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DOC: Fix format of basics.rst, following PEP-8 standard #23802

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Merged
merged 10 commits into from
Nov 21, 2018
Merged

DOC: Fix format of basics.rst, following PEP-8 standard #23802

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9516b83
Fix format of basics.rst
ywpark1 Nov 19, 2018
11d0e90
Fix the error E225, E902, E402
ywpark1 Nov 20, 2018
da683e6
Remove spaces after 'python'
ywpark1 Nov 20, 2018
9fd9796
Add missing spaces, adjust the indentation, remove 'In [0]'
ywpark1 Nov 20, 2018
0908967
Line alignment, replace print function, add 'pass'
ywpark1 Nov 20, 2018
5f2ff74
Fix the format of categorial.rst, following PEP8
ywpark1 Nov 21, 2018
b912b23
Revert "Fix the format of categorial.rst, following PEP8"
ywpark1 Nov 21, 2018
b216196
DOC: fix the format of basics.rst file (panda-dev#23792)
ywpark1 Nov 21, 2018
d273dbd
DOC: Fix format of basics.rst, following PEP-8 standard(panda-dev#23792)
ywpark1 Nov 21, 2018
9941f0f
Remove deprecated feature(Panel) from basics.rst
ywpark1 Nov 21, 2018
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72 changes: 36 additions & 36 deletions doc/source/basics.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -307,14 +307,13 @@ To evaluate single-element pandas objects in a boolean context, use the method

.. code-block:: python

>>> if df: # noqa: E999
...
>>> if df: # noqa: E999
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If the ... is giving problems with the validation I'd replace it by a pass, and we can also remove the noqa comment then.


Or

.. code-block:: python

>>> df and df2
>>> df and df2

These will both raise errors, as you are trying to compare multiple values.

Expand All @@ -330,17 +329,17 @@ Comparing if objects are equivalent
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Often you may find that there is more than one way to compute the same
result. As a simple example, consider ``df+df`` and ``df*2``. To test
result. As a simple example, consider ``df + df`` and ``df * 2``. To test
that these two computations produce the same result, given the tools
shown above, you might imagine using ``(df+df == df*2).all()``. But in
shown above, you might imagine using ``(df + df == df * 2).all()``. But in
fact, this expression is False:

.. ipython:: python

df + df == df * 2
(df + df == df * 2).all()

Notice that the boolean DataFrame ``df+df == df*2`` contains some False values!
Notice that the boolean DataFrame ``df + df == df * 2`` contains some False values!
This is because NaNs do not compare as equals:

.. ipython:: python
Expand Down Expand Up @@ -1506,14 +1505,15 @@ In short, basic iteration (``for i in object``) produces:

Thus, for example, iterating over a DataFrame gives you the column names:

.. ipython::
.. ipython:: python

df = pd.DataFrame(
{'col1': np.random.randn(3), 'col2': np.random.randn(3)},
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just a personal opinion here, but I think this style is easier to read:

df = pd.DataFrame({'col1': np.random.randn(3),
                   'col2': np.random.randn(3)},

index=['a', 'b', 'c'])

In [0]: df = pd.DataFrame({'col1': np.random.randn(3), 'col2': np.random.randn(3)},
...: index=['a', 'b', 'c'])
for col in df:
print(col)

In [0]: for col in df:
...: print(col)
...:

Pandas objects also have the dict-like :meth:`~DataFrame.iteritems` method to
iterate over the (key, value) pairs.
Expand Down Expand Up @@ -1576,12 +1576,11 @@ through key-value pairs:

For example:

.. ipython::
.. ipython:: python

In [0]: for item, frame in wp.iteritems():
...: print(item)
...: print(frame)
...:
for item, frame in wp.iteritems():
print(item)
print(frame)

.. _basics.iterrows:

Expand All @@ -1592,11 +1591,10 @@ iterrows
DataFrame as Series objects. It returns an iterator yielding each
index value along with a Series containing the data in each row:

.. ipython::
.. ipython:: python

In [0]: for row_index, row in df.iterrows():
...: print('%s\n%s' % (row_index, row))
...:
for row_index, row in df.iterrows():
print('%s\n%s' % (row_index, row))
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Do you mind changing this by print(row_index, row, sep='\n')


.. note::

Expand Down Expand Up @@ -1969,10 +1967,12 @@ 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(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')))
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could you move G to thenext line for consistency and clairity?

dft
dft.dtypes

Expand Down Expand Up @@ -2011,10 +2011,10 @@ 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(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'))))
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I think this is clearer here, and should fit in 79 characters:

C=pd.Series(np.array(np.random.randn(8),
                     dtype='uint8'))))

df2
df2.dtypes

Expand All @@ -2029,7 +2029,7 @@ The following will all result in ``int64`` dtypes.

pd.DataFrame([1, 2], columns=['a']).dtypes
pd.DataFrame({'a': [1, 2]}).dtypes
pd.DataFrame({'a': 1 }, index=list(range(2))).dtypes
pd.DataFrame({'a': 1}, index=list(range(2))).dtypes

Note that Numpy will choose *platform-dependent* types when creating arrays.
The following **WILL** result in ``int32`` on 32-bit platform.
Expand Down Expand Up @@ -2084,8 +2084,8 @@ Convert a subset of columns to a specified type using :meth:`~DataFrame.astype`.

.. ipython:: python

dft = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6], 'c': [7, 8, 9]})
dft[['a','b']] = dft[['a','b']].astype(np.uint8)
dft = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})
dft[['a', 'b']] = dft[['a', 'b']].astype(np.uint8)
dft
dft.dtypes

Expand All @@ -2095,7 +2095,7 @@ Convert certain columns to a specific dtype by passing a dict to :meth:`~DataFra

.. ipython:: python

dft1 = pd.DataFrame({'a': [1,0,1], 'b': [4,5,6], 'c': [7, 8, 9]})
dft1 = pd.DataFrame({'a': [1, 0, 1], 'b': [4, 5, 6], 'c': [7, 8, 9]})
dft1 = dft1.astype({'a': np.bool, 'c': np.float64})
dft1
dft1.dtypes
Expand All @@ -2108,7 +2108,7 @@ Convert certain columns to a specific dtype by passing a dict to :meth:`~DataFra

.. ipython:: python

dft = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6], 'c': [7, 8, 9]})
dft = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]})
dft.loc[:, ['a', 'b']].astype(np.uint8).dtypes
dft.loc[:, ['a', 'b']] = dft.loc[:, ['a', 'b']].astype(np.uint8)
dft.dtypes
Expand Down Expand Up @@ -2244,7 +2244,7 @@ See also :ref:`Support for integer NA <gotchas.intna>`.
dfi
dfi.dtypes

casted = dfi[dfi>0]
casted = dfi[dfi > 0]
casted
casted.dtypes

Expand All @@ -2256,7 +2256,7 @@ While float dtypes are unchanged.
dfa['A'] = dfa['A'].astype('float32')
dfa.dtypes

casted = dfa[df2>0]
casted = dfa[df2 > 0]
casted
casted.dtypes

Expand Down