Appending a List to a Pandas DataFrame Using Loc in Python

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💡 Problem Formulation: When working with data in Python, you might encounter situations where you need to append a list of values as a new row to an existing Pandas DataFrame. This operation is crucial when aggregating data collected over time or from various sources. For instance, you might have a DataFrame representing weekly sales data, and you want to add a new week’s worth of sales to the DataFrame without recreating it entirely. Below, various methods to achieve this using the loc indexer in Pandas are discussed.

Method 1: Appending a List as a New Row

This method involves directly appending a list as a new row to the DataFrame using the loc indexer. The loc method provides a fast and flexible way to access DataFrame rows and columns. It’s particularly useful when you want to expand your DataFrame by adding new rows.

Here’s an example:

import pandas as pd
df = pd.DataFrame(columns=['A', 'B', 'C'])
new_row = [1, 2, 3]
df.loc[len(df)] = new_row

Output:

   A  B  C
0  1  2  3

This code snippet creates a new DataFrame with empty columns A, B, and C. The list new_row is then appended as a new row to the DataFrame by using the loc indexer with len(df) to reference the next row index.

Method 2: Appending Multiple Lists as Multiple Rows

When you need to append several lists as new rows to your DataFrame, you can iterate through the lists and append each one using the loc indexer. This is a straightforward extension of Method 1 that allows processing multiple entries simultaneously.

Here’s an example:

new_rows = [[4, 5, 6], [7, 8, 9]]
for row in new_rows:
    df.loc[len(df)] = row

Output:

   A  B  C
0  1  2  3
1  4  5  6
2  7  8  9

Using a loop, the code iterates over a list of new rows, appending each one to the DataFrame by referencing its new index with len(df). This builds on the DataFrame created in Method 1, adding two more rows.

Method 3: Using a List of Series

If your new rows are represented as Pandas Series instead of lists, the process remains similar. Each Series must correspond to a row and have the same index labels as the DataFrame’s columns.

Here’s an example:

new_series_rows = [pd.Series([10, 11, 12], index=df.columns), pd.Series([13, 14, 15], index=df.columns)]
for series in new_series_rows:
    df.loc[len(df)] = series

Output:

    A   B   C
0   1   2   3
1   4   5   6
2   7   8   9
3  10  11  12
4  13  14  15

A list of Pandas Series with corresponding column indexes is appended using the loc method in a loop. Each Series is added as a new DataFrame row, demonstrating the DataFrame’s expansion with Series rather than lists.

Method 4: Appending Rows with Missing Data

When appending rows that might contain missing data, it’s important to handle the possibility of NaN values. By using the same loc technique, you can append rows with missing data, and Pandas will automatically handle the NaN values.

Here’s an example:

df.loc[len(df)] = [16, pd.NA, 18]
df.loc[len(df)] = pd.Series([19, 20], index=['A', 'B'])

Output:

      A     B     C
0     1     2     3
...  ...   ...   ...
4    13    14    15
5    16  <NA>    18
6    19    20   NaN

This code continues appending to the same DataFrame, demonstrating how Pandas handles appending rows when some column values are missing or explicitly marked as Pandas’ NA.

Bonus One-Liner Method 5: Appending a List Using at and len

If you prefer more concise code, you can use the at method along with len to quickly append a single row. The at method is used for setting scalar values and is highly optimized for performance.

Here’s an example:

df.at[len(df), :] = [21, 22, 23]

Output:

      A     B     C
0     1     2     3
...  ...   ...   ...
6    19    20   NaN
7    21    22    23

This one-liner appends a new row to the DataFrame using a combination of the at setter and the len function to specify the index at which to insert the row.

Summary/Discussion

  • Method 1: Appending a List as a New Row. This method is simple and effective for single-row insertions. However, for multiple rows, it requires a loop, which can become inefficient for large datasets.
  • Method 2: Appending Multiple Lists as Multiple Rows. This extension of Method 1 is practical for multiple entries but shares the same potential inefficiency with increasing dataset size.
  • Method 3: Using a List of Series. By using Series, data alignment is implicit, reducing errors when appending. It requires the Series to be specially prepared with correct indexing, which adds overhead.
  • Method 4: Appending Rows with Missing Data. This method smoothly handles incomplete data, a common real-world scenario, but necessitates care with data types and NaN handling.
  • Method 5: Bonus One-Liner. While concise, using at is best for single value insertions and may not be intuitive for users accustomed to list-like insertions.