.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples\pandas\plot_format04_therapy_all.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download__examples_pandas_plot_format04_therapy_all.py>`
        to download the full example code

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

.. _sphx_glr__examples_pandas_plot_format04_therapy_all.py:


04. Daily Aggregation of MIMIC-III ICU Antibiotic Therapy Data
===============================================================
04. Format MIMIC therapy (all)

This Python script processes and transforms antibiotic treatment data from the
ICU_diagnoses_antibiotics.csv file, likely derived from the MIMIC-III dataset.
It begins by loading the data, parsing starttime and stoptime columns, and
cleaning antibiotic names. The core of the script reshapes the data from a
record of treatment intervals into a daily summary. It achieves this by creating
a date range for each antibiotic administration and then "exploding" these
ranges into individual daily records. Finally, it groups the data by patient
stay and date to produce a final series showing the unique list of antibiotics
administered each day.

.. GENERATED FROM PYTHON SOURCE LINES 16-22

.. code-block:: default
   :lineno-start: 16

    # Generic libraries
    import pandas as pd

    # Show in terminal
    TERMINAL = False








.. GENERATED FROM PYTHON SOURCE LINES 23-24

First, lets load and do some basic formatting on the data.

.. GENERATED FROM PYTHON SOURCE LINES 24-71

.. code-block:: default
   :lineno-start: 25


    # -----------------------------
    # Constants
    # -----------------------------
    # Path
    path = './data/mimic-therapy/ICU_diagnoses_antibiotics.csv'

    # -----------------------------
    # Load data
    # -----------------------------
    # Read data
    data = pd.read_csv(path)

    # Keep only useful columns
    data = data[['subject_id',
                 'hadm_id',
                 'stay_id',
                 'icd_code',
                 'antibiotic',
                 'route',
                 'starttime',
                 'stoptime']]

    # .. note::  Converting datetime manually because the parse_dates
    #            value in read_csv returns the value unaltered as an object
    #            type when the the conversion is not possible, and this
    #            triggers errors while accessing using .dt.
    # Explicitly convert columns to datetime, coercing errors to NaT
    data['starttime'] = pd.to_datetime(data['starttime'], dayfirst=True, errors='coerce')
    data['stoptime'] = pd.to_datetime(data['stoptime'], dayfirst=True, errors='coerce')

    # Handle any rows where dates could not be parsed (Optional)
    data.dropna(subset=['starttime', 'stoptime'], inplace=True)

    # Reformat (time info and str)
    data.starttime = data.starttime.dt.date
    data.stoptime = data.stoptime.dt.date
    data.antibiotic = data.antibiotic \
        .str.lower() \
        .str.strip()

    # Show
    if TERMINAL:
        print("\nData:")
        print(data)
    data






.. 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>subject_id</th>
          <th>hadm_id</th>
          <th>stay_id</th>
          <th>icd_code</th>
          <th>antibiotic</th>
          <th>route</th>
          <th>starttime</th>
          <th>stoptime</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>10656173</td>
          <td>25778760</td>
          <td>30001555</td>
          <td>J95851</td>
          <td>ceftriaxone</td>
          <td>IV</td>
          <td>2177-09-27</td>
          <td>2177-02-10</td>
        </tr>
        <tr>
          <th>2</th>
          <td>10656173</td>
          <td>25778760</td>
          <td>37985659</td>
          <td>J95851</td>
          <td>cefepime</td>
          <td>IV</td>
          <td>2177-09-11</td>
          <td>2177-12-09</td>
        </tr>
        <tr>
          <th>3</th>
          <td>10656173</td>
          <td>25778760</td>
          <td>37985659</td>
          <td>J95851</td>
          <td>cefepime</td>
          <td>IV</td>
          <td>2177-09-10</td>
          <td>2177-11-09</td>
        </tr>
        <tr>
          <th>4</th>
          <td>10656173</td>
          <td>25778760</td>
          <td>37985659</td>
          <td>J95851</td>
          <td>cefepime</td>
          <td>IV</td>
          <td>2177-09-12</td>
          <td>2177-12-09</td>
        </tr>
        <tr>
          <th>8</th>
          <td>10656173</td>
          <td>25778760</td>
          <td>37985659</td>
          <td>J95851</td>
          <td>vancomycin</td>
          <td>IV</td>
          <td>2177-09-11</td>
          <td>2177-12-09</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>21526</th>
          <td>15689523</td>
          <td>23914765</td>
          <td>39918058</td>
          <td>99731</td>
          <td>meropenem</td>
          <td>IV</td>
          <td>2159-07-04</td>
          <td>2159-05-07</td>
        </tr>
        <tr>
          <th>21528</th>
          <td>15689523</td>
          <td>23914765</td>
          <td>39918058</td>
          <td>99731</td>
          <td>meropenem</td>
          <td>IV</td>
          <td>2159-07-03</td>
          <td>2159-04-07</td>
        </tr>
        <tr>
          <th>21532</th>
          <td>15689523</td>
          <td>23914765</td>
          <td>39918058</td>
          <td>99731</td>
          <td>vancomycin</td>
          <td>IV</td>
          <td>2159-07-03</td>
          <td>2159-04-07</td>
        </tr>
        <tr>
          <th>21540</th>
          <td>15689523</td>
          <td>23914765</td>
          <td>39918058</td>
          <td>99731</td>
          <td>levofloxacin</td>
          <td>IV</td>
          <td>2159-06-29</td>
          <td>2159-02-07</td>
        </tr>
        <tr>
          <th>21544</th>
          <td>15689523</td>
          <td>23914765</td>
          <td>39918058</td>
          <td>99731</td>
          <td>sulfamethoxazole-trimethoprim</td>
          <td>IV</td>
          <td>2159-06-26</td>
          <td>2159-02-07</td>
        </tr>
      </tbody>
    </table>
    <p>8474 rows × 8 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 72-80

Lets transform the data

.. note:: You might need to add ``NaNs`` for missing days per patient.
          The other sample included in this repository for a single patient
          :ref:`sphx_glr__examples_pandas_plot_format04_therapy_one.py`
          achieves this by using the following code: ``aux = aux.asfreq('1D')``

          Note it needs to be applied per patient!

.. GENERATED FROM PYTHON SOURCE LINES 80-109

.. code-block:: default
   :lineno-start: 81


    # -----------------------------
    # Transform data
    # -----------------------------
    # .. note: The closed parameter indicates whether to include
    #          the first and/or last samples. None will keep both,
    #          left will keep only start date and right will keep
    #          also the right date.
    # Create column with date range
    data['startdate'] = data.apply(lambda x:
        pd.date_range(start=x['starttime'],
                      end=x['stoptime'],
                      inclusive='left',         # ignoring right
                      freq='D') ,axis=1)

    # Explode such column
    data = data.explode('startdate')

    # Groupby
    groupby = ['subject_id',
               'hadm_id',
               'stay_id',
               'startdate']

    # Create daily therapies
    aux = data.groupby(groupby) \
        .apply(lambda x: sorted(x.antibiotic \
            .unique().tolist()))








.. GENERATED FROM PYTHON SOURCE LINES 110-111

Lets see the formatted data

.. GENERATED FROM PYTHON SOURCE LINES 111-119

.. code-block:: default
   :lineno-start: 112


    # Show
    if TERMINAL:
        print("\nFormatted:")
        print(aux)
    aux






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

 Out:

 .. code-block:: none


    subject_id  hadm_id   stay_id   startdate 
    10007818    22987108  32359580  2146-06-30                [meropenem]
                                    2146-07-01                [meropenem]
                                    2146-07-02    [meropenem, vancomycin]
                                    2146-07-03    [meropenem, vancomycin]
                                    2146-07-04    [meropenem, vancomycin]
                                                           ...           
    19997367    20617667  35616526  2126-10-31              [ceftriaxone]
                                    2126-11-01              [ceftriaxone]
                                    2126-11-02              [ceftriaxone]
                                    2126-11-03              [ceftriaxone]
                                    2126-11-04              [ceftriaxone]
    Length: 143037, dtype: object



.. GENERATED FROM PYTHON SOURCE LINES 120-121

Lets count the number of days

.. GENERATED FROM PYTHON SOURCE LINES 121-129

.. code-block:: default
   :lineno-start: 122


    # Show
    if TERMINAL:
        print("\nTherapies (number of days)")
        print(aux.value_counts())
    aux.value_counts()






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

 Out:

 .. code-block:: none


    [vancomycin]                                                               15480
    [cefepime, vancomycin]                                                     14006
    [piperacillin-tazobactam, vancomycin]                                       8398
    [cefepime]                                                                  7935
    [meropenem]                                                                 5345
                                                                               ...  
    [sulfameth/trimethoprim]                                                       1
    [cefepime, ceftriaxone, metronidazole (flagyl)]                                1
    [linezolid, meropenem, metronidazole (flagyl), piperacillin-tazobactam]        1
    [ceftaroline, meropenem, vancomycin]                                           1
    [gentamicin, linezolid, meropenem]                                             1
    Name: count, Length: 477, dtype: int64




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

   **Total running time of the script:** ( 0 minutes  13.875 seconds)


.. _sphx_glr_download__examples_pandas_plot_format04_therapy_all.py:


.. only :: html

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



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

     :download:`Download Python source code: plot_format04_therapy_all.py <plot_format04_therapy_all.py>`



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

     :download:`Download Jupyter notebook: plot_format04_therapy_all.ipynb <plot_format04_therapy_all.ipynb>`


.. only:: html

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

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