5 Best Ways to Insert a Row at the Top of a Python DataFrame

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💡 Problem Formulation:

When working with data in Python, it’s common to use Pandas DataFrames. Occasionally, there is a need to insert a new row of data at the top of an existing DataFrame. This could be due to a late arrival of important data points that need to be analyzed first or to emphasize new observations. Given a DataFrame, the problem is how to efficiently insert a new row at the top without disrupting the integrity of the existing data. For example, if your initial DataFrame contains monthly sales data, the input may look like a table of figures, and the desired output is a similar table with a new row added at the top to include the most recent month’s figures.

Method 1: Using pd.concat()

An effective way to add a row on top of a DataFrame is by concatenating the existing DataFrame with the new row DataFrame using the pd.concat() function. It provides flexibility and works well with larger DataFrames.

Here’s an example:

import pandas as pd

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

# The new row to insert
new_row = pd.DataFrame({'A': [0], 'B': [0]})

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

The output will be:

   A  B
0  0  0
1  1  3
2  2  4

This snippet creates a new DataFrame named new_row and concatenates it to the top of the existing DataFrame df. The reset_index(drop=True) part is used to reset the index of the new DataFrame so that it starts at 0 and avoids keeping the old index.

Method 2: Using the iloc[] indexer

For a direct approach to insert a row at a specific index, the iloc[] indexer combined with the append() and sort_index() functions can be used. This method is straightforward and quite readable.

Here’s an example:

new_row = pd.Series({'A': 0, 'B': 0})
df = df.append(new_row, ignore_index=True)
df = df.sort_index(axis=0, ascending=False).reset_index(drop=True)

The output:

   A  B
0  0  0
1  1  3
2  2  4

This method first appends the row using df.append() which adds the row at the bottom. Then, it sorts the DataFrame by index in descending order with df.sort_index() to bring the new row to the top. Finally, reset_index(drop=True) resets the index to start at 0.

Method 3: Using loc[] indexer with reindexing

The loc[] indexer can be used for reindexing to include the new row at the desired position. It works by assigning the new row to an index that precedes the DataFrame’s first index.

Here’s an example:

df.index = df.index + 1  # Shift the index
df.loc[0] = [0, 0]  # Insert the new row
df = df.sort_index()  # Sort the index

The output:

   A  B
0  0  0
1  1  3
2  2  4

Using df.index + 1, we shift all existing row indices by 1. Then we set the new row data at index 0 using df.loc[0]. After inserting the row, we sort the DataFrame indices so that the new row is positioned at the top.

Method 4: Recreating DataFrame with pd.DataFrame

Sometimes simply recreating the DataFrame by combining the new row with the existing data can be the simplest solution, especially if you’re dealing with small DataFrames and performance is not a concern.

Here’s an example:

df = pd.DataFrame([[0, 0]] + df.values.tolist(), columns=df.columns)

The output:

   A  B
0  0  0
1  1  3
2  2  4

This code snippet creates a list with the new row [0, 0] followed by the existing data rows converted to a list. It then creates a new DataFrame from this combined list using the same column names as the original.

Bonus One-Liner Method 5: Using pd.DataFrame.insert()

If you’re looking for a one-liner, the insert() method can come in handy, but it is generally used to insert columns. However, with a clever workaround, it can also be used to insert rows by transposing the DataFrame, inserting the column (which is the row transposed), and transposing it back.

Here’s an example:

df = df.T.insert(0, 'new_row', [0, 0]).T

The output:

   new_row  A  B
0        0  1  3
1        0  2  4

This one-liner transposes the DataFrame, inserts a new row, and then transposes it back to its original form. Note that this results in a new index column called ‘new_row’, so additional steps might be needed to adjust the DataFrame to the intended format.

Summary/Discussion

  • Method 1: Using pd.concat(). Strengths: Offers flexibility and maintains data integrity in larger DataFrames. Weaknesses: Slightly more complex syntax.
  • Method 2: Using the iloc[] indexer. Strengths: Readable code and straightforward appending process. Weaknesses: Requires sorting which may be less optimal for very large DataFrames.
  • Method 3: Using loc[] indexer with reindexing. Strengths: Direct control over DataFrame indices. Weaknesses: Increased complexity in handling indices and potential performance impact on larger DataFrames.
  • Method 4: Recreating DataFrame with pd.DataFrame. Strengths: Simple solution, good for small datasets. Weaknesses: Not efficient for large DataFrames as it recreates the entire DataFrame.
  • Bonus One-Liner Method 5: Using pd.DataFrame.insert(). Strengths: Quick one-liner for small-scale data manipulations. Weaknesses: Not intuitive, improper for this purpose, and may require additional processing.