It's time we learn how to store our data in structured form. For this, we'll use the pandas library, a very neat and visual way to structure tabula data.
You can find the pandas documentation here.
The examples in this section are from the website Rotten Tomatoes, as shown in the course.
Suppose we have a list for each movie information: a list for the movies titles (movie_names); a list for the movies years (years); a list for the movies scores (scores); another for the movies adjusted scores (final_adj); a list for the movies directors (final_directors); a list for the movies synopsis (synopsis_text); a list for the movies casts (cast) and another list for the movies consensus (consensus_text).
We'll create a pandas DataFrame to store the information on these movies - this means 'populating' it. This, in turn, will present itself as a table. The lists will be "columns" and the "keys" are the table head.
import pandas as pd
pd.set_option('display.max_colwidth', -1)
movies_info = pd.DataFrame()
movies_info["Movie Title"] = movies_names
movies_info["Year"] = years
movies_info["Score"] = scores
movies_info["Adjusted Score"] = final_adj
movies_info["Director"] = final_directors
movies_info["Synopsis"] = synopsis_text
movies_info["Cast"] = cast
movies_info["Consensus"] = consensus_textPS: pandas abreviate any text that does not fit into the screen. To make it not "truncate" it, we have to set an option: pd.set_option('display.max_colwidth', -1)
Now, data analysis can be carried on. It is desirable to expor the data to CSV (Comma Separated Values) or Excel, so we have it as file. And pandas provide simple methods to convert dataframes into these formats: .to_csv() and .to_excel(). They will create a file located in the same folder of your python code.
This is how it's done in our case. Check below.
# Exporting the DataFrame to a CSV file:
movies_info.to_csv("movies_info.csv", index=False, header=True)
# Exporting the DataFrame to an Excel file:
movies_info.to_excel("movies_info.xlsx", index=False, header=True)