Have you ever tried to add a new column to a Pandas DataFrame and ended up with the “Typeerror: incompatible index of inserted column with frame index” error message?
Well, this error occurs when you try to add a new column to a DataFrame using a Series with a different index than the DataFrame.
In this article, we will explore solutions to fix the “Typeerror: incompatible index of inserted column with frame index” error and provide example code, output, and explanations to help you understand and solve this common pandas error.
What is Typeerror: incompatible index of inserted column with frame index
This Typeerror: incompatible index of inserted column with frame index is an error that occurs when we try to add a column to a Pandas DataFrame.
However, the index of the new column does not match the index of the DataFrame. This error message is commonly encountered when using the “df.insert()” method to add a column to a DataFrame.
In short, the error message means that the DataFrame and the new column being added have different index values, and therefore cannot be combined.
To resolve this error, you will need to ensure that the index of the new column matches the index of the DataFrame.
Why this happen?
Suppose we have a DataFrame called “df” with the following data:
Name Age 0 John 30 1 Jane 25 2 Bob 35
Now, we want to add a new column called “Gender” to the DataFrame, so we use the following code:
df.insert(2, "Gender", ["Male", "Female", "Male"])This code will try to insert a new column at index position 2 with the values “Male”, “Female”, and “Male” for each row, respectively.
However, the index of the new column (i.e., the row labels) does not match the index of the DataFrame, which is currently [0, 1, 2].
This will result in the following error.
Typeerror: incompatible index of inserted column with frame indexHow to fix this incompatible index of inserted column with frame index error
This time here are the three solutions you can consider in fixing this Typeerror: incompatible index of inserted column with frame index error.
📌Solution 1: Set the index of the Series to match the DataFrame index
import pandas as pd
# create a dataframe with an index
df = pd.DataFrame({"Name": ["John", "Jane", "Bob"], "Age": [30, 25, 35]}, index=[1, 2, 4])
# create a new column with the same index as the dataframe
gender_col = pd.Series(["Male", "Female", "Male"], index=[1, 2, 4], name="Gender")
df["Gender"] = gender_col
# print the dataframe with the new column
print(df)
In this solution, we create a Series for the new column with the same index as the DataFrame using the index parameter of the pd.Series() method.
By setting the index to match the DataFrame index, we ensure that the new column is compatible with the DataFrame.
We then add the new column to the DataFrame using the square bracket notation ([]) and assign it the value of the Series.
This will be the Output:
Name Age Gender 1 John 30 Male 2 Jane 25 Female 4 Bob 35 Male
📌Solution 2: Reset the index of the DataFrame before adding the new column
import pandas as pd
# create a dataframe with an index
df = pd.DataFrame({"Name": ["John", "Jane", "Bob"], "Age": [30, 25, 35]}, index=[1, 2, 4])
# reset the index of the dataframe
df = df.reset_index(drop=True)
# create a new column with a different index
gender_col = pd.Series(["Male", "Female", "Male"], name="Gender")
# add the new column to the dataframe
df["Gender"] = gender_col
# print the dataframe with the new column
print(df)
In this code, we reset the index of the DataFrame using the reset_index() method with drop=True to avoid creating a new column with the old index.
We then create a new Series for the new column without specifying an index. Since the new Series has the default integer index, it is compatible with the DataFrame.
We then add the new column to the DataFrame using the square bracket notation ([]) and assign it the value of the Series.
This will output the following:
Name Age Gender 0 John 30 Male 1 Jane 25 Female 2 Bob 35 Male
📌Solution 3: Create new DataFrame with the same columns and new index
import pandas as pd
# create a dataframe with an index
df = pd.DataFrame({"Name": ["John", "Jane", "Bob"], "Age": [30, 25, 35]}, index=[1, 2, 4])
# create a new dataframe with the same columns and a new index
gender_col = pd.Series(["Male", "Female", "Male"], name="Gender")
new_df = pd.concat([df.reset_index(drop=True), gender_col], axis=1)
# print the new dataframe
print(new_df)
This time, we create a new DataFrame with the same columns as the original DataFrame and a new index using the pd.concat() method.
We first reset the index of the original DataFrame using the reset_index() method with drop=True to avoid creating a new column with the old index.
We then concatenate the reset DataFrame with the new Series along the axis=1 (column-wise) to create a new DataFrame with the same columns and the new column.
This will output the following:
Name Age Gender 0 John 30 Male 1 Jane 25 Female 2 Bob 35 Male
Anyway, we also have a solution for Typeerror: cannot perform reduce with flexible type errors, you might encounter.
Conclusion
All the given solutions above should fix the “Typeerror: incompatible index of inserted column with frame index” error and produce a new DataFrame with the added column.
Hence, the solution you choose may depend on your specific use case and the structure of your data.
We hope this article has helped you fix the error and get back to coding.
Thank you! 😊