# Pandas Read CSV convert values

For example in some cases 0 represents False and 1 represents True. If the CSV file contains 0 and 1 values in a column Pandas will automatically represent them as integers. We can convert them to False and True values respectively.

In another case we might have exit-codes in a column where 0 means success and any other number means failure. We might want to simplify that column and represent success by True and failure by False. (Yes, we loose the details of the failure, but maybe we are not interested in the details.)

This latter is what we can see in our example.

**examples/pandas/mixed_convert_values.py**

import pandas as pd import numpy as np df = pd.read_csv('mixed.csv', converters = { 'MyExit' : lambda x : x == '0' }) print( df.dtypes ) print( df )

MyText object MyInteger int64 MyFloat float64 MyBool bool MyExit bool dtype: object MyText MyInteger MyFloat MyBool MyExit 0 Joe 12 3.4 True True 1 Jane 3 4.0 False False 2 Mary 7 2.3 False False