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DOC: Fixing flake8 errors in cookbook.rst #23837

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merged 12 commits into from
Nov 24, 2018
Merged

DOC: Fixing flake8 errors in cookbook.rst #23837

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d467a5c
firt iter on errors
Nov 21, 2018
c3c26ea
compatible wiht PEP-8 standard
Nov 21, 2018
27b3245
DOC: compatible wiht PEP-8 standard
Nov 21, 2018
01d9826
DOC: PEP8 compatibility for cookbook.rst
Nov 21, 2018
2df61db
DOC: PEP8 compatibility for cookbook.rst
Nov 21, 2018
cd94d8d
DOC: PEP8 compatible v3
Nov 21, 2018
085ac5f
DOC: PEP8 compatible v4
Nov 21, 2018
ce05dba
DOC: PEP8 compatible v5
Nov 21, 2018
acfb29d
DOC: PEP8 compatible v6
Nov 21, 2018
8cde4a7
DOC: Modified the order of imports
Nov 22, 2018
8895fde
DOC: Changed code to iPython and conform to PEP8
Nov 22, 2018
d57be90
DOC: PEP8 standards - blank lines in import block
Nov 24, 2018
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87 changes: 45 additions & 42 deletions doc/source/cookbook.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,8 @@ These are some neat pandas ``idioms``

.. ipython:: python

df = pd.DataFrame({'AAA': [4, 5, 6, 7], 'BBB': [10, 20, 30, 40],
df = pd.DataFrame({'AAA': [4, 5, 6, 7],
'BBB': [10, 20, 30, 40],
'CCC': [100, 50, -30, -50]})
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df

Expand Down Expand Up @@ -85,7 +86,8 @@ Or use pandas where after you've set up a mask

.. ipython:: python

df_mask = pd.DataFrame({'AAA': [True] * 4, 'BBB': [False] * 4,
df_mask = pd.DataFrame({'AAA': [True] * 4,
'BBB': [False] * 4,
'CCC': [True, False] * 2})
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df.where(df_mask, -1000)

Expand All @@ -94,7 +96,8 @@ Or use pandas where after you've set up a mask

.. ipython:: python

df = pd.DataFrame({'AAA': [4, 5, 6, 7], 'BBB': [10, 20, 30, 40],
df = pd.DataFrame({'AAA': [4, 5, 6, 7],
'BBB': [10, 20, 30, 40],
'CCC': [100, 50, -30, -50]})
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df
df['logic'] = np.where(df['AAA'] > 5, 'high', 'low')
Expand All @@ -108,14 +111,13 @@ Splitting

.. ipython:: python

df = pd.DataFrame({'AAA': [4, 5, 6, 7], 'BBB': [10, 20, 30, 40],
df = pd.DataFrame({'AAA': [4, 5, 6, 7],
'BBB': [10, 20, 30, 40],
'CCC': [100, 50, -30, -50]})
df

dflow = df[df.AAA <= 5]
dflow
dfhigh = df[df.AAA > 5]
dfhigh
df[df.AAA <= 5]
df[df.AAA > 5]

Building Criteria
*****************
Expand All @@ -125,23 +127,22 @@ Building Criteria

.. ipython:: python

df = pd.DataFrame({'AAA': [4, 5, 6, 7], 'BBB': [10, 20, 30, 40],
df = pd.DataFrame({'AAA': [4, 5, 6, 7],
'BBB': [10, 20, 30, 40],
'CCC': [100, 50, -30, -50]})
df

...and (without assignment returns a Series)

.. ipython:: python

newseries = df.loc[(df['BBB'] < 25) & (df['CCC'] >= -40), 'AAA']
newseries
df.loc[(df['BBB'] < 25) & (df['CCC'] >= -40), 'AAA']

...or (without assignment returns a Series)

.. ipython:: python

newseries = df.loc[(df['BBB'] > 25) | (df['CCC'] >= -40), 'AAA']
newseries
df.loc[(df['BBB'] > 25) | (df['CCC'] >= -40), 'AAA']

...or (with assignment modifies the DataFrame.)

Expand All @@ -155,7 +156,8 @@ Building Criteria

.. ipython:: python

df = pd.DataFrame({'AAA': [4, 5, 6, 7], 'BBB': [10, 20, 30, 40],
df = pd.DataFrame({'AAA': [4, 5, 6, 7],
'BBB': [10, 20, 30, 40],
'CCC': [100, 50, -30, -50]})
df
aValue = 43.0
Expand All @@ -166,7 +168,8 @@ Building Criteria

.. ipython:: python

df = pd.DataFrame({'AAA': [4, 5, 6, 7], 'BBB': [10, 20, 30, 40],
df = pd.DataFrame({'AAA': [4, 5, 6, 7],
'BBB': [10, 20, 30, 40],
'CCC': [100, 50, -30, -50]})
df

Expand Down Expand Up @@ -204,7 +207,8 @@ The :ref:`indexing <indexing>` docs.

.. ipython:: python

df = pd.DataFrame({'AAA': [4, 5, 6, 7], 'BBB': [10, 20, 30, 40],
df = pd.DataFrame({'AAA': [4, 5, 6, 7],
'BBB': [10, 20, 30, 40],
'CCC': [100, 50, -30, -50]})
df

Expand All @@ -215,11 +219,11 @@ The :ref:`indexing <indexing>` docs.

.. ipython:: python

data = {'AAA': [4, 5, 6, 7],
'BBB': [10, 20, 30, 40],
'CCC': [100, 50, -30, -50]}
df = pd.DataFrame(data=data, index=['foo', 'bar', 'boo', 'kar'])
df
df = pd.DataFrame({'AAA': [4, 5, 6, 7],
'BBB': [10, 20, 30, 40],
'CCC': [100, 50, -30, -50]},
index=['foo', 'bar', 'boo', 'kar'])


There are 2 explicit slicing methods, with a third general case

Expand Down Expand Up @@ -295,7 +299,7 @@ New Columns
'CCC': [2, 1, 3, 1]})
df

source_cols = df.columns # or some subset would work too.
source_cols = df.columns # Or some subset would work too
new_cols = [str(x) + "_cat" for x in source_cols]
categories = {1: 'Alpha', 2: 'Beta', 3: 'Charlie'}

Expand Down Expand Up @@ -533,10 +537,10 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to

S = pd.Series([i / 100.0 for i in range(1, 11)])

def CumRet(x, y):
def cumRet(x, y):
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cum_ret would be the Pythonic way to name the function.

Can you also replace the names in the places where the function names are used below.

return x * (1 + y)

def Red(x):
def red(x):
return functools.reduce(CumRet, x, 1.0)

S.expanding().apply(Red, raw=True)
Expand Down Expand Up @@ -606,11 +610,11 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to

.. ipython:: python

df = pd.DataFrame({u'line_race': [10, 10, 8, 10, 10, 8],
u'beyer': [99, 102, 103, 103, 88, 100]},
index=[u'Last Gunfighter', u'Last Gunfighter',
u'Last Gunfighter', u'Paynter', u'Paynter',
u'Paynter'])
df = pd.DataFrame({'line_race': [10, 10, 8, 10, 10, 8],
'beyer': [99, 102, 103, 103, 88, 100]},
index=['Last Gunfighter', 'Last Gunfighter',
'Last Gunfighter', 'Paynter', 'Paynter',
'Paynter'])
df
df['beyer_shifted'] = df.groupby(level=0)['beyer'].shift(1)
df
Expand Down Expand Up @@ -759,9 +763,9 @@ Rolling Apply to multiple columns where function calculates a Series before a Sc
columns=['A', 'B'])
df

def gm(aDF, Const):
v = ((((aDF.A + aDF.B) + 1).cumprod()) - 1) * Const
return (aDF.index[0], v.iloc[-1])
def gm(df, const):
v = ((((df.A + df.B) + 1).cumprod()) - 1) * const
return (df.index[0], v.iloc[-1])

S = pd.Series(dict([gm(df.iloc[i:min(i + 51, len(df) - 1)], 5)
for i in range(len(df) - 50)]))
Expand Down Expand Up @@ -944,16 +948,16 @@ The :ref:`Plotting <visualization>` docs.
.. ipython:: python

df = pd.DataFrame(
{u'stratifying_var': np.random.uniform(0, 100, 20),
u'price': np.random.normal(100, 5, 20)})
{'stratifying_var': np.random.uniform(0, 100, 20),
'price': np.random.normal(100, 5, 20)})

df[u'quartiles'] = pd.qcut(
df[u'stratifying_var'],
df['quartiles'] = pd.qcut(
df['stratifying_var'],
4,
labels=[u'0-25%', u'25-50%', u'50-75%', u'75-100%'])
labels=['0-25%', '25-50%', '50-75%', '75-100%'])

@savefig quartile_boxplot.png
df.boxplot(column=u'price', by=u'quartiles')
df.boxplot(column='price', by='quartiles')

Data In/Out
-----------
Expand Down Expand Up @@ -1424,8 +1428,7 @@ of the data values:
rows = itertools.product(*data_dict.values())
return pd.DataFrame.from_records(rows, columns=data_dict.keys())

df = expand_grid(
{'height': [60, 70],
'weight': [100, 140, 180],
'sex': ['Male', 'Female']})
df = expand_grid({'height': [60, 70],
'weight': [100, 140, 180],
'sex': ['Male', 'Female']})
df