How to add one row in existing Pandas DataFrame? Last Updated : 03 Apr, 2025 Summarize Suggest changes Share Like Article Like Report Adding rows to a Pandas DataFrame is a common task in data manipulation and can be achieved using methods like loc[], and concat(). Method 1. Using loc[] - By Specifying its Index and ValuesThe loc[] method is ideal for directly modifying an existing DataFrame, making it more memory-efficient compared to append() which is now-deprecated. This approach is particularly useful when you know the index where the row should be inserted.Example : Adding One Row in an existing Pandas DataFrame using loc Python import pandas as pd data = {"Name": ["Alice", "Bob"], "Age": [25, 30]} df = pd.DataFrame(data) # Add a new row using loc[] df.loc[len(df)] = ["Charlie", 35] print(df) Output Name Age 0 Alice 25 1 Bob 30 2 Charlie 35 Method 2. Adding Row Using concat() The concat() function merges two DataFrames along rows (or columns). To add a single row, create it as a DataFrame and concatenate it with the original. Ideal for adding multiple rows or when working with external data sources. It avoids modifying the original DataFrame directly. Python import pandas as pd data = {"Name": ["Alice", "Bob"], "Age": [25, 30]} df = pd.DataFrame(data) new_row = pd.DataFrame({"Name": ["Eve"], "Age": [28]}) df = pd.concat([df, new_row], ignore_index=True) print(df) Output Name Age 0 Alice 25 1 Bob 30 2 Eve 28 Additional Examples of Adding Rows to Pandas DataFramesExample 1: Adding a Row with Default ValuesWhen you need to add a placeholder row with default values for further updates or processing. Python import pandas as pd data = {"Name": ["Alice", "Bob"], "Age": [25, 30]} df = pd.DataFrame(data) # Add a placeholder row df.loc[len(df)] = ["Unknown", 0] print(df) Output Name Age 0 Alice 25 1 Bob 30 2 Unknown 0 Example 2: Adding Multiple Rows with concat()Useful when merging datasets from different sources, such as combining population data for different cities, ensuring scalability for multiple rows and avoiding direct modification of the original DataFrame. Python import pandas as pd data = {"Name": ["Alice", "Bob"], "Age": [25, 30]} df = pd.DataFrame(data) # New rows as a DataFrame new_rows = pd.DataFrame({"Name": ["Charlie", "Diana"], "Age": [35, 28]}) df = pd.concat([df, new_rows], ignore_index=True) print(df) Output Name Age 0 Alice 25 1 Bob 30 2 Charlie 35 3 Diana 28 Example 3: Adding a Row with a DictionaryAdding a row dynamically with values generated programmatically. Python import pandas as pd data = {"Product": ["A", "B"], "Price": [100, 150]} df = pd.DataFrame(data) # Add a dynamically generated row new_row = {"Product": "C", "Price": 200} df.loc[len(df)] = new_row print(df) Output Product Price 0 A 100 1 B 150 2 C 200 Example 4: Adding Rows ConditionallyAdding rows only if certain conditions are met. Python import pandas as pd data = {"Employee": ["Alice", "Bob"], "Salary": [5000, 6000]} df = pd.DataFrame(data) # Add a row only if the salary is below a threshold if df["Salary"].max() < 7000: df.loc[len(df)] = ["Charlie", 7000] print(df) Output Employee Salary 0 Alice 5000 1 Bob 6000 2 Charlie 7000 Example 5: Adding Rows Using iloc and Index ReassignmentAdding rows at a specific position in the DataFrame. Python import pandas as pd data = {"City": ["NY", "LA"], "Population": [8_399_000, 3_990_000]} df = pd.DataFrame(data) # Insert a row in the middle df.loc[1.5] = ["Chicago", 2_705_000] # Add at a floating-point index df = df.sort_index().reset_index(drop=True) print(df) Output City Population 0 NY 8399000 1 LA 3990000 2 Chicago 2705000 Example 6: Adding Rows with Missing ColumnsHandling scenarios where the new row has fewer columns than the original DataFrame. Python import pandas as pd data = {"Name": ["Alice", "Bob"], "Age": [25, 30], "City": ["NY", "LA"]} df = pd.DataFrame(data) # Add a row missing one column new_row = {"Name": "Charlie", "Age": 35} # No "City" df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True) print(df) Output Name Age City 0 Alice 25 NY 1 Bob 30 LA 2 Charlie 35 NaN Advertise with us Next Article Pandas GroupBy P parasmadan15 Follow Similar Reads Pandas Tutorial Pandas is an open-source software library designed for data manipulation and analysis. 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