.. _10min:

.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   import pandas as pd
   import os
   np.random.seed(123456)
   np.set_printoptions(precision=4, suppress=True)
   import matplotlib
   matplotlib.style.use('ggplot')
   pd.options.display.max_rows = 15

   #### portions of this were borrowed from the
   #### Pandas cheatsheet
   #### created during the PyData Workshop-Sprint 2012
   #### Hannah Chen, Henry Chow, Eric Cox, Robert Mauriello


********************
10 Minutes to pandas
********************

This is a short introduction to pandas, geared mainly for new users.
You can see more complex recipes in the :ref:`Cookbook<cookbook>`

Customarily, we import as follows:

.. ipython:: python

   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt

Object Creation
---------------

See the :ref:`Data Structure Intro section <dsintro>`

Creating a :class:`Series` by passing a list of values, letting pandas create
a default integer index:

.. ipython:: python

   s = pd.Series([1,3,5,np.nan,6,8])
   s

Creating a :class:`DataFrame` by passing a numpy array, with a datetime index
and labeled columns:

.. ipython:: python

   dates = pd.date_range('20130101', periods=6)
   dates
   df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
   df

Creating a ``DataFrame`` by passing a dict of objects that can be converted to series-like.

.. ipython:: python

   df2 = pd.DataFrame({ 'A' : 1.,
                        'B' : pd.Timestamp('20130102'),
                        'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
                        'D' : np.array([3] * 4,dtype='int32'),
                        'E' : pd.Categorical(["test","train","test","train"]),
                        'F' : 'foo' })
   df2

Having specific :ref:`dtypes <basics.dtypes>`

.. ipython:: python

   df2.dtypes

If you're using IPython, tab completion for column names (as well as public
attributes) is automatically enabled. Here's a subset of the attributes that
will be completed:

.. ipython::

   @verbatim
   In [1]: df2.<TAB>
   df2.A                  df2.bool
   df2.abs                df2.boxplot
   df2.add                df2.C
   df2.add_prefix         df2.clip
   df2.add_suffix         df2.clip_lower
   df2.align              df2.clip_upper
   df2.all                df2.columns
   df2.any                df2.combine
   df2.append             df2.combine_first
   df2.apply              df2.compound
   df2.applymap           df2.consolidate
   df2.as_blocks          df2.convert_objects
   df2.asfreq             df2.copy
   df2.as_matrix          df2.corr
   df2.astype             df2.corrwith
   df2.at                 df2.count
   df2.at_time            df2.cov
   df2.axes               df2.cummax
   df2.B                  df2.cummin
   df2.between_time       df2.cumprod
   df2.bfill              df2.cumsum
   df2.blocks             df2.D

As you can see, the columns ``A``, ``B``, ``C``, and ``D`` are automatically
tab completed. ``E`` is there as well; the rest of the attributes have been
truncated for brevity.

Viewing Data
------------

See the :ref:`Basics section <basics>`

See the top & bottom rows of the frame

.. ipython:: python

   df.head()
   df.tail(3)

Display the index, columns, and the underlying numpy data

.. ipython:: python

   df.index
   df.columns
   df.values

Describe shows a quick statistic summary of your data

.. ipython:: python

   df.describe()

Transposing your data

.. ipython:: python

   df.T

Sorting by an axis

.. ipython:: python

   df.sort_index(axis=1, ascending=False)

Sorting by values

.. ipython:: python

   df.sort_values(by='B')

Selection
---------

.. note::

   While standard Python / Numpy expressions for selecting and setting are
   intuitive and come in handy for interactive work, for production code, we
   recommend the optimized pandas data access methods, ``.at``, ``.iat``,
   ``.loc``, ``.iloc`` and ``.ix``.

See the indexing documentation :ref:`Indexing and Selecting Data <indexing>` and :ref:`MultiIndex / Advanced Indexing <advanced>`

Getting
~~~~~~~

Selecting a single column, which yields a ``Series``,
equivalent to ``df.A``

.. ipython:: python

   df['A']

Selecting via ``[]``, which slices the rows.

.. ipython:: python

   df[0:3]
   df['20130102':'20130104']

Selection by Label
~~~~~~~~~~~~~~~~~~

See more in :ref:`Selection by Label <indexing.label>`

For getting a cross section using a label

.. ipython:: python

   df.loc[dates[0]]

Selecting on a multi-axis by label

.. ipython:: python

   df.loc[:,['A','B']]

Showing label slicing, both endpoints are *included*

.. ipython:: python

   df.loc['20130102':'20130104',['A','B']]

Reduction in the dimensions of the returned object

.. ipython:: python

   df.loc['20130102',['A','B']]

For getting a scalar value

.. ipython:: python

   df.loc[dates[0],'A']

For getting fast access to a scalar (equiv to the prior method)

.. ipython:: python

   df.at[dates[0],'A']

Selection by Position
~~~~~~~~~~~~~~~~~~~~~

See more in :ref:`Selection by Position <indexing.integer>`

Select via the position of the passed integers

.. ipython:: python

   df.iloc[3]

By integer slices, acting similar to numpy/python

.. ipython:: python

   df.iloc[3:5,0:2]

By lists of integer position locations, similar to the numpy/python style

.. ipython:: python

   df.iloc[[1,2,4],[0,2]]

For slicing rows explicitly

.. ipython:: python

   df.iloc[1:3,:]

For slicing columns explicitly

.. ipython:: python

   df.iloc[:,1:3]

For getting a value explicitly

.. ipython:: python

   df.iloc[1,1]

For getting fast access to a scalar (equiv to the prior method)

.. ipython:: python

   df.iat[1,1]

Boolean Indexing
~~~~~~~~~~~~~~~~

Using a single column's values to select data.

.. ipython:: python

   df[df.A > 0]

Selecting values from a DataFrame where a boolean condition is met.

.. ipython:: python

   df[df > 0]

Using the :func:`~Series.isin` method for filtering:

.. ipython:: python

   df2 = df.copy()
   df2['E'] = ['one', 'one','two','three','four','three']
   df2
   df2[df2['E'].isin(['two','four'])]

Setting
~~~~~~~

Setting a new column automatically aligns the data
by the indexes

.. ipython:: python

   s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))
   s1
   df['F'] = s1

Setting values by label

.. ipython:: python

   df.at[dates[0],'A'] = 0

Setting values by position

.. ipython:: python

   df.iat[0,1] = 0

Setting by assigning with a numpy array

.. ipython:: python

   df.loc[:,'D'] = np.array([5] * len(df))

The result of the prior setting operations

.. ipython:: python

   df

A ``where`` operation with setting.

.. ipython:: python

   df2 = df.copy()
   df2[df2 > 0] = -df2
   df2


Missing Data
------------

pandas primarily uses the value ``np.nan`` to represent missing data. It is by
default not included in computations. See the :ref:`Missing Data section
<missing_data>`

Reindexing allows you to change/add/delete the index on a specified axis. This
returns a copy of the data.

.. ipython:: python

   df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
   df1.loc[dates[0]:dates[1],'E'] = 1
   df1

To drop any rows that have missing data.

.. ipython:: python

   df1.dropna(how='any')

Filling missing data

.. ipython:: python

   df1.fillna(value=5)

To get the boolean mask where values are ``nan``

.. ipython:: python

   pd.isnull(df1)


Operations
----------

See the :ref:`Basic section on Binary Ops <basics.binop>`

Stats
~~~~~

Operations in general *exclude* missing data.

Performing a descriptive statistic

.. ipython:: python

   df.mean()

Same operation on the other axis

.. ipython:: python

   df.mean(1)

Operating with objects that have different dimensionality and need alignment.
In addition, pandas automatically broadcasts along the specified dimension.

.. ipython:: python

   s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)
   s
   df.sub(s, axis='index')


Apply
~~~~~

Applying functions to the data

.. ipython:: python

   df.apply(np.cumsum)
   df.apply(lambda x: x.max() - x.min())

Histogramming
~~~~~~~~~~~~~

See more at :ref:`Histogramming and Discretization <basics.discretization>`

.. ipython:: python

   s = pd.Series(np.random.randint(0, 7, size=10))
   s
   s.value_counts()

String Methods
~~~~~~~~~~~~~~

Series is equipped with a set of string processing methods in the `str`
attribute that make it easy to operate on each element of the array, as in the
code snippet below. Note that pattern-matching in `str` generally uses `regular
expressions <https://docs.python.org/2/library/re.html>`__ by default (and in
some cases always uses them). See more at :ref:`Vectorized String Methods
<text.string_methods>`.

.. ipython:: python

   s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
   s.str.lower()

Merge
-----

Concat
~~~~~~

pandas provides various facilities for easily combining together Series,
DataFrame, and Panel objects with various kinds of set logic for the indexes
and relational algebra functionality in the case of join / merge-type
operations.

See the :ref:`Merging section <merging>`

Concatenating pandas objects together with :func:`concat`:

.. ipython:: python

   df = pd.DataFrame(np.random.randn(10, 4))
   df

   # break it into pieces
   pieces = [df[:3], df[3:7], df[7:]]

   pd.concat(pieces)

Join
~~~~

SQL style merges. See the :ref:`Database style joining <merging.join>`

.. ipython:: python

   left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
   right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
   left
   right
   pd.merge(left, right, on='key')

Another example that can be given is:

.. ipython:: python

   left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})
   right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})
   left
   right
   pd.merge(left, right, on='key')


Append
~~~~~~

Append rows to a dataframe. See the :ref:`Appending <merging.concatenation>`

.. ipython:: python

   df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])
   df
   s = df.iloc[3]
   df.append(s, ignore_index=True)


Grouping
--------

By "group by" we are referring to a process involving one or more of the
following steps

 - **Splitting** the data into groups based on some criteria
 - **Applying** a function to each group independently
 - **Combining** the results into a data structure

See the :ref:`Grouping section <groupby>`

.. ipython:: python

   df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
                             'foo', 'bar', 'foo', 'foo'],
                      'B' : ['one', 'one', 'two', 'three',
                             'two', 'two', 'one', 'three'],
                      'C' : np.random.randn(8),
                      'D' : np.random.randn(8)})
   df

Grouping and then applying a function ``sum`` to the resulting groups.

.. ipython:: python

   df.groupby('A').sum()

Grouping by multiple columns forms a hierarchical index, which we then apply
the function.

.. ipython:: python

   df.groupby(['A','B']).sum()

Reshaping
---------

See the sections on :ref:`Hierarchical Indexing <advanced.hierarchical>` and
:ref:`Reshaping <reshaping.stacking>`.

Stack
~~~~~

.. ipython:: python

   tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
                        'foo', 'foo', 'qux', 'qux'],
                       ['one', 'two', 'one', 'two',
                        'one', 'two', 'one', 'two']]))
   index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
   df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
   df2 = df[:4]
   df2

The :meth:`~DataFrame.stack` method "compresses" a level in the DataFrame's
columns.

.. ipython:: python

   stacked = df2.stack()
   stacked

With a "stacked" DataFrame or Series (having a ``MultiIndex`` as the
``index``), the inverse operation of :meth:`~DataFrame.stack` is
:meth:`~DataFrame.unstack`, which by default unstacks the **last level**:

.. ipython:: python

   stacked.unstack()
   stacked.unstack(1)
   stacked.unstack(0)

Pivot Tables
~~~~~~~~~~~~
See the section on :ref:`Pivot Tables <reshaping.pivot>`.

.. ipython:: python

   df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,
                      'B' : ['A', 'B', 'C'] * 4,
                      'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
                      'D' : np.random.randn(12),
                      'E' : np.random.randn(12)})
   df

We can produce pivot tables from this data very easily:

.. ipython:: python

   pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])


Time Series
-----------

pandas has simple, powerful, and efficient functionality for performing
resampling operations during frequency conversion (e.g., converting secondly
data into 5-minutely data). This is extremely common in, but not limited to,
financial applications. See the :ref:`Time Series section <timeseries>`

.. ipython:: python

   rng = pd.date_range('1/1/2012', periods=100, freq='S')
   ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
   ts.resample('5Min').sum()

Time zone representation

.. ipython:: python

   rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
   ts = pd.Series(np.random.randn(len(rng)), rng)
   ts
   ts_utc = ts.tz_localize('UTC')
   ts_utc

Convert to another time zone

.. ipython:: python

   ts_utc.tz_convert('US/Eastern')

Converting between time span representations

.. ipython:: python

   rng = pd.date_range('1/1/2012', periods=5, freq='M')
   ts = pd.Series(np.random.randn(len(rng)), index=rng)
   ts
   ps = ts.to_period()
   ps
   ps.to_timestamp()

Converting between period and timestamp enables some convenient arithmetic
functions to be used. In the following example, we convert a quarterly
frequency with year ending in November to 9am of the end of the month following
the quarter end:

.. ipython:: python

   prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
   ts = pd.Series(np.random.randn(len(prng)), prng)
   ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
   ts.head()

Categoricals
------------

Since version 0.15, pandas can include categorical data in a ``DataFrame``. For full docs, see the
:ref:`categorical introduction <categorical>` and the :ref:`API documentation <api.categorical>`.

.. ipython:: python

    df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

Convert the raw grades to a categorical data type.

.. ipython:: python

    df["grade"] = df["raw_grade"].astype("category")
    df["grade"]

Rename the categories to more meaningful names (assigning to ``Series.cat.categories`` is inplace!)

.. ipython:: python

    df["grade"].cat.categories = ["very good", "good", "very bad"]

Reorder the categories and simultaneously add the missing categories (methods under ``Series
.cat`` return a new ``Series`` per default).

.. ipython:: python

    df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
    df["grade"]

Sorting is per order in the categories, not lexical order.

.. ipython:: python

    df.sort_values(by="grade")

Grouping by a categorical column shows also empty categories.

.. ipython:: python

    df.groupby("grade").size()


Plotting
--------

:ref:`Plotting <visualization>` docs.

.. ipython:: python
   :suppress:

   import matplotlib.pyplot as plt
   plt.close('all')

.. ipython:: python

   ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
   ts = ts.cumsum()

   @savefig series_plot_basic.png
   ts.plot()

On DataFrame, :meth:`~DataFrame.plot` is a convenience to plot all of the
columns with labels:

.. ipython:: python

   df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
                     columns=['A', 'B', 'C', 'D'])
   df = df.cumsum()

   @savefig frame_plot_basic.png
   plt.figure(); df.plot(); plt.legend(loc='best')

Getting Data In/Out
-------------------

CSV
~~~

:ref:`Writing to a csv file <io.store_in_csv>`

.. ipython:: python

   df.to_csv('foo.csv')

:ref:`Reading from a csv file <io.read_csv_table>`

.. ipython:: python

   pd.read_csv('foo.csv')

.. ipython:: python
   :suppress:

   os.remove('foo.csv')

HDF5
~~~~

Reading and writing to :ref:`HDFStores <io.hdf5>`

Writing to a HDF5 Store

.. ipython:: python

   df.to_hdf('foo.h5','df')

Reading from a HDF5 Store

.. ipython:: python

   pd.read_hdf('foo.h5','df')

.. ipython:: python
   :suppress:

   os.remove('foo.h5')

Excel
~~~~~

Reading and writing to :ref:`MS Excel <io.excel>`

Writing to an excel file

.. ipython:: python

   df.to_excel('foo.xlsx', sheet_name='Sheet1')

Reading from an excel file

.. ipython:: python

   pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])

.. ipython:: python
   :suppress:

   os.remove('foo.xlsx')

Gotchas
-------

If you are trying an operation and you see an exception like:

.. code-block:: python

    >>> if pd.Series([False, True, False]):
        print("I was true")
    Traceback
        ...
    ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().

See :ref:`Comparisons<basics.compare>` for an explanation and what to do.

See :ref:`Gotchas<gotchas>` as well.
