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DOC: use black to fix code style in doc pandas-dev#36777 #36813

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4f60f72
DOC: use black to fix code style in doc pandas-dev#36777
PrayagS Oct 2, 2020
ba99f24
DOC: fix flake8-rst errors
PrayagS Oct 2, 2020
569a71a
DOC: fix E131 in comparison_with_stata.rst
PrayagS Oct 2, 2020
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153 changes: 98 additions & 55 deletions doc/source/getting_started/comparison/comparison_with_r.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -122,16 +122,16 @@ Selecting multiple columns by name in ``pandas`` is straightforward

.. ipython:: python

df = pd.DataFrame(np.random.randn(10, 3), columns=list('abc'))
df[['a', 'c']]
df.loc[:, ['a', 'c']]
df = pd.DataFrame(np.random.randn(10, 3), columns=list("abc"))
df[["a", "c"]]
df.loc[:, ["a", "c"]]

Selecting multiple noncontiguous columns by integer location can be achieved
with a combination of the ``iloc`` indexer attribute and ``numpy.r_``.

.. ipython:: python

named = list('abcdefg')
named = list("abcdefg")
n = 30
columns = named + np.arange(len(named), n).tolist()
df = pd.DataFrame(np.random.randn(n, n), columns=columns)
Expand Down Expand Up @@ -160,14 +160,29 @@ 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]})

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

For more details and examples see :ref:`the groupby documentation
<groupby.split>`.
Expand Down Expand Up @@ -228,11 +243,14 @@ In ``pandas`` we may use :meth:`~pandas.pivot_table` method to handle this:
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)})
{
"team": ["team %d" % (x + 1) for x in range(5)] * 5,
"player": random.sample(list(string.ascii_lowercase), 25),
"batting avg": np.random.uniform(0.200, 0.400, 25),
}
)

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

For more details and examples see :ref:`the reshaping documentation
<reshaping.pivot>`.
Expand All @@ -256,10 +274,10 @@ index/slice as well as standard boolean indexing:

.. ipython:: python

df = pd.DataFrame({'a': np.random.randn(10), 'b': np.random.randn(10)})
df.query('a <= b')
df[df['a'] <= df['b']]
df.loc[df['a'] <= df['b']]
df = pd.DataFrame({"a": np.random.randn(10), "b": np.random.randn(10)})
df.query("a <= b")
df[df["a"] <= df["b"]]
df.loc[df["a"] <= df["b"]]

For more details and examples see :ref:`the query documentation
<indexing.query>`.
Expand All @@ -282,9 +300,9 @@ In ``pandas`` the equivalent expression, using the

.. ipython:: python

df = pd.DataFrame({'a': np.random.randn(10), 'b': np.random.randn(10)})
df.eval('a + b')
df['a'] + df['b'] # same as the previous expression
df = pd.DataFrame({"a": np.random.randn(10), "b": np.random.randn(10)})
df.eval("a + b")
df["a"] + df["b"] # same as the previous expression

In certain cases :meth:`~pandas.DataFrame.eval` will be much faster than
evaluation in pure Python. For more details and examples see :ref:`the eval
Expand Down Expand Up @@ -334,14 +352,18 @@ 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(1.0, 168.0, 120),
"y": np.random.uniform(7.0, 334.0, 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['x'].agg([np.mean, np.std])
grouped = df.groupby(["month", "week"])
grouped["x"].agg([np.mean, np.std])


For more details and examples see :ref:`the groupby documentation
Expand Down Expand Up @@ -410,13 +432,17 @@ 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
pd.melt(cheese, id_vars=["first", "last"])
cheese.set_index(["first", "last"]).stack() # alternative way

For more details and examples see :ref:`the reshaping documentation
<reshaping.melt>`.
Expand Down Expand Up @@ -444,15 +470,24 @@ 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})

mdf = pd.melt(df, id_vars=['month', 'week'])
pd.pivot_table(mdf, values='value', index=['variable', 'week'],
columns=['month'], aggfunc=np.mean)
df = pd.DataFrame(
{
"x": np.random.uniform(1.0, 168.0, 12),
"y": np.random.uniform(7.0, 334.0, 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,
)

Similarly for ``dcast`` which uses a data.frame called ``df`` in R to
aggregate information based on ``Animal`` and ``FeedType``:
Expand All @@ -475,21 +510,29 @@ using :meth:`~pandas.pivot_table`:

.. ipython:: python

df = pd.DataFrame({
'Animal': ['Animal1', 'Animal2', 'Animal3', 'Animal2', 'Animal1',
'Animal2', 'Animal3'],
'FeedType': ['A', 'B', 'A', 'A', 'B', 'B', 'A'],
'Amount': [10, 7, 4, 2, 5, 6, 2],
})

df.pivot_table(values='Amount', index='Animal', columns='FeedType',
aggfunc='sum')
df = pd.DataFrame(
{
"Animal": [
"Animal1",
"Animal2",
"Animal3",
"Animal2",
"Animal1",
"Animal2",
"Animal3",
],
"FeedType": ["A", "B", "A", "A", "B", "B", "A"],
"Amount": [10, 7, 4, 2, 5, 6, 2],
}
)

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 Down
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