From the course: Python for Data Science and Machine Learning Essential Training Part 1
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Concatenating and transforming - Python Tutorial
From the course: Python for Data Science and Machine Learning Essential Training Part 1
Concatenating and transforming
- [Voiceover] Knowing how to concatenate and transform data is really important in data analysis. Concatenation and data transformation are useful for getting your data into the structure and order you need for analysis. For example, imagine you're mailing out a piece of direct mail advertisement. You have one table with customer ID and name, and you have another table with customer ID, mailing address, and age. Your mailing address application requires you to supply it only one table that contained only customer name and address. You generate this table by concatenating your two tables by customer ID, row wise. Concatenating is simply combining data from separate sources. Transformation, on the other hand, is converting and reformatting data to the format necessary for your purposes. When you transform your data, you convert it into the format that's required to facilitate analysis. This could include dropping data, which is essentially just dropping variables or observations, can…
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