id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas-settingwithcopywarning0_99_0.txt | Indexing an array with a list of integers returns a copy of the original
array. The copy contains the elements from the original array whose indices
are present in the list:
Python | |
pandas_series/pandas_series_255_4.txt | as.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.contains.html)
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pandas_series/pandas_series_281_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_100_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas_series_39_6.txt | .html#pandas.Series "pandas.Series") :
>>> ser = pd.Series(['a', 'b', 'c', 'a'], dtype='category')
>>> ser.cat.ordered
False
>>> raw_cat = pd.Categorical(['a', 'b', 'c', 'a'], ordered=True)
>>> ser = pd.Series(raw_cat)
>>> ser.cat.ordered
True
For [ ` pandas.C... | |
pandas_series/pandas_series_311_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.conta... | |
pandas_series/pandas_series_93_6.txt | Series or DatetimeIndex
The same type as the original data with boolean values. Series will have the
same name and index. DatetimeIndex will have the same name.
See also
[ ` quarter ` ](pandas.Series.dt.quarter.html#pandas.Series.dt.quarter
"pandas.Series.dt.quarter")
Return the quarter of the date.
... | |
pandas_series/pandas_series_114_5.txt | pandas.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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Ser... | |
pandas_series/pandas_series_239_6.txt | ing [ ` Series.array `
](pandas.Series.array.html#pandas.Series.array "pandas.Series.array") or [ `
Series.to_numpy() ` ](pandas.Series.to_numpy.html#pandas.Series.to_numpy
"pandas.Series.to_numpy") , depending on whether you need a reference to the
underlying data or a NumPy array.
Returns :
numpy.ndarray o... | |
pandas_series/pandas-settingwithcopywarning0_76_0.txt | ` arr ` and ` copy_of_arr ` hold 144 bytes each. As you’ve seen previously, 48
bytes out of the 144 total are for the data elements. The remaining 96 bytes
are for other attributes. ` view_of_arr ` holds only those 96 bytes because it
doesn’t have its own data elements.
To illustrate the second difference between view... | |
pandas_series/pandas_series_132_5.txt | pandas.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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
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pandas_series/pandas_series_291_3.txt | ml)
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* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
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pandas_series/pandas_series_231_3.txt | .sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
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* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Series.dt.year ](pandas.Series.... | |
pandas_series/pandas_series_258_6.txt | operations over the specified axis.
Parameters :
**func** function, str, list or dict
Function to use for aggregating the data. If a function, must either work when
passed a Series or when passed to Series.apply.
Accepted combinations are:
* function
* string function name
* list of function... | |
pandas_series/pandas_series_105_6.txt | median of the values over the requested axis.
Parameters :
**axis** {index (0)}
Axis for the function to be applied on. For Series this parameter is unused
and defaults to 0.
For DataFrames, specifying ` axis=None ` will apply the aggregation across
both axes.
New in version 2.0.0.
**skipna** boo... | |
pandas_series/pandas_series_175_4.txt | ries.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
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pandas_series/pandas_series_148_3.txt | .sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
* [ pandas.Series.dt.date ](pandas.Series.dt.date.html)
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pandas_series/pandas_series_331_3.txt | ml)
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* [ pandas.Index.str ](pandas.Index.str.html)
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* [ pandas.Series.dt.time ](pandas.Series.dt.time.html)
* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Ser... | |
pandas_series/pandas_series_261_6.txt | ng indicated encoding.
Equivalent to [ ` str.encode() `
](https://docs.python.org/3/library/stdtypes.html#str.encode "\(in Python
v3.12\)") .
Parameters :
**encoding** str
**errors** str, optional
Returns :
Series/Index of objects
Examples
>>> ser = pd.Series(['cow',... | |
pandas_series/pandas_series_106_2.txt | ries.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
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* [ pandas.Series.head ](pandas.Series.head.html)
* [ pandas.Series.idxma... | |
pandas_series/pandas_series_103_6.txt |
Parameters :
**level** int, str, or list-like
If a string is given, must be the name of a level If list-like, elements must
be names or positional indexes of levels.
**axis** {0 or âindexâ, 1 or âcolumnsâ}, default 0
Axis along which the level(s) is removed:
* 0 or âindexâ: remov... | |
pandas_series/pandas_series_51_7.txt | False
2 True
3 False
dtype: bool
The ` s5.str.istitle ` method checks for whether all words are in title case
(whether only the first letter of each word is capitalized). Words are assumed
to be as any sequence of non-numeric characters separated by whitespace
characters.
>>> s... | |
pandas_series/pandas_series_181_7.txt | urce ](../../_sources/reference/api/pandas.Series.where.rst.txt)
© 2024, pandas via [ NumFOCUS, Inc. ](https://numfocus.org) Hosted by [
OVHcloud ](https://www.ovhcloud.com) .
Created using [ Sphinx ](https://www.sphinx-doc.org/) 7.2.6.
Built with the [ PyData Sphinx Theme ](https://pydata-sphinx-
theme.readthedoc... | |
pandas_series/pandas_series_248_6.txt | ([1, 2, 3])
>>> s.shape
(3,)
[ __ previous pandas.Series.dtype ](pandas.Series.dtype.html "previous
page") [ next pandas.Series.nbytes __ ](pandas.Series.nbytes.html "next
page")
__ On this page
* ` Series.shape `
[ __ Show Source ](../../_sources/reference/api/pandas.Series.shape.rst.txt)
© 20... | |
pandas_series/pandas_series_143_7.txt | xt pandas.Series.where __ ](pandas.Series.where.html "next page")
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[ __ Show Source
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Created using... | |
pandas_series/pandas_series_171_4.txt | as.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.Series.s... | |
pandas_series/pandas_series_325_3.txt | parse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
* [ pandas.Series.dt.date ](pandas.Series.dt.date.html)
* [ pandas.Series.dt.time ](pandas.Series.dt.time.html)
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* [ pandas.Series.dt.year ](pandas.Series.dt... | |
pandas_series/pandas-settingwithcopywarning0_138_0.txt | Python
>>> df[mask]["z"] = 0
__main__:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/in... | |
pandas_series/pandas_series_255_5.txt | s.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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Series.c... | |
pandas_series/pandas_series_265_7.txt | Windowing Operations ](../../user_guide/window.html#window-generic) 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
**window**
Rolling sum with a window length of 2 o... | |
pandas_series/pandas_series_338_3.txt | ml)
* [ pandas.DataFrame.sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
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* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Ser... | |
pandas_series/pandas_series_39_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.conta... | |
pandas_series/pandas_series_221_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.conta... | |
pandas_series/pandas_series_306_6.txt | ill_axis = _NoDefault.no_default _ , _ broadcast_axis =
_NoDefault.no_default _ ) [ [source] ](https://github.com/pandas-
dev/pandas/blob/v2.2.1/pandas/core/generic.py#L10182-L10474) #
Align two objects on their axes with the specified join method.
Join method is specified for each axis Index.
Parameter... | |
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pandas_series/pandas_series_148_6.txt | a new object will be created with a copy of the
calling objectâs data and indices. Modifications to the data or indices of
the copy will not be reflected in the original object (see notes below).
When ` deep=False ` , a new object will be created without copying the
calling objectâs data or index (only references... | |
pandas_series/pandas_series_43_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_293_6.txt | e largest value in the Series.
If the maximum is achieved in multiple locations, the first row position is
returned.
Parameters :
**axis** {None}
Unused. Parameter needed for compatibility with DataFrame.
**skipna** bool, default True
Exclude NA/null values when showing the result.
***args, **... | |
pandas_series/pandas_series_77_5.txt | pandas.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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Ser... | |
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pandas_series/pandas_series_244_6.txt | efault value if not found.
Parameters :
**key** object
Returns :
same type as items contained in object
Examples
>>> df = pd.DataFrame(
... [
... [24.3, 75.7, "high"],
... [31, 87.8, "high"],
... [22, 71.6, "medium"],
... ... | |
pandas_series/pandas_series_284_5.txt | (pandas.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.Se... | |
pandas_series/pandas_series_224_5.txt | (pandas.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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Se... | |
pandas_series/pandas_series_167_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas-settingwithcopywarning0_107_0.txt | Copied!
Keep in mind that these examples show how you can reference data in an
array. Referencing data returns views when slicing arrays and copies when
using index and mask arrays. Assignments , on the other hand, always modify
the original data of the array. | |
pandas_series/pandas-settingwithcopywarning0_174_0.txt | In the examples above, you avoid chained indexing both with accessors ( `
df.loc[["a", "b"], ("powers", "x")] ` ) and without them ( ` df["powers", "x"]
` ).
As you saw earlier, chained indexing can lead to a ` SettingWithCopyWarning `
: | |
pandas_series/pandas-settingwithcopywarning0_142_0.txt | ` df["z"][mask] = 0 ` succeeds and you get the modified ` df ` without a `
SettingWithCopyWarning ` .
Using the accessors is recommended, but you can run into trouble with them as
well: | |
pandas_series/pandas_series_273_5.txt | )
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
* [ pandas.Series.cat.add_categories ](pandas.Series.cat.add_categories.html)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Series.cat.remove_unused_categories ](panda... | |
pandas_series/pandas_series_251_2.txt | ies.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
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pandas_series/pandas_series_225_3.txt | arse ](pandas.DataFrame.sparse.html)
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* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Series.dt.year ](pandas.Series.dt.... | |
pandas_series/pandas_series_192_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
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* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas_series_2_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_21_5.txt | ndas.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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_55_4.txt | das.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.contains.html)
* [ pandas.Series.... | |
pandas_series/pandas_series_338_6.txt | values from a scipy.sparse.coo_matrix.
Parameters :
**A** scipy.sparse.coo_matrix
**dense_index** bool, default False
If False (default), the index consists of only the coords of the non-null
entries of the original coo_matrix. If True, the index consists of the full
sorted (row, col) coordinates... | |
pandas_series/pandas_series_47_3.txt | parse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
* [ pandas.Series.dt.date ](pandas.Series.dt.date.html)
* [ pandas.Series.dt.time ](pandas.Series.dt.time.html)
* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Series.dt.year ](pandas.Series.dt... | |
pandas_series/pandas-settingwithcopywarning0_195_0.txt | Mirko has a Ph.D. in Mechanical Engineering and works as a university
professor. He is a Pythonista who applies hybrid optimization and machine
learning methods to support decision making in the energy sector.
» More about Mirko | |
pandas_series/pandas_series_274_3.txt | ml)
* [ pandas.DataFrame.sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
* [ pandas.Series.dt.date ](pandas.Series.dt.date.html)
* pandas.Series.dt.time
* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Series.dt.year ](pandas.Series.dt.... | |
pandas_series/pandas-settingwithcopywarning0_117_0.txt | Combining index and mask arrays with slicing is also possible. In such cases,
you get copies.
Here are a few examples: | |
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pandas_series/pandas_series_311_5.txt | .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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Series.ca... | |
pandas_series/pandas_series_33_6.txt | This is convenient if
you want to create a lazy iterator.
Returns :
iterable
Iterable of tuples containing the (index, value) pairs from a Series.
See also
[ ` DataFrame.items ` ](pandas.DataFrame.items.html#pandas.DataFrame.items
"pandas.DataFrame.items")
Iterate over (column name, Series) pa... | |
pandas_series/pandas_series_234_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas_series_186_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas_series_294_5.txt | pandas.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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Ser... | |
pandas_series/pandas-settingwithcopywarning0_17_0.txt | Copied!
If you prefer Anaconda or Miniconda distributions, you can use the conda
package management system. To learn more about this approach, check out
Setting Up Python for Machine Learning on Windows . For now, it’ll be enough
to install NumPy and pandas in your environment : | |
pandas_series/pandas_series_88_0.txt | Skip to main content
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pandas_series/pandas_series_250_6.txt | ber of decimals.
Parameters :
**decimals** int, default 0
Number of decimal places to round to. If decimals is negative, it specifies
the number of positions to the left of the decimal point.
***args, **kwargs**
Additional arguments and keywords have no effect but might be accepted for
compatibi... | |
pandas_series/pandas_series_269_3.txt | ml)
* [ pandas.DataFrame.sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
* [ pandas.Series.dt.date ](pandas.Series.dt.date.html)
* [ pandas.Series.dt.time ](pandas.Series.dt.time.html)
* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Ser... | |
pandas_series/pandas_series_186_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
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* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_226_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.conta... | |
pandas_series/pandas_series_245_8.txt | abc
1 foo new
2 bait xyz
>>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True)
A B
0 new abc
1 foo bar
2 bait xyz
>>> df.replace(regex=r'^ba.$', value='new')
A B
0 new abc
1 foo new
2 bait xyz
... | |
pandas_series/pandas_series_274_4.txt | ndas.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.Series... | |
pandas_series/pandas_series_282_6.txt | type ` dtype ` .
Parameters :
**dtype** str, data type, Series or Mapping of column name -> data type
Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to cast entire
pandas object to the same type. Alternatively, use a mapping, e.g. {col:
dtype, â¦}, where col is a column label and dtype i... | |
pandas_series/pandas_series_190_3.txt | e ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
* [ pandas.Series.dt.date ](pandas.Series.dt.date.html)
* [ pandas.Series.dt.time ](pandas.Series.dt.time.html)
* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Series.dt.year ](pandas.Series.dt.yea... | |
pandas_series/pandas_series_140_5.txt | s.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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Series.c... | |
pandas_series/pandas_series_252_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_337_6.txt | the Series/Index.
Each of returned indexes corresponds to the position where the substring is
fully contained between [start:end]. Return -1 on failure. Equivalent to
standard [ ` str.find() `
](https://docs.python.org/3/library/stdtypes.html#str.find "\(in Python
v3.12\)") .
Parameters :
**sub** str
... | |
pandas_series/pandas_series_90_5.txt | as.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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Series.... | |
pandas_series/pandas_series_77_2.txt | ies.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
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* [ pandas.Series.head ](pandas.Series.head.html)
* [ pandas.Series.idxmax... | |
pandas_series/pandas_series_17_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)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.contains.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_92_3.txt | ml)
* [ pandas.DataFrame.sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
* [ pandas.Series.dt.date ](pandas.Series.dt.date.html)
* [ pandas.Series.dt.time ](pandas.Series.dt.time.html)
* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Ser... | |
pandas_series/pandas_series_55_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_108_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas_series_309_6.txt | ces along an axis.
This means that we are not indexing according to actual values in the index
attribute of the object. We are indexing according to the actual position of
the element in the object.
Parameters :
**indices** array-like
An array of ints indicating which positions to take.
**axis** {0 or... | |
pandas_series/pandas-settingwithcopywarning0_201_0.txt | Master Real-World Python Skills
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pandas_series/pandas_series_290_3.txt | ml)
* [ pandas.DataFrame.sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
* [ pandas.Series.dt.date ](pandas.Series.dt.date.html)
* [ pandas.Series.dt.time ](pandas.Series.dt.time.html)
* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Ser... | |
pandas_series/pandas_series_137_4.txt | das.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.contains.html)
* [ pandas.Series.... | |
pandas_series/pandas_series_336_2.txt | s.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
* [ pandas.Series.idxmax ]... | |
C++_Ranges_library/C++_Ranges_library_99_13.txt |
| GCC | Clang | MSVC | Apple Clang | EDG eccp | Intel C++ | IBM XL C++ | IBM Open XL C++ for AIX | IBM Open XL C++ for z/OS | Sun/Oracle C++ | Embarcadero C++ Builder | Cray | Nvidia HPC C++ (ex PGI)* | Nvidia nvcc |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|--- ... | |
C++_Ranges_library/C++_Ranges_library_82_5.txt | s_partitioned ](/w/cpp/algorithm/ranges/is_partitioned
"cpp/algorithm/ranges/is partitioned")
---
[ ranges::partition_point ](/w/cpp/algorithm/ranges/partition_point
"cpp/algorithm/ranges/partition point")
| [ ranges::partition ](/w/cpp/algorithm/ranges/partition
"cpp/algorithm/ranges/partition")
---
[... | |
C++_Ranges_library/C++_Ranges_library_63_0.txt | ##### [ cppreference.com ](/)
[ Log in
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##### Namespaces
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* [ Discussion ](/mwiki/index.php?... | |
C++_Ranges_library/C++_Ranges_library_150_3.txt | https://wg21.link/P1668R1)
Changing the active member of a union in constant evaluation | 202002L | (C++20) | [ P1330R0 ](https://wg21.link/P1330R0)
[ P2493R0 ](https://wg21.link/P2493R0)
Non- [ literal ](/w/cpp/named_req/LiteralType "cpp/named req/LiteralType") variables, labels, and [ ` goto ` ](/w/cpp/l... | |
C++_Ranges_library/C++_Ranges_library_59_40.txt | {*this, [ranges::end](http://en.cppreference.com/w/cpp/ranges-ranges-placeholder/end)(base_)};
}
constexpr auto end() const {
if constexpr (forward_range<V> && forward_range<const V> && common_range<const V>)
return __outer_iterator<true>{*this, [ranges::end](http://... | |
C++_Ranges_library/C++_Ranges_library_95_2.txt | ct initialization ](/w/cpp/language/direct_initialization "cpp/language/direct initialization") | 201902L | (C++20) | [ P0960R3 ](https://wg21.link/P0960R3)
` __cpp_alias_templates ` | [ Alias templates ](/w/cpp/language/type_alias "cpp/language/type alias") | 200704L | (C++11) | [ N2258 ](https://wg21.lin... | |
C++_Ranges_library/C++_Ranges_library_84_6.txt | l_t) and bool
( bound >= 0 ) is false .
4) Initializes ` _value__ ` and ` _bound__ ` through piecewise construction.
### Parameters
value | \- | the value to be repeatedly produced
---|---|---
bound | \- | the bound
## std::ranges::repeat_view:: begin
constexpr /*iterator*/ begin (... | |
C++_Ranges_library/C++_Ranges_library_126_7.txt | 3) | [ P0401R6 ](https://wg21.link/P0401R6)
[ P2652R2 ](https://wg21.link/P2652R2)
[ LWG3887 ](https://wg21.link/LWG3887)
` __cpp_lib_allocator_ traits_is_always_equal ` | [ ` std::allocator_traits::is_always_equal ` ](/w/cpp/memory/allocator_traits "cpp/memory/allocator traits") | 201411L | [ ` <memory> ... |
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