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How to disallow duplicate labels in a Pandas DataFrame?
By default, Pandas allows duplicate labels in a DataFrame. However, it can cause issues because some Pandas methods won't work if there are duplicates in a DataFrame. In this article, we will see how to prevent Pandas from allowing duplicate labels or catch them at the compile time.
Example
Take a look at the following code. We have a DataFrame with two columns having the same column name "Name". Still, Pandas will produce the required output without any issues.
import pandas as pd
df = pd.DataFrame(
[
['John', 89, 'Maths'],
['Jacob', 23, 'Physics'],
['Tom', 100, 'Chemistry']],
columns=['Name', 'Name', 'Subjects'])
print("Input DataFrame is:\n", df)
Output
It will produce the following output −
Input DataFrame is:
Name Name Subjects
0 John 89 Maths
1 Jacob 23 Physics
2 Tom 100 Chemistry
Now, let's see how to prevent Pandas from allowing duplicate labels.
We can use .set_flags(allows_duplicate_labels=False) which will automatically detect the duplicate labels in the DataFrame and throw an error.
Now, run the same code with allows_duplicate_labels set to False and it will produce errors −
import pandas as pd
df = pd.DataFrame(
[
['John', 89, 'Maths'],
['Jacob', 23, 'Physics'],
['Tom', 100, 'Chemistry']],
columns=['Name', 'Name', 'Subjects']
).set_flags(allows_duplicate_labels=False)
print("Input DataFrame is:\n", df)
Now, it will catch the duplicate labels and produce the following error −
pandas.errors.DuplicateLabelError: Index has duplicates. label positions Name [0, 1]
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