Beautiful Tables in Matplotlib, a Tutorial¶

In this tutorial, we will explore how to use matplotlib to create highly customized tables.

Imports¶

In [153]:

import pandas as pd
from tabulate import tabulate
import matplotlib.pyplot as plt

from PIL import Image
import urllib
import os

A Minimal Example¶

In [154]:

# This is random data.
data = {
    'teams': ['Real Madrid', 'Barcelona', 'Villarreal', 'Sevilla'],
    'goals_scored': [23, 15, 28, 12]
}

In [155]:

fig = plt.figure(figsize=(4,3), dpi=200)
ax = plt.subplot(111)

ncols = 2
nrows = 4

ax.set_xlim(0, ncols)
ax.set_ylim(0, nrows)
ax.set_axis_off()

for y in range(0, nrows):
    ax.annotate(
        xy=(0.5,y),
        text=data['teams'][y],
        ha='center'
    )
    ax.annotate(
        xy=(1.5,y),
        text=data['goals_scored'][y],
        ha='center'
    )

ax.annotate(
    xy=(0.5, nrows),
    text='TEAM',
    weight='bold',
    ha='center'
)
ax.annotate(
    xy=(1.5, nrows),
    text='GOALS\nSCORED',
    weight='bold',
    ha='center'
)

plt.savefig(
    'figures/a_very_basic_table.png',
    dpi=300,
    transparent=True
)

Reading some basic data¶

In [156]:

df = pd.read_csv('data/real_madrid_playing_time.csv', header=[1])
# print(tabulate(df.head(), tablefmt='pipe', headers='keys'))
df.head()

Out[156]:

	Player	Nation	Pos	Age	MP	Min	Mn/MP	Min%	90s	Starts	...	+/-	+/-90	On-Off	onxG	onxGA	xG+/-	xG+/-90	On-Off.1	Matches	-9999
0	Thibaut Courtois	be BEL	GK	29.0	36	3240.0	90.0	94.7	36.0	36	...	50.0	1.39	1.89	71.3	41.4	29.8	0.83	1.95	Matches	1840e36d
1	Éder Militão	br BRA	DF	23.0	34	3031.0	89.0	88.6	33.7	34	...	33.0	0.98	-2.72	62.5	41.0	21.4	0.64	-0.79	Matches	2784f898
2	Karim Benzema	fr FRA	FW	33.0	32	2593.0	81.0	75.8	28.8	31	...	41.0	1.42	0.55	57.5	30.6	26.9	0.93	0.86	Matches	70d74ece
3	Casemiro	br BRA	MF	29.0	32	2569.0	80.0	75.1	28.5	31	...	29.0	1.02	-1.10	51.2	34.3	16.9	0.59	-0.54	Matches	4d224fe8
4	Vinicius Júnior	br BRA	FW	21.0	35	2690.0	77.0	78.7	29.9	30	...	43.0	1.44	0.70	56.6	32.9	23.8	0.80	0.32	Matches	7111d552

5 rows × 28 columns

For our first example, we'll begin by looking at players with more than a thousand minutes under their belt. For this sample, we'll look at the total of matches they were named in the squad and how many of them they actually played in.

In [157]:

df_example_1 = df[df['Min'] >= 1000].reset_index(drop=True)
df_example_1 = df_example_1[['Player', 'Pos', 'MP', 'Starts', 'Subs', 'unSub']]
print(tabulate(df_example_1.head(), tablefmt='pipe', headers='keys'))
# df_example_1.head()

|    | Player           | Pos   |   MP |   Starts |   Subs |   unSub |
|---:|:-----------------|:------|-----:|---------:|-------:|--------:|
|  0 | Thibaut Courtois | GK    |   36 |       36 |      0 |       1 |
|  1 | Éder Militão     | DF    |   34 |       34 |      0 |       2 |
|  2 | Karim Benzema    | FW    |   32 |       31 |      1 |       1 |
|  3 | Casemiro         | MF    |   32 |       31 |      1 |       2 |
|  4 | Vinicius Júnior  | FW    |   35 |       30 |      5 |       0 |

In [158]:

df_example_1['InSquad'] = df_example_1['MP'] + df_example_1['unSub']
df_example_1 = df_example_1.sort_values(by='Starts').reset_index(drop=True)
df_example_1 = df_example_1[~df_example_1['Pos'].isna()]
df_example_1.head()

Out[158]:

	Player	Pos	MP	Starts	Subs	unSub	InSquad
0	Eduardo Camavinga	MF	26	13	13	8	34
1	Nacho	DF	28	17	11	8	36
2	Dani Carvajal	DF	24	17	7	4	28
3	Rodrygo	FW	33	17	16	1	34
4	Federico Valverde	MF	31	19	12	2	33

In [159]:

fig = plt.figure(figsize=(7,10), dpi=300)
ax = plt.subplot()

ncols = 5
nrows = df_example_1.shape[0]

ax.set_xlim(0, ncols + 1)
ax.set_ylim(0, nrows)

positions = [0.25, 2.5, 3.5, 4.5, 5.5]
columns = ['Player', 'Pos', 'MP', 'Starts', 'InSquad']

# Add table's main text
for i in range(nrows):
    for j, column in enumerate(columns):
        if j == 0:
            ha = 'left'
        else:
            ha = 'center'
        ax.annotate(
            xy=(positions[j], i),
            text=df_example_1[column].iloc[i],
            ha=ha,
            va='center'
        )

# Add column names
column_names = ['Player', 'Position', 'Matches\nPlayed', 'Starts', 'In Squad']
for index, c in enumerate(column_names):
        if index == 0:
            ha = 'left'
        else:
            ha = 'center'
        ax.annotate(
            xy=(positions[index], nrows),
            text=column_names[index],
            ha=ha,
            va='bottom',
            weight='bold'
        )

ax.set_axis_off()
plt.savefig(
    'figures/first_useful_table.png',
    dpi=300,
    transparent=True,
    bbox_inches='tight'
)

Making it pretty¶

In [160]:

df_example_2 = df[df['Min'] >= 1000].reset_index(drop=True)
df_example_2 = df_example_2[['Player', 'Pos', 'Min', 'MP', 'Starts', 'Subs', 'unSub']]
print(tabulate(df_example_2.head(), tablefmt='pipe', headers='keys'))

|    | Player           | Pos   |   Min |   MP |   Starts |   Subs |   unSub |
|---:|:-----------------|:------|------:|-----:|---------:|-------:|--------:|
|  0 | Thibaut Courtois | GK    |  3240 |   36 |       36 |      0 |       1 |
|  1 | Éder Militão     | DF    |  3031 |   34 |       34 |      0 |       2 |
|  2 | Karim Benzema    | FW    |  2593 |   32 |       31 |      1 |       1 |
|  3 | Casemiro         | MF    |  2569 |   32 |       31 |      1 |       2 |
|  4 | Vinicius Júnior  | FW    |  2690 |   35 |       30 |      5 |       0 |

In [161]:

df_example_2['InSquad'] = df_example_2['MP'] + df_example_2['unSub']
df_example_2 = df_example_2.sort_values(by='Min').reset_index(drop=True)
df_example_2 = df_example_2[~df_example_2['Pos'].isna()]

In [162]:

df_example_2['Starts_InSquad'] = [f'{x}/{y}' for x,y in zip(df_example_2['Starts'], df_example_2['InSquad'])]
df_example_2.head()

Out[162]:

	Player	Pos	Min	MP	Starts	Subs	unSub	InSquad	Starts_InSquad
0	Eduardo Camavinga	MF	1236.0	26	13	13	8	34	13/34
1	Rodrygo	FW	1523.0	33	17	16	1	34	17/34
2	Dani Carvajal	DF	1551.0	24	17	7	4	28	17/28
3	Nacho	DF	1555.0	28	17	11	8	36	17/36
4	Marco Asensio	FWMF	1731.0	31	19	12	2	33	19/33

In [163]:

fig = plt.figure(figsize=(7,10), dpi=300)
ax = plt.subplot()

ncols = 5
nrows = df_example_2.shape[0]

ax.set_xlim(0, ncols + 1)
ax.set_ylim(0, nrows + 1)

positions = [0.25, 2.5, 3.5, 4.5, 5.5]
columns = ['Player', 'Pos', 'Min', 'MP', 'Starts_InSquad']

# Add table's main text
for i in range(nrows):
    for j, column in enumerate(columns):
        if j == 0:
            ha = 'left'
        else:
            ha = 'center'
        if column == 'Min':
            text_label = f'{df_example_2[column].iloc[i]:,.0f}'
            weight = 'bold'
        else:
            text_label = f'{df_example_2[column].iloc[i]}'
            weight = 'normal'
        ax.annotate(
            xy=(positions[j], i + .5),
            text=text_label,
            ha=ha,
            va='center',
            weight=weight
        )

# Add column names
column_names = ['Player', 'Position', 'Minutes', 'Matches\nPlayed', 'Starts /\nIn Squad']
for index, c in enumerate(column_names):
        if index == 0:
            ha = 'left'
        else:
            ha = 'center'
        ax.annotate(
            xy=(positions[index], nrows + .25),
            text=column_names[index],
            ha=ha,
            va='bottom',
            weight='bold'
        )

# Add dividing lines
ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [nrows, nrows], lw=1.5, color='black', marker='', zorder=4)
ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [0, 0], lw=1.5, color='black', marker='', zorder=4)
for x in range(1, nrows):
    ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [x, x], lw=1.15, color='gray', ls=':', zorder=3 , marker='')

ax.set_axis_off()
plt.savefig(
    'figures/pretty_example.png',
    dpi=300,
    transparent=True,
    bbox_inches='tight'
)

Time to get fancy¶

In [164]:

df_final = df[df['Min'] >= 1000].reset_index(drop=True)
df_final = df_final[['Player', 'Nation', 'Pos', 'Min', 'MP', 'Starts', 'Subs', 'unSub']]
df_final['InSquad'] = df_final['MP'] + df_final['unSub']
df_final = df_final.sort_values(by='Min').reset_index(drop=True)
df_final = df_final[~df_final['Pos'].isna()]
df_final['Nation'] = [x.split(' ')[1].lower() for x in df_final['Nation']]
df_final['Starts_InSquad'] = [f'{x}/{y}' for x,y in zip(df_final['Starts'], df_final['InSquad'])]
print(tabulate(df_final.head(), tablefmt='pipe', headers='keys'))

|    | Player            | Nation   | Pos   |   Min |   MP |   Starts |   Subs |   unSub |   InSquad | Starts_InSquad   |
|---:|:------------------|:---------|:------|------:|-----:|---------:|-------:|--------:|----------:|:-----------------|
|  0 | Eduardo Camavinga | fra      | MF    |  1236 |   26 |       13 |     13 |       8 |        34 | 13/34            |
|  1 | Rodrygo           | bra      | FW    |  1523 |   33 |       17 |     16 |       1 |        34 | 17/34            |
|  2 | Dani Carvajal     | esp      | DF    |  1551 |   24 |       17 |      7 |       4 |        28 | 17/28            |
|  3 | Nacho             | esp      | DF    |  1555 |   28 |       17 |     11 |       8 |        36 | 17/36            |
|  4 | Marco Asensio     | esp      | FWMF  |  1731 |   31 |       19 |     12 |       2 |        33 | 19/33            |

Function to get the country logo

In [165]:

def ax_logo(team_id, ax):
    '''
    Plots the logo of the team at a specific axes.
    Args:
        team_id (int): the id of the team according to Fotmob. You can find it in the url of the team page.
        ax (object): the matplotlib axes where we'll draw the image.
    '''
    fotmob_url = 'https://images.fotmob.com/image_resources/logo/teamlogo/'
    club_icon = Image.open(urllib.request.urlopen(f'{fotmob_url}{team_id}.png'))
    ax.imshow(club_icon)
    ax.axis('off')
    return ax

In [166]:

fig = plt.figure(figsize=(8,10), dpi=300)
ax = plt.subplot()

ncols = 6
nrows = df_final.shape[0]

ax.set_xlim(0, ncols + 1)
ax.set_ylim(0, nrows + 1)

positions = [0.25, 3.5, 4.5, 5.5, 6.5]
columns = ['Player', 'Pos', 'Min', 'MP', 'Starts_InSquad']

# -- Add table's main text
for i in range(nrows):
    for j, column in enumerate(columns):
        if j == 0:
            ha = 'left'
        else:
            ha = 'center'
        if column == 'Min':
            text_label = f'{df_final[column].iloc[i]:,.0f}'
            weight = 'bold'
        else:
            text_label = f'{df_final[column].iloc[i]}'
            weight = 'normal'
        ax.annotate(
            xy=(positions[j], i + .5),
            text=text_label,
            ha=ha,
            va='center',
            weight=weight
        )

# -- Transformation functions
DC_to_FC = ax.transData.transform
FC_to_NFC = fig.transFigure.inverted().transform
# -- Take data coordinates and transform them to normalized figure coordinates
DC_to_NFC = lambda x: FC_to_NFC(DC_to_FC(x))
# -- Add nation axes
ax_point_1 = DC_to_NFC([2.25, 0.25])
ax_point_2 = DC_to_NFC([2.75, 0.75])
ax_width = abs(ax_point_1[0] - ax_point_2[0])
ax_height = abs(ax_point_1[1] - ax_point_2[1])
for x in range(0, nrows):
    ax_coords = DC_to_NFC([2.25, x + .25])
    flag_ax = fig.add_axes(
        [ax_coords[0], ax_coords[1], ax_width, ax_height]
    )
    ax_logo(df_final['Nation'].iloc[x], flag_ax)

# -- Add column names
column_names = ['Player', 'Position', 'Minutes', 'Matches\nPlayed', 'Starts /\nIn Squad']
for index, c in enumerate(column_names):
        if index == 0:
            ha = 'left'
        else:
            ha = 'center'
        ax.annotate(
            xy=(positions[index], nrows + .25),
            text=column_names[index],
            ha=ha,
            va='bottom',
            weight='bold'
        )

# Add dividing lines
ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [nrows, nrows], lw=1.5, color='black', marker='', zorder=4)
ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [0, 0], lw=1.5, color='black', marker='', zorder=4)
for x in range(1, nrows):
    ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [x, x], lw=1.15, color='gray', ls=':', zorder=3 , marker='')

ax.set_axis_off()
plt.savefig(
    'figures/flag_example.png',
    dpi=300,
    transparent=True,
    bbox_inches='tight'
)

In [169]:

def minutes_battery(minutes, ax):
    '''
    This function takes an integer and an axes and 
    plots a battery chart.
    '''
    ax.set_xlim(0,1)
    ax.set_ylim(0,1)
    ax.barh([0.5], [1], fc = '#EFE9E6', ec='black', height=.35)
    ax.barh([0.5], [minutes/(90*38)], fc = '#00529F', height=.35)
    text_ = ax.annotate(
        xy=(minutes/(90*38), .5),
        text=f'{minutes/(90*38):.0%}',
        xytext=(-8,0),
        textcoords='offset points',
        weight='bold',
        color='#EFE9E6',
        va='center',
        ha='center',
        size=5
    )
    ax.set_axis_off()
    return ax

In [173]:

fig = plt.figure(figsize=(8,10), dpi=300, facecolor='#EFE9E6')
ax = plt.subplot()

ncols = 6
nrows = df_final.shape[0]

ax.set_xlim(0, ncols + 1)
ax.set_ylim(0, nrows + 1)

positions = [0.25, 3.5, 4.5, 5.5, 6.5]
columns = ['Player', 'Pos', 'Min', 'MP', 'Starts_InSquad']

# -- Add table's main text
for i in range(nrows):
    for j, column in enumerate(columns):
        if j == 0:
            ha = 'left'
        else:
            ha = 'center'
        if column == 'Min':
            continue
        else:
            text_label = f'{df_final[column].iloc[i]}'
            weight = 'normal'
        ax.annotate(
            xy=(positions[j], i + .5),
            text=text_label,
            ha=ha,
            va='center',
            weight=weight
        )

# -- Transformation functions
DC_to_FC = ax.transData.transform
FC_to_NFC = fig.transFigure.inverted().transform
# -- Take data coordinates and transform them to normalized figure coordinates
DC_to_NFC = lambda x: FC_to_NFC(DC_to_FC(x))
# -- Add nation axes
ax_point_1 = DC_to_NFC([2.25, 0.25])
ax_point_2 = DC_to_NFC([2.75, 0.75])
ax_width = abs(ax_point_1[0] - ax_point_2[0])
ax_height = abs(ax_point_1[1] - ax_point_2[1])
for x in range(0, nrows):
    ax_coords = DC_to_NFC([2.25, x + .25])
    flag_ax = fig.add_axes(
        [ax_coords[0], ax_coords[1], ax_width, ax_height]
    )
    ax_logo(df_final['Nation'].iloc[x], flag_ax)

ax_point_1 = DC_to_NFC([4, 0.05])
ax_point_2 = DC_to_NFC([5, 0.95])
ax_width = abs(ax_point_1[0] - ax_point_2[0])
ax_height = abs(ax_point_1[1] - ax_point_2[1])
for x in range(0, nrows):
    ax_coords = DC_to_NFC([4, x + .025])
    bar_ax = fig.add_axes(
        [ax_coords[0], ax_coords[1], ax_width, ax_height]
    )
    minutes_battery(df_final['Min'].iloc[x], bar_ax)

# -- Add column names
column_names = ['Player', 'Position', '% of Min.\nPlayed', 'Matches\nPlayed', 'Starts /\nIn Squad']
for index, c in enumerate(column_names):
        if index == 0:
            ha = 'left'
        else:
            ha = 'center'
        ax.annotate(
            xy=(positions[index], nrows + .25),
            text=column_names[index],
            ha=ha,
            va='bottom',
            weight='bold'
        )

# Add dividing lines
ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [nrows, nrows], lw=1.5, color='black', marker='', zorder=4)
ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [0, 0], lw=1.5, color='black', marker='', zorder=4)
for x in range(1, nrows):
    ax.plot([ax.get_xlim()[0], ax.get_xlim()[1]], [x, x], lw=1.15, color='gray', ls=':', zorder=3 , marker='')

ax.fill_between(
    x=[0,2],
    y1=nrows,
    y2=0,
    color='lightgrey',
    alpha=0.5,
    ec='None'
)

ax.set_axis_off()
# -- Final details
logo_ax = fig.add_axes(
    [0.825, 0.89, .05, .05]
)
ax_logo(8633, logo_ax)
fig.text(
    x=0.15, y=.91,
    s='Real Madrid 2021/2022 Squad Playing Time',
    ha='left',
    va='bottom',
    weight='bold',
    size=12
)
plt.savefig(
    'figures/final_table.png',
    dpi=300,
    transparent=True,
    bbox_inches='tight'
)
plt.savefig(
    'figures/final_table_bkg.png',
    dpi=300,
    transparent=False,
    faceolor='#EFE9E6',
    bbox_inches='tight'
)

c:\Users\migue\Documents\Personal\Son of a corner\matplotlib-tutorials\.TutorialsVenv\lib\site-packages\ipykernel_launcher.py:116: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument "faceolor" which is no longer supported as of 3.3 and will become an error in 3.6