How to drop "Unnamed: 0" column from DataFrame

There are situations when an Unnamed: 0 column in pandas comes when you are reading CSV file . The simplest solution would be to read the "Unnamed: 0" column as the index. So, what you have to do is to specify an index_col=[0] argument to read_csv() function, then it reads in the first column as the index.

pd.read_csv('file.csv', index_col=[0])

index_col=[0]

While you read csv file, if you set index_col=[0] you're explicitly stating to treat the first column as the index.

import pandas as pd import numpy as np from io import StringIO df = pd.DataFrame(np.random.randn(5,3), columns=list('xyz')) pd.read_csv(io.StringIO(df.to_csv()))

Unnamed: 0 x y z 0 0 -0.515264 -0.167118 0.695661 1 1 -1.226441 -1.232471 -1.087333 2 2 0.049460 0.904160 -0.923499 3 3 -1.411504 0.617604 2.769514 4 4 -0.847113 0.530300 1.254127

You can solve this issue by using index_col=0 in you read_csv() function.

pd.read_csv(io.StringIO(df.to_csv()), index_col=[0])

x y z 0 -0.279695 -0.217099 -1.208364 1 2.165306 -0.394201 -1.721362 2 1.436819 1.195225 1.570140 3 0.271943 0.940938 -0.230880 4 -1.044362 1.399811 0.725777

index=False

df.to_csv('file.csv', index=False)

In most cases, it is caused by your to_csv() having been saved along with an "Unnamed: 0" index. You could have avoided this mistakes in the first place by using "index=False" if the output CSV was created in DataFrame.

df = pd.DataFrame(np.random.randn(5,3), columns=list('xyz')) pd.read_csv(io.StringIO(df.to_csv(index=False)))

x y z 0 1.025470 0.799474 0.078352 1 0.295817 0.115101 0.386230 2 1.306253 -0.988141 1.123844 3 -1.764021 -1.214755 0.592631 4 0.404928 -1.562189 0.921208

Using regex

You can get ride of all Unnamed columns from your DataFrame by using regex.

df.drop(df.filter(regex="Unname"),axis=1, inplace=True)

Finally, you can simply delete that column using: del df['column_name'] .

del df['column_name']

Next : ValueError: cannot convert float NaN to integer

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