int' 对象不可迭代", '发生在索引 i

Question

I`m trying to apply a .mean function in a dataframe 'combined_sf2' which contains the shape '43722 rows × 62 columns'.

I would like to calculate the mean of some values in a range of different attributes from my dataframe for each row. Then generating a new attribute/column with the name 'wkQtyEXTMean', which will contain the mean of this selected range of attributes values for each row.

I tried to create the follow function applying the .mean function from statistics method:

    #function create to take the range of the selected attributes, if the sum is zero, so return the message 'thre is no mean', if not, calculate the mean
    
import statistics
    def wkQtyEXTMean(row):
        if (row['wk13QtyEXT']+row['wk12QtyEXT']) == 0:
            return 'No mean'
       else:
            return statistics.mean(row['wk13QtyEXT']+row['wk12QtyEXT'])

    #generating new column
    combined_sf2['wkQtyEXTMean'] = combined_sf2.apply(wkQtyEXTMean, axis=1)

But i`m getting the follow error:

("'int' object is not iterable", 'occurred at index 43721')

Expected result

Any suggestion what should I do ?

Answer 1

Please find updated answer.

# Online Python compiler (interpreter) to run Python online.

import pandas as pd
import statistics
# Creating the dataframe 
df = pd.DataFrame({"A":[12, 4, 5, None, 1],
                   "B":[7, 2, 54, 3, None],
                   "C":[20, 16, 11, 3, 8],
                   "D":[14, 3, None, 2, 6]})
  
# skip the Na values while finding the mean
print(df.mean(axis = 1, skipna = True))
te = df.mean(axis = 1, skipna = True)
print(statistics.mean(te))

0 13.250000

1 6.250000 2 23.333333

3 2.666667

4 5.000000

dtype: float64

This is answer. 10.1

Old conversation:

This is the syntax for mean.

# Importing the statistics module
import statistics
  
# list of positive integer numbers
data1 = [1, 3, 4, 5, 7, 9, 2]
  
x = statistics.mean(data1)

Now what you are doing wrong is

return statistics.mean(row['wk13QtyEXT']+row['wk12QtyEXT']) is wrong
 
return statistics.mean(row) is right

Answer 2

Try (your approach):

import statistics

def wkQtyEXTMean(row):
    if row['wk13QtyEXT'] == 0 and row['wk12QtyEXT'] == 0:
        return 'No mean'
    return statistics.mean([row['wk13QtyEXT'], row['wk12QtyEXT']])

combined_sf2['wkQtyEXTMean'] = combined_sf2.apply(wkQtyEXTMean, axis=1)

Or without statistics.mean :

def wkQtyEXTMean(row):
    if row['wk13QtyEXT'] == 0 and row['wk12QtyEXT'] == 0:
        return 'No mean'
    return (row['wk13QtyEXT'] + row['wk12QtyEXT']) / 2

combined_sf2['wkQtyEXTMean'] = combined_sf2.apply(wkQtyEXTMean, axis=1)

Or without apply :

combined_sf2['wkQtyEXTMean'] = (combined_sf2['wk13QtyEXT']
                                + combined_sf2['wk12QtyEXT']) / 2
combined_sf2.loc[combined_sf2['wk13QtyEXT'].eq(0)
                 & combined_sf2['wk12QtyEXT'].eq(0), 'wkQtyEXTMean'] = 'No mean'

Or if you are already using NumPy ( np ):

combined_sf2['wkQtyEXTMean'] = np.where(
            combined_sf2['wk13QtyEXT'].eq(0) & combined_sf2['wk12QtyEXT'].eq(0),
            'No mean',
            (combined_sf2['wk13QtyEXT'] + combined_sf2['wk12QtyEXT']) / 2
        )