.. currentmodule:: pandas
.. _compare_with_sql:

Comparison with SQL
********************
Since many potential pandas users have some familiarity with
`SQL <http://en.wikipedia.org/wiki/SQL>`_, this page is meant to provide some examples of how
various SQL operations would be performed using pandas.

If you're new to pandas, you might want to first read through :ref:`10 Minutes to pandas<10min>`
to familiarize yourself with the library.

As is customary, we import pandas and numpy as follows:

.. ipython:: python

    import pandas as pd
    import numpy as np

Most of the examples will utilize the ``tips`` dataset found within pandas tests.  We'll read
the data into a DataFrame called `tips` and assume we have a database table of the same name and
structure.

.. ipython:: python

    url = 'https://raw.github.com/pydata/pandas/master/pandas/tests/data/tips.csv'
    tips = pd.read_csv(url)
    tips.head()

SELECT
------
In SQL, selection is done using a comma-separated list of columns you'd like to select (or a ``*``
to select all columns):

.. code-block:: sql

    SELECT total_bill, tip, smoker, time
    FROM tips
    LIMIT 5;

With pandas, column selection is done by passing a list of column names to your DataFrame:

.. ipython:: python

    tips[['total_bill', 'tip', 'smoker', 'time']].head(5)

Calling the DataFrame without the list of column names would display all columns (akin to SQL's
``*``).

WHERE
-----
Filtering in SQL is done via a WHERE clause.

.. code-block:: sql

    SELECT *
    FROM tips
    WHERE time = 'Dinner'
    LIMIT 5;

DataFrames can be filtered in multiple ways; the most intuitive of which is using
`boolean indexing <http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing>`_.

.. ipython:: python

    tips[tips['time'] == 'Dinner'].head(5)

The above statement is simply passing a ``Series`` of True/False objects to the DataFrame,
returning all rows with True.

.. ipython:: python

    is_dinner = tips['time'] == 'Dinner'
    is_dinner.value_counts()
    tips[is_dinner].head(5)

Just like SQL's OR and AND, multiple conditions can be passed to a DataFrame using | (OR) and &
(AND).

.. code-block:: sql

    -- tips of more than $5.00 at Dinner meals
    SELECT *
    FROM tips
    WHERE time = 'Dinner' AND tip > 5.00;

.. ipython:: python

    # tips of more than $5.00 at Dinner meals
    tips[(tips['time'] == 'Dinner') & (tips['tip'] > 5.00)]

.. code-block:: sql

    -- tips by parties of at least 5 diners OR bill total was more than $45
    SELECT *
    FROM tips
    WHERE size >= 5 OR total_bill > 45;

.. ipython:: python

    # tips by parties of at least 5 diners OR bill total was more than $45
    tips[(tips['size'] >= 5) | (tips['total_bill'] > 45)]

NULL checking is done using the :meth:`~pandas.Series.notnull` and :meth:`~pandas.Series.isnull`
methods.

.. ipython:: python

    frame = pd.DataFrame({'col1': ['A', 'B', np.NaN, 'C', 'D'],
                          'col2': ['F', np.NaN, 'G', 'H', 'I']})
    frame

Assume we have a table of the same structure as our DataFrame above. We can see only the records
where ``col2`` IS NULL with the following query:

.. code-block:: sql

    SELECT *
    FROM frame
    WHERE col2 IS NULL;

.. ipython:: python

    frame[frame['col2'].isnull()]

Getting items where ``col1`` IS NOT NULL can be done with :meth:`~pandas.Series.notnull`.

.. code-block:: sql

    SELECT *
    FROM frame
    WHERE col1 IS NOT NULL;

.. ipython:: python

    frame[frame['col1'].notnull()]


GROUP BY
--------
In pandas, SQL's GROUP BY operations performed using the similarly named
:meth:`~pandas.DataFrame.groupby` method. :meth:`~pandas.DataFrame.groupby` typically refers to a
process where we'd like to split a dataset into groups, apply some function (typically aggregation)
, and then combine the groups together.

A common SQL operation would be getting the count of records in each group throughout a dataset.
For instance, a query getting us the number of tips left by sex:

.. code-block:: sql

    SELECT sex, count(*)
    FROM tips
    GROUP BY sex;
    /*
    Female     87
    Male      157
    */


The pandas equivalent would be:

.. ipython:: python

    tips.groupby('sex').size()

Notice that in the pandas code we used :meth:`~pandas.DataFrameGroupBy.size` and not
:meth:`~pandas.DataFrameGroupBy.count`. This is because :meth:`~pandas.DataFrameGroupBy.count`
applies the function to each column, returning the number of ``not null`` records within each.

.. ipython:: python

    tips.groupby('sex').count()

Alternatively, we could have applied the :meth:`~pandas.DataFrameGroupBy.count` method to an
individual column:

.. ipython:: python

    tips.groupby('sex')['total_bill'].count()

Multiple functions can also be applied at once. For instance, say we'd like to see how tip amount
differs by day of the week - :meth:`~pandas.DataFrameGroupBy.agg` allows you to pass a dictionary
to your grouped DataFrame, indicating which functions to apply to specific columns.

.. code-block:: sql

    SELECT day, AVG(tip), COUNT(*)
    FROM tips
    GROUP BY day;
    /*
    Fri   2.734737   19
    Sat   2.993103   87
    Sun   3.255132   76
    Thur  2.771452   62
    */

.. ipython:: python

    tips.groupby('day').agg({'tip': np.mean, 'day': np.size})

Grouping by more than one column is done by passing a list of columns to the
:meth:`~pandas.DataFrame.groupby` method.

.. code-block:: sql

    SELECT smoker, day, COUNT(*), AVG(tip)
    FROM tips
    GROUP BY smoker, day;
    /*
    smoker day
    No     Fri      4  2.812500
           Sat     45  3.102889
           Sun     57  3.167895
           Thur    45  2.673778
    Yes    Fri     15  2.714000
           Sat     42  2.875476
           Sun     19  3.516842
           Thur    17  3.030000
    */

.. ipython:: python

    tips.groupby(['smoker', 'day']).agg({'tip': [np.size, np.mean]})

.. _compare_with_sql.join:

JOIN
----
JOINs can be performed with :meth:`~pandas.DataFrame.join` or :meth:`~pandas.merge`. By default,
:meth:`~pandas.DataFrame.join` will join the DataFrames on their indices. Each method has
parameters allowing you to specify the type of join to perform (LEFT, RIGHT, INNER, FULL) or the
columns to join on (column names or indices).

.. ipython:: python

    df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'],
                        'value': np.random.randn(4)})
    df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'],
                        'value': np.random.randn(4)})

Assume we have two database tables of the same name and structure as our DataFrames.

Now let's go over the various types of JOINs.

INNER JOIN
~~~~~~~~~~
.. code-block:: sql

    SELECT *
    FROM df1
    INNER JOIN df2
      ON df1.key = df2.key;

.. ipython:: python

    # merge performs an INNER JOIN by default
    pd.merge(df1, df2, on='key')

:meth:`~pandas.merge` also offers parameters for cases when you'd like to join one DataFrame's
column with another DataFrame's index.

.. ipython:: python

    indexed_df2 = df2.set_index('key')
    pd.merge(df1, indexed_df2, left_on='key', right_index=True)

LEFT OUTER JOIN
~~~~~~~~~~~~~~~
.. code-block:: sql

    -- show all records from df1
    SELECT *
    FROM df1
    LEFT OUTER JOIN df2
      ON df1.key = df2.key;

.. ipython:: python

    # show all records from df1
    pd.merge(df1, df2, on='key', how='left')

RIGHT JOIN
~~~~~~~~~~
.. code-block:: sql

    -- show all records from df2
    SELECT *
    FROM df1
    RIGHT OUTER JOIN df2
      ON df1.key = df2.key;

.. ipython:: python

    # show all records from df2
    pd.merge(df1, df2, on='key', how='right')

FULL JOIN
~~~~~~~~~
pandas also allows for FULL JOINs, which display both sides of the dataset, whether or not the
joined columns find a match. As of writing, FULL JOINs are not supported in all RDBMS (MySQL).

.. code-block:: sql

    -- show all records from both tables
    SELECT *
    FROM df1
    FULL OUTER JOIN df2
      ON df1.key = df2.key;

.. ipython:: python

    # show all records from both frames
    pd.merge(df1, df2, on='key', how='outer')


UNION
-----
UNION ALL can be performed using :meth:`~pandas.concat`.

.. ipython:: python

    df1 = pd.DataFrame({'city': ['Chicago', 'San Francisco', 'New York City'],
                        'rank': range(1, 4)})
    df2 = pd.DataFrame({'city': ['Chicago', 'Boston', 'Los Angeles'],
                        'rank': [1, 4, 5]})

.. code-block:: sql

    SELECT city, rank
    FROM df1
    UNION ALL
    SELECT city, rank
    FROM df2;
    /*
             city  rank
          Chicago     1
    San Francisco     2
    New York City     3
          Chicago     1
           Boston     4
      Los Angeles     5
    */

.. ipython:: python

    pd.concat([df1, df2])

SQL's UNION is similar to UNION ALL, however UNION will remove duplicate rows.

.. code-block:: sql

    SELECT city, rank
    FROM df1
    UNION
    SELECT city, rank
    FROM df2;
    -- notice that there is only one Chicago record this time
    /*
             city  rank
          Chicago     1
    San Francisco     2
    New York City     3
           Boston     4
      Los Angeles     5
    */

In pandas, you can use :meth:`~pandas.concat` in conjunction with
:meth:`~pandas.DataFrame.drop_duplicates`.

.. ipython:: python

    pd.concat([df1, df2]).drop_duplicates()


UPDATE
------


DELETE
------
