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DOC: fixing override of built-in functions in docs #47632

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fixing override of built-in functions in docs
Transurgeon Jul 8, 2022
bb0276d
removing additional override of built-in parameter 'columns' with 'co…
Transurgeon Jul 8, 2022
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4 changes: 2 additions & 2 deletions doc/source/getting_started/comparison/comparison_with_r.rst
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Expand Up @@ -133,8 +133,8 @@ with a combination of the ``iloc`` indexer attribute and ``numpy.r_``.

named = list("abcdefg")
n = 30
columns = named + np.arange(len(named), n).tolist()
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columns is not a built-in function. what are you trying to do here?

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Hi. I am sorry for the mistake. I explained my reasoning in this issue

Could you take a look at it?

Also I was wondering if it was just this file ("comparison_with_r") that was wrong or all of them?

Thanks for the feedback

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Commented on the issue

df = pd.DataFrame(np.random.randn(n, n), columns=columns)
column_schema = named + np.arange(len(named), n).tolist()
df = pd.DataFrame(np.random.randn(n, n), columns=column_schema)

df.iloc[:, np.r_[:10, 24:30]]

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4 changes: 2 additions & 2 deletions doc/source/user_guide/categorical.rst
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Expand Up @@ -626,12 +626,12 @@ even if some categories are not present in the data:

.. ipython:: python

columns = pd.Categorical(
column_schema = pd.Categorical(
["One", "One", "Two"], categories=["One", "Two", "Three"], ordered=True
)
df = pd.DataFrame(
data=[[1, 2, 3], [4, 5, 6]],
columns=pd.MultiIndex.from_arrays([["A", "B", "B"], columns]),
columns=pd.MultiIndex.from_arrays([["A", "B", "B"], column_schema]),
)
df.groupby(axis=1, level=1).sum()

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4 changes: 2 additions & 2 deletions doc/source/user_guide/cookbook.rst
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Expand Up @@ -1171,9 +1171,9 @@ Option 2: read column names and then data
.. ipython:: python

pd.read_csv(StringIO(data), sep=";", header=10, nrows=10).columns
columns = pd.read_csv(StringIO(data), sep=";", header=10, nrows=10).columns
column_schema = pd.read_csv(StringIO(data), sep=";", header=10, nrows=10).columns
pd.read_csv(
StringIO(data), sep=";", index_col=0, header=12, parse_dates=True, names=columns
StringIO(data), sep=";", index_col=0, header=12, parse_dates=True, names=column_schema
)


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6 changes: 3 additions & 3 deletions doc/source/user_guide/groupby.rst
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Expand Up @@ -839,10 +839,10 @@ Alternatively, the built-in methods could be used to produce the same outputs.

.. ipython:: python

max = ts.groupby(lambda x: x.year).transform("max")
min = ts.groupby(lambda x: x.year).transform("min")
max_ts = ts.groupby(lambda x: x.year).transform("max")
min_ts = ts.groupby(lambda x: x.year).transform("min")

max - min
max_ts - min_ts

Another common data transform is to replace missing data with the group mean.

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4 changes: 2 additions & 2 deletions doc/source/user_guide/indexing.rst
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Expand Up @@ -1581,8 +1581,8 @@ The name, if set, will be shown in the console display:
.. ipython:: python

index = pd.Index(list(range(5)), name='rows')
columns = pd.Index(['A', 'B', 'C'], name='cols')
df = pd.DataFrame(np.random.randn(5, 3), index=index, columns=columns)
column_schema = pd.Index(['A', 'B', 'C'], name='cols')
df = pd.DataFrame(np.random.randn(5, 3), index=index, columns=column_schema)
df
df['A']

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8 changes: 4 additions & 4 deletions doc/source/user_guide/reshaping.rst
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Expand Up @@ -179,7 +179,7 @@ processed individually.

.. ipython:: python

columns = pd.MultiIndex.from_tuples(
column_schema = pd.MultiIndex.from_tuples(
[
("A", "cat", "long"),
("B", "cat", "long"),
Expand All @@ -188,7 +188,7 @@ processed individually.
],
names=["exp", "animal", "hair_length"],
)
df = pd.DataFrame(np.random.randn(4, 4), columns=columns)
df = pd.DataFrame(np.random.randn(4, 4), columns=column_schema)
df

df.stack(level=["animal", "hair_length"])
Expand All @@ -212,7 +212,7 @@ calling :meth:`~DataFrame.sort_index`, of course). Here is a more complex exampl

.. ipython:: python

columns = pd.MultiIndex.from_tuples(
column_schema = pd.MultiIndex.from_tuples(
[
("A", "cat"),
("B", "dog"),
Expand All @@ -224,7 +224,7 @@ calling :meth:`~DataFrame.sort_index`, of course). Here is a more complex exampl
index = pd.MultiIndex.from_product(
[("bar", "baz", "foo", "qux"), ("one", "two")], names=["first", "second"]
)
df = pd.DataFrame(np.random.randn(8, 4), index=index, columns=columns)
df = pd.DataFrame(np.random.randn(8, 4), index=index, columns=column_schema)
df2 = df.iloc[[0, 1, 2, 4, 5, 7]]
df2

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6 changes: 3 additions & 3 deletions doc/source/user_guide/scale.rst
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Expand Up @@ -73,15 +73,15 @@ Option 1 loads in all the data and then filters to what we need.

.. ipython:: python

columns = ["id_0", "name_0", "x_0", "y_0"]
column_schema = ["id_0", "name_0", "x_0", "y_0"]

pd.read_parquet("timeseries_wide.parquet")[columns]
pd.read_parquet("timeseries_wide.parquet")[column_schema]

Option 2 only loads the columns we request.

.. ipython:: python

pd.read_parquet("timeseries_wide.parquet", columns=columns)
pd.read_parquet("timeseries_wide.parquet", columns=column_schema)

.. ipython:: python
:suppress:
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