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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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Bit-wise OR in NumPy and Pandas

schedule Mar 5, 2023
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The pipe character (|) is used to perform a bit-wise OR operation in NumPy and Pandas:

import numpy as np
x = np.array([True, False, False])
y = np.array([True, True, False])
x | y
array([ True, True, False])

A bit-wise operation returns True when there is at least one True value - the reason we get False for the third element is that the third element in x and y is False.

Note that as long as one operand (either x or y) is a NumPy array or a Pandas Series, the bit-wise operation would work:

np.array([False, False]) | [True,False]
array([ True, False])
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Published by Isshin Inada
Edited by 0 others
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