.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/pandas/groupby.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_pandas_groupby.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_pandas_groupby.py:


=============
5.6 groupby
=============
This lesson shows the usage of groupby in pandas

.. important::
  This lesson is still under development.

.. GENERATED FROM PYTHON SOURCE LINES 13-16

The material in this lesson is mostly inspired from
`pandas documentation for groupby <https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html>`_
and `realpython <https://realpython.com/pandas-groupby/>`_ .

.. GENERATED FROM PYTHON SOURCE LINES 16-26

.. code-block:: default


    import time

    import numpy as np
    import pandas as pd

    print(time.asctime())
    print(np.__version__)
    print(pd.__version__)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Mon Nov 11 07:57:11 2024
    1.26.4
    1.5.3




.. GENERATED FROM PYTHON SOURCE LINES 27-42

.. code-block:: default


    url = "https://danepubliczne.imgw.pl/data/dane_pomiarowo_obserwacyjne/dane_hydrologiczne/dobowe/2020/codz_2020_01.zip"

    df = pd.read_csv(
        url, compression='zip', encoding="ISO-8859-1",  engine='python', 
        on_bad_lines="skip",
        names=['stn_id', 'year', 'day', 'water_level_cm', 'q_cms', 'temp_C', 'month'],
        usecols=[0, 3, 5, 6, 7, 8, 9],
        dtype={'stn_id': int, 'year': 'int', 'day': 'int', 'water_level_cm': np.float32, 'q_cms': np.float32, 'temp_C': np.float32, 'month': 'int'},
        parse_dates={'date': ['year', 'month', 'day']},
        index_col='date'
        )

    df






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>stn_id</th>
          <th>water_level_cm</th>
          <th>q_cms</th>
          <th>temp_C</th>
        </tr>
        <tr>
          <th>date</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>2020-11-01</th>
          <td>149180020</td>
          <td>82.0</td>
          <td>12.90</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-02</th>
          <td>149180020</td>
          <td>80.0</td>
          <td>12.50</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-03</th>
          <td>149180020</td>
          <td>79.0</td>
          <td>12.10</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-04</th>
          <td>149180020</td>
          <td>81.0</td>
          <td>12.60</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-05</th>
          <td>149180020</td>
          <td>84.0</td>
          <td>13.40</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>2020-11-26</th>
          <td>149190250</td>
          <td>142.0</td>
          <td>0.44</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-27</th>
          <td>149190250</td>
          <td>142.0</td>
          <td>0.44</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-28</th>
          <td>149190250</td>
          <td>142.0</td>
          <td>0.44</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-29</th>
          <td>149190250</td>
          <td>142.0</td>
          <td>0.44</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-30</th>
          <td>149190250</td>
          <td>142.0</td>
          <td>0.44</td>
          <td>99.900002</td>
        </tr>
      </tbody>
    </table>
    <p>24913 rows × 4 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 43-46

.. code-block:: default


    print(df.shape)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    (24913, 4)




.. GENERATED FROM PYTHON SOURCE LINES 47-50

.. code-block:: default


    df['stn_id'].unique()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    array([149180020, 149180300, 149180010, 150180060, 150180030, 150170240,
           150170290, 150170130, 150170090, 150170040, 151170030, 151160170,
           151160150, 151160130, 151160060, 151150150, 152150130, 152150050,
           152140130, 152140090, 152140050, 152140060, 152140020, 152140010,
           153140020, 153140030, 153140050, 153140190, 150170160, 150170170,
           149180130, 149180060, 149180070, 149180030, 149180050, 149180040,
           150180040, 150180090, 150180280, 150180130, 150180110, 150180140,
           150180120, 150180080, 150180250, 150180220, 150180180, 150180150,
           150180070, 150180170, 150180160, 150180010, 150170180, 150170110,
           150170080, 150170220, 150180190, 150180100, 150180050, 150180020,
           150180230, 150160190, 150160170, 150160180, 150160220, 150170060,
           150170100, 150170140, 150160210, 150160150, 150160230, 150160200,
           150160110, 150160080, 150160100, 150160270, 150170070, 150170050,
           150170120, 150170150, 150170200, 150170210, 150170010, 150170030,
           150160250, 150160280, 151160230, 150160060, 150160070, 150160120,
           150160160, 151160190, 150160140, 150160130, 150160290, 150160030,
           150160090, 151160180, 150160020, 151170090, 151170050, 151170010,
           151150170, 151160020, 151160050, 151160100, 151160090, 151160070,
           151160040, 151160080, 151150160, 151150180, 151170070, 151170040,
           151160140, 151170080, 151170060, 151160220, 151160200, 151160160,
           151160030, 151160260, 151150190, 152150170, 151160250, 151160010,
           152150060, 150150120, 150150130, 150160010, 150150100, 150150080,
           150150060, 151150140, 151150120, 151150080, 151150050, 151150040,
           152150020, 150150090, 150150110, 150150030, 150150050, 150150070,
           150150190, 150150200, 150150040, 151150130, 150150010, 151150110,
           151150060, 151150100, 151150090, 150150020, 151150070, 151150030,
           150140010, 150140020, 151140060, 151140040, 151140010, 150140100,
           150140030, 151150020, 151140050, 151150010, 151140030, 151140020,
           152140110, 152140080, 150190240, 150190200, 150190150, 150190220,
           151190060, 151180130, 151180100, 151180120, 151180080, 151180110,
           152180120, 152180050, 152170130, 152170080, 152170060, 152170010,
           152160140, 152160100, 152160050, 152150200, 152150110, 152150080,
           152150040, 152150010, 152140070, 150180210, 151190010, 151180090,
           151190020, 151180170, 151180140, 151190030, 151180180, 151180150,
           151190040, 151180160, 152180150, 152180140, 152180110, 152180160,
           152180060, 152170150, 152170140, 152170160, 152170190, 152170120,
           151180070, 151180040, 151180020, 151170110, 151180030, 151180060,
           151180010, 151180050, 152160090, 152160130, 152160060, 152169998,
           152160150, 152170020, 152170050, 152170030, 152170040, 152160110,
           152160120, 152160170, 152160080, 152160040, 152150270, 152150220,
           152150100, 152160030, 152150120, 152180100, 152180090, 152180170,
           152180080, 152180030, 153170100, 153170010, 153160170, 153160160,
           152160070, 152160010, 152150190, 152150140, 152150090, 152180010,
           152180020, 152170100, 152170090, 152170110, 153170040, 153160300,
           153160200, 153160210, 153160180, 153160150, 153160220, 153160260,
           153160250, 153160050, 153160290, 153160270, 153160070, 153160190,
           153160110, 153160060, 153170160, 153170020, 153170150, 153160280,
           153160030, 153160040, 153150120, 153150140, 153150100, 152150240,
           153160010, 153150180, 153150160, 153150150, 153150170, 152150150,
           152150180, 152150230, 152140120, 152140100, 152140030, 152140270,
           153140110, 153140200, 153140100, 153150010, 153140090, 153150060,
           153150020, 153140080, 153140060, 153150090, 153150080, 153150050,
           154150010, 153150030, 153150070, 154150020, 153160080, 153160020,
           154150050, 154150040, 154160080, 154160020, 153160240, 154160010,
           154160030, 154160040, 154160050, 154170040, 154160120, 154160070,
           154160130, 154160100, 154160090, 154160060, 154170120, 154170070,
           154170010, 154160140, 154170030, 154170020, 154170140, 154170230,
           154170150, 154170080, 154170060, 154170050, 154180020, 154170160,
           154170110, 154170090, 154170210, 154170130, 154180050, 154180030,
           154180080, 154180070, 150160040, 149180200, 149180180, 149180160,
           149180140, 149180110, 149180100, 149180080, 149180210, 149180240,
           149190060, 150190140, 150190170, 150190360, 150190260, 149190230,
           150190340, 150200060, 150200100, 150200130, 150200150, 150210020,
           150210150, 150210170, 150210190, 150210180, 151210190, 151210120,
           151210050, 152210040, 152210170, 152200110, 152200150, 152200030,
           152190120, 152190030, 153180090, 153180020, 153180080, 153180100,
           154180150, 154180220, 154180190, 154180200, 154180210, 154190040,
           154190020, 154180160, 154180170, 154180180, 149180120, 149180250,
           149180230, 149190030, 149190010, 149180090, 149180220, 150190060,
           150190050, 150190190, 150190120, 150190130, 150190080, 150190180,
           150190210, 150190010, 150180270, 150180260, 150190070, 150190270,
           150190250, 150190100, 149190050, 149190080, 149190100, 149190120,
           150190160, 149190040, 149190020, 149190090, 149190150, 149190070,
           149190140, 149190290, 149190260, 149190210, 149190180, 149190170,
           149190190, 149190220, 149190200, 149190160, 149190270, 150190310,
           150190330, 149190340, 149200060, 149200040, 149190310, 149200090,
           149200170, 149200080, 149190370, 149190350, 149200130, 150200070,
           150200140, 150200110, 149190280, 149200030, 149200050, 149200140,
           149200160, 149200190, 149200240, 149200230, 149200280, 150200170,
           149190300, 149190360, 149190390, 149190380, 149200020, 149190320,
           149200010, 149200100, 149200110, 149200070, 149200120, 149200180,
           149200150, 149200300, 149200290, 149200220, 149200270, 149200250,
           149200260, 149200200, 149200310, 149200330, 149200320, 150200010,
           150200030, 150200080, 150200020, 150200160, 150200120, 150200040,
           150200090, 150200050, 150210070, 150210010, 150210060, 150210100,
           150210030, 150210050, 149210090, 149210070, 149210050, 149210040,
           150210130, 150210120, 149210010, 149210030, 149210060, 149210020,
           149210100, 149210080, 150210110, 150210140, 150210160, 150210200,
           149220150, 149220130, 149220060, 149220030, 149220040, 149220190,
           150220100, 150220090, 150220070, 150220030, 150210210, 149220180,
           149220140, 149220080, 149220100, 149220110, 149220070, 149220020,
           149220050, 149220160, 149220200, 149220210, 150220140, 150220130,
           149210150, 149210160, 149210110, 149210130, 150220010, 150220080,
           149220010, 149210140, 149210120, 150220060, 150220040, 150220160,
           150220050, 150220110, 150220020, 151210130, 151200100, 151210010,
           151210020, 151210040, 150210090, 151210090, 150210040, 150210080,
           150210220, 151210080, 151220040, 150220170, 150230080, 150230010,
           150230020, 150230040, 151230010, 151220090, 151220010, 150220120,
           150230030, 150230050, 150230060, 151220130, 151220140, 151220070,
           151220100, 151220110, 151220080, 151220120, 151220060, 151220050,
           151200110, 151210060, 151210030, 151210110, 151210100, 151210070,
           150190280, 151190090, 151190100, 151200020, 151200090, 151200120,
           150190350, 151200060, 151190120, 151200070, 151190080, 151190110,
           151200040, 151200080, 152210070, 152210020, 152230120, 152230110,
           152230090, 152230040, 152220080, 153220270, 153220130, 153220070,
           153220010, 153210210, 153210090, 152210060, 152200130, 152230190,
           152230100, 152230060, 152230030, 153230160, 153230170, 153230110,
           153230010, 153230140, 153230080, 153230060, 153220230, 153220180,
           153230070, 153220260, 153220170, 153220100, 153230130, 154220070,
           153220240, 153220300, 153220280, 153220290, 153230050, 153230040,
           153230020, 154220060, 153220110, 153220120, 153220150, 153220190,
           153220320, 153220200, 153220220, 154220020, 154220030, 153220060,
           153220050, 153220080, 153220140, 153220160, 153220310, 153220090,
           154210090, 153210110, 153210130, 153210200, 153210190, 153210170,
           153210220, 154210040, 153210100, 153210050, 153210080, 153210240,
           153210230, 153210180, 153210140, 153210040, 153210120, 153210010,
           153210070, 153200160, 152210100, 153210020, 152210030, 150240020,
           150240010, 151230060, 151230040, 152230080, 152230200, 152220050,
           152229999, 152210090, 152210150, 150230070, 151230050, 151230030,
           151230080, 152230020, 152230070, 152230050, 151230020, 152220060,
           152230010, 152220070, 152220030, 152220090, 152210130, 152220010,
           152210120, 152210140, 152210160, 152210050, 153190160, 152200020,
           152200120, 153200050, 153200020, 152200100, 152200060, 152200080,
           152190050, 152199992, 152200050, 151190070, 152190100, 152200010,
           152200090, 152200070, 152190060, 152190080, 152190110, 152190020,
           152190040, 152180130, 153200010, 153190170, 153190140, 153190120,
           153190090, 153190050, 153180140, 153190080, 153190100, 153190150,
           153190130, 153190180, 153190070, 153190190, 153190060, 153170050,
           153170080, 153170060, 153170120, 153170140, 153179996, 153170070,
           153170110, 153170090, 154170180, 153170130, 153180010, 153180050,
           153180060, 153180040, 153190020, 153180150, 153190010, 154180010,
           153180030, 154180110, 153180110, 154179991, 154170190, 154180230,
           154170240, 154180040, 154180060, 154180270, 154180260, 154190010,
           153190030, 153180130, 153190040, 154190080, 154190060, 154190100,
           154190110, 153200040, 153200030, 154190170, 154190150, 154190160,
           154190140, 154200020, 154200010, 153200070, 154200030, 154210010,
           153200140, 153200090, 154200040, 154210020, 154210030, 154210060,
           154210070, 154210080, 154210100, 154220050, 154220110, 154220010,
           154220100, 154230010, 154220080, 154220090, 154230020, 154230030,
           153230120, 153230090, 154230040, 149220170, 149190240, 149190250])



.. GENERATED FROM PYTHON SOURCE LINES 51-54

.. code-block:: default


    df['stn_id'].nunique()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    840



.. GENERATED FROM PYTHON SOURCE LINES 55-60

.. code-block:: default


    grouper = df.groupby('stn_id')

    grouper





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f0c7f2ef7f0>



.. GENERATED FROM PYTHON SOURCE LINES 61-64

.. code-block:: default


    type(grouper)








.. GENERATED FROM PYTHON SOURCE LINES 65-68

.. code-block:: default


    grouper.ngroups





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    840



.. GENERATED FROM PYTHON SOURCE LINES 69-72

.. code-block:: default


    grouper.groups





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    {149180010: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149180020: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 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2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 2020-11-20 00:00:00, 2020-11-21 00:00:00, 2020-11-22 00:00:00, 2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], 149210060: [2020-11-01 00:00:00, 2020-11-02 00:00:00, 2020-11-03 00:00:00, 2020-11-04 00:00:00, 2020-11-05 00:00:00, 2020-11-06 00:00:00, 2020-11-07 00:00:00, 2020-11-08 00:00:00, 2020-11-09 00:00:00, 2020-11-10 00:00:00, 2020-11-11 00:00:00, 2020-11-12 00:00:00, 2020-11-13 00:00:00, 2020-11-14 00:00:00, 2020-11-15 00:00:00, 2020-11-16 00:00:00, 2020-11-17 00:00:00, 2020-11-18 00:00:00, 2020-11-19 00:00:00, 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2020-11-23 00:00:00, 2020-11-24 00:00:00, 2020-11-25 00:00:00, 2020-11-26 00:00:00, 2020-11-27 00:00:00, 2020-11-28 00:00:00, 2020-11-29 00:00:00, 2020-11-30 00:00:00], ...}



.. GENERATED FROM PYTHON SOURCE LINES 73-77

.. code-block:: default


    grouper.get_group(149180020)







.. raw:: html

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          <th></th>
          <th>stn_id</th>
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        <tr>
          <th>date</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>2020-11-01</th>
          <td>149180020</td>
          <td>82.0</td>
          <td>12.9</td>
          <td>99.900002</td>
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        <tr>
          <th>2020-11-02</th>
          <td>149180020</td>
          <td>80.0</td>
          <td>12.5</td>
          <td>99.900002</td>
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        <tr>
          <th>2020-11-03</th>
          <td>149180020</td>
          <td>79.0</td>
          <td>12.1</td>
          <td>99.900002</td>
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        <tr>
          <th>2020-11-04</th>
          <td>149180020</td>
          <td>81.0</td>
          <td>12.6</td>
          <td>99.900002</td>
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        <tr>
          <th>2020-11-05</th>
          <td>149180020</td>
          <td>84.0</td>
          <td>13.4</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>2020-11-26</th>
          <td>149180020</td>
          <td>90.0</td>
          <td>15.3</td>
          <td>99.900002</td>
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        <tr>
          <th>2020-11-27</th>
          <td>149180020</td>
          <td>91.0</td>
          <td>15.8</td>
          <td>99.900002</td>
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        <tr>
          <th>2020-11-28</th>
          <td>149180020</td>
          <td>90.0</td>
          <td>15.3</td>
          <td>99.900002</td>
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        <tr>
          <th>2020-11-29</th>
          <td>149180020</td>
          <td>87.0</td>
          <td>14.4</td>
          <td>99.900002</td>
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        <tr>
          <th>2020-11-30</th>
          <td>149180020</td>
          <td>86.0</td>
          <td>14.0</td>
          <td>99.900002</td>
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    <p>30 rows × 4 columns</p>
    </div>
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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 78-81

.. code-block:: default


    type(grouper.get_group(149180020))








.. GENERATED FROM PYTHON SOURCE LINES 82-85

.. code-block:: default


    grouper.get_group(149180020).shape





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    (30, 4)



.. GENERATED FROM PYTHON SOURCE LINES 86-89

.. code-block:: default


    grouper.get_group(149180020).head()






.. raw:: html

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        <tr>
          <th>date</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
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      </thead>
      <tbody>
        <tr>
          <th>2020-11-01</th>
          <td>149180020</td>
          <td>82.0</td>
          <td>12.9</td>
          <td>99.900002</td>
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          <th>2020-11-02</th>
          <td>149180020</td>
          <td>80.0</td>
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          <th>2020-11-03</th>
          <td>149180020</td>
          <td>79.0</td>
          <td>12.1</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-04</th>
          <td>149180020</td>
          <td>81.0</td>
          <td>12.6</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-05</th>
          <td>149180020</td>
          <td>84.0</td>
          <td>13.4</td>
          <td>99.900002</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 90-93

.. code-block:: default


    grouper.get_group(149180020).tail()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>stn_id</th>
          <th>water_level_cm</th>
          <th>q_cms</th>
          <th>temp_C</th>
        </tr>
        <tr>
          <th>date</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>2020-11-26</th>
          <td>149180020</td>
          <td>90.0</td>
          <td>15.3</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-27</th>
          <td>149180020</td>
          <td>91.0</td>
          <td>15.8</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-28</th>
          <td>149180020</td>
          <td>90.0</td>
          <td>15.3</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-29</th>
          <td>149180020</td>
          <td>87.0</td>
          <td>14.4</td>
          <td>99.900002</td>
        </tr>
        <tr>
          <th>2020-11-30</th>
          <td>149180020</td>
          <td>86.0</td>
          <td>14.0</td>
          <td>99.900002</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 94-99

.. code-block:: default


    for name, group in grouper:
        print(name, group.shape)
        break





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    149180010 (30, 4)




.. GENERATED FROM PYTHON SOURCE LINES 100-106

.. code-block:: default


    for idx, (name, group) in enumerate(grouper):
        print(name, group.shape)
        if idx > 5:
            break
  




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    149180010 (30, 4)
    149180020 (30, 4)
    149180030 (30, 4)
    149180040 (30, 4)
    149180050 (30, 4)
    149180060 (30, 4)
    149180070 (30, 4)




.. GENERATED FROM PYTHON SOURCE LINES 107-113

.. code-block:: default


    for idx, (name, group) in enumerate(grouper):
        pass

    print(idx)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    839




.. GENERATED FROM PYTHON SOURCE LINES 114-115

What is the mean discharge for each station?

.. GENERATED FROM PYTHON SOURCE LINES 115-121

.. code-block:: default


    for idx, (name, group) in enumerate(grouper):
        print(name, group['q_cms'].mean())
        if idx > 5:
            break
    




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    149180010 25.900002
    149180020 17.079998
    149180030 7.032334
    149180040 0.58933336
    149180050 0.6753333
    149180060 3.5083337
    149180070 0.15820001




.. GENERATED FROM PYTHON SOURCE LINES 122-123

What is the mean, min and max temperature for each station?

.. GENERATED FROM PYTHON SOURCE LINES 123-129

.. code-block:: default


    for idx, (name, group) in enumerate(grouper):
        print(name, group['temp_C'].mean(), group['temp_C'].min(), group['temp_C'].max())
        if idx > 5:
            break





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    149180010 99.89999 99.9 99.9
    149180020 99.89999 99.9 99.9
    149180030 99.89999 99.9 99.9
    149180040 99.89999 99.9 99.9
    149180050 99.89999 99.9 99.9
    149180060 99.89999 99.9 99.9
    149180070 99.89999 99.9 99.9




.. GENERATED FROM PYTHON SOURCE LINES 130-131

replace values > 99.8 and less than 100 with np.nan

.. GENERATED FROM PYTHON SOURCE LINES 131-133

.. code-block:: default

    df.loc[(df['temp_C']>99.8) & (df['temp_C']<100.0), 'temp_C'] = np.nan








.. GENERATED FROM PYTHON SOURCE LINES 134-140

.. code-block:: default


    for idx, (name, group) in enumerate(grouper):
        if not np.isnan(group['temp_C'].mean()):
          print(name, group['temp_C'].mean(), group['temp_C'].min(), group['temp_C'].max())
        if idx > 50:
            break
  



.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    149180100 7.933334 5.6 10.9
    149180240 5.520001 5.0 6.3
    149190240 4.55 2.0 7.5
    149190310 9.059999 8.2 10.6





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  1.289 seconds)


.. _sphx_glr_download_auto_examples_pandas_groupby.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/AtrCheema/python-seekho/master?urlpath=lab/tree/notebooks/auto_examples/pandas/groupby.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: groupby.py <groupby.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: groupby.ipynb <groupby.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_