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