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Append DataFrame Pandas: How to Add Rows and Columns Like a Pro

Rajiv Chandra
Name
Rajiv Chandra

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Pandas, a highly efficient open-source Python library, is a go-to tool for data scientists worldwide. Its power lies in the flexibility and ease of manipulating structured data. The DataFrame, one of Pandas' fundamental data structures, is widely used due to its ability to handle large data sets efficiently.

One common task while working with Pandas DataFrames is appending data. This operation can involve adding rows, adding columns, or even appending entire DataFrames. It can seem quite challenging at first, but once you master the append function, it's a breeze. So, let's dive deep into how we can leverage the DataFrame append function in Pandas.

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Pandas DataFrame Append Function

The Pandas DataFrame append function is used to append rows of other DataFrame objects and returns a new DataFrame. It's essential to understand that this function does not alter the original DataFrame but creates a new one that combines the original and the appended data.

Syntax of Append Function in Pandas

The basic syntax for append() function is as follows:

DataFrame.append(other, ignore_index=False, sort=False)
  • other: This can be a DataFrame, Series, dictionary, or list of these, defining the data to append.
  • ignore_index: If True, the resulting DataFrame’s index will be labeled 0, 1, …, n. Default is False.
  • sort: This defines whether to sort the non-concatenation axis. The default is False.

Let’s see the append function in action through an example.

Append Row to DataFrame

import pandas as pd
 
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
                    'B': ['B0', 'B1', 'B2'],
                    'C': ['C0', 'C1', 'C2'],
                    'D': ['D0', 'D1', 'D2']},
                    index=[0, 1, 2])
 
df2 = pd.DataFrame({'A': 'A3',
                    'B': 'B3',
                    'C': 'C3',
                    'D': 'D3'},
                    index=[3])
 
df1 = df1.append(df2)
 
print(df1)

In the above example, df1 is the original DataFrame, and df2 is appended to it. The resulting DataFrame will include all the rows of df1 and df2.

Append Multiple DataFrames

Appending multiple DataFrames is also a straightforward process. The append function can take a list of DataFrames to append together. Consider the following example:

df3 = pd.DataFrame({'A': 'A4',
                    'B': 'B4',
                    'C': 'C4',
                    'D': 'D4'},
                    index=[4])
 
df1 = df1.append([df2, df3])
 
print(df1)

In this example, we're appending df2 and df3 to df1 simultaneously.

DataFrame Append vs Concat in Pandas

You might wonder about the difference between the append() and concat() functions in Pandas, as both seem to serve a similar purpose. While append() is essentially a specific case of concat(), the concat() function provides more flexibility, such as the ability to add data along either the row axis (axis=0) or column axis (axis=1). This broader functionality means that concat() can be a more powerful tool for more complex data manipulation tasks. However, for simple appending tasks, append() is often more than sufficient.

Append Column to DataFrame Pandas

Appending a column to a DataFrame can be achieved by simply assigning data to a new column in the DataFrame. For instance:

df1['E'] = ['E0', 'E1', 'E2', 'E3', 'E4']
print(df1)

In this example, a new column 'E' is added to df1. The new column is initialized with values 'E0', 'E1', 'E2', 'E3', 'E4'.

Append Output of For Loop in a Python DataFrame

You can also append the output of a for loop to a DataFrame. This can be useful in scenarios where you're processing or generating data in a loop. Let’s take a look at an example:

df = pd.DataFrame(columns = ['A', 'B', 'C'])
 
for i in range(5):
    df = df.append({'A': i, 'B': i*2, 'C': i+3}, ignore_index=True)
 
print(df)

In this example, for each iteration of the loop, a new row is created and appended to the DataFrame df.

Best Practices for Appending DataFrame Rows in Pandas

While the append() function is an easy-to-use tool for adding data to a DataFrame, it might not always be the most efficient. That's because append() always returns a new DataFrame, and in the case of appending rows in a loop, this can lead to significant memory consumption.

In scenarios where you need to append a large number of rows, it's often more efficient to create a list of the rows, and then create a DataFrame in one go:

rows_list = []
for i in range(100000):
    dict1 = {'A': i, 'B': i*2, 'C': i+3}
    rows_list.append(dict1)
 
df = pd.DataFrame(rows_list)

In this example, the DataFrame is created only once, saving memory and processing time.

How to Merge Pandas DataFrame Using Append()

While merge() and join() functions are specifically designed for merging or joining DataFrames, the append() function can also achieve this if the DataFrames have the same columns:

df1 = df1.append(df2, ignore_index=True)

In this example, df2 is appended at the end of df1, effectively merging the two DataFrames.

Conclusion

In this article, we've covered a lot of ground on how to use the append() function in Pandas to add rows and columns to a DataFrame, how to append multiple DataFrames, how to add a column, and how to append output from a for loop. Remember that while append() is convenient and easy to use, in cases of large data sets, other methods might be more efficient.

Frequently Asked Questions

  1. What is Pandas DataFrame append function used for?

    The Pandas DataFrame append function is used to append rows of other DataFrame objects to the end of the given DataFrame, returning a new DataFrame object. It doesn't modify the original DataFrame; instead, it creates a new one that includes the original and appended data.

  2. Can you append multiple DataFrames using append()?

    Yes, you can append multiple DataFrames using the append() function. It can take a list of DataFrames to append together.

  3. Is it recommended to use append() method to add data to a DataFrame?

    While the append() function is easy to use and suitable for many cases, for large DataFrames, it might not be the most efficient method because it always returns a new DataFrame. Instead, consider creating a list of rows or columns and then converting this list to a DataFrame in one go.