From the course: Data Analysis with Python and Pandas

Unlock this course with a free trial

Join today to access over 24,900 courses taught by industry experts.

Key takeaways

Key takeaways

From the course: Data Analysis with Python and Pandas

Start my 1-month free trial Buy for my team

Key takeaways

“

- [Instructor] All right, so we just walked through a lot on importing and exporting data. Let's summarize with some key takeaways. The read_csv function is capable of significant data preprocessing. Once you've cleaned your data, take time to incorporate those steps into the import phase to save time and memory the next time you work with the data. Pandas lets you easily read in and write to flat files like CSV and Excel. Just make sure to specify the correct delimiter for .tsv and .txt files and the desired sheet name for your Excel workbooks. You can create a DataFrame from any SQL query. With the help of SQLAlchemy, we can create a connection to any SQL database, and the read_sql function lets us pass a query into the database to create a DataFrame. This is what many real world Pandas workflows are built on. And finally, many additional formats can be read in using Pandas. If you ever need to tackle a JSON file or a pickle file, check the documentation or Google some examples…

Contents