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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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Converting all object-typed columns to categorical type in Pandas DataFrame
schedule May 20, 2023
Last updated local_offer
Tags Python●Pandas
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Consider the following DataFrame where all the columns are of type object:
A B0 3 51 4 6
Solution
To convert object-typed columns to categorical:
Explanation
We first obtain a list of the column labels where the column is of type object:
We then iterate over this list and perform the type conversion using astype("category").
Published by Isshin Inada
Edited by 0 others
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