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import numpy as np |
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import pandas as pd |
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def join_cols(): |
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# Reads in csv files to be manipulated |
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dfg = pd.read_csv('data_preparation/data/games_ranked.csv') |
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# Creates the new dataframe where each date is a unique column, and gets the number of dates |
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unique_dates = pd.DataFrame(dfg["Date"].unique()).to_numpy() |
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unique_rows = unique_dates.shape[0] |
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daily_elos = np.array(unique_rows).astype(float) |
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print(unique_rows) |
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# Creates two numpy arrays to perform some operations on |
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dates = dfg["Date"].to_numpy() |
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e_change = dfg["eloChangeAdjusted"].to_numpy() |
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rows = dates.shape()[0] |
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# sums up the elo change on a given day and then exports it to a unique .csv file |
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x = 0 |
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for i in range(0, rows): |
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if not (dates[i] == unique_dates[x]): |
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x = x + 1 |
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daily_elos[x] = daily_elos[x] + e_change[i] |
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# Creates a new dataframe from the two unique date array and the daily elo change array |
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df_dec = pd.DataFrame() |
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df_dec["Date"] = unique_dates |
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df_dec["DailyElo"] = daily_elos |
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print(df_dec) |
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