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Append data to an empty Pandas DataFrame

Pandas Append Rows & Columns to Empty DataFrame

Last Updated : 19 Nov, 2024

Appending rows and columns to an empty DataFrame in pandas is useful when you want to incrementally add data to a table without predefining its structure. To immediately grasp the concept, here’s a quick example of appending rows and columns to an empty DataFrame using the concat() method, which is frequently used and highly efficient.

Python

import pandas as pd df = pd.DataFrame()   # an empty DataFrame # Append columns by directly assigning values df['Name'] = ['Alice', 'Bob'] df['Age'] = [30, 22]  # appending columns  # Create new rows as separate DataFrames new_row1 = pd.DataFrame({'Name': ['Charlie'], 'Age': [28]}) new_row2 = pd.DataFrame({'Name': ['David'], 'Age': [35]})  df = pd.concat([df, new_row1, new_row2], ignore_index=True) # Appending new rows using concat() display(df)

Output:

Append-Rows-Columns-to-Empty-DataFrame

Append Rows & Columns to Empty DataFrame

In this example:

Columns are added directly by assigning lists to column names.
Rows are appended using the concat() method, which is efficient and recommended for appending rows.

There are multiple ways to append rows and columns to an empty Pandas DataFrame, here we will implement each of them.

Using Loc for Appending Rows to an Empty DataFrame

The concat() function is the recommended method for appending rows, especially since append() has been deprecated since Pandas version 2.0. We have already seen an example using concat. Now Using loc[] for Rows, that allows to append rows by specifying the index explicitly.

Python

import pandas as pd # Create an empty DataFrame with column names df = pd.DataFrame(columns=['Name', 'Age'])  # Append rows using loc[] df.loc[0] = ['Alice', 30] df.loc[1] = ['Bob', 22]  print(df)

Output:

    Name  Age
0  Alice   30
1    Bob   22

Appending Columns to an Empty DataFrame

Using Direct Assignment for Columns is the simplest and most frequently used method to append columns to a DataFrame, as demonstrated in the first example. Now, let’s look at other methods for appending columns dynamically to a DataFrame.

1. Using `assign()` for Columns

The assign() method allows you to add one or more columns to a DataFrame in a chainable manner.

Python

import pandas as pd df = pd.DataFrame()  # Append columns using assign() df = df.assign(Name=['Alice', 'Bob'], Age=[30, 22]) print(df)

Output:

    Name  Age
0  Alice   30
1    Bob   22

2. Using `insert()` for Columns

The insert() method allows you to add a column at a specific position in the DataFrame.

Python

import pandas as pd  # Create an empty DataFrame with some columns df = pd.DataFrame({'Name': ['Alice', 'Bob']})  # Insert a new column at position 1 (second column) df.insert(1, 'Age', [30, 22]) print(df)

Output:

    Name  Age
0  Alice   30
1    Bob   22

Summary: Adding Rows and Columns to an Empty DataFrame:

Method	Description
Direct Assignment (Columns)	Easiest way to append columns by assigning values directly.
`assign()` (Columns)	Allows adding multiple columns in a chainable manner; returns a new DataFrame.
`insert()` (Columns)	Adds a column at a specific position in the DataFrame.
`concat()` (Rows)	Efficiently appends rows by concatenating two or more DataFrames.
`loc[]` (Rows)	Appends rows by specifying the index and assigning values directly.

Appending Rows and Columns to Empty DataFrame : Practical Examples

Example 1: Create a complete empty DataFrame without any column name or indices and then append columns in Pandas one by one to it.

Python

import pandas as pd df = pd.DataFrame() print(df)  # append columns to an empty DataFrame df['Name'] = ['Ankit', 'Ankita', 'Yashvardhan'] df['Articles'] = [97, 600, 200] df['Improved'] = [2200, 75, 100]  print(df)

Output:

Pandas Append Rows & Columns to Empty DataFrame

Appending Rows and Columns to Empty DataFrame

Example 2: This method will create a new Dataframe with a new column added to the old Dataframe using assign in Pandas.

Python

import pandas as pd  # Define a dictionary containing Students data data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'],'Height': [5.1, 6.2, 5.1, 5.2],'Qualification': ['Msc', 'MA', 'Msc', 'Msc']} df = pd.DataFrame(data)  # Using 'Address' as the column name and equating it to the list df2 = df.assign(address=['Delhi', 'Bangalore', 'Chennai', 'Patna']) print(df2)

Output:

Pandas Append Rows & Columns to Empty DataFrame

Appending Rows and Columns to Empty DataFrame

Example 3: Create an empty DataFrame with a column name and indices and then append rows one by one to it using the loc[] method.

Python

import pandas as pd # create an Empty DataFrame object With column names and indices df = pd.DataFrame(columns = ['Name', 'Articles', 'Improved'],         index = ['a', 'b', 'c']) print("Empty DataFrame With NaN values : \n\n", df)  # adding rows to an empty dataframe at existing index df.loc['a'] = ['Ankita', 50, 100] df.loc['b'] = ['Ankit', 60, 120] df.loc['c'] = ['Harsh', 30, 60]  print(df)

Output:

Pandas Append Rows & Columns to Empty DataFrame

Append data to an empty Pandas DataFrame

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Improve

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