Styling Pandas DataFrame

Source 1

Source 2

Import library

import numpy as np
import pandas as pd

Download and select data

! wget https://raw.githubusercontent.com/dataprofessor/data/master/nba-player-stats-2019.csv

--2021-04-17 15:38:29--  https://raw.githubusercontent.com/dataprofessor/data/master/nba-player-stats-2019.csv
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 90308 (88K) [text/plain]
Saving to: ‘nba-player-stats-2019.csv.1’

nba-player-stats-20 100%[===================>]  88.19K   334KB/s    in 0.3s    

2021-04-17 15:38:31 (334 KB/s) - ‘nba-player-stats-2019.csv.1’ saved [90308/90308]

df = pd.read_csv('nba-player-stats-2019.csv')

df.head()

	Player	Pos	Age	Tm	G	GS	MP	FG	FGA	FG%	...	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	Álex Abrines	SG	25	OKC	31	2	19.0	1.8	5.1	0.357	...	0.923	0.2	1.4	1.5	0.6	0.5	0.2	0.5	1.7	5.3
1	Quincy Acy	PF	28	PHO	10	0	12.3	0.4	1.8	0.222	...	0.700	0.3	2.2	2.5	0.8	0.1	0.4	0.4	2.4	1.7
2	Jaylen Adams	PG	22	ATL	34	1	12.6	1.1	3.2	0.345	...	0.778	0.3	1.4	1.8	1.9	0.4	0.1	0.8	1.3	3.2
3	Steven Adams	C	25	OKC	80	80	33.4	6.0	10.1	0.595	...	0.500	4.9	4.6	9.5	1.6	1.5	1.0	1.7	2.6	13.9
4	Bam Adebayo	C	21	MIA	82	28	23.3	3.4	5.9	0.576	...	0.735	2.0	5.3	7.3	2.2	0.9	0.8	1.5	2.5	8.9

5 rows × 29 columns

df = df[['Player','Pos','Age','Tm','G','3P%','2P%','FT%','AST','STL','BLK','PTS']]

df.head()

	Player	Pos	Age	Tm	G	3P%	2P%	FT%	AST	STL	BLK	PTS
0	Álex Abrines	SG	25	OKC	31	0.323	0.500	0.923	0.6	0.5	0.2	5.3
1	Quincy Acy	PF	28	PHO	10	0.133	0.667	0.700	0.8	0.1	0.4	1.7
2	Jaylen Adams	PG	22	ATL	34	0.338	0.361	0.778	1.9	0.4	0.1	3.2
3	Steven Adams	C	25	OKC	80	0.000	0.596	0.500	1.6	1.5	1.0	13.9
4	Bam Adebayo	C	21	MIA	82	0.200	0.588	0.735	2.2	0.9	0.8	8.9

Style Pandas Table with CSS

df.head(20).style.set_table_styles(
[{'selector': 'th',
  'props': [('background', '#7CCE00'), 
            ('color', 'white'),
            ('font-family', 'verdana')]},
 
 {'selector': 'td',
  'props': [('font-family', 'verdana')]},

 {'selector': 'tr:nth-of-type(odd)',
  'props': [('background', '#DCDCDC')]}, 

 {'selector': 'tr:nth-of-type(even)',
  'props': [('background', 'white')]},
 
]
).hide_index()

Player	Pos	Age	Tm	G	3P%	2P%	FT%	AST	STL	BLK	PTS
Álex Abrines	SG	25	OKC	31	0.323000	0.500000	0.923000	0.600000	0.500000	0.200000	5.300000
Quincy Acy	PF	28	PHO	10	0.133000	0.667000	0.700000	0.800000	0.100000	0.400000	1.700000
Jaylen Adams	PG	22	ATL	34	0.338000	0.361000	0.778000	1.900000	0.400000	0.100000	3.200000
Steven Adams	C	25	OKC	80	0.000000	0.596000	0.500000	1.600000	1.500000	1.000000	13.900000
Bam Adebayo	C	21	MIA	82	0.200000	0.588000	0.735000	2.200000	0.900000	0.800000	8.900000
Deng Adel	SF	21	CLE	19	0.261000	0.385000	1.000000	0.300000	0.100000	0.200000	1.700000
DeVaughn Akoon-Purcell	SG	25	DEN	7	0.000000	0.500000	0.500000	0.900000	0.300000	0.000000	1.000000
LaMarcus Aldridge	C	33	SAS	81	0.238000	0.528000	0.847000	2.400000	0.500000	1.300000	21.300000
Rawle Alkins	SG	21	CHI	10	0.250000	0.370000	0.667000	1.300000	0.100000	0.000000	3.700000
Grayson Allen	SG	23	UTA	38	0.323000	0.443000	0.750000	0.700000	0.200000	0.200000	5.600000
Jarrett Allen	C	20	BRK	80	0.133000	0.629000	0.709000	1.400000	0.500000	1.500000	10.900000
Kadeem Allen	SG	26	NYK	19	0.472000	0.457000	0.778000	4.000000	0.800000	0.200000	9.900000
Al-Farouq Aminu	PF	28	POR	81	0.343000	0.514000	0.867000	1.300000	0.800000	0.400000	9.400000
Justin Anderson	SF	25	ATL	48	0.312000	0.500000	0.743000	0.500000	0.500000	0.300000	3.700000
Kyle Anderson	SF	25	MEM	43	0.265000	0.583000	0.578000	3.000000	1.300000	0.900000	8.000000
Ryan Anderson	PF	30	TOT	25	0.225000	0.414000	0.750000	0.800000	0.200000	0.000000	2.500000
Ryan Anderson	PF	30	PHO	15	0.206000	0.462000	0.786000	1.100000	0.200000	0.100000	3.700000
Ryan Anderson	PF	30	MIA	10	0.333000	0.000000	0.500000	0.200000	0.100000	0.000000	0.700000
Ike Anigbogu	C	20	IND	3	0.000000	0.000000	0.000000	0.300000	0.000000	0.300000	0.000000
Giannis Antetokounmpo	PF	24	MIL	72	0.256000	0.641000	0.729000	5.900000	1.300000	1.500000	27.700000

Adding yellow highlight upon hover

df.head(20).style.set_table_styles(
[{'selector': 'th',
  'props': [('background', '#7CCE00'), 
            ('color', 'white'),
            ('font-family', 'verdana')]},
 
 {'selector': 'td',
  'props': [('font-family', 'verdana')]},

 {'selector': 'tr:nth-of-type(odd)',
  'props': [('background', '#DCDCDC')]}, 
 
 {'selector': 'tr:nth-of-type(even)',
  'props': [('background', 'white')]},
 
 {'selector': 'tr:hover',
  'props': [('background-color', 'yellow')]}

]
).hide_index()

Player	Pos	Age	Tm	G	3P%	2P%	FT%	AST	STL	BLK	PTS
Álex Abrines	SG	25	OKC	31	0.323000	0.500000	0.923000	0.600000	0.500000	0.200000	5.300000
Quincy Acy	PF	28	PHO	10	0.133000	0.667000	0.700000	0.800000	0.100000	0.400000	1.700000
Jaylen Adams	PG	22	ATL	34	0.338000	0.361000	0.778000	1.900000	0.400000	0.100000	3.200000
Steven Adams	C	25	OKC	80	0.000000	0.596000	0.500000	1.600000	1.500000	1.000000	13.900000
Bam Adebayo	C	21	MIA	82	0.200000	0.588000	0.735000	2.200000	0.900000	0.800000	8.900000
Deng Adel	SF	21	CLE	19	0.261000	0.385000	1.000000	0.300000	0.100000	0.200000	1.700000
DeVaughn Akoon-Purcell	SG	25	DEN	7	0.000000	0.500000	0.500000	0.900000	0.300000	0.000000	1.000000
LaMarcus Aldridge	C	33	SAS	81	0.238000	0.528000	0.847000	2.400000	0.500000	1.300000	21.300000
Rawle Alkins	SG	21	CHI	10	0.250000	0.370000	0.667000	1.300000	0.100000	0.000000	3.700000
Grayson Allen	SG	23	UTA	38	0.323000	0.443000	0.750000	0.700000	0.200000	0.200000	5.600000
Jarrett Allen	C	20	BRK	80	0.133000	0.629000	0.709000	1.400000	0.500000	1.500000	10.900000
Kadeem Allen	SG	26	NYK	19	0.472000	0.457000	0.778000	4.000000	0.800000	0.200000	9.900000
Al-Farouq Aminu	PF	28	POR	81	0.343000	0.514000	0.867000	1.300000	0.800000	0.400000	9.400000
Justin Anderson	SF	25	ATL	48	0.312000	0.500000	0.743000	0.500000	0.500000	0.300000	3.700000
Kyle Anderson	SF	25	MEM	43	0.265000	0.583000	0.578000	3.000000	1.300000	0.900000	8.000000
Ryan Anderson	PF	30	TOT	25	0.225000	0.414000	0.750000	0.800000	0.200000	0.000000	2.500000
Ryan Anderson	PF	30	PHO	15	0.206000	0.462000	0.786000	1.100000	0.200000	0.100000	3.700000
Ryan Anderson	PF	30	MIA	10	0.333000	0.000000	0.500000	0.200000	0.100000	0.000000	0.700000
Ike Anigbogu	C	20	IND	3	0.000000	0.000000	0.000000	0.300000	0.000000	0.300000	0.000000
Giannis Antetokounmpo	PF	24	MIL	72	0.256000	0.641000	0.729000	5.900000	1.300000	1.500000	27.700000

Conditional Coloring

def color_red(val):
  if val > 20:
    color = 'green'
  elif val > 5:
    color = 'red'
  else:
    color = 'black'
  return 'color: %s' % color


df[['PTS']].head(20).style.applymap(color_red)

	PTS
0	5.300000
1	1.700000
2	3.200000
3	13.900000
4	8.900000
5	1.700000
6	1.000000
7	21.300000
8	3.700000
9	5.600000
10	10.900000
11	9.900000
12	9.400000
13	3.700000
14	8.000000
15	2.500000
16	3.700000
17	0.700000
18	0.000000
19	27.700000

Further reading

• Shiu-Tang Li. 10 Python Pandas tips to make data analysis faster, 2019.
• HTML Color Codes. (Note: This website is awesome for visualizing the colors of hex color codes as well as choosing color variants).