How to Append Data to a Pandas DataFrame in Python

Understanding Pandas DataFrames and Appending Data

When working with Pandas data frames, it’s essential to understand how they are created, manipulated, and appended. In this article, we’ll explore the basics of Pandas data frames and discuss a common issue that arises when trying to append data from multiple excel files.

Introduction to Pandas DataFrames

A Pandas DataFrame is a two-dimensional labeled data structure with columns of potentially different types. It’s similar to an Excel spreadsheet or a table in a relational database. Each column represents a variable, and each row represents an observation or record. The DataFrame class provides various methods for filtering, sorting, grouping, merging, reshaping, and pivoting data.

Creating a Pandas DataFrame

There are several ways to create a Pandas DataFrame:

From a dictionary: A dictionary can be passed directly to the DataFrame constructor.
From a list of lists: A list of lists can be used to create a DataFrame where each inner list represents a row in the data frame, and the first element of each inner list is assumed to be the index label.
From an Excel file: The pd.read_excel() function can be used to read an Excel file into a DataFrame.

Appending Data to a Pandas DataFrame

Appending data to a Pandas DataFrame involves creating new rows or adding existing columns while keeping other unchanged.

Using the Append Method

When working with pandas, it is often useful to have multiple dataframes that contain different pieces of information. There are many ways to combine these into one dataframe, however appending is the most common.

Here’s an example of how you can append new rows to an existing DataFrame:

import pandas as pd
import os

# create an empty DataFrame
df = pd.DataFrame()

# read multiple Excel files and append data to df
for filename in os.listdir('data1'):
    if filename.endswith(".xls"):
        print(f'appending {filename}')
        data = pd.read_excel(os.path.join("data1", filename), sheet_name=0)
        display(data)
        # Append the new DataFrame to the existing one
        df = df.append(data)

In this example, df.append(data) is used to append each new DataFrame (data) to the existing one. However, as we’ll see later, there’s a problem with this approach.

Problem: df is an empty DataFrame

When trying to use df.append(data), it turns out that df remains an empty DataFrame throughout the process. This issue lies in how append() works internally, and it can be easily fixed using a different method.

The Solution: Using Concatenation

Instead of df.append(data), you should use df = pd.concat([df, data]). Here’s why:

When you pass a DataFrame to the append() function in pandas, it creates an entirely new DataFrame. It doesn’t modify the original DataFrame.
So if we were trying to “append” our new dataframe (data) to the existing one (df), what was actually happening was that a brand new, empty DataFrame (new_df = pd.DataFrame()) was being passed instead of df.
And when you’re concatenating DataFrames using pd.concat([df, data]), pandas will add all rows from each DataFrame, maintaining their original order.

Example:

import pandas as pd
import os

# create an empty DataFrame
df = pd.DataFrame()

for filename in os.listdir('data1'):
    if filename.endswith(".xls"):
        print(f'appending {filename}')
        data = pd.read_excel(os.path.join("data1", filename), sheet_name=0)
        # Append using concatenation
        df = pd.concat([df, data])

This way, df will contain all rows from the multiple Excel files.

Frequently Asked Questions

Q: How do I add a column of values that are based on another value in the same row?

A: You can use the assign() method:

df = df.assign(new_column=df['value1'])

Or if you want to perform some operation before adding a new column, use the apply() function:

df['new_column'] = df['value1'].apply(operation)

Q: How do I sort a DataFrame by multiple columns?

A: Use the sort_values() method with keyword arguments for sorting:

df = df.sort_values(by=['column1', 'column2'], ascending=False)

Q: How can I merge two DataFrames on specific values in their respective rows?

A: Use the merge() function:

merged_df = pd.merge(df1, df2, on='value')

This approach assumes you’re merging based on one common value. If there are many columns to match on, consider using a more advanced merge strategy like the inner or left method.

Q: How can I handle missing values in a DataFrame?

A: There are several strategies for handling missing values:

Dropping rows with missing values (df.dropna())
Filling missing values with a specific value (df.fillna())

For more information and examples, see the pandas documentation.

Last modified on 2023-06-09