Pandas Drop First Column From DataFrame

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  • Post category:Pandas
  • Post last modified:May 17, 2024
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To drop the first column from a pandas DataFrame, you can use the drop() method along with specifying the column name or index. In this article, I will explain how to remove the first column from Pandas DataFrame by using either iloc[] property, drop(), pop() functions, and del keyword.

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Key Points –

  • Use the drop() function in pandas to remove columns from a DataFrame.
  • Specify the column to drop either by its name or index using the appropriate parameter.
  • Set axis=1 to indicate that you’re dropping a column (by default, axis=0 drops rows).
  • Utilize the drop() method with the appropriate axis parameter (axis=1 or columns) to remove columns from the DataFrame.
  • Access column labels using the .columns attribute and column indexes using integer positions.

Related: In Pandas, you can drop the first/last n columns from DataFrame.

Quick Examples of Drop First Column

If you are in a hurry, below are some quick examples of how to remove the first column from DataFrame.


# Quick examples of drop first column

# Example 1: Drop first column of dataframe 
# Using iloc[]
df2 = df.iloc[: , 1:]

# Example 2: select all columns
# Except the first column
df2 = df[df.columns[1:]]

# Example 3: Drop first column of dataframe 
# Using drop()
df.drop(columns=df.columns[0], axis=1,  inplace=True)

# Example 4: Using drop() function 
# To delete first column
df2 = df.drop(df.columns[0],axis=1)

# Example 5: Use del keywords to drop 
# First column of dataframe
del df[df.columns[0]]

# Example 6: Drop first column of dataframe 
# Using pop()
df.pop(df.columns[0])

Now, let’s create a DataFrame with a few rows and columns, execute these examples, and validate results. Our DataFrame contains column names Courses, Fee, Duration, and Discount.


import pandas as pd
import numpy as np
technologies= ({
    'Courses':["Spark","PySpark","Hadoop","Pandas"],
    'Fee': [22000,25000,30000,35000],
    'Duration':['30days','50days','40days','35days'],
    'Discount':[1000,2000,2500,1500]
              })
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print("DataFrame:\n", df)

Yields below output.

pandas drop first column

Drop First Column of DataFrame Using iloc[]

To drop the first column of a DataFrame using iloc[], you can select all rows (:) and all columns starting from the second column index onwards.


# Drop first column of dataframe 
# Using iloc[]
df2 = df.iloc[: , 1:]
print("After dropping the first column:\n", df2)

In the above examples, df.iloc[:, 1:] selects all rows (:) and all columns starting from the second column index (1) onwards. By assigning this selection back to the variable df, you effectively drop the first column from the DataFrame.

pandas drop first column

Drop First Column of DataFrame Using drop()

To drop the first column of a DataFrame using the drop() function, you can specify the column to drop either by its name or index.


# Drop first column of dataframe using drop()
df.drop(columns=df.columns[0], axis=1,  inplace=True)
print("After dropping the first column:\n", df)

# Using drop() function to delete first column
df2 = df.drop(df.columns[0],axis=1)
print("After dropping the first column:\n", df2)

# Output:
# After dropping the first column:
#        Fee Duration  Discount
# r1  22000   30days      1000
# r2  25000   50days      2000
# r3  30000   40days      2500
# r4  35000   35days      1500

In the above examples, drop the first column of the DataFrame df using the drop() function. df.columns[0] selects the name of the first column, and axis=1 specifies that you are dropping a column. Finally, by assigning the result back to the variable df, the first column is effectively dropped from the DataFrame.

Use del Keyword to Drop the First Column

Alternatively, you can also use del df[df.columns[0]] to remove the first column of the Pandas DataFrame. Here, it will select all columns except the first column of DataFrame.


# Use del keywords to drop 
# First column of dataframe
del df[df.columns[0]]
print("After dropping the first column:\n", df)

# Output:
# After dropping the first column:
#        Fee Duration  Discount
# r1  22000   30days      1000
# r2  25000   50days      2000
# r3  30000   40days      2500
# r4  35000   35days      1500

In the above examples, del df[df.columns[0]] remove the first column of the DataFrame df by selecting it using df.columns[0]. After executing this line, the first column is deleted from the DataFrame.

Drop the First Column of DataFrame Using pop()

Similarly, you can use DataFrame.pop() function to drop the first column of pandas DataFrame and also return the remove column as a series. So, you can select the first column of DataFrame by using df.columns[0] and passing this to the pop(), it will select all columns except the first column of DataFrame.


# Drop first column of dataframe using pop()
df.pop(df.columns[0])
print("After dropping the first column:\n", df)

# Output:
# After dropping the first column:
#        Fee Duration  Discount
# r1  22000   30days      1000
# r2  25000   50days      2000
# r3  30000   40days      2500
# r4  35000   35days      1500

In the above examples, df.pop(df.columns[0]) drops the first column of the DataFrame df and returns it. The returned column is assigned to the variable df, and the modified DataFrame without the first column is printed.

Complete Example For Remove First Column 


import pandas as pd
import numpy as np
technologies= ({
    'Courses':["Spark","PySpark","Hadoop","Pandas"],
    'Fee': [22000,25000,30000,35000],
    'Duration':['30days','50days','40days','35days'],
    'Discount':[1000,2000,2500,1500]
              })
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print(df)

# Drop first column of dataframe 
# Using iloc[]
df2 = df.iloc[: , 1:]
print(df2)

# Select all columns
# Except the first column
df2 = df[df.columns[1:]]
print(df2)

# Drop first column of dataframe using drop()
df.drop(columns=df.columns[0], axis=1,  inplace=True)
print(df)

# Using drop() function to delete first column
df2 = df.drop(df.columns[0],axis=1)
print(df2)

# Use del keywords to drop 
# First column of dataframe
del df[df.columns[0]]
print(df)

# Drop first column of dataframe 
# Using pop()
df.pop(df.columns[0])
print(df)

Frequently Asked Questions on Drop First Column from Pandas DataFrame

How can I drop the first column without specifying the column name?

You can use integer indexing to drop the first column without explicitly specifying the column name. For example, df1 = df.drop(df.columns[0], axis=1)

Is there a way to drop the first column in place without assigning to a new variable?

You can use the inplace=True parameter to modify the existing DataFrame in place instead of creating a copy of the DataFrame. For example, df.drop(df.columns[0], axis=1, inplace=True)

How do I drop multiple columns, including the first one?

If you want to drop multiple columns, including the first one, you can pass a list of column names to the drop() method: For example, columns_to_drop = [df.columns[0], 'Column2', 'Column3']
df = df.drop(columns = columns_to_drop)

Is there a way to drop the first N columns?

You can use the slicing technique to drop the first N columns from the DataFrame. For example,
n = 3
df = df.iloc[:, n:]

Conclusion

In conclusion, we’ve explored various methods to drop the first column from a Pandas DataFrame, including DataFrame.iloc[], DataFrame.drop(), DataFrame.pop(), and the del keyword. Each method offers its own advantages and can be selected based on specific use cases and preferences.

Happy Learning !!

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