handling missing or NaN values in pandas dataframe
# Six(6) ways to handle NaN values # 1. Drop/delete any rows with NaN values df.dropna(axis = 0) #row is axis = 0 # 2. Drop/delete any columns with NaN values df.dropna(axis = 1) #column is axis = 1 # 3. Replace all NaN values with 0 df.fillna(0) # 4. Replace NaN values with the previous value in the column, Fill Forward df.fillna(method = 'ffill', axis = 0) #OR axis = 1 for rows # 5. Replace NaN values with the next value in the column, Fill Backward df.fillna(method = 'backfill', axis = 0) #OR axis =1 for rows # 6. replace NaN values by using linear interpolation using column values df.interpolate(method = 'linear', axis = 0) #OR axis = 1 for rows #NB: 1. For the last three options, depending on the method, changes to NaN # in the first row, last row, first column or last column may not be effected. # 2. Remember to include inplace = True if you want the original dataframe to #be modified, else the changes will revert back to the original when you #reference the dataframe again. Eg. df.dropna(axis = 0, inplace = True)