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64 lines
2.2 KiB
64 lines
2.2 KiB
import pandas as pd
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import numpy as np
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def vectorize_mult(column, dictionary, postfix, df, file=None):
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newCol = column + postfix
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df[newCol] = df[column].map(dictionary)
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if file is not None:
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df.to_csv('cleaned/{}.csv'.format(file))
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csv = pd.read_csv("cleaned/notnormalized_with_continuousNan_rpe.csv")
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# vectorize_mult("Training", {"No": 0, "Yes": 1}, "", csv)
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#
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# mapping = {"Mobility/Recovery": 1, "Game": 0, "Skills": 0, "Conditioning": 0,
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# "Strength": 0, "Combat": 0, "Speed": 0, np.nan: 0}
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# vectorize_mult("SessionType", mapping, "Mobility/Recovery", csv)
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# mapping["Mobility/Recovery"] = 0
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# mapping["Game"] = 1
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# vectorize_mult("SessionType", mapping, "Game", csv)
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# mapping["Game"] = 0
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# mapping["Skills"] = 1
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# vectorize_mult("SessionType", mapping, "Skills", csv)
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# mapping["Skills"] = 0
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# mapping["Conditioning"] = 1
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# vectorize_mult("SessionType", mapping, "Conditioning", csv)
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# mapping["Conditioning"] = 0
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# mapping["Strength"] = 1
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# vectorize_mult("SessionType", mapping, "Strength", csv)
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# mapping["Strength"] = 0
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# mapping["Combat"] = 1
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# vectorize_mult("SessionType", mapping, "Combat", csv)
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# mapping["Combat"] = 0
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# mapping["Speed"] = 1
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# vectorize_mult("SessionType", mapping, "Speed", csv)
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# mapping["Speed"] = 0
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# mapping[np.nan] = 1
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# vectorize_mult("SessionType", mapping, "Unknown", csv)
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# mapping[np.nan] = 0
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#
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# mapping = {"Not at all": 1, "Absolutely": 0, "Somewhat": 0, np.nan: 0}
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# vectorize_mult("BestOutOfMyself", mapping, "NotAtAll", csv)
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# mapping["Not at all"] = 0
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# mapping["Absolutely"] = 1
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# vectorize_mult("BestOutOfMyself", mapping, "Absolutely", csv)
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# mapping["Absolutely"] = 0
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# mapping["Somewhat"] = 1
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# vectorize_mult('BestOutOfMyself', mapping, "Somewhat", csv)
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# mapping["Somewhat"] = 0
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# mapping[np.nan] = 1
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# vectorize_mult('BestOutOfMyself', mapping, "Unknown", csv)
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# mapping[np.nan] = 0
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#
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# csv.to_csv("cleaned/notnormalized_with_continuousNan_rpe.csv")
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for i in range(len(csv.dtypes)):
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type = csv.dtypes[i]
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if type != "object":
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colname = csv.columns[i]
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if csv[colname].hasnans:
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print(colname)
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csv[colname] = csv[colname].fillna(0)
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#print(csv.isnull().sum())
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csv.to_csv("cleaned/notnormalized_with_0Nan_rpe.csv")
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