68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
"""
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Download dataset between 10-20 mb,
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Split it into train/dev/test
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Return dataset info (length, max, min etc.)
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"""
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import string
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn import preprocessing
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import kaggle
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kaggle.api.authenticate()
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kaggle.api.dataset_download_files("pustola/9900-imdb-movies", path=".", unzip=True)
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movies_data = pd.read_csv("imdb_movies.csv")
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# Drop rows with missing values
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movies_data.dropna(inplace=True)
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# Remove not interesting columns
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drop_columns = ["title_id", "certificate", "title", "plot"]
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movies_data.drop(labels=drop_columns, axis=1, inplace=True)
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# Normalize data, lowercase str
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for column_name in ["original_title", "countries", "genres", "director", "cast"]:
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movies_data[column_name] = (
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movies_data[column_name]
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.str.translate(str.maketrans("", "", string.punctuation))
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.str.lower()
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)
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# Remove ',' from votes number and change type to int
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movies_data["votes_number"] = (movies_data["votes_number"].str.replace(",", "")).astype(
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int
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)
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# Normalize number values
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scaler = preprocessing.MinMaxScaler()
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movies_data[["rating", "votes_number", "year", "runtime"]] = scaler.fit_transform(
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movies_data[["rating", "votes_number", "year", "runtime"]]
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)
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# Split set to train/dev/test 6:2:2 ratio and save to .csv file
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train, dev = train_test_split(movies_data, train_size=0.6, test_size=0.4, shuffle=True)
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dev, test = train_test_split(dev, train_size=0.5, test_size=0.5, shuffle=True)
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train.to_csv("train.csv")
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dev.to_csv("dev.csv")
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test.to_csv("test.csv")
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# Get length of given sets
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print(f"Test dataset length: {len(test)}")
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print(f"Dev dataset length: {len(dev)}")
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print(f"Train dataset length: {len(train)}")
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print(f"Whole dataset length: {len(movies_data)}, \n")
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# Print information of given columns
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for column in ["year", "rating", "runtime", "votes_number"]:
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column_data = movies_data[column]
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print(f"Information on {column}")
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print(f"Min: {column_data.min()}")
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print(f"Max: {column_data.max()}")
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print(f"Mean: {column_data.mean()}")
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print(f"Median: {column_data.median()}")
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print(f"Standard deviation: {column_data.std()}, \n")
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