# You can use "astype" method
# suppose you want to correct your "sales" column data type
df['sales'] = df['sales'].astype('float64')# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)df['myvar'] = df['myvar'].astype(str) # Transform as character
df['myvar'] = df['myvar'].astype(float) # Transform as float
df['myvar'] = df['myvar'].astype(int) # Transform as numeric>>> df.astype({'col1': 'int32'}).dtypes
col1 int32
col2 int64
dtype: object# select columns that need to be converted
cols = df.select_dtypes(include=['float64']).columns.to_list()
df = df.astype({col:int for col in cols})convert pandas datatype