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import pandas as pd
from datetime import datetime
def process_actors_data(keep_alive: bool = True):
current_year = datetime.now().year
# Read actors data
df = pd.read_csv("data/name.basics.tsv", sep="\t")
df["birthYear"] = pd.to_numeric(df["birthYear"], errors="coerce")
df["deathYear"] = pd.to_numeric(df["deathYear"], errors="coerce")
# Prepare and cleanup actors data
if keep_alive:
df = df[df["deathYear"].isna()]
df = df[df.knownForTitles.apply(lambda x: len(x)) > 0]
df = df.dropna(subset=["primaryProfession"])
df = df[df.primaryProfession.apply(lambda x: any([p in {"actor", "actress"} for p in x.split(",")]))]
df = df[df.knownForTitles != "\\N"]
df = df.dropna(subset=["birthYear"])
#df["knownForTitles"] = df["knownForTitles"].apply(lambda x: x.split(","))
#dfat = df[["nconst", "knownForTitles"]].explode("knownForTitles")
#dfat.columns = ["nconst", "tconst"]
dfat = pd.read_csv("data/title.principals.tsv.gz", sep="\t")
dfat = dfat[dfat.category.isin(["actor", "actress", "self"])][["tconst", "nconst"]]
# Get data for the movies/shows the actors were known for
dftr = pd.read_csv("data/title.ratings.tsv", sep="\t")
dftb = pd.read_csv("data/title.basics.tsv", sep="\t")
dftb["startYear"] = pd.to_numeric(dftb["startYear"], errors="coerce")
dftb["endYear"] = pd.to_numeric(dftb["endYear"], errors="coerce")
# Estimate last year the show/movie was released (TV shows span several years and might still be active)
dftb.loc[(dftb.titleType.isin(["tvSeries", "tvMiniSeries"]) & (dftb.endYear.isna())), "lastYear"] = current_year
dftb["lastYear"] = dftb["lastYear"].fillna(dftb["startYear"])
dftb = dftb.dropna(subset=["lastYear"])
dftb = dftb[dftb.isAdult == 0]
# Aggregate stats for all movies the actor was known for
dft = pd.merge(dftb, dftr, how="inner", on="tconst")
del dftb, dftr
dfat = pd.merge(dfat, dft, how="inner", on="tconst")
del dft
dfat["totalRating"] = dfat.averageRating*dfat.numVotes
dfat = dfat.groupby("nconst").agg({"averageRating": "mean", "totalRating": "sum", "numVotes": "sum", "tconst": "count", "startYear": "min", "lastYear": "max"})
# Merge everything with actor data and cleanup
df = df.drop(["deathYear", "knownForTitles", "primaryProfession"], axis=1)
df = pd.merge(df, dfat, how="inner", on="nconst").sort_values("totalRating", ascending=False)
df = df.dropna(subset=["birthYear", "startYear", "lastYear"])
df[["birthYear", "startYear", "lastYear"]] = df[["birthYear", "startYear", "lastYear"]].astype(int)
df = df.round(2)
return df
if __name__ == "__main__":
df = process_actors_data()
df.to_csv("data/imdb_actors.csv", index=False) |