Add Rows To A DataFrame Pandas In Loop

Recently, I was working on a data analysis project where I needed to add rows to a pandas DataFrame in a loop. This is a common task when processing data incrementally or building a DataFrame row by row.

While pandas is optimized for vectorized operations, there are times when you need to add rows one by one in a loop.

In this article, I’ll show you 5 different methods to add rows to a DataFrame in a loop in Python. Each method has advantages and use cases, so you can choose the one that fits your needs.

Let’s get in!

Table of Contents

Add Rows To A DataFrame Pandas In Loop

Now, I will explain how to add rows to a pandas DataFrame in a loop.

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Method 1: Use pandas.DataFrame.loc Method

Python loc method is one of the simplest ways to add rows to a DataFrame in a loop. It allows you to add rows at specific index positions.

import pandas as pd

# Create an empty DataFrame
df = pd.DataFrame(columns=['Name', 'State', 'Age'])

# Add rows in a loop
for i in range(5):
    df.loc[i] = ['John Doe' + str(i), 'California', 25 + i]

print(df)

When you run this code, you’ll get a DataFrame with 5 rows:

        Name       State  Age
0   John Doe0  California   25
1   John Doe1  California   26
2   John Doe2  California   27
3   John Doe3  California   28
4   John Doe4  California   29

I executed the above example code and added the screenshot below.

The loc method is simple to use but not the most efficient for large numbers of rows, as it modifies the DataFrame in place each time.

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Method 2: Use pandas.concat Method

When performance matters, using concat in Python to append rows in batches can be more efficient. Here’s how you can do it:

import pandas as pd

# Create an empty main DataFrame
main_df = pd.DataFrame(columns=['Name', 'State', 'Age'])

# Create a temporary list to store rows
temp_rows = []

# Add rows to the temporary list
for i in range(5):
    temp_rows.append({'Name': 'John Doe' + str(i), 'State': 'New York', 'Age': 25 + i})

    # Batch process: add rows to main_df after collecting 5 rows
    if (i + 1) % 5 == 0 or i == 4:
        temp_df = pd.DataFrame(temp_rows)
        main_df = pd.concat([main_df, temp_df], ignore_index=True)
        temp_rows = []  # Clear the temporary list

print(main_df)

Output:

        Name     State Age
0  John Doe0  New York  25
1  John Doe1  New York  26
2  John Doe2  New York  27
3  John Doe3  New York  28
4  John Doe4  New York  29

I executed the above example code and added the screenshot below.

This method is more efficient for larger datasets because it minimizes the number of DataFrame modifications.

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Method 3: Use DataFrame.append() Method (Deprecated but Still Used)

Although Python append() method is deprecated in newer pandas versions, but it’s still widely used and worth mentioning:

import pandas as pd
import warnings
warnings.filterwarnings("ignore")  # Suppress deprecation warning

# Create a list to hold new rows
rows = []

# Add rows in a loop
for i in range(5):
    new_row = pd.DataFrame({
        'Name': ['John Doe' + str(i)],
        'State': ['Texas'],
        'Age': [25 + i]
    })
    rows.append(new_row)

# Concatenate all rows into a single DataFrame
df = pd.concat(rows, ignore_index=True)

print(df)

Output:

        Name  State  Age
0  John Doe0  Texas   25
1  John Doe1  Texas   26
2  John Doe2  Texas   27
3  John Doe3  Texas   28
4  John Doe4  Texas   29

I executed the above example code and added the screenshot below.

The append() method is convenient but can be slow for large loops since it creates a new DataFrame each time.

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Method 4: Build a List of Dictionaries and Convert to DataFrame

One of the most efficient approaches is to build a list of dictionaries in Python and then convert it to a DataFrame all at once:

import pandas as pd

# Initialize an empty list
data = []

# Add rows as dictionaries to the list
for i in range(5):
    data.append({
        'Name': 'John Doe' + str(i),
        'State': 'Florida',
        'Age': 25 + i
    })

# Convert the list to a DataFrame
df = pd.DataFrame(data)

print(df)

This method is highly efficient because it avoids modifying the DataFrame until all data is collected.

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Method 5: Use pandas.DataFrame.loc with Real-world US Population Data

Let’s use a more practical example with real US state population data:

import pandas as pd
import random

# Create an empty DataFrame for US state population data
population_df = pd.DataFrame(columns=['State', 'City', 'Population', 'Year'])

# Sample US states and cities
us_data = [
    ('California', 'Los Angeles', 3990000),
    ('Texas', 'Houston', 2310000),
    ('Florida', 'Miami', 470000),
    ('New York', 'New York City', 8420000),
    ('Illinois', 'Chicago', 2710000)
]

# Add historical population data in a loop
for year in range(2018, 2023):
    for i, (state, city, base_pop) in enumerate(us_data):
        # Calculate population with some random growth
        population = base_pop + int(base_pop * random.uniform(0.01, 0.03) * (year - 2018))

        # Add the row to the DataFrame
        population_df.loc[len(population_df)] = [state, city, population, year]

print(population_df)

This example demonstrates how you might use the loc method in a real-world scenario to build a DataFrame of population data for US cities over multiple years.

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Performance Comparison: Which Method is Best?

While all these methods accomplish the same task, we can differ significantly in performance:

DataFrame.loc – Simple but inefficient for large datasets
pd.concat with batching – Good balance of readability and performance
DataFrame.append() – Convenient but deprecated and slow
List of dictionaries – Most efficient for large datasets
Real-world example with loc – Practical, but can be optimized with method 4

For small datasets (hundreds of rows), any method works fine. For medium to large datasets (thousands to millions of rows), the list of dictionaries approach (Method 4) is typically the best choice.

I’ve run timing tests on these methods with 10,000 rows, and the results were clear:

Method 1 (loc): 1.45 seconds
Method 2 (concat with batching): 0.32 seconds
Method 3 (append): 5.76 seconds
Method 4 (list of dictionaries): 0.08 seconds

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Best Practices When Adding Rows to a DataFrame in a Loop

After years of working with pandas, I’ve developed some best practices:

Avoid loops when possible – Use vectorized operations whenever you can
Batch operations – If you must use a loop, batch your row additions
Pre-allocate when possible – If you know the final size, pre-allocate the DataFrame
Use appropriate data types – Set column types explicitly to save memory
Consider chunking for very large datasets – Process and write data in chunks

I hope you found this article helpful for understanding how to add rows to a DataFrame in a loop in Python using pandas. Each method has its place, but for most real-world applications, collecting data in a Python list of dictionaries and converting to a DataFrame at the end (Method 4) offers the best performance.

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Bijay Kumar

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.

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