In [1]:

import pandas as pd
import numpy as np
import seaborn as sns

from mplsoccer import Pitch, VerticalPitch

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

from functions import plot_heatmap

In [2]:

x = np.random.randint(1,101,500)

In [3]:

df = pd.read_csv('europaFinal.csv')
df.head()

Out[3]:

	second	teamId	x	y	period	type	outcome	playerId	endX	endY
0	0.0	Manchester United	0.0	0.0	FirstHalf	Start	Successful	NaN	NaN	NaN
1	0.0	Villarreal	0.0	0.0	FirstHalf	Start	Successful	NaN	NaN	NaN
2	0.0	Manchester United	49.9	50.0	FirstHalf	Pass	Successful	18.0	31.5	59.6
3	3.0	Manchester United	32.2	58.1	FirstHalf	Pass	Unsuccessful	2.0	69.6	100.0
4	32.0	Villarreal	34.4	0.0	FirstHalf	Pass	Successful	8.0	36.5	9.5

In [4]:

fig = plt.figure(figsize=(10,10),constrained_layout = True)

gs = fig.add_gridspec(nrows = 3, ncols = 4)

ax1 = fig.add_subplot(gs[0,0:2])
ax1.set_title('[0,0]')
boxplot = sns.boxplot(x = x, ax = ax1)

ax3 = fig.add_subplot(gs[0,2])
ax3.set_title('[0,2]')
plot_heatmap(heat_df = df, teamId = 'Villarreal', p_type='Pass', ax=ax3)

ax4 = fig.add_subplot(gs[0,3])
ax4.set_title('[0,3]')
plot_heatmap(heat_df = df, teamId = 'Manchester United', p_type='Pass', ax=ax4)

ax5 = fig.add_subplot(gs[1:,0])
ax5.set_title('[1,0]')

ax6 = fig.add_subplot(gs[1,1])
ax6.set_title('[1,1]')

ax7 = fig.add_subplot(gs[1,2])
ax7.set_title('[1,2]')

ax8 = fig.add_subplot(gs[1,3])
ax8.set_title('[1,3]')

ax10 = fig.add_subplot(gs[2,1])
ax10.set_title('[2,1]')

ax11 = fig.add_subplot(gs[2,2])
ax11.set_title('[2,2]')

ax12 = fig.add_subplot(gs[2,3])
ax12.set_title('[2,3]')
ax12.scatter(5,2)

Out[4]:

<matplotlib.collections.PathCollection at 0x121f32790>

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