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DOC: use black to fix code style in doc #36777 (#36813)
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‎doc/source/getting_started/comparison/comparison_with_r.rst

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@@ -122,16 +122,16 @@ Selecting multiple columns by name in ``pandas`` is straightforward
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.. ipython:: python
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df = pd.DataFrame(np.random.randn(10, 3), columns=list('abc'))
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df[['a', 'c']]
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df.loc[:, ['a', 'c']]
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df = pd.DataFrame(np.random.randn(10, 3), columns=list("abc"))
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df[["a", "c"]]
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df.loc[:, ["a", "c"]]
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Selecting multiple noncontiguous columns by integer location can be achieved
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with a combination of the ``iloc`` indexer attribute and ``numpy.r_``.
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.. ipython:: python
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named = list('abcdefg')
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named = list("abcdefg")
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n = 30
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columns = named + np.arange(len(named), n).tolist()
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df = pd.DataFrame(np.random.randn(n, n), columns=columns)
@@ -160,14 +160,29 @@ function.
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.. ipython:: python
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df = pd.DataFrame(
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{'v1': [1, 3, 5, 7, 8, 3, 5, np.nan, 4, 5, 7, 9],
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'v2': [11, 33, 55, 77, 88, 33, 55, np.nan, 44, 55, 77, 99],
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'by1': ["red", "blue", 1, 2, np.nan, "big", 1, 2, "red", 1, np.nan, 12],
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'by2': ["wet", "dry", 99, 95, np.nan, "damp", 95, 99, "red", 99, np.nan,
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np.nan]})
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{
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"v1": [1, 3, 5, 7, 8, 3, 5, np.nan, 4, 5, 7, 9],
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"v2": [11, 33, 55, 77, 88, 33, 55, np.nan, 44, 55, 77, 99],
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"by1": ["red", "blue", 1, 2, np.nan, "big", 1, 2, "red", 1, np.nan, 12],
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"by2": [
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"wet",
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"dry",
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99,
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95,
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np.nan,
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"damp",
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95,
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99,
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"red",
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99,
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np.nan,
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np.nan,
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],
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}
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)
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g = df.groupby(['by1', 'by2'])
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g[['v1', 'v2']].mean()
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g = df.groupby(["by1", "by2"])
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g[["v1", "v2"]].mean()
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For more details and examples see :ref:`the groupby documentation
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<groupby.split>`.
@@ -228,11 +243,14 @@ In ``pandas`` we may use :meth:`~pandas.pivot_table` method to handle this:
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import string
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baseball = pd.DataFrame(
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{'team': ["team %d" % (x + 1) for x in range(5)] * 5,
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'player': random.sample(list(string.ascii_lowercase), 25),
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'batting avg': np.random.uniform(.200, .400, 25)})
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{
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"team": ["team %d" % (x + 1) for x in range(5)] * 5,
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"player": random.sample(list(string.ascii_lowercase), 25),
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"batting avg": np.random.uniform(0.200, 0.400, 25),
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}
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)
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baseball.pivot_table(values='batting avg', columns='team', aggfunc=np.max)
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baseball.pivot_table(values="batting avg", columns="team", aggfunc=np.max)
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For more details and examples see :ref:`the reshaping documentation
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<reshaping.pivot>`.
@@ -256,10 +274,10 @@ index/slice as well as standard boolean indexing:
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.. ipython:: python
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df = pd.DataFrame({'a': np.random.randn(10), 'b': np.random.randn(10)})
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df.query('a <= b')
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df[df['a'] <= df['b']]
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df.loc[df['a'] <= df['b']]
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df = pd.DataFrame({"a": np.random.randn(10), "b": np.random.randn(10)})
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df.query("a <= b")
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df[df["a"] <= df["b"]]
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df.loc[df["a"] <= df["b"]]
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For more details and examples see :ref:`the query documentation
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<indexing.query>`.
@@ -282,9 +300,9 @@ In ``pandas`` the equivalent expression, using the
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.. ipython:: python
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df = pd.DataFrame({'a': np.random.randn(10), 'b': np.random.randn(10)})
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df.eval('a + b')
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df['a'] + df['b'] # same as the previous expression
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df = pd.DataFrame({"a": np.random.randn(10), "b": np.random.randn(10)})
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df.eval("a + b")
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df["a"] + df["b"] # same as the previous expression
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In certain cases :meth:`~pandas.DataFrame.eval` will be much faster than
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evaluation in pure Python. For more details and examples see :ref:`the eval
@@ -334,14 +352,18 @@ In ``pandas`` the equivalent expression, using the
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.. ipython:: python
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df = pd.DataFrame({'x': np.random.uniform(1., 168., 120),
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'y': np.random.uniform(7., 334., 120),
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'z': np.random.uniform(1.7, 20.7, 120),
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'month': [5, 6, 7, 8] * 30,
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'week': np.random.randint(1, 4, 120)})
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df = pd.DataFrame(
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{
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"x": np.random.uniform(1.0, 168.0, 120),
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"y": np.random.uniform(7.0, 334.0, 120),
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"z": np.random.uniform(1.7, 20.7, 120),
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"month": [5, 6, 7, 8] * 30,
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"week": np.random.randint(1, 4, 120),
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}
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)
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grouped = df.groupby(['month', 'week'])
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grouped['x'].agg([np.mean, np.std])
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grouped = df.groupby(["month", "week"])
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grouped["x"].agg([np.mean, np.std])
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For more details and examples see :ref:`the groupby documentation
@@ -410,13 +432,17 @@ In Python, the :meth:`~pandas.melt` method is the R equivalent:
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.. ipython:: python
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cheese = pd.DataFrame({'first': ['John', 'Mary'],
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'last': ['Doe', 'Bo'],
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'height': [5.5, 6.0],
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'weight': [130, 150]})
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cheese = pd.DataFrame(
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{
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"first": ["John", "Mary"],
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"last": ["Doe", "Bo"],
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"height": [5.5, 6.0],
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"weight": [130, 150],
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}
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)
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pd.melt(cheese, id_vars=['first', 'last'])
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cheese.set_index(['first', 'last']).stack() # alternative way
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pd.melt(cheese, id_vars=["first", "last"])
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cheese.set_index(["first", "last"]).stack() # alternative way
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For more details and examples see :ref:`the reshaping documentation
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<reshaping.melt>`.
@@ -444,15 +470,24 @@ In Python the best way is to make use of :meth:`~pandas.pivot_table`:
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.. ipython:: python
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df = pd.DataFrame({'x': np.random.uniform(1., 168., 12),
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'y': np.random.uniform(7., 334., 12),
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'z': np.random.uniform(1.7, 20.7, 12),
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'month': [5, 6, 7] * 4,
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'week': [1, 2] * 6})
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df = pd.DataFrame(
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{
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"x": np.random.uniform(1.0, 168.0, 12),
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"y": np.random.uniform(7.0, 334.0, 12),
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"z": np.random.uniform(1.7, 20.7, 12),
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"month": [5, 6, 7] * 4,
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"week": [1, 2] * 6,
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}
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)
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mdf = pd.melt(df, id_vars=['month', 'week'])
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pd.pivot_table(mdf, values='value', index=['variable', 'week'],
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columns=['month'], aggfunc=np.mean)
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mdf = pd.melt(df, id_vars=["month", "week"])
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pd.pivot_table(
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mdf,
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values="value",
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index=["variable", "week"],
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columns=["month"],
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aggfunc=np.mean,
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)
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Similarly for ``dcast`` which uses a data.frame called ``df`` in R to
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aggregate information based on ``Animal`` and ``FeedType``:
@@ -475,21 +510,29 @@ using :meth:`~pandas.pivot_table`:
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.. ipython:: python
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df = pd.DataFrame({
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'Animal': ['Animal1', 'Animal2', 'Animal3', 'Animal2', 'Animal1',
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'Animal2', 'Animal3'],
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'FeedType': ['A', 'B', 'A', 'A', 'B', 'B', 'A'],
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'Amount': [10, 7, 4, 2, 5, 6, 2],
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})
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df = pd.DataFrame(
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{
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"Animal": [
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"Animal1",
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"Animal2",
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"Animal3",
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"Animal2",
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"Animal1",
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"Animal2",
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"Animal3",
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],
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"FeedType": ["A", "B", "A", "A", "B", "B", "A"],
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"Amount": [10, 7, 4, 2, 5, 6, 2],
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}
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)
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df.pivot_table(values='Amount', index='Animal', columns='FeedType',
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aggfunc='sum')
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df.pivot_table(values="Amount", index="Animal", columns="FeedType", aggfunc="sum")
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The second approach is to use the :meth:`~pandas.DataFrame.groupby` method:
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.. ipython:: python
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df.groupby(['Animal', 'FeedType'])['Amount'].sum()
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df.groupby(["Animal", "FeedType"])["Amount"].sum()
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For more details and examples see :ref:`the reshaping documentation
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<reshaping.pivot>` or :ref:`the groupby documentation<groupby.split>`.

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