Tuesday Tip #40: Build your DataFrame from multiple files ๐Ÿ—๏ธ

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๐Ÿ‘‰ Tip #40: Build a DataFrame from multiple files

Letโ€™s say that your dataset is spread across multiple files, but you want to read the dataset into a single pandas DataFrame.

For example, I have a tiny dataset of stock market data in which each CSV file only includes a single day. Hereโ€™s the first day:

Hereโ€™s the second day:

And hereโ€™s the third day:

You could read each CSV file into its own DataFrame, combine them together, and then delete the original DataFrames, but that would be memory inefficient and require a lot of code.

A better solution is to use Pythonโ€™s built-in glob module:

You can pass a pattern to the glob() function, including wildcard characters, and it will return a list of all files that match that pattern.

In this case, glob() is looking in the โ€œdataโ€ subdirectory for all CSV files that start with the word โ€œstocksโ€ followed by one or more characters:

glob returns filenames in an arbitrary order, which is why we sorted the list using Pythonโ€™s built-in sorted() function.

We can then use a generator expression to read each of the files using read_csv() and pass the results to the concat() function, which will concatenate the rows into a single DataFrame:

Unfortunately, there are now duplicate values in the index. To avoid that, we can tell the concat() function to ignore the index and instead use the default integer index:

Pretty cool, right?

Need to build a DataFrame column-wise instead? Use the same code as above, except pass axis='columns' to concat()!

๐Ÿ‘‹ Until next time

Did you like this weekโ€™s tip? Please forward it to a friend or share this link in your favorite Slack team. It really helps me out! ๐Ÿ™Œ

See you next Tuesday!

- Kevin

P.S. Would you wear pajamas during a Zoom call?โ€‹

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