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chevron_left Series Cookbook
Appending values to a SeriesApplying a function to SeriesBinning values in a SeriesChanging data type of SeriesChecking if a value is NaN in Pandas SeriesChecking if all values are NaN in SeriesChecking if all values in a Series are uniqueChecking if Series has missing valuesConverting Python list to SeriesConverting Series of lists into DataFrameConverting Series to a Numpy arrayConverting Series to Python listCounting frequency of values in SeriesCreating a Series of zeroesCreating a Series with constant valueFiltering strings based on length in SeriesFiltering values of a SeriesGetting frequency counts of values in intervalsGetting index of largest valueGetting index of smallest valueGetting index of value in SeriesGetting integer index of largest valueGetting integer index of smallest valueGetting integer index of value in SeriesGetting intersection of SeriesGetting length of each string in SeriesGetting list of integer indices where value is boolean True in SeriesGetting the index of the nth value in SeriesGetting the most frequent value in SeriesGetting value of Series using integer indexGrouping Series by its valuesHandling error - "Truth value of a Series is ambiguous"Inverting a Series of booleansRemoving missing values from a SeriesRemoving substrings from strings in a SeriesRemoving values from SeriesResetting index of SeriesSorting values in a SeriesSplitting strings based on spaceStripping leading and trailing whitespaceTaking the floor or ceiling of values in SeriesUsing index.get_loc(~) for multiple values
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chevron_left Series Cookbook
Appending values to a SeriesApplying a function to SeriesBinning values in a SeriesChanging data type of SeriesChecking if a value is NaN in Pandas SeriesChecking if all values are NaN in SeriesChecking if all values in a Series are uniqueChecking if Series has missing valuesConverting Python list to SeriesConverting Series of lists into DataFrameConverting Series to a Numpy arrayConverting Series to Python listCounting frequency of values in SeriesCreating a Series of zeroesCreating a Series with constant valueFiltering strings based on length in SeriesFiltering values of a SeriesGetting frequency counts of values in intervalsGetting index of largest valueGetting index of smallest valueGetting index of value in SeriesGetting integer index of largest valueGetting integer index of smallest valueGetting integer index of value in SeriesGetting intersection of SeriesGetting length of each string in SeriesGetting list of integer indices where value is boolean True in SeriesGetting the index of the nth value in SeriesGetting the most frequent value in SeriesGetting value of Series using integer indexGrouping Series by its valuesHandling error - "Truth value of a Series is ambiguous"Inverting a Series of booleansRemoving missing values from a SeriesRemoving substrings from strings in a SeriesRemoving values from SeriesResetting index of SeriesSorting values in a SeriesSplitting strings based on spaceStripping leading and trailing whitespaceTaking the floor or ceiling of values in SeriesUsing index.get_loc(~) for multiple values
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Removing values from Series in Pandas

Pandas
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Series Cookbook
schedule Jul 1, 2022
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Removing values using integer indices

To remove values using integer indices, use the drop(~) method like so:

import pandas as pd
s = pd.Series([3,5,4,5])
s.drop(index=2)
0 3
1 5
3 5
dtype: int64

Here, a new Series is returned and so the original s is kept intact. To directly modify s, set inplace=True.

Removing values using index labels

To remove values using index labels, use the drop(~) method:

s = pd.Series([3,5,4,5], index=["a","b","c","d"])
s.drop("a")
b 5
c 4
d 5
dtype: int64

Removing occurrences of values

To remove all occurrences of the value 5:

s = pd.Series([3,5,4,5])
s.loc[s != 5]
0 3
2 4
dtype: int64

To explain, we first fetch a boolean mask where True indicates a value that is not 5:

s != 5
0 True
1 False
2 True
3 False
dtype: bool

We then pass this mask into the loc property to fetch the entries that correspond to True in the mask.

Removing values using slicing

To remove the values from integer index 1 (inclusive) to 3 (exclusive):

s = pd.Series([3,5,4,5])
s.drop(s.index[1:3])
0 3
3 5
dtype: int64
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Published by Isshin Inada
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