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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_basic_stainer_example.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_basic_stainer_example.py:


Basic Usage of Stainers (no DirtyDF)
====================================

This page shows some basic examples of using stainers to directly transform panda dataframes.

.. GENERATED FROM PYTHON SOURCE LINES 8-13

.. code-block:: default


    import pandas as pd
    import numpy as np
    from ddf.stainer import ShuffleStainer, InflectionStainer








.. GENERATED FROM PYTHON SOURCE LINES 14-16

ShuffleStainer Example
^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 18-19

For the first example, let us use a basic dataset containing only 6 rows and 2 columns, an integer ID and an animal class.

.. GENERATED FROM PYTHON SOURCE LINES 19-23

.. code-block:: default


    df = pd.DataFrame([(0, 'Cat'), (1, 'Dog'), (2, 'Rabbit'), (3, 'Cat'), (4, 'Cat'), (5, 'Dog')], columns=('id', 'class'))
    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>id</th>
          <th>class</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0</td>
          <td>Cat</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1</td>
          <td>Dog</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2</td>
          <td>Rabbit</td>
        </tr>
        <tr>
          <th>3</th>
          <td>3</td>
          <td>Cat</td>
        </tr>
        <tr>
          <th>4</th>
          <td>4</td>
          <td>Cat</td>
        </tr>
        <tr>
          <th>5</th>
          <td>5</td>
          <td>Dog</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 24-26

We now apply a ShuffleStainer to shuffle the rows in this dataset. Note that we require to pass in a numpy random generator for
random generation.

.. GENERATED FROM PYTHON SOURCE LINES 28-30

The stainer's transform method will output 3 objects: the transformed dataframe, a row map which maps the rows in the old dataframe
to the new one, and a column map which maps the columns in the old dataframe to the new one.

.. GENERATED FROM PYTHON SOURCE LINES 30-37

.. code-block:: default


    shuffle_stainer = ShuffleStainer()
    rng = np.random.default_rng(42)

    new_df, row_map, col_map = shuffle_stainer.transform(df, rng)
    new_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>id</th>
          <th>class</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>3</td>
          <td>Cat</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2</td>
          <td>Rabbit</td>
        </tr>
        <tr>
          <th>2</th>
          <td>5</td>
          <td>Dog</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4</td>
          <td>Cat</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1</td>
          <td>Dog</td>
        </tr>
        <tr>
          <th>5</th>
          <td>0</td>
          <td>Cat</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 38-40

Also, we can check the row map to determine which rows in the old dataframe were mapped to the new ones. (Note that ShuffleStainer
does not affect or alter columns, so the column map is simply an empty dictionary)

.. GENERATED FROM PYTHON SOURCE LINES 40-43

.. code-block:: default


    row_map





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

 Out:

 .. code-block:: none


    {3: [0], 2: [1], 5: [2], 4: [3], 1: [4], 0: [5]}



.. GENERATED FROM PYTHON SOURCE LINES 44-46

The output shows that the 3rd row index (0-based indexing) from the original dataframe is mapped to the 0-th row in the new
dataframe, as well as others. You may check with the ID column, or with the original dataframe above to verify that this is true.

.. GENERATED FROM PYTHON SOURCE LINES 48-50

Furthermore, you may use the stainer's `get_history()` method to get the name of the stainer, a description of how the stainer 
had transformed the dataframe, and the time taken for said transformation.

.. GENERATED FROM PYTHON SOURCE LINES 50-53

.. code-block:: default


    shuffle_stainer.get_history()





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

 Out:

 .. code-block:: none


    ('Shuffle', 'Order of rows randomized', 0.0019943714141845703)



.. GENERATED FROM PYTHON SOURCE LINES 54-56

InflectionStainer Example
^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 58-61

For this next example, we will be using a randomly generated dataset of 100 rows and 3 columns, an integer ID, and 2 animal class
columns (this dataset has no 'meaning', it is simply for demo). In particular, we will demonstrate using the InflectionStainer to
generate string inflections of the animal categories.

.. GENERATED FROM PYTHON SOURCE LINES 61-68

.. code-block:: default


    rng = np.random.default_rng(42) # reinitialize random generator
    df2 = pd.DataFrame(zip(range(100), rng.choice(['Cat','Dog','Rabbit'], 100), rng.choice(['Cow', 'Sheep', 'Goat', 'Horse'], 100)),
                      columns=('id', 'class', 'class2'))

    df2.head()






.. 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>id</th>
          <th>class</th>
          <th>class2</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0</td>
          <td>Cat</td>
          <td>Horse</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1</td>
          <td>Rabbit</td>
          <td>Cow</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2</td>
          <td>Dog</td>
          <td>Horse</td>
        </tr>
        <tr>
          <th>3</th>
          <td>3</td>
          <td>Dog</td>
          <td>Cow</td>
        </tr>
        <tr>
          <th>4</th>
          <td>4</td>
          <td>Dog</td>
          <td>Horse</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 69-70

Here are the distributions of the animal classes.

.. GENERATED FROM PYTHON SOURCE LINES 70-71

.. code-block:: default

    df2['class'].value_counts()




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

 Out:

 .. code-block:: none


    Rabbit    40
    Dog       33
    Cat       27
    Name: class, dtype: int64



.. GENERATED FROM PYTHON SOURCE LINES 72-74

.. code-block:: default

    df2['class2'].value_counts()





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

 Out:

 .. code-block:: none


    Sheep    27
    Goat     26
    Cow      24
    Horse    23
    Name: class2, dtype: int64



.. GENERATED FROM PYTHON SOURCE LINES 75-77

We inflect on the 2 animal columns (index 1 and 2), use only 3 inflection formats (original, lowercase, and pluralize), 
and ignore inflections on the 'Dog' category in the first class and 'Cow' & 'Sheep' categories in the second class.

.. GENERATED FROM PYTHON SOURCE LINES 77-83

.. code-block:: default

    inflect_stainer = InflectionStainer(col_idx=[1, 2], num_format = 3, formats=['original', 'lowercase', 'pluralize'], 
                        ignore_cats={1: ['Dog'], 2: ['Cow', 'Sheep']})

    new_df2, row_map2, col_map2 = inflect_stainer.transform(df2, rng)
    new_df2.head()






.. 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>id</th>
          <th>class</th>
          <th>class2</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0</td>
          <td>Cats</td>
          <td>Horses</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1</td>
          <td>Rabbit</td>
          <td>Cow</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2</td>
          <td>Dog</td>
          <td>Horses</td>
        </tr>
        <tr>
          <th>3</th>
          <td>3</td>
          <td>Dog</td>
          <td>Cow</td>
        </tr>
        <tr>
          <th>4</th>
          <td>4</td>
          <td>Dog</td>
          <td>Horse</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 84-85

We can see the new distributions.

.. GENERATED FROM PYTHON SOURCE LINES 85-86

.. code-block:: default

    new_df2['class'].value_counts()




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

 Out:

 .. code-block:: none


    Dog        33
    rabbit     16
    Rabbits    15
    cat        10
    Cat        10
    Rabbit      9
    Cats        7
    Name: class, dtype: int64



.. GENERATED FROM PYTHON SOURCE LINES 87-89

.. code-block:: default

    new_df2['class2'].value_counts()





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

 Out:

 .. code-block:: none


    Sheep     27
    Cow       24
    Goats     12
    Horses    11
    Goat       8
    Horse      6
    horse      6
    goat       6
    Name: class2, dtype: int64



.. GENERATED FROM PYTHON SOURCE LINES 90-91

We can also check the description of the stainer's transform from its history (the 2nd element in the history tuple).

.. GENERATED FROM PYTHON SOURCE LINES 91-93

.. code-block:: default

    print(inflect_stainer.get_history()[1])





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

 Out:

 .. code-block:: none

    Categorical inflections on:
    {'class': {'Cat': ['Cat', 'Cats', 'cat'], 'Rabbit': ['Rabbits', 'rabbit', 'Rabbit']}, 'class2': {'Horse': ['horse', 'Horses', 'Horse'], 'Goat': ['Goats', 'goat', 'Goat']}}




.. GENERATED FROM PYTHON SOURCE LINES 94-94

For more info on each of the stainer's use-cases and input parameters, do check their respective documentations.


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

   **Total running time of the script:** ( 0 minutes  0.035 seconds)


.. _sphx_glr_download_auto_examples_plot_basic_stainer_example.py:


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 .. container:: sphx-glr-footer
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     :download:`Download Python source code: plot_basic_stainer_example.py <plot_basic_stainer_example.py>`



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