How to Create Test and Train Samples from One DataFrame with Pandas

If you are working with a large dataset in pandas, it is often necessary to split the data into training and testing sets for machine learning or other analytical tasks. In this article, we will go over how to create test and train samples from one DataFrame using pandas.

Step 1: Import the necessary libraries

Before we can start splitting the data, we need to import the pandas library. If you do not have it installed, you can do so by running the following command in your terminal:


$ pip install pandas


Step 2: Load the data into a DataFrame

Once we have pandas installed, we can load our data into a DataFrame. This can be done by reading in a CSV file, querying a database, or any other method that you prefer. For this example, let’s assume we have a CSV file called ‘data.csv’ that contains our dataset.


import pandas as pd
data = pd.read_csv('data.csv')


Step 3: Split the data into test and train sets

Now that we have our data loaded into a DataFrame, we can split it into test and train sets. We can do this using the train_test_split method from the scikit-learn library.


from sklearn.model_selection import train_test_split

# Split the data into train and test sets
train, test = train_test_split(data, test_size=0.2)


In this example, we are splitting the data into 80% training and 20% testing sets. You can adjust the test_size parameter to fit your specific needs.

Step 4: Use the train and test sets for analysis

Now that we have our train and test sets, we can use them for any analytical tasks that we need. For example, we can use the training set to train a machine learning model and then use the testing set to evaluate its performance.

By following these steps, you can easily create test and train samples from one DataFrame with pandas. This can be incredibly useful for a wide range of analytical tasks, and it can save you a lot of time and effort when working with large datasets.