AttributeError: 'Index' object has no attribute 'replace'

Question

I am making some script, wherein my requirement is to remove some special characters from column names and to make data frame as multi header. code

import pandas as pd
import numpy as np
cars = {'day':['aug','aug','sep','sep','aug'],
        'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4','Hyundai Elite i20'],
        'Type':['sedan,','sedan','hatchback','hatchback','hatchback'],
        'Down Price': [22000,25000,27000,35000,10000]
        }

df = pd.DataFrame(cars, columns = ['day','Brand', 'Type','Down Price'])
dfpivot=pd.pivot_table(df,index=['day'],columns=['Brand','Type'],values=['Down Price'],aggfunc=np.max)
dfpivot=pd.DataFrame(dfpivot.to_records())

Post this my requirement is to replace "(" & ")" from column name & to split column name with "," to make it multi header What i tried :

dfpivot.columns=dfpivot.columns.str.replace("(","").replace(")","")
dfpivot.columns = dfpivot.columns.str.split(',', expand=True)

I am getting the below error, I also tried to check similar questions posted, but it is not helping, will be grateful if someone can help to resolve.

AttributeError Traceback (most recent call last) in 10 dfpivot=pd.pivot_table(df,index=['day'],columns=['Brand','Type'],values=['Down Price'],aggfunc=np.max) 11 dfpivot=pd.DataFrame(dfpivot.to_records()) ---> 12 dfpivot.columns=dfpivot.columns.str.replace("(","").replace(")","") 13 dfpivot

AttributeError: 'Index' object has no attribute 'replace'

tdy · Accepted Answer · 2021-11-21 12:40:22Z

2

The error is because the second replace is missing its str accessor:

dfpivot.columns = dfpivot.columns.str.replace("(", "").str.replace(")", "")
#                                                      ^^^

However it's simpler to use str.strip for this:

dfpivot.columns = dfpivot.columns.str.strip("()").str.split(",", expand=True)

#    day                                                                     'Down Price'
#    NaN    'Audi A4'  'Ford Focus'  'Honda Civic'  'Hyundai Elite i20'  'Toyota Corolla'
#    NaN  'hatchback'   'hatchback'        'sedan'          'hatchback'           'sedan'
# 0  aug          NaN           NaN        22000.0              10000.0           25000.0
# 1  sep      35000.0       27000.0            NaN                  NaN               NaN

But note that this will leave single quotes in the column names. If you don't want those quotes, instead use str.replace with regex:

dfpivot.columns = dfpivot.columns.str.replace(r"[()']", "", regex=True).str.split(",", expand=True)

#    day                                                             Down Price                                                            
#    NaN    Audi A4  Ford Focus  Honda Civic  Hyundai Elite i20  Toyota Corolla
#    NaN  hatchback   hatchback        sedan          hatchback           sedan
# 0  aug        NaN         NaN      22000.0            10000.0         25000.0
# 1  sep    35000.0     27000.0          NaN                NaN             NaN

edited Nov 21, 2021 at 12:40

answered Nov 21, 2021 at 12:27

tdy

42.1k42 gold badges124 silver badges125 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

DD08 Over a year ago

Thank you for your time and such a detailed explanation

wwnde · Accepted Answer · 2021-11-21 12:40:52Z

1

Replace all except alpha numerics, white space and comma, split and expand in the columns.

dfpivot.columns=dfpivot.columns.str.replace('[^\w\,\s]','',regex=True).str.split(',', expand=True)

edited Nov 21, 2021 at 12:40

answered Nov 21, 2021 at 12:31

wwnde

26.7k6 gold badges22 silver badges38 bronze badges

Comments

tromgy · Accepted Answer · 2021-11-21 12:27:50Z

1

Pandas replace returns an Index, so you need to use another str on it:

dfpivot.columns=dfpivot.columns.str.replace("(","").str.replace(")","")

answered Nov 21, 2021 at 12:27

tromgy

5,9234 gold badges24 silver badges24 bronze badges

Collectives™ on Stack Overflow

AttributeError: 'Index' object has no attribute 'replace'

3 Answers 3

1 Comment

Comments

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

3 Answers 3

1 Comment

Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Related