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Stratified sampling #33762

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fb9cec6
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flaboss Apr 15, 2020
f6ea6bd
ENH: Added new method stratified sample
flaboss Apr 17, 2020
153858b
ENH: Added docstrings to stratified sample function
flaboss Apr 17, 2020
bdbf267
ENH: Add test_stratified_sample.py
flaboss Apr 23, 2020
6aa6359
ENH: Create stratified_sample method for DataFrame
flaboss Apr 23, 2020
3b47523
ENH: Added methods for stratified sampling.
flaboss Apr 24, 2020
766d2d1
ENH: Added method stratified_sample
flaboss Apr 24, 2020
9a35384
ENH: Added test for stratified_sample
flaboss Apr 24, 2020
191e4f6
ENH: Added stratified_sample method
flaboss Apr 24, 2020
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1 change: 1 addition & 0 deletions doc/source/whatsnew/index.rst
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Expand Up @@ -24,6 +24,7 @@ Version 1.0
.. toctree::
:maxdepth: 2

v1.0.4
v1.0.3
v1.0.2
v1.0.1
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69 changes: 69 additions & 0 deletions doc/source/whatsnew/v1.0.4.rst
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@@ -0,0 +1,69 @@
.. _whatsnew_104:

What's new in 1.0.4 (??)
------------------------

These are the changes in pandas 1.0.4. See :ref:`release` for a full changelog
including other versions of pandas.


.. ---------------------------------------------------------------------------

Enhancements
~~~~~~~~~~~~

.. _whatsnew_104.stratified_sample:

Stratified Sampling in Pandas
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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if this is a new feature, it would go in 1.1


We've added :meth:`~DataFrame.stratified_sample` for sampling a DataFrame object using strata.

A stratified sample is used when it is desired to keep the same proportions of the variables in the original population when sampling.

For example:

.. code-block:: ipython

In [1]: df = pd.DataFrame({'gender': ['male', 'male', 'female', 'female', 'female',
... 'female', 'female', 'female', 'male', 'male'],
... 'age': [25, 26, 25, 26, 30, 25, 25, 30, 30, 25],
... 'country': ['US', 'CAN', 'MEX', 'CAN', 'IN', 'CAN',
... 'CAN', 'US', 'CAN', 'IN'],
... 'income_K' : [100, 110, 99, 110, 110, 100, 100, 110,
... 100, 99]}
)
In [2]: df.stratified_sample(strata=['age'], n=5, random_state=0, reset_index=True)
Out[2]:
gender age country income_K
0 female 25 CAN 100
1 male 25 US 100
2 female 26 CAN 110
3 male 30 CAN 100
4 female 30 US 110

We've also added :meth:`~DataFrame.stratified_sample_counts` to return the counts that would be generated by a stratified sampling.

.. code-block:: ipython

In [1]: df = pd.DataFrame({'gender': ['male', 'male', 'female', 'female', 'female',
... 'female', 'female', 'female', 'male', 'male'],
... 'age': [25, 26, 25, 26, 30, 25, 25, 30, 30, 25],
... 'country': ['US', 'CAN', 'MEX', 'CAN', 'IN', 'CAN',
... 'CAN', 'US', 'CAN', 'IN'],
... 'income_K' : [100, 110, 99, 110, 110, 100, 100, 110,
... 100, 99]}
)
In [2]: df.stratified_sample_counts(strata=['age'], n=5)
Out[2]:
age size sample_size
0 25 5 2
1 26 2 1
2 30 3 2


.. _whatsnew_104.contributors:

Contributors
~~~~~~~~~~~~
* Flavio Bossolan +
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