Overview

Dataset statistics

Number of variables6
Number of observations865
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory190.2 KiB
Average record size in memory225.2 B

Variable types

Text3
Numeric3

Alerts

Code has unique valuesUnique
Name has unique valuesUnique
R has 81 (9.4%) zerosZeros
G has 58 (6.7%) zerosZeros
B has 80 (9.2%) zerosZeros

Reproduction

Analysis started2024-05-07 00:02:16.128006
Analysis finished2024-05-07 00:02:17.290504
Duration1.16 second
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Code
Text

UNIQUE 

Distinct865
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
2024-05-07T00:02:17.545413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length39
Median length26
Mean length11.375723
Min length3

Characters and Unicode

Total characters9840
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique865 ?
Unique (%)100.0%

Sample

1st rowair_force_blue_raf
2nd rowair_force_blue_usaf
3rd rowair_superiority_blue
4th rowalabama_crimson
5th rowalice_blue
ValueCountFrequency (%)
air_force_blue_raf 1
 
0.1%
amethyst 1
 
0.1%
arsenic 1
 
0.1%
air_superiority_blue 1
 
0.1%
alabama_crimson 1
 
0.1%
alice_blue 1
 
0.1%
alizarin_crimson 1
 
0.1%
alloy_orange 1
 
0.1%
almond 1
 
0.1%
amaranth 1
 
0.1%
Other values (855) 855
98.8%
2024-05-07T00:02:17.951232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1201
 
12.2%
_ 799
 
8.1%
r 796
 
8.1%
a 788
 
8.0%
l 695
 
7.1%
n 626
 
6.4%
i 558
 
5.7%
o 519
 
5.3%
t 396
 
4.0%
u 373
 
3.8%
Other values (21) 3089
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1201
 
12.2%
_ 799
 
8.1%
r 796
 
8.1%
a 788
 
8.0%
l 695
 
7.1%
n 626
 
6.4%
i 558
 
5.7%
o 519
 
5.3%
t 396
 
4.0%
u 373
 
3.8%
Other values (21) 3089
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1201
 
12.2%
_ 799
 
8.1%
r 796
 
8.1%
a 788
 
8.0%
l 695
 
7.1%
n 626
 
6.4%
i 558
 
5.7%
o 519
 
5.3%
t 396
 
4.0%
u 373
 
3.8%
Other values (21) 3089
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1201
 
12.2%
_ 799
 
8.1%
r 796
 
8.1%
a 788
 
8.0%
l 695
 
7.1%
n 626
 
6.4%
i 558
 
5.7%
o 519
 
5.3%
t 396
 
4.0%
u 373
 
3.8%
Other values (21) 3089
31.4%

Name
Text

UNIQUE 

Distinct865
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size58.3 KiB
2024-05-07T00:02:18.233857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length41
Median length28
Mean length11.591908
Min length3

Characters and Unicode

Total characters10027
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique865 ?
Unique (%)100.0%

Sample

1st rowAir Force Blue (Raf)
2nd rowAir Force Blue (Usaf)
3rd rowAir Superiority Blue
4th rowAlabama Crimson
5th rowAlice Blue
ValueCountFrequency (%)
blue 98
 
6.0%
green 78
 
4.8%
pink 47
 
2.9%
dark 45
 
2.8%
red 42
 
2.6%
yellow 31
 
1.9%
rose 28
 
1.7%
light 25
 
1.5%
lavender 23
 
1.4%
orange 23
 
1.4%
Other values (606) 1190
73.0%
2024-05-07T00:02:18.680858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1168
 
11.6%
765
 
7.6%
a 737
 
7.4%
r 661
 
6.6%
l 611
 
6.1%
n 609
 
6.1%
i 536
 
5.3%
o 463
 
4.6%
u 345
 
3.4%
t 328
 
3.3%
Other values (59) 3804
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1168
 
11.6%
765
 
7.6%
a 737
 
7.4%
r 661
 
6.6%
l 611
 
6.1%
n 609
 
6.1%
i 536
 
5.3%
o 463
 
4.6%
u 345
 
3.4%
t 328
 
3.3%
Other values (59) 3804
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1168
 
11.6%
765
 
7.6%
a 737
 
7.4%
r 661
 
6.6%
l 611
 
6.1%
n 609
 
6.1%
i 536
 
5.3%
o 463
 
4.6%
u 345
 
3.4%
t 328
 
3.3%
Other values (59) 3804
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1168
 
11.6%
765
 
7.6%
a 737
 
7.4%
r 661
 
6.6%
l 611
 
6.1%
n 609
 
6.1%
i 536
 
5.3%
o 463
 
4.6%
u 345
 
3.4%
t 328
 
3.3%
Other values (59) 3804
37.9%

Hex
Text

Distinct765
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size54.0 KiB
2024-05-07T00:02:18.991679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.7988439
Min length4

Characters and Unicode

Total characters5881
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique684 ?
Unique (%)79.1%

Sample

1st row#5d8aa8
2nd row#00308f
3rd row#72a0c1
4th row#a32638
5th row#f0f8ff
ValueCountFrequency (%)
c19a6b 5
 
0.6%
967117 4
 
0.5%
fada5e 4
 
0.5%
808080 3
 
0.3%
0f0 3
 
0.3%
a52a2a 3
 
0.3%
483c32 3
 
0.3%
d2691e 3
 
0.3%
fad6a5 3
 
0.3%
dda0dd 3
 
0.3%
Other values (755) 831
96.1%
2024-05-07T00:02:19.510934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
# 865
14.7%
0 665
 
11.3%
f 625
 
10.6%
8 317
 
5.4%
c 300
 
5.1%
a 292
 
5.0%
e 269
 
4.6%
4 268
 
4.6%
b 268
 
4.6%
3 267
 
4.5%
Other values (7) 1745
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5881
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
# 865
14.7%
0 665
 
11.3%
f 625
 
10.6%
8 317
 
5.4%
c 300
 
5.1%
a 292
 
5.0%
e 269
 
4.6%
4 268
 
4.6%
b 268
 
4.6%
3 267
 
4.5%
Other values (7) 1745
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5881
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
# 865
14.7%
0 665
 
11.3%
f 625
 
10.6%
8 317
 
5.4%
c 300
 
5.1%
a 292
 
5.0%
e 269
 
4.6%
4 268
 
4.6%
b 268
 
4.6%
3 267
 
4.5%
Other values (7) 1745
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5881
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
# 865
14.7%
0 665
 
11.3%
f 625
 
10.6%
8 317
 
5.4%
c 300
 
5.1%
a 292
 
5.0%
e 269
 
4.6%
4 268
 
4.6%
b 268
 
4.6%
3 267
 
4.5%
Other values (7) 1745
29.7%

R
Real number (ℝ)

ZEROS 

Distinct221
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.59884
Minimum0
Maximum255
Zeros81
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-07T00:02:19.732218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1101
median178
Q3236
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)135

Descriptive statistics

Standard deviation85.338432
Coefficient of variation (CV)0.53807726
Kurtosis-0.92645087
Mean158.59884
Median Absolute Deviation (MAD)66
Skewness-0.59367921
Sum137188
Variance7282.6479
MonotonicityNot monotonic
2024-05-07T00:02:19.943177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 110
 
12.7%
0 81
 
9.4%
250 15
 
1.7%
204 13
 
1.5%
150 11
 
1.3%
128 11
 
1.3%
227 10
 
1.2%
153 10
 
1.2%
244 10
 
1.2%
240 9
 
1.0%
Other values (211) 585
67.6%
ValueCountFrequency (%)
0 81
9.4%
1 4
 
0.5%
2 1
 
0.1%
3 2
 
0.2%
5 1
 
0.1%
6 1
 
0.1%
8 4
 
0.5%
10 1
 
0.1%
11 1
 
0.1%
13 1
 
0.1%
ValueCountFrequency (%)
255 110
12.7%
254 7
 
0.8%
253 8
 
0.9%
252 6
 
0.7%
251 9
 
1.0%
250 15
 
1.7%
249 4
 
0.5%
248 8
 
0.9%
247 3
 
0.3%
246 2
 
0.2%

G
Real number (ℝ)

ZEROS 

Distinct234
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.68324
Minimum0
Maximum255
Zeros58
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-07T00:02:20.145878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q164
median123
Q3190
95-th percentile250
Maximum255
Range255
Interquartile range (IQR)126

Descriptive statistics

Standard deviation76.270225
Coefficient of variation (CV)0.61171194
Kurtosis-1.0978467
Mean124.68324
Median Absolute Deviation (MAD)63
Skewness0.052233472
Sum107851
Variance5817.1472
MonotonicityNot monotonic
2024-05-07T00:02:20.356164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58
 
6.7%
255 35
 
4.0%
128 13
 
1.5%
105 12
 
1.4%
51 11
 
1.3%
204 11
 
1.3%
66 9
 
1.0%
160 9
 
1.0%
218 9
 
1.0%
102 9
 
1.0%
Other values (224) 689
79.7%
ValueCountFrequency (%)
0 58
6.7%
1 2
 
0.2%
2 2
 
0.2%
3 2
 
0.2%
6 2
 
0.2%
8 2
 
0.2%
10 3
 
0.3%
11 2
 
0.2%
12 3
 
0.3%
14 2
 
0.2%
ValueCountFrequency (%)
255 35
4.0%
254 3
 
0.3%
253 2
 
0.2%
252 2
 
0.2%
251 1
 
0.1%
250 5
 
0.6%
249 1
 
0.1%
248 4
 
0.5%
247 2
 
0.2%
246 1
 
0.1%

B
Real number (ℝ)

ZEROS 

Distinct230
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.08786
Minimum0
Maximum255
Zeros80
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-07T00:02:20.557979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q153
median119
Q3186
95-th percentile253.6
Maximum255
Range255
Interquartile range (IQR)133

Descriptive statistics

Standard deviation78.343862
Coefficient of variation (CV)0.65786606
Kurtosis-1.13796
Mean119.08786
Median Absolute Deviation (MAD)66
Skewness0.10728769
Sum103011
Variance6137.7608
MonotonicityNot monotonic
2024-05-07T00:02:20.763322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 80
 
9.2%
255 41
 
4.7%
107 15
 
1.7%
128 14
 
1.6%
204 10
 
1.2%
94 9
 
1.0%
120 9
 
1.0%
50 8
 
0.9%
51 8
 
0.9%
153 8
 
0.9%
Other values (220) 663
76.6%
ValueCountFrequency (%)
0 80
9.2%
2 3
 
0.3%
3 1
 
0.1%
5 2
 
0.2%
7 2
 
0.2%
8 3
 
0.3%
9 1
 
0.1%
10 2
 
0.2%
11 3
 
0.3%
12 3
 
0.3%
ValueCountFrequency (%)
255 41
4.7%
254 3
 
0.3%
252 1
 
0.1%
251 1
 
0.1%
250 7
 
0.8%
249 1
 
0.1%
245 3
 
0.3%
244 2
 
0.2%
241 1
 
0.1%
240 6
 
0.7%

Interactions

2024-05-07T00:02:16.804437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T00:02:16.229616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T00:02:16.518889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T00:02:16.899247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T00:02:16.329357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T00:02:16.614312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T00:02:16.994190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T00:02:16.424744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T00:02:16.707778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-07T00:02:20.885890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
BGR
B1.0000.2890.010
G0.2891.0000.256
R0.0100.2561.000

Missing values

2024-05-07T00:02:17.121027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-07T00:02:17.238918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CodeNameHexRGB
0air_force_blue_rafAir Force Blue (Raf)#5d8aa893138168
1air_force_blue_usafAir Force Blue (Usaf)#00308f048143
2air_superiority_blueAir Superiority Blue#72a0c1114160193
3alabama_crimsonAlabama Crimson#a326381633856
4alice_blueAlice Blue#f0f8ff240248255
5alizarin_crimsonAlizarin Crimson#e326362273854
6alloy_orangeAlloy Orange#c462101969816
7almondAlmond#efdecd239222205
8amaranthAmaranth#e52b502294380
9amberAmber#ffbf002551910
CodeNameHexRGB
855yale_blueYale Blue#0f4d921577146
856yellowYellow#ff02552550
857yellow_greenYellow-Green#9acd3215420550
858yellow_munsellYellow (Munsell)#efcc002392040
859yellow_ncsYellow (Ncs)#ffd3002552110
860yellow_orangeYellow Orange#ffae4225517466
861yellow_processYellow (Process)#ffef002552390
862yellow_rybYellow (Ryb)#fefe3325425451
863zaffreZaffre#0014a8020168
864zinnwaldite_brownZinnwaldite Brown#2c160844228