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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_pandas_pivot_vs_melt.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_pivot_vs_melt.py:


============================
5.9 pivot vs melt
============================

.. GENERATED FROM PYTHON SOURCE LINES 6-12

.. code-block:: default

    import time
    import pandas as pd

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





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

 .. code-block:: none

    Mon Nov 11 07:57:23 2024
    1.5.3




.. GENERATED FROM PYTHON SOURCE LINES 13-15

Let's consider a dataframe which consists of daily streamflow data from 840 polish stations for the month of January 2020.
The data is available in a zip file. We can read the data using the `read_csv` method.

.. GENERATED FROM PYTHON SOURCE LINES 15-31

.. 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', 'q_cms', 'month'],
        usecols=[0, 3, 5, 7, 9],
        dtype={'stn_id': 'str', 'year': 'int', 'day': 'int', 'q_cms': 'float', '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>q_cms</th>
        </tr>
        <tr>
          <th>date</th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>2020-11-01</th>
          <td>149180020</td>
          <td>12.90</td>
        </tr>
        <tr>
          <th>2020-11-02</th>
          <td>149180020</td>
          <td>12.50</td>
        </tr>
        <tr>
          <th>2020-11-03</th>
          <td>149180020</td>
          <td>12.10</td>
        </tr>
        <tr>
          <th>2020-11-04</th>
          <td>149180020</td>
          <td>12.60</td>
        </tr>
        <tr>
          <th>2020-11-05</th>
          <td>149180020</td>
          <td>13.40</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>2020-11-26</th>
          <td>149190250</td>
          <td>0.44</td>
        </tr>
        <tr>
          <th>2020-11-27</th>
          <td>149190250</td>
          <td>0.44</td>
        </tr>
        <tr>
          <th>2020-11-28</th>
          <td>149190250</td>
          <td>0.44</td>
        </tr>
        <tr>
          <th>2020-11-29</th>
          <td>149190250</td>
          <td>0.44</td>
        </tr>
        <tr>
          <th>2020-11-30</th>
          <td>149190250</td>
          <td>0.44</td>
        </tr>
      </tbody>
    </table>
    <p>24913 rows × 2 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 32-35

.. code-block:: default


    print(df.shape)





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

 .. code-block:: none

    (24913, 2)




.. GENERATED FROM PYTHON SOURCE LINES 36-37

The above dataframe consists of data for 480 stations, each stacked on top of the other.

.. GENERATED FROM PYTHON SOURCE LINES 37-39

.. code-block:: default

    len(df['stn_id'].unique())





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

 .. code-block:: none


    840



.. GENERATED FROM PYTHON SOURCE LINES 40-41

If we want data for each station in a separate column, we can use the `pivot_table` method.

.. GENERATED FROM PYTHON SOURCE LINES 41-46

.. code-block:: default


    pivoted_table = df.pivot_table(index=df.index, columns="stn_id", values="q_cms")

    pivoted_table






.. 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>stn_id</th>
          <th>149180010</th>
          <th>149180020</th>
          <th>149180030</th>
          <th>149180040</th>
          <th>149180050</th>
          <th>...</th>
          <th>154220110</th>
          <th>154230010</th>
          <th>154230020</th>
          <th>154230030</th>
          <th>154230040</th>
        </tr>
        <tr>
          <th>date</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>2020-11-01</th>
          <td>17.6</td>
          <td>12.9</td>
          <td>3.44</td>
          <td>0.47</td>
          <td>0.53</td>
          <td>...</td>
          <td>0.41</td>
          <td>1.30</td>
          <td>99999.999</td>
          <td>2.38</td>
          <td>0.69</td>
        </tr>
        <tr>
          <th>2020-11-02</th>
          <td>17.0</td>
          <td>12.5</td>
          <td>3.53</td>
          <td>0.47</td>
          <td>0.86</td>
          <td>...</td>
          <td>0.42</td>
          <td>1.26</td>
          <td>99999.999</td>
          <td>2.38</td>
          <td>0.72</td>
        </tr>
        <tr>
          <th>2020-11-03</th>
          <td>16.6</td>
          <td>12.1</td>
          <td>3.47</td>
          <td>0.43</td>
          <td>0.95</td>
          <td>...</td>
          <td>0.42</td>
          <td>1.26</td>
          <td>99999.999</td>
          <td>2.41</td>
          <td>0.70</td>
        </tr>
        <tr>
          <th>2020-11-04</th>
          <td>16.8</td>
          <td>12.6</td>
          <td>3.42</td>
          <td>0.47</td>
          <td>1.87</td>
          <td>...</td>
          <td>0.44</td>
          <td>1.30</td>
          <td>99999.999</td>
          <td>2.41</td>
          <td>0.71</td>
        </tr>
        <tr>
          <th>2020-11-05</th>
          <td>18.9</td>
          <td>13.4</td>
          <td>4.52</td>
          <td>0.47</td>
          <td>1.62</td>
          <td>...</td>
          <td>0.45</td>
          <td>1.30</td>
          <td>99999.999</td>
          <td>2.41</td>
          <td>0.75</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>2020-11-26</th>
          <td>21.5</td>
          <td>15.3</td>
          <td>4.57</td>
          <td>0.60</td>
          <td>0.39</td>
          <td>...</td>
          <td>0.45</td>
          <td>1.25</td>
          <td>99999.999</td>
          <td>2.49</td>
          <td>0.76</td>
        </tr>
        <tr>
          <th>2020-11-27</th>
          <td>22.4</td>
          <td>15.8</td>
          <td>4.79</td>
          <td>0.60</td>
          <td>0.47</td>
          <td>...</td>
          <td>0.46</td>
          <td>1.29</td>
          <td>99999.999</td>
          <td>2.53</td>
          <td>0.76</td>
        </tr>
        <tr>
          <th>2020-11-28</th>
          <td>21.8</td>
          <td>15.3</td>
          <td>4.70</td>
          <td>0.56</td>
          <td>0.53</td>
          <td>...</td>
          <td>0.47</td>
          <td>1.26</td>
          <td>99999.999</td>
          <td>2.46</td>
          <td>0.76</td>
        </tr>
        <tr>
          <th>2020-11-29</th>
          <td>20.7</td>
          <td>14.4</td>
          <td>4.96</td>
          <td>0.60</td>
          <td>0.47</td>
          <td>...</td>
          <td>0.48</td>
          <td>1.26</td>
          <td>99999.999</td>
          <td>2.50</td>
          <td>0.78</td>
        </tr>
        <tr>
          <th>2020-11-30</th>
          <td>20.9</td>
          <td>14.0</td>
          <td>5.46</td>
          <td>0.64</td>
          <td>0.42</td>
          <td>...</td>
          <td>0.51</td>
          <td>1.27</td>
          <td>99999.999</td>
          <td>2.50</td>
          <td>0.79</td>
        </tr>
      </tbody>
    </table>
    <p>30 rows × 840 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 47-50

.. code-block:: default


    pivoted_table.shape





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

 .. code-block:: none


    (30, 840)



.. GENERATED FROM PYTHON SOURCE LINES 51-54

.. code-block:: default


    pivoted_table.columns





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

 .. code-block:: none


    Index([' 149180010', ' 149180020', ' 149180030', ' 149180040', ' 149180050',
           ' 149180060', ' 149180070', ' 149180080', ' 149180090', ' 149180100',
           ...
           ' 154220060', ' 154220070', ' 154220080', ' 154220090', ' 154220100',
           ' 154220110', ' 154230010', ' 154230020', ' 154230030', ' 154230040'],
          dtype='object', name='stn_id', length=840)



.. GENERATED FROM PYTHON SOURCE LINES 55-58

.. code-block:: default


    len(pivoted_table.columns)





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

 .. code-block:: none


    840



.. GENERATED FROM PYTHON SOURCE LINES 59-62

Melt
====
Melt is kind of opposite to that of pivot. It stacks the columns on top of each other.

.. GENERATED FROM PYTHON SOURCE LINES 62-66

.. code-block:: default


    melted_table = df.melt(id_vars=["stn_id"], value_vars=["q_cms"])

    melted_table





.. 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>variable</th>
          <th>value</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>149180020</td>
          <td>q_cms</td>
          <td>12.90</td>
        </tr>
        <tr>
          <th>1</th>
          <td>149180020</td>
          <td>q_cms</td>
          <td>12.50</td>
        </tr>
        <tr>
          <th>2</th>
          <td>149180020</td>
          <td>q_cms</td>
          <td>12.10</td>
        </tr>
        <tr>
          <th>3</th>
          <td>149180020</td>
          <td>q_cms</td>
          <td>12.60</td>
        </tr>
        <tr>
          <th>4</th>
          <td>149180020</td>
          <td>q_cms</td>
          <td>13.40</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>24908</th>
          <td>149190250</td>
          <td>q_cms</td>
          <td>0.44</td>
        </tr>
        <tr>
          <th>24909</th>
          <td>149190250</td>
          <td>q_cms</td>
          <td>0.44</td>
        </tr>
        <tr>
          <th>24910</th>
          <td>149190250</td>
          <td>q_cms</td>
          <td>0.44</td>
        </tr>
        <tr>
          <th>24911</th>
          <td>149190250</td>
          <td>q_cms</td>
          <td>0.44</td>
        </tr>
        <tr>
          <th>24912</th>
          <td>149190250</td>
          <td>q_cms</td>
          <td>0.44</td>
        </tr>
      </tbody>
    </table>
    <p>24913 rows × 3 columns</p>
    </div>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** ( 0 minutes  1.761 seconds)


.. _sphx_glr_download_auto_examples_pandas_pivot_vs_melt.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/pivot_vs_melt.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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