cannot convert float nan to integer
# 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)
Source: stackoverflow.com
ValueError: cannot convert float NaN to integer
# import pandas library import 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 \n",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 \n",df)
Source: itsmycode.com
ValueError: cannot convert float NaN to integer
# import pandas library import 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 \n",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 \n",df)
Source: itsmycode.com
ValueError: cannot convert float NaN to integer
# import pandas library import 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 \n",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 \n",df)
Source: itsmycode.com