id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_56_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/pandas_series_265_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_178_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.DataFrame.mask.html4_23_0.txt |
>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0 NaN
1 1.0
2 2.0
3 3.0
4 4.0
dtype: float64
>>> s.mask(s > 0)
0 0.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
>>> s = pd.Series(range(5))
>>> t = pd.Ser... | |
pandas_series/pandas_series_180_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_5_6.txt | pat as columns in a DataFrame.
For each subject string in the Series, extract groups from the first match of
regular expression pat .
Parameters :
**pat** str
Regular expression pattern with capturing groups.
**flags** int, default 0 (no flags)
Flags from the ` re ` module, e.g. ` re.IGNOR... | |
pandas_series/pandas_series_188_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)
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* [ pandas.Series.str.contains ](pandas.Series.str.contains.html)
* [ pandas.Seri... | |
pandas_series/pandas_series_116_7.txt | lse
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.
>>> s5.s... | |
pandas_series/pandas_series_143_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_278_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_227_6.txt | :
Categorical
Categorical with unused categories dropped.
See also
[ ` rename_categories `
](pandas.Series.cat.rename_categories.html#pandas.Series.cat.rename_categories
"pandas.Series.cat.rename_categories")
Rename categories.
[ ` reorder_categories `
](pandas.Series.cat.reorder_categories.ht... | |
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pandas_series/pandas_series_207_2.txt | ies.drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas_series_275_1.txt | html)
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pandas_series/pandas_series_130_2.txt | ies.drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas-settingwithcopywarning0_103_0.txt |
>>> mask = [False, True, False, True, False, False]
>>> d = arr[mask]
>>> d
array([2, 8])
>>> d.base is None
True
>>> d.flags.owndata
True
Copied! | |
pandas_series/pandas_series_230_2.txt | vel.html)
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... | |
pandas_series/pandas_series_86_1.txt | html)
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pandas_series/pandas_series_287_6.txt | ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='s'))
>>> ser
0 0 days 00:00:01
1 0 days 00:00:02
2 0 days 00:00:03
dtype: timedelta64[ns]
>>> ser.dt.seconds
0 1
1 2
2 3
dtype: int32
For TimedeltaIndex:
>>> tdelta_idx = pd.to_timedelta([1, 2, 3... | |
pandas_series/dataframe-indexing.html6_9_0.txt | # Indexing into Dask DataFrames ¶
Dask DataFrame supports some of Pandas’ indexing behavior. | |
pandas_series/pandas_series_242_6.txt | =
False _ , _ compression = 'infer' _ , _ index = None _ , _ indent =
None _ , _ storage_options = None _ , _ mode = 'w' _ ) [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.1/pandas/core/generic.py#L2425-L2714) #
Convert the object to a JSON string.
Note NaNâs and None will be c... | |
pandas_series/pandas_series_238_3.txt | ml)
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pandas_series/pandas_series_256_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
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pandas_series/pandas_series_98_4.txt | (pandas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
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pandas_series/pandas_series_209_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_series/pandas_series_13_2.txt | vel.html)
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* [ pandas.Series.first ](pandas.Series.first.html)
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... | |
pandas_series/pandas_series_17_3.txt | parse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas-settingwithcopywarning0_55_0.txt |
>>> arr = np.array([1, 2, 4, 8, 16, 32])
>>> arr
array([ 1, 2, 4, 8, 16, 32])
Copied! | |
pandas_series/pandas_series_254_2.txt | ries.drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas_series_138_1.txt | html)
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pandas_series/pandas-settingwithcopywarning0_30_0.txt | You create the first two arrays with the routine ` numpy.arange() ` and the
last one with ` numpy.array() ` . To learn more about ` arange() ` , check
out NumPy arange(): How to Use np.arange() .
The list attached to the variable ` index ` contains the strings ` "a" ` ,
` "b" ` , ` "c" ` , ` "d" ` , and ` "e" ... | |
pandas_series/pandas_series_35_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_309_4.txt | (pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_183_6.txt | The length of each list.
Examples
>>> import pyarrow as pa
>>> s = pd.Series(
... [
... [1, 2, 3],
... [3],
... ],
... dtype=pd.ArrowDtype(pa.list_(
... pa.int64()
... ))
... )
>>> s.list.len()
0 3
1 1
dtyp... | |
pandas_series/pandas_series_51_1.txt | html)
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pandas_series/pandas-settingwithcopywarning0_51_0.txt | * Avoid chained assignments that combine two or more indexing operations like ` df["z"][mask] = 0 ` and ` df.loc[mask]["z"] = 0 ` .
* Apply single assignments with just one indexing operation like ` df.loc[mask, "z"] = 0 ` . This might (or might not) involve the use of accessors, but they are certainly very usef... | |
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pandas_series/pandas_series_45_7.txt | 0+01:00
5 2018-10-28 03:00:00+01:00
6 2018-10-28 03:30:00+01:00
dtype: datetime64[ns, CET]
In some cases, inferring the DST is impossible. In such cases, you can pass an
ndarray to the ambiguous parameter to set the DST explicitly
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pandas_series/pandas_series_91_9.txt | 64
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pandas_series/pandas_series_178_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.
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pandas_series/pandas_series_214_6.txt | alse _ , _ key = None _ ) [ [source] ](https://github.com/pandas-
dev/pandas/blob/v2.2.1/pandas/core/series.py#L3927-L4070) #
Sort Series by index labels.
Returns a new Series sorted by label if inplace argument is ` False ` ,
otherwise updates the original series and returns None.
Parameters :
... | |
pandas_series/pandas-settingwithcopywarning0_105_0.txt | Note: Instead of a list, you can use another NumPy array of integers, but
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To recap, here are the variables you’ve created so far that reference ` arr `
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pandas_series/pandas_series_92_6.txt | luded in the old categories. Values which were in the
removed categories will be set to NaN
Parameters :
**removals** category or list of categories
The categories which should be removed.
Returns :
Categorical
Categorical with removed categories.
Raises :
ValueError
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pandas_series/pandas-settingwithcopywarning0_154_0.txt | Let’s focus on the data types in this example:
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pandas_series/pandas_series_196_6.txt | 3/library/datetime.html#datetime.datetime "\(in
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Deprecated since version 2.1.0: The current behavior of dt.to_pydatetime is
deprecated. In a future version this will return a Series containing python
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Timezone information is retained if present.
Warnin... | |
pandas_series/pandas_series_332_6.txt | rseArray
>>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
>>> s.npoints
3
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pandas_series/pandas_series_18_6.txt | element in the Series or Index.
Parameters :
**start** int, optional
Start position for slice operation.
**stop** int, optional
Stop position for slice operation.
**step** int, optional
Step size for slice operation.
Returns :
Series or Index of object
Series or Index fro... | |
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