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[Solved] TypeError: cannot concatenate object of type ‘‘; only Series and DataFrame objs are valid

Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error TypeError: cannot concatenate object of type ”; only Series and DataFrame objs are valid in python. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

How TypeError: cannot concatenate object of type ”; only Series and DataFrame objs are valid Error Occurs?

Today I get the following error TypeError: cannot concatenate object of type ”; only Series and DataFrame objs are valid in python.

How To Solve TypeError: cannot concatenate object of type ”; only Series and DataFrame objs are valid Error ?

  1. How To Solve TypeError: cannot concatenate object of type ''; only Series and DataFrame objs are valid Error ?

    To Solve TypeError: cannot concatenate object of type ''; only Series and DataFrame objs are valid Error You can use itertools.product to build a set of all of the pairs, filter to remove when the source and destination are the same location, and then construct a new data frame.

  2. TypeError: cannot concatenate object of type ''; only Series and DataFrame objs are valid

    To Solve TypeError: cannot concatenate object of type ''; only Series and DataFrame objs are valid Error You can use itertools.product to build a set of all of the pairs, filter to remove when the source and destination are the same location, and then construct a new data frame.

Solution 1

You can use itertools.product to build a set of all of the pairs, filter to remove when the source and destination are the same location, and then construct a new data frame.

import pandas as pd
from itertools import product

df = pd.DataFrame({'sources': [
    "CAULAINCOURT",
    "MARCHE DE L'EUROPE",
    "AU MAIRE"
]})

df_pairs = pd.DataFrame(
    filter(lambda x: x[0]!=x[1], product(df.sources, df.sources)), 
    columns=['source', 'dest']
)

df_pairs
# returns:
               source                dest
0        CAULAINCOURT  MARCHE DE L'EUROPE
1        CAULAINCOURT            AU MAIRE
2  MARCHE DE L'EUROPE        CAULAINCOURT
3  MARCHE DE L'EUROPE            AU MAIRE
4            AU MAIRE        CAULAINCOURT
5            AU MAIRE  MARCHE DE L'EUROPE

Solution 2

itertools.permutations removes the duplicates for you.

import pandas as pd
from itertools import permutations

df = pd.DataFrame({'sources': [
    "CAULAINCOURT",
    "MARCHE DE L'EUROPE",
    "AU MAIRE"
]})

df_pairs = pd.DataFrame(
    [x for x in permutations(df.sources, 2)],
    columns=['source', 'dest'])

df_pairs
# returns
               source                dest
0        CAULAINCOURT  MARCHE DE L'EUROPE
1        CAULAINCOURT            AU MAIRE
2  MARCHE DE L'EUROPE        CAULAINCOURT
3  MARCHE DE L'EUROPE            AU MAIRE
4            AU MAIRE        CAULAINCOURT
5            AU MAIRE  MARCHE DE L'EUROPE

Summery

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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