# x contained NaN
df = df[~df['x'].isnull()]# Y contained some other garbage, so null check was not enough
df = df[df['y'].str.isnumeric()]# final conversion now worked
df[['x']]= df[['x']].astype(int)
df[['y']]= df[['y']].astype(int)
# import pandas libraryimport numpy as np
import pandas as pd
# create pandas DataFrame
df = pd.DataFrame({'Antivirus':['Windows Defender','AVG Antivirus','Mcafee Antivirus','Kaspersky Security','Norton Antivirus'],'quantity':[10,4,8,3,5],'price':[23.55, np.nan,32.78,33.0, np.nan]})print("Before conversion
",df)print("Data type of Price column is",df['price'].dtype)# replace the NaN values for specific column
df['price']= df['price'].replace(np.nan,0)#attempt to convert 'price' column from float to integer
df['price']= df['price'].astype(int)print("After conversion
",df)
# import pandas libraryimport numpy as np
import pandas as pd
# create pandas DataFrame
df = pd.DataFrame({'Antivirus':['Windows Defender','AVG Antivirus','Mcafee Antivirus','Kaspersky Security','Norton Antivirus'],'quantity':[10,4,8,3,5],'price':[23.55, np.nan,32.78,33.0, np.nan]})print("Before conversion
",df)print("Data type of Price column is",df['price'].dtype)# drop the rows which has NaN
df = df.dropna()#attempt to convert 'price' column from float to integer
df['price']= df['price'].astype(int)print("After conversion
",df)
# import pandas libraryimport numpy as np
import pandas as pd
# create pandas DataFrame
df = pd.DataFrame({'Antivirus':['Windows Defender','AVG Antivirus','Mcafee Antivirus','Kaspersky Security','Norton Antivirus'],'quantity':[10,4,8,3,5],'price':[23.55, np.nan,32.78,33.0, np.nan]})print("Before conversion
",df)print("Data type of Price column is",df['price'].dtype)# fill the NaN values with 0
df = df.fillna(0)#attempt to convert 'price' column from float to integer
df['price']= df['price'].astype(int)print("After conversion
",df)