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DOC: Fix PEP-8 issues in 10min.rst #23908

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Nov 27, 2018
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DOC: Fix PEP-8 issues in 10min.rst #23908

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Fix PEP-8 issues in 10min.rst
FHaase Nov 25, 2018
ece28ac
Review of @datapythonista
FHaase Nov 25, 2018
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77 changes: 38 additions & 39 deletions doc/source/10min.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -2,23 +2,6 @@

.. currentmodule:: pandas

.. ipython:: python
:suppress:

import numpy as np
import pandas as pd
import os
np.random.seed(123456)
np.set_printoptions(precision=4, suppress=True)
import matplotlib
# matplotlib.style.use('default')
pd.options.display.max_rows = 15

#### portions of this were borrowed from the
#### Pandas cheatsheet
#### created during the PyData Workshop-Sprint 2012
#### Hannah Chen, Henry Chow, Eric Cox, Robert Mauriello


********************
10 Minutes to pandas
Expand All @@ -31,9 +14,22 @@ Customarily, we import as follows:

.. ipython:: python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

.. ipython:: python
:suppress:
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why not at the beginning and with numpy and pandas imports in the same suppress block? I think this is what we do in all the other pages, right?


import os

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

# portions of this were borrowed from the
# Pandas cheatsheet
# created during the PyData Workshop-Sprint 2012
# Hannah Chen, Henry Chow, Eric Cox, Robert Mauriello

Object Creation
---------------
Expand All @@ -55,7 +51,7 @@ and labeled columns:

dates = pd.date_range('20130101', periods=6)
dates
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
df

Creating a ``DataFrame`` by passing a dict of objects that can be converted to series-like.
Expand All @@ -64,7 +60,7 @@ Creating a ``DataFrame`` by passing a dict of objects that can be converted to s

df2 = pd.DataFrame({'A': 1.,
'B': pd.Timestamp('20130102'),
'C': pd.Series(1, index=list(range(4)),dtype='float32'),
'C': pd.Series(1, index=list(range(4)), dtype='float32'),
'D': np.array([3] * 4, dtype='int32'),
'E': pd.Categorical(["test", "train", "test", "train"]),
'F': 'foo'})
Expand Down Expand Up @@ -190,31 +186,31 @@ Selecting on a multi-axis by label:

.. ipython:: python

df.loc[:,['A','B']]
df.loc[:, ['A', 'B']]

Showing label slicing, both endpoints are *included*:

.. ipython:: python

df.loc['20130102':'20130104',['A','B']]
df.loc['20130102':'20130104', ['A', 'B']]

Reduction in the dimensions of the returned object:

.. ipython:: python

df.loc['20130102',['A','B']]
df.loc['20130102', ['A', 'B']]

For getting a scalar value:

.. ipython:: python

df.loc[dates[0],'A']
df.loc[dates[0], 'A']

For getting fast access to a scalar (equivalent to the prior method):

.. ipython:: python

df.at[dates[0],'A']
df.at[dates[0], 'A']

Selection by Position
~~~~~~~~~~~~~~~~~~~~~
Expand All @@ -231,37 +227,37 @@ By integer slices, acting similar to numpy/python:

.. ipython:: python

df.iloc[3:5,0:2]
df.iloc[3:5, 0:2]

By lists of integer position locations, similar to the numpy/python style:

.. ipython:: python

df.iloc[[1,2,4],[0,2]]
df.iloc[[1, 2, 4], [0, 2]]

For slicing rows explicitly:

.. ipython:: python

df.iloc[1:3,:]
df.iloc[1:3, :]

For slicing columns explicitly:

.. ipython:: python

df.iloc[:,1:3]
df.iloc[:, 1:3]

For getting a value explicitly:

.. ipython:: python

df.iloc[1,1]
df.iloc[1, 1]

For getting fast access to a scalar (equivalent to the prior method):

.. ipython:: python

df.iat[1,1]
df.iat[1, 1]

Boolean Indexing
~~~~~~~~~~~~~~~~
Expand Down Expand Up @@ -303,19 +299,19 @@ Setting values by label:

.. ipython:: python

df.at[dates[0],'A'] = 0
df.at[dates[0], 'A'] = 0

Setting values by position:

.. ipython:: python

df.iat[0,1] = 0
df.iat[0, 1] = 0

Setting by assigning with a NumPy array:

.. ipython:: python

df.loc[:,'D'] = np.array([5] * len(df))
df.loc[:, 'D'] = np.array([5] * len(df))

The result of the prior setting operations.

Expand Down Expand Up @@ -345,7 +341,7 @@ returns a copy of the data.
.. ipython:: python

df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
df1.loc[dates[0]:dates[1],'E'] = 1
df1.loc[dates[0]:dates[1], 'E'] = 1
df1

To drop any rows that have missing data.
Expand Down Expand Up @@ -653,7 +649,8 @@ pandas can include categorical data in a ``DataFrame``. For full docs, see the

.. ipython:: python

df = pd.DataFrame({"id":[1, 2, 3, 4, 5, 6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})
df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],
"raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']})

Convert the raw grades to a categorical data type.

Expand All @@ -674,7 +671,8 @@ Reorder the categories and simultaneously add the missing categories (methods un

.. ipython:: python

df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])
df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium",
"good", "very good"])
df["grade"]

Sorting is per order in the categories, not lexical order.
Expand Down Expand Up @@ -703,7 +701,8 @@ See the :ref:`Plotting <visualization>` docs.

.. ipython:: python

ts = pd.Series(np.random.randn(1000), index=pd.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()

@savefig series_plot_basic.png
Expand Down