Movie

Data

The IMBD data is here.

• I unzipped before importing.

The filmtv data is here.

• I checked a few titles and I think joining on title would be pretty successful.

Variables added

• Genres :contains list of multiple genres the film is apart of.
• production_companies :which production companies are involved.
• production_countries :location of companies.
• cast_names :contians list of all actors and actresses names.
• mf_ratio :female to male ratio of actors and actresses.
• gender_lead :the leads gender.
• animation :If the movie is animatied.

What next

I think if we get a list of

• top actors
• most funny actors
• most attractive actors
• actors with their award count
• movie award count and combine it with cast or on index we'll have a very interesting set of variables.

Code explained

def get_out(list,key):
  new_list=[]
  for i in list:
    new_list.append(i[key])
  return new_list

Gets value out of a list of dictionaries.

def clean_it_even(list_char,element):
  for i in list_char:
    element=element.replace(i,"")
  element=element.split(",")
  del element[::2]
  for i in range(len(element)):
    element[i]=element[i].replace("name:","")
  return element

Designed for that weird dictionary format in imdb_movies.

takes in list of charecters you want to get rid of and a string.

iterates over the list of charecters and removes them.

splits element by comma.

deletes ever other element from list created from split.

gets rid of 'name:' in each element of list.

returns list.

and odd does the same but odd instead of even in a list.

list_of_char=['[',']','{','}','"', " "]

The bad list of charecters.

sum(get_out(x,"gender"))/(2*len(x)

sum a list of genders (genders in a 1 and 2 context) and then divided by length to get avg and then divided by 2 to get ratio.

df_new['cast'] = df_new['cast'].apply(ast.literal_eval)

converts row to normal python form instead of string.