Data Transformation

Data transformation is a crucial step in the data ingestion and ETL process. It involves converting raw data into a format that is suitable for analysis and processing. Data engineers perform various transformations to enhance the quality and usefulness of the data.

In the world of data science, Python is a popular programming language for data transformation tasks. Let's take a look at an example of how you can perform data transformation using Python and the pandas library.

1import pandas as pd
2
3# Read data from a CSV file
4# Apply data transformation
5# Print the transformed data

In the code snippet above, we start by reading data from a CSV file using the read_csv() function from the pandas library. Once we have the data, we can apply various transformation operations to manipulate the data according to our requirements.

For example, consider a scenario where we have a dataset of sales records, and we want to apply a 10% discount to the sales amount. We can use the apply() function along with a lambda function to apply the transformation to each value in the 'sales' column.

1transformed_data = data['sales'].apply(lambda x: x * 0.1)

In this example, we multiply each value in the 'sales' column by 0.1 to apply the 10% discount. The result is a new column called 'transformed_data' that contains the transformed values.

After performing the data transformation, you can continue with further analysis and processing tasks on the transformed data. Data transformation is a crucial step that allows you to derive meaningful insights and make informed decisions based on the data.

Remember, data transformation is not limited to simple mathematical operations. You can perform a wide range of transformations, such as data cleaning, normalization, aggregation, and feature engineering, depending on the specific requirements of your project.

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if __name__ == '__main__':

    import pandas as pd

​

    # Read data from a CSV file

    data = pd.read_csv('data.csv')

​

    # Apply data transformation

    transformed_data = data['sales'].apply(lambda x: x * 0.1)

​

    # Print the transformed data

    print(transformed_data)