id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_207_4.txt | (pandas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
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pandas_series/pandas_series_78_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)
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pandas_series/pandas_series_318_7.txt | ask() `
[ __ Show Source ](../../_sources/reference/api/pandas.Series.mask.rst.txt)
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pandas_series/pandas_series_162_1.txt | truediv ](pandas.Series.rtruediv.html)
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pandas_series/pandas_series_276_6.txt | stead of 365) including 29th of
February as an intercalary day. Leap years are years which are multiples of
four with the exception of years divisible by 100 but not by 400.
Returns :
Series or ndarray
Booleans indicating if dates belong to a leap year.
Examples
This method is available on Series with... | |
pandas_series/pandas_series_175_3.txt | pandas.DataFrame.sparse.html)
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pandas_series/pandas_series_300_5.txt | s.cat.rename_categories.html)
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pandas_series/pandas_series_149_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_16_5.txt | ies.cat.rename_categories ](pandas.Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
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pandas_series/pandas_series_88_6.txt |
**locale** str, optional
Locale determining the language in which to return the month name. Default is
English locale ( ` 'en_US.utf8' ` ). Use the command ` locale -a ` on your
terminal on Unix systems to find your locale language code.
Returns :
Series or Index
Series or Index of month n... | |
pandas_series/pandas_series_96_6.txt | to of series and other, element-wise (binary
operator le ).
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 Ind... | |
pandas_series/pandas_series_260_5.txt | s.Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
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pandas_series/pandas_series_98_3.txt | .sparse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas_series_273_3.txt | ml)
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pandas_series/pandas_series_1_3.txt | ml)
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pandas_series/pandas_series_130_6.txt | n 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** bool, def... | |
pandas_series/pandas_series_23_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_341_6.txt |
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
[ ` is_year_end `
](pandas.Series.dt.is_year_end.html#pandas.Series.dt.is_year_end
"pandas.Series.dt.is_year_end")
Similar property indicating the last day of ... | |
pandas_series/pandas_series_326_4.txt | ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_224_2.txt | vel.html)
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... | |
pandas_series/pandas_series_225_6.txt | l
Examples
>>> s = pd.Series([1, 2, 3, None])
>>> s
0 1.0
1 2.0
2 3.0
3 NaN
dtype: float64
>>> s.hasnans
True
[ __ previous pandas.Series.memory_usage ](pandas.Series.memory_usage.html
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pandas_series/pandas_series_150_6.txt | 9:30AM).
Parameters :
**time** datetime.time or str
The values to select.
**axis** {0 or âindexâ, 1 or âcolumnsâ}, default 0
For Series this parameter is unused and defaults to 0.
Returns :
Series or DataFrame
Raises :
TypeError
If the index is not a [ ` ... | |
pandas_series/pandas-settingwithcopywarning0_205_0.txt | * * *
Looking for a real-time conversation? Visit the Real Python Community Chat
or join the next “Office Hours” Live Q&A Session . Happy Pythoning! | |
pandas_series/pandas_series_167_6.txt | art** 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 da... | |
pandas_series/pandas_series_283_6.txt | ://github.com/pandas-
dev/pandas/blob/v2.2.1/pandas/core/series.py#L5127-L5152) #
Conform Series to new index with optional filling logic.
Places NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and `
copy=False ` .
Param... | |
pandas_series/pandas_series_323_3.txt | ml)
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pandas_series/pandas_series_138_6.txt | tions 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 functions and... | |
pandas_series/pandas_series_213_6.txt | s <= right.
This function returns a boolean vector containing True wherever the
corresponding Series element is between the boundary values left and right
. NA values are treated as False .
Parameters :
**left** scalar or list-like
Left boundary.
**right** scalar or list-like
Right bound... | |
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pandas_series/pandas-settingwithcopywarning0_53_0.txt | Understanding views and copies is an important part of getting to know how
NumPy and pandas manipulate data. It can also help you avoid errors and
performance bottlenecks. Sometimes data is copied from one part of memory to
another, but in other cases two or more objects can share the same data,
saving both time and me... | |
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pandas_series/pandas_series_329_6.txt | 23-L1017) #
Return a Series containing counts of unique values.
The resulting object will be in descending order so that the first element is
the most frequently-occurring element. Excludes NA values by default.
Parameters :
**normalize** bool, default False
If True then the object returned will... | |
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pandas_series/pandas.DataFrame.mask.html4_26_0.txt | * ` DataFrame.mask() `
Show Source | |
pandas_series/pandas-settingwithcopywarning0_63_0.txt | Copied!
You’ve obtained the array ` view_of_arr ` , which is a view, or shallow copy,
of the original array ` arr ` . The attribute ` .base ` of ` view_of_arr ` is
` arr ` itself. In other words, ` view_of_arr ` doesn’t own any data—it uses
the data that belongs to ` arr ` . You can also verify this with the attribut... | |
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pandas_series/selecting-in-pandas-using-where-and-mask2_7_0.txt | You’ll notice that our result here is only 5 elements even though the original
` Series ` contains 10 elements. This is the whole point of indexing,
selecting the values you want. But what happens if you want the shape of your
result to match your original data? In this case, you use ` where ` . The
values that are sel... | |
pandas_series/pandas.DataFrame.mask.html4_8_0.txt |
cond bool Series/DataFrame, array-like, or callable | |
pandas_series/pandas_series_69_2.txt | vel.html)
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pandas_series/pandas_series_237_6.txt | -
dev/pandas/blob/v2.2.1/pandas/core/generic.py#L5973-L6121) #
Return a random sample of items from an axis of object.
You can use random_state for reproducibility.
Parameters :
**n** int, optional
Number of items from axis to return. Cannot be used with frac . Default = 1
if frac = None.
... | |
pandas_series/selecting-in-pandas-using-where-and-mask2_33_0.txt | ## free pandas e-book
Master the basics of indexing and selecting data in pandas with my free
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pandas_series/pandas_series_323_6.txt | art_time `
](pandas.Period.start_time.html#pandas.Period.start_time
"pandas.Period.start_time")
Return the start Timestamp.
[ ` Period.dayofyear ` ](pandas.Period.dayofyear.html#pandas.Period.dayofyear
"pandas.Period.dayofyear")
Return the day of year.
[ ` Period.daysinmonth `
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pandas_series/pandas_series_62_1.txt | html)
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pandas_series/pandas_series_217_3.txt | (pandas.DataFrame.sparse.html)
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pandas_series/pandas_series_167_3.txt | ml)
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pandas_series/pandas_series_225_2.txt | .drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas_series_281_6.txt |
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
[ ` is_year_start `
](pandas.Series.dt.is_year_start.html#pandas.Series.dt.is_year_start
"pandas.Series.dt.is_year_start")
Similar property indicating the start... | |
pandas_series/pandas_series_137_3.txt | ml)
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pandas_series/pandas_series_124_5.txt | das.Series.cat.rename_categories.html)
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pandas_series/pandas_series_137_2.txt | vel.html)
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pandas_series/pandas_series_13_5.txt | ndas.Series.cat.rename_categories.html)
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pandas_series/pandas_series_194_4.txt | .Series.str.capitalize.html)
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pandas_series/pandas_series_202_6.txt | abel based, but may also be used with a boolean
array.
Allowed inputs are:
* A single label, e.g. ` 5 ` or ` 'a' ` , (note that ` 5 ` is interpreted as a _label_ of the index, and **never** as an integer position along the index).
* A list or array of labels, e.g. ` ['a', 'b', 'c'] ` .
* A slice objec... | |
pandas_series/pandas-settingwithcopywarning0_143_0.txt | Python
>>> df = pd.DataFrame(data=data, index=index)
>>> df.loc[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: http... | |
pandas_series/pandas_series_25_6.txt | ries(['Ant', 'Bear', 'Cow'])
>>> s
0 Ant
1 Bear
2 Cow
dtype: object
>>> s.T
0 Ant
1 Bear
2 Cow
dtype: object
For Index:
>>> idx = pd.Index([1, 2, 3])
>>> idx.T
Index([1, 2, 3], dtype='int64')
[ __ previous pandas.Series.si... | |
pandas_series/pandas-settingwithcopywarning0_203_0.txt | Rate this article:
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pandas_series/pandas_series_293_1.txt | html)
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pandas_series/pandas_series_182_6.txt | e of each
individual category).
The assigned value has to be a list-like object. All items must be unique and
the number of items in the new categories must be the same as the number of
items in the old categories.
Raises :
ValueError
If the new categories do not validate as categories or if the number... | |
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pandas_series/pandas_series_313_5.txt | .Series.cat.rename_categories.html)
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pandas_series/pandas_series_184_1.txt | html)
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pandas_series/pandas_series_135_3.txt | pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
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* [ pandas.Series.dt.year ](pandas.Series.dt.year.ht... | |
pandas_series/pandas_series_162_6.txt | tems), meaning any of the axes
are of length 0.
Returns :
bool
If Series/DataFrame is empty, return True, if not return False.
See also
[ ` Series.dropna ` ](pandas.Series.dropna.html#pandas.Series.dropna
"pandas.Series.dropna")
Return series without null values.
[ ` DataFrame.dropna ` ](pan... | |
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pandas_series/pandas_series_247_6.txt | deprecated and will be removed in
future version of pandas. For ` Series ` use ` pandas.Series.item ` .
This must be a boolean scalar value, either True or False. It will raise a
ValueError if the Series or DataFrame does not have exactly 1 element, or that
element is not boolean (integer values 0 and 1 will also ra... | |
pandas_series/selecting-in-pandas-using-where-and-mask2_34_0.txt | Invalid email address
I promise not to spam you, and you can unsubscribe at any time. | |
pandas_series/pandas_series_90_6.txt | the elements of a Series are lists themselves, join the content of these
lists using the delimiter passed to the function. This function is an
equivalent to [ ` str.join() `
](https://docs.python.org/3/library/stdtypes.html#str.join "\(in Python
v3.12\)") .
Parameters :
**sep** str
Delimiter to use bet... | |
pandas_series/pandas_series_286_5.txt | .Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
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pandas_series/pandas_series_173_5.txt | andas.Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
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pandas_series/pandas_series_38_6.txt | tatistics.
Descriptive statistics include those that summarize the central tendency,
dispersion and shape of a datasetâs distribution, excluding ` NaN ` values.
Analyzes both numeric and object series, as well as ` DataFrame ` column sets
of mixed data types. The output will vary depending on what is provided. Re... | |
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* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
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