5 Best Ways to Append a DataFrame Row to Another DataFrame in Python

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💡 Problem Formulation: When working with pandas DataFrames in Python, a common operation is appending a row from one DataFrame to another. Suppose you have two DataFrames, df1 and df2, where df1 contains data regarding monthly sales and df2 holds a new entry for the current month. The goal is to append the row from df2 to df1 to update the sales record effectively.

Method 1: Using DataFrame.append()

The DataFrame.append() method is a straightforward way to add a single row or multiple rows to the end of a DataFrame. It doesn’t modify the original DataFrame but returns a new DataFrame instead. This method maintains the DataFrame’s structure by aligning the columns.

Here’s an example:

import pandas as pd

# Existing DataFrame
df1 = pd.DataFrame({'Month': ['Jan', 'Feb', 'Mar'], 'Sales': [200, 210, 190]})
# DataFrame to append
df2 = pd.DataFrame({'Month': ['Apr'], 'Sales': [220]})

# Appending df2 to df1
result = df1.append(df2, ignore_index=True)
print(result)

Output:

  Month  Sales
0   Jan    200
1   Feb    210
2   Mar    190
3   Apr    220

This code snippet creates two DataFrames, df1 and df2, with sales data for different months. The append() method is used to add df2 to df1, creating a new DataFrame result with the combined data. The ignore_index=True parameter is optional, but it creates a new continuous index for the resulting DataFrame.

Method 2: Using pandas.concat()

The pandas.concat() function is more versatile than append() and can concatenate along a particular axis while performing optional set logic. This approach is suitable when you’re dealing with multiple DataFrames or Series objects that you want to stack together vertically or horizontally.

Here’s an example:

import pandas as pd

# Existing DataFrame
df1 = pd.DataFrame({'Month': ['Jan', 'Feb', 'Mar'], 'Sales': [200, 210, 190]})
# DataFrame to append
df2 = pd.DataFrame({'Month': ['Apr'], 'Sales': [220]})

# Concatenating df1 and df2
result = pd.concat([df1, df2], ignore_index=True)
print(result)

Output:

  Month  Sales
0   Jan    200
1   Feb    210
2   Mar    190
3   Apr    220

In this example, the pd.concat() function is used to combine df1 and df2 into a single DataFrame result. The ignore_index=True parameter resets the index of the resultant DataFrame, much like in append().

Method 3: Using DataFrame.loc[]

The DataFrame.loc[] property is a powerful indexing feature in pandas that allows you to access a group of rows and columns by labels or a boolean array. You can use it to append a new row by specifying a new index that does not exist in the original DataFrame.

Here’s an example:

import pandas as pd

# Existing DataFrame
df1 = pd.DataFrame({'Month': ['Jan', 'Feb', 'Mar'], 'Sales': [200, 210, 190]})
# New row to append
new_row = {'Month': 'Apr', 'Sales': 220}

# Appending new_row to df1 using loc
df1.loc[len(df1)] = new_row
print(df1)

Output:

  Month  Sales
0   Jan    200
1   Feb    210
2   Mar    190
3   Apr    220

This snippet demonstrates appending a new row to df1 using the loc[] indexer. The expression len(df1) provides the next index value which doesn’t exist in df1, effectively appending the new data as the last row of the DataFrame.

Method 4: Using DataFrame.iloc[] and numpy

The combination of DataFrame.iloc[], which allows integer-location based indexing, and the numpy library can also achieve row appendage. By creating a numpy array from the new row’s data, it can be added at a specific integer index position at the end of the DataFrame.

Here’s an example:

import pandas as pd
import numpy as np

# Existing DataFrame
df1 = pd.DataFrame({'Month': ['Jan', 'Feb', 'Mar'], 'Sales': [200, 210, 190]})
# New row as numpy array
new_row = np.array(['Apr', 220])

# Appending new row to df1 using iloc
df1.iloc[len(df1)] = new_row
print(df1)

Output:

  Month  Sales
0   Jan    200
1   Feb    210
2   Mar    190
3   Apr    220

In the above code snippet, df1 is appended with a new row created from a numpy array. Although similar to Method 3, this approach utilizes numpy for array creation, which can be convenient when dealing with numerical computations or complex data manipulations.

Bonus One-Liner Method 5: Using direct assignment with index

Python’s direct assignment can also be utilized to append a row to a DataFrame by simply adding a new index and assigning the row’s values. This method is the most straightforward and least verbose.

Here’s an example:

import pandas as pd

# Existing DataFrame
df1 = pd.DataFrame({'Month': ['Jan', 'Feb', 'Mar'], 'Sales': [200, 210, 190]})
# Row to append
new_row = {'Month': 'Apr', 'Sales': 220}

# Appending new_row to df1 using direct assignment
df1.loc[df1.index.max() + 1] = new_row
print(df1)

Output:

  Month  Sales
0   Jan    200
1   Feb    210
2   Mar    190
3   Apr    220

With this elegant one-liner, the DataFrame, df1, is effortlessly appended with the new row by merely assigning the row’s values to a new index, calculated to be one greater than the maximum current index.

Summary/Discussion

  • Method 1: DataFrame.append(): Simple to use. Creates a new DataFrame. May be less efficient with large data due to data copying.
  • Method 2: pandas.concat(): More flexible with multiple objects. Can concatenate along different axes. Potentially more overhead than append().
  • Method 3: DataFrame.loc[]: Effective and intuitive for appending single rows. Does not return a new DataFrame, which can save memory.
  • Method 4: DataFrame.iloc[] and numpy: Good for numerical data or when numpy is already being used. Slightly more complex due to numpy array creation.
  • Method 5: Direct assignment: Quick and elegant for simple row appendage. Ideal for relatively few row insertions.