id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_271_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_65_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_158_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_116_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_201_6.txt | the minimum value.
If multiple values equal the minimum, the first row label with that value is
returned.
Parameters :
**axis** {0 or âindexâ}
Unused. Parameter needed for compatibility with DataFrame.
**skipna** bool, default True
Exclude NA/null values. If the entire Series is NA, the res... | |
pandas_series/pandas_series_220_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_223_6.txt | g the Python string method [ ` str.isdecimal() `
](https://docs.python.org/3/library/stdtypes.html#str.isdecimal "\(in Python
v3.12\)") for each element of the Series/Index. If a string has zero
characters, ` False ` is returned for that check.
Returns :
Series or Index of bool
Series or Index of bool... | |
pandas_series/pandas_series_250_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_163_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)
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pandas_series/pandas_series_71_0.txt | Skip to main content
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pandas_series/pandas_series_67_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_273_4.txt | * [ 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.str.count.... | |
pandas_series/pandas_series_15_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_76_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_142_6.txt | /v2.2.1/pandas/core/series.py#L1615-L1764) #
Generate a new DataFrame or Series with the index reset.
This is useful when the index needs to be treated as a column, or when the
index is meaningless and needs to be reset to the default before another
operation.
Parameters :
**level** int, str, tuple, or... | |
pandas_series/pandas-settingwithcopywarning0_122_0.txt |
>>> arr[0, 1] = 100
>>> arr
array([[ 1, 100, 4, 8],
[ 16, 32, 64, 128],
[ 256, 512, 1024, 2048]])
>>> a
array([[ 100, 4],
[ 32, 64],
[ 512, 1024]])
>>> b
array([[ 100, 8],
[ 32, 128],
... | |
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pandas_series/pandas_series_240_6.txt | eries with duplicate values removed.
Parameters :
**keep** {âfirstâ, âlastâ, ` False ` }, default âfirstâ
Method to handle dropping duplicates:
* âfirstâ : Drop duplicates except for the first occurrence.
* âlastâ : Drop duplicates except for the last occurrence.
* ` Fals... | |
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pandas_series/pandas_series_146_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_161_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)
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* [ pandas.Serie... | |
pandas_series/pandas_series_180_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_104_3.txt | arse ](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_179_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_238_4.txt | eries.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
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* [ pandas.Series.str.c... | |
pandas_series/pandas_series_120_6.txt | ing starts with a match of a regular expression.
Parameters :
**pat** str
Character sequence.
**case** bool, default True
If True, case sensitive.
**flags** int, default 0 (no flags)
Regex module flags, e.g. re.IGNORECASE.
**na** scalar, optional
Fill value for missing values. Th... | |
pandas_series/pandas_series_142_3.txt | ](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_136_3.txt | .sparse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas_series_4_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.Ser... | |
pandas_series/pandas_series_268_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_145_1.txt | html)
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pandas_series/pandas_series_338_5.txt | ies.cat.rename_categories ](pandas.Series.cat.rename_categories.html)
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pandas_series/pandas_series_19_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
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... | |
pandas_series/pandas_series_91_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
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pandas_series/pandas_series_151_1.txt | html)
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pandas_series/pandas_series_38_8.txt | 2.0
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pandas_series/pandas_series_104_5.txt | das.Series.cat.rename_categories.html)
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pandas_series/pandas.DataFrame.mask.html4_28_0.txt | Built with the PyData Sphinx Theme 0.14.4.
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pandas_series/pandas_series_91_3.txt | rse ](pandas.DataFrame.sparse.html)
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pandas_series/selecting-in-pandas-using-where-and-mask2_26_0.txt |
>>> sal['hourly_rate_all'] = np.where(sal['salary_or_hourly'] == 'Salary',
sal['annual_salary'] / (52 * 40),
sal['hourly_rate'])
If you have a much more complex scenario, you can use ` np.select ` . Think of
` np.select `... | |
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pandas_series/pandas_series_75_6.txt | de
= 'w' _ , _ encoding = None _ , _ compression = 'infer' _ , _ quoting
= None _ , _ quotechar = '"' _ , _ lineterminator = None _ , _
chunksize = None _ , _ date_format = None _ , _ doublequote = True _
, _ escapechar = None _ , _ decimal = '.' _ , _ errors = 'strict' _ ,
_ storage_o... | |
pandas_series/dataframe-indexing.html6_14_0.txt |
>>> ddf['A']
Dask Series Structure:
npartitions=1
a int64
c ...
Name: A, dtype: int64
Dask Name: getitem, 2 tasks
Slicing rows and (optionally) columns with ` .loc ` : | |
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pandas_series/pandas_series_267_6.txt | formatted strings specified by date_format, which supports
the same string format as the python standard library. Details of the string
format can be found in [ python string format doc
](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-
behavior) .
Formats supported by the C strftime API but no... | |
pandas_series/pandas_series_209_6.txt | sed on position. It
is useful for quickly testing if your object has the right type of data in it.
For negative values of n , this function returns all rows except the last
|n| rows, equivalent to ` df[:n] ` .
If n is larger than the number of rows, this function returns all rows.
Parameters :
**n** int,... | |
pandas_series/pandas-settingwithcopywarning0_48_0.txt | However, using accessors sometimes isn’t enough. They might also return
copies, in which case you can get a ` SettingWithCopyWarning ` :
Python | |
pandas_series/pandas_series_9_3.txt | ml)
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pandas_series/pandas-settingwithcopywarning0_194_0.txt | Send Me Python Tricks »
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pandas_series/pandas-settingwithcopywarning0_112_0.txt | Python
>>> arr = np.array([1, 2, 4, 8, 16, 32])
>>> arr[1:4:2][0] = 64
>>> arr
array([ 1, 64, 4, 8, 16, 32])
>>> arr = np.array([1, 2, 4, 8, 16, 32])
>>> arr[[1, 3]][0] = 64
>>> arr
array([ 1, 2, 4, 8, 16, 32])
| |
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... | |
pandas_series/pandas-settingwithcopywarning0_89_0.txt | Copied!
` df ` and ` view_of_df ` share the same row and column labels, while `
copy_of_df ` has separate index instances. Keep in mind that you can’t modify
particular elements of ` .index ` and ` .columns ` . They are immutable
objects. | |
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pandas_series/pandas_series_165_6.txt | ob/v2.2.1/pandas/core/series.py#L5190-L5206) #
Set the name of the axis for the index or columns.
Parameters :
**mapper** scalar, list-like, optional
Value to set the axis name attribute.
**index, columns** scalar, list-like, dict-like or function, optional
A scalar, list-like, dict-like ... | |
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pandas_series/pandas_series_325_6.txt | over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative sum.
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 def... | |
pandas_series/pandas_series_316_5.txt | das.Series.cat.rename_categories.html)
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... | |
pandas_series/pandas_series_279_7.txt | > df2
temp_celsius windspeed
2014-02-12 28.0 low
2014-02-13 30.0 low
2014-02-15 35.1 medium
>>> df2.reindex_like(df1)
temp_celsius temp_fahrenheit windspeed
2014-02-12 28.0 NaN low
... | |
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