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DOC: Fix PEP-8 issues in computation.rst and comparison_*.rst #24002

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ca6a8de
Fix PEP-8 issues in comparison*.rst
FHaase Nov 29, 2018
1621277
Fix PEP-8 issues in computation.rst
FHaase Nov 29, 2018
c9c8db4
Merge branch 'master' into fix-pep8
FHaase Nov 30, 2018
4c09acf
Merge branch 'master' into fix-pep8
FHaase Dec 2, 2018
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88 changes: 43 additions & 45 deletions doc/source/comparison_with_r.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@

import pandas as pd
import numpy as np
pd.options.display.max_rows=15
pd.options.display.max_rows = 15

Comparison with R / R libraries
*******************************
Expand Down Expand Up @@ -165,16 +165,15 @@ function.

.. ipython:: python

df = pd.DataFrame({
'v1': [1,3,5,7,8,3,5,np.nan,4,5,7,9],
'v2': [11,33,55,77,88,33,55,np.nan,44,55,77,99],
'by1': ["red", "blue", 1, 2, np.nan, "big", 1, 2, "red", 1, np.nan, 12],
'by2': ["wet", "dry", 99, 95, np.nan, "damp", 95, 99, "red", 99, np.nan,
np.nan]
})
df = pd.DataFrame(
{'v1': [1, 3, 5, 7, 8, 3, 5, np.nan, 4, 5, 7, 9],
'v2': [11, 33, 55, 77, 88, 33, 55, np.nan, 44, 55, 77, 99],
'by1': ["red", "blue", 1, 2, np.nan, "big", 1, 2, "red", 1, np.nan, 12],
'by2': ["wet", "dry", 99, 95, np.nan, "damp", 95, 99, "red", 99, np.nan,
np.nan]})

g = df.groupby(['by1','by2'])
g[['v1','v2']].mean()
g = df.groupby(['by1', 'by2'])
g[['v1', 'v2']].mean()

For more details and examples see :ref:`the groupby documentation
<groupby.split>`.
Expand All @@ -195,7 +194,7 @@ The :meth:`~pandas.DataFrame.isin` method is similar to R ``%in%`` operator:

.. ipython:: python

s = pd.Series(np.arange(5),dtype=np.float32)
s = pd.Series(np.arange(5), dtype=np.float32)
s.isin([2, 4])

The ``match`` function returns a vector of the positions of matches
Expand Down Expand Up @@ -234,11 +233,11 @@ In ``pandas`` we may use :meth:`~pandas.pivot_table` method to handle this:
import random
import string

baseball = pd.DataFrame({
'team': ["team %d" % (x+1) for x in range(5)]*5,
'player': random.sample(list(string.ascii_lowercase),25),
'batting avg': np.random.uniform(.200, .400, 25)
})
baseball = pd.DataFrame(
{'team': ["team %d" % (x + 1) for x in range(5)] * 5,
'player': random.sample(list(string.ascii_lowercase), 25),
'batting avg': np.random.uniform(.200, .400, 25)})

baseball.pivot_table(values='batting avg', columns='team', aggfunc=np.max)

For more details and examples see :ref:`the reshaping documentation
Expand Down Expand Up @@ -341,15 +340,13 @@ In ``pandas`` the equivalent expression, using the

.. ipython:: python

df = pd.DataFrame({
'x': np.random.uniform(1., 168., 120),
'y': np.random.uniform(7., 334., 120),
'z': np.random.uniform(1.7, 20.7, 120),
'month': [5,6,7,8]*30,
'week': np.random.randint(1,4, 120)
})
df = pd.DataFrame({'x': np.random.uniform(1., 168., 120),
'y': np.random.uniform(7., 334., 120),
'z': np.random.uniform(1.7, 20.7, 120),
'month': [5, 6, 7, 8] * 30,
'week': np.random.randint(1, 4, 120)})

grouped = df.groupby(['month','week'])
grouped = df.groupby(['month', 'week'])
grouped['x'].agg([np.mean, np.std])


Expand All @@ -374,8 +371,8 @@ In Python, since ``a`` is a list, you can simply use list comprehension.

.. ipython:: python

a = np.array(list(range(1,24))+[np.NAN]).reshape(2,3,4)
pd.DataFrame([tuple(list(x)+[val]) for x, val in np.ndenumerate(a)])
a = np.array(list(range(1, 24)) + [np.NAN]).reshape(2, 3, 4)
pd.DataFrame([tuple(list(x) + [val]) for x, val in np.ndenumerate(a)])

|meltlist|_
~~~~~~~~~~~~
Expand All @@ -393,7 +390,7 @@ In Python, this list would be a list of tuples, so

.. ipython:: python

a = list(enumerate(list(range(1,5))+[np.NAN]))
a = list(enumerate(list(range(1, 5)) + [np.NAN]))
pd.DataFrame(a)

For more details and examples see :ref:`the Into to Data Structures
Expand All @@ -419,12 +416,13 @@ In Python, the :meth:`~pandas.melt` method is the R equivalent:

.. 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]})

pd.melt(cheese, id_vars=['first', 'last'])
cheese.set_index(['first', 'last']).stack() # alternative way
cheese.set_index(['first', 'last']).stack() # alternative way

For more details and examples see :ref:`the reshaping documentation
<reshaping.melt>`.
Expand Down Expand Up @@ -452,16 +450,15 @@ In Python the best way is to make use of :meth:`~pandas.pivot_table`:

.. ipython:: python

df = pd.DataFrame({
'x': np.random.uniform(1., 168., 12),
'y': np.random.uniform(7., 334., 12),
'z': np.random.uniform(1.7, 20.7, 12),
'month': [5,6,7]*4,
'week': [1,2]*6
})
df = pd.DataFrame({'x': np.random.uniform(1., 168., 12),
'y': np.random.uniform(7., 334., 12),
'z': np.random.uniform(1.7, 20.7, 12),
'month': [5, 6, 7] * 4,
'week': [1, 2] * 6})

mdf = pd.melt(df, id_vars=['month', 'week'])
pd.pivot_table(mdf, values='value', index=['variable','week'],
columns=['month'], aggfunc=np.mean)
pd.pivot_table(mdf, values='value', index=['variable', 'week'],
columns=['month'], aggfunc=np.mean)

Similarly for ``dcast`` which uses a data.frame called ``df`` in R to
aggregate information based on ``Animal`` and ``FeedType``:
Expand Down Expand Up @@ -491,13 +488,14 @@ using :meth:`~pandas.pivot_table`:
'Amount': [10, 7, 4, 2, 5, 6, 2],
})

df.pivot_table(values='Amount', index='Animal', columns='FeedType', aggfunc='sum')
df.pivot_table(values='Amount', index='Animal', columns='FeedType',
aggfunc='sum')

The second approach is to use the :meth:`~pandas.DataFrame.groupby` method:

.. ipython:: python

df.groupby(['Animal','FeedType'])['Amount'].sum()
df.groupby(['Animal', 'FeedType'])['Amount'].sum()

For more details and examples see :ref:`the reshaping documentation
<reshaping.pivot>` or :ref:`the groupby documentation<groupby.split>`.
Expand All @@ -516,8 +514,8 @@ In pandas this is accomplished with ``pd.cut`` and ``astype("category")``:

.. ipython:: python

pd.cut(pd.Series([1,2,3,4,5,6]), 3)
pd.Series([1,2,3,2,2,3]).astype("category")
pd.cut(pd.Series([1, 2, 3, 4, 5, 6]), 3)
pd.Series([1, 2, 3, 2, 2, 3]).astype("category")

For more details and examples see :ref:`categorical introduction <categorical>` and the
:ref:`API documentation <api.categorical>`. There is also a documentation regarding the
Expand Down
20 changes: 9 additions & 11 deletions doc/source/comparison_with_sql.rst
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Expand Up @@ -23,7 +23,8 @@ structure.

.. ipython:: python

url = 'https://raw.github.com/pandas-dev/pandas/master/pandas/tests/data/tips.csv'
url = ('https://raw.github.com/pandas-dev'
'/pandas/master/pandas/tests/data/tips.csv')
tips = pd.read_csv(url)
tips.head()

Expand Down Expand Up @@ -387,7 +388,7 @@ Top N rows with offset

.. ipython:: python

tips.nlargest(10+5, columns='tip').tail(10)
tips.nlargest(10 + 5, columns='tip').tail(10)

Top N rows per group
~~~~~~~~~~~~~~~~~~~~
Expand All @@ -411,8 +412,7 @@ Top N rows per group
.groupby(['day'])
.cumcount() + 1)
.query('rn < 3')
.sort_values(['day','rn'])
)
.sort_values(['day', 'rn']))

the same using `rank(method='first')` function

Expand All @@ -421,8 +421,7 @@ the same using `rank(method='first')` function
(tips.assign(rnk=tips.groupby(['day'])['total_bill']
.rank(method='first', ascending=False))
.query('rnk < 3')
.sort_values(['day','rnk'])
)
.sort_values(['day', 'rnk']))

.. code-block:: sql

Expand All @@ -445,11 +444,10 @@ Notice that when using ``rank(method='min')`` function
.. ipython:: python

(tips[tips['tip'] < 2]
.assign(rnk_min=tips.groupby(['sex'])['tip']
.rank(method='min'))
.query('rnk_min < 3')
.sort_values(['sex','rnk_min'])
)
.assign(rnk_min=tips.groupby(['sex'])['tip']
.rank(method='min'))
.query('rnk_min < 3')
.sort_values(['sex', 'rnk_min']))


UPDATE
Expand Down
23 changes: 11 additions & 12 deletions doc/source/comparison_with_stata.rst
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Expand Up @@ -102,9 +102,7 @@ and the values are the data.

.. ipython:: python

df = pd.DataFrame({
'x': [1, 3, 5],
'y': [2, 4, 6]})
df = pd.DataFrame({'x': [1, 3, 5], 'y': [2, 4, 6]})
df


Expand All @@ -128,7 +126,8 @@ the data set if presented with a url.

.. ipython:: python

url = 'https://raw.github.com/pandas-dev/pandas/master/pandas/tests/data/tips.csv'
url = ('https://raw.github.com/pandas-dev'
'/pandas/master/pandas/tests/data/tips.csv')
tips = pd.read_csv(url)
tips.head()

Expand Down Expand Up @@ -278,17 +277,17 @@ see the :ref:`timeseries documentation<timeseries>` for more details.
tips['date1_year'] = tips['date1'].dt.year
tips['date2_month'] = tips['date2'].dt.month
tips['date1_next'] = tips['date1'] + pd.offsets.MonthBegin()
tips['months_between'] = (tips['date2'].dt.to_period('M') -
tips['date1'].dt.to_period('M'))
tips['months_between'] = (tips['date2'].dt.to_period('M')
- tips['date1'].dt.to_period('M'))

tips[['date1','date2','date1_year','date2_month',
'date1_next','months_between']].head()
tips[['date1', 'date2', 'date1_year', 'date2_month', 'date1_next',
'months_between']].head()

.. ipython:: python
:suppress:

tips = tips.drop(['date1','date2','date1_year',
'date2_month','date1_next','months_between'], axis=1)
tips = tips.drop(['date1', 'date2', 'date1_year', 'date2_month',
'date1_next', 'months_between'], axis=1)

Selection of Columns
~~~~~~~~~~~~~~~~~~~~
Expand Down Expand Up @@ -472,7 +471,7 @@ The following tables will be used in the merge examples
'value': np.random.randn(4)})
df1
df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'],
'value': np.random.randn(4)})
'value': np.random.randn(4)})
df2

In Stata, to perform a merge, one data set must be in memory
Expand Down Expand Up @@ -661,7 +660,7 @@ In pandas this would be written as:

.. ipython:: python

tips.groupby(['sex','smoker']).first()
tips.groupby(['sex', 'smoker']).first()


Other Considerations
Expand Down
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