How to drop unnamed column in pandas ? 5 Steps Only

An unnamed column in pandas comes when you are reading the CSV file using it. Sometimes we require to drop columns in the dataset that we are not required. It not only saves memory but is also helpful in analyzing the data efficiently. One approach is removing the NaN value or some other value. The second approach is to drop unnamed columns in pandas. In this entire tutorial, I will discuss how to easily remove unnamed column errors while reading a CSV file.

Steps by Step to drop unnamed column in pandas

Step 1: Import all the necessary libraries.

The first and basic step is to import Python libraries. Here in our example, We are using only pandas. So let’s import them. However, if you have not installed pandas in your system, you can read How to install pandas in the dedicated tutorial.

import pandas as pd

Step 2: Create a DataFrame.

Now let’s create a Dataframe for demonstrating purpose.  You can do it by using pandas.Dataframe() method.

df = pd.DataFrame('x', index=range(5), columns=list('abc'))

The following argument I am passing. It is done only for creation purposes.

x: It allows us to put value in the entire row as “x”.

index: It will create an index column. In our example rows from 0 to 4.

columns: Name of the columns.

Output

Step 3: Export or Save it as CSV File.

The next step is to save this dataframe as CSV. Some readers might have asked, Why I am doing so? The answer is simple. I want to read the CSV file that outputs the dataframe with the unnamed column.  You can export any dataframe using the to_csv() method.

df.to_csv('demo_file.csv')

It will save dataframe with the filename “demo_file.csv“.

Step 4: Read the Exported CSV File

After exporting the dataframe as a CSV file, let’s now read it. You can read the CSV file using the read_csv() method.

Execute the following code to read the dataframe.

df2= pd.read_csv("demo_file.csv")

If you output the dataframe you will also get the unnamed column error like below.

And if you also print the columns using df2.columns you will see the unnamed columns also.

Index(['Unnamed: 0', 'a', 'b', 'c'], dtype='object')

Step 5: Follow the following method to drop unnamed column in pandas

Method 1: Use the index = False argument

In this method, you have to not directly output the dataframe to the CSV file. But you should also include index = False argument. It will automatically drop the unnamed column in pandas.  And if you want to set the index for the dataframe then you can call the df.set_index() method on any column. You will get the output below.

Method 2: Filtering the Unnamed Column

The second method to drop unnamed columns is filtering the dataframe using str.match. It can be also known as continual filtering.

Execute the code below to drop the column.

df2.columns.str.match("Unnamed")
df2.loc[:,~df2.columns.str.match("Unnamed")]

You will get the following output.

Method 3: Drop the Unnamed Column in Pandas using drop() method

In this example, you will use the drop() method. You have to pass the “Unnamed: 0” as its argument.  Execute the code below.

df2.drop("Unnamed: 0",axis=1)

You will get the following output.

Conclusion

These are the method to remove the issue of the drop unnamed column. You should note that while exporting the dataset in form of CSV you should always include index = False. It will remove the error automatically. However, if you have already had dataframe that outputs this column then you can try other methods.

Hope this tutorial has solved your issue. If you have any queries then you can contact us for more information.

Source:

Pandas Read CSV Method

Pandas Drop Method

 

Other Questions

How to remove the index from dataframe pandas

Sometimes you want to remove the index from the dataframe. If the column is the index you have to first reset the index and then drop the column. Use the following line of code to remove the index from the dataframe.

df.reset_index(drop=True, inplace=True)

You can also first reset the index column and then use the drop() method on the column name you want to remove.

How to access unnamed columns in pandas

Sometimes you want to access unnamed columns in pandas. You can do so by renaming the column. You will use the rename() function for that.

data.rename( columns={'Unnamed: 0':'new column name'}, inplace=True )

The above code will rename the column with your new column name and now you can access the column.

How to drop a specific column in pandas?

The above solution was to remove unnamed columns from datatframe using pandas. But how to remove a specific column in pandas. For this, you have to use the dataframe.drop() method. For example, you want to remove column a from the dataframe then you will pass the column name and axis =1 as an argument. Execute the below code to remove it.

df2.drop("a",axis=1)

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