5 Best Ways to Append New Rows to DataFrame Using a Template in Python Pandas

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πŸ’‘ Problem Formulation: Data manipulation often involves adding new rows to an existing DataFrame in Python’s Pandas library. Users may need to add multiple entries with shared characteristics or based on a predefined template. For example, suppose we have a DataFrame representing a classroom’s student records. We may want to append a new row for each additional student joining the class, using a template that includes default values or structures.

Method 1: Using DataFrame.append()

Appending new rows to a DataFrame through the append() method involves creating a template as a DataFrame or a dictionary, and then using this method to append it to the original DataFrame. This method is straightforward and widely used for its simplicity and readability.

Here’s an example:

import pandas as pd

# Original DataFrame
df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [25, 30]})

# Template for the new row
new_row = {'Name': 'Charlie', 'Age': 28}

# Append the new row to the DataFrame
df = df.append(new_row, ignore_index=True)

print(df)

The output:

      Name  Age
0    Alice   25
1      Bob   30
2  Charlie   28

This example demonstrates appending a new row to the existing DataFrame by providing a dictionary that maps column names to new data values. The ignore_index=True argument is used to re-index the DataFrame automatically.

Method 2: Using pd.concat()

The pd.concat() function is powerful for combining DataFrames along a particular axis. When it comes to appending rows, this method is particularly useful when you have multiple rows to append as it is optimized for concatenation operations and can be more efficient than the append() method.

Here’s an example:

import pandas as pd

# Original DataFrame
df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [25, 30]})

# Template DataFrame for new rows
new_rows = pd.DataFrame({'Name': ['Charlie', 'Denise'], 'Age': [28, 22]})

# Append the new rows using concat
df = pd.concat([df, new_rows], ignore_index=True)

print(df)

The output:

      Name  Age
0    Alice   25
1      Bob   30
2  Charlie   28
3   Denise   22

In this snippet, we create a new DataFrame from a list of dictionaries, corresponding to the rows to be appended. The pd.concat() function is then called with both DataFrames as arguments, producing a combined DataFrame.

Method 3: Using DataFrame’s loc[] method

The loc[] method is particularly useful for appending rows based on an index label. This technique is perfect for cases where the index of the DataFrame is meaningful, and you want to preserve its context when appending new data.

Here’s an example:

import pandas as pd

# Original DataFrame with an index
df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [25, 30]}, index=['student1', 'student2'])

# Template for the new row with an index label
new_row = pd.Series({'Name': 'Charlie', 'Age': 28}, name='student3')

# Append the new row
df = df.append(new_row)

print(df)

The output:

             Name  Age
student1    Alice   25
student2      Bob   30
student3  Charlie   28

This code adds a new row using the loc[] method where we specify the index label for the new entry directly. The result is that the new row is added with a specified index, preserving the DataFrame context.

Method 4: Using DataFrame.iloc[] and numpy.nan

For more control over the index and to handle the possibility of missing data, we can employ the iloc[] method in combination with NumPy’s nan to append a new row. This approach is beneficial when dealing with larger DataFrames and you want to preallocate the size for performance reasons.

Here’s an example:

import pandas as pd
import numpy as np

# Original DataFrame
df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [25, 30]})

# Prepare a new row with numpy.nan and set values afterward
df.loc[df.index[-1] + 1] = np.nan
df.iloc[-1] = pd.Series({'Name': 'Charlie', 'Age': 28})

print(df)

The output:

      Name  Age
0    Alice   25
1      Bob   30
2  Charlie   28

The code first adds a new row filled with np.nan to prepare the DataFrame for the new data. It then uses the iloc[] method to populate the last row with the actual data we want to append.

Bonus One-Liner Method 5: Using pd.DataFrame.append() with a List of Series

This one-liner method is a concise and elegant way to append multiple rows using a list of Pandas Series. It’s very handy when you need to append just a few rows without much preparation.

Here’s an example:

import pandas as pd

# Original DataFrame
df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [25, 30]})

# List of Series to append
new_rows = [pd.Series(['Charlie', 28], index=df.columns), pd.Series(['Denise', 22], index=df.columns)]

# Append the list of Series
df = df.append(new_rows, ignore_index=True)

print(df)

The output:

      Name  Age
0    Alice   25
1      Bob   30
2  Charlie   28
3   Denise   22

This code snippet showcases appending new rows by creating a list of Pandas Series objects, which represent the new rows. Each Series must have the same index as the DataFrame columns to align the data correctly.

Summary/Discussion

  • Method 1: DataFrame.append(). Strengths: Simple and readable for small additions. Weaknesses: Not the most efficient for a large number of appends.
  • Method 2: pd.concat(). Strengths: Highly efficient for concatenating multiple data structures. Weaknesses: Slightly more complex syntax.
  • Method 3: loc[] method. Strengths: Provides index control and is intuitive for adding single rows. Weaknesses: Can be slower when appending many rows.
  • Method 4: iloc[] method with np.nan. Strengths: Offers preallocation of DataFrame for better performance with large data. Weaknesses: Involves multiple steps and handling of np.nan.
  • Bonus Method 5: One-liner with a list of Series. Strengths: Extremely concise for adding multiple rows. Weaknesses: Requires that all data for each row be known upfront and structured correctly.