Understanding the AttributeError: ‘DataFrame’ Object Has No Attribute ‘append’

Resolving a Common Pandas Error

Encountering errors while working with Python libraries like Pandas is common. One such error is the AttributeError when dealing with Pandas DataFrames. Today, let’s focus on fixing a specific mistake – using appand instead of the correct method name.

What You Will Learn

In this comprehensive guide, you will grasp how to understand and resolve the AttributeError: ‘DataFrame’ object has no attribute ‘appand’. By the end of this tutorial, you will be adept at correctly appending data to your DataFrame.

Introduction to the Problem and Solution

When handling Pandas DataFrames in Python, tasks like appending rows or merging DataFrames are routine. However, small typos or confusion regarding method names can lead to errors. The error message ‘DataFrame’ object has no attribute ‘appand’ typically occurs due to such mistakes – attempting to use a non-existent method because of a typo.

To tackle this issue effectively, our solution revolves around identifying and rectifying these errors by employing proper syntax and methods provided by Pandas. We will delve into utilizing the .append() method correctly for adding rows to our DataFrame. This entails understanding its parameters and distinguishing its behavior from similar operations like .concat(). Let’s explore some code examples illustrating the correct approach.


import pandas as pd

# Creating sample DataFrames
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5], 'B': [6]})

# Correctly appending df2 onto df1
result_df = df1.append(df2)


# Copyright PHD


Here’s a snippet demonstrating how we can merge two DataFrames (df1 and df2) by appending one onto another using the .append() function from the Pandas library. Note that my previous attempt failed due to typing “appand” instead of “append”. Precision in spelling when invoking library functions is crucial.

The .append() function does not alter the original DataFrames (df1, df2) but generates a new DataFrame (result_df) by vertically stacking them (i.e., placing one on top of the other). For extensive datasets or frequent appends, consider using pd.concat() for improved efficiency as .append() creates a new object upon each invocation.

  1. How do I append multiple rows at once?

  2. You can append an entire DataFrame in one go as shown above or directly combine lists into an existing DataFrame using .loc[], ensuring index uniqueness.

  3. Can I use .append() with Series objects?

  4. Yes! When appending Series objects as rows into your DataFrame, ensure they align properly (same columns) or specify ignore_index=True if column alignment isn’t exact.

  5. Does .append() modify my original DataFrame?

  6. No, .append() returns a new DataFrame without altering your original ones unless explicitly reassigned e.g., df = df.append(…).

  7. What are alternatives for large-scale row additions?

  8. For substantial datasets or repetitive row additions, opt for pandas.concat(), which is optimized for such operations.

  9. Are there any performance considerations with .append()?

  10. Given that .append() creates a new object each time it’s called, it may be less efficient compared to concat(), tailored for handling larger merges more efficiently.


Understanding how to correctly utilize methods like .append() is vital in avoiding errors such as AttributeError: ‘DataFrame’ object has no attribute ‘appand’. By adhering to best practices and experimenting safely, we can harness Pandas’ robust data manipulation features effectively. Remember always to double-check method names for typos!

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