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chevron_leftData Manipulation Cookbook
Adding a prefix to column valuesAdding leading zeros to strings of a columnAdding new column using listsAdding padding to a column of stringsBit-wise ORChanging column type to stringConditionally updating values of a DataFrameConverting all object-typed columns to categorical typeConverting column type to dateConverting column type to floatConverting column type to integerConverting K and M to numerical formConverting string categories or labels to numeric valuesEncoding categorical variablesExpanding lists vertically in a DataFrameExpanding strings vertically in a DataFrameExtracting numbers from columnFilling missing value in Index of DataFrameFiltering column values using boolean masksLogical AND operationMaking DataFrame string column lowercaseMapping True and False to 1 and 0 respectivelyMapping values of a DataFrame using a dictionaryModifying a single value in a DataFrameRemoving characters from columnsRemoving comma from column valuesRemoving first n characters from column valuesRemoving last n characters from column valuesRemoving leading substringRemoving trailing substringReplacing infinities with another value in DataFrameReplacing values in a DataFrameRounding valuesSorting categorical columnsUsing previous row to create new columns
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Logical AND operation in Pandas DataFrame

schedule Aug 10, 2023
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Use & to perform a logical AND operation in Pandas DataFrame.

Consider the following DataFrame:

import pandas as pd
df = pd.DataFrame({"A":[True,False,False],"B":[True,True,False]})
df
A B
0 True True
1 False True
2 False False

To perform a bit-wise logical AND operation between columns A and B:

df["A"] & df["B"]
0 True
1 False
2 False
dtype: bool

Here, a True is returned for the first row because both columns A and B have True as the first row.

robocat
Published by Isshin Inada
Edited by 0 others
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