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DOC: remove use of head() in the comparison docs (#38935)
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‎doc/source/getting_started/comparison/comparison_with_sas.rst‎

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Comparison with SAS
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********************
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For potential users coming from `SAS <https://en.wikipedia.org/wiki/SAS_(software)>`__
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this page is meant to demonstrate how different SAS operations would be
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performed in pandas.
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.. include:: includes/introduction.rst
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.. note::
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Throughout this tutorial, the pandas ``DataFrame`` will be displayed by calling
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``df.head()``, which displays the first N (default 5) rows of the ``DataFrame``.
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This is often used in interactive work (e.g. `Jupyter notebook
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<https://jupyter.org/>`_ or terminal) - the equivalent in SAS would be:
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.. code-block:: sas
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proc print data=df(obs=5);
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run;
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Data structures
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---------------
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"pandas/master/pandas/tests/io/data/csv/tips.csv"
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)
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tips = pd.read_csv(url)
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tips.head()
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tips
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Like ``PROC IMPORT``, ``read_csv`` can take a number of parameters to specify
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such as Excel, HDF5, and SQL databases. These are all read via a ``pd.read_*``
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function. See the :ref:`IO documentation<io>` for more details.
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Limiting output
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~~~~~~~~~~~~~~~
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.. include:: includes/limit.rst
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The equivalent in SAS would be:
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.. code-block:: sas
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proc print data=df(obs=5);
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run;
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Exporting data
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~~~~~~~~~~~~~~
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new_bill = total_bill / 2;
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run;
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pandas provides similar vectorized operations by
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specifying the individual ``Series`` in the ``DataFrame``.
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New columns can be assigned in the same way.
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.. include:: includes/column_operations.rst
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.. ipython:: python
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tips["total_bill"] = tips["total_bill"] - 2
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tips["new_bill"] = tips["total_bill"] / 2.0
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tips.head()
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.. ipython:: python
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:suppress:
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tips = tips.drop("new_bill", axis=1)
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Filtering
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~~~~~~~~~
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rename total_bill=total_bill_2;
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run;
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The same operations are expressed in pandas below.
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.. ipython:: python
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# keep
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tips[["sex", "total_bill", "tip"]].head()
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# drop
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tips.drop("sex", axis=1).head()
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# rename
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tips.rename(columns={"total_bill": "total_bill_2"}).head()
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.. include:: includes/column_selection.rst
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Sorting by values
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Missing data
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------------
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Both pandas and SAS have a representation for missing data.
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.. include:: includes/missing_intro.rst
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One difference is that missing data cannot be compared to its sentinel value.

‎doc/source/getting_started/comparison/comparison_with_sql.rst‎

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"/pandas/master/pandas/tests/io/data/csv/tips.csv"
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)
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tips = pd.read_csv(url)
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tips.head()
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tips
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SELECT
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------
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.. code-block:: sql
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SELECT total_bill, tip, smoker, time
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FROM tips
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LIMIT 5;
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FROM tips;
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With pandas, column selection is done by passing a list of column names to your DataFrame:
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.. ipython:: python
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tips[["total_bill", "tip", "smoker", "time"]].head(5)
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tips[["total_bill", "tip", "smoker", "time"]]
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Calling the DataFrame without the list of column names would display all columns (akin to SQL's
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``*``).
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.. code-block:: sql
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SELECT *, tip/total_bill as tip_rate
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FROM tips
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LIMIT 5;
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FROM tips;
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With pandas, you can use the :meth:`DataFrame.assign` method of a DataFrame to append a new column:
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.. ipython:: python
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tips.assign(tip_rate=tips["tip"] / tips["total_bill"]).head(5)
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tips.assign(tip_rate=tips["tip"] / tips["total_bill"])
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WHERE
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-----
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pd.concat([df1, df2]).drop_duplicates()
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LIMIT
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-----
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.. code-block:: sql
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SELECT * FROM tips
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LIMIT 10;
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.. ipython:: python
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tips.head(10)
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pandas equivalents for some SQL analytic and aggregate functions
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----------------------------------------------------------------
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‎doc/source/getting_started/comparison/comparison_with_stata.rst‎

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.. include:: includes/introduction.rst
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.. note::
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Throughout this tutorial, the pandas ``DataFrame`` will be displayed by calling
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``df.head()``, which displays the first N (default 5) rows of the ``DataFrame``.
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This is often used in interactive work (e.g. `Jupyter notebook
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<https://jupyter.org/>`_ or terminal) -- the equivalent in Stata would be:
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.. code-block:: stata
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list in 1/5
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Data structures
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---------------
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"/pandas/master/pandas/tests/io/data/csv/tips.csv"
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)
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tips = pd.read_csv(url)
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tips.head()
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tips
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Like ``import delimited``, :func:`read_csv` can take a number of parameters to specify
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how the data should be parsed. For example, if the data were instead tab delimited,
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function. See the :ref:`IO documentation<io>` for more details.
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Limiting output
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~~~~~~~~~~~~~~~
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.. include:: includes/limit.rst
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The equivalent in Stata would be:
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.. code-block:: stata
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list in 1/5
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Exporting data
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~~~~~~~~~~~~~~
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generate new_bill = total_bill / 2
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drop new_bill
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pandas provides similar vectorized operations by
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specifying the individual ``Series`` in the ``DataFrame``.
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New columns can be assigned in the same way. The :meth:`DataFrame.drop` method
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drops a column from the ``DataFrame``.
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.. include:: includes/column_operations.rst
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.. ipython:: python
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tips["total_bill"] = tips["total_bill"] - 2
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tips["new_bill"] = tips["total_bill"] / 2
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tips.head()
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tips = tips.drop("new_bill", axis=1)
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Filtering
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~~~~~~~~~
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rename total_bill total_bill_2
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The same operations are expressed in pandas below. Note that in contrast to Stata, these
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operations do not happen in place. To make these changes persist, assign the operation back
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to a variable.
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.. ipython:: python
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# keep
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tips[["sex", "total_bill", "tip"]].head()
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# drop
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tips.drop("sex", axis=1).head()
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# rename
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tips.rename(columns={"total_bill": "total_bill_2"}).head()
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.. include:: includes/column_selection.rst
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Sorting by values
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restore
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merge 1:n key using df2.dta
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.. include:: includes/merge_setup.rst
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.. include:: includes/merge.rst
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Missing data
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------------
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Both pandas and Stata have a representation for missing data.
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.. include:: includes/missing_intro.rst
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One difference is that missing data cannot be compared to its sentinel value.

‎doc/source/getting_started/comparison/includes/column_operations.rst‎

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pandas provides similar vectorized operations by specifying the individual ``Series`` in the
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``DataFrame``. New columns can be assigned in the same way. The :meth:`DataFrame.drop` method drops
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a column from the ``DataFrame``.
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.. ipython:: python
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tips["total_bill"] = tips["total_bill"] - 2
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tips["new_bill"] = tips["total_bill"] / 2
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tips
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tips = tips.drop("new_bill", axis=1)

‎doc/source/getting_started/comparison/includes/column_selection.rst‎

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The same operations are expressed in pandas below. Note that these operations do not happen in
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place. To make these changes persist, assign the operation back to a variable.
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Keep certain columns
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''''''''''''''''''''
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.. ipython:: python
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tips[["sex", "total_bill", "tip"]]
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Drop a column
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'''''''''''''
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.. ipython:: python
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tips.drop("sex", axis=1)
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Rename a column
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'''''''''''''''
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.. ipython:: python
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tips.rename(columns={"total_bill": "total_bill_2"})

‎doc/source/getting_started/comparison/includes/extract_substring.rst‎

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.. ipython:: python
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tips["sex"].str[0:1].head()
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tips["sex"].str[0:1]

‎doc/source/getting_started/comparison/includes/find_substring.rst‎

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.. ipython:: python
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tips["sex"].str.find("ale").head()
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tips["sex"].str.find("ale")

‎doc/source/getting_started/comparison/includes/groupby.rst‎

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.. ipython:: python
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tips_summed = tips.groupby(["sex", "smoker"])[["total_bill", "tip"]].sum()
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tips_summed.head()
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tips_summed

‎doc/source/getting_started/comparison/includes/if_then.rst‎

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.. ipython:: python
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tips["bucket"] = np.where(tips["total_bill"] < 10, "low", "high")
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tips.head()
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tips
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.. ipython:: python
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:suppress:

‎doc/source/getting_started/comparison/includes/length.rst‎

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.. ipython:: python
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tips["time"].str.len().head()
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tips["time"].str.rstrip().str.len().head()
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tips["time"].str.len()
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tips["time"].str.rstrip().str.len()

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