id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_208_5.txt | 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_2_5.txt | ies.cat.rename_categories ](pandas.Series.cat.rename_categories.html)
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* [ pandas.Series.cat.add_categories ](pandas.Series.cat.add_categories.html)
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pandas_series/pandas_series_130_3.txt | .sparse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas_series_143_6.txt | eries or DataFrame before and after some index value.
This is a useful shorthand for boolean indexing based on index values above or
below certain thresholds.
Parameters :
**before** date, str, int
Truncate all rows before this index value.
**after** date, str, int
Truncate all rows after this ... | |
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pandas_series/pandas_series_299_12.txt | values by propagating the last valid observation to next valid.
[ ` pct_change ` ](pandas.Series.pct_change.html#pandas.Series.pct_change "pandas.Series.pct_change") ([periods, fill_method, limit, freq]) | Fractional change between the current and a prior element.
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pandas_series/pandas_series_333_6.txt | of series and other, element-wise (binary operator
pow ).
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... | |
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pandas_series/pandas_series_110_6.txt | s = pd.to_datetime(s)
>>> s
0 2020-01-01 10:00:00+00:00
1 2020-02-01 11:00:00+00:00
dtype: datetime64[ns, UTC]
>>> s.dt.daysinmonth
0 31
1 29
dtype: int32
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pandas_series/selecting-in-pandas-using-where-and-mask2_21_0.txt | ## More examples
Let’s go back to a data set from a previous post . This is salary info for
City of Chicago employees for both hourly and salaried employees. | |
pandas_series/pandas_series_118_7.txt | alse
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.... | |
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pandas_series/pandas-settingwithcopywarning0_100_0.txt |
>>> c = arr[[1, 3]]
>>> c
array([2, 8])
>>> c.base is None
True
>>> c.flags.owndata
True
Copied! | |
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pandas_series/pandas_series_91_8.txt | arting
0 10 50 2018-01-07
1 11 60 2018-01-14
2 9 40 2018-01-21
3 13 100 2018-01-28
4 14 50 2018-02-04
5 18 100 2018-02-11
6 17 40 2018-02-18
7 19 50 2018-02-25
>>> df.resample('ME', on='wee... | |
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pandas_series/pandas_series_38_7.txt | will include a union of attributes of each
type.
The include and exclude parameters can be used to limit which columns in a
` DataFrame ` are analyzed for the output. The parameters are ignored when
analyzing a ` Series ` .
Examples
Describing a numeric ` Series ` .
>>> s = pd.Series([1, 2, 3])... | |
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pandas_series/pandas_series_141_6.txt | e smallest value in the Series.
If the minimum is achieved in multiple locations, the first row position is
returned.
Parameters :
**axis** {None}
Unused. Parameter needed for compatibility with DataFrame.
**skipna** bool, default True
Exclude NA/null values when showing the result.
***args, *... | |
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pandas_series/pandas_series_107_6.txt | rs :
**freq** str, default None
Frequency associated with the PeriodIndex.
**copy** bool, default True
Whether or not to return a copy.
Note
The copy keyword will change behavior in pandas 3.0. [ Copy-on-Write
](https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html) will be
enabled... | |
pandas_series/pandas_series_187_6.txt | plot.
An area plot displays quantitative data visually. This function wraps the
matplotlib area function.
Parameters :
**x** label or position, optional
Coordinates for the X axis. By default uses the index.
**y** label or position, optional
Column to plot. By default uses all columns.
**stack... | |
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pandas_series/pandas_series_116_6.txt | he Python string method [ ` str.isdigit() `
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pandas_series/pandas_series_177_6.txt | :
bool
Examples
>>> s = pd.Series([1, 2, 2])
>>> s.is_monotonic_increasing
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Return this many descending sorted values.
**keep** {âfirstâ, âlastâ, âallâ}, default âfirstâ
When there are duplicate values that cannot all fit in a Series of n
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2 2002-12-31
dtype: datetime64[ns]
>>> datetime_series.dt.year
0 2000
1 2001
2 2002
dtype: int32
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