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DOC: 10min import fixes
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@jreback
jreback committed Aug 1, 2013
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Expand Up @@ -9,18 +9,16 @@
import random
import os
np.random.seed(123456)
from pandas import *
from pandas import options
import pandas as pd
randn = np.random.randn
randint = np.random.randint
np.set_printoptions(precision=4, suppress=True)
options.display.mpl_style='default'
from pandas.compat import lrange, lzip
#### portions of this were borrowed from the
#### Pandas cheatsheet
#### created during the PyData Workshop-Sprint 2012
#### Hannah Chen, Henry Chow, Eric Cox, Robert Mauriello
#### Pandas cheatsheet
#### created during the PyData Workshop-Sprint 2012
#### Hannah Chen, Henry Chow, Eric Cox, Robert Mauriello
********************
Expand All @@ -42,7 +40,7 @@ Object Creation

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

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

.. ipython:: python
Expand All @@ -63,10 +61,10 @@ Creating a ``DataFrame`` by passing a dict of objects that can be converted to s

.. ipython:: python
df2 = pd.DataFrame({ 'A' : 1.,
'B' : pd.Timestamp('20130102'),
df2 = pd.DataFrame({ 'A' : 1.,
'B' : pd.Timestamp('20130102'),
'C' : pd.Series(1,index=lrange(4),dtype='float32'),
'D' : np.array([3] * 4,dtype='int32'),
'D' : np.array([3] * 4,dtype='int32'),
'E' : 'foo' })
df2
Expand Down Expand Up @@ -123,7 +121,7 @@ Sorting by values
Selection
---------

.. note::
.. note::

While standard Python / Numpy expressions for selecting and setting are
intuitive and come in handy for interactive work, for production code, we
Expand Down Expand Up @@ -248,7 +246,7 @@ error.
x[4:10]
x[8:10]
Pandas will detect this and raise ``IndexError``, rather than return an empty
Pandas will detect this and raise ``IndexError``, rather than return an empty
structure.

::
Expand Down Expand Up @@ -280,7 +278,7 @@ by the indexes

.. ipython:: python
s1 = pd.Series([1,2,3,4,5,6],index=date_range('20130102',periods=6))
s1 = pd.Series([1,2,3,4,5,6],index=pd.date_range('20130102',periods=6))
s1
df['F'] = s1
Expand Down Expand Up @@ -401,7 +399,7 @@ See more at :ref:`Histogramming and Discretization <basics.discretization>`

.. ipython:: python
s = Series(np.random.randint(0,7,size=10))
s = pd.Series(np.random.randint(0,7,size=10))
s
s.value_counts()
Expand All @@ -412,7 +410,7 @@ See more at :ref:`Vectorized String Methods <basics.string_methods>`

.. ipython:: python
s = Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
s.str.lower()
Merge
Expand All @@ -428,7 +426,7 @@ operations.

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

Concatenating pandas objects together
Concatenating pandas objects together

.. ipython:: python
Expand All @@ -438,7 +436,7 @@ Concatenating pandas objects together
# break it into pieces
pieces = [df[:3], df[3:7], df[7:]]
concat(pieces)
pd.concat(pieces)
Join
~~~~
Expand All @@ -451,7 +449,7 @@ SQL style merges. See the :ref:`Database style joining <merging.join>`
right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
left
right
merge(left, right, on='key')
pd.merge(left, right, on='key')
Append
~~~~~~
Expand Down Expand Up @@ -484,7 +482,8 @@ See the :ref:`Grouping section <groupby>`
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : randn(8), 'D' : randn(8)})
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
df
Grouping and then applying a function ``sum`` to the resulting groups.
Expand All @@ -493,7 +492,7 @@ Grouping and then applying a function ``sum`` to the resulting groups.
df.groupby('A').sum()
Grouping by multiple columns forms a hierarchical index, which we then apply
Grouping by multiple columns forms a hierarchical index, which we then apply
the function.

.. ipython:: python
Expand All @@ -516,7 +515,7 @@ Stack
['one', 'two', 'one', 'two',
'one', 'two', 'one', 'two']])
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(randn(8, 2), index=index, columns=['A', 'B'])
df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
df2 = df[:4]
df2
Expand All @@ -543,18 +542,18 @@ See the section on :ref:`Pivot Tables <reshaping.pivot>`.

.. ipython:: python
df = 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 = 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
pivot_table(df, values='D', rows=['A', 'B'], cols=['C'])
pd.pivot_table(df, values='D', rows=['A', 'B'], cols=['C'])
Time Series
Expand All @@ -568,15 +567,15 @@ 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(randint(0, 500, len(rng)), index=rng)
ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
ts.resample('5Min', how='sum')
Time zone representation

.. ipython:: python
rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
ts = pd.Series(randn(len(rng)), rng)
ts = pd.Series(np.random.randn(len(rng)), rng)
ts_utc = ts.tz_localize('UTC')
ts_utc
Expand All @@ -591,7 +590,7 @@ Converting between time span representations
.. ipython:: python
rng = pd.date_range('1/1/2012', periods=5, freq='M')
ts = pd.Series(randn(len(rng)), index=rng)
ts = pd.Series(np.random.randn(len(rng)), index=rng)
ts
ps = ts.to_period()
ps
Expand All @@ -604,8 +603,8 @@ the quarter end:

.. ipython:: python
prng = period_range('1990Q1', '2000Q4', freq='Q-NOV')
ts = Series(randn(len(prng)), prng)
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()
Expand All @@ -624,7 +623,7 @@ Plotting
.. ipython:: python
ts = pd.Series(randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
@savefig series_plot_basic.png
Expand All @@ -634,7 +633,7 @@ On DataFrame, ``plot`` is a convenience to plot all of the columns with labels:

.. ipython:: python
df = pd.DataFrame(randn(1000, 4), index=ts.index,
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
columns=['A', 'B', 'C', 'D'])
df = df.cumsum()
Expand Down Expand Up @@ -679,7 +678,7 @@ Reading from a HDF5 Store

.. ipython:: python
read_hdf('foo.h5','df')
pd.read_hdf('foo.h5','df')
.. ipython:: python
:suppress:
Expand All @@ -701,7 +700,7 @@ Reading from an excel file

.. ipython:: python
read_excel('foo.xlsx', 'sheet1', index_col=None, na_values=['NA'])
pd.read_excel('foo.xlsx', 'sheet1', index_col=None, na_values=['NA'])
.. ipython:: python
:suppress:
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