Pandas | How To Make Good Reproducible Pandas Examples?

In this tutorial, we will learn How to make well -reproducible pandas examples As Pandas is one of the most important libraries of python which is very useful when we have to deal with manipulating data which could be numerical, time or information related to anything.



Here we have given some very useful reproducible examples in pandas involves so let’s us understand them one by one with some examples given below.

  1. Importing the necessary libraries: Importing means loading the library to the project we created and Import it and any other libraries that are required for your example. It is important to include version information for these libraries, as different versions may produce different results.
  2. Creating a sample dataset: As it work on data manipulations so we need to Create a sample dataset that can be used to demonstrate the functionality of it. This dataset should be small enough to be easily understood and reproduced.
  3. Providing code that reproduces the example: Provide a code snippet that demonstrates its functionality of it using the sample dataset. The code should be self-contained and include any necessary imports.
  4. Including comments and explanations: Include comments and explanations throughout the code to make it easier for others to understand what the code is doing.
  5. Using random seeds: If your example involves random data or functions, set a random seed to ensure that the results are reproducible.
  6. Providing expected output: Provide the expected output for your example to help others understand what the code should be producing.
  7. Using a Jupyter Notebook or Python script: Provide your example in a Jupyter Notebook format, which makes it easier for others to run and reproduce.

To understand the concerts and the things done above we can simply follow the code to get a clear idea of it how it works and what are the features of it.

# Import libraries
import pandas as pd
import  numpy as np

print(" panda version:", pd .__version__)
print(" numpy version:", np .__version__)

# Create sample dataset
df = pd. DataFrame({'name': ['Alice', 'Bob', 'Charlie', 'David'],
                   'age': [25, 30, 35, 40]})

# Set random seed
np.random. seed(42)

# Add a new column with random values
df['score'] = np.random. randint(0, 100, size=len(df))

# Sort by score in descending order
df_sorted = df.sort_values( by='score', ascending= False)

# Print expected output
print( df_sorted. head())

It imports it library and performs useful operations which we need to perform and then produces the result as we want.

To learn more about It visit Pandas in python by stack overflow.

To learn more about it solutions and tutorials on different concepts visit: Python Tutorials And Problems to get the list of tutorials we want.

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