If you are encountering the error InvalidIndexError: Reindexing only valid with uniquely valued Index objects in Python and have difficulties fixing it. Let’s follow this guide with the explanation and examples below to solve it.
How does the error “InvalidIndexerror: reindexing only valid with uniquely valued Index objects” occur?
Generally, this error occurs when you have duplicated column names.
Look at the example below to know more about this error.
import pandas as pd
from pandas import Timestamp
df1 = pd.DataFrame(
{
'Title': ['Tutorial', 'Learn To Share IT', 'Fix Error', 'Guide'],
'Author': ['crvt4722', 'noname', 'hq09', 'LSI'],
'Comments': [9999, 6789, 5678, 4722]
},
index = pd.DatetimeIndex(['2022-10-1', '2022-6-1', '2022-10-1', '2022-5-6'])
)
df2 = pd.DataFrame(
{
'New follow': [99, 88, 77, 66, 55],
'Interaction': ['Good', 'Bad', 'Excellent', 'Not Good', 'Not Bad'],
'Reader': [
'Cristiano Ronaldo',
'Lionel Messi',
'Neymar',
'Marcus Rashford',
'Harry Kane'
],
'Donation': [100000, 400000, 400000, 400000, 400000]
},
index = pd.DatetimeIndex(
['2022-10-1', '2022-6-1', '2022-1-2', '2022-5-6', '2022-5-1']
)
)
result = pd.concat([df1, df2], axis = 1)
print(result)Output
pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued Index objectsHow to solve this error?
Solution 1: Use reset_index() function in pandas DataFrame
This function will remove the data that is duplicated.
Syntax:
Parameters:
- df: The data frame.
Return value: DataFrame or None.
Look at the example below.
import pandas as pd
from pandas import Timestamp
df1 = pd.DataFrame(
{
'Title': ['Tutorial', 'Learn To Share IT', 'Fix Error', 'Guide'],
'Author': ['crvt4722', 'noname', 'hq09', 'LSI'],
'Comments': [9999, 6789, 5678, 4722]
},
index = pd.DatetimeIndex(['2022-10-1', '2022-6-1', '2022-10-1', '2022-5-6'])
)
df1 = df1.reset_index()
df2 = pd.DataFrame(
{
'New follow': [99, 88, 77, 66, 55],
'Interaction': ['Good', 'Bad', 'Excellent', 'Not Good', 'Not Bad'],
'Reader': [
'Cristiano Ronaldo',
'Lionel Messi',
'Neymar',
'Marcus Rashford',
'Harry Kane'
],
'Donation': [100000, 400000, 400000, 400000, 400000]
},
index = pd.DatetimeIndex(
['2022-10-1', '2022-6-1', '2022-1-2', '2022-5-6', '2022-5-1']
)
)
df2 = df2.reset_index()
result = pd.concat([df1, df2], axis = 1)
print(result)Output
index Title ... Reader Donation
0 2022-10-01 Tutorial ... Cristiano Ronaldo 100000
1 2022-06-01 Learn To Share IT ... Lionel Messi 400000
2 2022-10-01 Fix Error ... Neymar 400000
3 2022-05-06 Guide ... Marcus Rashford 400000
4 NaT NaN ... Harry Kane 400000
[5 rows x 9 columns]Solution 2: Use DataFrame.loc method
This method accesses a group of rows and columns by label(s) or a boolean array.
Syntax:
Return Value: Scalar, Series, DataFrame.
Look at the example below to learn more about this method.
import pandas as pd
from pandas import Timestamp
df1 = pd.DataFrame(
{
'Title': ['Tutorial', 'Learn To Share IT', 'Fix Error', 'Guide'],
'Author': ['crvt4722', 'noname', 'hq09', 'LSI'],
'Comments': [9999, 6789, 5678, 4722]
},
index = pd.DatetimeIndex(['2022-10-1', '2022-6-1', '2022-10-1', '2022-5-6'])
)
df1 = df1.loc[~df1.index.duplicated(keep = 'first')]
df2 = pd.DataFrame(
{
'New follow': [99, 88, 77, 66, 55],
'Interaction': ['Good', 'Bad', 'Excellent', 'Not Good', 'Not Bad'],
'Reader': [
'Cristiano Ronaldo',
'Lionel Messi',
'Neymar',
'Marcus Rashford',
'Harry Kane'
],
'Donation': [100000, 400000, 400000, 400000, 400000]
},
index = pd.DatetimeIndex(
['2022-10-1', '2022-6-1', '2022-1-2', '2022-5-6', '2022-5-1']
)
)
df2 = df2.loc[~df2.index.duplicated(keep = 'first')]
result = pd.concat([df1, df2], axis = 1)
print(result)Output:
Title Author ... Reader Donation
2022-01-02 NaN NaN ... Neymar 400000
2022-05-01 NaN NaN ... Harry Kane 400000
2022-05-06 Guide LSI ... Marcus Rashford 400000
2022-06-01 Learn To Share IT noname ... Lionel Messi 400000
2022-10-01 Tutorial crvt4722 ... Cristiano Ronaldo 100000
[5 rows x 7 columns]Summary
You can use the reset_index() method or the DataFrame.loc[] method in pandas to solve the error reindexing only valid with uniquely valued index objects. Choose the solution that is the most suitable for you. We hope this tutorial is helpful to you. Have an exciting learning experience. Thanks!
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