|pypi| |downloads|
edc-analytics
-------------
Build analytic tables from EDC data
Read your data into a dataframe, for example an EDC screening table:
.. code-block:: python
qs_screening = SubjectScreening.objects.all()
df = read_frame(qs_screening)
Convert all numerics to ``pandas`` numerics:
.. code-block:: python
cols = [
"age_in_years",
"dia_blood_pressure_avg",
"fbg_value",
"hba1c_value",
"ogtt_value",
"sys_blood_pressure_avg",
]
df[cols] = df[cols].apply(pd.to_numeric)
Pass the dataframe to each ``Table`` class
.. code-block:: python
gender_tbl = GenderTable(main_df=df)
age_tbl = AgeTable(main_df=df)
bp_table = BpTable(main_df=df)
In the ``Table`` instance,
* ``data_df`` is the supporting dataframe
* ``table_df`` is the dataframe to display. The ``table_df`` displays formatted data in the first 5 columns ("Characteristic", "Statistic", "F", "M", "All"). The ``table_df`` has additional columns that contain the statistics used for the statistics displayed in columns ["F", "M", "All"].
From above, ``gender_tbl.table_df`` is just a dataframe and can be combined with other ``table_df`` dataframes using ``pd.concat()`` to make a single ``table_df``.
.. code-block:: python
table_df = pd.concat(
[gender_tbl.table_df, age_tbl.table_df, bp_table.table_df]
)
Show just the first 5 columns:
.. code-block:: python
table_df.iloc[:, :5]
Like any dataframe, you can export to csv:
.. code-block:: python
path = "my/path/to/csv/folder/table_df.csv"
table_df.to_csv(path_or_buf=path, encoding="utf-8", index=0, sep="|")
.. |pypi| image:: https://img.shields.io/pypi/v/edc-analytics.svg
:target: https://pypi.python.org/pypi/edc-analytics
.. |downloads| image:: https://pepy.tech/badge/edc-analytics
:target: https://pepy.tech/project/edc-analytics
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"description": "|pypi| |downloads|\n\n\nedc-analytics\n-------------\n\nBuild analytic tables from EDC data\n\n\nRead your data into a dataframe, for example an EDC screening table:\n\n.. code-block:: python\n\n qs_screening = SubjectScreening.objects.all()\n df = read_frame(qs_screening)\n\n\nConvert all numerics to ``pandas`` numerics:\n\n.. code-block:: python\n\n cols = [\n \"age_in_years\",\n \"dia_blood_pressure_avg\",\n \"fbg_value\",\n \"hba1c_value\",\n \"ogtt_value\",\n \"sys_blood_pressure_avg\",\n ]\n df[cols] = df[cols].apply(pd.to_numeric)\n\n\nPass the dataframe to each ``Table`` class\n\n.. code-block:: python\n\n gender_tbl = GenderTable(main_df=df)\n age_tbl = AgeTable(main_df=df)\n bp_table = BpTable(main_df=df)\n\n\nIn the ``Table`` instance,\n\n* ``data_df`` is the supporting dataframe\n* ``table_df`` is the dataframe to display. The ``table_df`` displays formatted data in the first 5 columns (\"Characteristic\", \"Statistic\", \"F\", \"M\", \"All\"). The ``table_df`` has additional columns that contain the statistics used for the statistics displayed in columns [\"F\", \"M\", \"All\"].\n\nFrom above, ``gender_tbl.table_df`` is just a dataframe and can be combined with other ``table_df`` dataframes using ``pd.concat()`` to make a single ``table_df``.\n\n.. code-block:: python\n\n table_df = pd.concat(\n [gender_tbl.table_df, age_tbl.table_df, bp_table.table_df]\n )\n\nShow just the first 5 columns:\n\n.. code-block:: python\n\n table_df.iloc[:, :5]\n\n\nLike any dataframe, you can export to csv:\n\n.. code-block:: python\n\n path = \"my/path/to/csv/folder/table_df.csv\"\n table_df.to_csv(path_or_buf=path, encoding=\"utf-8\", index=0, sep=\"|\")\n\n\n\n.. |pypi| image:: https://img.shields.io/pypi/v/edc-analytics.svg\n :target: https://pypi.python.org/pypi/edc-analytics\n\n.. |downloads| image:: https://pepy.tech/badge/edc-analytics\n :target: https://pepy.tech/project/edc-analytics\n",
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