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33 lines
1.1 KiB
33 lines
1.1 KiB
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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