id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_134_6.txt | abel-based lookups. Use ` at ` if
you only need to get or set a single value in a DataFrame or Series.
Raises :
KeyError
If getting a value and âlabelâ does not exist in a DataFrame or Series.
ValueError
If row/column label pair is not a tuple or if any label from the pair is not a
scalar f... | |
pandas_series/pandas_series_23_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
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* [ 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_189_6.txt | s and other, element-wise (binary operator add ).
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 o... | |
pandas_series/pandas_series_306_3.txt | sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
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pandas_series/pandas-settingwithcopywarning0_171_0.txt | Copied!
In this example, you specify that you want the intersection of the rows ` a `
and ` b ` with the column ` x ` , which is below ` powers ` . To get a single
column, you pass the tuple of indices ` ("powers", "x") ` and get a ` Series `
object as the result. | |
pandas_series/pandas_series_78_4.txt | as.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)
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pandas_series/pandas_series_60_3.txt | rse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
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* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
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pandas_series/pandas_series_175_2.txt | vel.html)
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... | |
pandas_series/pandas_series_174_6.txt | orical
Unordered Categorical.
Examples
For [ ` pandas.Series ` ](pandas.Series.html#pandas.Series "pandas.Series") :
>>> raw_cat = pd.Categorical(['a', 'b', 'c', 'a'], ordered=True)
>>> ser = pd.Series(raw_cat)
>>> ser.cat.ordered
True
>>> ser = ser.cat.as_unordered()
>>> se... | |
pandas_series/pandas_series_124_6.txt | le element are squeezed to a scalar.
DataFrames with a single column or a single row are squeezed to a Series.
Otherwise the object is unchanged.
This method is most useful when you donât know if your object is a Series or
DataFrame, but you do know it has just a single column. In that case you can
safely call sque... | |
pandas_series/pandas_series_48_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
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pandas_series/pandas_series_113_2.txt | vel.html)
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... | |
pandas_series/pandas_series_206_7.txt | ument has
changed, as has the default value to ârâ.
**multirow** bool, default True
Use multirow to enhance MultiIndex rows. Requires adding a
usepackage{{multirow}} to your LaTeX preamble. Will print centered labels
(instead of top-aligned) across the contained rows, separating groups via
clines. The defau... | |
pandas_series/pandas-settingwithcopywarning0_60_0.txt | This might seem odd at the first sight. The difference is in the fact that `
arr[1:4:2] ` returns a shallow copy , while ` arr[[1, 3]] ` returns a deep
copy . Understanding this difference is essential not only for dealing with
the ` SettingWithCopyWarning ` but also for manipulating big data with NumPy
and pandas.... | |
pandas_series/dataframe-indexing.html6_18_0.txt | Trying to select specific rows with ` iloc ` will raise an exception:
>>> ddf.iloc[[0, 2], [1]]
Traceback (most recent call last)
File "<stdin>", line 1, in <module>
ValueError: 'DataFrame.iloc' does not support slicing rows. The indexer must be a 2-tuple whose first item is 'slice(None)'.... | |
pandas_series/pandas_series_148_5.txt | pandas.Series.cat.rename_categories.html)
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pandas_series/pandas_series_105_3.txt | parse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas_series_299_10.txt | orical variable.
[ ` ffill ` ](pandas.Series.ffill.html#pandas.Series.ffill "pandas.Series.ffill") (*[, axis, inplace, limit, limit_area, ...]) | Fill NA/NaN values by propagating the last valid observation to next valid.
[ ` fillna ` ](pandas.Series.fillna.html#pandas.Series.fillna "pandas.Series.fillna... | |
pandas_series/pandas_series_123_3.txt | sparse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas_series_9_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_95_6.txt | are not NA. Non-
missing values get mapped to True. Characters such as empty strings ` '' ` or
` numpy.inf ` are not considered NA values (unless you set `
pandas.options.mode.use_inf_as_na = True ` ). NA values, such as None or `
numpy.NaN ` , get mapped to False values.
Returns :
Series
Mask of... | |
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* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
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pandas_series/pandas_series_183_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
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pandas_series/pandas_series_234_6.txt | exes for row labels can be changed by assigning a list-like or Index.
Parameters :
**labels** list-like, Index
The values for the new index.
**axis** {0 or âindexâ}, default 0
The axis to update. The value 0 identifies the rows. For Series this
parameter is unused and defaults to 0.
**cop... | |
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pandas_series/pandas-settingwithcopywarning0_192_0.txt | If you have questions or comments, then please put them in the comment section
below.
Mark as Completed | |
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pandas_series/pandas-settingwithcopywarning0_202_0.txt | Level Up Your Python Skills »
What Do You Think? | |
pandas_series/pandas_series_191_5.txt | .Series.cat.rename_categories.html)
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pandas_series/pandas_series_277_6.txt | pd.date_range("2000-01-01", periods=3, freq="ME")
... )
>>> datetime_series
0 2000-01-31
1 2000-02-29
2 2000-03-31
dtype: datetime64[ns]
>>> datetime_series.dt.month
0 1
1 2
2 3
dtype: int32
[ __ previous pandas.Series.dt.year ](pandas.Series.dt.year.ht... | |
pandas_series/pandas-settingwithcopywarning0_74_0.txt | However, if you use ` sys.getsizeof() ` to get the memory amount directly
attributed to each array, then you’ll see the difference:
Python | |
pandas_series/pandas_series_279_2.txt | vel.html)
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pandas_series/pandas-settingwithcopywarning0_57_0.txt |
>>> arr[1:4:2]
array([2, 8])
>>> arr[[1, 3]]
array([2, 8]))
Copied! | |
pandas_series/pandas-settingwithcopywarning0_33_0.txt | Now that you have a DataFrame to work with, let’s try to get a `
SettingWithCopyWarning ` . You’ll take all values from column ` z ` that are
less than fifty and replace them with zeros. You can start by creating a mask,
or a filter with pandas Boolean operators :
Python | |
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pandas_series/pandas-settingwithcopywarning0_23_0.txt | * NumPy Quickstart Tutorial
* Look Ma, No ` for ` Loops: Array Programming With NumPy
* Python Plotting With Matplotlib
To remind yourself about pandas, you can read the following: | |
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pandas_series/pandas_series_286_6.txt | andas/core/generic.py#L11980-L12164) #
Fractional change between the current and a prior element.
Computes the fractional change from the immediately previous row by default.
This is useful in comparing the fraction of change in a time series of
elements.
Note
Despite the name of this method, it calculates fr... | |
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pandas_series/pandas-settingwithcopywarning0_50_0.txt | In this example, as in the previous one, you use the accessor ` .loc[] ` . The
assignment fails because ` df.loc[mask] ` returns a new DataFrame with a copy
of the data from ` df ` . Then ` df.loc[mask]["z"] = 0 ` modifies the new
DataFrame, not ` df ` .
Generally, to avoid a ` SettingWithCopyWarning ` in pandas, you ... | |
pandas_series/pandas_series_315_7.txt | e.rst.txt)
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pandas_series/pandas-settingwithcopywarning0_126_0.txt | Note: Indexing in pandas is a very wide topic. It’s essential for using
pandas data structures properly. You can use a variety of techniques:
* Dictionary-like notation
* Attribute-like (dot) notation
* The accessors ` .loc[] ` , ` .iloc[] ` , ` .at[] ` , and ` .iat ` | |
pandas_series/pandas_series_64_5.txt | ies.cat.rename_categories ](pandas.Series.cat.rename_categories.html)
* pandas.Series.cat.reorder_categories
* [ pandas.Series.cat.add_categories ](pandas.Series.cat.add_categories.html)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Series.cat.remove_unu... | |
pandas_series/pandas_series_291_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
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* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas_series_276_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_123_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
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* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_332_3.txt | ml)
* [ pandas.DataFrame.sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
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pandas_series/pandas_series_94_1.txt | html)
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pandas_series/pandas_series_294_4.txt | (pandas.Series.str.capitalize.html)
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* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
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pandas_series/pandas_series_54_1.txt | html)
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* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_220_6.txt | 0", "4/1/2020 11:00:00+00:00"])
>>> s = pd.to_datetime(s)
>>> s
0 2020-01-01 10:00:00+00:00
1 2020-04-01 11:00:00+00:00
dtype: datetime64[ns, UTC]
>>> s.dt.quarter
0 1
1 2
dtype: int32
For DatetimeIndex:
>>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00... | |
pandas_series/pandas_series_124_3.txt | arse ](pandas.DataFrame.sparse.html)
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* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Series.dt.year ](pandas.Series.dt.... | |
pandas_series/pandas_series_242_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)
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pandas_series/pandas-settingwithcopywarning0_1_0.txt | * More
Learner Stories Python Newsletter Python Job Board Meet the Team Become a
Tutorial Writer Become a Video Instructor | |
pandas_series/pandas_series_110_4.txt | eries.str.casefold ](pandas.Series.str.casefold.html)
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* [ pandas.Series.str.count ](pandas.Series.str.count.html)
* ... | |
pandas_series/pandas_series_303_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
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* [ pandas.Series.rpow ](pandas.Series.rpow.html)
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* [ pandas.Serie... | |
pandas_series/pandas_series_327_4.txt | s.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.Series.st... | |
pandas_series/pandas-settingwithcopywarning0_158_0.txt | This mirrors the behavior that you’ve seen in the article so far. `
df["b":"d"] ` returns a view and allows you to modify the original data.
That’s why the assignment ` df["b":"d"]["z"] = 0 ` succeeds. Notice that in
this case you get a ` SettingWithCopyWarning ` regardless of the successful
change to ` df ` .
If your... | |
pandas_series/pandas-settingwithcopywarning0_133_0.txt | ## Use of Views and Copies in pandas
As you’ve already learned, pandas can issue a ` SettingWithCopyWarning ` when
you try to modify the copy of data instead of the original. This often follows
chained indexing. | |
pandas_series/pandas_series_149_5.txt | ies.cat.rename_categories ](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_categor... | |
pandas_series/pandas-settingwithcopywarning0_61_0.txt | #### Views in NumPy
A shallow copy or view is a NumPy array that doesn’t have its own data. It
looks at, or “views,” the data contained in the original array. You can create
a view of an array with ` .view() ` : | |
pandas_series/pandas_series_331_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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Series.c... | |
pandas_series/pandas_series_120_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_306_7.txt | >>> left
D B E A
1 1.0 2.0 3.0 4.0
2 6.0 7.0 8.0 9.0
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
>>> right
A B C D
1 NaN NaN NaN NaN
2 10.0 20.0 30.0 40.0
3 60.0 70.0 80.0 90.0
4 600.0 700.0 800.0 900.0... | |
pandas_series/pandas_series_47_4.txt | andas.Series.str.capitalize.html)
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* [ pandas.Series.str.contains ](pandas.Series.str.contains.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_94_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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* pandas.Series.str.contains
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pandas_series/pandas_series_90_2.txt | vel.html)
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pandas_series/pandas_series_278_6.txt | ch as a string, tuple or list) or a
collection (such as a dictionary).
Returns :
Series or Index of int
A Series or Index of integer values indicating the length of each element in
the Series or Index.
See also
` str.len `
Python built-in function returning the length of an object.
[ ` Series... | |
pandas_series/pandas-settingwithcopywarning0_132_0.txt | The copy has a different ` .base ` than ` df ` .
In the next section, you’ll find more details related to indexing DataFrames
and returning views and copies. You’ll see some cases where the behavior of
pandas becomes more complex and differs from NumPy. | |
pandas_series/pandas_series_299_4.txt | ize ](pandas.Series.str.capitalize.html)
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