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merged 5 commits into from
Oct 5, 2020
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DOC: ran blacken docs tool and checked output to improve formatting #36777 #36802

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fb42c5a
ran blacken docs tool and checked output to improve formatting
maria-ilie Oct 2, 2020
7031ad3
fixing setup.cfg
maria-ilie Oct 2, 2020
56b6ec2
Merge branch 'master' into fixing_code_style_in_documentation
maria-ilie Oct 2, 2020
9616e06
fixed indentation
maria-ilie Oct 4, 2020
9d00e5e
Update doc/source/user_guide/basics.rst
maria-ilie Oct 5, 2020
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111 changes: 57 additions & 54 deletions doc/source/user_guide/10min.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -34,9 +34,9 @@ and labeled columns:

.. ipython:: python

dates = pd.date_range('20130101', periods=6)
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 :class:`DataFrame` by passing a dict of objects that can be converted to series-like.
Expand Down Expand Up @@ -156,7 +156,7 @@ Sorting by values:

.. ipython:: python

df.sort_values(by='B')
df.sort_values(by="B")

Selection
---------
Expand All @@ -178,14 +178,14 @@ equivalent to ``df.A``:

.. ipython:: python

df['A']
df["A"]

Selecting via ``[]``, which slices the rows.

.. ipython:: python

df[0:3]
df['20130102':'20130104']
df["20130102":"20130104"]

Selection by label
~~~~~~~~~~~~~~~~~~
Expand All @@ -202,31 +202,31 @@ 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 Down Expand Up @@ -282,7 +282,7 @@ Using a single column's values to select data.

.. ipython:: python

df[df['A'] > 0]
df[df["A"] > 0]

Selecting values from a DataFrame where a boolean condition is met.

Expand All @@ -295,9 +295,9 @@ Using the :func:`~Series.isin` method for filtering:
.. ipython:: python

df2 = df.copy()
df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']
df2["E"] = ["one", "one", "two", "three", "four", "three"]
df2
df2[df2['E'].isin(['two', 'four'])]
df2[df2["E"].isin(["two", "four"])]

Setting
~~~~~~~
Expand All @@ -307,15 +307,15 @@ by the indexes.

.. ipython:: python

s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20130102', periods=6))
s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range("20130102", periods=6))
s1
df['F'] = s1
df["F"] = s1

Setting values by label:

.. ipython:: python

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

Setting values by position:

Expand All @@ -327,7 +327,7 @@ 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 @@ -356,15 +356,15 @@ 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 = df.reindex(index=dates[0:4], columns=list(df.columns) + ["E"])
df1.loc[dates[0] : dates[1], "E"] = 1
df1

To drop any rows that have missing data.

.. ipython:: python

df1.dropna(how='any')
df1.dropna(how="any")

Filling missing data.

Expand Down Expand Up @@ -408,7 +408,7 @@ In addition, pandas automatically broadcasts along the specified dimension.

s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2)
s
df.sub(s, axis='index')
df.sub(s, axis="index")


Apply
Expand Down Expand Up @@ -444,7 +444,7 @@ some cases always uses them). See more at :ref:`Vectorized String Methods

.. ipython:: python

s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
s = pd.Series(["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"])
s.str.lower()

Merge
Expand Down Expand Up @@ -486,21 +486,21 @@ SQL style merges. See the :ref:`Database style joining <merging.join>` section.

.. ipython:: python

left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
left = pd.DataFrame({"key": ["foo", "foo"], "lval": [1, 2]})
right = pd.DataFrame({"key": ["foo", "foo"], "rval": [4, 5]})
left
right
pd.merge(left, right, on='key')
pd.merge(left, right, on="key")

Another example that can be given is:

.. ipython:: python

left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})
right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})
left = pd.DataFrame({"key": ["foo", "bar"], "lval": [1, 2]})
right = pd.DataFrame({"key": ["foo", "bar"], "rval": [4, 5]})
left
right
pd.merge(left, right, on='key')
pd.merge(left, right, on="key")

Grouping
--------
Expand Down Expand Up @@ -531,14 +531,14 @@ groups.

.. ipython:: python

df.groupby('A').sum()
df.groupby("A").sum()

Grouping by multiple columns forms a hierarchical index, and again we can
apply the :meth:`~pandas.core.groupby.GroupBy.sum` function.

.. ipython:: python

df.groupby(['A', 'B']).sum()
df.groupby(["A", "B"]).sum()

Reshaping
---------
Expand All @@ -559,8 +559,8 @@ Stack
]
)
)
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])
df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=["A", "B"])
df2 = df[:4]
df2

Expand Down Expand Up @@ -603,7 +603,7 @@ We can produce pivot tables from this data very easily:

.. ipython:: python

pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
pd.pivot_table(df, values="D", index=["A", "B"], columns=["C"])


Time series
Expand All @@ -616,31 +616,31 @@ financial applications. See the :ref:`Time Series section <timeseries>`.

.. ipython:: python

rng = pd.date_range('1/1/2012', periods=100, freq='S')
rng = pd.date_range("1/1/2012", periods=100, freq="S")
ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
ts.resample('5Min').sum()
ts.resample("5Min").sum()

Time zone representation:

.. ipython:: python

rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
rng = pd.date_range("3/6/2012 00:00", periods=5, freq="D")
ts = pd.Series(np.random.randn(len(rng)), rng)
ts
ts_utc = ts.tz_localize('UTC')
ts_utc = ts.tz_localize("UTC")
ts_utc

Converting to another time zone:

.. ipython:: python

ts_utc.tz_convert('US/Eastern')
ts_utc.tz_convert("US/Eastern")

Converting between time span representations:

.. ipython:: python

rng = pd.date_range('1/1/2012', periods=5, freq='M')
rng = pd.date_range("1/1/2012", periods=5, freq="M")
ts = pd.Series(np.random.randn(len(rng)), index=rng)
ts
ps = ts.to_period()
Expand All @@ -654,9 +654,9 @@ the quarter end:

.. ipython:: python

prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
prng = pd.period_range("1990Q1", "2000Q4", freq="Q-NOV")
ts = pd.Series(np.random.randn(len(prng)), prng)
ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
ts.index = (prng.asfreq("M", "e") + 1).asfreq("H", "s") + 9
ts.head()

Categoricals
Expand All @@ -667,9 +667,10 @@ pandas can include categorical data in a :class:`DataFrame`. For full docs, see

.. 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"]}
)
Comment on lines +670 to +672
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@MarcoGorelli MarcoGorelli Oct 4, 2020 •
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Looks like this block got extra-indented by one space, this is causing the CI error, could you move it back?




Convert the raw grades to a categorical data type.
Expand Down Expand Up @@ -718,7 +719,8 @@ We use the standard convention for referencing the matplotlib API:
.. ipython:: python

import matplotlib.pyplot as plt
plt.close('all')

plt.close("all")

.. ipython:: python

Expand Down Expand Up @@ -754,19 +756,20 @@ CSV

.. ipython:: python

df.to_csv('foo.csv')
df.to_csv("foo.csv")

:ref:`Reading from a csv file. <io.read_csv_table>`

.. ipython:: python

pd.read_csv('foo.csv')
pd.read_csv("foo.csv")

.. ipython:: python
:suppress:

import os
os.remove('foo.csv')

os.remove("foo.csv")

HDF5
~~~~
Expand All @@ -777,18 +780,18 @@ Writing to a HDF5 Store.

.. ipython:: python

df.to_hdf('foo.h5', 'df')
df.to_hdf("foo.h5", "df")

Reading from a HDF5 Store.

.. ipython:: python

pd.read_hdf('foo.h5', 'df')
pd.read_hdf("foo.h5", "df")

.. ipython:: python
:suppress:

os.remove('foo.h5')
os.remove("foo.h5")

Excel
~~~~~
Expand All @@ -799,18 +802,18 @@ Writing to an excel file.

.. ipython:: python

df.to_excel('foo.xlsx', sheet_name='Sheet1')
df.to_excel("foo.xlsx", sheet_name="Sheet1")

Reading from an excel file.

.. ipython:: python

pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])
pd.read_excel("foo.xlsx", "Sheet1", index_col=None, na_values=["NA"])

.. ipython:: python
:suppress:

os.remove('foo.xlsx')
os.remove("foo.xlsx")

Gotchas
-------
Expand Down
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