Skip to content

DOC: Fix PEP-8 issues in basics.rst #23910

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Nov 25, 2018
Merged

DOC: Fix PEP-8 issues in basics.rst #23910

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
56 changes: 30 additions & 26 deletions doc/source/basics.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@

import numpy as np
import pandas as pd

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

Expand Down Expand Up @@ -173,8 +174,9 @@ Furthermore you can align a level of a MultiIndexed DataFrame with a Series.
.. ipython:: python

dfmi = df.copy()
dfmi.index = pd.MultiIndex.from_tuples([
(1, 'a'), (1, 'b'), (1, 'c'), (2, 'a')], names=['first', 'second'])
dfmi.index = pd.MultiIndex.from_tuples([(1, 'a'), (1, 'b'),
(1, 'c'), (2, 'a')],
names=['first', 'second'])
dfmi.sub(column, axis=0, level='second')

With Panel, describing the matching behavior is a bit more difficult, so
Expand Down Expand Up @@ -565,8 +567,8 @@ course):
series = pd.Series(np.random.randn(1000))
series[::2] = np.nan
series.describe()
frame = pd.DataFrame(
np.random.randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])
frame = pd.DataFrame(np.random.randn(1000, 5),
columns=['a', 'b', 'c', 'd', 'e'])
frame.iloc[::2] = np.nan
frame.describe()

Expand Down Expand Up @@ -1088,8 +1090,10 @@ a single value and returning a single value. For example:
.. ipython:: python

df4

def f(x):
len(str(x))
return len(str(x))

df4['one'].map(f)
df4.applymap(f)

Expand Down Expand Up @@ -1433,10 +1437,8 @@ Thus, for example, iterating over a DataFrame gives you the column names:

.. ipython:: python

df = pd.DataFrame({
'col1': np.random.randn(3),
'col2': np.random.randn(3)},
index=['a', 'b', 'c'])
df = pd.DataFrame({'col1': np.random.randn(3),
'col2': np.random.randn(3)}, index=['a', 'b', 'c'])

for col in df:
print(col)
Expand Down Expand Up @@ -1556,7 +1558,7 @@ For instance, a contrived way to transpose the DataFrame would be:
print(df2)
print(df2.T)

df2_t = pd.DataFrame(dict((idx, values) for idx, values in df2.iterrows()))
df2_t = pd.DataFrame({idx: values for idx, values in df2.iterrows()})
print(df2_t)

itertuples
Expand Down Expand Up @@ -1732,8 +1734,9 @@ to use to determine the sorted order.

.. ipython:: python

df1 = pd.DataFrame({
'one': [2, 1, 1, 1], 'two': [1, 3, 2, 4], 'three': [5, 4, 3, 2]})
df1 = pd.DataFrame({'one': [2, 1, 1, 1],
'two': [1, 3, 2, 4],
'three': [5, 4, 3, 2]})
df1.sort_values(by='two')

The ``by`` parameter can take a list of column names, e.g.:
Expand Down Expand Up @@ -1843,8 +1846,9 @@ all levels to ``by``.

.. ipython:: python

df1.columns = pd.MultiIndex.from_tuples([
('a', 'one'), ('a', 'two'), ('b', 'three')])
df1.columns = pd.MultiIndex.from_tuples([('a', 'one'),
('a', 'two'),
('b', 'three')])
df1.sort_values(by=('a', 'two'))


Expand Down Expand Up @@ -1894,13 +1898,13 @@ with the data type of each column.

.. ipython:: python

dft = pd.DataFrame(dict(A=np.random.rand(3),
B=1,
C='foo',
D=pd.Timestamp('20010102'),
E=pd.Series([1.0] * 3).astype('float32'),
F=False,
G=pd.Series([1] * 3, dtype='int8')))
dft = pd.DataFrame({'A': np.random.rand(3),
'B': 1,
'C': 'foo',
'D': pd.Timestamp('20010102'),
'E': pd.Series([1.0] * 3).astype('float32'),
'F': False,
'G': pd.Series([1] * 3, dtype='int8')})
dft
dft.dtypes

Expand Down Expand Up @@ -1939,10 +1943,10 @@ different numeric dtypes will **NOT** be combined. The following example will gi
df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
df1
df1.dtypes
df2 = pd.DataFrame(dict(A=pd.Series(np.random.randn(8), dtype='float16'),
B=pd.Series(np.random.randn(8)),
C=pd.Series(np.array(np.random.randn(8),
dtype='uint8'))))
df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float16'),
'B': pd.Series(np.random.randn(8)),
'C': pd.Series(np.array(np.random.randn(8),
dtype='uint8'))})
df2
df2.dtypes

Expand Down Expand Up @@ -2057,7 +2061,7 @@ to the correct type.
df = pd.DataFrame([[1, 2],
['a', 'b'],
[datetime.datetime(2016, 3, 2),
datetime.datetime(2016, 3, 2)]])
datetime.datetime(2016, 3, 2)]])
df = df.T
df
df.dtypes
Expand Down