id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_312_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_253_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_230_5.txt | ies.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.cat.re... | |
pandas_series/pandas_series_139_2.txt | ies.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... | |
pandas_series/pandas_series_253_6.txt | er of series and other, element-wise (binary operator
rpow ).
Equivalent to ` other ** series ` , but with support to substitute a
fill_value for missing data in either one of the inputs.
Parameters :
**other** Series or scalar value
**level** int or name
Broadcast across a level, matching In... | |
pandas_series/pandas_series_218_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_27_5.txt | das.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_219_4.txt | 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.contains.html)
* [ pandas.Seri... | |
pandas_series/pandas_series_215_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_187_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_95_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_280_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_125_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_87_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_146_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_65_3.txt | e.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.Series.dt.year ](pandas.Series... | |
pandas_series/pandas_series_227_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_92_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-settingwithcopywarning0_8_0.txt | Mark as Completed
Share Share Email | |
pandas_series/pandas_series_273_6.txt | andas.Series.dt.qyear __
](pandas.Series.dt.qyear.html "next page")
__ On this page
* ` Series.dt.as_unit() `
[ __ Show Source
](../../_sources/reference/api/pandas.Series.dt.as_unit.rst.txt)
© 2024, pandas via [ NumFOCUS, Inc. ](https://numfocus.org) Hosted by [
OVHcloud ](https://www.ovhcloud.com) .
Created ... | |
pandas_series/pandas_series_292_6.txt | over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative
product.
Parameters :
**axis** {0 or âindexâ, 1 or âcolumnsâ}, default 0
The index or the name of the axis. 0 is equivalent to None or âindexâ. For
Series this parameter is unused and... | |
pandas_series/pandas_series_210_6.txt | particular method. Patterned after
Pythonâs string methods, with some inspiration from Râs stringr package.
Examples
>>> s = pd.Series(["A_Str_Series"])
>>> s
0 A_Str_Series
dtype: object
>>> s.str.split("_")
0 [A, Str, Series]
dtype: object
... | |
pandas_series/pandas_series_35_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_36_6.txt | alize() `
](https://docs.python.org/3/library/stdtypes.html#str.capitalize "\(in Python
v3.12\)") .
Returns :
Series or Index of object
See also
[ ` Series.str.lower ` ](pandas.Series.str.lower.html#pandas.Series.str.lower
"pandas.Series.str.lower")
Converts all characters to lowercase.
[ ` S... | |
pandas_series/pandas_series_51_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_239_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 ]... | |
pandas_series/pandas-settingwithcopywarning0_180_0.txt | Python
>>> df = pd.DataFrame(
... data={("powers", "x"): 2**np.arange(5),
... ("powers", "y"): 3**np.arange(5),
... ("random", "z"): np.array([45, 98, 24, 11, 64], dtype=float)},
... index=["a", "b", "c", "d", "e"]
... )
>>> df["powers", "x"] = 0
... | |
pandas_series/pandas_series_318_4.txt | (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.contains.html)
* [ pandas.Ser... | |
pandas_series/pandas_series_198_3.txt | ](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.year.... | |
pandas_series/pandas_series_211_6.txt | ge("2000-01-01", periods=3, freq="h")
... )
>>> datetime_series
0 2000-01-01 00:00:00
1 2000-01-01 01:00:00
2 2000-01-01 02:00:00
dtype: datetime64[ns]
>>> datetime_series.dt.hour
0 0
1 1
2 2
dtype: int32
[ __ previous pandas.Series.dt.day ](pandas.Seri... | |
pandas_series/pandas_series_105_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_172_5.txt | eries.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.cat.... | |
pandas_series/pandas_series_260_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_63_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_120_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_283_7.txt | d `
NaN ` .
>>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
... 'Chrome']
>>> df.reindex(new_index)
http_status response_time
Safari 404.0 0.07
Iceweasel NaN NaN
Comodo Dragon N... | |
pandas_series/pandas_series_164_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_149_6.txt | ys import SparseArray
>>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
>>> s.density
0.6
[ __ previous pandas.Series.sparse.npoints
](pandas.Series.sparse.npoints.html "previous page") [ next
pandas.Series.sparse.fill_value __ ](pandas.Series.sparse.fill_value.html
"next page")
__ On this page
... | |
pandas_series/pandas_series_254_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_222_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_257_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_213_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_204_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_184_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_52_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_107_1.txt | div ](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.Series.combine_first ](pandas.Series.c... | |
pandas_series/pandas_series_193_7.txt |
Plot DataFrame versus the desired column
>>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
>>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
>>> index = ['snail', 'pig', 'elephant',
... 'rabbit', 'gi... | |
pandas_series/pandas_series_1_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_73_3.txt | e.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.Series.dt.year ](pandas.Series... | |
pandas_series/pandas_series_220_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-settingwithcopywarning0_162_0.txt | Hierarchical indexing , or MultiIndex , is a pandas feature that enables
you to organize your row or column indices on multiple levels according to a
hierarchy. It’s a powerful feature that increases the flexibility of pandas
and enables working with data in more than two dimensions.
Hierarchical indices are create... | |
pandas_series/pandas_series_233_4.txt | ](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.contains.html)
* [ pandas.Se... | |
pandas_series/pandas_series_201_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_48_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_181_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_297_7.txt | ât like the default colours, you can specify how youâd like each
column to be colored.
>>> axes = df.plot.bar(
... rot=0, subplots=True, color={"speed": "red", "lifespan": "green"}
... )
>>> axes[1].legend(loc=2)
* [ 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.y... | |
pandas_series/pandas_series_121_5.txt | das.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_296_5.txt | ies.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.cat.re... | |
pandas_series/pandas_series_182_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-settingwithcopywarning0_54_0.txt | Let’s start by creating a NumPy array :
Python | |
pandas_series/pandas_series_330_4.txt | s.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.str.count... | |
pandas_series/pandas_series_214_4.txt | s.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.st... | |
pandas_series/pandas_series_71_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_171_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_85_3.txt | .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.Series.dt.year ](pandas.Series.... | |
pandas_series/pandas_series_96_3.txt | me.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.Series.dt.year ](pandas.Serie... | |
pandas_series/pandas.DataFrame.mask.html4_10_0.txt | other scalar, Series/DataFrame, or callable
| |
pandas_series/pandas_series_323_4.txt | .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.str... | |
pandas_series/pandas_series_129_3.txt | e.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.Series.dt.year ](pandas.Series... | |
pandas_series/pandas_series_319_4.txt | 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.contains.html)
* [ pandas.Seri... | |
pandas_series/pandas_series_212_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_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_329_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_304_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-settingwithcopywarning0_118_0.txt | Python
>>> arr = np.array([[ 1, 2, 4, 8],
... [ 16, 32, 64, 128],
... [256, 512, 1024, 2048]])
>>> arr
array([[ 1, 2, 4, 8],
[ 16, 32, 64, 128],
[ 256, 512, 1024, 2048]])
>>> a = arr[:, 1:3] # T... | |
pandas_series/pandas_series_106_3.txt | e.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.Series.dt.year ](pandas.Series... | |
pandas_series/pandas-settingwithcopywarning0_128_0.txt | Python
>>> df = pd.DataFrame(data=data, index=index)
>>> df["a":"c"]
x y z
a 1 1 45
b 2 3 98
c 4 9 24
>>> df["a":"c"].to_numpy().base
array([[ 1, 2, 4, 8, 16],
[ 1, 3, 9, 27, 81],
[45, 98, 24, 11, 64]])
>>> df["a":"c... | |
pandas_series/pandas_series_247_2.txt | ies.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... | |
pandas_series/pandas-settingwithcopywarning0_166_0.txt | Python
>>> df["powers"]
x y
a 1 1
b 2 3
c 4 9
d 8 27
e 16 81
>>> df["powers", "x"]
a 1
b 2
c 4
d 8
e 16
Name: (powers, x), dtype: int64
>>> df["powers", "x"] = 0
>>> df
powers random
... | |
pandas_series/pandas_series_50_6.txt | 0.
This is useful in cases, when the time does not matter. Length is unaltered.
The timezones are unaffected.
This method is available on Series with datetime values under the ` .dt `
accessor, and directly on Datetime Array/Index.
Returns :
DatetimeArray, DatetimeIndex or Series
The same type as the ... | |
pandas_series/pandas.DataFrame.mask.html4_5_0.txt | * * API reference
* DataFrame
* pandas.DataF...
# pandas.DataFrame.mask # | |
pandas_series/pandas_series_100_6.txt | ent to standard [ ` str.translate() `
](https://docs.python.org/3/library/stdtypes.html#str.translate "\(in Python
v3.12\)") .
Parameters :
**table** dict
Table is a mapping of Unicode ordinals to Unicode ordinals, strings, or None.
Unmapped characters are left untouched. Characters mapped to None are ... | |
pandas_series/pandas_series_29_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_142_4.txt | .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.str... | |
pandas_series/selecting-in-pandas-using-where-and-mask2_18_0.txt | Invalid email address
I promise not to spam you, and you can unsubscribe at any time. | |
pandas_series/pandas_series_13_6.txt |
>>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]")
>>> ser.sparse.density
0.6
>>> ser.sparse.sp_values
array([2, 2, 2])
[ __ previous pandas.Series.dt ](pandas.Series.dt.html "previous page") [
next pandas.DataFrame.sparse __ ](pandas.DataFrame.sparse.html "next page")
__ On this ... | |
pandas_series/selecting-in-pandas-using-where-and-mask2_24_0.txt |
>>> sal['total_pay2'] = sal['annual_salary']
>>> mask = sal['salary_or_hourly'] != 'Salary'
>>> sal.loc[mask, 'total_pay2'] = sal.loc[mask, 'typical_hours'] * sal.loc[mask, 'hourly_rate'] * 52
So using ` where ` can result in a slightly more simple expression, even if
it’s a little long. | |
pandas_series/pandas_series_121_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.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.contains.html)
* [ pandas.Series... | |
pandas_series/pandas_series_36_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_109_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_48_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_103_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_133_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_110_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_223_5.txt | ries.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.cat.r... | |
pandas_series/pandas_series_157_6.txt | arameters :
**dropna** bool, default True
Donât include NaN in the count.
Returns :
int
See also
[ ` DataFrame.nunique `
](pandas.DataFrame.nunique.html#pandas.DataFrame.nunique
"pandas.DataFrame.nunique")
Method nunique for DataFrame.
[ ` Series.count ` ](pandas.Series.count... | |
pandas_series/pandas_series_79_0.txt | Skip to main content
__ Back to top
__ ` Ctrl ` \+ ` K `
[ 
](../../index.html)
Site Navigation
* [ Getting started ](../../getting_started/index.html)
* [ User Guide ](../../user_guide/index.html)
* [ API reference ](../index.html)
* [ Developm... | |
pandas_series/pandas_series_226_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_169_6.txt | ies and other, element-wise (binary operator gt ).
Equivalent to ` series > other ` , but with support to substitute a
fill_value for missing data in either one of the inputs.
Parameters :
**other** Series or scalar value
**level** int or name
Broadcast across a level, matching Index values o... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.