Add a Row at Top in Pandas DataFrame – Explained

by

Adding rows to a DataFrame is a common task when manipulating data in Pandas. While adding a row at the end of a DataFrame is straightforward, inserting a row at the top requires a different approach. In this blog post, we’ll explore various methods to add a row at the top of a Pandas DataFrame.

Introduction

Pandas is a powerful library for data manipulation and analysis. Adding a row at the top of a DataFrame can be useful for tasks such as inserting headers, prepending data, or including summary rows. Let’s look at different ways to achieve this.

1. Using concat()

The pd.concat() function is a versatile method that can concatenate two or more DataFrames along a specified axis. We can use it to add a new row at the top of an existing DataFrame.

Example:
Python
import pandas as pd

# Existing DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Row to add
new_row = pd.DataFrame({'A': [0], 'B': [0]})

# Adding the new row at the top using pd.concat()
df = pd.concat([new_row, df]).reset_index(drop=True)

print(df)

In this example, we create a new row as a DataFrame and use pd.concat() to concatenate it with the existing DataFrame. The reset_index(drop=True) function is used to reset the index of the concatenated DataFrame.

Output:
   A  B
0  0  0
1  1  4
2  2  5
3  3  6
3. Using _append()

The _append() method in Pandas is typically used to append rows to the end of a DataFrame. However, we can modify its usage to add a row at the top by appending the existing DataFrame to the new row.

Example:
Python
import pandas as pd

# Existing DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Row to add
new_row = pd.DataFrame({'A': [0], 'B': [0]})

# Adding the new row at the top using append()
df = new_row._append(df, ignore_index=True)

print(df)

In this example, we append the existing DataFrame to the new row DataFrame. The ignore_index=True parameter ensures that the index is reset.

Output:
   A  B
0  0  0
1  1  4
2  2  5
3  3  6

Note: The append method has been deprecated since Pandas version 1.4.0 and removed in Pandas version 2.0.0. Then append method has been replaced by _append in recent versions of pandas, but it might be removed in future releases. It’s advisable to use it with caution. In modern Pandas applications, it is recommended to use the pd.concat function to concatenate DataFrames.

4. Using loc and Index Shifting

Another method involves using the loc method and shifting the index of the existing DataFrame.

Example:
Python
import pandas as pd

# Existing DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Row to add
new_row = {'A': 0, 'B': 0}

# Shifting the index
df.index = df.index + 1

# Adding the new row at the top using loc
df.loc[0] = new_row

# Sorting the DataFrame by index
df = df.sort_index().reset_index(drop=True)

print(df)

In this example, we shift the index of the existing DataFrame by 1, insert the new row at index 0 using loc, and then sort the DataFrame by index to maintain the correct order. Finally, we reset the index.

Output:
   A  B
0  0  0
1  1  4
2  2  5
3  3  6
Conclusion

Adding a row at the top of a Pandas DataFrame can be accomplished using several methods. Whether you choose to use pd.concat(), pd.DataFrame._append(), or loc with index shifting, each approach provides a flexible way to manipulate your DataFrame according to your needs.

By mastering these techniques, you can efficiently handle data insertion tasks and enhance your data manipulation skills in Pandas.

Happy coding!

Also Explore:

Leave a Comment