How to Replace Pandas Append With Concat?

9 minutes read

To replace pandas append with concat, you can use the pd.concat() function instead. This function combines DataFrames along a particular axis, allowing you to concatenate multiple DataFrames into one. Simply pass a list of DataFrames to pd.concat() and specify the axis along which you want to concatenate them (0 for rows, 1 for columns). This way, you can avoid using the append method and achieve the same result more efficiently.

Best Python Books to Read in 2024

1
Fluent Python: Clear, Concise, and Effective Programming

Rating is 5 out of 5

Fluent Python: Clear, Concise, and Effective Programming

2
Learning Python, 5th Edition

Rating is 4.9 out of 5

Learning Python, 5th Edition

3
Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming

Rating is 4.8 out of 5

Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming

4
Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners

Rating is 4.7 out of 5

Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners

  • Language: english
  • Book - automate the boring stuff with python, 2nd edition: practical programming for total beginners
  • It is made up of premium quality material.
5
Python 3: The Comprehensive Guide to Hands-On Python Programming

Rating is 4.6 out of 5

Python 3: The Comprehensive Guide to Hands-On Python Programming

6
Python Programming for Beginners: The Complete Guide to Mastering Python in 7 Days with Hands-On Exercises – Top Secret Coding Tips to Get an Unfair Advantage and Land Your Dream Job!

Rating is 4.5 out of 5

Python Programming for Beginners: The Complete Guide to Mastering Python in 7 Days with Hands-On Exercises – Top Secret Coding Tips to Get an Unfair Advantage and Land Your Dream Job!

7
Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

Rating is 4.4 out of 5

Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

8
Python All-in-One For Dummies (For Dummies (Computer/Tech))

Rating is 4.3 out of 5

Python All-in-One For Dummies (For Dummies (Computer/Tech))

9
Python QuickStart Guide: The Simplified Beginner's Guide to Python Programming Using Hands-On Projects and Real-World Applications (QuickStart Guides™ - Technology)

Rating is 4.2 out of 5

Python QuickStart Guide: The Simplified Beginner's Guide to Python Programming Using Hands-On Projects and Real-World Applications (QuickStart Guides™ - Technology)

10
The Big Book of Small Python Projects: 81 Easy Practice Programs

Rating is 4.1 out of 5

The Big Book of Small Python Projects: 81 Easy Practice Programs


What is the best practice for replacing append with concat in pandas?

The best practice for replacing append with concat in pandas is to use the concat function whenever you need to combine multiple dataframes along a specific axis. This is because the concat function is more versatile and efficient than the append method, especially when dealing with a large number of dataframes or when concatenating along multiple axes.


Here is an example of how to replace append with concat:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
import pandas as pd

# Using append
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})

result = df1.append(df2)

# Using concat
result = pd.concat([df1, df2])


In this example, both append and concat achieve the same result of combining df1 and df2 along the rows. However, using concat is generally preferred as it allows for more flexibility and control over the concatenation process.


What is the output of concatenating empty dataframes in pandas?

When you concatenate empty DataFrames in pandas, the resulting DataFrame will also be empty.


How to concatenate dataframes with matching column names in pandas?

You can concatenate dataframes with matching column names in pandas using the pd.concat() function. This function takes a list of dataframes as input and concatenates them along a specified axis. In this case, you would want to concatenate the dataframes along the axis that represents columns (axis=1).


Here's an example of how you can concatenate two dataframes with matching column names:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
import pandas as pd

# Create two dataframes with matching column names
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})

# Concatenate the dataframes along the columns axis
result = pd.concat([df1, df2], axis=1)

print(result)


In this example, the pd.concat() function is used to concatenate df1 and df2 along the columns axis. The resulting dataframe result will have the columns 'A' and 'B' from both df1 and df2.


Alternatively, you can also use the merge() function to concatenate dataframes with matching column names. Here's an example using the merge() function:

1
2
3
result = pd.merge(df1, df2, left_index=True, right_index=True)

print(result)


In this example, the merge() function is used to concatenate df1 and df2 based on their index. The resulting dataframe result will have the columns 'A' and 'B' from both df1 and df2.


What is the default axis value for concat in pandas?

The default axis value for the concat function in pandas is 0, which means concatenation will be done along the rows (index).

Facebook Twitter LinkedIn Whatsapp Pocket

Related Posts:

To add multiple series in pandas correctly, you can follow these steps:Import the pandas library: Begin by importing the pandas library into your Python environment. import pandas as pd Create each series: Define each series separately using the pandas Series ...
To parse a CSV (comma-separated values) file into a pandas dataframe, you can follow these steps:Import the pandas library: Begin by importing the pandas library using the following command: import pandas as pd Load the CSV file into a dataframe: Use the read_...
To effectively loop within groups in pandas, you can use the groupby() function along with a combination of other pandas functions and methods. Here's a brief explanation of how to achieve this:First, import the pandas library: import pandas as pd Next, lo...