# If I used column_names directly instead of p, all my numerical columns would turn into Booleans. # I then used the final piece of the code from unutbu calling the indexes of the rows which had the max = 1 as stored in my variable p. # These indexes are the same as the indexes from my original dataframe 'df'.

# I then extracted the indexes of the rows from n and stored them in temporary variable p. # I then did a filter like BrenBarn showed in another post to extract the rows which had the max = 1 and stored it in a temporary variable n. For instance, you may want an efficient way to transform a ‘yes’/’no’ column into boolean True/False values.

M=df).columns].max().reset_index(name='max') In data processing and analysis with Python’s Pandas library, converting different data types to boolean values can be crucial for feature engineering, masking, or condition checking. # re-extracting the columns of numerical type (using awesome np.number1 :)) then getting the max of those and storing them in a temporary variable m. # code as per unutbuĬolumn_names = df.select_dtypes(include=).columns So after identifying the numerical columns like unutbu showed us, I filtered out the columns which had a maximum more than 1. I had numerical columns like age and ID which I did not want to convert to Boolean. Reference: Stack Overflow unutbu (Jan 9 at 13:25), BrenBarn (Sep 18 2017) To convert the whole dataframe, one can do, for example, the following df = df.astype(bool) For additional ways, read this: How do I get time of a Python program's execution? There are additional ways to measure the time of execution. Using numpy.where as follows import numpy as npĭf = np.where(df = 1, True, False)įor this specific case one has used time.perf_counter() to measure the time of execution. Using and a custom lambda function as follows df = df.apply(lambda x: True if x = 1 else False) There are various ways to achieve that, below one will see various options:Īs OP didn't specify the dataframe, in this answer I will be using the following dataframe import pandas as pdĭf = pd.DataFrame()ĭf = df.replace(, ) If you see many Null values, return the data type to String and proceed to Create a calculation using the DATEPARSE function to correct the issue.

Convert string to boolean pandas