How to drop "Unnamed: 0" column from DataFrame

To drop the "Unnamed: 0" column from a DataFrame, you can use the drop() method. Here's how you can do it:

import pandas as pd # Assuming df is your DataFrame with the "Unnamed: 0" column # To drop the column in-place (modify the original DataFrame): df.drop(columns="Unnamed: 0", inplace=True) # Alternatively, to create a new DataFrame without the "Unnamed: 0" column: df_without_unnamed = df.drop(columns="Unnamed: 0")

The drop() method allows you to specify the column you want to remove using the columns parameter. Setting inplace=True will modify the original DataFrame, while omitting it or setting it to False will create a new DataFrame without the specified column.

"Unnamed: 0"

In certain scenarios, a situation may arise where an "Unnamed: 0" column appears in a pandas DataFrame when reading a CSV file. To address this, a straightforward solution involves treating the "Unnamed: 0" column as the index. To achieve this, you can specify the index_col=[0] argument in the read_csv() function, enabling it to interpret the first column as the DataFrame's index.

pd.read_csv('file.csv', 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


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

In many instances, the presence of the "Unnamed: 0" index in your DataFrame when using to_csv() is a result of saving the DataFrame with the default index. This can be avoided by using "index=False" as an option while creating the output CSV file, particularly when the DataFrame's index is not required to be included in the saved data.

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

how to drop

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']


To remove the "Unnamed: 0" column from a DataFrame, you can use the drop() method in pandas. Simply specify the column name using the columns parameter, and set inplace=True to modify the original DataFrame or omit it to create a new DataFrame without the specified column.