id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
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pandas_series/pandas-settingwithcopywarning0_71_0.txt | 1. Views don’t need additional storage for data, but copies do.
2. Modifying the original array affects its views, and vice versa. However, modifying the original array will not affect its copy.
To illustrate the first difference between views and copies, let’s compare the
sizes of ` arr ` , ` view_of_arr ` , ... | |
pandas_series/pandas_series_297_5.txt | ies.cat.rename_categories ](pandas.Series.cat.rename_categories.html)
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pandas_series/pandas_series_262_6.txt | ndas.MultiIndex.html#pandas.MultiIndex "pandas.MultiIndex") .
Default is to swap the two innermost levels of the index.
Parameters :
**i, j** int or str
Levels of the indices to be swapped. Can pass level name as string.
**copy** bool, default True
Whether to copy underlying data.
Note
The c... | |
pandas_series/pandas_series_56_2.txt | vel.html)
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pandas_series/pandas_series_242_8.txt | "name": "index",
"type": "string"
},
{
"name": "col 1",
"type": "string"
},
{
"name": "col 2",
"type": "string"
}
],
... | |
pandas_series/pandas_series_226_6.txt | tps://docs.python.org/3/library/stdtypes.html#str.upper "\(in Python
v3.12\)") .
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.
` Series.str.upper `
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pandas_series/pandas-settingwithcopywarning0_28_0.txt |
>>> data = {"x": 2**np.arange(5),
... "y": 3**np.arange(5),
... "z": np.array([45, 98, 24, 11, 64])}
>>> index = ["a", "b", "c", "d", "e"]
>>> df = pd.DataFrame(data=data, index=index)
>>> df
x y z
a 1 1 45
b 2 3 98
c 4 9 ... | |
pandas_series/pandas_series_311_2.txt | vel.html)
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pandas_series/pandas-settingwithcopywarning0_16_0.txt | Shell
$ python -m pip install -U "numpy==1.18.*" "pandas==1.0.*"
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* [ pandas.Ser... | |
pandas_series/pandas_series_311_6.txt | ) or a set of specified characters from
each string in the Series/Index from left side. Replaces any non-strings in
Series with NaNs. Equivalent to [ ` str.lstrip() `
](https://docs.python.org/3/library/stdtypes.html#str.lstrip "\(in Python
v3.12\)") .
Parameters :
**to_strip** str or None, default None
... | |
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pandas_series/pandas_series_218_2.txt | ies.drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas_series_287_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)
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* [ pandas.Series.ca... | |
pandas_series/pandas_series_289_6.txt | d for substituting each value in a Series with another value, that may be
derived from a function, a ` dict ` or a [ ` Series `
](pandas.Series.html#pandas.Series "pandas.Series") .
Parameters :
**arg** function, collections.abc.Mapping subclass or Series
Mapping correspondence.
**na_action** {None, ... | |
pandas_series/dataframe-indexing.html6_19_0.txt | ## Partition Indexing ¶
In addition to pandas-style indexing, Dask DataFrame also supports indexing at
a partition level with ` DataFrame.get_partition() ` and `
DataFrame.partitions ` . These can be used to select subsets of the data by
partition, rather than by position in the entire DataFrame or index label. | |
pandas_series/pandas_series_282_7.txt | 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.readthedocs.io/en/stable/index.html) 0.14.4.
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pandas_series/pandas.DataFrame.mask.html4_18_0.txt | See also
` DataFrame.where() ` | |
pandas_series/pandas_series_53_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_32_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.
>>> ... | |
pandas_series/pandas_series_317_6.txt | 1, 2, 3], unit='d'))
>>> ser
0 1 days
1 2 days
2 3 days
dtype: timedelta64[ns]
>>> ser.dt.days
0 1
1 2
2 3
dtype: int64
For TimedeltaIndex:
>>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"])
>>> tdelta_idx
TimedeltaIndex... | |
pandas_series/pandas_series_300_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_241_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_86_6.txt | 30) #
Return the product 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.
Warning
The behavior of DataFrame.prod with ` axis=None ` is deprecated, in a future
version this w... | |
pandas_series/pandas_series_303_6.txt | up to width.
Parameters :
**width** int
Minimum width of resulting string; additional characters will be filled with
character defined in fillchar .
**side** {âleftâ, ârightâ, âbothâ}, default âleftâ
Side from which to fill resulting string.
**fillchar** str, default â â
... | |
pandas_series/pandas_series_167_5.txt | at.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.remove_... | |
pandas_series/pandas_series_88_4.txt | eries.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.c... | |
pandas_series/pandas_series_236_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_202_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_59_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_324_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_166_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_38_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_22_0.txt | Note: This article requires you to have some prior pandas knowledge. You’ll
also need some knowledge of NumPy for the later sections.
To refresh your NumPy skills, you can check out the following resources: | |
pandas_series/dataframe-indexing.html6_12_0.txt |
>>> import dask.dataframe as dd
>>> import pandas as pd
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]},
... index=['a', 'b', 'c'])
>>> ddf = dd.from_pandas(df, npartitions=2)
>>> ddf
Dask DataFrame Structure:
A B
npartitions... | |
pandas_series/pandas_series_109_0.txt | Skip to main content
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pandas_series/pandas_series_57_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_144_6.txt |
**q** float or array-like, default 0.5 (50% quantile)
The quantile(s) to compute, which can lie in range: 0 <= q <= 1.
**interpolation** {âlinearâ, âlowerâ, âhigherâ, âmidpointâ,
ânearestâ}
This optional parameter specifies the interpolation method to use, when the
desired qua... | |
pandas_series/pandas_series_305_6.txt | of series and other, element-wise (binary operator rmul
).
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 Index ... | |
pandas_series/pandas_series_18_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_166_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_217_0.txt | Skip to main content
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__ ` Ctrl ` \+ ` K `
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* [ User Guide ](../../user_guide/index.html)
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pandas_series/pandas-settingwithcopywarning0_140_0.txt | You’ve also seen that in pandas, evaluation order matters . In some cases,
you can switch the order of operations to make the code work:
Python | |
pandas_series/pandas_series_331_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)
* [ pandas.Series.s... | |
pandas_series/pandas-settingwithcopywarning0_150_0.txt | The recommended way of performing such operations is to avoid chained
indexing. Accessors can be of great help with that:
Python | |
pandas_series/pandas_series_57_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_285_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_335_6.txt | nteger indices that would sort the Series values.
Override ndarray.argsort. Argsorts the value, omitting NA/null values, and
places the result in the same locations as the non-NA values.
Parameters :
**axis** {0 or âindexâ}
Unused. Parameter needed for compatibility with DataFrame.
**kind** {âme... | |
pandas_series/pandas_series_64_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-settingwithcopywarning0_183_0.txt | The ` SettingWithCopyWarning ` is a warning, not an error. Your code will
still execute when it’s issued, even though it may not work as intended.
To change this behavior, you can modify the pandas ` mode.chained_assignment `
option with ` pandas.set_option() ` . You can use the following settings: | |
pandas_series/pandas_series_20_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_244_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_265_6.txt | _ ) [ [source] ](https://github.com/pandas-
dev/pandas/blob/v2.2.1/pandas/core/generic.py#L12527-L12588) #
Provide rolling window calculations.
Parameters :
**window** int, timedelta, str, offset, or BaseIndexer subclass
Size of the moving window.
If an integer, the fixed number of observati... | |
pandas_series/pandas_series_44_2.txt | ries.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.idxma... | |
pandas_series/pandas_series_292_3.txt | arse ](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.... |
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