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Some column in dataframe df, df.column, is stored as datatype int64.
The values are all 1s or 0s.
Is there a way to replace these values with boolean values?
622
dtype. dtype('int64') The type int64 tells us that Python is storing each value within this column as a 64 bit integer. We can use the dat. dtypes command to view the data type for each column in a DataFrame (all at once).
to_numeric() The best way to convert one or more columns of a DataFrame to numeric values is to use pandas. to_numeric() . This function will try to change non-numeric objects (such as strings) into integers or floating-point numbers as appropriate.
Change data type of a series in Pandas Use a numpy. dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy. dtype or Python type to cast one or more of the DataFrame's columns to column-specific types.
astype() method is used to cast a pandas object to a specified dtype. astype() function also provides the capability to convert any suitable existing column to categorical type. DataFrame. astype() function comes very handy when we want to case a particular column data type to another data type.
df['column_name'] = df['column_name'].astype('bool') For example:
import pandas as pd import numpy as np df = pd.DataFrame(np.random.random_integers(0,1,size=5), columns=['foo']) print(df) # foo # 0 0 # 1 1 # 2 0 # 3 1 # 4 1 df['foo'] = df['foo'].astype('bool') print(df) yields
foo 0 False 1 True 2 False 3 True 4 True Given a list of column_names, you could convert multiple columns to bool dtype using:
df[column_names] = df[column_names].astype(bool) If you don't have a list of column names, but wish to convert, say, all numeric columns, then you could use
column_names = df.select_dtypes(include=[np.number]).columns df[column_names] = df[column_names].astype(bool) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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