sportowe_wizualizacja/MovingAvg.ipynb

import pandas as pd
df = pd.read_csv('fcb_xg.csv')

df

	Date	Time	GF	GA	xG	xGA
0	2019-08-16	21:00 (00:30)	0	1	1.1	0.5
1	2019-08-25	21:00 (00:30)	5	2	2.0	0.2
2	2019-08-31	17:00 (20:30)	2	2	0.5	1.6
3	2019-09-14	21:00 (00:30)	5	2	2.0	1.3
4	2019-09-17	21:00 (00:30)	0	0	0.6	2.6
5	2019-09-21	21:00 (00:30)	0	2	0.5	1.7
6	2019-09-24	21:00 (00:30)	2	1	0.7	0.7
7	2019-09-28	16:00 (19:30)	2	0	1.4	0.7
8	2019-10-02	21:00 (00:30)	2	1	1.2	1.1
9	2019-10-06	21:00 (00:30)	4	0	2.4	1.8
10	2019-10-19	13:00 (16:30)	3	0	2.2	0.4
11	2019-10-23	21:00 (00:30)	2	1	2.3	1.4
12	2019-10-29	21:15 (01:45)	5	1	3.4	0.7
13	2019-11-02	16:00 (20:30)	1	3	2.2	0.9
14	2019-11-05	18:55 (23:25)	0	0	2.2	0.2
15	2019-11-09	21:00 (01:30)	4	1	1.9	0.4
16	2019-11-23	13:00 (17:30)	2	1	1.8	0.4
17	2019-11-27	21:00 (01:30)	3	1	2.6	1.2
18	2019-12-01	21:00 (01:30)	1	0	1.2	1.5
19	2019-12-07	21:00 (01:30)	5	2	3.6	1.3

#change this cell to drive the whole IPYNB
graph_var1= 'GA'
graph_var2= 'xGA'
pand_out1 = 'RollingGA'
pand_out2 = 'RollingxGA'
hue_disp1 = 'GA vs xGA'
hue_disp2 = 'GA and xGA'

def movavg(pandas_df,col1,col2,out1,out2):    
    pandas_df['X_axis'] = df.index
    for i in range(0,pandas_df.shape[0] - 4):
        pandas_df.loc[pandas_df.index[i+4],out1] = (pandas_df.iloc[i][col1] + pandas_df.iloc[i+1][col1] + pandas_df.iloc[i+2][col1] + pandas_df.iloc[i + 3][col1] + pandas_df.iloc[i+4][col1])/5
        pandas_df.loc[pandas_df.index[i+4],out2] = (pandas_df.iloc[i][col2] + pandas_df.iloc[i+1][col2] + pandas_df.iloc[i+2][col2] + pandas_df.iloc[i + 3][col2] + pandas_df.iloc[i+4][col2])/5
    
    return pandas_df[['X_axis',out1,out2]]

main_df = movavg(df,graph_var1,graph_var2,pand_out1,pand_out2)

main_df.head()

	X_axis	RollingGA	RollingxGA
0	0	NaN	NaN
1	1	NaN	NaN
2	2	NaN	NaN
3	3	NaN	NaN
4	4	1.4	1.24

def melt(pandas_df,x_axis):
    pandas_df = pandas_df.melt(x_axis,var_name=hue_disp1, value_name=hue_disp2)
    return pandas_df

df_for_disp = melt(main_df,'X_axis')

df_for_disp.head()

	X_axis	GA vs xGA	GA and xGA
0	0	RollingGA	NaN
1	1	RollingGA	NaN
2	2	RollingGA	NaN
3	3	RollingGA	NaN
4	4	RollingGA	1.4

import seaborn as sns
import matplotlib.pyplot as plt

a4_dims = (14 ,12)
bg = "#181818"

flatui = flatui = ["firebrick", "seagreen"]
palette = sns.color_palette(flatui)
sns.set(rc={ 'grid.color': '#5c5b5b','grid.linestyle': ' ','axes.edgecolor': '#000000','axes.facecolor':bg, 'figure.facecolor':bg,'ytick.color':'white','xtick.color':'white' ,'axes.labelcolor': 'white','text.color': 'white'})
fig, ax = plt.subplots(figsize=a4_dims)
#with plt.xkcd():
cur_axes = plt.gca()
cur_axes.axes.get_xaxis().set_ticks([])
plt.title('Barcelona ' + hue_disp1 + ' Rolling 5 average | All Competitions',loc = 'left',color='white',weight = 'semibold')

ax = sns.lineplot(x='X_axis', y=hue_disp2,hue=hue_disp1, data=df_for_disp,palette = palette)