I was wondering if there has been a progression of Black movies being made since the question why is the oscars so white . I wondered what it would look like to see the black films being made throughout the years. It was hard to say what qualifies as a black movies as there are a significant amount of movies made that targetted a black audience but wasn’t necessarily created by black artist....
It is time to do a little clean up with the information collected from the Scraper. Another pass through using Director Film Dataframe to see if there are new movies to add and to add IMDB id to director film list import pandas as pd from imdb import Cinemagoer import re import os.path ia = Cinemagoer() file = 'missing_directors.csv' check_missing_directors_file = os.path.isfile(file) df_director_films = pd.read_csv('imdb_movies_directors.csv', encoding= 'unicode_escape') df_director_names = pd.read_csv('director_names.csv', encoding='unicode_escape') for name in df_director_names....
import pandas as pd import re import imdb import time ia = imdb.Cinemagoer() df_director_films = pd.read_csv('Director_Movies_Clean.csv', encoding= 'utf-8-sig') df_director_names = pd.read_csv('director_names.csv', encoding='unicode_escape') kinds = {} df_director_films Movie_ID Title Year Person_ID Name 0 3501074 Madam Secretary (2015-2017) 0 151 Morgan Freeman 1 106464 Bopha! 1993 151 Morgan Freeman 2 995854 A Journal for Jordan 2021 243 Denzel Washington 3 2671706 Fences 2016 243 Denzel Washington 4 413573 Grey's Anatomy 2016 243 Denzel Washington ....
I worked on this project to get an understanding on what exactly is involved in Data Engineering. I found this very helpful “Uber Data Analytics” youtube tutorial that went through all the steps from beginning to end of Extract, Transform, and Load of this Uber dataset. I still need to add in the steps to this post, but for now is an outline of what I completed. import pandas as pd df = pd....
For this regression data analysis, I will be using a dataset of homeless population in New Orleans from 1960 until today. The dataset contains information on the year, the estimated homeless population, and the number of homeless shelters in the city. Before we start analyzing the data, let’s import the necessary libraries and load the dataset: import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns Load the dataset df = pd....