import pandas as pd import numpy as np import re #Modulo de Expresiones Regulares

moviesDf = pd.read_csv('movies_dataset.csv', sep=',')

#Verificamos el dataset moviesDf moviesDf.head()

moviesDf.info()

#Función para extraer y concatenar 'name' def extract_concatenate_name(value): try: pattern = r"'name':\s+'(.*?)'" match = re.findall(pattern, value) return ', '.join(match) except (TypeError, AttributeError): return ''

#Normalizamos la columna "belongs_to_collection" moviesDf['collection'] = moviesDf['belongs_to_collection'].apply(extract_concatenate_name)

moviesDf['collection'].head(10)

#Normalizamos la columna "genres" moviesDf['genre'] = moviesDf['genres'].apply(extract_concatenate_name)

moviesDf['genre'].head(10)

#Normalizamos la columna la columna "production_companies" moviesDf['company'] = moviesDf['production_companies'].apply(extract_concatenate_name)

moviesDf['company'].head(10)

#Normalizamos la columna "production_countries" moviesDf['country'] = moviesDf['production_countries'].apply(extract_concatenate_name)

moviesDf['country'].head(10)

#Normalizamos la columna "spoken_languages" moviesDf['language'] = moviesDf['spoken_languages'].apply(extract_concatenate_name)

moviesDf['language'].head(10)

moviesDf.head(0)

moviesDf['budget'].fillna(0, inplace=True) print('# de valores NULL en el campo budget:', moviesDf.budget.isna().sum())

moviesDf['revenue'].fillna(0, inplace=True) print('# de valores NULL en el campo revenue:', moviesDf.revenue.isna().sum())

#Damos formato a todos los valores de la columna 'release_date' #A los que no se les puede dar formato se les asigna NaT (Not a Time) moviesDf['release_date'] = pd.to_datetime(moviesDf['release_date'], errors='coerce')

#Eliminamos los registros NaT moviesDf.dropna(subset = ['release_date'], inplace=True)

#Verificamor que no existan NaT moviesDf['release_date'].isnull().sum()

#Verificamos que todos los registros tengan el mismo formato AAAA-MM-DD moviesDf['release_date'] = pd.to_datetime(moviesDf['release_date']).dt.strftime('%Y-%m-%d')

#Creamos la columna 'release_year' que va a contener el año de la columna 'release_date' moviesDf['release_year'] = pd.to_datetime(moviesDf['release_date']).dt.year

#Verificamos las columnas 'release_date','release_year' moviesDf[['release_date','release_year']].tail()

#Damos formato a los registros de las columnas 'budget' y 'revenue' a tipo numerico #A los que no se les puede dar formato se les asigna el valor NaN (Not a Number) moviesDf['budget'] = pd.to_numeric(moviesDf['budget'], errors='coerce') moviesDf['revenue'] = pd.to_numeric(moviesDf['revenue'], errors='coerce')

#Realizamos el calculo del retorno de inversión 'return' moviesDf['return'] = moviesDf['revenue']/moviesDf['budget'] moviesDf['return'].fillna(0, inplace=True)

moviesDf = moviesDf[['id', 'title', 'tagline', 'overview', 'runtime', 'original_language', 'genre', 'collection', 'status', 'release_date', 'release_year', 'popularity','vote_average', 'company', 'country', 'language', 'revenue', 'budget', 'return']]

moviesDf.head()

movie_listing = moviesDf.to_csv('movie_listing.csv', index=False, header=True, sep=';')