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DOC: Fix PEP-8 issues in reshaping.rst #23897

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DOC: Fix PEP-8 issues in reshaping.rst #23897

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57 changes: 29 additions & 28 deletions doc/source/reshaping.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,10 @@
:suppress:

import numpy as np
np.random.seed(123456)
import pandas as pd
pd.options.display.max_rows=15

np.random.seed(123456)
pd.options.display.max_rows = 15
np.set_printoptions(precision=4, suppress=True)

**************************
Expand All @@ -19,21 +20,21 @@ Reshaping by pivoting DataFrame objects

.. image:: _static/reshaping_pivot.png

.. ipython::
.. ipython:: python
:suppress:

In [1]: import pandas.util.testing as tm; tm.N = 3
import pandas.util.testing as tm
tm.N = 3

In [2]: def unpivot(frame):
...: N, K = frame.shape
...: data = {'value' : frame.values.ravel('F'),
...: 'variable' : np.asarray(frame.columns).repeat(N),
...: 'date' : np.tile(np.asarray(frame.index), K)}
...: columns = ['date', 'variable', 'value']
...: return pd.DataFrame(data, columns=columns)
...:
def unpivot(frame):
N, K = frame.shape
data = {'value': frame.values.ravel('F'),
'variable': np.asarray(frame.columns).repeat(N),
'date': np.tile(np.asarray(frame.index), K)}
columns = ['date', 'variable', 'value']
return pd.DataFrame(data, columns=columns)

In [3]: df = unpivot(tm.makeTimeDataFrame())
df = unpivot(tm.makeTimeDataFrame())

Data is often stored in so-called "stacked" or "record" format:

Expand Down Expand Up @@ -184,7 +185,7 @@ will result in a **sorted** copy of the original ``DataFrame`` or ``Series``:

.. ipython:: python

index = pd.MultiIndex.from_product([[2,1], ['a', 'b']])
index = pd.MultiIndex.from_product([[2, 1], ['a', 'b']])
df = pd.DataFrame(np.random.randn(4), index=index, columns=['A'])
df
all(df.unstack().stack() == df.sort_index())
Expand All @@ -204,9 +205,8 @@ processed individually.
.. ipython:: python

columns = pd.MultiIndex.from_tuples([
('A', 'cat', 'long'), ('B', 'cat', 'long'),
('A', 'dog', 'short'), ('B', 'dog', 'short')
],
('A', 'cat', 'long'), ('B', 'cat', 'long'),
('A', 'dog', 'short'), ('B', 'dog', 'short')],
names=['exp', 'animal', 'hair_length']
)
df = pd.DataFrame(np.random.randn(4, 4), columns=columns)
Expand Down Expand Up @@ -301,10 +301,10 @@ For instance,

.. ipython:: python

cheese = pd.DataFrame({'first' : ['John', 'Mary'],
'last' : ['Doe', 'Bo'],
'height' : [5.5, 6.0],
'weight' : [130, 150]})
cheese = pd.DataFrame({'first': ['John', 'Mary'],
'last': ['Doe', 'Bo'],
'height': [5.5, 6.0],
'weight': [130, 150]})
cheese
cheese.melt(id_vars=['first', 'last'])
cheese.melt(id_vars=['first', 'last'], var_name='quantity')
Expand All @@ -315,11 +315,11 @@ user-friendly.

.. ipython:: python

dft = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
"A1980" : {0 : "d", 1 : "e", 2 : "f"},
"B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
"B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
"X" : dict(zip(range(3), np.random.randn(3)))
dft = pd.DataFrame({"A1970": {0: "a", 1: "b", 2: "c"},
"A1980": {0: "d", 1: "e", 2: "f"},
"B1970": {0: 2.5, 1: 1.2, 2: .7},
"B1980": {0: 3.2, 1: 1.3, 2: .1},
"X": dict(zip(range(3), np.random.randn(3)))
})
dft["id"] = dft.index
dft
Expand Down Expand Up @@ -390,7 +390,8 @@ We can produce pivot tables from this data very easily:

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

The result object is a ``DataFrame`` having potentially hierarchical indexes on the
rows and columns. If the ``values`` column name is not given, the pivot table
Expand Down Expand Up @@ -658,7 +659,7 @@ When a column contains only one level, it will be omitted in the result.

.. ipython:: python

df = pd.DataFrame({'A':list('aaaaa'),'B':list('ababc')})
df = pd.DataFrame({'A': list('aaaaa'), 'B': list('ababc')})

pd.get_dummies(df)

Expand Down