id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_185_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_159_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_7_0.txt | Table of Contents
* Prerequisites
* Example of a SettingWithCopyWarning
* Views and Copies in NumPy and pandas
* Understanding Views and Copies in NumPy
* Understanding Views and Copies in pandas
* Indices and Slices in NumPy and pandas
* Indexing in NumPy: Copies and Views
* Indexing in... | |
pandas_series/pandas_series_219_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_74_1.txt | .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.Series.combine_first ](pandas.Se... | |
pandas_series/pandas_series_26_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.Ser... | |
pandas_series/pandas_series_154_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
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* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_131_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_249_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)
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* [ pandas.Series.dt.year ](pandas.Series.dt.year.h... | |
pandas_series/pandas_series_56_6.txt | e.date "\(in Python
v3.12\)") objects.
Namely, the date part of Timestamps without time and timezone information.
Examples
For Series:
>>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+00:00"])
>>> s = pd.to_datetime(s)
>>> s
0 2020-01-01 10:00:00+00:00
1 2020-02-01 ... | |
pandas_series/pandas_series_325_4.txt | andas.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.Serie... | |
pandas_series/pandas_series_271_6.txt | eters :
**into** class, default dict
The collections.abc.MutableMapping subclass to use as the return object. Can
be the actual class or an empty instance of the mapping type you want. If you
want a collections.defaultdict, you must pass it initialized.
Returns :
collections.abc.MutableMapping
... | |
pandas_series/pandas_series_115_6.txt | the original string will be returned.
Parameters :
**suffix** str
Remove the suffix of the string.
Returns :
Series/Index: object
The Series or Index with given suffix removed.
See also
[ ` Series.str.removeprefix `
](pandas.Series.str.removeprefix.html#pandas.Series.str.removeprefix
... | |
pandas_series/pandas_series_0_4.txt | ](pandas.Series.str.capitalize.html)
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* [ 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_131_6.txt | lit strings around given separator/delimiter.
Splits the string in the Series/Index from the beginning, at the specified
delimiter string.
Parameters :
**pat** str or compiled regex, optional
String or regular expression to split on. If not specified, split on
whitespace.
**n** int, default -1 (all)
... | |
pandas_series/pandas_series_60_6.txt | een the Series and its
shifted self.
Parameters :
**lag** int, default 1
Number of lags to apply before performing autocorrelation.
Returns :
float
The Pearson correlation between self and self.shift(lag).
See also
[ ` Series.corr ` ](pandas.Series.corr.html#pandas.Series.corr
"pandas... | |
pandas_series/pandas_series_340_3.txt | ml)
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* [ pandas.Ser... | |
pandas_series/pandas-settingwithcopywarning0_15_0.txt | ## Prerequisites
To follow the examples in this article, you’ll need Python 3.7 or 3.8 , as
well as the libraries NumPy and pandas . This article is written for NumPy
version 1.18.1 and pandas version 1.0.3. You can install them with ` pip ` : | |
pandas_series/pandas_series_320_1.txt | truediv ](pandas.Series.rtruediv.html)
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pandas_series/pandas-settingwithcopywarning0_182_0.txt | Remove ads
## Change the Default ` SettingWithCopyWarning ` Behavior | |
pandas_series/pandas_series_22_4.txt | das.Series.str.capitalize.html)
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* [ pandas.Series.... | |
pandas_series/pandas_series_177_2.txt | ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
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* [ 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_187_2.txt | vel.html)
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* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/selecting-in-pandas-using-where-and-mask2_3_0.txt | # Selecting in Pandas using where and mask
Leave a Comment / Pandas , Python / By Matt Wright | |
pandas_series/pandas_series_135_5.txt | ies.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_332_2.txt | vel.html)
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... | |
pandas_series/pandas_series_113_3.txt | ml)
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... | |
pandas_series/pandas_series_108_4.txt | s.Series.str.capitalize.html)
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pandas_series/pandas-settingwithcopywarning0_156_0.txt | You’ve created the DataFrame with all integer columns. The fact that all three
columns have the same data types is important here! In this case, you can
select rows with a slice and get a view:
Python | |
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pandas_series/pandas_series_145_6.txt |
Return unbiased standard error of the mean over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument
Parameters :
**axis** {index (0)}
For Series this parameter is unused and defaults to 0.
Warning
The behavior of DataFrame.sem with ` axis=None ` is deprec... | |
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pandas_series/pandas-settingwithcopywarning0_187_0.txt | Python
>>> pd.get_option("mode.chained_assignment")
'raise'
| |
pandas_series/pandas_series_20_4.txt | ndas.Series.str.capitalize.html)
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* [ pandas.Serie... | |
pandas_series/dataframe-indexing.html6_15_0.txt |
>>> ddf.loc[['b', 'c'], ['A']]
Dask DataFrame Structure:
A
npartitions=1
b int64
c ...
Dask Name: loc, 2 tasks
>>> ddf.loc[df["A"] > 1, ["B"]]
Dask DataFrame Structure:
B
npartitions=1
a ... | |
pandas_series/pandas-settingwithcopywarning0_45_0.txt | This works! You’ve modified ` df ` . Here’s what this process looks like:
Here’s a breakdown of the image:: | |
pandas_series/pandas_series_205_5.txt | ries.cat.rename_categories.html)
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* [ pandas.Series.cat.r... | |
pandas_series/pandas_series_212_6.txt | ng indicated encoding.
Equivalent to ` str.decode() ` in python2 and [ ` bytes.decode() `
](https://docs.python.org/3/library/stdtypes.html#bytes.decode "\(in Python
v3.12\)") in python3.
Parameters :
**encoding** str
**errors** str, optional
Returns :
Series or Index
Examples
Fo... | |
pandas_series/pandas_series_131_2.txt | vel.html)
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pandas_series/pandas-settingwithcopywarning0_12_0.txt | Remove ads
NumPy and pandas are very comprehensive, efficient, and flexible Python
tools for data manipulation. An important concept for proficient users of
these two libraries to understand is how data are referenced as shallow
copies ( views ) and deep copies (or just copies ). pandas sometimes
issues a `... | |
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* [ pandas.Series.c... | |
pandas_series/dataframe-indexing.html6_20_0.txt | Use ` DataFrame.get_partition() ` to select a single partition by position.
>>> import dask
>>> ddf = dask.datasets.timeseries(start="2021-01-01", end="2021-01-07", freq="1h")
>>> ddf.get_partition(0)
Dask DataFrame Structure:
name id x y
npartitio... | |
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pandas_series/pandas-settingwithcopywarning0_83_0.txt | Python
>>> view_of_df.to_numpy().base is df.to_numpy().base
True
>>> copy_of_df.to_numpy().base is df.to_numpy().base
False
| |
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pandas_series/pandas_series_156_3.txt | rse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas_series_178_6.txt | g the Python string method [ ` str.istitle() `
](https://docs.python.org/3/library/stdtypes.html#str.istitle "\(in Python
v3.12\)") 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 boolean ... | |
pandas_series/pandas_series_127_1.txt | html)
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pandas_series/pandas_series_210_1.txt | html)
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pandas_series/pandas_series_295_6.txt | gnore_index** bool, default False
If True, the resulting index will be labeled 0, 1, â¦, n - 1.
Returns :
Series
Exploded lists to rows; index will be duplicated for these rows.
See also
[ ` Series.str.split ` ](pandas.Series.str.split.html#pandas.Series.str.split
"pandas.Series.str.split")
... | |
pandas_series/pandas_series_199_0.txt | Skip to main content
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pandas_series/selecting-in-pandas-using-where-and-mask2_10_0.txt | Using ` where ` will always return a copy of the existing data. But if you
want to modify the original, you can by using the ` inplace ` argument,
similar to many other functions in pandas (like ` fillna ` or ` ffill ` and
others).
>>> s.where(s % 2 != 0, -s, inplace=True)
>>> s
>>> s
0 0
... | |
pandas_series/pandas_series_320_3.txt | sparse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas-settingwithcopywarning0_114_0.txt | In the first case, ` arr[1:4:2] ` returns a view that references the data of `
arr ` and contains the elements ` 2 ` and ` 8 ` . The statement `
arr[1:4:2][0] = 64 ` modifies the first of these elements to ` 64 ` . The
change is visible in both ` arr ` and the view returned by ` arr[1:4:2] ` .
In the second case, ` ar... | |
pandas_series/pandas_series_7_6.txt | ies and other, element-wise (binary operator radd ).
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 value... | |
pandas_series/pandas_series_136_1.txt | html)
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pandas_series/pandas_series_232_6.txt |
type of index
Examples
For Series:
>>> s = pd.Series([None, 3, 4])
>>> s.first_valid_index()
1
>>> s.last_valid_index()
2
>>> s = pd.Series([None, None])
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None
If all ... | |
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pandas_series/pandas_series_315_6.txt | ot using Gaussian kernels.
In statistics, [ kernel density estimation
](https://en.wikipedia.org/wiki/Kernel_density_estimation) (KDE) is a non-
parametric way to estimate the probability density function (PDF) of a random
variable. This function uses Gaussian kernels and includes automatic bandwidth
determination.
P... | |
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pandas_series/pandas_series_63_5.txt | s.Series.cat.rename_categories.html)
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pandas_series/pandas_series_62_6.txt | s) or a set of specified characters from
each string in the Series/Index from right side. Replaces any non-strings in
Series with NaNs. Equivalent to [ ` str.rstrip() `
](https://docs.python.org/3/library/stdtypes.html#str.rstrip "\(in Python
v3.12\)") .
Parameters :
**to_strip** str or None, default None
... | |
pandas_series/pandas_series_42_1.txt | html)
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pandas_series/pandas_series_158_6.txt | re information on the forms, see the [ ` unicodedata.normalize() `
](https://docs.python.org/3/library/unicodedata.html#unicodedata.normalize
"\(in Python v3.12\)") .
Parameters :
**form** {âNFCâ, âNFKCâ, âNFDâ, âNFKDâ}
Unicode form.
Returns :
Series/Index of objects
Exa... | |
pandas_series/pandas_series_200_6.txt |
Equivalent to [ ` str.endswith() `
](https://docs.python.org/3/library/stdtypes.html#str.endswith "\(in Python
v3.12\)") .
Parameters :
**pat** str or tuple[str, â¦]
Character sequence or tuple of strings. Regular expressions are not accepted.
**na** object, default NaN
Object shown if elemen... | |
pandas_series/pandas-settingwithcopywarning0_160_0.txt | Copied!
In this case, you used .astype() to create a DataFrame that has two integer
columns and one floating-point column. Contrary to the previous example, `
df["b":"d"] ` now returns a copy, so the assignment ` df["b":"d"]["z"] = 0 `
fails and ` df ` remains unchanged. | |
pandas_series/pandas_series_50_2.txt | vel.html)
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... | |
pandas_series/pandas_series_37_2.txt | vel.html)
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pandas_series/pandas_series_31_5.txt | s.Series.cat.rename_categories.html)
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pandas_series/pandas_series_132_3.txt | .sparse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas-settingwithcopywarning0_13_0.txt | In this article, you’ll learn:
* What views and copies are in NumPy and pandas
* How to properly work with views and copies in NumPy and pandas
* Why the ` SettingWithCopyWarning ` happens in pandas
* How to avoid getting a ` SettingWithCopyWarning ` in pandas | |
pandas_series/pandas_series_85_5.txt | pandas.Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
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* [ pandas.Ser... | |
pandas_series/selecting-in-pandas-using-where-and-mask2_25_0.txt | ## NumPy ` where ` and ` select ` for more complicated updates
There are times where you want to create new columns with some sort of
complicated condition on a dataframe that might need to be applied across
multiple columns. Using NumPy ` where ` can be helpful for these situations.
For example, we can creating an h... | |
pandas_series/pandas_series_262_4.txt | as.Series.str.capitalize.html)
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pandas_series/pandas_series_0_1.txt | html)
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pandas_series/pandas_series_227_1.txt | html)
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pandas_series/pandas_series_148_2.txt | ies.drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas_series_81_6.txt | series and other, element-wise (binary operator rsub
).
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 val... |
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