id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_237_4.txt | andas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
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pandas_series/pandas-settingwithcopywarning0_72_0.txt | Python
>>> arr.nbytes
48
>>> view_of_arr.nbytes
48
>>> copy_of_arr.nbytes
48
| |
pandas_series/pandas_series_75_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas_series_21_3.txt | parse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas-settingwithcopywarning0_111_0.txt | #### Chained Indexing in NumPy
Does this behavior with ` a ` and ` b ` look at all similar to the earlier
pandas examples? It might, because the concept of chained indexing applies
in NumPy, too: | |
pandas_series/pandas_series_308_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
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pandas_series/pandas_series_334_6.txt | 109) #
Trim values at input threshold(s).
Assigns values outside boundary to boundary values. Thresholds can be singular
values or array like, and in the latter case the clipping is performed
element-wise in the specified axis.
Parameters :
**lower** float or array-like, default None
Minimum thr... | |
pandas_series/pandas.DataFrame.mask.html4_2_0.txt | * Getting started
* User Guide
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pandas_series/pandas-settingwithcopywarning0_70_0.txt | #### Differences Between Views and Copies
There are two very important differences between views and copies: | |
pandas_series/selecting-in-pandas-using-where-and-mask2_4_0.txt | This is the fifth post in a series on indexing and selecting in pandas. If you
are jumping in the middle and want to get caught up, here’s what has been
discussed so far:
* Basic indexing, selecting by label and location
* Slicing in pandas
* Selecting by boolean indexing
* Selecting by callable | |
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pandas_series/pandas-settingwithcopywarning0_113_0.txt | Copied!
This example illustrates the difference between copies and views when using
chained indexing in NumPy. | |
pandas_series/pandas_series_158_5.txt | ries.cat.rename_categories.html)
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pandas_series/pandas_series_129_6.txt | at both provide integer-based lookups. Use ` iat
` if you only need to get or set a single value in a DataFrame or Series.
Raises :
IndexError
When integer position is out of bounds.
See also
[ ` DataFrame.at ` ](pandas.DataFrame.at.html#pandas.DataFrame.at
"pandas.DataFrame.at")
Access a sing... | |
pandas_series/pandas_series_104_4.txt | ndas.Series.str.capitalize.html)
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pandas_series/pandas_series_266_6.txt | github.com/pandas-
dev/pandas/blob/v2.2.1/pandas/core/series.py#L3678-L3877) #
Sort by the values.
Sort a Series in ascending or descending order by some criterion.
Parameters :
**axis** {0 or âindexâ}
Unused. Parameter needed for compatibility with DataFrame.
**ascending** bool or list of ... | |
pandas_series/pandas_series_8_6.txt | sep .
This method splits the string at the last occurrence of sep , and returns 3
elements containing the part before the separator, the separator itself, and
the part after the separator. If the separator is not found, return 3 elements
containing two empty strings, followed by the string itself.
Parameters :
... | |
pandas_series/pandas_series_43_4.txt | .Series.str.capitalize.html)
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pandas_series/pandas_series_119_5.txt | eries.cat.rename_categories.html)
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* [ pandas.Series.cat.... | |
pandas_series/pandas_series_57_6.txt |
For Series, returns a Series with boolean values. For DatetimeIndex, returns a
boolean array.
See also
` is_month_start `
Return a boolean indicating whether the date is the first day of the month.
[ ` is_month_end `
](pandas.Series.dt.is_month_end.html#pandas.Series.dt.is_month_end
"pandas.Series.dt.is_m... | |
pandas_series/pandas_series_322_1.txt | ediv ](pandas.Series.rtruediv.html)
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pandas_series/pandas_series_218_6.txt | ev/pandas/blob/v2.2.1/pandas/core/series.py#L5250-L5355) #
Return Series with specified index labels removed.
Remove elements of a Series based on specifying the index labels. When using a
multi-index, labels on different levels can be removed by specifying the
level.
Parameters :
**labels** single lab... | |
pandas_series/pandas_series_45_2.txt | vel.html)
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... | |
pandas_series/pandas_series_74_6.txt | ** label
Index of the element that needs to be removed.
Returns :
Value that is popped from series.
Examples
>>> ser = pd.Series([1, 2, 3])
>>> ser.pop(0)
1
>>> ser
1 2
2 3
dtype: int64
[ __ previous pandas.Series.k... | |
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pandas_series/pandas_series_151_7.txt | tabase.
>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)
Create a table from scratch with 3 rows.
>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
name
0 User 1
1 User 2
2 User 3
... | |
pandas_series/pandas-settingwithcopywarning0_46_0.txt | * ` df["z"] ` returns a ` Series ` object (outlined in purple) that points to the same data as the column ` z ` in ` df ` , not its copy.
* ` df["z"][mask] = 0 ` modifies this ` Series ` object by using chained assignment to set the masked values (highlighted in green) to zero.
* ` df ` is modified as well ... | |
pandas_series/pandas-settingwithcopywarning0_168_0.txt | Python
>>> df = pd.DataFrame(
... data={("powers", "x"): 2**np.arange(5),
... ("powers", "y"): 3**np.arange(5),
... ("random", "z"): np.array([45, 98, 24, 11, 64])},
... index=["a", "b", "c", "d", "e"]
... )
>>> df.loc[["a", "b"], "powers"]
... | |
pandas_series/pandas_series_257_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_280_6.txt | and other, element-wise (binary operator eq ).
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 values on ... | |
pandas_series/pandas_series_108_5.txt | .Series.cat.rename_categories.html)
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... | |
pandas_series/pandas_series_111_6.txt | >>> s = pd.Series([1, 2, 3], index=[0, 1, 2])
>>> s.keys()
Index([0, 1, 2], dtype='int64')
[ __ previous pandas.Series.items ](pandas.Series.items.html "previous
page") [ next pandas.Series.pop __ ](pandas.Series.pop.html "next page")
__ On this page
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[ __ Show Source ](../.... | |
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pandas_series/pandas-settingwithcopywarning0_84_0.txt | Copied!
Here, ` .to_numpy() ` returns the NumPy array that holds the data of the
DataFrames. You can see that ` df ` and ` view_of_df ` have the same ` .base `
and share the same data. On the other hand, ` copy_of_df ` contains different
data. | |
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pandas_series/pandas_series_48_6.txt | imum over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative
maximum.
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... | |
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pandas_series/pandas_series_78_6.txt | st of tuples of conditions and expected replacements
Takes the form: ` (condition0, replacement0) ` , ` (condition1,
replacement1) ` , ⦠. ` condition ` should be a 1-D boolean array-like
object or a callable. If ` condition ` is a callable, it is computed on the
Series and should return a boolean Series ... | |
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pandas_series/pandas_series_219_6.txt | /pandas/core/generic.py#L7481-L7603) #
Fill NA/NaN values by propagating the last valid observation to next valid.
Parameters :
**axis** {0 or âindexâ} for Series, {0 or âindexâ, 1 or âcolumnsâ}
for DataFrame
Axis along which to fill missing values. For Series this parameter is unus... | |
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pandas_series/pandas_series_302_6.txt | ", "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 11:00:00+00:00
dtype: datetime64[ns, UTC]
>>> s.dt.daysinmonth
0 31
1 29
dtype: int32
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pandas_series/pandas-settingwithcopywarning0_127_0.txt | For more information, check out the official documentation and The pandas
DataFrame: Make Working With Data Delightful .
In this section, you’ll see two examples of how pandas behaves similarly to
NumPy. First, you can see that accessing the first three rows of ` df ` with a
slice returns a view: | |
pandas_series/pandas_series_140_4.txt | as.Series.str.capitalize.html)
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pandas_series/selecting-in-pandas-using-where-and-mask2_23_0.txt |
>>> # you should be able to grab this dataset as an unauthenticated user, but you can be rate limited
>>> # it also only returns 1000 rows (or at least it did for me without an API key)
>>> sal = pd.read_json("https://data.cityofchicago.org/resource/xzkq-xp2w.json")
sal = sal.drop('name', axis... | |
pandas_series/pandas.DataFrame.mask.html4_14_0.txt | Alignment axis if needed. For Series this parameter is unused and defaults
to 0.
level int, default None | |
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pandas_series/dataframe-indexing.html6_1_0.txt | Getting Started
* Install Dask
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* Additional Information
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