id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_33_5.txt | andas.Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
* [ pandas.Series.cat.add_categories ](pandas.Series.cat.add_categories.html)
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pandas_series/pandas_series_265_2.txt | .drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas-settingwithcopywarning0_149_0.txt | Copied!
In these two cases, you select the first three rows with slices and get views.
The assignments succeed both on the views and on ` df ` . But you still
receive a ` SettingWithCopyWarning ` . | |
pandas_series/pandas_series_126_6.txt | he Series/DataFrame.
This method takes a key argument to select data at a particular level of a
MultiIndex.
Parameters :
**key** label or tuple of label
Label contained in the index, or partially in a MultiIndex.
**axis** {0 or âindexâ, 1 or âcolumnsâ}, default 0
Axis to retrieve cros... | |
pandas_series/pandas_series_288_5.txt | ies.cat.rename_categories ](pandas.Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
* pandas.Series.cat.add_categories
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pandas_series/pandas-settingwithcopywarning0_191_0.txt | * Indexing-based assignments in NumPy and pandas can return either views or copies .
* Both views and copies can be useful, but they have different behaviors .
* Special care must be taken to avoid setting unwanted values on copies.
* Accessors in pandas are very useful objects for properly assignin... | |
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* Development
* Release notes | |
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pandas_series/pandas_series_177_3.txt | taFrame.sparse.html)
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pandas_series/pandas_series_56_3.txt | ml)
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pandas_series/pandas_series_160_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_10_5.txt | ies.cat.rename_categories.html)
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pandas_series/pandas_series_145_2.txt | ries.drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas_series_70_1.txt | html)
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pandas_series/pandas_series_179_5.txt | ](pandas.Series.cat.rename_categories.html)
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pandas_series/pandas_series_119_2.txt | vel.html)
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pandas_series/pandas_series_3_6.txt | , _ min_rows = None _ ) [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.1/pandas/core/series.py#L1809-L1891) #
Render a string representation of the Series.
Parameters :
**buf** StringIO-like, optional
Buffer to write to.
**na_rep** str, optional
String representation of ... | |
pandas_series/pandas_series_337_5.txt | as.Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
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pandas_series/pandas_series_216_6.txt | s = pd.Series(['Ant', 'Bear', 'Cow'])
>>> s
0 Ant
1 Bear
2 Cow
dtype: object
>>> s.nbytes
24
For Index:
>>> idx = pd.Index([1, 2, 3])
>>> idx
Index([1, 2, 3], dtype='int64')
>>> idx.nbytes
24
[ __ previous pandas.Series.shape ](pandas.... | |
pandas_series/pandas_series_290_5.txt | ies.cat.rename_categories ](pandas.Series.cat.rename_categories.html)
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pandas_series/pandas_series_341_2.txt | vel.html)
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pandas_series/pandas_series_37_4.txt | andas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
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* [ pandas.Series.str.contains ](pandas.Series.str.contains.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_301_6.txt |
Return a new Series with missing values removed.
See the [ User Guide ](../../user_guide/missing_data.html#missing-data) for
more on which values are considered missing, and how to work with missing
data.
Parameters :
**axis** {0 or âindexâ}
Unused. Parameter needed for compatibility with DataFr... | |
pandas_series/pandas_series_293_3.txt | parse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
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pandas_series/pandas_series_160_1.txt | html)
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* [ pandas.Serie... | |
pandas_series/pandas_series_154_6.txt | n Monday, which is
denoted by 0 and ends on Sunday which is denoted by 6. This method is
available on both Series with datetime values (using the dt accessor) or
DatetimeIndex.
Returns :
Series or Index
Containing integers indicating the day number.
See also
[ ` Series.dt.dayofweek `
](pandas.Serie... | |
pandas_series/pandas_series_2_6.txt | ct, optional
The passed name should substitute for the series name (if it has one).
Returns :
DataFrame
DataFrame representation of Series.
Examples
>>> s = pd.Series(["a", "b", "c"],
... name="vals")
>>> s.to_frame()
vals
0 a
1 b
2 ... | |
pandas_series/pandas_series_178_1.txt | html)
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pandas_series/pandas_series_106_4.txt | ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_66_2.txt | vel.html)
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... | |
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pandas_series/pandas_series_293_5.txt | ndas.Series.cat.rename_categories.html)
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pandas_series/pandas_series_169_5.txt | ](pandas.Series.cat.rename_categories.html)
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... | |
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pandas_series/pandas-settingwithcopywarning0_188_0.txt | Copied!
You get ` "raise" ` in this case because you changed the behavior with `
set_option() ` . Normally, ` pd.get_option("mode.chained_assignment") `
returns ` "warn" ` . | |
pandas_series/pandas_series_123_6.txt | /pandas/core/generic.py#L7674-L7807) #
Fill NA/NaN values by using the next valid observation to fill the gap.
Parameters :
**axis** {0 or âindexâ} for Series, {0 or âindexâ, 1 or âcolumnsâ}
for DataFrame
Axis along which to fill missing values. For Series this parameter is unused
a... | |
pandas_series/pandas_series_26_6.txt | s = pd.Series(['Ant', 'Bear', 'Cow'])
>>> s
0 Ant
1 Bear
2 Cow
dtype: object
>>> s.size
3
For Index:
>>> idx = pd.Index([1, 2, 3])
>>> idx
Index([1, 2, 3], dtype='int64')
>>> idx.size
3
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pandas_series/pandas.DataFrame.mask.html4_21_0.txt | The signature for ` DataFrame.where() ` differs from ` numpy.where() ` .
Roughly ` df1.where(m, df2) ` is equivalent to ` np.where(m, df1, df2) `
.
For further details and examples see the ` mask ` documentation in indexing
. | |
pandas_series/pandas_series_73_1.txt | html)
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pandas_series/pandas_series_66_5.txt | s.Series.cat.rename_categories.html)
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* [ pandas.Series.cat.add_categories ](pandas.Series.cat.add_categories.html)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Series.c... | |
pandas_series/pandas_series_180_7.txt | formations.
Notes
See [ Windowing Operations ](../../user_guide/window.html#window-
exponentially-weighted) for further usage details and examples.
Examples
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
B
0 0.0
1 1.0
2 2.0
3 NaN
4 4.0
... | |
pandas_series/pandas_series_303_2.txt | vel.html)
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pandas_series/pandas_series_27_1.txt | html)
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pandas_series/pandas_series_16_6.txt | sts in the series flattened.
Examples
>>> import pyarrow as pa
>>> s = pd.Series(
... [
... [1, 2, 3],
... [3],
... ],
... dtype=pd.ArrowDtype(pa.list_(
... pa.int64()
... ))
... )
>>> s.list.flatten()
0 1
1 2
... | |
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pandas_series/pandas_series_278_5.txt | das.Series.cat.rename_categories.html)
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... | |
pandas_series/pandas_series_313_6.txt | target time zone.
Parameters :
**tz** str or tzinfo object or None
Target time zone. Passing ` None ` will convert to UTC and remove the
timezone information.
**axis** {0 or âindexâ, 1 or âcolumnsâ}, default 0
The axis to convert
**level** int, str, default None
If axis is a Mul... | |
pandas_series/pandas_series_146_6.txt | row (for each element in where , if list) without any NaN is taken.
In case of a [ ` DataFrame ` ](pandas.DataFrame.html#pandas.DataFrame
"pandas.DataFrame") , the last row without NaN considering only the subset of
columns (if not None )
If there is no good value, NaN is returned for a Series or a Series of NaN... | |
pandas_series/pandas_series_202_7.txt | s
>>> df.loc[(df['max_speed'] > 4) | (df['shield'] < 5)]
max_speed shield
cobra 1 2
sidewinder 7 8
Please ensure that each condition is wrapped in parentheses ` () ` . See the
[ user guide ](../../user_guide/indexing.html#indexing-boolea... | |
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pandas_series/pandas_series_318_6.txt | c.py#L11019-L11076) #
Replace values where the condition is True.
Parameters :
**cond** bool Series/DataFrame, array-like, or callable
Where cond is False, keep the original value. Where True, replace with
corresponding value from other . If cond is callable, it is computed on
the Series/Da... | |
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pandas_series/pandas_series_185_6.txt | n Series/Index.
Each of the returned indexes corresponds to the position where the substring
is fully contained between [start:end]. This is the same as ` str.rfind `
except instead of returning -1, it raises a ValueError when the substring is
not found. Equivalent to standard ` str.rindex ` .
Parameters :
... | |
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pandas_series/pandas_series_14_6.txt | . There can be multiple modes.
Always returns Series even if only one value is returned.
Parameters :
**dropna** bool, default True
Donât consider counts of NaN/NaT.
Returns :
Series
Modes of the Series in sorted order.
Examples
>>> s = pd.Series([2, 4, 2, 2, 4, None])... | |
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* [ pandas.Serie... | |
pandas_series/pandas_series_321_6.txt |
>>> s = pd.Series([1, 2, 3])
>>> s.is_unique
True
>>> s = pd.Series([1, 2, 3, 1])
>>> s.is_unique
False
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* [ pandas.Ser... | |
pandas_series/pandas-settingwithcopywarning0_96_0.txt |
>>> arr = np.array([1, 2, 4, 8, 16, 32])
>>> a = arr[1:3]
>>> a
array([2, 4])
>>> a.base
array([ 1, 2, 4, 8, 16, 32])
>>> a.base is arr
True
>>> a.flags.owndata
False
>>> b = arr[1:4:2]
>>> b
array([2, 8])
>>> b.base
array([ 1, 2, 4, ... | |
pandas_series/pandas_series_227_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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