Jeffery Russell 5 years ago
parent
commit
7afec92078
2 changed files with 51 additions and 0 deletions
  1. +18
    -0
      data_preparation/cleaned/time_series_days_ranked.csv
  2. +33
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      elo_per_day.py

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data_preparation/cleaned/time_series_days_ranked.csv View File

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,Date,DailyElo
0,121,0.0
1,122,-3.714599999999998
2,178,0.04346000000000028
3,179,2.1916710000000013
4,180,0.0
5,255,0.0
6,256,0.0
7,257,-2.520374784999996
8,263,-2.0880156214999985
9,264,-1.7032140593500005
10,284,-0.6130256153877235
11,285,2.620463284090865
12,311,-2.076954630427971
13,312,1.0960590427574828
14,313,1.8954531384817344
15,353,-0.2940921753664384
16,354,-1.8646829578297937

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elo_per_day.py View File

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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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