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DOC: clean up / consistent imports (GH9886) #9987

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May 5, 2015
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DOC: clean up / consistent imports (GH9886) #9987

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41 changes: 20 additions & 21 deletions doc/source/computation.rst
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Original file line number Diff line number Diff line change
@@ -1,23 +1,22 @@
.. currentmodule:: pandas
.. _computation:

.. ipython:: python
:suppress:

import numpy as np
np.random.seed(123456)
from pandas import *
import pandas.util.testing as tm
randn = np.random.randn
np.set_printoptions(precision=4, suppress=True)
import pandas as pd
import matplotlib
try:
matplotlib.style.use('ggplot')
except AttributeError:
options.display.mpl_style = 'default'
pd.options.display.mpl_style = 'default'
import matplotlib.pyplot as plt
plt.close('all')
options.display.max_rows=15
pd.options.display.max_rows=15

.. _computation:

Computational tools
===================
Expand All @@ -36,13 +35,13 @@ NA/null values *before* computing the percent change).

.. ipython:: python

ser = Series(randn(8))
ser = pd.Series(np.random.randn(8))

ser.pct_change()

.. ipython:: python

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

df.pct_change(periods=3)

Expand All @@ -56,8 +55,8 @@ The ``Series`` object has a method ``cov`` to compute covariance between series

.. ipython:: python

s1 = Series(randn(1000))
s2 = Series(randn(1000))
s1 = pd.Series(np.random.randn(1000))
s2 = pd.Series(np.random.randn(1000))
s1.cov(s2)

Analogously, ``DataFrame`` has a method ``cov`` to compute pairwise covariances
Expand All @@ -78,7 +77,7 @@ among the series in the DataFrame, also excluding NA/null values.

.. ipython:: python

frame = DataFrame(randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])
frame = pd.DataFrame(np.random.randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])
frame.cov()

``DataFrame.cov`` also supports an optional ``min_periods`` keyword that
Expand All @@ -87,7 +86,7 @@ in order to have a valid result.

.. ipython:: python

frame = DataFrame(randn(20, 3), columns=['a', 'b', 'c'])
frame = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c'])
frame.ix[:5, 'a'] = np.nan
frame.ix[5:10, 'b'] = np.nan

Expand Down Expand Up @@ -123,7 +122,7 @@ All of these are currently computed using pairwise complete observations.

.. ipython:: python

frame = DataFrame(randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])
frame = pd.DataFrame(np.random.randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])
frame.ix[::2] = np.nan

# Series with Series
Expand All @@ -140,7 +139,7 @@ Like ``cov``, ``corr`` also supports the optional ``min_periods`` keyword:

.. ipython:: python

frame = DataFrame(randn(20, 3), columns=['a', 'b', 'c'])
frame = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c'])
frame.ix[:5, 'a'] = np.nan
frame.ix[5:10, 'b'] = np.nan

Expand All @@ -157,8 +156,8 @@ objects.

index = ['a', 'b', 'c', 'd', 'e']
columns = ['one', 'two', 'three', 'four']
df1 = DataFrame(randn(5, 4), index=index, columns=columns)
df2 = DataFrame(randn(4, 4), index=index[:4], columns=columns)
df1 = pd.DataFrame(np.random.randn(5, 4), index=index, columns=columns)
df2 = pd.DataFrame(np.random.randn(4, 4), index=index[:4], columns=columns)
df1.corrwith(df2)
df2.corrwith(df1, axis=1)

Expand All @@ -172,7 +171,7 @@ of the ranks (by default) for the group:

.. ipython:: python

s = Series(np.random.randn(5), index=list('abcde'))
s = pd.Series(np.random.np.random.randn(5), index=list('abcde'))
s['d'] = s['b'] # so there's a tie
s.rank()

Expand All @@ -181,7 +180,7 @@ or the columns (``axis=1``). ``NaN`` values are excluded from the ranking.

.. ipython:: python

df = DataFrame(np.random.randn(10, 6))
df = pd.DataFrame(np.random.np.random.randn(10, 6))
df[4] = df[2][:5] # some ties
df
df.rank(1)
Expand Down Expand Up @@ -253,7 +252,7 @@ These functions can be applied to ndarrays or Series objects:

.. ipython:: python

ts = Series(randn(1000), index=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()

ts.plot(style='k--')
Expand All @@ -271,7 +270,7 @@ sugar for applying the moving window operator to all of the DataFrame's columns:

.. ipython:: python

df = 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 @@ -310,7 +309,7 @@ keyword. The list of recognized types are:

.. ipython:: python

ser = Series(randn(10), index=date_range('1/1/2000', periods=10))
ser = pd.Series(np.random.randn(10), index=pd.date_range('1/1/2000', periods=10))

rolling_window(ser, 5, 'triang')

Expand Down
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