Python Tutorial: Power and flexibility

Want to learn more? Take the full course at https://learn.datacamp.com/courses/pandas-joins-for-spreadsheet-users at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this video you'll learn how the flexibility and power of pandas can make you more effective and more productive. Pandas is excellent for working with large datasets. Even with a typical laptop you can manipulate millions of rows of data and go well beyond the size limit of spreadsheets. There's no hard limit on data frame size. If the data is too large for your machine to handle, you can scale up to a machine with more memory and more processing power. If that's not an option, pandas has built-in functions to work with data in chunks. For example, the pandas .read_csv() function includes a parameter to set the chunk size. You can use this parameter in combination with other functions and Python code to conserve memory. Even when you reach a limit with Pandas alone, you can combine it with other packages to take advantage of distributed computing and parallel processing. Handling datasets with hundreds of millions of rows is quite possible! Pandas can also save you time when joining data. You can join datasets by any number of columns if the data logically matches. For instance, if you want to join data by month and day, and both datasets contain those columns, you can join directly on the columns. There's no need to create a new column for the date or combine text columns as you might for a spreadsheet. The code behind pandas is written to make things as easy as possible. When two data frames have the same names for overlapping columns, the statement can be super simple. This merge statement joins two data frames by their common columns and common row indexes. We refer to the first data frame mentioned as the left data frame, and the data frame inside the parentheses as the right data frame. This basic example is just a st