Introduction to Appending DataFrames in Columns

In this article, we will explore the concept of appending dataframes in columns using pandas, a popular Python library for data manipulation and analysis. We will delve into the details of how to achieve this and provide examples along the way.

Understanding DataFrames and Appending

A DataFrame is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet or a SQL table. In pandas, DataFrames are created using the pd.DataFrame() function, which takes in a dictionary where the keys represent the column names and the values represent the corresponding data.

Appending to a DataFrame involves adding new rows to the existing data. However, when we append dataframes in columns, we want to add new columns instead of just new rows. This is where things get interesting!

The Problem with Original Code

The original code attempts to achieve this by using the append() method, which adds a single row at a time. However, this approach has several issues:

• It creates multiple DataFrames and then concatenates them together, resulting in slow performance.
• It doesn’t create new columns; instead, it tries to append to existing columns, leading to inconsistent data types.

The Solution: Creating New Columns

To achieve our goal, we need to create a new column for each iteration of the loop. We can do this by using a list comprehension to generate a list of values and then assigning that list to a new column in the DataFrame.

import pandas as pd
import random

# Create an empty DataFrame
df = pd.DataFrame([])

# Define the performance data
perf = [650, 875, 400, 200, 630, 950, 850, 800]

# Loop through each iteration of the performance data
for i in range(0,8):
    # Generate a list of values for the new column
    values = [random.randint(100, 1000) for val in perf]
    
    # Create a new column with the generated values
    df['Pp'+str(i)] = values

# Print the resulting DataFrame
print(df)

Understanding the Code

Let’s break down what’s happening in this code:

• We first import the necessary libraries: pandas and random.
• We create an empty DataFrame using pd.DataFrame([]).
• We define a list called perf, which contains our performance data.
• We loop through each iteration of the perf list using range(0,8).
• Inside the loop, we use a list comprehension to generate a list of values for the new column. This is done by iterating over each value in the perf list and generating a random integer between 100 and 1000.
• We then create a new column with the generated values using df['Pp'+str(i)] = values.
• Finally, we print the resulting DataFrame.

Output

When you run this code, you should see an output that looks something like this:

   Pp0  Pp1  Pp2  Pp3  Pp4  Pp5  Pp6  Pp7
0  963  394  165  750  918  687  637  164
1  642  217  154  455  173  807  995  649
2  508  399  833  853  686  834  529  992
3  688  178  328  101  469  559  455  844
4  145  113  416  927  503  882  725  326
5  171  548  394  952  459  725  460  625
6  189  129  136  541  280  131  956  356
7  906  562  779  773  412  423  429  769

As you can see, this output has eight columns and sixty-four rows, just like the desired output.

Conclusion

In conclusion, appending dataframes in columns using pandas requires a bit of creative thinking. By using list comprehensions to generate new values for each column and then assigning those values to the DataFrame, we can achieve our goal of creating multiple columns from a single dataset. This technique is useful when working with large datasets or when you need to add new columns dynamically.

I hope this article has provided you with a comprehensive understanding of how to append dataframes in columns using pandas. Happy coding!

Last modified on 2023-09-21