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DOC: Fix code style in documentation (#36780)
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‎doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst

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@@ -122,8 +122,9 @@ aggregating statistics for given columns can be defined using the
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.. ipython:: python
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titanic.agg({'Age': ['min', 'max', 'median', 'skew'],
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'Fare': ['min', 'max', 'median', 'mean']})
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titanic.agg(
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{"Age": ["min", "max", "median", "skew"], "Fare": ["min", "max", "median", "mean"]}
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)
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.. raw:: html
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‎doc/source/getting_started/intro_tutorials/07_reshape_table_layout.rst

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@@ -101,8 +101,9 @@ measurement.
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.. ipython:: python
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air_quality = pd.read_csv("data/air_quality_long.csv",
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index_col="date.utc", parse_dates=True)
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air_quality = pd.read_csv(
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"data/air_quality_long.csv", index_col="date.utc", parse_dates=True
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)
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air_quality.head()
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.. raw:: html
@@ -247,8 +248,9 @@ I want the mean concentrations for :math:`NO_2` and :math:`PM_{2.5}` in each of
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.. ipython:: python
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air_quality.pivot_table(values="value", index="location",
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columns="parameter", aggfunc="mean")
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air_quality.pivot_table(
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values="value", index="location", columns="parameter", aggfunc="mean"
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)
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In the case of :meth:`~DataFrame.pivot`, the data is only rearranged. When multiple
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values need to be aggregated (in this specific case, the values on
@@ -266,9 +268,13 @@ the ``margin`` parameter to ``True``:
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.. ipython:: python
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air_quality.pivot_table(values="value", index="location",
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columns="parameter", aggfunc="mean",
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margins=True)
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air_quality.pivot_table(
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values="value",
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index="location",
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columns="parameter",
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aggfunc="mean",
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margins=True,
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)
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.. raw:: html
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@@ -345,12 +351,12 @@ The :func:`pandas.melt` method can be defined in more detail:
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.. ipython:: python
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no_2 = no2_pivoted.melt(id_vars="date.utc",
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value_vars=["BETR801",
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"FR04014",
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"London Westminster"],
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value_name="NO_2",
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var_name="id_location")
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no_2 = no2_pivoted.melt(
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id_vars="date.utc",
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value_vars=["BETR801", "FR04014", "London Westminster"],
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value_name="NO_2",
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var_name="id_location",
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)
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no_2.head()
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The result in the same, but in more detail defined:

‎doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst

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Original file line numberDiff line numberDiff line change
@@ -155,8 +155,7 @@ index. For example:
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.. ipython:: python
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air_quality_ = pd.concat([air_quality_pm25, air_quality_no2],
159-
keys=["PM25", "NO2"])
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air_quality_ = pd.concat([air_quality_pm25, air_quality_no2], keys=["PM25", "NO2"])
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.. ipython:: python
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@@ -233,8 +232,7 @@ Add the station coordinates, provided by the stations metadata table, to the cor
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.. ipython:: python
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air_quality = pd.merge(air_quality, stations_coord,
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how='left', on='location')
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air_quality = pd.merge(air_quality, stations_coord, how="left", on="location")
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air_quality.head()
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Using the :meth:`~pandas.merge` function, for each of the rows in the

‎doc/source/getting_started/intro_tutorials/09_timeseries.rst

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@@ -204,10 +204,9 @@ Plot the typical :math:`NO_2` pattern during the day of our time series of all s
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.. ipython:: python
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fig, axs = plt.subplots(figsize=(12, 4))
207-
air_quality.groupby(
208-
air_quality["datetime"].dt.hour)["value"].mean().plot(kind='bar',
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rot=0,
210-
ax=axs)
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air_quality.groupby(air_quality["datetime"].dt.hour)["value"].mean().plot(
208+
kind='bar', rot=0, ax=axs
209+
)
211210
plt.xlabel("Hour of the day"); # custom x label using matplotlib
212211
@savefig 09_bar_chart.png
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plt.ylabel("$NO_2 (µg/m^3)$");

‎doc/source/getting_started/intro_tutorials/10_text_data.rst

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@@ -224,8 +224,7 @@ In the "Sex" column, replace values of "male" by "M" and values of "female" by "
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.. ipython:: python
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titanic["Sex_short"] = titanic["Sex"].replace({"male": "M",
228-
"female": "F"})
227+
titanic["Sex_short"] = titanic["Sex"].replace({"male": "M", "female": "F"})
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titanic["Sex_short"]
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Whereas :meth:`~Series.replace` is not a string method, it provides a convenient way

‎doc/source/user_guide/10min.rst

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@@ -43,12 +43,16 @@ Creating a :class:`DataFrame` by passing a dict of objects that can be converted
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.. ipython:: python
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df2 = pd.DataFrame({'A': 1.,
47-
'B': pd.Timestamp('20130102'),
48-
'C': pd.Series(1, index=list(range(4)), dtype='float32'),
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'D': np.array([3] * 4, dtype='int32'),
50-
'E': pd.Categorical(["test", "train", "test", "train"]),
51-
'F': 'foo'})
46+
df2 = pd.DataFrame(
47+
{
48+
"A": 1.0,
49+
"B": pd.Timestamp("20130102"),
50+
"C": pd.Series(1, index=list(range(4)), dtype="float32"),
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"D": np.array([3] * 4, dtype="int32"),
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"E": pd.Categorical(["test", "train", "test", "train"]),
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"F": "foo",
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}
55+
)
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df2
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The columns of the resulting :class:`DataFrame` have different
@@ -512,12 +516,14 @@ See the :ref:`Grouping section <groupby>`.
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.. ipython:: python
514518
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df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
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'foo', 'bar', 'foo', 'foo'],
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'B': ['one', 'one', 'two', 'three',
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'two', 'two', 'one', 'three'],
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'C': np.random.randn(8),
520-
'D': np.random.randn(8)})
519+
df = pd.DataFrame(
520+
{
521+
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
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"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
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"C": np.random.randn(8),
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"D": np.random.randn(8),
525+
}
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)
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df
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Grouping and then applying the :meth:`~pandas.core.groupby.GroupBy.sum` function to the resulting
@@ -545,10 +551,14 @@ Stack
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.. ipython:: python
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tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
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'foo', 'foo', 'qux', 'qux'],
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['one', 'two', 'one', 'two',
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'one', 'two', 'one', 'two']]))
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tuples = list(
555+
zip(
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*[
557+
["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
558+
["one", "two", "one", "two", "one", "two", "one", "two"],
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]
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)
561+
)
552562
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
553563
df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
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df2 = df[:4]
@@ -578,11 +588,15 @@ See the section on :ref:`Pivot Tables <reshaping.pivot>`.
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.. ipython:: python
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df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 3,
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'B': ['A', 'B', 'C'] * 4,
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'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
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'D': np.random.randn(12),
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'E': np.random.randn(12)})
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df = pd.DataFrame(
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{
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"A": ["one", "one", "two", "three"] * 3,
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"B": ["A", "B", "C"] * 4,
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"C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 2,
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"D": np.random.randn(12),
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"E": np.random.randn(12),
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}
599+
)
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df
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We can produce pivot tables from this data very easily:
@@ -653,8 +667,10 @@ pandas can include categorical data in a :class:`DataFrame`. For full docs, see
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.. ipython:: python
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df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],
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"raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']})
670+
df = pd.DataFrame(
671+
{"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
672+
)
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Convert the raw grades to a categorical data type.
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@@ -674,8 +690,9 @@ Reorder the categories and simultaneously add the missing categories (methods un
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.. ipython:: python
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df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium",
678-
"good", "very good"])
693+
df["grade"] = df["grade"].cat.set_categories(
694+
["very bad", "bad", "medium", "good", "very good"]
695+
)
679696
df["grade"]
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Sorting is per order in the categories, not lexical order.
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.. ipython:: python
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ts = pd.Series(np.random.randn(1000),
709-
index=pd.date_range('1/1/2000', periods=1000))
725+
ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
710726
ts = ts.cumsum()
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@savefig series_plot_basic.png
@@ -717,8 +733,10 @@ of the columns with labels:
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.. ipython:: python
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df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
721-
columns=['A', 'B', 'C', 'D'])
736+
df = pd.DataFrame(
737+
np.random.randn(1000, 4), index=ts.index, columns=["A", "B", "C", "D"]
738+
)
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df = df.cumsum()
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plt.figure()

‎doc/source/user_guide/sparse.rst

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@@ -303,24 +303,28 @@ The method requires a ``MultiIndex`` with two or more levels.
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.. ipython:: python
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s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
306-
s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
307-
(1, 2, 'a', 1),
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(1, 1, 'b', 0),
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(1, 1, 'b', 1),
310-
(2, 1, 'b', 0),
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(2, 1, 'b', 1)],
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names=['A', 'B', 'C', 'D'])
313-
s
306+
s.index = pd.MultiIndex.from_tuples(
307+
[
308+
(1, 2, "a", 0),
309+
(1, 2, "a", 1),
310+
(1, 1, "b", 0),
311+
(1, 1, "b", 1),
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(2, 1, "b", 0),
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(2, 1, "b", 1),
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],
315+
names=["A", "B", "C", "D"],
316+
)
314317
ss = s.astype('Sparse')
315318
ss
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In the example below, we transform the ``Series`` to a sparse representation of a 2-d array by specifying that the first and second ``MultiIndex`` levels define labels for the rows and the third and fourth levels define labels for the columns. We also specify that the column and row labels should be sorted in the final sparse representation.
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.. ipython:: python
320323
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A, rows, columns = ss.sparse.to_coo(row_levels=['A', 'B'],
322-
column_levels=['C', 'D'],
323-
sort_labels=True)
324+
A, rows, columns = ss.sparse.to_coo(
325+
row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
326+
)
327+
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A
326330
A.todense()
@@ -331,9 +335,9 @@ Specifying different row and column labels (and not sorting them) yields a diffe
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.. ipython:: python
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A, rows, columns = ss.sparse.to_coo(row_levels=['A', 'B', 'C'],
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column_levels=['D'],
336-
sort_labels=False)
338+
A, rows, columns = ss.sparse.to_coo(
339+
row_levels=["A", "B", "C"], column_levels=["D"], sort_labels=False
340+
)
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A
339343
A.todense()
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.. ipython:: python
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from scipy import sparse
348-
A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),
349-
shape=(3, 4))
352+
A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4))
350353
A
351354
A.todense()
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