My first project as a Data Science student @ Flatiron

  • bom.movie_gross.csv.gz
  • imdb.name.basics.csv.gz
  • imdb.title.akas.csv.gz
  • imdb.title.basics.csv.gz
  • imdb.title.crew.csv.gz
  • imdb.title.principals.csv.gz
  • imdb.titlee.ratings.csv.gz
  • tmdb.movies.csv.gz
  • tn.movie_budgets.csv.gz
  • rt.movies_info.tsv.gz
  • rt_reviews.tsv.gz
Snapshot of my first attempt to clean a dataframe in Jupyter Notebook using pandas
Info() output for the DataFrame df_bm
Snapshot from Jupyter Notebook showing the sum of NaN values in the df_bm dataframe
Snapshot from Jupyter Notebook showing the sum of NaN values per column
Creating the SQL Database with the cleaned 11 dataset
Preliminary SQL Schema
SQL query with df output below
First dataframe showing the mean of the worldwide_gross and profit per season
Second dataframe showing mean of the worldwide_gross and profit per calendar month
Code for creating subplot using two different dataframes and the Seaborn library
Subplots created in Seaborn diplaying the mean profit per season (left) and mean profit per month (right)
Code for 4x1 subplots illustrating the mean profit per month separated by seasons
Mean profit per month plotted per season

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Data Scientist formerly an engineer with 20 years of experience in the energy industry. Fan of ML and DL and everything related to AI

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Javier Herbas

Javier Herbas

Data Scientist formerly an engineer with 20 years of experience in the energy industry. Fan of ML and DL and everything related to AI

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