ggcorrplot function#
In this notebook, we’ll describe the python package ggcorrplot for displaying easily a correlation matrix using ‘plotnine’.
mtcars dataset#
the mtcars data set will be used in the following python code. The function cor_pmat [in ggcorrplot] computes a matrix of correlation p-values
[1]:
#disable warnings
from warnings import simplefilter, filterwarnings
simplefilter(action='ignore', category=FutureWarning)
filterwarnings("ignore")
[2]:
#load mtcars dataset form plotnine
from plotnine.data import mtcars
print(mtcars)
name mpg cyl disp hp drat wt qsec vs am \
0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1
1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1
2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1
3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0
4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0
5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0
6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0
7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0
8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0
9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0
10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0
11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0
12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0
13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0
14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0
15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0
16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0
17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1
18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1
19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1
20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0
21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0
22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0
23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0
24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0
25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1
26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1
27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1
28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1
29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1
30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1
31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1
gear carb
0 4 4
1 4 4
2 4 1
3 3 1
4 3 2
5 3 1
6 3 4
7 4 2
8 4 2
9 4 4
10 4 4
11 3 3
12 3 3
13 3 3
14 3 4
15 3 4
16 3 4
17 4 1
18 4 2
19 4 1
20 3 1
21 3 2
22 3 2
23 3 4
24 3 2
25 4 1
26 5 2
27 5 2
28 5 4
29 5 6
30 5 8
31 4 2
We set name as rownames
[3]:
# Set name as index
mtcars = mtcars.set_index("name")
print(mtcars)
mpg cyl disp hp drat wt qsec vs am gear \
name
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4
carb
name
Mazda RX4 4
Mazda RX4 Wag 4
Datsun 710 1
Hornet 4 Drive 1
Hornet Sportabout 2
Valiant 1
Duster 360 4
Merc 240D 2
Merc 230 2
Merc 280 4
Merc 280C 4
Merc 450SE 3
Merc 450SL 3
Merc 450SLC 3
Cadillac Fleetwood 4
Lincoln Continental 4
Chrysler Imperial 4
Fiat 128 1
Honda Civic 2
Toyota Corolla 1
Toyota Corona 1
Dodge Challenger 2
AMC Javelin 2
Camaro Z28 4
Pontiac Firebird 2
Fiat X1-9 1
Porsche 914-2 2
Lotus Europa 2
Ford Pantera L 4
Ferrari Dino 6
Maserati Bora 8
Volvo 142E 2
Compute a correlation matrix#
[4]:
# Compute a correlation matrix
corr = mtcars.corr()
print(corr.round(4))
mpg cyl disp hp drat wt qsec vs am \
mpg 1.0000 -0.8522 -0.8476 -0.7762 0.6812 -0.8677 0.4187 0.6640 0.5998
cyl -0.8522 1.0000 0.9020 0.8324 -0.6999 0.7825 -0.5912 -0.8108 -0.5226
disp -0.8476 0.9020 1.0000 0.7909 -0.7102 0.8880 -0.4337 -0.7104 -0.5912
hp -0.7762 0.8324 0.7909 1.0000 -0.4488 0.6587 -0.7082 -0.7231 -0.2432
drat 0.6812 -0.6999 -0.7102 -0.4488 1.0000 -0.7124 0.0912 0.4403 0.7127
wt -0.8677 0.7825 0.8880 0.6587 -0.7124 1.0000 -0.1747 -0.5549 -0.6925
qsec 0.4187 -0.5912 -0.4337 -0.7082 0.0912 -0.1747 1.0000 0.7445 -0.2299
vs 0.6640 -0.8108 -0.7104 -0.7231 0.4403 -0.5549 0.7445 1.0000 0.1683
am 0.5998 -0.5226 -0.5912 -0.2432 0.7127 -0.6925 -0.2299 0.1683 1.0000
gear 0.4803 -0.4927 -0.5556 -0.1257 0.6996 -0.5833 -0.2127 0.2060 0.7941
carb -0.5509 0.5270 0.3950 0.7498 -0.0908 0.4276 -0.6562 -0.5696 0.0575
gear carb
mpg 0.4803 -0.5509
cyl -0.4927 0.5270
disp -0.5556 0.3950
hp -0.1257 0.7498
drat 0.6996 -0.0908
wt -0.5833 0.4276
qsec -0.2127 -0.6562
vs 0.2060 -0.5696
am 0.7941 0.0575
gear 1.0000 0.2741
carb 0.2741 1.0000
Correlation matrix p-value#
[5]:
# Compute a matrix of correlation p-values
from ggcorrplot import cor_pmat
p_mat = cor_pmat(mtcars)
print(p_mat.round(4))
mpg cyl disp hp drat wt qsec vs am \
mpg 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0171 0.0000 0.0003
cyl 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0004 0.0000 0.0022
disp 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0131 0.0000 0.0004
hp 0.0000 0.0000 0.0000 0.0000 0.0100 0.0000 0.0000 0.0000 0.1798
drat 0.0000 0.0000 0.0000 0.0100 0.0000 0.0000 0.6196 0.0117 0.0000
wt 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.3389 0.0010 0.0000
qsec 0.0171 0.0004 0.0131 0.0000 0.6196 0.3389 0.0000 0.0000 0.2057
vs 0.0000 0.0000 0.0000 0.0000 0.0117 0.0010 0.0000 0.0000 0.3570
am 0.0003 0.0022 0.0004 0.1798 0.0000 0.0000 0.2057 0.3570 0.0000
gear 0.0054 0.0042 0.0010 0.4930 0.0000 0.0005 0.2425 0.2579 0.0000
carb 0.0011 0.0019 0.0253 0.0000 0.6212 0.0146 0.0000 0.0007 0.7545
gear carb
mpg 0.0054 0.0011
cyl 0.0042 0.0019
disp 0.0010 0.0253
hp 0.4930 0.0000
drat 0.0000 0.6212
wt 0.0005 0.0146
qsec 0.2425 0.0000
vs 0.2579 0.0007
am 0.0000 0.7545
gear 0.0000 0.1290
carb 0.1290 0.0000
Correlation matrix visualization#
[6]:
from ggcorrplot import ggcorrplot
method = “squared” (default)#
[7]:
# method = "square" (default)
p = ggcorrplot(corr)
p
[7]:
method = “circle”#
[8]:
# method = "circle"
p = ggcorrplot(corr,method = "circle")
p
[8]:
Reordering the correlation matrix#
[9]:
# using hierarchical clustering
p = ggcorrplot(corr,
hc_order = True,
outline_color = "white")
p
[9]:
Types of correlogram layout#
Get the lower triangle#
[10]:
# Get the lower triangle
p = ggcorrplot(corr,
hc_order = True,
type = "lower",
outline_color = "white")
p
[10]:
Get the upper triangle#
[11]:
# Get the upper triangle
p = ggcorrplot(corr,
hc_order = True,
type = "upper",
outline_color = "white")
p
[11]:
Change colors and theme#
[12]:
# Argument colors
from plotnine import theme_gray
p = ggcorrplot(corr,
hc_order = True,
type = "lower",
outline_color = "white",
ggtheme = theme_gray(),
colors = ("#6D9EC1", "white", "#E46726"))
p
[12]:
Add correlation coefficients#
[13]:
# argument label = True
p = ggcorrplot(corr,
hc_order = True,
type = "lower",
label = True)
p
[13]:
Add correlation significance level#
[14]:
# Argument p_mat
# Barring the no significant coefficient
p = ggcorrplot(corr,
hc_order = True,
type = "lower",
p_mat = p_mat)
p
[14]:
Leave blank on no significant coefficient#
[15]:
# Leave blank on no significant coefficient
p = ggcorrplot(corr,
p_mat = p_mat,
hc_order = True,
type = "lower",
insig = "blank")
p
[15]:
Using original data#
[16]:
#usinf original dataset
p = ggcorrplot(mtcars,
matrix_type="completed")
p
[16]:
