Python Tutorial: Building DataFrames from scratch

Want to learn more? Take the full course at https://learn.datacamp.com/courses/pandas-foundations at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- We've seen how to work with DataFrames in memory. But how do we get them in memory? In the Intermediate Python for Data Science course, we used read_csv to load a DataFrame from a comma-separated-values file. For instance, here we use a file users dot csv to create a DataFrame called users. The file records visitors to a blog for a band and who signed up for the newsletter. By tracking where visitors come from, this information can help design tours later. DataFrames can also be rolled by hand using dictionaries. Remember, dictionaries (or associative arrays) are a core data structure in Python. Here, we construct a dictioary of lists with the same users data. The keys of the dictionary data are used as column labels. Notice, with no index specified, the row labels are the integers zero to three by default. Let's build the DataFrame users up a different way, using conforming lists cities, signups, visitors and weekdays for the column data. It is useful to be able to build DataFrames from lists because lists are a common Python data structure; it's natural that we might receive data accumulated in lists. We can then define two other lists: list_labels (containing the column labels) and list_cols (containing the column entries for each column). Notice list_cols is a list of lists. Using Python's list and zip functions constructs a list called zipped of tuples (column names and columns) to feed to the dict command. Calling dict(zipped) creates a dict data which is then used with pd dot DataFrame to build the DataFrame. Let's look again at broadcasting, a convenient technique in NumPy & Pandas. With users in memory, a new column, say fees, can be created on the fly. By using the new column label fees and by assigning the scalar value zero, th