Overview

Brought to you by YData

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 started2025-09-23 16:03:04.258804
Analysis finished2025-09-23 16:03:05.174003
Duration0.92 seconds
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
2025-09-23T16:03:05.309008image/svg+xmlMatplotlib v3.10.0, 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_usaf1
 
0.1%
wild_watermelon1
 
0.1%
wine1
 
0.1%
wine_dregs1
 
0.1%
wisteria1
 
0.1%
wood_brown1
 
0.1%
xanadu1
 
0.1%
yale_blue1
 
0.1%
yellow1
 
0.1%
yellow_green1
 
0.1%
Other values (855)855
98.8%
2025-09-23T16:03:05.573903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e1201
 
12.2%
_799
 
8.1%
r796
 
8.1%
a788
 
8.0%
l695
 
7.1%
n626
 
6.4%
i558
 
5.7%
o519
 
5.3%
t396
 
4.0%
u373
 
3.8%
Other values (21)3089
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)9840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1201
 
12.2%
_799
 
8.1%
r796
 
8.1%
a788
 
8.0%
l695
 
7.1%
n626
 
6.4%
i558
 
5.7%
o519
 
5.3%
t396
 
4.0%
u373
 
3.8%
Other values (21)3089
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1201
 
12.2%
_799
 
8.1%
r796
 
8.1%
a788
 
8.0%
l695
 
7.1%
n626
 
6.4%
i558
 
5.7%
o519
 
5.3%
t396
 
4.0%
u373
 
3.8%
Other values (21)3089
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1201
 
12.2%
_799
 
8.1%
r796
 
8.1%
a788
 
8.0%
l695
 
7.1%
n626
 
6.4%
i558
 
5.7%
o519
 
5.3%
t396
 
4.0%
u373
 
3.8%
Other values (21)3089
31.4%

Name
Text

Unique 

Distinct865
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size58.3 KiB
2025-09-23T16:03:05.770622image/svg+xmlMatplotlib v3.10.0, 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 (%)
blue98
 
6.0%
green78
 
4.8%
pink47
 
2.9%
dark45
 
2.8%
red42
 
2.6%
yellow31
 
1.9%
rose28
 
1.7%
light25
 
1.5%
lavender23
 
1.4%
orange23
 
1.4%
Other values (606)1190
73.0%
2025-09-23T16:03:06.063684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e1168
 
11.6%
765
 
7.6%
a737
 
7.4%
r661
 
6.6%
l611
 
6.1%
n609
 
6.1%
i536
 
5.3%
o463
 
4.6%
u345
 
3.4%
t328
 
3.3%
Other values (59)3804
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1168
 
11.6%
765
 
7.6%
a737
 
7.4%
r661
 
6.6%
l611
 
6.1%
n609
 
6.1%
i536
 
5.3%
o463
 
4.6%
u345
 
3.4%
t328
 
3.3%
Other values (59)3804
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1168
 
11.6%
765
 
7.6%
a737
 
7.4%
r661
 
6.6%
l611
 
6.1%
n609
 
6.1%
i536
 
5.3%
o463
 
4.6%
u345
 
3.4%
t328
 
3.3%
Other values (59)3804
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1168
 
11.6%
765
 
7.6%
a737
 
7.4%
r661
 
6.6%
l611
 
6.1%
n609
 
6.1%
i536
 
5.3%
o463
 
4.6%
u345
 
3.4%
t328
 
3.3%
Other values (59)3804
37.9%

Hex
Text

Distinct765
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size54.0 KiB
2025-09-23T16:03:06.280660image/svg+xmlMatplotlib v3.10.0, 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 (%)
c19a6b5
 
0.6%
fada5e4
 
0.5%
9671174
 
0.5%
d2691e3
 
0.3%
0f03
 
0.3%
cf03
 
0.3%
9003
 
0.3%
8080803
 
0.3%
0080003
 
0.3%
0ff3
 
0.3%
Other values (755)831
96.1%
2025-09-23T16:03:06.573862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
#865
14.7%
0665
 
11.3%
f625
 
10.6%
8317
 
5.4%
c300
 
5.1%
a292
 
5.0%
e269
 
4.6%
4268
 
4.6%
b268
 
4.6%
3267
 
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%
0665
 
11.3%
f625
 
10.6%
8317
 
5.4%
c300
 
5.1%
a292
 
5.0%
e269
 
4.6%
4268
 
4.6%
b268
 
4.6%
3267
 
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%
0665
 
11.3%
f625
 
10.6%
8317
 
5.4%
c300
 
5.1%
a292
 
5.0%
e269
 
4.6%
4268
 
4.6%
b268
 
4.6%
3267
 
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%
0665
 
11.3%
f625
 
10.6%
8317
 
5.4%
c300
 
5.1%
a292
 
5.0%
e269
 
4.6%
4268
 
4.6%
b268
 
4.6%
3267
 
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
2025-09-23T16:03:06.667373image/svg+xmlMatplotlib v3.10.0, 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
2025-09-23T16:03:06.763074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255110
 
12.7%
081
 
9.4%
25015
 
1.7%
20413
 
1.5%
15011
 
1.3%
12811
 
1.3%
24410
 
1.2%
15310
 
1.2%
22710
 
1.2%
2519
 
1.0%
Other values (211)585
67.6%
ValueCountFrequency (%)
081
9.4%
14
 
0.5%
21
 
0.1%
32
 
0.2%
51
 
0.1%
61
 
0.1%
84
 
0.5%
101
 
0.1%
111
 
0.1%
131
 
0.1%
ValueCountFrequency (%)
255110
12.7%
2547
 
0.8%
2538
 
0.9%
2526
 
0.7%
2519
 
1.0%
25015
 
1.7%
2494
 
0.5%
2488
 
0.9%
2473
 
0.3%
2462
 
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
2025-09-23T16:03:06.857103image/svg+xmlMatplotlib v3.10.0, 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
2025-09-23T16:03:06.953928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058
 
6.7%
25535
 
4.0%
12813
 
1.5%
10512
 
1.4%
20411
 
1.3%
5111
 
1.3%
2189
 
1.0%
1029
 
1.0%
669
 
1.0%
1609
 
1.0%
Other values (224)689
79.7%
ValueCountFrequency (%)
058
6.7%
12
 
0.2%
22
 
0.2%
32
 
0.2%
62
 
0.2%
82
 
0.2%
103
 
0.3%
112
 
0.2%
123
 
0.3%
142
 
0.2%
ValueCountFrequency (%)
25535
4.0%
2543
 
0.3%
2532
 
0.2%
2522
 
0.2%
2511
 
0.1%
2505
 
0.6%
2491
 
0.1%
2484
 
0.5%
2472
 
0.2%
2461
 
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
2025-09-23T16:03:07.048830image/svg+xmlMatplotlib v3.10.0, 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
2025-09-23T16:03:07.142560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
080
 
9.2%
25541
 
4.7%
10715
 
1.7%
12814
 
1.6%
20410
 
1.2%
949
 
1.0%
1209
 
1.0%
338
 
0.9%
1538
 
0.9%
508
 
0.9%
Other values (220)663
76.6%
ValueCountFrequency (%)
080
9.2%
23
 
0.3%
31
 
0.1%
52
 
0.2%
72
 
0.2%
83
 
0.3%
91
 
0.1%
102
 
0.2%
113
 
0.3%
123
 
0.3%
ValueCountFrequency (%)
25541
4.7%
2543
 
0.3%
2521
 
0.1%
2511
 
0.1%
2507
 
0.8%
2491
 
0.1%
2453
 
0.3%
2442
 
0.2%
2411
 
0.1%
2406
 
0.7%

Interactions

2025-09-23T16:03:04.761360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:03:04.366144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:03:04.566547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:03:04.828269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:03:04.436164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:03:04.631256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:03:04.892000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:03:04.501204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-23T16:03:04.696398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-23T16:03:07.201008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BGR
B1.0000.2890.010
G0.2891.0000.256
R0.0100.2561.000

Missing values

2025-09-23T16:03:04.983612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-23T16:03:05.036236image/svg+xmlMatplotlib v3.10.0, 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