Adding Rows to a Pandas DataFrame Row by Row

Table of Contents#

Core Concepts
Typical Usage Methods
Common Practices
Best Practices
Code Examples
Conclusion
FAQ
References

Core Concepts#

A Pandas DataFrame is a two - dimensional labeled data structure with columns of potentially different types. Each row in a DataFrame can be thought of as a record, and adding rows row by row means appending new records to the existing DataFrame.

The underlying data in a DataFrame is stored in a NumPy array, and when you add a row, Pandas needs to manage memory allocation, type checking, and index updates. This can have performance implications, especially when adding a large number of rows, as it may involve copying the entire DataFrame multiple times.

Typical Usage Methods#

Using `append()` Method#

The append() method in Pandas can be used to add a single row (or multiple rows) to a DataFrame. You can pass a dictionary, a Series, or another DataFrame to the append() method.

Using `loc[]` Indexer#

The loc[] indexer can be used to add a new row at a specific index. If the index does not exist, Pandas will create a new row with the given values.

Common Practices#

Adding a Single Row as a Dictionary#

When adding a single row, it is common to represent the row as a dictionary where the keys are the column names and the values are the corresponding data.

Adding Rows in a Loop#

If you have a list of data points and you want to add them to a DataFrame one by one, you can use a loop to iterate over the list and add each data point as a row.

Best Practices#

Use a List of Dictionaries#

Instead of adding rows one by one in a loop, it is more efficient to create a list of dictionaries representing the rows and then convert the list to a DataFrame in one go. This reduces the number of memory re - allocations.

Consider Performance#

When adding a large number of rows, avoid using the append() method in a loop as it can be very slow. Instead, use the pd.concat() function with a list of DataFrames.

Code Examples#

import pandas as pd
 
# Create an empty DataFrame
df = pd.DataFrame(columns=['Name', 'Age', 'City'])
 
# Method 1: Using append() with a dictionary
new_row = {'Name': 'John', 'Age': 30, 'City': 'New York'}
df = df.append(new_row, ignore_index=True)
 
# Method 2: Using loc[]
df.loc[len(df)] = ['Alice', 25, 'Los Angeles']
 
# Adding rows in a loop
data = [
    {'Name': 'Bob', 'Age': 35, 'City': 'Chicago'},
    {'Name': 'Eve', 'Age': 22, 'City': 'Miami'}
]
 
for row in data:
    df = df.append(row, ignore_index=True)
 
# Best practice: Using a list of dictionaries
data_list = [
    {'Name': 'Frank', 'Age': 40, 'City': 'Dallas'},
    {'Name': 'Grace', 'Age': 28, 'City': 'Seattle'}
]
new_df = pd.DataFrame(data_list)
df = pd.concat([df, new_df], ignore_index=True)
 
print(df)

In the above code:

First, we create an empty DataFrame with three columns: 'Name', 'Age', and 'City'.
We then add a row using the append() method with a dictionary.
Next, we add a row using the loc[] indexer.
After that, we use a loop to add multiple rows using the append() method.
Finally, we demonstrate the best practice of creating a list of dictionaries, converting it to a DataFrame, and then concatenating it with the original DataFrame.

Conclusion#

Adding rows to a Pandas DataFrame row by row is a common operation in data analysis. While there are multiple ways to achieve this, it is important to understand the performance implications of each method. For small datasets, using append() or loc[] can be sufficient. However, for large datasets, it is recommended to use a list of dictionaries and pd.concat() to improve performance.

FAQ#

Q1: Why is adding rows in a loop using `append()` slow?#

A1: The append() method creates a new DataFrame each time it is called, which involves copying the existing data. When done in a loop, this leads to multiple memory re - allocations and copies, resulting in poor performance.

Q2: Can I add rows with missing values?#

A2: Yes, if you pass a dictionary or a Series with missing keys (column names), Pandas will fill the corresponding cells with NaN values.

Q3: How can I add a row with a specific index?#

A3: You can use the loc[] indexer and specify the index value. For example, df.loc['new_index'] = [value1, value2, value3].

References#

Pandas official documentation: https://pandas.pydata.org/docs/
Python Data Science Handbook by Jake VanderPlas

This blog provides a comprehensive guide on adding rows to a Pandas DataFrame row by row, covering core concepts, usage methods, best practices, and code examples. By following these guidelines, developers can efficiently handle row - by - row data addition in real - world scenarios.