TypeError: 'Series' objects are mutable, thus they cannot be hashed

Question

I know this error is common, I tried some solutions I looked up and still can't understand what is wrong. I guess it is due to the mutable form of row and row1, but i can't figure it out

What am I trying to do ? I have 2 dataframes. I need to iterate over the rows of the first 1, and for each line of the first one iterate through the second and check the value of the cell for some columns. My code and different attempts :

a=0
b=0
  for row in Correction.iterrows():
        b+=1
        for row1 in dataframe.iterrows():
            c+=1
            a=0
            print('Handling correction '+str(b)+' and deal '+str(c))
            if (Correction.loc[row,['BO Branch Code']]==dataframe.loc[row1,['wings Branch']] and Correction.loc[row,['Profit Center']]==dataframe.loc[row1,['Profit Center']] and Correction.loc[row,['Back Office']]==dataframe.loc[row1,['Back Office']]
                and Correction.loc[row,['BO System Code']]==dataframe.loc[row1,['BO System Code']]):

I also tried

a=0
b=0
 for row in Correction.iterrows():
        b+=1
        for row1 in dataframe.iterrows():
            c+=1
            a=0
            print('Handling correction '+str(b)+' and deal '+str(c))
            if (Correction[row]['BO Branch Code']==dataframe[row1]['wings Branch'] and Correction[row]['Profit Center']==dataframe[row1]['Profit Center'] and Correction[row]['Back Office']==dataframe[row1]['Back Office']
                and Correction[row]['BO System Code']==dataframe[row1]['BO System Code']):

And

a=0
b=0
 for row in Correction.iterrows():
        b+=1
        for row1 in dataframe.iterrows():
            c+=1
            a=0
            print('Handling correction '+str(b)+' and deal '+str(c))
            if (Correction.loc[row,['BO Branch Code']]==dataframe[row1,['wings Branch']] and Correction[row,['Profit Center']]==dataframe[row1,['Profit Center']] and Correction[row,['Back Office']]==dataframe[row1,['Back Office']]
                and Correction[row,['BO System Code']]==dataframe[row1,['BO System Code']]):

score 0 · Answer 1 · answered Feb 28 '17 at 09:43

I found a way around by changing my for loop now my code is :

a=0
b=0
 for index in Correction.index:
        b+=1
        for index1 in dataframe.index:
            c+=1
            a=0
            print('Handling correction '+str(b)+' and deal '+str(c))
            if (Correction.loc[row,'BO Branch Code']==dataframe.loc[row1,'Wings Branch]] and Correction.loc[row,'Profit Center']==dataframe.loc[row1,'Profit Center'] and Correction.loc[row,'Back Office']==dataframe.loc[row1,'Back Office']
                and Correction.loc[row,'BO System Code']==dataframe.loc[row1,'BO System Code']):

score 0 · Answer 2 · edited May 23 '17 at 10:29

I think you are iterating your df wrong

for row in Correction.itertuples():
    bo_branch_code = row['BO Branch Code']
    for row1 in dataframe.itertuples():
        if row1['wings Branch'] == bo_branch_code:
            # do stuff here

reference how to iterate DataFrame: https://github.com/vi3k6i5/pandas_basics/blob/master/2.A%20Iterate%20over%20a%20dataframe.ipynb

I timed your index approach and iteraterows approach. Here are the results:

import pandas as pd
import numpy as np
import time

df = pd.DataFrame(np.random.randint(0,100,size=(10, 4)), columns=list('ABCD'))

df_2 = pd.DataFrame(np.random.randint(0,100,size=(10, 4)), columns=list('ABCD'))

def test_time():
    for index in df.index:
        for index1 in df_2.index:
            if (df.loc[index, 'A'] == df_2.loc[index1, 'A']):
                continue

def test_time_2():
    for idx, row in df.iterrows():
        a_val = row['A']
        for idy, row_1 in df_2.iterrows():
            if (a_val == row_1['A']):
                continue

start= time.clock()
test_time()
end= time.clock()
print(end-start)
# 0.038514999999999855

start= time.clock()
test_time_2()
end= time.clock()
print(end-start)
# 0.009272000000000169

Simply saying iterrows is way faster than your approach.

Reference on good approaches to loop over a dataframe What is the most efficient way to loop through dataframes with pandas?

TypeError: 'Series' objects are mutable, thus they cannot be hashed

2 Answers2