{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"cell_id":"00000-9c96fc7c-eaba-433b-bf8d-7044f600854b","deepnote_cell_type":"markdown"},"source":["# Pandas - Introduction\n","\n","This notebook explans how to use the `pandas` library for analysis of tabular data."]},{"cell_type":"code","execution_count":1,"metadata":{"cell_id":"00001-5b6f28ed-c26f-468f-b0a4-9e0c93cbc187","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611579348294,"source_hash":"dbbf1542"},"outputs":[],"source":["# Start using pandas (default import convention)\n","import pandas as pd\n","import numpy as np"]},{"cell_type":"code","execution_count":2,"metadata":{"cell_id":"00002-f8d6b212-25bf-4955-b5d6-a559c86af20b","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":5,"execution_start":1611579350193,"scrolled":true,"source_hash":"a1d46cae"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","pandas - a powerful data analysis and manipulation library for Python\n","=====================================================================\n","\n","**pandas** is a Python package providing fast, flexible, and expressive data\n","structures designed to make working with \"relational\" or \"labeled\" data both\n","easy and intuitive. It aims to be the fundamental high-level building block for\n","doing practical, **real world** data analysis in Python. Additionally, it has\n","the broader goal of becoming **the most powerful and flexible open source data\n","analysis / manipulation tool available in any language**. It is already well on\n","its way toward this goal.\n","\n","Main Features\n","-------------\n","Here are just a few of the things that pandas does well:\n","\n","  - Easy handling of missing data in floating point as well as non-floating\n","    point data.\n","  - Size mutability: columns can be inserted and deleted from DataFrame and\n","    higher dimensional objects\n","  - Automatic and explicit data alignment: objects can be explicitly aligned\n","    to a set of labels, or the user can simply ignore the labels and let\n","    `Series`, `DataFrame`, etc. automatically align the data for you in\n","    computations.\n","  - Powerful, flexible group by functionality to perform split-apply-combine\n","    operations on data sets, for both aggregating and transforming data.\n","  - Make it easy to convert ragged, differently-indexed data in other Python\n","    and NumPy data structures into DataFrame objects.\n","  - Intelligent label-based slicing, fancy indexing, and subsetting of large\n","    data sets.\n","  - Intuitive merging and joining data sets.\n","  - Flexible reshaping and pivoting of data sets.\n","  - Hierarchical labeling of axes (possible to have multiple labels per tick).\n","  - Robust IO tools for loading data from flat files (CSV and delimited),\n","    Excel files, databases, and saving/loading data from the ultrafast HDF5\n","    format.\n","  - Time series-specific functionality: date range generation and frequency\n","    conversion, moving window statistics, date shifting and lagging.\n","\n"]}],"source":["# Let pandas speak for themselves\n","print(pd.__doc__)"]},{"cell_type":"markdown","metadata":{"cell_id":"00003-3ee27f5c-4aef-4dc3-8653-c90b1146e494","deepnote_cell_type":"markdown"},"source":["Visit the official website for a nicely written documentation: https://pandas.pydata.org"]},{"cell_type":"code","execution_count":3,"metadata":{"cell_id":"00004-d90c37d8-7234-413e-a89c-f8a34d58e39d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611579356214,"source_hash":"30ba3d9f"},"outputs":[{"name":"stdout","output_type":"stream","text":["2.2.2\n"]}],"source":["# Current version (should be 1.5+ in 2023)\n","print(pd.__version__)"]},{"cell_type":"markdown","metadata":{"cell_id":"00005-68938e1d-9143-4228-9d63-9e0a36b8d1d1","deepnote_cell_type":"markdown"},"source":["## Basic objects "]},{"cell_type":"markdown","metadata":{"cell_id":"00006-fdd69bd6-cb3e-4fca-8220-671dfa4b2b88","deepnote_cell_type":"markdown"},"source":["The **pandas** library has a vast API with many useful functions. However, most of this revolves\n","around two important classes:\n","\n","* Series\n","* DataFrame\n","\n","In this introduction, we will focus on them - what each of them does and how they relate to each other\n","and numpy objects."]},{"cell_type":"markdown","metadata":{"cell_id":"00007-b550553e-6541-4081-9dcd-a68fa7b896d1","deepnote_cell_type":"markdown"},"source":["### Series\n","\n","Series is a one-dimensional data structure, central to pandas. \n","\n","For a complete API, visit https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html"]},{"cell_type":"code","execution_count":4,"metadata":{"cell_id":"00008-576737c0-f7f4-48ce-996d-28c0b635e3aa","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611579361045,"source_hash":"c76e8792"},"outputs":[{"data":{"text/plain":["0    1\n","1    2\n","2    3\n","dtype: int64"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# My first series\n","series = pd.Series([1, 2, 3])\n","series"]},{"cell_type":"markdown","metadata":{"cell_id":"00009-b63b590b-e993-42c8-bb33-166b2702ba3e","deepnote_cell_type":"markdown"},"source":["This looks a bit like a Numpy array, does it not?\n","\n","Actually, in most cases the Series wraps a Numpy array..."]},{"cell_type":"code","execution_count":5,"metadata":{"cell_id":"00010-d06edb1a-2143-4c1b-a5b0-aaadfb315e7d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611567760034,"source_hash":"44e6fad0"},"outputs":[{"data":{"text/plain":["array([1, 2, 3])"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["series.values  # The result is a Numpy array"]},{"cell_type":"markdown","metadata":{"cell_id":"00013-b8611001-c48a-4a1e-a482-b6c781cf77ff","deepnote_cell_type":"markdown"},"source":["But there is something more. Alongside the values, we see that each item (or \"row\") has a certain label. The collection of labels is called **index**."]},{"cell_type":"code","execution_count":6,"metadata":{"cell_id":"00014-54885ee0-c17a-4340-8039-2675e8b38c77","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567760829,"source_hash":"8f0869f4"},"outputs":[{"data":{"text/plain":["RangeIndex(start=0, stop=3, step=1)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["series.index"]},{"cell_type":"markdown","metadata":{"cell_id":"00015-7b104328-754a-4e7f-bb62-14c4d2dd1917","deepnote_cell_type":"markdown"},"source":["This index (see below) can be used, as its name suggests, to index items of the series."]},{"cell_type":"code","execution_count":7,"metadata":{"cell_id":"00016-863ac09e-7c3f-4e03-959e-c66c510c596b","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567761660,"source_hash":"4a387647"},"outputs":[{"data":{"text/plain":["2"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["# Return an element from the series\n","series.loc[1]"]},{"cell_type":"code","execution_count":8,"metadata":{"cell_id":"00015-91f2ee46-a5f9-45a9-abb6-12bd4a463ac9","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611567762195,"source_hash":"ec5f55d9","tags":[]},"outputs":[{"data":{"text/plain":["2"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["# Or\n","series[1]"]},{"cell_type":"code","execution_count":9,"metadata":{"cell_id":"00017-43b59fa1-d72d-46ba-86d6-0fed76ccd1f0","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567762576,"source_hash":"e117364f"},"outputs":[{"data":{"text/plain":["a    2\n","b    4\n","dtype: int64"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["# Construction from a dictionary\n","series_ab = pd.Series({\"a\": 2, \"b\": 4})\n","series_ab"]},{"cell_type":"markdown","metadata":{"cell_id":"00018-370c0ee0-646c-419e-b994-f9a6d344a42b","deepnote_cell_type":"markdown"},"source":["**Exercise**: Create a series with 5 elements."]},{"cell_type":"code","execution_count":10,"metadata":{"cell_id":"00019-b9b88043-d813-4c7f-bc2f-e82f2390f14a","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567763650,"source_hash":"a257d19f"},"outputs":[],"source":["result = ..."]},{"cell_type":"markdown","metadata":{"cell_id":"00020-6aa98d6a-363c-4907-8fb1-30c32e070e9f","deepnote_cell_type":"markdown"},"source":["### DataFrame"]},{"cell_type":"markdown","metadata":{"cell_id":"00021-06c635d1-efc4-4a34-9ab0-870db0778bf0","deepnote_cell_type":"markdown"},"source":["A **DataFrame** is pandas' answer to Excel sheets - it is a collection of named columns (or, in our case, a collection of **Series**).\n","Quite often, we directly read data frames from an external source, but it is possible to create them from:\n","* a dict of Series, numpy arrays or other array-like objects\n","* from an iterable of rows (where rows are Series, lists, dictionaries, ...)"]},{"cell_type":"code","execution_count":11,"metadata":{"cell_id":"00022-b08d7be1-128a-48c5-9b57-806f68113d63","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567767842,"source_hash":"3d0048f"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>a</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>b</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>c</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   0  1\n","0  a  1\n","1  b  3\n","2  c  5"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# List of lists (no column names)\n","table = [\n","    ['a', 1],\n","    ['b', 3],\n","    ['c', 5]\n","]\n","table_df = pd.DataFrame(table)\n","table_df"]},{"cell_type":"code","execution_count":12,"metadata":{"cell_id":"00023-460aa85e-6503-4335-8b70-b695c45b6121","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":5,"execution_start":1611567768383,"source_hash":"433789ee"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>number</th>\n","      <th>letter</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>a</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>b</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>c</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>d</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   number letter\n","0       1      a\n","1       2      b\n","2       3      c\n","3       4      d"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["# Dict of Series (with column names)\n","df = pd.DataFrame({\n","    'number': pd.Series([1, 2, 3, 4], dtype=np.int8),\n","    'letter': pd.Series(['a', 'b', 'c', 'd'])\n","})\n","df"]},{"cell_type":"code","execution_count":13,"metadata":{"cell_id":"00024-ce04e0d4-525f-4e35-8ecd-e5d1c808b5f8","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567769751,"scrolled":true,"source_hash":"31c05ea5"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>a</th>\n","      <th>b</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-0.623829</td>\n","      <td>-1.850478</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.541544</td>\n","      <td>-0.816918</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.353791</td>\n","      <td>0.470390</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-0.411840</td>\n","      <td>2.111382</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.180242</td>\n","      <td>-0.081106</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>-0.773012</td>\n","      <td>1.676229</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.667790</td>\n","      <td>-0.968450</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>-1.076493</td>\n","      <td>0.050577</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.920813</td>\n","      <td>-1.891053</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>-0.132454</td>\n","      <td>-0.447746</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          a         b\n","0 -0.623829 -1.850478\n","1 -0.541544 -0.816918\n","2  1.353791  0.470390\n","3 -0.411840  2.111382\n","4  1.180242 -0.081106\n","5 -0.773012  1.676229\n","6  0.667790 -0.968450\n","7 -1.076493  0.050577\n","8  0.920813 -1.891053\n","9 -0.132454 -0.447746"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["# Numpy array (10x2), specify column names\n","data = np.random.normal(0, 1, (10, 2))\n","\n","df = pd.DataFrame(data, columns=['a', 'b'])\n","df"]},{"cell_type":"code","execution_count":14,"metadata":{"cell_id":"00025-47de99ba-8288-4905-8877-85452ec86841","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611567773984,"source_hash":"ed5550ef"},"outputs":[{"data":{"text/plain":["RangeIndex(start=0, stop=10, step=1)"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# A DataFrame also has an index.\n","df.index"]},{"cell_type":"code","execution_count":15,"metadata":{"cell_id":"00027-6c934155-da4c-4080-a0be-f51d4fd62f3f","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611567783708,"source_hash":"342beafe"},"outputs":[{"data":{"text/plain":["True"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["# ...that is shared by all columns\n","df.index is df[\"a\"].index"]},{"cell_type":"code","execution_count":16,"metadata":{"cell_id":"00026-c0ad1aba-1e06-411d-9301-9a3fcebca0e1","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611567783032,"source_hash":"90ddc3f8","tags":[]},"outputs":[{"data":{"text/plain":["Index(['a', 'b'], dtype='object')"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["# The columns also form an index.\n","df.columns"]},{"cell_type":"markdown","metadata":{"cell_id":"00047-2671c08d-d825-4fe1-ae69-a9c8beba058e","deepnote_cell_type":"markdown"},"source":["**Exercise:** Create `DataFrame` whose `x`-column is $0, \\frac{1}{4}\\pi, \\frac{1}{2}\\pi, .. 2\\pi $, `y` column is `cos(x)` and index are `fractions` `0, 1/4, 1/2 ... 2`"]},{"cell_type":"code","execution_count":18,"metadata":{"cell_id":"00048-1ec15e17-145f-44a3-96c5-131a08b8b0d4","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":111,"execution_start":1611480561408,"source_hash":"2ccc186a"},"outputs":[{"ename":"TypeError","evalue":"'RangeIndex' object cannot be interpreted as an integer","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[18], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfractions\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m index \u001b[38;5;241m=\u001b[39m [fractions\u001b[38;5;241m.\u001b[39mFraction(n, ___) \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m___\u001b[49m\u001b[43m)\u001b[49m]\n\u001b[1;32m      4\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39m___([___ \u001b[38;5;28;01mfor\u001b[39;00m ___ \u001b[38;5;129;01min\u001b[39;00m ___])\n\u001b[1;32m      5\u001b[0m y \u001b[38;5;241m=\u001b[39m ___\n","\u001b[0;31mTypeError\u001b[0m: 'RangeIndex' object cannot be interpreted as an integer"]}],"source":["import fractions\n","\n","index = [fractions.Fraction(n, ___) for n in range(___)]\n","x = np.___([___ for ___ in ___])\n","y = ___\n","\n","df = pd.DataFrame(___, index = ___)\n","\n","# display\n","df"]},{"cell_type":"markdown","metadata":{"cell_id":"00028-aa94d7ea-8bba-4294-9b86-6a391209884f","deepnote_cell_type":"markdown"},"source":["## D(ata) types\n","\n","Pandas builds upon the numpy data types (mentioned earlier) and adds a couple of more."]},{"cell_type":"code","execution_count":19,"metadata":{"cell_id":"00029-8f0e7b0b-dc86-4248-be41-2ecdcd29719d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":20,"execution_start":1611567785414,"source_hash":"6af9f238"},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/dm/gbbql3p121z0tr22r2z98vy00000gn/T/ipykernel_78670/1417085050.py:10: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n","  \"datetime\": pd.date_range('2018-01-01', periods=5, freq='3M'),\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>bool</th>\n","      <th>int</th>\n","      <th>int[nan]</th>\n","      <th>float</th>\n","      <th>complex</th>\n","      <th>object</th>\n","      <th>string?</th>\n","      <th>string!</th>\n","      <th>datetime</th>\n","      <th>timedelta</th>\n","      <th>category</th>\n","      <th>period</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>True</td>\n","      <td>0</td>\n","      <td>&lt;NA&gt;</td>\n","      <td>0.00</td>\n","      <td>1.0+2.0j</td>\n","      <td>None</td>\n","      <td>a</td>\n","      <td>a</td>\n","      <td>2018-01-31</td>\n","      <td>0 days 00:00:00</td>\n","      <td>animal</td>\n","      <td>2018-01</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>False</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3.14</td>\n","      <td>2.0+3.0j</td>\n","      <td>1</td>\n","      <td>b</td>\n","      <td>b</td>\n","      <td>2018-04-30</td>\n","      <td>0 days 00:00:01</td>\n","      <td>plant</td>\n","      <td>2018-02</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>True</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>6.28</td>\n","      <td>3.0+4.0j</td>\n","      <td>2</td>\n","      <td>c</td>\n","      <td>c</td>\n","      <td>2018-07-31</td>\n","      <td>0 days 00:00:02</td>\n","      <td>animal</td>\n","      <td>2018-03</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>False</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>9.42</td>\n","      <td>4.0+5.0j</td>\n","      <td>[3, 4]</td>\n","      <td>d</td>\n","      <td>d</td>\n","      <td>2018-10-31</td>\n","      <td>0 days 00:00:03</td>\n","      <td>animal</td>\n","      <td>2018-04</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>True</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>12.56</td>\n","      <td>5.0+6.0j</td>\n","      <td>(5+6j)</td>\n","      <td>e</td>\n","      <td>e</td>\n","      <td>2019-01-31</td>\n","      <td>0 days 00:00:04</td>\n","      <td>plant</td>\n","      <td>2018-05</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["    bool  int  int[nan]  float   complex  object string? string!   datetime  \\\n","0   True    0      <NA>   0.00  1.0+2.0j    None       a       a 2018-01-31   \n","1  False    1         0   3.14  2.0+3.0j       1       b       b 2018-04-30   \n","2   True    2         1   6.28  3.0+4.0j       2       c       c 2018-07-31   \n","3  False    3         2   9.42  4.0+5.0j  [3, 4]       d       d 2018-10-31   \n","4   True    4         3  12.56  5.0+6.0j  (5+6j)       e       e 2019-01-31   \n","\n","        timedelta category   period  \n","0 0 days 00:00:00   animal  2018-01  \n","1 0 days 00:00:01    plant  2018-02  \n","2 0 days 00:00:02   animal  2018-03  \n","3 0 days 00:00:03   animal  2018-04  \n","4 0 days 00:00:04    plant  2018-05  "]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["typed_df = pd.DataFrame({\n","  \"bool\": np.arange(5) % 2 == 0,\n","  \"int\": range(5),\n","  \"int[nan]\": pd.Series([np.nan, 0, 1, 2, 3], dtype=\"Int64\"),\n","  \"float\": np.arange(5) * 3.14,\n","  \"complex\": np.array([1 + 2j, 2 + 3j, 3 + 4j, 4 + 5j, 5 + 6j]),\n","  \"object\": [None, 1, \"2\", [3, 4], 5 + 6j],\n","  \"string?\": [\"a\", \"b\", \"c\", \"d\", \"e\"],\n","  \"string!\": pd.Series([\"a\", \"b\", \"c\", \"d\", \"e\"], dtype=\"string\"),\n","  \"datetime\": pd.date_range('2018-01-01', periods=5, freq='3M'),\n","  \"timedelta\": pd.timedelta_range(0, freq=\"1s\", periods=5),\n","  \"category\": pd.Series([\"animal\", \"plant\", \"animal\", \"animal\", \"plant\"], dtype=\"category\"),\n","  \"period\": pd.period_range('2018-01-01', periods=5, freq='M'),\n","})\n","typed_df"]},{"cell_type":"code","execution_count":20,"metadata":{"cell_id":"00030-80fb51df-1921-4c0f-b380-cb3812a3d059","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611567785980,"source_hash":"4f0d2239"},"outputs":[{"data":{"text/plain":["bool                    bool\n","int                    int64\n","int[nan]               Int64\n","float                float64\n","complex           complex128\n","object                object\n","string?               object\n","string!       string[python]\n","datetime      datetime64[ns]\n","timedelta    timedelta64[ns]\n","category            category\n","period             period[M]\n","dtype: object"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["typed_df.dtypes"]},{"cell_type":"markdown","metadata":{"cell_id":"00031-9aa5fa0e-e065-4f81-858d-93a4ff13c158","deepnote_cell_type":"markdown"},"source":["We will see some of the types practically used in further analysis."]},{"cell_type":"markdown","metadata":{"cell_id":"00032-d28f532f-1cc0-45a3-9275-869490ca3cc4","deepnote_cell_type":"markdown"},"source":["## Indices & indexing\n","\n"]},{"cell_type":"code","execution_count":21,"metadata":{"cell_id":"00033-35f9e0dc-1e10-4354-9301-37cdf5dfc108","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567788444,"source_hash":"19f8a65a"},"outputs":[{"data":{"text/plain":["a    0\n","b    1\n","c    2\n","dtype: int64"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["abc_series = pd.Series(range(3), index=[\"a\", \"b\", \"c\"])\n","abc_series"]},{"cell_type":"code","execution_count":22,"metadata":{"cell_id":"00034-53dceb0f-f3ea-4376-a3fc-0b9132b15c2d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611567788966,"source_hash":"7cd8d5bf"},"outputs":[{"data":{"text/plain":["Index(['a', 'b', 'c'], dtype='object')"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["abc_series.index"]},{"cell_type":"code","execution_count":23,"metadata":{"cell_id":"00035-f24e5c70-36f0-4d63-a7a4-cae2c798bf94","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567789584,"source_hash":"2812f42b"},"outputs":[{"data":{"text/plain":["letter\n","c    0\n","d    1\n","e    2\n","dtype: int64"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["abc_series.index = [\"c\", \"d\", \"e\"]  # Changes the labels in-place!\n","abc_series.index.name = \"letter\"\n","abc_series"]},{"cell_type":"code","execution_count":24,"metadata":{"cell_id":"00036-c789e02a-2403-487e-a08d-01df0da17840","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611567790118,"source_hash":"90a443a4"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>alpha</th>\n","      <th>beta</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>first</th>\n","      <td>a</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>second</th>\n","      <td>b</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>third</th>\n","      <td>c</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       alpha  beta\n","first      a     1\n","second     b     3\n","third      c     5"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["table = [\n","    ['a', 1],\n","    ['b', 3],\n","    ['c', 5]\n","]\n","table_df = pd.DataFrame(\n","    table,\n","    index=[\"first\", \"second\", \"third\"],\n","    columns=[\"alpha\", \"beta\"]\n",")\n","table_df"]},{"cell_type":"code","execution_count":25,"metadata":{"cell_id":"00037-1bf7a004-e61f-4a7b-ab79-809ed090af1f","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611567790746,"source_hash":"13d66294"},"outputs":[{"data":{"text/plain":["first     a\n","second    b\n","third     c\n","Name: alpha, dtype: object"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["alpha = table_df[\"alpha\"]  # Simple [] indexing in DataFrame returns Series\n","alpha"]},{"cell_type":"code","execution_count":26,"metadata":{"cell_id":"00038-c890f83c-1e8a-4952-abdf-c2799352d49b","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":5,"execution_start":1611567791382,"source_hash":"ca4ed67d"},"outputs":[{"data":{"text/plain":["'b'"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["alpha[\"second\"]             # Simple [] indexing in Series returns scalar values."]},{"cell_type":"markdown","metadata":{"cell_id":"00042-b5bea60b-507a-4dea-8a4f-9acbe6c598a0","deepnote_cell_type":"markdown"},"source":["A slice with a `[\"list\", \"of\", \"columns\"]` yields a `DataFrame` with those columns. \n","\n","For example:"]},{"cell_type":"code","execution_count":null,"metadata":{"cell_id":"00043-b297bd6b-85ff-4770-879c-9bdf09de6abf","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567794413,"source_hash":"b512d0f2"},"outputs":[],"source":["table_df[[\"beta\", \"alpha\"]]"]},{"cell_type":"markdown","metadata":{"cell_id":"00044-096ecd23-878e-4b06-bcb9-b60dfa8da461","deepnote_cell_type":"markdown"},"source":["`[[\"column_name\"]]` returs a `DataFrame` as well, not `Series`:"]},{"cell_type":"code","execution_count":28,"metadata":{"cell_id":"00045-a862e2cf-389b-4a9b-9444-cf2a2c7520a4","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611567795450,"source_hash":"9f86bc6a"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>alpha</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>first</th>\n","      <td>a</td>\n","    </tr>\n","    <tr>\n","      <th>second</th>\n","      <td>b</td>\n","    </tr>\n","    <tr>\n","      <th>third</th>\n","      <td>c</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       alpha\n","first      a\n","second     b\n","third      c"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["table_df[[\"alpha\"]]"]},{"cell_type":"markdown","metadata":{"cell_id":"00046-94cc193e-ed78-4239-97bd-ad01ad286aaf","deepnote_cell_type":"markdown"},"source":["There are two ways how to properly index rows & cells in the DataFrame:\n","\n","- `loc` for label-based indexing\n","- `iloc` for order-based indexing (it does not use the **index** at all)\n","\n","Note the square brackets. The mentioned attributes actually are not methods\n","but special \"indexer\" objects. They accept one or two arguments specifying\n","the position along one or both axes."]},{"cell_type":"markdown","metadata":{"cell_id":"00049-67ec2b8c-3dc9-4fbd-849f-fa4aa6fc6050","deepnote_cell_type":"markdown"},"source":["#### loc\n"]},{"cell_type":"code","execution_count":29,"metadata":{"cell_id":"00050-aedef5e1-79bd-4581-88a5-462122c3b77e","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567804258,"source_hash":"eaeea355"},"outputs":[{"data":{"text/plain":["alpha    a\n","beta     1\n","Name: first, dtype: object"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["first = table_df.loc[\"first\"]\n","first"]},{"cell_type":"code","execution_count":30,"metadata":{"cell_id":"00051-4d9528ae-b57e-401d-97a2-87f103e0c72f","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611567805002,"source_hash":"f6fdb961"},"outputs":[{"data":{"text/plain":["1"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["table_df.loc[\"first\", \"beta\"]"]},{"cell_type":"code","execution_count":31,"metadata":{"cell_id":"00052-50078dce-3868-4300-8675-aab20929e97f","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611567805683,"source_hash":"9f0543fa"},"outputs":[{"data":{"text/plain":["first     1\n","second    3\n","Name: beta, dtype: int64"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["table_df.loc[\"first\":\"second\", \"beta\"]   # Use ranges (inclusive)"]},{"cell_type":"markdown","metadata":{"cell_id":"00053-9b0ffb86-5974-4cd5-b8f4-f4b4fe979771","deepnote_cell_type":"markdown"},"source":["#### iloc"]},{"cell_type":"code","execution_count":32,"metadata":{"cell_id":"00054-91fa79d6-941e-428f-9ceb-1a480634cd6b","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567807968,"source_hash":"589116e5"},"outputs":[{"data":{"text/plain":["alpha    b\n","beta     3\n","Name: second, dtype: object"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["table_df.iloc[1]"]},{"cell_type":"code","execution_count":33,"metadata":{"cell_id":"00055-6ab2d6bf-c83e-42b2-95fa-0d5f085de251","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567808790,"source_hash":"22edf038"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>alpha</th>\n","      <th>beta</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>first</th>\n","      <td>a</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>third</th>\n","      <td>c</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      alpha  beta\n","first     a     1\n","third     c     5"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["table_df.iloc[0:4:2]   # Select every second row"]},{"cell_type":"code","execution_count":34,"metadata":{"cell_id":"00056-310d81a9-5114-40c7-bc1c-794b7bca3b8b","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567810031,"source_hash":"9381f832"},"outputs":[{"data":{"text/plain":["1"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["table_df.at[\"first\", \"beta\"]"]},{"cell_type":"code","execution_count":35,"metadata":{"cell_id":"00057-a4f407fc-3a40-4853-b374-170c1626ab70","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567811230,"source_hash":"5ab74605"},"outputs":[{"data":{"text/plain":["pandas.core.indexing._AtIndexer"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["type(table_df.at)"]},{"cell_type":"markdown","metadata":{"cell_id":"00058-a1b50a76-2ede-4e10-b170-6493642d8d7f","deepnote_cell_type":"markdown"},"source":["## Modifying DataFrames\n","\n","Adding a new column is like adding a key/value pair to a dict.\n","Note that this operation, unlike most others, does modify the DataFrame."]},{"cell_type":"code","execution_count":36,"metadata":{"cell_id":"00059-380e843a-7de8-48f6-a0b9-5bdd5fba08a0","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567839334,"source_hash":"19406c5f"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>alpha</th>\n","      <th>beta</th>\n","      <th>now</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>first</th>\n","      <td>a</td>\n","      <td>1</td>\n","      <td>2024-04-25 12:45:05.181057</td>\n","    </tr>\n","    <tr>\n","      <th>second</th>\n","      <td>b</td>\n","      <td>3</td>\n","      <td>2024-04-25 12:45:05.181057</td>\n","    </tr>\n","    <tr>\n","      <th>third</th>\n","      <td>c</td>\n","      <td>5</td>\n","      <td>2024-04-25 12:45:05.181057</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       alpha  beta                        now\n","first      a     1 2024-04-25 12:45:05.181057\n","second     b     3 2024-04-25 12:45:05.181057\n","third      c     5 2024-04-25 12:45:05.181057"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["from datetime import datetime\n","table_df[\"now\"] = datetime.now()\n","table_df"]},{"cell_type":"markdown","metadata":{"cell_id":"00060-24f384c8-eecd-41ce-b22e-df7c651d6e35","deepnote_cell_type":"markdown"},"source":["Non-destructive version that returns a new DataFrame, uses the `assign` method:"]},{"cell_type":"code","execution_count":37,"metadata":{"cell_id":"00061-30216bc8-5872-4928-af41-bb55fb8331e6","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611567842415,"source_hash":"6960fea5"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>alpha</th>\n","      <th>beta</th>\n","      <th>now</th>\n","      <th>delta</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>first</th>\n","      <td>a</td>\n","      <td>1</td>\n","      <td>2024-04-25 12:45:05.181057</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>second</th>\n","      <td>b</td>\n","      <td>3</td>\n","      <td>2024-04-25 12:45:05.181057</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>third</th>\n","      <td>c</td>\n","      <td>5</td>\n","      <td>2024-04-25 12:45:05.181057</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       alpha  beta                        now  delta\n","first      a     1 2024-04-25 12:45:05.181057   True\n","second     b     3 2024-04-25 12:45:05.181057  False\n","third      c     5 2024-04-25 12:45:05.181057   True"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["table_df.assign(delta = [True, False, True])"]},{"cell_type":"code","execution_count":38,"metadata":{"cell_id":"00062-0de7f7d1-8f77-4997-bc82-423b64c0dcc9","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":2,"execution_start":1611567844125,"source_hash":"17c762c7"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>alpha</th>\n","      <th>beta</th>\n","      <th>now</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>first</th>\n","      <td>a</td>\n","      <td>1</td>\n","      <td>2024-04-25 12:45:05.181057</td>\n","    </tr>\n","    <tr>\n","      <th>second</th>\n","      <td>b</td>\n","      <td>3</td>\n","      <td>2024-04-25 12:45:05.181057</td>\n","    </tr>\n","    <tr>\n","      <th>third</th>\n","      <td>c</td>\n","      <td>5</td>\n","      <td>2024-04-25 12:45:05.181057</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       alpha  beta                        now\n","first      a     1 2024-04-25 12:45:05.181057\n","second     b     3 2024-04-25 12:45:05.181057\n","third      c     5 2024-04-25 12:45:05.181057"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["# However, the original DataFrame is not changed\n","table_df"]},{"cell_type":"markdown","metadata":{"cell_id":"00063-aec93e64-9522-4eee-a75e-e8c652479d4b","deepnote_cell_type":"markdown"},"source":["Deleting a column is very easy too."]},{"cell_type":"code","execution_count":39,"metadata":{"cell_id":"00064-7ed9a380-a1d9-467b-b6a4-e927ee4a73a1","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611567854337,"source_hash":"c9dffb9a"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>alpha</th>\n","      <th>beta</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>first</th>\n","      <td>a</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>second</th>\n","      <td>b</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>third</th>\n","      <td>c</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       alpha  beta\n","first      a     1\n","second     b     3\n","third      c     5"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["del table_df[\"now\"]\n","table_df"]},{"cell_type":"markdown","metadata":{"cell_id":"00065-6ab3e7f7-7ac8-4b93-bb9f-b7c2d3518b9e","deepnote_cell_type":"markdown"},"source":["The **drop** method works with both rows and columns (creating a new data frame), returning a new object."]},{"cell_type":"code","execution_count":40,"metadata":{"cell_id":"00066-960e696f-9ef1-41f4-ba03-e8652f58dd7a","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":5,"execution_start":1611567856256,"source_hash":"ade330b4"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>alpha</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>first</th>\n","      <td>a</td>\n","    </tr>\n","    <tr>\n","      <th>second</th>\n","      <td>b</td>\n","    </tr>\n","    <tr>\n","      <th>third</th>\n","      <td>c</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       alpha\n","first      a\n","second     b\n","third      c"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["table_df.drop(\"beta\", axis=1)"]},{"cell_type":"code","execution_count":41,"metadata":{"cell_id":"00067-3f951069-898a-49a3-9629-21f00ad27e0a","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611567857838,"source_hash":"ce27d27f"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>alpha</th>\n","      <th>beta</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>first</th>\n","      <td>a</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>third</th>\n","      <td>c</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      alpha  beta\n","first     a     1\n","third     c     5"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["table_df.drop(\"second\", axis=0)"]},{"cell_type":"markdown","metadata":{"cell_id":"00068-86559d86-2c01-4fdb-a288-98f8313d4336","deepnote_cell_type":"markdown"},"source":["**Exercise:** Use a combination of `reset_index`, `drop` and `set_index` to transform `table_df` into `pd.DataFrame({'index': table_df.index}, index=table_df[\"alpha\"])`"]},{"cell_type":"code","execution_count":null,"metadata":{"cell_id":"00069-b97999ec-63f2-42e9-a862-e541115baf44","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611480611376,"source_hash":"d5f2663e"},"outputs":[],"source":["results = table_df.___.___.___\n","\n","# display\n","result"]},{"cell_type":"markdown","metadata":{"cell_id":"00070-a7144d25-1150-4589-ab81-aee7173db134","deepnote_cell_type":"markdown"},"source":["**Let's get some real data!**"]},{"cell_type":"markdown","metadata":{"cell_id":"00071-521ee5f8-d794-4fd1-b772-186b84e3deac","deepnote_cell_type":"markdown"},"source":["## I/O in pandas\n","\n","Pandas can read (and write to) a huge variety of file formats. More details can be found in the official documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/io.html\n","\n","Most of the functions for reading data are named `pandas.read_XXX`, where XXX is the format used. We will look at three commonly used ones."]},{"cell_type":"code","execution_count":43,"metadata":{"cell_id":"00073-a1058079-31e9-43e2-b07c-f898af75de5f","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":2,"execution_start":1611568168910,"source_hash":"dc3b3f60"},"outputs":[{"name":"stdout","output_type":"stream","text":["read_clipboard\n","read_csv\n","read_excel\n","read_feather\n","read_fwf\n","read_gbq\n","read_hdf\n","read_html\n","read_json\n","read_orc\n","read_parquet\n","read_pickle\n","read_sas\n","read_spss\n","read_sql\n","read_sql_query\n","read_sql_table\n","read_stata\n","read_table\n","read_xml\n"]}],"source":["# List functions for input in pandas.\n","\n","print(\"\\n\".join(method for method in dir(pd) if method.startswith(\"read_\")))"]},{"cell_type":"markdown","metadata":{"cell_id":"00074-36ecee81-4869-4699-ae4f-a6dfc68ace99","deepnote_cell_type":"markdown"},"source":["### Read CSV\n","\n","Nowadays, a lot of data comes in the textual Comma-separated values format (CSV).\n","Although not properly standardized, it is the de-facto standard for files that are not\n","huge and are meant to be read by human eyes too.\n","\n","Let's read the population of U.S. states that we will need later:"]},{"cell_type":"code","execution_count":45,"metadata":{"cell_id":"00075-9f192cd5-ad0f-4601-9e2e-0848f708b35f","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":17,"execution_start":1611579778720,"source_hash":"6caee636"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Territory</th>\n","      <th>Population</th>\n","      <th>Population 2010</th>\n","      <th>Code</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>California</td>\n","      <td>39029342.0</td>\n","      <td>37253956</td>\n","      <td>CA</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Texas</td>\n","      <td>30029572.0</td>\n","      <td>25145561</td>\n","      <td>TX</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Florida</td>\n","      <td>22244823.0</td>\n","      <td>18801310</td>\n","      <td>FL</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>New York</td>\n","      <td>19677151.0</td>\n","      <td>19378102</td>\n","      <td>NY</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Pennsylvania</td>\n","      <td>12972008.0</td>\n","      <td>12702379</td>\n","      <td>PA</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Illinois</td>\n","      <td>12582032.0</td>\n","      <td>12830632</td>\n","      <td>IL</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Ohio</td>\n","      <td>11756058.0</td>\n","      <td>11536504</td>\n","      <td>OH</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Georgia</td>\n","      <td>10912876.0</td>\n","      <td>9687653</td>\n","      <td>GA</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>North Carolina</td>\n","      <td>10698973.0</td>\n","      <td>9535483</td>\n","      <td>NC</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        Territory  Population  Population 2010 Code\n","0      California  39029342.0         37253956   CA\n","1           Texas  30029572.0         25145561   TX\n","2         Florida  22244823.0         18801310   FL\n","3        New York  19677151.0         19378102   NY\n","4    Pennsylvania  12972008.0         12702379   PA\n","5        Illinois  12582032.0         12830632   IL\n","6            Ohio  11756058.0         11536504   OH\n","7         Georgia  10912876.0          9687653   GA\n","8  North Carolina  10698973.0          9535483   NC"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["territories = pd.read_csv(\"data/us_state_population.csv\")\n","territories.head(9)"]},{"cell_type":"markdown","metadata":{"cell_id":"00076-b6058815-262c-43ce-b7a6-2a40ecade892","deepnote_cell_type":"markdown"},"source":["The automatic data type parsing converts columns to appropriate types:"]},{"cell_type":"code","execution_count":46,"metadata":{"cell_id":"00077-0c4b98ee-3ee5-418d-ac4c-d2530614af5c","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":3,"execution_start":1611568175175,"source_hash":"ec8087cf"},"outputs":[{"data":{"text/plain":["Territory           object\n","Population         float64\n","Population 2010      int64\n","Code                object\n","dtype: object"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["territories.dtypes"]},{"cell_type":"markdown","metadata":{"cell_id":"00078-cd5bd76c-c0c7-4029-8438-2d9ef7096ab9","deepnote_cell_type":"markdown"},"source":["Sometimes the CSV input does not work out of the box. Although pandas automatically understands and reads zipped files,\n","it usually does not automatically infer the file format and its variations - for details, see the `read_csv` documentation here: \n","https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html"]},{"cell_type":"code","execution_count":48,"metadata":{"cell_id":"00079-8a8d808f-3ae6-436d-8588-58368dc13d07","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611568224402,"source_hash":"44c0777d"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Sepal length\\tSepal width\\tPetal length\\tPetal width\\tSpecies</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1\\t3.5\\t1.4\\t0.2\\tI. setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9\\t3.0\\t1.4\\t0.2\\tI. setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7\\t3.2\\t1.3\\t0.2\\tI. setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6\\t3.1\\t1.5\\t0.2\\tI. setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0\\t3.6\\t1.4\\t0.2\\tI. setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Sepal length\\tSepal width\\tPetal length\\tPetal width\\tSpecies\n","0                      5.1\\t3.5\\t1.4\\t0.2\\tI. setosa           \n","1                      4.9\\t3.0\\t1.4\\t0.2\\tI. setosa           \n","2                      4.7\\t3.2\\t1.3\\t0.2\\tI. setosa           \n","3                      4.6\\t3.1\\t1.5\\t0.2\\tI. setosa           \n","4                      5.0\\t3.6\\t1.4\\t0.2\\tI. setosa           "]},"execution_count":48,"metadata":{},"output_type":"execute_result"}],"source":["pd.read_csv('data/iris.tsv.gz')"]},{"cell_type":"markdown","metadata":{"cell_id":"00080-bb60d975-5ead-4f4d-9220-bbd3e57fb292","deepnote_cell_type":"markdown"},"source":["...in this case, the CSV file does not use commas to separate values. Therefore, we need to specify an extra argument:"]},{"cell_type":"code","execution_count":49,"metadata":{"cell_id":"00081-ecbb62ad-cd8e-4759-a682-18168f4ef43d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611568226154,"source_hash":"e6cc840f"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Sepal length</th>\n","      <th>Sepal width</th>\n","      <th>Petal length</th>\n","      <th>Petal width</th>\n","      <th>Species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>I. setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>I. setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>I. setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>I. setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>I. setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Sepal length  Sepal width  Petal length  Petal width    Species\n","0           5.1          3.5           1.4          0.2  I. setosa\n","1           4.9          3.0           1.4          0.2  I. setosa\n","2           4.7          3.2           1.3          0.2  I. setosa\n","3           4.6          3.1           1.5          0.2  I. setosa\n","4           5.0          3.6           1.4          0.2  I. setosa"]},"execution_count":49,"metadata":{},"output_type":"execute_result"}],"source":["pd.read_csv(\"data/iris.tsv.gz\", sep='\\t')"]},{"cell_type":"markdown","metadata":{"cell_id":"00085-e8f867c0-6fe2-4ba4-b5a1-c6ea29baed08","deepnote_cell_type":"markdown"},"source":["See the difference?"]},{"cell_type":"markdown","metadata":{"cell_id":"00087-30bc5c76-4595-48c7-8256-c26fe5e87343","deepnote_cell_type":"markdown"},"source":["### Read Excel\n","\n","Let's read the list of U.S. incidents when lasers interfered with airplanes."]},{"cell_type":"code","execution_count":51,"metadata":{"cell_id":"00088-d81339af-9950-4692-87b1-1f1380c2e215","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1509,"execution_start":1611568236481,"source_hash":"f73cd15e"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Incident Date</th>\n","      <th>Incident Time</th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2019-01-01</td>\n","      <td>35</td>\n","      <td>N3EG</td>\n","      <td>C421</td>\n","      <td>2500</td>\n","      <td>SAT</td>\n","      <td>Green</td>\n","      <td>No</td>\n","      <td>San Antonio</td>\n","      <td>Texas</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2019-01-01</td>\n","      <td>43</td>\n","      <td>RPA3469</td>\n","      <td>E75S</td>\n","      <td>4000</td>\n","      <td>IAH</td>\n","      <td>Green</td>\n","      <td>No</td>\n","      <td>Houston</td>\n","      <td>Texas</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2019-01-01</td>\n","      <td>44</td>\n","      <td>UAL1607</td>\n","      <td>A319</td>\n","      <td>4000</td>\n","      <td>IAH</td>\n","      <td>Green</td>\n","      <td>No</td>\n","      <td>Houston</td>\n","      <td>Texas</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2019-01-01</td>\n","      <td>110</td>\n","      <td>N205TM</td>\n","      <td>BE20</td>\n","      <td>2500</td>\n","      <td>HDC</td>\n","      <td>Green</td>\n","      <td>No</td>\n","      <td>Hammond</td>\n","      <td>Louisiana</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2019-01-01</td>\n","      <td>115</td>\n","      <td>JIA5233</td>\n","      <td>CRJ9</td>\n","      <td>2000</td>\n","      <td>JAX</td>\n","      <td>Green</td>\n","      <td>No</td>\n","      <td>Jacksonville</td>\n","      <td>Florida</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>6131</th>\n","      <td>2019-12-31</td>\n","      <td>845</td>\n","      <td>ASH5861</td>\n","      <td>CRJ9</td>\n","      <td>3000</td>\n","      <td>JAN</td>\n","      <td>Green</td>\n","      <td>No</td>\n","      <td>Jackson</td>\n","      <td>Mississippi</td>\n","    </tr>\n","    <tr>\n","      <th>6132</th>\n","      <td>2019-12-31</td>\n","      <td>929</td>\n","      <td>N22P</td>\n","      <td>CRUZ</td>\n","      <td>2500</td>\n","      <td>HNL</td>\n","      <td>Green</td>\n","      <td>No</td>\n","      <td>Honolulu</td>\n","      <td>Hawaii</td>\n","    </tr>\n","    <tr>\n","      <th>6133</th>\n","      <td>2019-12-31</td>\n","      <td>2310</td>\n","      <td>GTH530</td>\n","      <td>GLF4</td>\n","      <td>500</td>\n","      <td>SJU</td>\n","      <td>White</td>\n","      <td>No</td>\n","      <td>Carolina</td>\n","      <td>Puerto Rico</td>\n","    </tr>\n","    <tr>\n","      <th>6134</th>\n","      <td>2019-12-31</td>\n","      <td>2312</td>\n","      <td>AMF6916</td>\n","      <td>SW4</td>\n","      <td>600</td>\n","      <td>SJU</td>\n","      <td>Green</td>\n","      <td>No</td>\n","      <td>Carolina</td>\n","      <td>Puerto Rico</td>\n","    </tr>\n","    <tr>\n","      <th>6135</th>\n","      <td>2019-12-31</td>\n","      <td>2327</td>\n","      <td>N715TH</td>\n","      <td>C172</td>\n","      <td>3000</td>\n","      <td>CHO</td>\n","      <td>Green</td>\n","      <td>No</td>\n","      <td>Charlottesville</td>\n","      <td>Virginia</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>6136 rows × 10 columns</p>\n","</div>"],"text/plain":["     Incident Date  Incident Time Flight ID Aircraft Altitude Airport  \\\n","0       2019-01-01             35      N3EG     C421     2500     SAT   \n","1       2019-01-01             43   RPA3469     E75S     4000     IAH   \n","2       2019-01-01             44   UAL1607     A319     4000     IAH   \n","3       2019-01-01            110    N205TM     BE20     2500     HDC   \n","4       2019-01-01            115   JIA5233     CRJ9     2000     JAX   \n","...            ...            ...       ...      ...      ...     ...   \n","6131    2019-12-31            845   ASH5861     CRJ9     3000     JAN   \n","6132    2019-12-31            929      N22P     CRUZ     2500     HNL   \n","6133    2019-12-31           2310    GTH530     GLF4      500     SJU   \n","6134    2019-12-31           2312   AMF6916      SW4      600     SJU   \n","6135    2019-12-31           2327    N715TH     C172     3000     CHO   \n","\n","     Laser Color Injury              City        State  \n","0          Green     No       San Antonio        Texas  \n","1          Green     No           Houston        Texas  \n","2          Green     No           Houston        Texas  \n","3          Green     No           Hammond    Louisiana  \n","4          Green     No      Jacksonville      Florida  \n","...          ...    ...               ...          ...  \n","6131       Green     No          Jackson   Mississippi  \n","6132       Green     No          Honolulu       Hawaii  \n","6133       White     No          Carolina  Puerto Rico  \n","6134       Green     No          Carolina  Puerto Rico  \n","6135       Green     No  Charlottesville      Virginia  \n","\n","[6136 rows x 10 columns]"]},"execution_count":51,"metadata":{},"output_type":"execute_result"}],"source":["pd.read_excel(\"data/laser_incidents_2019.xlsx\")"]},{"cell_type":"markdown","metadata":{"cell_id":"00083-77ddcd20-cd38-4097-a860-c3937f532502","deepnote_cell_type":"markdown"},"source":["Note: This reads just the first sheet from the file. If you want to extract more sheets, you will need to use the `pandas.'ExcelFile` class. See the [relevant part](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#reading-excel-files) of the documentation."]},{"cell_type":"markdown","metadata":{"cell_id":"00095-f0b29e54-ff51-488c-84af-4e69ffd6ecde","deepnote_cell_type":"markdown"},"source":["### Read HTML (Optional)\n","\n","Pandas is able to scrape data from tables embedded in web pages using the `read_html` function.\n","This might or might not bring you good results and probably you will have to tweak your\n","data frame manually. But it is a good starting point - much better than being forced to parse\n","the HTML ourselves!"]},{"cell_type":"code","execution_count":53,"metadata":{"cell_id":"00096-5eb09cb2-b8f9-4c84-b2c5-cb8d57a8fd20","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":128,"execution_start":1611568290755,"source_hash":"5920828f"},"outputs":[{"data":{"text/plain":["(list, 9)"]},"execution_count":53,"metadata":{},"output_type":"execute_result"}],"source":["tables = pd.read_html(\"https://en.wikipedia.org/wiki/List_of_laser_types\")\n","type(tables), len(tables)"]},{"cell_type":"code","execution_count":54,"metadata":{"cell_id":"00086-b66016d7-5d0d-4a7f-975b-fd7d023d0967","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":8,"execution_start":1611568296111,"source_hash":"6717b7e4"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Laser gain medium and type</th>\n","      <th>Operation wavelength(s)</th>\n","      <th>Pump source</th>\n","      <th>Applications and notes</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Helium–neon laser</td>\n","      <td>632.8 nm (543.5 nm, 593.9 nm, 611.8 nm, 1.1523...</td>\n","      <td>Electrical discharge</td>\n","      <td>Interferometry, holography, spectroscopy, barc...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Argon laser</td>\n","      <td>454.6 nm, 488.0 nm, 514.5 nm (351 nm, 363.8, 4...</td>\n","      <td>Electrical discharge</td>\n","      <td>Retinal phototherapy (for diabetes), lithograp...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Krypton laser</td>\n","      <td>416 nm, 530.9 nm, 568.2 nm, 647.1 nm, 676.4 nm...</td>\n","      <td>Electrical discharge</td>\n","      <td>Scientific research, mixed with argon to creat...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Xenon ion laser</td>\n","      <td>Many lines throughout visible spectrum extendi...</td>\n","      <td>Electrical discharge</td>\n","      <td>Scientific research.</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Nitrogen laser</td>\n","      <td>337.1 nm</td>\n","      <td>Electrical discharge</td>\n","      <td>Pumping of dye lasers, measuring air pollution...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Carbon dioxide laser</td>\n","      <td>10.6 μm, (9.4 μm)</td>\n","      <td>Transverse (high-power) or longitudinal (low-p...</td>\n","      <td>Material processing (laser cutting, laser beam...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Carbon monoxide laser</td>\n","      <td>2.6 to 4 μm, 4.8 to 8.3 μm</td>\n","      <td>Electrical discharge</td>\n","      <td>Material processing (engraving, welding, etc.)...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Excimer laser</td>\n","      <td>157 nm (F2), 193.3 nm (ArF), 248 nm (KrF), 308...</td>\n","      <td>Excimer recombination via electrical discharge</td>\n","      <td>Ultraviolet lithography for semiconductor manu...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Laser gain medium and type  \\\n","0          Helium–neon laser   \n","1                Argon laser   \n","2              Krypton laser   \n","3            Xenon ion laser   \n","4             Nitrogen laser   \n","5       Carbon dioxide laser   \n","6      Carbon monoxide laser   \n","7              Excimer laser   \n","\n","                             Operation wavelength(s)  \\\n","0  632.8 nm (543.5 nm, 593.9 nm, 611.8 nm, 1.1523...   \n","1  454.6 nm, 488.0 nm, 514.5 nm (351 nm, 363.8, 4...   \n","2  416 nm, 530.9 nm, 568.2 nm, 647.1 nm, 676.4 nm...   \n","3  Many lines throughout visible spectrum extendi...   \n","4                                           337.1 nm   \n","5                                  10.6 μm, (9.4 μm)   \n","6                         2.6 to 4 μm, 4.8 to 8.3 μm   \n","7  157 nm (F2), 193.3 nm (ArF), 248 nm (KrF), 308...   \n","\n","                                         Pump source  \\\n","0                               Electrical discharge   \n","1                               Electrical discharge   \n","2                               Electrical discharge   \n","3                               Electrical discharge   \n","4                               Electrical discharge   \n","5  Transverse (high-power) or longitudinal (low-p...   \n","6                               Electrical discharge   \n","7     Excimer recombination via electrical discharge   \n","\n","                              Applications and notes  \n","0  Interferometry, holography, spectroscopy, barc...  \n","1  Retinal phototherapy (for diabetes), lithograp...  \n","2  Scientific research, mixed with argon to creat...  \n","3                               Scientific research.  \n","4  Pumping of dye lasers, measuring air pollution...  \n","5  Material processing (laser cutting, laser beam...  \n","6  Material processing (engraving, welding, etc.)...  \n","7  Ultraviolet lithography for semiconductor manu...  "]},"execution_count":54,"metadata":{},"output_type":"execute_result"}],"source":["tables[1]"]},{"cell_type":"code","execution_count":55,"metadata":{"cell_id":"00087-6c3dba25-3a3b-4dd2-b50a-38f402dfd000","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":14,"execution_start":1611568297875,"source_hash":"c007c36a"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Laser gain medium and type</th>\n","      <th>Operation wavelength(s)</th>\n","      <th>Pump source</th>\n","      <th>Applications and notes</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Hydrogen fluoride laser</td>\n","      <td>2.7 to 2.9 μm for hydrogen fluoride (&lt;80% atmo...</td>\n","      <td>Chemical reaction in a burning jet of ethylene...</td>\n","      <td>Used in research for laser weaponry, operated ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Deuterium fluoride laser</td>\n","      <td>~3800 nm (3.6 to 4.2 μm) (~90% atm. transmitta...</td>\n","      <td>chemical reaction</td>\n","      <td>US military laser prototypes.</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>COIL (chemical oxygen–iodine laser)</td>\n","      <td>1.315 μm (&lt;70% atmospheric transmittance)</td>\n","      <td>Chemical reaction in a jet of singlet delta ox...</td>\n","      <td>Military lasers, scientific and materials rese...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Agil (All gas-phase iodine laser)</td>\n","      <td>1.315 μm (&lt;70% atmospheric transmittance)</td>\n","      <td>Chemical reaction of chlorine atoms with gaseo...</td>\n","      <td>Scientific, weaponry, aerospace.</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            Laser gain medium and type  \\\n","0              Hydrogen fluoride laser   \n","1             Deuterium fluoride laser   \n","2  COIL (chemical oxygen–iodine laser)   \n","3    Agil (All gas-phase iodine laser)   \n","\n","                             Operation wavelength(s)  \\\n","0  2.7 to 2.9 μm for hydrogen fluoride (<80% atmo...   \n","1  ~3800 nm (3.6 to 4.2 μm) (~90% atm. transmitta...   \n","2          1.315 μm (<70% atmospheric transmittance)   \n","3          1.315 μm (<70% atmospheric transmittance)   \n","\n","                                         Pump source  \\\n","0  Chemical reaction in a burning jet of ethylene...   \n","1                                  chemical reaction   \n","2  Chemical reaction in a jet of singlet delta ox...   \n","3  Chemical reaction of chlorine atoms with gaseo...   \n","\n","                              Applications and notes  \n","0  Used in research for laser weaponry, operated ...  \n","1                      US military laser prototypes.  \n","2  Military lasers, scientific and materials rese...  \n","3                   Scientific, weaponry, aerospace.  "]},"execution_count":55,"metadata":{},"output_type":"execute_result"}],"source":["tables[2]"]},{"cell_type":"markdown","metadata":{"cell_id":"00099-84a117bf-82b7-420c-8897-b6b7ccac564a","deepnote_cell_type":"markdown"},"source":["### Write CSV\n","\n","Pandas is able to write to many various formats but the usage is similar. "]},{"cell_type":"code","execution_count":56,"metadata":{"cell_id":"00100-43addc0e-d2c6-4fb4-8008-aafc629b4bd6","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":7,"execution_start":1611568306930,"source_hash":"5d15ec0c"},"outputs":[],"source":["tables[1].to_csv(\"gas_lasers.csv\", index=False)"]},{"cell_type":"markdown","metadata":{"cell_id":"00108-01ae14f6-543b-47d2-8e7b-349ae95ed116","deepnote_cell_type":"markdown"},"source":["## Data analysis (very basics)\n","\n","Let's extend the data of laser incidents to a broader time range and read the data from a summary CSV file:"]},{"cell_type":"code","execution_count":58,"metadata":{"cell_id":"00211-63078b55-fe36-4053-af3d-2e62e30fe3d1","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":109,"execution_start":1611579768500,"source_hash":"cd0f9759","tags":[]},"outputs":[],"source":["laser_incidents_raw = pd.read_csv(\"data/laser_incidents_2015-2020.csv\")"]},{"cell_type":"markdown","metadata":{"cell_id":"00106-2b91f7bb-016c-4e79-ba08-114067e1538e","deepnote_cell_type":"markdown"},"source":["Let's see what we have here..."]},{"cell_type":"code","execution_count":59,"metadata":{"cell_id":"00109-7585c80e-026a-42ee-a30c-818618f792fb","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":89,"execution_start":1611570270491,"source_hash":"8bb9bcc1"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Incident Date</th>\n","      <th>Incident Time</th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>2020-01-01</td>\n","      <td>148.0</td>\n","      <td>N424RP</td>\n","      <td>DA42/A</td>\n","      <td>8500.0</td>\n","      <td>SBA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Santa Barbara</td>\n","      <td>California</td>\n","      <td>2020-01-01 01:48:00</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2020-01-01</td>\n","      <td>155.0</td>\n","      <td>AMF1829</td>\n","      <td>B190</td>\n","      <td>40000.0</td>\n","      <td>SSF</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>San Antonio</td>\n","      <td>Texas</td>\n","      <td>2020-01-01 01:55:00</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>2020-01-01</td>\n","      <td>214.0</td>\n","      <td>NKS1881</td>\n","      <td>A320</td>\n","      <td>2500.0</td>\n","      <td>TPA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Tampa</td>\n","      <td>Florida</td>\n","      <td>2020-01-01 02:14:00</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>2020-01-01</td>\n","      <td>217.0</td>\n","      <td>FDX3873</td>\n","      <td>B763</td>\n","      <td>3000.0</td>\n","      <td>DFW</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Fort Worth</td>\n","      <td>Texas</td>\n","      <td>2020-01-01 02:17:00</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>2020-01-01</td>\n","      <td>218.0</td>\n","      <td>SWA3635</td>\n","      <td>B739</td>\n","      <td>11000.0</td>\n","      <td>MOD</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Modesto</td>\n","      <td>California</td>\n","      <td>2020-01-01 02:18:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Unnamed: 0 Incident Date  Incident Time Flight ID Aircraft  Altitude  \\\n","0           0    2020-01-01          148.0    N424RP   DA42/A    8500.0   \n","1           1    2020-01-01          155.0   AMF1829     B190   40000.0   \n","2           2    2020-01-01          214.0   NKS1881     A320    2500.0   \n","3           3    2020-01-01          217.0   FDX3873     B763    3000.0   \n","4           4    2020-01-01          218.0   SWA3635     B739   11000.0   \n","\n","  Airport Laser Color Injury           City       State            timestamp  \n","0     SBA       green  False  Santa Barbara  California  2020-01-01 01:48:00  \n","1     SSF       green  False    San Antonio       Texas  2020-01-01 01:55:00  \n","2     TPA       green  False          Tampa     Florida  2020-01-01 02:14:00  \n","3     DFW       green  False    Fort Worth        Texas  2020-01-01 02:17:00  \n","4     MOD       green  False        Modesto  California  2020-01-01 02:18:00  "]},"execution_count":59,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents_raw.head()"]},{"cell_type":"code","execution_count":60,"metadata":{"cell_id":"00110-5ecc71e7-2c54-4854-825e-06f53abc63bd","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":29,"execution_start":1611570286251,"source_hash":"2e516b33"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Incident Date</th>\n","      <th>Incident Time</th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>36458</th>\n","      <td>36458</td>\n","      <td>2015-12-31</td>\n","      <td>525.0</td>\n","      <td>VRD917</td>\n","      <td>A320 (AIRBUS - A-32</td>\n","      <td>8000.0</td>\n","      <td>LAS</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Las Vegas</td>\n","      <td>Nevada</td>\n","      <td>2015-12-31 05:25:00</td>\n","    </tr>\n","    <tr>\n","      <th>36459</th>\n","      <td>36459</td>\n","      <td>2015-12-31</td>\n","      <td>623.0</td>\n","      <td>DAL2371</td>\n","      <td>B738 (BOEING - 737-</td>\n","      <td>11000.0</td>\n","      <td>LHM</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Lincoln</td>\n","      <td>California</td>\n","      <td>2015-12-31 06:23:00</td>\n","    </tr>\n","    <tr>\n","      <th>36460</th>\n","      <td>36460</td>\n","      <td>2015-12-31</td>\n","      <td>1111.0</td>\n","      <td>Unknown</td>\n","      <td>Unknown</td>\n","      <td>2000.0</td>\n","      <td>FOK</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Westhampton Beach</td>\n","      <td>New York</td>\n","      <td>2015-12-31 11:11:00</td>\n","    </tr>\n","    <tr>\n","      <th>36461</th>\n","      <td>36461</td>\n","      <td>2015-12-31</td>\n","      <td>1147.0</td>\n","      <td>UAL197</td>\n","      <td>B737</td>\n","      <td>300.0</td>\n","      <td>GUM</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Guam</td>\n","      <td>Guam</td>\n","      <td>2015-12-31 11:47:00</td>\n","    </tr>\n","    <tr>\n","      <th>36462</th>\n","      <td>36462</td>\n","      <td>2015-12-31</td>\n","      <td>2314.0</td>\n","      <td>EJA336</td>\n","      <td>E55P/L</td>\n","      <td>1000.0</td>\n","      <td>APF</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Naples</td>\n","      <td>Florida</td>\n","      <td>2015-12-31 23:14:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Unnamed: 0 Incident Date  Incident Time Flight ID             Aircraft  \\\n","36458       36458    2015-12-31          525.0    VRD917  A320 (AIRBUS - A-32   \n","36459       36459    2015-12-31          623.0   DAL2371  B738 (BOEING - 737-   \n","36460       36460    2015-12-31         1111.0   Unknown              Unknown   \n","36461       36461    2015-12-31         1147.0    UAL197                 B737   \n","36462       36462    2015-12-31         2314.0    EJA336               E55P/L   \n","\n","       Altitude Airport Laser Color Injury               City       State  \\\n","36458    8000.0     LAS       green  False          Las Vegas      Nevada   \n","36459   11000.0     LHM       green  False            Lincoln  California   \n","36460    2000.0     FOK       green  False  Westhampton Beach    New York   \n","36461     300.0     GUM       green  False               Guam        Guam   \n","36462    1000.0     APF       green  False             Naples     Florida   \n","\n","                 timestamp  \n","36458  2015-12-31 05:25:00  \n","36459  2015-12-31 06:23:00  \n","36460  2015-12-31 11:11:00  \n","36461  2015-12-31 11:47:00  \n","36462  2015-12-31 23:14:00  "]},"execution_count":60,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents_raw.tail()"]},{"cell_type":"markdown","metadata":{"cell_id":"00111-a4b4c017-88ac-46b5-8108-540b9a009d64","deepnote_cell_type":"markdown"},"source":["For an unknown, potentially unevenly distributed dataset, looking at the beginning / end is typically not the best idea. We'd rather sample randomly:"]},{"cell_type":"code","execution_count":61,"metadata":{"cell_id":"00112-e1b12e12-d950-4faa-a3b8-f26c2c7700f0","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":9,"execution_start":1611570332518,"source_hash":"f966e514"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Incident Date</th>\n","      <th>Incident Time</th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>13749</th>\n","      <td>13749</td>\n","      <td>2018-10-23</td>\n","      <td>306.0</td>\n","      <td>DAL356</td>\n","      <td>B738</td>\n","      <td>8000.0</td>\n","      <td>MSP</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Minneapolis</td>\n","      <td>Minnesota</td>\n","      <td>2018-10-23 03:06:00</td>\n","    </tr>\n","    <tr>\n","      <th>14397</th>\n","      <td>14397</td>\n","      <td>2018-12-04</td>\n","      <td>17.0</td>\n","      <td>LBQ784</td>\n","      <td>PC12/G</td>\n","      <td>15000.0</td>\n","      <td>JAX</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Jacksonville</td>\n","      <td>Florida</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>11645</th>\n","      <td>11645</td>\n","      <td>2018-06-01</td>\n","      <td>601.0</td>\n","      <td>AAL301</td>\n","      <td>B738</td>\n","      <td>6000.0</td>\n","      <td>SLC</td>\n","      <td>red</td>\n","      <td>False</td>\n","      <td>Salt Lake City</td>\n","      <td>Utah</td>\n","      <td>2018-06-01 06:01:00</td>\n","    </tr>\n","    <tr>\n","      <th>5772</th>\n","      <td>5772</td>\n","      <td>2019-06-20</td>\n","      <td>355.0</td>\n","      <td>PFT574</td>\n","      <td>C560</td>\n","      <td>5000.0</td>\n","      <td>LAS</td>\n","      <td>blue</td>\n","      <td>False</td>\n","      <td>Las Vegas</td>\n","      <td>Nevada</td>\n","      <td>2019-06-20 03:55:00</td>\n","    </tr>\n","    <tr>\n","      <th>22561</th>\n","      <td>22561</td>\n","      <td>2016-02-08</td>\n","      <td>600.0</td>\n","      <td>UAL249</td>\n","      <td>Unknown</td>\n","      <td>13000.0</td>\n","      <td>T41</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>LA Porte</td>\n","      <td>Texas</td>\n","      <td>2016-02-08 06:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>18574</th>\n","      <td>18574</td>\n","      <td>2017-08-11</td>\n","      <td>445.0</td>\n","      <td>DAL2974</td>\n","      <td>B712</td>\n","      <td>4000.0</td>\n","      <td>PDX</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Portland</td>\n","      <td>Oregon</td>\n","      <td>2017-08-11 04:45:00</td>\n","    </tr>\n","    <tr>\n","      <th>14960</th>\n","      <td>14960</td>\n","      <td>2018-12-31</td>\n","      <td>623.0</td>\n","      <td>SWA2042</td>\n","      <td>B737</td>\n","      <td>6000.0</td>\n","      <td>HWD</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Hayward</td>\n","      <td>California</td>\n","      <td>2018-12-31 06:23:00</td>\n","    </tr>\n","    <tr>\n","      <th>24922</th>\n","      <td>24922</td>\n","      <td>2016-06-17</td>\n","      <td>524.0</td>\n","      <td>N522LG</td>\n","      <td>COL4</td>\n","      <td>7000.0</td>\n","      <td>ELP</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>El Paso</td>\n","      <td>Texas</td>\n","      <td>2016-06-17 05:24:00</td>\n","    </tr>\n","    <tr>\n","      <th>31425</th>\n","      <td>31425</td>\n","      <td>2015-06-10</td>\n","      <td>213.0</td>\n","      <td>RPA4266</td>\n","      <td>E170</td>\n","      <td>2500.0</td>\n","      <td>IND</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Indianapolis</td>\n","      <td>Indiana</td>\n","      <td>2015-06-10 02:13:00</td>\n","    </tr>\n","    <tr>\n","      <th>668</th>\n","      <td>668</td>\n","      <td>2020-02-07</td>\n","      <td>248.0</td>\n","      <td>SWA1251</td>\n","      <td>B737</td>\n","      <td>10500.0</td>\n","      <td>SAT</td>\n","      <td>green and red</td>\n","      <td>False</td>\n","      <td>San Antonio</td>\n","      <td>Texas</td>\n","      <td>2020-02-07 02:48:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Unnamed: 0 Incident Date  Incident Time Flight ID Aircraft  Altitude  \\\n","13749       13749    2018-10-23          306.0    DAL356     B738    8000.0   \n","14397       14397    2018-12-04           17.0    LBQ784   PC12/G   15000.0   \n","11645       11645    2018-06-01          601.0    AAL301     B738    6000.0   \n","5772         5772    2019-06-20          355.0    PFT574     C560    5000.0   \n","22561       22561    2016-02-08          600.0    UAL249  Unknown   13000.0   \n","18574       18574    2017-08-11          445.0   DAL2974     B712    4000.0   \n","14960       14960    2018-12-31          623.0   SWA2042     B737    6000.0   \n","24922       24922    2016-06-17          524.0    N522LG     COL4    7000.0   \n","31425       31425    2015-06-10          213.0   RPA4266     E170    2500.0   \n","668           668    2020-02-07          248.0   SWA1251     B737   10500.0   \n","\n","      Airport    Laser Color Injury            City       State  \\\n","13749     MSP          green  False     Minneapolis   Minnesota   \n","14397     JAX          green  False    Jacksonville     Florida   \n","11645     SLC            red  False  Salt Lake City        Utah   \n","5772      LAS           blue  False       Las Vegas      Nevada   \n","22561     T41          green  False        LA Porte       Texas   \n","18574     PDX          green  False        Portland      Oregon   \n","14960     HWD          green  False         Hayward  California   \n","24922     ELP          green  False         El Paso       Texas   \n","31425     IND          green  False    Indianapolis     Indiana   \n","668       SAT  green and red  False     San Antonio       Texas   \n","\n","                 timestamp  \n","13749  2018-10-23 03:06:00  \n","14397                  NaN  \n","11645  2018-06-01 06:01:00  \n","5772   2019-06-20 03:55:00  \n","22561  2016-02-08 06:00:00  \n","18574  2017-08-11 04:45:00  \n","14960  2018-12-31 06:23:00  \n","24922  2016-06-17 05:24:00  \n","31425  2015-06-10 02:13:00  \n","668    2020-02-07 02:48:00  "]},"execution_count":61,"metadata":{},"output_type":"execute_result"}],"source":["# Show a few examples\n","laser_incidents_raw.sample(10)"]},{"cell_type":"code","execution_count":62,"metadata":{"cell_id":"00223-f8a014e3-05b5-4aad-a946-9f9318503bcb","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611579998912,"scrolled":true,"source_hash":"c825f3d4","tags":[]},"outputs":[{"data":{"text/plain":["Unnamed: 0         int64\n","Incident Date     object\n","Incident Time    float64\n","Flight ID         object\n","Aircraft          object\n","Altitude         float64\n","Airport           object\n","Laser Color       object\n","Injury            object\n","City              object\n","State             object\n","timestamp         object\n","dtype: object"]},"execution_count":62,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents_raw.dtypes"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["The topic of data cleaning and pre-processing is very broad. We will limit ourselves to dropping unused columns and converting one to a proper type."]},{"cell_type":"code","execution_count":63,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>N424RP</td>\n","      <td>DA42/A</td>\n","      <td>8500.0</td>\n","      <td>SBA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Santa Barbara</td>\n","      <td>California</td>\n","      <td>2020-01-01 01:48:00</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>AMF1829</td>\n","      <td>B190</td>\n","      <td>40000.0</td>\n","      <td>SSF</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>San Antonio</td>\n","      <td>Texas</td>\n","      <td>2020-01-01 01:55:00</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>NKS1881</td>\n","      <td>A320</td>\n","      <td>2500.0</td>\n","      <td>TPA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Tampa</td>\n","      <td>Florida</td>\n","      <td>2020-01-01 02:14:00</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>FDX3873</td>\n","      <td>B763</td>\n","      <td>3000.0</td>\n","      <td>DFW</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Fort Worth</td>\n","      <td>Texas</td>\n","      <td>2020-01-01 02:17:00</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>SWA3635</td>\n","      <td>B739</td>\n","      <td>11000.0</td>\n","      <td>MOD</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Modesto</td>\n","      <td>California</td>\n","      <td>2020-01-01 02:18:00</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>36458</th>\n","      <td>VRD917</td>\n","      <td>A320 (AIRBUS - A-32</td>\n","      <td>8000.0</td>\n","      <td>LAS</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Las Vegas</td>\n","      <td>Nevada</td>\n","      <td>2015-12-31 05:25:00</td>\n","    </tr>\n","    <tr>\n","      <th>36459</th>\n","      <td>DAL2371</td>\n","      <td>B738 (BOEING - 737-</td>\n","      <td>11000.0</td>\n","      <td>LHM</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Lincoln</td>\n","      <td>California</td>\n","      <td>2015-12-31 06:23:00</td>\n","    </tr>\n","    <tr>\n","      <th>36460</th>\n","      <td>Unknown</td>\n","      <td>Unknown</td>\n","      <td>2000.0</td>\n","      <td>FOK</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Westhampton Beach</td>\n","      <td>New York</td>\n","      <td>2015-12-31 11:11:00</td>\n","    </tr>\n","    <tr>\n","      <th>36461</th>\n","      <td>UAL197</td>\n","      <td>B737</td>\n","      <td>300.0</td>\n","      <td>GUM</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Guam</td>\n","      <td>Guam</td>\n","      <td>2015-12-31 11:47:00</td>\n","    </tr>\n","    <tr>\n","      <th>36462</th>\n","      <td>EJA336</td>\n","      <td>E55P/L</td>\n","      <td>1000.0</td>\n","      <td>APF</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Naples</td>\n","      <td>Florida</td>\n","      <td>2015-12-31 23:14:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>36463 rows × 9 columns</p>\n","</div>"],"text/plain":["      Flight ID             Aircraft  Altitude Airport Laser Color Injury  \\\n","0        N424RP               DA42/A    8500.0     SBA       green  False   \n","1       AMF1829                 B190   40000.0     SSF       green  False   \n","2       NKS1881                 A320    2500.0     TPA       green  False   \n","3       FDX3873                 B763    3000.0     DFW       green  False   \n","4       SWA3635                 B739   11000.0     MOD       green  False   \n","...         ...                  ...       ...     ...         ...    ...   \n","36458    VRD917  A320 (AIRBUS - A-32    8000.0     LAS       green  False   \n","36459   DAL2371  B738 (BOEING - 737-   11000.0     LHM       green  False   \n","36460   Unknown              Unknown    2000.0     FOK       green  False   \n","36461    UAL197                 B737     300.0     GUM       green  False   \n","36462    EJA336               E55P/L    1000.0     APF       green  False   \n","\n","                    City       State           timestamp  \n","0          Santa Barbara  California 2020-01-01 01:48:00  \n","1            San Antonio       Texas 2020-01-01 01:55:00  \n","2                  Tampa     Florida 2020-01-01 02:14:00  \n","3            Fort Worth        Texas 2020-01-01 02:17:00  \n","4                Modesto  California 2020-01-01 02:18:00  \n","...                  ...         ...                 ...  \n","36458          Las Vegas      Nevada 2015-12-31 05:25:00  \n","36459            Lincoln  California 2015-12-31 06:23:00  \n","36460  Westhampton Beach    New York 2015-12-31 11:11:00  \n","36461               Guam        Guam 2015-12-31 11:47:00  \n","36462             Naples     Florida 2015-12-31 23:14:00  \n","\n","[36463 rows x 9 columns]"]},"execution_count":63,"metadata":{},"output_type":"execute_result"}],"source":["# The first three are not needed\n","laser_incidents = laser_incidents_raw.drop(columns=laser_incidents_raw.columns[:3])\n","\n","# We convert the timestamp\n","laser_incidents = laser_incidents.assign(\n","    timestamp = pd.to_datetime(laser_incidents[\"timestamp\"])\n",")\n","laser_incidents"]},{"cell_type":"code","execution_count":64,"metadata":{},"outputs":[{"data":{"text/plain":["Flight ID              object\n","Aircraft               object\n","Altitude              float64\n","Airport                object\n","Laser Color            object\n","Injury                 object\n","City                   object\n","State                  object\n","timestamp      datetime64[ns]\n","dtype: object"]},"execution_count":64,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents.dtypes"]},{"cell_type":"markdown","metadata":{"cell_id":"00116-c11f30a8-213d-43d0-a266-05c178f5bdb1","deepnote_cell_type":"markdown"},"source":["#### Categorical dtype (Optional)"]},{"cell_type":"markdown","metadata":{"cell_id":"00118-40976ed8-11f5-4d81-9edf-4ecbb8a509c2","deepnote_cell_type":"markdown"},"source":["To analyze **Laser Color**, we can look at its typical values."]},{"cell_type":"code","execution_count":65,"metadata":{"cell_id":"00119-1185825a-60ae-4324-9791-75307c0c4d40","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611580058236,"source_hash":"7a451c24"},"outputs":[{"data":{"text/plain":["count     36461\n","unique       73\n","top       green\n","freq      32787\n","Name: Laser Color, dtype: object"]},"execution_count":65,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[\"Laser Color\"].describe()"]},{"cell_type":"markdown","metadata":{"cell_id":"00120-530cf793-62f5-4dc9-85d3-9322a290acda","deepnote_cell_type":"markdown"},"source":["Not too many different values."]},{"cell_type":"code","execution_count":66,"metadata":{"cell_id":"00121-fc1d1882-0e50-4e84-81b5-b07428dcec93","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611580061642,"source_hash":"bb7bc601"},"outputs":[{"data":{"text/plain":["array(['green', 'purple', 'blue', 'unknown', 'red', 'white',\n","       'green and white', 'white and green', 'green and yellow',\n","       'multiple', 'unknwn', 'green and purple', 'green and red',\n","       'red and green', 'green and blue', 'blue and purple',\n","       'red white and blue', 'blue and green', 'blue or purple',\n","       'blue or green', 'yellow/orange', 'blue/purple', 'unkwn', 'orange',\n","       'multi', 'yellow and white', 'blue and white', 'white or amber',\n","       'red and white', 'yellow', 'amber', 'yellow and green',\n","       'white and blue', 'red, blue, and green', 'purple-blue',\n","       'red and blue', 'magenta', 'phx', 'green or blue', 'red or green',\n","       'green or red', 'green, blue or purple', 'blue and red', 'unkn',\n","       'blue-green', 'multi-colored', nan, 'blue-yellow',\n","       'white or green', 'green and orange', 'white-green-red',\n","       'multicolored', 'green-white', 'blue or white', 'green red blue',\n","       'green or white', 'blue -green', 'green-red', 'green-blue',\n","       'multi-color', 'green-yellow', 'red-white', 'blue-purple',\n","       'white-yellow', 'green-purple', 'lavender', 'orange-red',\n","       'blue-white', 'blue-red', 'yellow-white', 'red-green',\n","       'white-green', 'white-blue', 'white-red'], dtype=object)"]},"execution_count":66,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[\"Laser Color\"].unique()"]},{"cell_type":"code","execution_count":67,"metadata":{"cell_id":"00122-71fc39cc-49e9-4fdf-a691-f327233079ec","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611580067236,"scrolled":true,"source_hash":"e58027f8"},"outputs":[{"data":{"text/plain":["Laser Color\n","green             0.899235\n","blue              0.046790\n","red               0.012260\n","white             0.010395\n","unkn              0.009051\n","                    ...   \n","red or green      0.000027\n","white or green    0.000027\n","blue-yellow       0.000027\n","multi-colored     0.000027\n","white-red         0.000027\n","Name: proportion, Length: 73, dtype: float64"]},"execution_count":67,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[\"Laser Color\"].value_counts(normalize=True)"]},{"cell_type":"markdown","metadata":{"cell_id":"00123-029b8467-6cef-43db-b365-382cf1a7391a","deepnote_cell_type":"markdown"},"source":["This column is a very good candidate to turn into a pandas-special, **Categorical** data type. (See https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html)"]},{"cell_type":"code","execution_count":68,"metadata":{"cell_id":"00124-fd191f1d-375f-4f3f-a0c8-da2cc5a619cd","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":5,"execution_start":1611580076427,"source_hash":"e2654309"},"outputs":[{"data":{"text/plain":["2261216"]},"execution_count":68,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[\"Laser Color\"].memory_usage(deep=True)   # ~60 bytes per item"]},{"cell_type":"code","execution_count":69,"metadata":{"cell_id":"00125-49305ac3-2935-4f85-960e-34417275872a","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611580080121,"source_hash":"ac02b36b"},"outputs":[{"data":{"text/plain":["16027    green\n","17144    green\n","18133    green\n","6309     green\n","26757    green\n","6907     green\n","36042    green\n","21744    green\n","34013    green\n","17587    green\n","Name: Laser Color, dtype: category\n","Categories (73, object): ['amber', 'blue', 'blue -green', 'blue and green', ..., 'yellow and green', 'yellow and white', 'yellow-white', 'yellow/orange']"]},"execution_count":69,"metadata":{},"output_type":"execute_result"}],"source":["color_category = laser_incidents[\"Laser Color\"].astype(\"category\")\n","color_category.sample(10)"]},{"cell_type":"code","execution_count":70,"metadata":{"cell_id":"00126-c2413840-6826-454a-8c34-383331e41144","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611580083584,"scrolled":false,"source_hash":"31548793"},"outputs":[{"data":{"text/plain":["43668"]},"execution_count":70,"metadata":{},"output_type":"execute_result"}],"source":["color_category.memory_usage(deep=True)              # ~1-2 bytes per item"]},{"cell_type":"markdown","metadata":{"cell_id":"00127-ed3d49a2-d39b-4558-81cb-b7260f4c9ca8","deepnote_cell_type":"markdown"},"source":["**Exercise:** Are there any other columns in the dataset that you would suggest for conversion to categorical?"]},{"cell_type":"markdown","metadata":{"cell_id":"00123-f82612c3-e4f2-43e3-ab76-e5523d66b1ef","deepnote_cell_type":"markdown","tags":[]},"source":["#### Integer vs. float"]},{"cell_type":"markdown","metadata":{"cell_id":"00128-322171db-56d4-43fe-bfbc-7d17ed3b7082","deepnote_cell_type":"markdown"},"source":["Pandas is generally quite good at guessing (inferring) number types. \n","You may wonder why `Altitude` is float and not int though. \n","This is a consequence of not having an integer nan in numpy. There's been many discussions about this."]},{"cell_type":"code","execution_count":71,"metadata":{"cell_id":"00129-af1ce659-ef22-4ba5-a25a-28421dfcf478","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611580131568,"scrolled":true,"source_hash":"8bc46429"},"outputs":[{"data":{"text/plain":["0         8500.0\n","1        40000.0\n","2         2500.0\n","3         3000.0\n","4        11000.0\n","          ...   \n","36458     8000.0\n","36459    11000.0\n","36460     2000.0\n","36461      300.0\n","36462     1000.0\n","Name: Altitude, Length: 36463, dtype: float64"]},"execution_count":71,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[\"Altitude\"]"]},{"cell_type":"code","execution_count":72,"metadata":{"cell_id":"00130-75ec543c-44c3-4559-8383-2a2255de17f0","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":194,"execution_start":1611580134594,"scrolled":true,"source_hash":"acd13dfc"},"outputs":[{"ename":"IntCastingNaNError","evalue":"Cannot convert non-finite values (NA or inf) to integer","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mIntCastingNaNError\u001b[0m                        Traceback (most recent call last)","Cell \u001b[0;32mIn[72], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mlaser_incidents\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAltitude\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/mambaforge/envs/python-fjfi/lib/python3.9/site-packages/pandas/core/generic.py:6643\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   6637\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m   6638\u001b[0m         ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m   6639\u001b[0m     ]\n\u001b[1;32m   6641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   6642\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 6643\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6644\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m   6645\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m~/mambaforge/envs/python-fjfi/lib/python3.9/site-packages/pandas/core/internals/managers.py:430\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[1;32m    428\u001b[0m     copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    431\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    434\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    435\u001b[0m \u001b[43m    \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/mambaforge/envs/python-fjfi/lib/python3.9/site-packages/pandas/core/internals/managers.py:363\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    361\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    362\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 363\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    364\u001b[0m     result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m    366\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n","File \u001b[0;32m~/mambaforge/envs/python-fjfi/lib/python3.9/site-packages/pandas/core/internals/blocks.py:758\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[1;32m    755\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not squeeze with more than one column.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    756\u001b[0m     values \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m0\u001b[39m, :]  \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[0;32m--> 758\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    760\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m    762\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","File \u001b[0;32m~/mambaforge/envs/python-fjfi/lib/python3.9/site-packages/pandas/core/dtypes/astype.py:237\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    234\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 237\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    238\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m    239\u001b[0m     \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m    240\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[1;32m    241\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n","File \u001b[0;32m~/mambaforge/envs/python-fjfi/lib/python3.9/site-packages/pandas/core/dtypes/astype.py:182\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m    179\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 182\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n","File \u001b[0;32m~/mambaforge/envs/python-fjfi/lib/python3.9/site-packages/pandas/core/dtypes/astype.py:101\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mensure_string_array(\n\u001b[1;32m     97\u001b[0m         arr, skipna\u001b[38;5;241m=\u001b[39mskipna, convert_na_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m     98\u001b[0m     )\u001b[38;5;241m.\u001b[39mreshape(shape)\n\u001b[1;32m    100\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(arr\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39mfloating) \u001b[38;5;129;01mand\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_astype_float_to_int_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m    104\u001b[0m     \u001b[38;5;66;03m# if we have a datetime/timedelta array of objects\u001b[39;00m\n\u001b[1;32m    105\u001b[0m     \u001b[38;5;66;03m# then coerce to datetime64[ns] and use DatetimeArray.astype\u001b[39;00m\n\u001b[1;32m    107\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_np_dtype(dtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n","File \u001b[0;32m~/mambaforge/envs/python-fjfi/lib/python3.9/site-packages/pandas/core/dtypes/astype.py:145\u001b[0m, in \u001b[0;36m_astype_float_to_int_nansafe\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;124;03mastype with a check preventing converting NaN to an meaningless integer value.\u001b[39;00m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misfinite(values)\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m--> 145\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m IntCastingNaNError(\n\u001b[1;32m    146\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert non-finite values (NA or inf) to integer\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    147\u001b[0m     )\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;66;03m# GH#45151\u001b[39;00m\n\u001b[1;32m    150\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (values \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mall():\n","\u001b[0;31mIntCastingNaNError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"]}],"source":["laser_incidents[\"Altitude\"].astype(int)"]},{"cell_type":"markdown","metadata":{"cell_id":"00126-2c8993ce-8946-4375-bb20-2ee0ab461bde","deepnote_cell_type":"markdown","tags":[]},"source":["Quite recently, Pandas introduced nullable types for [working with missing data](https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html#missing-data), \n","for example [nullable integer](https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html#integer-na)."]},{"cell_type":"code","execution_count":73,"metadata":{"cell_id":"00131-e63ba474-cb21-404b-96b2-09b7a189a4bb","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611580390420,"scrolled":true,"source_hash":"4026b12"},"outputs":[{"data":{"text/plain":["0         8500\n","1        40000\n","2         2500\n","3         3000\n","4        11000\n","         ...  \n","36458     8000\n","36459    11000\n","36460     2000\n","36461      300\n","36462     1000\n","Name: Altitude, Length: 36463, dtype: Int64"]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[\"Altitude\"].astype(\"Int64\")"]},{"cell_type":"markdown","metadata":{"cell_id":"00132-b2a25fe0-7a24-4da1-ad6a-a60fb3ce368f","deepnote_cell_type":"markdown"},"source":["### Filtering\n","\n","Indexing in pandas Series / DataFrames (`[]`) support also boolean (masked) arrays. These arrays can be obtained by applying boolean operations on them.\n","\n","You can also use standard **comparison operators** like `<`, `<=`, `==`, `>=`, `>`, `!=`. \n","\n","It is possible to perform **logical operations** with boolean series too. You need to use `|`, `&`, `^` operators though, not `and`, `or`, `not` keywords. \n","\n","As an example, find all California incidents:"]},{"cell_type":"code","execution_count":74,"metadata":{"cell_id":"00133-8752eece-4640-482d-b740-1865e8f9545f","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611580444784,"source_hash":"6cd53a8d"},"outputs":[{"data":{"text/plain":["2330     False\n","8288     False\n","16691    False\n","35831    False\n","26067    False\n","21937    False\n","22719    False\n","6498     False\n","24045     True\n","11533    False\n","Name: State, dtype: bool"]},"execution_count":74,"metadata":{},"output_type":"execute_result"}],"source":["is_california = laser_incidents.State == \"California\"\n","is_california.sample(10)"]},{"cell_type":"markdown","metadata":{"cell_id":"00134-292a372c-6fa0-4fb7-beed-a676584f7161","deepnote_cell_type":"markdown"},"source":["Now we can directly apply the boolean mask. (Note: This is no magic. You can construct the mask yourself)"]},{"cell_type":"code","execution_count":75,"metadata":{"cell_id":"00135-06d17dd7-36b4-41f5-86a0-50e90566c04b","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611581549688,"source_hash":"c49f68f2"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>10611</th>\n","      <td>N377YG</td>\n","      <td>EVSS</td>\n","      <td>2000.0</td>\n","      <td>SNA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Santa Ana</td>\n","      <td>California</td>\n","      <td>2018-03-19 04:53:00</td>\n","    </tr>\n","    <tr>\n","      <th>29171</th>\n","      <td>N254CA</td>\n","      <td>GLF4</td>\n","      <td>4000.0</td>\n","      <td>SNA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Santa Ana</td>\n","      <td>California</td>\n","      <td>2015-01-05 02:36:00</td>\n","    </tr>\n","    <tr>\n","      <th>29266</th>\n","      <td>QXE472</td>\n","      <td>DH8D (DE HAVILLAND</td>\n","      <td>8000.0</td>\n","      <td>STS</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Santa Rosa</td>\n","      <td>California</td>\n","      <td>2015-01-12 05:39:00</td>\n","    </tr>\n","    <tr>\n","      <th>31416</th>\n","      <td>VVT5423</td>\n","      <td>UH60</td>\n","      <td>4000.0</td>\n","      <td>SNA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Santa Ana</td>\n","      <td>California</td>\n","      <td>2015-06-09 03:36:00</td>\n","    </tr>\n","    <tr>\n","      <th>4202</th>\n","      <td>JBU471</td>\n","      <td>A320</td>\n","      <td>2000.0</td>\n","      <td>SJC</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>San Jose</td>\n","      <td>California</td>\n","      <td>2019-03-09 04:46:00</td>\n","    </tr>\n","    <tr>\n","      <th>544</th>\n","      <td>SWR41</td>\n","      <td>B77W</td>\n","      <td>50000.0</td>\n","      <td>LAX</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Los Angeles</td>\n","      <td>California</td>\n","      <td>2020-01-30 03:31:00</td>\n","    </tr>\n","    <tr>\n","      <th>22330</th>\n","      <td>N2121V</td>\n","      <td>PA28</td>\n","      <td>4000.0</td>\n","      <td>ONT</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Ontario</td>\n","      <td>California</td>\n","      <td>2016-01-29 04:20:00</td>\n","    </tr>\n","    <tr>\n","      <th>27317</th>\n","      <td>N615PG</td>\n","      <td>E135</td>\n","      <td>6000.0</td>\n","      <td>SBP</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>San Luis Obispo</td>\n","      <td>California</td>\n","      <td>2016-10-26 02:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>32297</th>\n","      <td>SWA4201</td>\n","      <td>B737</td>\n","      <td>5000.0</td>\n","      <td>SAN</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>San Diego</td>\n","      <td>California</td>\n","      <td>2015-07-25 04:44:00</td>\n","    </tr>\n","    <tr>\n","      <th>28747</th>\n","      <td>SKW5786</td>\n","      <td>CRJ2</td>\n","      <td>3800.0</td>\n","      <td>BUR</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Burbank</td>\n","      <td>California</td>\n","      <td>2016-12-17 01:52:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      Flight ID            Aircraft  Altitude Airport Laser Color Injury  \\\n","10611    N377YG                EVSS    2000.0     SNA       green  False   \n","29171    N254CA                GLF4    4000.0     SNA       green  False   \n","29266    QXE472  DH8D (DE HAVILLAND    8000.0     STS       green  False   \n","31416   VVT5423                UH60    4000.0     SNA       green  False   \n","4202     JBU471                A320    2000.0     SJC       green  False   \n","544       SWR41                B77W   50000.0     LAX       green  False   \n","22330    N2121V                PA28    4000.0     ONT       green  False   \n","27317    N615PG                E135    6000.0     SBP       green  False   \n","32297   SWA4201                B737    5000.0     SAN       green  False   \n","28747   SKW5786                CRJ2    3800.0     BUR       green  False   \n","\n","                  City       State           timestamp  \n","10611        Santa Ana  California 2018-03-19 04:53:00  \n","29171        Santa Ana  California 2015-01-05 02:36:00  \n","29266       Santa Rosa  California 2015-01-12 05:39:00  \n","31416        Santa Ana  California 2015-06-09 03:36:00  \n","4202          San Jose  California 2019-03-09 04:46:00  \n","544        Los Angeles  California 2020-01-30 03:31:00  \n","22330          Ontario  California 2016-01-29 04:20:00  \n","27317  San Luis Obispo  California 2016-10-26 02:00:00  \n","32297        San Diego  California 2015-07-25 04:44:00  \n","28747          Burbank  California 2016-12-17 01:52:00  "]},"execution_count":75,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[is_california].sample(10)"]},{"cell_type":"markdown","metadata":{"cell_id":"00136-34b33725-4010-4b87-9c80-9a83353feebb","deepnote_cell_type":"markdown"},"source":["Or maybe we should include the whole West coast?"]},{"cell_type":"code","execution_count":76,"metadata":{"cell_id":"00137-e0dd79ab-d7dc-4122-a338-f11a93e60cd1","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":33,"execution_start":1611581559244,"source_hash":"59870149"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>8140</th>\n","      <td>DAL8945</td>\n","      <td>B752</td>\n","      <td>2000.0</td>\n","      <td>LAX</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Los Angeles</td>\n","      <td>California</td>\n","      <td>2019-11-02 07:16:00</td>\n","    </tr>\n","    <tr>\n","      <th>11670</th>\n","      <td>JBU687</td>\n","      <td>A321</td>\n","      <td>10500.0</td>\n","      <td>SMO</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Santa Monica</td>\n","      <td>California</td>\n","      <td>2018-06-03 03:30:00</td>\n","    </tr>\n","    <tr>\n","      <th>16188</th>\n","      <td>N01J</td>\n","      <td>R44</td>\n","      <td>500.0</td>\n","      <td>LGB</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Long Beach</td>\n","      <td>California</td>\n","      <td>2017-03-10 01:50:00</td>\n","    </tr>\n","    <tr>\n","      <th>31950</th>\n","      <td>SHERFF2</td>\n","      <td>HELO</td>\n","      <td>12000.0</td>\n","      <td>SEA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Seattle</td>\n","      <td>Washington</td>\n","      <td>2015-07-11 04:30:00</td>\n","    </tr>\n","    <tr>\n","      <th>14821</th>\n","      <td>SKW3258</td>\n","      <td>CRJ7</td>\n","      <td>6000.0</td>\n","      <td>LGB</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Long Beach</td>\n","      <td>California</td>\n","      <td>2018-12-23 05:48:00</td>\n","    </tr>\n","    <tr>\n","      <th>24680</th>\n","      <td>AAL95</td>\n","      <td>B739</td>\n","      <td>9000.0</td>\n","      <td>SAN</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>San Diego</td>\n","      <td>California</td>\n","      <td>2016-05-31 03:09:00</td>\n","    </tr>\n","    <tr>\n","      <th>4749</th>\n","      <td>JCM615</td>\n","      <td>H25B</td>\n","      <td>23000.0</td>\n","      <td>DAG</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Daggett</td>\n","      <td>California</td>\n","      <td>2019-04-08 06:16:00</td>\n","    </tr>\n","    <tr>\n","      <th>32642</th>\n","      <td>UAL1247</td>\n","      <td>B739/L</td>\n","      <td>15000.0</td>\n","      <td>OXR</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Oxnard</td>\n","      <td>California</td>\n","      <td>2015-08-10 04:09:00</td>\n","    </tr>\n","    <tr>\n","      <th>33940</th>\n","      <td>REH1</td>\n","      <td>EC35</td>\n","      <td>1200.0</td>\n","      <td>STS</td>\n","      <td>green</td>\n","      <td>True</td>\n","      <td>Santa Rosa</td>\n","      <td>California</td>\n","      <td>2015-10-05 03:10:00</td>\n","    </tr>\n","    <tr>\n","      <th>1127</th>\n","      <td>N816KW</td>\n","      <td>C172</td>\n","      <td>2500.0</td>\n","      <td>BFL</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Bakersfield</td>\n","      <td>California</td>\n","      <td>2020-03-05 02:25:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      Flight ID Aircraft  Altitude Airport Laser Color Injury          City  \\\n","8140    DAL8945     B752    2000.0     LAX       green  False   Los Angeles   \n","11670    JBU687     A321   10500.0     SMO       green  False  Santa Monica   \n","16188      N01J      R44     500.0     LGB       green  False    Long Beach   \n","31950   SHERFF2     HELO   12000.0     SEA       green  False       Seattle   \n","14821   SKW3258     CRJ7    6000.0     LGB       green  False    Long Beach   \n","24680     AAL95     B739    9000.0     SAN       green  False     San Diego   \n","4749     JCM615     H25B   23000.0     DAG       green  False       Daggett   \n","32642   UAL1247   B739/L   15000.0     OXR       green  False        Oxnard   \n","33940      REH1     EC35    1200.0     STS       green   True    Santa Rosa   \n","1127     N816KW     C172    2500.0     BFL       green  False   Bakersfield   \n","\n","            State           timestamp  \n","8140   California 2019-11-02 07:16:00  \n","11670  California 2018-06-03 03:30:00  \n","16188  California 2017-03-10 01:50:00  \n","31950  Washington 2015-07-11 04:30:00  \n","14821  California 2018-12-23 05:48:00  \n","24680  California 2016-05-31 03:09:00  \n","4749   California 2019-04-08 06:16:00  \n","32642  California 2015-08-10 04:09:00  \n","33940  California 2015-10-05 03:10:00  \n","1127   California 2020-03-05 02:25:00  "]},"execution_count":76,"metadata":{},"output_type":"execute_result"}],"source":["# isin takes an array of possible values\n","west_coast = laser_incidents[laser_incidents.State.isin([\"California\", \"Oregon\", \"Washington\"])]\n","west_coast.sample(10)"]},{"cell_type":"markdown","metadata":{"cell_id":"00135-a92f3da1-aafa-4184-835f-3bee4e7c5b7f","deepnote_cell_type":"markdown","tags":[]},"source":["Or low-altitude incidents?"]},{"cell_type":"code","execution_count":77,"metadata":{"cell_id":"00135-c3c9b74c-7eb6-4a86-9be4-75889555210d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611581572207,"source_hash":"d4d1b2e","tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>71</th>\n","      <td>AAL633</td>\n","      <td>A21N</td>\n","      <td>0.0</td>\n","      <td>ELP</td>\n","      <td>unknown</td>\n","      <td>False</td>\n","      <td>El Paso</td>\n","      <td>Texas</td>\n","      <td>2020-01-04 04:02:00</td>\n","    </tr>\n","    <tr>\n","      <th>267</th>\n","      <td>17223</td>\n","      <td>C172</td>\n","      <td>200.0</td>\n","      <td>SRQ</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Sarasota</td>\n","      <td>Florida</td>\n","      <td>2020-01-14 01:12:00</td>\n","    </tr>\n","    <tr>\n","      <th>400</th>\n","      <td>N106NK</td>\n","      <td>C172</td>\n","      <td>0.0</td>\n","      <td>ADS</td>\n","      <td>red</td>\n","      <td>False</td>\n","      <td>Addison</td>\n","      <td>Texas</td>\n","      <td>2020-01-21 20:49:00</td>\n","    </tr>\n","    <tr>\n","      <th>613</th>\n","      <td>FDX57</td>\n","      <td>DC10</td>\n","      <td>100.0</td>\n","      <td>BQN</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Aguadilla</td>\n","      <td>Puerto Rico</td>\n","      <td>2020-02-03 01:50:00</td>\n","    </tr>\n","    <tr>\n","      <th>1066</th>\n","      <td>CR6562</td>\n","      <td>HELI</td>\n","      <td>200.0</td>\n","      <td>PBI</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>West Palm Beach</td>\n","      <td>Florida</td>\n","      <td>2020-03-01 05:14:00</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>35801</th>\n","      <td>N80298</td>\n","      <td>C172</td>\n","      <td>200.0</td>\n","      <td>MIA</td>\n","      <td>red</td>\n","      <td>False</td>\n","      <td>Miami</td>\n","      <td>Florida</td>\n","      <td>NaT</td>\n","    </tr>\n","    <tr>\n","      <th>35892</th>\n","      <td>N488SR</td>\n","      <td>C525</td>\n","      <td>160.0</td>\n","      <td>DUA</td>\n","      <td>unkn</td>\n","      <td>False</td>\n","      <td>Durant</td>\n","      <td>Oklahoma</td>\n","      <td>2015-12-11 01:35:00</td>\n","    </tr>\n","    <tr>\n","      <th>36089</th>\n","      <td>UPS1337</td>\n","      <td>B763</td>\n","      <td>170.0</td>\n","      <td>LEX</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Lexington</td>\n","      <td>Kentucky</td>\n","      <td>2015-12-16 03:51:00</td>\n","    </tr>\n","    <tr>\n","      <th>36156</th>\n","      <td>UPS1295</td>\n","      <td>A306</td>\n","      <td>170.0</td>\n","      <td>LEX</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Lexington</td>\n","      <td>Kentucky</td>\n","      <td>2015-12-18 04:54:00</td>\n","    </tr>\n","    <tr>\n","      <th>36206</th>\n","      <td>NKS631</td>\n","      <td>A320</td>\n","      <td>172.0</td>\n","      <td>TDZ</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Toledo</td>\n","      <td>Ohio</td>\n","      <td>2015-12-19 23:53:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>274 rows × 9 columns</p>\n","</div>"],"text/plain":["      Flight ID Aircraft  Altitude Airport Laser Color Injury  \\\n","71       AAL633     A21N       0.0     ELP     unknown  False   \n","267       17223     C172     200.0     SRQ       green  False   \n","400      N106NK     C172       0.0     ADS         red  False   \n","613       FDX57     DC10     100.0     BQN       green  False   \n","1066     CR6562     HELI     200.0     PBI       green  False   \n","...         ...      ...       ...     ...         ...    ...   \n","35801    N80298     C172     200.0     MIA         red  False   \n","35892    N488SR     C525     160.0     DUA        unkn  False   \n","36089   UPS1337     B763     170.0     LEX       green  False   \n","36156   UPS1295     A306     170.0     LEX       green  False   \n","36206    NKS631     A320     172.0     TDZ       green  False   \n","\n","                   City        State           timestamp  \n","71              El Paso        Texas 2020-01-04 04:02:00  \n","267            Sarasota      Florida 2020-01-14 01:12:00  \n","400             Addison        Texas 2020-01-21 20:49:00  \n","613           Aguadilla  Puerto Rico 2020-02-03 01:50:00  \n","1066   West Palm Beach       Florida 2020-03-01 05:14:00  \n","...                 ...          ...                 ...  \n","35801             Miami      Florida                 NaT  \n","35892            Durant     Oklahoma 2015-12-11 01:35:00  \n","36089         Lexington     Kentucky 2015-12-16 03:51:00  \n","36156         Lexington     Kentucky 2015-12-18 04:54:00  \n","36206            Toledo         Ohio 2015-12-19 23:53:00  \n","\n","[274 rows x 9 columns]"]},"execution_count":77,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[laser_incidents.Altitude < 300]"]},{"cell_type":"markdown","metadata":{"cell_id":"00140-aa930bf4-8a66-4c8d-99b2-35dfcbc7fdba","deepnote_cell_type":"markdown"},"source":["### Visualization intermezzo\n","\n","Without much further ado, let's create our first plot."]},{"cell_type":"code","execution_count":78,"metadata":{"cell_id":"00141-bb2545a7-a777-4bfb-9062-79a61293ea08","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":3,"execution_start":1611581593826,"source_hash":"bea6c559"},"outputs":[{"data":{"text/plain":["State\n","California        7268\n","Texas             3620\n","Florida           2702\n","Arizona           1910\n","Colorado           988\n","Washington         982\n","Kentucky           952\n","Illinois           946\n","New York           921\n","Puerto Rico        912\n","Oregon             895\n","Tennessee          888\n","Nevada             837\n","Pennsylvania       826\n","Indiana            812\n","Utah               789\n","Ohio               750\n","Georgia            714\n","North Carolina     605\n","Missouri           547\n","Name: count, dtype: int64"]},"execution_count":78,"metadata":{},"output_type":"execute_result"}],"source":["# Most frequent states\n","laser_incidents[\"State\"].value_counts()[:20]"]},{"cell_type":"code","execution_count":79,"metadata":{"cell_id":"00139-073b6cf5-5911-43d7-a088-0cc9f534e515","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":239,"execution_start":1611581616421,"source_hash":"8c96c670","tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["laser_incidents[\"State\"].value_counts()[:20].plot(kind=\"bar\");"]},{"cell_type":"markdown","metadata":{"cell_id":"00151-73214bfe-e291-4213-97d5-14bdbd713252","deepnote_cell_type":"markdown"},"source":["## Sorting"]},{"cell_type":"code","execution_count":80,"metadata":{"cell_id":"00152-cec80388-3999-45b7-9206-2470ba4bb2f5","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":36,"execution_start":1611581638540,"source_hash":"673790e"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>21173</th>\n","      <td>ROU1628</td>\n","      <td>B763</td>\n","      <td>240000.0</td>\n","      <td>PBI</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>West Palm Beach</td>\n","      <td>Florida</td>\n","      <td>2017-12-04 11:49:00</td>\n","    </tr>\n","    <tr>\n","      <th>12017</th>\n","      <td>UPS797</td>\n","      <td>A306</td>\n","      <td>125000.0</td>\n","      <td>ABQ</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Albuquerque</td>\n","      <td>New Mexico</td>\n","      <td>2018-06-30 03:15:00</td>\n","    </tr>\n","    <tr>\n","      <th>27807</th>\n","      <td>LSFD1</td>\n","      <td>EC</td>\n","      <td>100000.0</td>\n","      <td>SJC</td>\n","      <td>blue</td>\n","      <td>False</td>\n","      <td>San Jose</td>\n","      <td>California</td>\n","      <td>2016-11-13 02:53:00</td>\n","    </tr>\n","    <tr>\n","      <th>21049</th>\n","      <td>ASQ5334</td>\n","      <td>CRJ7</td>\n","      <td>100000.0</td>\n","      <td>RDU</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Raleigh</td>\n","      <td>North Carolina</td>\n","      <td>2017-12-01 01:32:00</td>\n","    </tr>\n","    <tr>\n","      <th>27785</th>\n","      <td>ASH6193</td>\n","      <td>CRJ7</td>\n","      <td>98400.0</td>\n","      <td>IND</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Indianapolis</td>\n","      <td>Indiana</td>\n","      <td>2016-11-12 23:11:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      Flight ID Aircraft  Altitude Airport Laser Color Injury  \\\n","21173   ROU1628     B763  240000.0     PBI       green  False   \n","12017    UPS797     A306  125000.0     ABQ       green  False   \n","27807     LSFD1       EC  100000.0     SJC        blue  False   \n","21049   ASQ5334     CRJ7  100000.0     RDU       green  False   \n","27785   ASH6193     CRJ7   98400.0     IND       green  False   \n","\n","                  City           State           timestamp  \n","21173  West Palm Beach         Florida 2017-12-04 11:49:00  \n","12017      Albuquerque      New Mexico 2018-06-30 03:15:00  \n","27807         San Jose      California 2016-11-13 02:53:00  \n","21049          Raleigh  North Carolina 2017-12-01 01:32:00  \n","27785     Indianapolis         Indiana 2016-11-12 23:11:00  "]},"execution_count":80,"metadata":{},"output_type":"execute_result"}],"source":["# Display 5 incidents with the highest altitude\n","laser_incidents.sort_values(\"Altitude\", ascending=False).head(5)"]},{"cell_type":"code","execution_count":81,"metadata":{"cell_id":"00155-56db32fd-12d9-485b-9dc8-4393d81beb0d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":39,"execution_start":1611581645435,"source_hash":"a18fbdfc"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>21173</th>\n","      <td>ROU1628</td>\n","      <td>B763</td>\n","      <td>240000.0</td>\n","      <td>PBI</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>West Palm Beach</td>\n","      <td>Florida</td>\n","      <td>2017-12-04 11:49:00</td>\n","    </tr>\n","    <tr>\n","      <th>12017</th>\n","      <td>UPS797</td>\n","      <td>A306</td>\n","      <td>125000.0</td>\n","      <td>ABQ</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Albuquerque</td>\n","      <td>New Mexico</td>\n","      <td>2018-06-30 03:15:00</td>\n","    </tr>\n","    <tr>\n","      <th>21049</th>\n","      <td>ASQ5334</td>\n","      <td>CRJ7</td>\n","      <td>100000.0</td>\n","      <td>RDU</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Raleigh</td>\n","      <td>North Carolina</td>\n","      <td>2017-12-01 01:32:00</td>\n","    </tr>\n","    <tr>\n","      <th>27807</th>\n","      <td>LSFD1</td>\n","      <td>EC</td>\n","      <td>100000.0</td>\n","      <td>SJC</td>\n","      <td>blue</td>\n","      <td>False</td>\n","      <td>San Jose</td>\n","      <td>California</td>\n","      <td>2016-11-13 02:53:00</td>\n","    </tr>\n","    <tr>\n","      <th>27785</th>\n","      <td>ASH6193</td>\n","      <td>CRJ7</td>\n","      <td>98400.0</td>\n","      <td>IND</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Indianapolis</td>\n","      <td>Indiana</td>\n","      <td>2016-11-12 23:11:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      Flight ID Aircraft  Altitude Airport Laser Color Injury  \\\n","21173   ROU1628     B763  240000.0     PBI       green  False   \n","12017    UPS797     A306  125000.0     ABQ       green  False   \n","21049   ASQ5334     CRJ7  100000.0     RDU       green  False   \n","27807     LSFD1       EC  100000.0     SJC        blue  False   \n","27785   ASH6193     CRJ7   98400.0     IND       green  False   \n","\n","                  City           State           timestamp  \n","21173  West Palm Beach         Florida 2017-12-04 11:49:00  \n","12017      Albuquerque      New Mexico 2018-06-30 03:15:00  \n","21049          Raleigh  North Carolina 2017-12-01 01:32:00  \n","27807         San Jose      California 2016-11-13 02:53:00  \n","27785     Indianapolis         Indiana 2016-11-12 23:11:00  "]},"execution_count":81,"metadata":{},"output_type":"execute_result"}],"source":["# Alternative\n","laser_incidents.nlargest(5, \"Altitude\")"]},{"cell_type":"markdown","metadata":{"cell_id":"00156-3f27c790-049f-4d99-aa4e-032acaf43cdc","deepnote_cell_type":"markdown"},"source":["**Exercise:** Find the last 3 incidents with blue laser."]},{"cell_type":"markdown","metadata":{"cell_id":"00160-e3c9a342-9395-4fd6-9012-e55cf5c755bc","deepnote_cell_type":"markdown"},"source":["## Arithmetics and string manipulation"]},{"attachments":{},"cell_type":"markdown","metadata":{"cell_id":"00161-e326e9e8-fa3c-4788-be8b-c1ccc99b67ff","deepnote_cell_type":"markdown"},"source":["Standard **arithmetic operators** work on numerical columns too. And so do mathematical functions. Note all such operations are performed in a vector-like fashion."]},{"cell_type":"code","execution_count":82,"metadata":{"cell_id":"00162-d302c4f4-bdeb-46ad-ae72-e7c9c6bd8d6f","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":6,"execution_start":1611581670970,"source_hash":"fcb3ad67"},"outputs":[{"data":{"text/plain":["21880    2438.4\n","7556     1828.8\n","5124      762.0\n","1619     2286.0\n","28230    1066.8\n","8119     1066.8\n","4171      609.6\n","20169    4267.2\n","5014     2743.2\n","22018    1219.2\n","Name: Altitude, dtype: float64"]},"execution_count":82,"metadata":{},"output_type":"execute_result"}],"source":["altitude_meters = laser_incidents[\"Altitude\"] * .3048\n","altitude_meters.sample(10)"]},{"cell_type":"markdown","metadata":{"cell_id":"00147-4d2eda07-3d5b-4005-8521-4a46fc230acc","deepnote_cell_type":"markdown","tags":[]},"source":["You may mix columns and scalars, the string arithmetics also works as expected."]},{"cell_type":"code","execution_count":83,"metadata":{"cell_id":"00147-afccd665-426e-4152-830c-44f42a32b72e","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611581692952,"source_hash":"a5bbd6da","tags":[]},"outputs":[{"data":{"text/plain":["0          Santa Barbara, California\n","1                 San Antonio, Texas\n","2                     Tampa, Florida\n","3                 Fort Worth , Texas\n","4                Modesto, California\n","                    ...             \n","36458              Las Vegas, Nevada\n","36459            Lincoln, California\n","36460    Westhampton Beach, New York\n","36461                     Guam, Guam\n","36462                Naples, Florida\n","Length: 36463, dtype: object"]},"execution_count":83,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[\"City\"] + \", \" + laser_incidents[\"State\"]"]},{"cell_type":"markdown","metadata":{"cell_id":"00167-e72d2c71-c04a-4885-bbb7-c3ea7e4fe9fb","deepnote_cell_type":"markdown"},"source":["### Summary statistics\n","\n","The `describe` method shows summary statistics for all the columns:"]},{"cell_type":"code","execution_count":84,"metadata":{"cell_id":"00150-99f5fb5e-394e-4090-9e1b-94a124803b69","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":14,"execution_start":1611581705765,"source_hash":"eebca140","tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Altitude</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>36218.000000</td>\n","      <td>33431</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>7358.314264</td>\n","      <td>2017-08-31 03:32:36.253776384</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","      <td>2015-01-01 02:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>2500.000000</td>\n","      <td>2016-03-25 06:09:30</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>5000.000000</td>\n","      <td>2017-08-01 04:10:00</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>9700.000000</td>\n","      <td>2019-01-14 17:07:00</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>240000.000000</td>\n","      <td>2020-08-01 10:49:00</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>7642.686712</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            Altitude                      timestamp\n","count   36218.000000                          33431\n","mean     7358.314264  2017-08-31 03:32:36.253776384\n","min         0.000000            2015-01-01 02:00:00\n","25%      2500.000000            2016-03-25 06:09:30\n","50%      5000.000000            2017-08-01 04:10:00\n","75%      9700.000000            2019-01-14 17:07:00\n","max    240000.000000            2020-08-01 10:49:00\n","std      7642.686712                            NaN"]},"execution_count":84,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents.describe()"]},{"cell_type":"code","execution_count":87,"metadata":{"cell_id":"00150-b7ee6698-dad2-424d-a64b-f377e28f5f5f","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":117,"execution_start":1611581709658,"source_hash":"be4eb1ee","tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>36451</td>\n","      <td>36411</td>\n","      <td>36218.000000</td>\n","      <td>36450</td>\n","      <td>36461</td>\n","      <td>36445</td>\n","      <td>36460</td>\n","      <td>36457</td>\n","      <td>33431</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>24788</td>\n","      <td>1731</td>\n","      <td>NaN</td>\n","      <td>2019</td>\n","      <td>73</td>\n","      <td>2</td>\n","      <td>2254</td>\n","      <td>73</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>UNKN</td>\n","      <td>B737</td>\n","      <td>NaN</td>\n","      <td>LAX</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Phoenix</td>\n","      <td>California</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>49</td>\n","      <td>3817</td>\n","      <td>NaN</td>\n","      <td>988</td>\n","      <td>32787</td>\n","      <td>36261</td>\n","      <td>1157</td>\n","      <td>7268</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>7358.314264</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2017-08-31 03:32:36.253776384</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2015-01-01 02:00:00</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2500.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2016-03-25 06:09:30</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>5000.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2017-08-01 04:10:00</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>9700.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2019-01-14 17:07:00</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>240000.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2020-08-01 10:49:00</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>7642.686712</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Flight ID Aircraft       Altitude Airport Laser Color Injury     City  \\\n","count      36451    36411   36218.000000   36450       36461  36445    36460   \n","unique     24788     1731            NaN    2019          73      2     2254   \n","top         UNKN     B737            NaN     LAX       green  False  Phoenix   \n","freq          49     3817            NaN     988       32787  36261     1157   \n","mean         NaN      NaN    7358.314264     NaN         NaN    NaN      NaN   \n","min          NaN      NaN       0.000000     NaN         NaN    NaN      NaN   \n","25%          NaN      NaN    2500.000000     NaN         NaN    NaN      NaN   \n","50%          NaN      NaN    5000.000000     NaN         NaN    NaN      NaN   \n","75%          NaN      NaN    9700.000000     NaN         NaN    NaN      NaN   \n","max          NaN      NaN  240000.000000     NaN         NaN    NaN      NaN   \n","std          NaN      NaN    7642.686712     NaN         NaN    NaN      NaN   \n","\n","             State                      timestamp  \n","count        36457                          33431  \n","unique          73                            NaN  \n","top     California                            NaN  \n","freq          7268                            NaN  \n","mean           NaN  2017-08-31 03:32:36.253776384  \n","min            NaN            2015-01-01 02:00:00  \n","25%            NaN            2016-03-25 06:09:30  \n","50%            NaN            2017-08-01 04:10:00  \n","75%            NaN            2019-01-14 17:07:00  \n","max            NaN            2020-08-01 10:49:00  \n","std            NaN                            NaN  "]},"execution_count":87,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents.describe(include=\"all\")"]},{"cell_type":"code","execution_count":88,"metadata":{"cell_id":"00168-1752bdfb-7095-4f88-a554-2ba6b76ce988","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611581715842,"source_hash":"74ea3361"},"outputs":[{"data":{"text/plain":["7358.314263625822"]},"execution_count":88,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[\"Altitude\"].mean()"]},{"cell_type":"code","execution_count":89,"metadata":{"cell_id":"00169-dad58f25-491a-465a-b6eb-b9a3a71e9659","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611581717244,"source_hash":"e4b059b7"},"outputs":[{"data":{"text/plain":["7642.6867120945535"]},"execution_count":89,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[\"Altitude\"].std()"]},{"cell_type":"code","execution_count":90,"metadata":{"cell_id":"00170-45be576d-7c75-47de-8006-a72f978805ee","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":6,"execution_start":1611581718678,"source_hash":"bee76775"},"outputs":[{"data":{"text/plain":["240000.0"]},"execution_count":90,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[\"Altitude\"].max()"]},{"cell_type":"markdown","metadata":{"cell_id":"00174-4d46bfa9-0dc5-46cb-b599-418e7e35c7d0","deepnote_cell_type":"markdown"},"source":["### Basic string operations (Optional)\n","\n","These are typically accessed using the `.str` \"accessor\" of the Series like this:\n","    \n","- series.str.lower\n","- series.str.split\n","- series.str.startswith\n","- series.str.contains\n","- ...\n","\n","See more in the [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html)."]},{"cell_type":"code","execution_count":96,"metadata":{"cell_id":"00155-3f479a59-7d72-470c-a13d-2cd0b02c3894","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611581730292,"source_hash":"7b152063","tags":[]},"outputs":[{"data":{"text/plain":["array(['Panama City', 'Oklahoma City', 'Salt Lake City', 'Bullhead City',\n","       'Garden City', 'Atlantic City', 'Panama City  ', 'New York City',\n","       'Jefferson City', 'Kansas City', 'Rapid City', 'Tremont City',\n","       'Boulder City', 'Traverse City', 'Cross City', 'Brigham City',\n","       'Carson City', 'Midland City', 'Johnson City', 'Ponca City',\n","       'Panama City Beach', 'Sioux City', 'Bay City', 'Silver City',\n","       'Pueblo City', 'Iowa City', 'Calvert City', 'Crescent City',\n","       'Oak City', 'Falls City', 'Salt Lake City ', 'Royse City',\n","       'Kansas City ', 'Bossier City', 'Baker City', 'Ellwood City',\n","       'Dodge City', 'Garden City ', 'Union City', 'King City',\n","       'Kansas City    ', 'Mason City', 'Plant City   ', 'Lanai City',\n","       'Tell City', 'Yuba City', 'Kansas City  ', 'Salt Lake City    ',\n","       'Kansas City   ', 'Ocean City', 'Cedar City', 'City of Commerce',\n","       'Lake City', 'Beach City', 'Alexander City', 'Siler City',\n","       'Charles City', 'Malad City ', 'Rush City', 'Webster City',\n","       'Plant City'], dtype=object)"]},"execution_count":96,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[laser_incidents[\"City\"].str.contains(\"City\", na=False)][\"City\"].unique()"]},{"cell_type":"code","execution_count":97,"metadata":{"cell_id":"00156-1c7c826c-3fb7-4501-a16a-d680c1bc5c7c","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":9,"execution_start":1611581740902,"source_hash":"1b2868f1","tags":[]},"outputs":[{"data":{"text/plain":["array(['Panama City', 'Oklahoma City', 'Salt Lake City', 'Bullhead City',\n","       'Garden City', 'Atlantic City', 'New York City', 'Jefferson City',\n","       'Kansas City', 'Rapid City', 'Tremont City', 'Boulder City',\n","       'Traverse City', 'Cross City', 'Brigham City', 'Carson City',\n","       'Midland City', 'Johnson City', 'Ponca City', 'Panama City Beach',\n","       'Sioux City', 'Bay City', 'Silver City', 'Pueblo City',\n","       'Iowa City', 'Calvert City', 'Crescent City', 'Oak City',\n","       'Falls City', 'Royse City', 'Bossier City', 'Baker City',\n","       'Ellwood City', 'Dodge City', 'Union City', 'King City',\n","       'Mason City', 'Plant City', 'Lanai City', 'Tell City', 'Yuba City',\n","       'Ocean City', 'Cedar City', 'City of Commerce', 'Lake City',\n","       'Beach City', 'Alexander City', 'Siler City', 'Charles City',\n","       'Malad City', 'Rush City', 'Webster City'], dtype=object)"]},"execution_count":97,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents[laser_incidents[\"City\"].str.contains(\"City\", na=False)][\"City\"].str.strip().unique()"]},{"cell_type":"markdown","metadata":{"cell_id":"00224-bb92354d-7d1e-46f8-b068-924bdd26114a","deepnote_cell_type":"markdown"},"source":["## Merging data\n","\n","It is a common situation where we have two or more datasets with different columns that we need to bring together.\n","This operation is called *merging* and the Pandas apparatus is to a great detail described in [the documentation](https://pandas.pydata.org/docs/user_guide/merging.html).\n","\n","In our case, we would like to attach the state populations to the dataset. \n"]},{"cell_type":"code","execution_count":98,"metadata":{"cell_id":"00225-9cb43465-3c64-409d-93ae-c0bc774235b9","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":44,"execution_start":1611581774832,"source_hash":"98a1918d","tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Territory</th>\n","      <th>Population</th>\n","      <th>Population 2010</th>\n","      <th>Code</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>California</td>\n","      <td>39029342.0</td>\n","      <td>37253956</td>\n","      <td>CA</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Texas</td>\n","      <td>30029572.0</td>\n","      <td>25145561</td>\n","      <td>TX</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Florida</td>\n","      <td>22244823.0</td>\n","      <td>18801310</td>\n","      <td>FL</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>New York</td>\n","      <td>19677151.0</td>\n","      <td>19378102</td>\n","      <td>NY</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Pennsylvania</td>\n","      <td>12972008.0</td>\n","      <td>12702379</td>\n","      <td>PA</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Illinois</td>\n","      <td>12582032.0</td>\n","      <td>12830632</td>\n","      <td>IL</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Ohio</td>\n","      <td>11756058.0</td>\n","      <td>11536504</td>\n","      <td>OH</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Georgia</td>\n","      <td>10912876.0</td>\n","      <td>9687653</td>\n","      <td>GA</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>North Carolina</td>\n","      <td>10698973.0</td>\n","      <td>9535483</td>\n","      <td>NC</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Michigan</td>\n","      <td>10034113.0</td>\n","      <td>9883640</td>\n","      <td>MI</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>New Jersey</td>\n","      <td>9261699.0</td>\n","      <td>8791894</td>\n","      <td>NJ</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Virginia</td>\n","      <td>8683619.0</td>\n","      <td>8001024</td>\n","      <td>VA</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Washington</td>\n","      <td>7785786.0</td>\n","      <td>6724540</td>\n","      <td>WA</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Arizona</td>\n","      <td>7359197.0</td>\n","      <td>6392017</td>\n","      <td>AZ</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Tennessee</td>\n","      <td>7051339.0</td>\n","      <td>6346105</td>\n","      <td>TN</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Massachusetts</td>\n","      <td>6981974.0</td>\n","      <td>6547629</td>\n","      <td>MA</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Indiana</td>\n","      <td>6833037.0</td>\n","      <td>6483802</td>\n","      <td>IN</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Missouri</td>\n","      <td>6177957.0</td>\n","      <td>5988927</td>\n","      <td>MO</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Maryland</td>\n","      <td>6164660.0</td>\n","      <td>5773552</td>\n","      <td>MD</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Wisconsin</td>\n","      <td>5892539.0</td>\n","      <td>5686986</td>\n","      <td>WI</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>Colorado</td>\n","      <td>5839926.0</td>\n","      <td>5029196</td>\n","      <td>CO</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>Minnesota</td>\n","      <td>5717184.0</td>\n","      <td>5303925</td>\n","      <td>MN</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>South Carolina</td>\n","      <td>5282634.0</td>\n","      <td>4625364</td>\n","      <td>SC</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>Alabama</td>\n","      <td>5074296.0</td>\n","      <td>4779736</td>\n","      <td>AL</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>Louisiana</td>\n","      <td>4590241.0</td>\n","      <td>4533372</td>\n","      <td>LA</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>Kentucky</td>\n","      <td>4512310.0</td>\n","      <td>4339367</td>\n","      <td>KY</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>Oregon</td>\n","      <td>4240137.0</td>\n","      <td>3831074</td>\n","      <td>OR</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>Oklahoma</td>\n","      <td>4019800.0</td>\n","      <td>3751351</td>\n","      <td>OK</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>Connecticut</td>\n","      <td>3626205.0</td>\n","      <td>3574097</td>\n","      <td>CT</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>Utah</td>\n","      <td>3380800.0</td>\n","      <td>2763885</td>\n","      <td>UT</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>Puerto Rico</td>\n","      <td>3221789.0</td>\n","      <td>3725789</td>\n","      <td>PR</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>Iowa</td>\n","      <td>3200517.0</td>\n","      <td>3046355</td>\n","      <td>IA</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>Nevada</td>\n","      <td>3177772.0</td>\n","      <td>2700551</td>\n","      <td>NV</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>Arkansas</td>\n","      <td>3045637.0</td>\n","      <td>2915918</td>\n","      <td>AR</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>Mississippi</td>\n","      <td>2940057.0</td>\n","      <td>2967297</td>\n","      <td>MS</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>Kansas</td>\n","      <td>2937150.0</td>\n","      <td>2853118</td>\n","      <td>KS</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>New Mexico</td>\n","      <td>2113344.0</td>\n","      <td>2059179</td>\n","      <td>NM</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>Nebraska</td>\n","      <td>1967923.0</td>\n","      <td>1826341</td>\n","      <td>NE</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>Idaho</td>\n","      <td>1939033.0</td>\n","      <td>1567582</td>\n","      <td>ID</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>West Virginia</td>\n","      <td>1775156.0</td>\n","      <td>1852994</td>\n","      <td>WV</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>Hawaii</td>\n","      <td>1440196.0</td>\n","      <td>1360301</td>\n","      <td>HI</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>New Hampshire</td>\n","      <td>1395231.0</td>\n","      <td>1316470</td>\n","      <td>NH</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>Maine</td>\n","      <td>1385340.0</td>\n","      <td>1328361</td>\n","      <td>ME</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>Montana</td>\n","      <td>1122867.0</td>\n","      <td>989415</td>\n","      <td>MT</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>Rhode Island</td>\n","      <td>1093734.0</td>\n","      <td>1052567</td>\n","      <td>RI</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>Delaware</td>\n","      <td>1018396.0</td>\n","      <td>897934</td>\n","      <td>DE</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>South Dakota</td>\n","      <td>909824.0</td>\n","      <td>814180</td>\n","      <td>SD</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>North Dakota</td>\n","      <td>779261.0</td>\n","      <td>672591</td>\n","      <td>ND</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>Alaska</td>\n","      <td>733583.0</td>\n","      <td>710231</td>\n","      <td>AK</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>District of Columbia</td>\n","      <td>671803.0</td>\n","      <td>601723</td>\n","      <td>DC</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>Vermont</td>\n","      <td>647064.0</td>\n","      <td>625741</td>\n","      <td>VT</td>\n","    </tr>\n","    <tr>\n","      <th>51</th>\n","      <td>Wyoming</td>\n","      <td>581381.0</td>\n","      <td>563626</td>\n","      <td>WY</td>\n","    </tr>\n","    <tr>\n","      <th>52</th>\n","      <td>Guam</td>\n","      <td>NaN</td>\n","      <td>159358</td>\n","      <td>GU</td>\n","    </tr>\n","    <tr>\n","      <th>53</th>\n","      <td>U.S. Virgin Islands</td>\n","      <td>NaN</td>\n","      <td>106405</td>\n","      <td>VI</td>\n","    </tr>\n","    <tr>\n","      <th>54</th>\n","      <td>American Samoa</td>\n","      <td>NaN</td>\n","      <td>55519</td>\n","      <td>AS</td>\n","    </tr>\n","    <tr>\n","      <th>55</th>\n","      <td>Northern Mariana Islands</td>\n","      <td>NaN</td>\n","      <td>53883</td>\n","      <td>MP</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                   Territory  Population  Population 2010 Code\n","0                 California  39029342.0         37253956   CA\n","1                      Texas  30029572.0         25145561   TX\n","2                    Florida  22244823.0         18801310   FL\n","3                   New York  19677151.0         19378102   NY\n","4               Pennsylvania  12972008.0         12702379   PA\n","5                   Illinois  12582032.0         12830632   IL\n","6                       Ohio  11756058.0         11536504   OH\n","7                    Georgia  10912876.0          9687653   GA\n","8             North Carolina  10698973.0          9535483   NC\n","9                   Michigan  10034113.0          9883640   MI\n","10                New Jersey   9261699.0          8791894   NJ\n","11                  Virginia   8683619.0          8001024   VA\n","12                Washington   7785786.0          6724540   WA\n","13                   Arizona   7359197.0          6392017   AZ\n","14                 Tennessee   7051339.0          6346105   TN\n","15             Massachusetts   6981974.0          6547629   MA\n","16                   Indiana   6833037.0          6483802   IN\n","17                  Missouri   6177957.0          5988927   MO\n","18                  Maryland   6164660.0          5773552   MD\n","19                 Wisconsin   5892539.0          5686986   WI\n","20                  Colorado   5839926.0          5029196   CO\n","21                 Minnesota   5717184.0          5303925   MN\n","22            South Carolina   5282634.0          4625364   SC\n","23                   Alabama   5074296.0          4779736   AL\n","24                 Louisiana   4590241.0          4533372   LA\n","25                  Kentucky   4512310.0          4339367   KY\n","26                    Oregon   4240137.0          3831074   OR\n","27                  Oklahoma   4019800.0          3751351   OK\n","28               Connecticut   3626205.0          3574097   CT\n","29                      Utah   3380800.0          2763885   UT\n","30               Puerto Rico   3221789.0          3725789   PR\n","31                      Iowa   3200517.0          3046355   IA\n","32                    Nevada   3177772.0          2700551   NV\n","33                  Arkansas   3045637.0          2915918   AR\n","34               Mississippi   2940057.0          2967297   MS\n","35                    Kansas   2937150.0          2853118   KS\n","36                New Mexico   2113344.0          2059179   NM\n","37                  Nebraska   1967923.0          1826341   NE\n","38                     Idaho   1939033.0          1567582   ID\n","39             West Virginia   1775156.0          1852994   WV\n","40                    Hawaii   1440196.0          1360301   HI\n","41             New Hampshire   1395231.0          1316470   NH\n","42                     Maine   1385340.0          1328361   ME\n","43                   Montana   1122867.0           989415   MT\n","44              Rhode Island   1093734.0          1052567   RI\n","45                  Delaware   1018396.0           897934   DE\n","46              South Dakota    909824.0           814180   SD\n","47              North Dakota    779261.0           672591   ND\n","48                    Alaska    733583.0           710231   AK\n","49      District of Columbia    671803.0           601723   DC\n","50                   Vermont    647064.0           625741   VT\n","51                   Wyoming    581381.0           563626   WY\n","52                      Guam         NaN           159358   GU\n","53       U.S. Virgin Islands         NaN           106405   VI\n","54            American Samoa         NaN            55519   AS\n","55  Northern Mariana Islands         NaN            53883   MP"]},"execution_count":98,"metadata":{},"output_type":"execute_result"}],"source":["population = pd.read_csv(\"data/us_state_population.csv\")\n","population"]},{"cell_type":"markdown","metadata":{"cell_id":"00226-8147dc9b-914f-41f6-966b-b0cdcd7fdd03","deepnote_cell_type":"markdown","tags":[]},"source":["We will of course use the state name as the merge *key*. Before actually doing the merge, we can explore a bit whether all state names\n","from the laser incidents dataset are present in our population table."]},{"cell_type":"code","execution_count":99,"metadata":{"cell_id":"00227-6a0c8e33-2b0b-49a0-8f0f-32203eccfd66","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611581780420,"source_hash":"5907dbcd","tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["There are 82 rows with unknown states.\n","Unknown state values are: \n","[nan, 'Virgin Islands', 'Miami', 'North Hampshire', 'Marina Islands', 'Teas', 'Mexico', 'DC', 'VA', 'Northern Marina Islands', 'Mariana Islands', 'Oho', 'Northern Marianas Is', 'UNKN', 'Massachussets', 'FLorida', 'D.C.', 'MIchigan', 'Northern Mariana Is', 'Micronesia'].\n"]}],"source":["unknown_states = laser_incidents.loc[~laser_incidents[\"State\"].isin(population[\"Territory\"]), \"State\"]\n","print(f\"There are {unknown_states.count()} rows with unknown states.\")\n","print(f\"Unknown state values are: \\n{list(unknown_states.unique())}.\")"]},{"attachments":{},"cell_type":"markdown","metadata":{"cell_id":"00228-b71602fc-802c-4d49-9352-842e9ff9a591","deepnote_cell_type":"markdown"},"source":["We could certainly clean the data by correcting some of the typos. Since the number of the rows with unknown states is not large\n","(compared to the length of the whole dataset), we will deliberetly not fix the state names.\n","Instead, we will remove those rows from the merged dataset by using the *inner* type of merge.\n","All the merge types: *left*, *inner*, *outer* and *right* are well explained by the schema below:\n","\n","![merge types](pandas-joins.png)"]},{"cell_type":"markdown","metadata":{"cell_id":"00229-7ec9ba49-b9d3-489a-86bd-1e7965a512b0","deepnote_cell_type":"markdown","tags":[]},"source":["We can use the [`merge`](https://pandas.pydata.org/docs/reference/api/pandas.merge.html) function to add the `\"Population\"` values."]},{"cell_type":"code","execution_count":103,"metadata":{"cell_id":"00230-ff4a24b6-527b-45f1-b552-a955156b6482","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":51,"execution_start":1611581804022,"scrolled":true,"source_hash":"a9c59a48"},"outputs":[],"source":["laser_incidents_w_population = pd.merge(\n","    laser_incidents, population, left_on=\"State\", right_on=\"Territory\", how=\"inner\"\n",")"]},{"cell_type":"code","execution_count":104,"metadata":{"cell_id":"00231-6479b93f-1c71-4938-99c7-3ec9c177fc67","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611581807612,"source_hash":"13cf3d59"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","      <th>Territory</th>\n","      <th>Population</th>\n","      <th>Population 2010</th>\n","      <th>Code</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>N424RP</td>\n","      <td>DA42/A</td>\n","      <td>8500.0</td>\n","      <td>SBA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Santa Barbara</td>\n","      <td>California</td>\n","      <td>2020-01-01 01:48:00</td>\n","      <td>California</td>\n","      <td>39029342.0</td>\n","      <td>37253956</td>\n","      <td>CA</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>AMF1829</td>\n","      <td>B190</td>\n","      <td>40000.0</td>\n","      <td>SSF</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>San Antonio</td>\n","      <td>Texas</td>\n","      <td>2020-01-01 01:55:00</td>\n","      <td>Texas</td>\n","      <td>30029572.0</td>\n","      <td>25145561</td>\n","      <td>TX</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>NKS1881</td>\n","      <td>A320</td>\n","      <td>2500.0</td>\n","      <td>TPA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Tampa</td>\n","      <td>Florida</td>\n","      <td>2020-01-01 02:14:00</td>\n","      <td>Florida</td>\n","      <td>22244823.0</td>\n","      <td>18801310</td>\n","      <td>FL</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>FDX3873</td>\n","      <td>B763</td>\n","      <td>3000.0</td>\n","      <td>DFW</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Fort Worth</td>\n","      <td>Texas</td>\n","      <td>2020-01-01 02:17:00</td>\n","      <td>Texas</td>\n","      <td>30029572.0</td>\n","      <td>25145561</td>\n","      <td>TX</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>SWA3635</td>\n","      <td>B739</td>\n","      <td>11000.0</td>\n","      <td>MOD</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Modesto</td>\n","      <td>California</td>\n","      <td>2020-01-01 02:18:00</td>\n","      <td>California</td>\n","      <td>39029342.0</td>\n","      <td>37253956</td>\n","      <td>CA</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>36370</th>\n","      <td>VRD917</td>\n","      <td>A320 (AIRBUS - A-32</td>\n","      <td>8000.0</td>\n","      <td>LAS</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Las Vegas</td>\n","      <td>Nevada</td>\n","      <td>2015-12-31 05:25:00</td>\n","      <td>Nevada</td>\n","      <td>3177772.0</td>\n","      <td>2700551</td>\n","      <td>NV</td>\n","    </tr>\n","    <tr>\n","      <th>36371</th>\n","      <td>DAL2371</td>\n","      <td>B738 (BOEING - 737-</td>\n","      <td>11000.0</td>\n","      <td>LHM</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Lincoln</td>\n","      <td>California</td>\n","      <td>2015-12-31 06:23:00</td>\n","      <td>California</td>\n","      <td>39029342.0</td>\n","      <td>37253956</td>\n","      <td>CA</td>\n","    </tr>\n","    <tr>\n","      <th>36372</th>\n","      <td>Unknown</td>\n","      <td>Unknown</td>\n","      <td>2000.0</td>\n","      <td>FOK</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Westhampton Beach</td>\n","      <td>New York</td>\n","      <td>2015-12-31 11:11:00</td>\n","      <td>New York</td>\n","      <td>19677151.0</td>\n","      <td>19378102</td>\n","      <td>NY</td>\n","    </tr>\n","    <tr>\n","      <th>36373</th>\n","      <td>UAL197</td>\n","      <td>B737</td>\n","      <td>300.0</td>\n","      <td>GUM</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Guam</td>\n","      <td>Guam</td>\n","      <td>2015-12-31 11:47:00</td>\n","      <td>Guam</td>\n","      <td>NaN</td>\n","      <td>159358</td>\n","      <td>GU</td>\n","    </tr>\n","    <tr>\n","      <th>36374</th>\n","      <td>EJA336</td>\n","      <td>E55P/L</td>\n","      <td>1000.0</td>\n","      <td>APF</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Naples</td>\n","      <td>Florida</td>\n","      <td>2015-12-31 23:14:00</td>\n","      <td>Florida</td>\n","      <td>22244823.0</td>\n","      <td>18801310</td>\n","      <td>FL</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>36375 rows × 13 columns</p>\n","</div>"],"text/plain":["      Flight ID             Aircraft  Altitude Airport Laser Color Injury  \\\n","0        N424RP               DA42/A    8500.0     SBA       green  False   \n","1       AMF1829                 B190   40000.0     SSF       green  False   \n","2       NKS1881                 A320    2500.0     TPA       green  False   \n","3       FDX3873                 B763    3000.0     DFW       green  False   \n","4       SWA3635                 B739   11000.0     MOD       green  False   \n","...         ...                  ...       ...     ...         ...    ...   \n","36370    VRD917  A320 (AIRBUS - A-32    8000.0     LAS       green  False   \n","36371   DAL2371  B738 (BOEING - 737-   11000.0     LHM       green  False   \n","36372   Unknown              Unknown    2000.0     FOK       green  False   \n","36373    UAL197                 B737     300.0     GUM       green  False   \n","36374    EJA336               E55P/L    1000.0     APF       green  False   \n","\n","                    City       State           timestamp   Territory  \\\n","0          Santa Barbara  California 2020-01-01 01:48:00  California   \n","1            San Antonio       Texas 2020-01-01 01:55:00       Texas   \n","2                  Tampa     Florida 2020-01-01 02:14:00     Florida   \n","3            Fort Worth        Texas 2020-01-01 02:17:00       Texas   \n","4                Modesto  California 2020-01-01 02:18:00  California   \n","...                  ...         ...                 ...         ...   \n","36370          Las Vegas      Nevada 2015-12-31 05:25:00      Nevada   \n","36371            Lincoln  California 2015-12-31 06:23:00  California   \n","36372  Westhampton Beach    New York 2015-12-31 11:11:00    New York   \n","36373               Guam        Guam 2015-12-31 11:47:00        Guam   \n","36374             Naples     Florida 2015-12-31 23:14:00     Florida   \n","\n","       Population  Population 2010 Code  \n","0      39029342.0         37253956   CA  \n","1      30029572.0         25145561   TX  \n","2      22244823.0         18801310   FL  \n","3      30029572.0         25145561   TX  \n","4      39029342.0         37253956   CA  \n","...           ...              ...  ...  \n","36370   3177772.0          2700551   NV  \n","36371  39029342.0         37253956   CA  \n","36372  19677151.0         19378102   NY  \n","36373         NaN           159358   GU  \n","36374  22244823.0         18801310   FL  \n","\n","[36375 rows x 13 columns]"]},"execution_count":104,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents_w_population"]},{"cell_type":"code","execution_count":106,"metadata":{"cell_id":"00232-d6b25695-d24a-46de-948d-e21f0d7d044d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":129,"execution_start":1611581811551,"source_hash":"e8ac57e1"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","      <th>timestamp</th>\n","      <th>Territory</th>\n","      <th>Population</th>\n","      <th>Population 2010</th>\n","      <th>Code</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>36363</td>\n","      <td>36323</td>\n","      <td>36137.000000</td>\n","      <td>36365</td>\n","      <td>36374</td>\n","      <td>36359</td>\n","      <td>36374</td>\n","      <td>36375</td>\n","      <td>33361</td>\n","      <td>36375</td>\n","      <td>3.634300e+04</td>\n","      <td>3.637500e+04</td>\n","      <td>36375</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>24735</td>\n","      <td>1726</td>\n","      <td>NaN</td>\n","      <td>2009</td>\n","      <td>73</td>\n","      <td>2</td>\n","      <td>2239</td>\n","      <td>54</td>\n","      <td>NaN</td>\n","      <td>54</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>54</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>UNKN</td>\n","      <td>B737</td>\n","      <td>NaN</td>\n","      <td>LAX</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Phoenix</td>\n","      <td>California</td>\n","      <td>NaN</td>\n","      <td>California</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>CA</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>49</td>\n","      <td>3811</td>\n","      <td>NaN</td>\n","      <td>988</td>\n","      <td>32715</td>\n","      <td>36177</td>\n","      <td>1156</td>\n","      <td>7268</td>\n","      <td>NaN</td>\n","      <td>7268</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>7268</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>7363.934333</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2017-08-31 14:04:42.552681472</td>\n","      <td>NaN</td>\n","      <td>1.679960e+07</td>\n","      <td>1.542564e+07</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2015-01-01 02:00:00</td>\n","      <td>NaN</td>\n","      <td>5.813810e+05</td>\n","      <td>1.064050e+05</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2500.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2016-03-26 02:47:00</td>\n","      <td>NaN</td>\n","      <td>5.282634e+06</td>\n","      <td>4.779736e+06</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>5000.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2017-08-02 02:40:00</td>\n","      <td>NaN</td>\n","      <td>1.069897e+07</td>\n","      <td>9.687653e+06</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>9800.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2019-01-15 04:00:00</td>\n","      <td>NaN</td>\n","      <td>3.002957e+07</td>\n","      <td>2.514556e+07</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>240000.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2020-08-01 10:49:00</td>\n","      <td>NaN</td>\n","      <td>3.902934e+07</td>\n","      <td>3.725396e+07</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>7645.507063</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1.378730e+07</td>\n","      <td>1.287534e+07</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Flight ID Aircraft       Altitude Airport Laser Color Injury     City  \\\n","count      36363    36323   36137.000000   36365       36374  36359    36374   \n","unique     24735     1726            NaN    2009          73      2     2239   \n","top         UNKN     B737            NaN     LAX       green  False  Phoenix   \n","freq          49     3811            NaN     988       32715  36177     1156   \n","mean         NaN      NaN    7363.934333     NaN         NaN    NaN      NaN   \n","min          NaN      NaN       0.000000     NaN         NaN    NaN      NaN   \n","25%          NaN      NaN    2500.000000     NaN         NaN    NaN      NaN   \n","50%          NaN      NaN    5000.000000     NaN         NaN    NaN      NaN   \n","75%          NaN      NaN    9800.000000     NaN         NaN    NaN      NaN   \n","max          NaN      NaN  240000.000000     NaN         NaN    NaN      NaN   \n","std          NaN      NaN    7645.507063     NaN         NaN    NaN      NaN   \n","\n","             State                      timestamp   Territory    Population  \\\n","count        36375                          33361       36375  3.634300e+04   \n","unique          54                            NaN          54           NaN   \n","top     California                            NaN  California           NaN   \n","freq          7268                            NaN        7268           NaN   \n","mean           NaN  2017-08-31 14:04:42.552681472         NaN  1.679960e+07   \n","min            NaN            2015-01-01 02:00:00         NaN  5.813810e+05   \n","25%            NaN            2016-03-26 02:47:00         NaN  5.282634e+06   \n","50%            NaN            2017-08-02 02:40:00         NaN  1.069897e+07   \n","75%            NaN            2019-01-15 04:00:00         NaN  3.002957e+07   \n","max            NaN            2020-08-01 10:49:00         NaN  3.902934e+07   \n","std            NaN                            NaN         NaN  1.378730e+07   \n","\n","        Population 2010   Code  \n","count      3.637500e+04  36375  \n","unique              NaN     54  \n","top                 NaN     CA  \n","freq                NaN   7268  \n","mean       1.542564e+07    NaN  \n","min        1.064050e+05    NaN  \n","25%        4.779736e+06    NaN  \n","50%        9.687653e+06    NaN  \n","75%        2.514556e+07    NaN  \n","max        3.725396e+07    NaN  \n","std        1.287534e+07    NaN  "]},"execution_count":106,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents_w_population.describe(include=\"all\")"]},{"cell_type":"markdown","metadata":{"cell_id":"00233-a55496fd-adeb-4df6-acc8-95de93fb56fc","deepnote_cell_type":"markdown"},"source":["## Grouping & aggregation\n","\n","A common pattern in data analysis is grouping (or binning) data based on some property and getting some aggredate statistics.\n","\n","*Example:* Group this workshop participants by nationality a get the cardinality (the size) of each group."]},{"cell_type":"markdown","metadata":{"cell_id":"00234-cd96e084-45a7-4d0c-92b7-3b36e019f3dc","deepnote_cell_type":"markdown","tags":[]},"source":["Possibly the simplest group and aggregation is the `value_counts` method, which groups by the respective column value\n","and yields the number (or normalized frequency) of each unique value in the data."]},{"cell_type":"code","execution_count":107,"metadata":{"cell_id":"00235-ff04b023-4ba1-4c9e-95d2-ace15161ee6c","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611581834504,"source_hash":"17d3e2ac","tags":[]},"outputs":[{"data":{"text/plain":["State\n","California              7268\n","Texas                   3620\n","Florida                 2702\n","Arizona                 1910\n","Colorado                 988\n","Washington               982\n","Kentucky                 952\n","Illinois                 946\n","New York                 921\n","Puerto Rico              912\n","Oregon                   895\n","Tennessee                888\n","Nevada                   837\n","Pennsylvania             826\n","Indiana                  812\n","Utah                     789\n","Ohio                     750\n","Georgia                  714\n","North Carolina           605\n","Missouri                 547\n","Minnesota                531\n","New Jersey               519\n","Michigan                 505\n","Hawaii                   500\n","Alabama                  473\n","Virginia                 412\n","Oklahoma                 412\n","New Mexico               401\n","Louisiana                351\n","Massachusetts            346\n","South Carolina           306\n","Maryland                 255\n","Idaho                    237\n","Arkansas                 237\n","Wisconsin                207\n","Iowa                     200\n","Connecticut              185\n","District of Columbia     183\n","Kansas                   172\n","Mississippi              156\n","Montana                  134\n","Nebraska                 112\n","West Virginia            108\n","North Dakota              92\n","New Hampshire             86\n","Rhode Island              81\n","Alaska                    67\n","Maine                     66\n","South Dakota              52\n","Delaware                  43\n","Guam                      31\n","Vermont                   28\n","Wyoming                   22\n","U.S. Virgin Islands        1\n","Name: count, dtype: int64"]},"execution_count":107,"metadata":{},"output_type":"execute_result"}],"source":["laser_incidents_w_population[\"State\"].value_counts(normalize=False)"]},{"cell_type":"markdown","metadata":{"cell_id":"00236-162b4e43-c0a5-4e6e-81e7-c67d07c9727c","deepnote_cell_type":"markdown","tags":[]},"source":["This is just a primitive grouping and aggregation operation, we will look into more advanced patterns. \n","Let us say we would like to get some numbers (statistics) for individual states.\n","We can [`groupby`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html) the dataset by the `\"State\"` column:"]},{"cell_type":"code","execution_count":108,"metadata":{"cell_id":"00237-40cb5372-c1dd-48e6-a1f7-78c8508ee65d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611581860156,"source_hash":"93ed3a20"},"outputs":[],"source":["grouped_by_state = laser_incidents_w_population.groupby(\"State\")"]},{"cell_type":"markdown","metadata":{"cell_id":"00238-34236922-5ddf-42fe-9e33-0739ace84d5d","deepnote_cell_type":"markdown"},"source":["What did we get? "]},{"cell_type":"code","execution_count":109,"metadata":{"cell_id":"00239-180ff963-2b88-45b6-9229-da93d83943ae","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611581861359,"source_hash":"67295c6d"},"outputs":[{"data":{"text/plain":["<pandas.core.groupby.generic.DataFrameGroupBy object at 0x16d45faf0>"]},"execution_count":109,"metadata":{},"output_type":"execute_result"}],"source":["grouped_by_state"]},{"cell_type":"markdown","metadata":{"cell_id":"00240-02705e76-6766-43d1-bea8-92907f6ba93e","deepnote_cell_type":"markdown"},"source":["What is this `DataFrameGroupBy` object? [Its use case is](http://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html):\n","* Splitting the data into groups based on some criteria.\n","* Applying a function to each group independently.\n","* Combining the results into a data structure.\n"]},{"cell_type":"markdown","metadata":{"cell_id":"00241-6f20b354-c6fa-4f80-aab2-a5bf03b1bc58","deepnote_cell_type":"markdown"},"source":["Let's try a simple aggregate: the mean of altitude for each state:"]},{"cell_type":"code","execution_count":110,"metadata":{"cell_id":"00242-53cf6c41-e0fc-401e-86da-9feecba8c0d8","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611581866153,"source_hash":"96a64c50"},"outputs":[{"data":{"text/plain":["State\n","Puerto Rico              3552.996703\n","Hawaii                   4564.536585\n","Florida                  4970.406773\n","Alaska                   5209.848485\n","Wisconsin                5529.951220\n","New York                 5530.208743\n","Guam                     5800.000000\n","Maryland                 6071.739130\n","District of Columbia     6087.144444\n","New Jersey               6204.306950\n","Illinois                 6306.310566\n","Massachusetts            6473.763848\n","Texas                    6487.493759\n","Delaware                 6602.380952\n","Arizona                  6678.333158\n","Nevada                   6730.037485\n","California               6919.705613\n","Washington               7110.687629\n","Louisiana                7276.276353\n","Nebraska                 7277.321429\n","Michigan                 7330.459082\n","Oregon                   7411.285231\n","South Dakota             7419.607843\n","North Dakota             7455.434783\n","Ohio                     7482.409880\n","Pennsylvania             7518.614724\n","Connecticut              7519.562842\n","Vermont                  7610.714286\n","Idaho                    7636.756410\n","Oklahoma                 7678.803440\n","Montana                  7780.620155\n","Virginia                 7903.889976\n","Rhode Island             8186.875000\n","Minnesota                8191.869811\n","South Carolina           8593.535948\n","Kansas                   8661.994152\n","Indiana                  8664.055693\n","Maine                    8733.333333\n","Alabama                  8821.210191\n","Mississippi              8828.685897\n","Tennessee                8987.354402\n","North Carolina           9251.180763\n","New Hampshire            9591.764706\n","Utah                     9892.935197\n","Iowa                    10174.619289\n","Missouri                10548.161468\n","New Mexico              10714.706030\n","U.S. Virgin Islands     11000.000000\n","Georgia                 11130.663854\n","Arkansas                11203.483051\n","Colorado                11301.869388\n","Kentucky                11583.086225\n","West Virginia           12108.386792\n","Wyoming                 18238.095238\n","Name: Altitude, dtype: float64"]},"execution_count":110,"metadata":{},"output_type":"execute_result"}],"source":["grouped_by_state[\"Altitude\"].mean().sort_values()"]},{"cell_type":"markdown","metadata":{"cell_id":"00243-7ed92fb7-0072-4778-a14b-d361a388eedb","deepnote_cell_type":"markdown"},"source":["What if we were to group by year? We don't have a year column but we can just extract the year from the date and use it for `groupby`."]},{"cell_type":"code","execution_count":111,"metadata":{"cell_id":"00244-fa3bee11-be57-4f94-9952-dfcdef83d3e5","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":0,"execution_start":1611581884104,"source_hash":"fc8d42a7"},"outputs":[],"source":["grouped_by_year = laser_incidents_w_population.groupby(laser_incidents_w_population[\"timestamp\"].dt.year)"]},{"cell_type":"markdown","metadata":{"cell_id":"00245-151ab8cf-19db-4991-aa09-10228e1f2ece","deepnote_cell_type":"markdown","tags":[]},"source":["You may have noticed how we extracted the year using the [`.dt` accessor](https://pandas.pydata.org/docs/user_guide/basics.html#basics-dt-accessors).\n","We will use `.dt` even more below."]},{"cell_type":"markdown","metadata":{"cell_id":"00246-e25b5d25-0169-46dd-830a-0f370853727b","deepnote_cell_type":"markdown","tags":[]},"source":["Let's calculate the mean altitude of laser incidents per year. Are the lasers getting more powerful? 🤔"]},{"cell_type":"code","execution_count":112,"metadata":{"cell_id":"00247-a6f39663-ecf7-40c6-9dee-9f55a04d8976","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":4,"execution_start":1611581890788,"source_hash":"63689ae0","tags":[]},"outputs":[{"data":{"text/plain":["timestamp\n","2015.0    6564.621830\n","2016.0    7063.288912\n","2017.0    7420.971064\n","2018.0    7602.049323\n","2019.0    8242.586268\n","2020.0    8618.242465\n","Name: Altitude, dtype: float64"]},"execution_count":112,"metadata":{},"output_type":"execute_result"}],"source":["mean_altitude_per_year = grouped_by_year[\"Altitude\"].mean().sort_index()\n","mean_altitude_per_year"]},{"cell_type":"markdown","metadata":{"cell_id":"00248-b0e4965f-87d6-417f-be95-e135dc69eb65","deepnote_cell_type":"markdown","tags":[]},"source":["We can also quickly plot the results, more on plotting in the next lessons."]},{"cell_type":"code","execution_count":113,"metadata":{"cell_id":"00249-3a86526f-bd32-406b-a34e-4dc9c21e3364","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":153,"execution_start":1611581894917,"source_hash":"f6c49ffd","tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["mean_altitude_per_year.plot(kind=\"bar\");"]},{"cell_type":"markdown","metadata":{"cell_id":"00250-cc141cd5-94c6-4cc0-ad6c-55b447ffa775","deepnote_cell_type":"markdown","tags":[]},"source":["**Exercise:** Calculate the `sum` of injuries per year. Use the fact that `True + True = 2` ;)"]},{"cell_type":"markdown","metadata":{"cell_id":"00251-730c3364-9926-4709-ac1f-cf5c999862f4","deepnote_cell_type":"markdown","tags":[]},"source":["We can also create a new `Series` if the corresponding column does not exist in the dataframe and group it by another `Series`\n","(which in this case is a column from the dataframe). Important is that the grouped and the by series have the same index."]},{"cell_type":"code","execution_count":114,"metadata":{"cell_id":"00252-b9071b33-09a3-4608-9134-1c6740dced6e","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1,"execution_start":1611581920044,"source_hash":"27735942","tags":[]},"outputs":[{"data":{"text/plain":["State\n","Hawaii                  347.174968\n","Puerto Rico             283.072541\n","District of Columbia    272.401284\n","Nevada                  263.392087\n","Arizona                 259.539186\n","Utah                    233.376716\n","Oregon                  211.078085\n","Kentucky                210.978412\n","New Mexico              189.746676\n","California              186.218871\n","Colorado                169.180226\n","Washington              126.127279\n","Tennessee               125.933528\n","Idaho                   122.225872\n","Florida                 121.466464\n","Texas                   120.547839\n","Montana                 119.337375\n","Indiana                 118.834422\n","North Dakota            118.060573\n","Oklahoma                102.492661\n","Alabama                  93.214901\n","Minnesota                92.877892\n","Alaska                   91.332542\n","Missouri                 88.540597\n","Arkansas                 77.816234\n","Louisiana                76.466573\n","Illinois                 75.186584\n","Rhode Island             74.058226\n","Georgia                  65.427299\n","Ohio                     63.796895\n","Pennsylvania             63.675570\n","Iowa                     62.489904\n","New Hampshire            61.638539\n","West Virginia            60.839723\n","Kansas                   58.560169\n","South Carolina           57.925648\n","South Dakota             57.153911\n","Nebraska                 56.912796\n","North Carolina           56.547484\n","New Jersey               56.037235\n","Mississippi              53.060196\n","Connecticut              51.017524\n","Michigan                 50.328315\n","Massachusetts            49.556186\n","Maine                    47.641734\n","Virginia                 47.445656\n","New York                 46.805556\n","Vermont                  43.272381\n","Delaware                 42.223261\n","Maryland                 41.364812\n","Wyoming                  37.840934\n","Wisconsin                35.129169\n","U.S. Virgin Islands       0.000000\n","Guam                      0.000000\n","Name: Population, dtype: float64"]},"execution_count":114,"metadata":{},"output_type":"execute_result"}],"source":["# how many incidents per million inhabitants are there for each state?\n","incidents_per_million = (1_000_000 / laser_incidents_w_population[\"Population\"]).groupby(laser_incidents_w_population[\"State\"]).sum()\n","incidents_per_million.sort_values(ascending=False)"]},{"cell_type":"code","execution_count":115,"metadata":{"cell_id":"00253-2cf35a58-2508-425c-aef7-063c528317ea","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":1414,"execution_start":1611581937715,"source_hash":"707c2558","tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["incidents_per_million.sort_values(ascending=False).plot(kind=\"bar\", figsize=(15, 3));"]},{"attachments":{},"cell_type":"markdown","metadata":{"cell_id":"00254-d21271e7-9449-41fe-a96d-a57561b91e50","deepnote_cell_type":"markdown","tags":[]},"source":["## Time series operations (Optional)\n","\n","We will briefly look at some more specific operation for time series data (data with a natural time axis).\n","Typical operations for time series are resampling or rolling window transformations such as filtering.\n","Note that Pandas is not a general digital signal processing library - there are other (more capable) tools for this purpose.\n","\n","First, we set the index to `\"timestamp\"` to make our dataframe inherently time indexed. This will make doing further time operations easier."]},{"cell_type":"code","execution_count":116,"metadata":{"cell_id":"00255-c54372ae-004e-41f2-8334-35239a345c2d","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":26,"execution_start":1611581958884,"source_hash":"255850a5","tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Flight ID</th>\n","      <th>Aircraft</th>\n","      <th>Altitude</th>\n","      <th>Airport</th>\n","      <th>Laser Color</th>\n","      <th>Injury</th>\n","      <th>City</th>\n","      <th>State</th>\n","    </tr>\n","    <tr>\n","      <th>timestamp</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2020-01-01 01:48:00</th>\n","      <td>N424RP</td>\n","      <td>DA42/A</td>\n","      <td>8500.0</td>\n","      <td>SBA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Santa Barbara</td>\n","      <td>California</td>\n","    </tr>\n","    <tr>\n","      <th>2020-01-01 01:55:00</th>\n","      <td>AMF1829</td>\n","      <td>B190</td>\n","      <td>40000.0</td>\n","      <td>SSF</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>San Antonio</td>\n","      <td>Texas</td>\n","    </tr>\n","    <tr>\n","      <th>2020-01-01 02:14:00</th>\n","      <td>NKS1881</td>\n","      <td>A320</td>\n","      <td>2500.0</td>\n","      <td>TPA</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Tampa</td>\n","      <td>Florida</td>\n","    </tr>\n","    <tr>\n","      <th>2020-01-01 02:17:00</th>\n","      <td>FDX3873</td>\n","      <td>B763</td>\n","      <td>3000.0</td>\n","      <td>DFW</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Fort Worth</td>\n","      <td>Texas</td>\n","    </tr>\n","    <tr>\n","      <th>2020-01-01 02:18:00</th>\n","      <td>SWA3635</td>\n","      <td>B739</td>\n","      <td>11000.0</td>\n","      <td>MOD</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Modesto</td>\n","      <td>California</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2015-12-31 05:25:00</th>\n","      <td>VRD917</td>\n","      <td>A320 (AIRBUS - A-32</td>\n","      <td>8000.0</td>\n","      <td>LAS</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Las Vegas</td>\n","      <td>Nevada</td>\n","    </tr>\n","    <tr>\n","      <th>2015-12-31 06:23:00</th>\n","      <td>DAL2371</td>\n","      <td>B738 (BOEING - 737-</td>\n","      <td>11000.0</td>\n","      <td>LHM</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Lincoln</td>\n","      <td>California</td>\n","    </tr>\n","    <tr>\n","      <th>2015-12-31 11:11:00</th>\n","      <td>Unknown</td>\n","      <td>Unknown</td>\n","      <td>2000.0</td>\n","      <td>FOK</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Westhampton Beach</td>\n","      <td>New York</td>\n","    </tr>\n","    <tr>\n","      <th>2015-12-31 11:47:00</th>\n","      <td>UAL197</td>\n","      <td>B737</td>\n","      <td>300.0</td>\n","      <td>GUM</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Guam</td>\n","      <td>Guam</td>\n","    </tr>\n","    <tr>\n","      <th>2015-12-31 23:14:00</th>\n","      <td>EJA336</td>\n","      <td>E55P/L</td>\n","      <td>1000.0</td>\n","      <td>APF</td>\n","      <td>green</td>\n","      <td>False</td>\n","      <td>Naples</td>\n","      <td>Florida</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>36463 rows × 8 columns</p>\n","</div>"],"text/plain":["                    Flight ID             Aircraft  Altitude Airport  \\\n","timestamp                                                              \n","2020-01-01 01:48:00    N424RP               DA42/A    8500.0     SBA   \n","2020-01-01 01:55:00   AMF1829                 B190   40000.0     SSF   \n","2020-01-01 02:14:00   NKS1881                 A320    2500.0     TPA   \n","2020-01-01 02:17:00   FDX3873                 B763    3000.0     DFW   \n","2020-01-01 02:18:00   SWA3635                 B739   11000.0     MOD   \n","...                       ...                  ...       ...     ...   \n","2015-12-31 05:25:00    VRD917  A320 (AIRBUS - A-32    8000.0     LAS   \n","2015-12-31 06:23:00   DAL2371  B738 (BOEING - 737-   11000.0     LHM   \n","2015-12-31 11:11:00   Unknown              Unknown    2000.0     FOK   \n","2015-12-31 11:47:00    UAL197                 B737     300.0     GUM   \n","2015-12-31 23:14:00    EJA336               E55P/L    1000.0     APF   \n","\n","                    Laser Color Injury               City       State  \n","timestamp                                                              \n","2020-01-01 01:48:00       green  False      Santa Barbara  California  \n","2020-01-01 01:55:00       green  False        San Antonio       Texas  \n","2020-01-01 02:14:00       green  False              Tampa     Florida  \n","2020-01-01 02:17:00       green  False        Fort Worth        Texas  \n","2020-01-01 02:18:00       green  False            Modesto  California  \n","...                         ...    ...                ...         ...  \n","2015-12-31 05:25:00       green  False          Las Vegas      Nevada  \n","2015-12-31 06:23:00       green  False            Lincoln  California  \n","2015-12-31 11:11:00       green  False  Westhampton Beach    New York  \n","2015-12-31 11:47:00       green  False               Guam        Guam  \n","2015-12-31 23:14:00       green  False             Naples     Florida  \n","\n","[36463 rows x 8 columns]"]},"execution_count":116,"metadata":{},"output_type":"execute_result"}],"source":["incidents_w_time_index = laser_incidents.set_index(\"timestamp\")\n","incidents_w_time_index"]},{"cell_type":"markdown","metadata":{"cell_id":"00256-c91d8bbe-0421-40ea-ad11-0294041054c9","deepnote_cell_type":"markdown","tags":[]},"source":["First, turn the data into a time series of incidents per hour. This can be done by resampling to 1 hour and using \n","`count` (basically on any column or on any column that has any non-NA value) to count the number of incidents."]},{"cell_type":"code","execution_count":117,"metadata":{"cell_id":"00257-6b22d2e5-f4c0-4444-b231-4a84c8b33f77","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":19,"execution_start":1611581973826,"source_hash":"a673441c","tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/dm/gbbql3p121z0tr22r2z98vy00000gn/T/ipykernel_78670/3245646514.py:1: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n","  incidents_hourly = incidents_w_time_index.notna().any(axis=\"columns\").resample(\"1H\").count().rename(\"incidents per hour\")\n"]},{"data":{"text/plain":["timestamp\n","2015-01-01 02:00:00    1\n","2015-01-01 03:00:00    2\n","2015-01-01 04:00:00    1\n","2015-01-01 05:00:00    3\n","2015-01-01 06:00:00    0\n","                      ..\n","2020-08-01 06:00:00    0\n","2020-08-01 07:00:00    1\n","2020-08-01 08:00:00    1\n","2020-08-01 09:00:00    0\n","2020-08-01 10:00:00    3\n","Name: incidents per hour, Length: 48945, dtype: int64"]},"execution_count":117,"metadata":{},"output_type":"execute_result"}],"source":["incidents_hourly = incidents_w_time_index.notna().any(axis=\"columns\").resample(\"1H\").count().rename(\"incidents per hour\")\n","incidents_hourly"]},{"cell_type":"markdown","metadata":{"cell_id":"00258-2e038a7b-dfdf-4f1a-9db6-602b72bfed15","deepnote_cell_type":"markdown","tags":[]},"source":["Looking at those data gives us a bit too detailed information."]},{"cell_type":"code","execution_count":118,"metadata":{"cell_id":"00259-400f3d4a-95df-4434-9554-b6abc9b19084","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":458,"execution_start":1611581983970,"source_hash":"9cc2db73","tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["incidents_hourly.sort_index().plot(kind=\"line\", figsize=(15, 3));"]},{"cell_type":"markdown","metadata":{"cell_id":"00260-5084b047-e81c-4677-8c13-0e1c8b7e25b5","deepnote_cell_type":"markdown","tags":[]},"source":["A daily mean, the result of resampling to 1 day periods and calculating the mean, is already something more digestible. \n","Though still a bit noisy."]},{"cell_type":"code","execution_count":119,"metadata":{"cell_id":"00261-41a60c90-c1d7-4254-911a-a0ec99619f65","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":174,"execution_start":1611581992792,"source_hash":"b08f63b6","tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["incidents_daily = incidents_hourly.resample(\"1D\").mean()\n","incidents_daily.plot.line(figsize=(15, 3));"]},{"cell_type":"markdown","metadata":{"cell_id":"00262-0b0a784a-ad1b-4466-93f0-38e3b8fc7357","deepnote_cell_type":"markdown","tags":[]},"source":["We can look at filtered data by rolling mean with, e.g., 28 days window size."]},{"cell_type":"code","execution_count":120,"metadata":{"cell_id":"00263-4cf9649d-4308-4ca1-b27d-0a426d499edd","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":242,"execution_start":1611582000330,"source_hash":"c3e5c2ca","tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x300 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["incidents_daily_filtered = incidents_daily.rolling(\"28D\").mean()\n","incidents_daily.plot.line(figsize=(15, 3));\n","incidents_daily_filtered.plot.line(figsize=(15, 3));"]}],"metadata":{"deepnote":{},"deepnote_execution_queue":[],"deepnote_notebook_id":"c07be921-4e1e-454b-99b6-9e0865476bab","kernelspec":{"display_name":"python-course-2023","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.16"},"vscode":{"interpreter":{"hash":"11b62e27f0bdc93aa8ef666f0d0fce5f6f1147a16767b8591ab6102cbec3074e"}}},"nbformat":4,"nbformat_minor":4}