Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations800
Missing cells772
Missing cells (%)6.9%
Duplicate rows2
Duplicate rows (%)0.2%
Total size in memory120.6 KiB
Average record size in memory154.3 B

Variable types

Numeric10
Categorical2
Boolean1
Text1

Alerts

Dataset has 2 (0.2%) duplicate rowsDuplicates
# is highly overall correlated with GenerationHigh correlation
Attack is highly overall correlated with Defense and 3 other fieldsHigh correlation
Defense is highly overall correlated with Attack and 3 other fieldsHigh correlation
Generation is highly overall correlated with #High correlation
HP is highly overall correlated with Attack and 2 other fieldsHigh correlation
Legendary is highly overall correlated with Sp. Atk and 2 other fieldsHigh correlation
Sp. Atk is highly overall correlated with Legendary and 3 other fieldsHigh correlation
Sp. Def is highly overall correlated with Defense and 3 other fieldsHigh correlation
Speed is highly overall correlated with Total and 1 other fieldsHigh correlation
Total is highly overall correlated with Attack and 7 other fieldsHigh correlation
combine is highly overall correlated with Attack and 7 other fieldsHigh correlation
Legendary is highly imbalanced (59.3%) Imbalance
Type 2 has 386 (48.2%) missing values Missing
Type 1 + Type 2 has 386 (48.2%) missing values Missing
# is uniformly distributed Uniform

Reproduction

Analysis started2025-06-20 09:27:00.735511
Analysis finished2025-06-20 09:27:14.800485
Duration14.06 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

#
Real number (ℝ)

High correlation  Uniform 

Distinct721
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean362.81375
Minimum1
Maximum721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2025-06-20T09:27:14.914822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34.95
Q1184.75
median364.5
Q3539.25
95-th percentile689.05
Maximum721
Range720
Interquartile range (IQR)354.5

Descriptive statistics

Standard deviation208.3438
Coefficient of variation (CV)0.57424449
Kurtosis-1.1657051
Mean362.81375
Median Absolute Deviation (MAD)177.5
Skewness-0.0011225028
Sum290251
Variance43407.138
MonotonicityIncreasing
2025-06-20T09:27:15.050479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
479 6
 
0.8%
710 4
 
0.5%
711 4
 
0.5%
386 4
 
0.5%
413 3
 
0.4%
6 3
 
0.4%
646 3
 
0.4%
150 3
 
0.4%
492 2
 
0.2%
229 2
 
0.2%
Other values (711) 766
95.8%
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
3 2
0.2%
4 1
 
0.1%
5 1
 
0.1%
6 3
0.4%
7 1
 
0.1%
8 1
 
0.1%
9 2
0.2%
10 1
 
0.1%
ValueCountFrequency (%)
721 1
0.1%
720 2
0.2%
719 2
0.2%
718 1
0.1%
717 1
0.1%
716 1
0.1%
715 1
0.1%
714 1
0.1%
713 1
0.1%
712 1
0.1%

Type 1
Categorical

Distinct18
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size44.8 KiB
Water
112 
Normal
98 
Grass
70 
Bug
69 
Psychic
57 
Other values (13)
394 

Length

Max length8
Median length7
Mean length5.26
Min length3

Characters and Unicode

Total characters4208
Distinct characters28
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrass
2nd rowGrass
3rd rowGrass
4th rowGrass
5th rowFire

Common Values

ValueCountFrequency (%)
Water 112
14.0%
Normal 98
12.2%
Grass 70
 
8.8%
Bug 69
 
8.6%
Psychic 57
 
7.1%
Fire 52
 
6.5%
Rock 44
 
5.5%
Electric 44
 
5.5%
Ground 32
 
4.0%
Ghost 32
 
4.0%
Other values (8) 190
23.8%

Length

2025-06-20T09:27:15.175814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
water 112
14.0%
normal 98
12.2%
grass 70
 
8.8%
bug 69
 
8.6%
psychic 57
 
7.1%
fire 52
 
6.5%
rock 44
 
5.5%
electric 44
 
5.5%
ground 32
 
4.0%
ghost 32
 
4.0%
Other values (8) 190
23.8%

Most occurring characters

ValueCountFrequency (%)
r 488
 
11.6%
a 360
 
8.6%
o 294
 
7.0%
e 286
 
6.8%
c 270
 
6.4%
s 257
 
6.1%
i 256
 
6.1%
t 242
 
5.8%
l 173
 
4.1%
g 159
 
3.8%
Other values (18) 1423
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 488
 
11.6%
a 360
 
8.6%
o 294
 
7.0%
e 286
 
6.8%
c 270
 
6.4%
s 257
 
6.1%
i 256
 
6.1%
t 242
 
5.8%
l 173
 
4.1%
g 159
 
3.8%
Other values (18) 1423
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 488
 
11.6%
a 360
 
8.6%
o 294
 
7.0%
e 286
 
6.8%
c 270
 
6.4%
s 257
 
6.1%
i 256
 
6.1%
t 242
 
5.8%
l 173
 
4.1%
g 159
 
3.8%
Other values (18) 1423
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 488
 
11.6%
a 360
 
8.6%
o 294
 
7.0%
e 286
 
6.8%
c 270
 
6.4%
s 257
 
6.1%
i 256
 
6.1%
t 242
 
5.8%
l 173
 
4.1%
g 159
 
3.8%
Other values (18) 1423
33.8%

Type 2
Categorical

Missing 

Distinct18
Distinct (%)4.3%
Missing386
Missing (%)48.2%
Memory size44.8 KiB
Flying
97 
Ground
35 
Poison
34 
Psychic
33 
Fighting
26 
Other values (13)
189 

Length

Max length8
Median length7
Mean length5.6521739
Min length3

Characters and Unicode

Total characters2340
Distinct characters28
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoison
2nd rowPoison
3rd rowPoison
4th rowPoison
5th rowFlying

Common Values

ValueCountFrequency (%)
Flying 97
 
12.1%
Ground 35
 
4.4%
Poison 34
 
4.2%
Psychic 33
 
4.1%
Fighting 26
 
3.2%
Grass 25
 
3.1%
Fairy 23
 
2.9%
Steel 22
 
2.8%
Dark 20
 
2.5%
Dragon 18
 
2.2%
Other values (8) 81
 
10.1%
(Missing) 386
48.2%

Length

2025-06-20T09:27:15.291656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flying 97
23.4%
ground 35
 
8.5%
poison 34
 
8.2%
psychic 33
 
8.0%
fighting 26
 
6.3%
grass 25
 
6.0%
fairy 23
 
5.6%
steel 22
 
5.3%
dark 20
 
4.8%
dragon 18
 
4.3%
Other values (8) 81
19.6%

Most occurring characters

ValueCountFrequency (%)
i 257
 
11.0%
n 210
 
9.0%
g 170
 
7.3%
F 158
 
6.8%
r 157
 
6.7%
y 153
 
6.5%
o 153
 
6.5%
s 131
 
5.6%
l 129
 
5.5%
c 106
 
4.5%
Other values (18) 716
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 257
 
11.0%
n 210
 
9.0%
g 170
 
7.3%
F 158
 
6.8%
r 157
 
6.7%
y 153
 
6.5%
o 153
 
6.5%
s 131
 
5.6%
l 129
 
5.5%
c 106
 
4.5%
Other values (18) 716
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 257
 
11.0%
n 210
 
9.0%
g 170
 
7.3%
F 158
 
6.8%
r 157
 
6.7%
y 153
 
6.5%
o 153
 
6.5%
s 131
 
5.6%
l 129
 
5.5%
c 106
 
4.5%
Other values (18) 716
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 257
 
11.0%
n 210
 
9.0%
g 170
 
7.3%
F 158
 
6.8%
r 157
 
6.7%
y 153
 
6.5%
o 153
 
6.5%
s 131
 
5.6%
l 129
 
5.5%
c 106
 
4.5%
Other values (18) 716
30.6%

Total
Real number (ℝ)

High correlation 

Distinct200
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean435.1025
Minimum180
Maximum780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2025-06-20T09:27:15.410426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile250
Q1330
median450
Q3515
95-th percentile630
Maximum780
Range600
Interquartile range (IQR)185

Descriptive statistics

Standard deviation119.96304
Coefficient of variation (CV)0.27571214
Kurtosis-0.50746071
Mean435.1025
Median Absolute Deviation (MAD)85
Skewness0.15252992
Sum348082
Variance14391.131
MonotonicityNot monotonic
2025-06-20T09:27:15.574870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 37
 
4.6%
405 26
 
3.2%
500 23
 
2.9%
580 23
 
2.9%
300 19
 
2.4%
490 18
 
2.2%
525 16
 
2.0%
330 15
 
1.9%
495 15
 
1.9%
480 15
 
1.9%
Other values (190) 593
74.1%
ValueCountFrequency (%)
180 1
 
0.1%
190 1
 
0.1%
194 1
 
0.1%
195 3
0.4%
198 1
 
0.1%
200 3
0.4%
205 5
0.6%
210 3
0.4%
213 1
 
0.1%
215 1
 
0.1%
ValueCountFrequency (%)
780 3
 
0.4%
770 2
 
0.2%
720 1
 
0.1%
700 9
1.1%
680 13
1.6%
670 4
 
0.5%
660 1
 
0.1%
640 1
 
0.1%
635 1
 
0.1%
634 2
 
0.2%

HP
Real number (ℝ)

High correlation 

Distinct94
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.25875
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2025-06-20T09:27:15.710036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.95
Q150
median65
Q380
95-th percentile110
Maximum255
Range254
Interquartile range (IQR)30

Descriptive statistics

Standard deviation25.534669
Coefficient of variation (CV)0.3686851
Kurtosis7.2320784
Mean69.25875
Median Absolute Deviation (MAD)15
Skewness1.5682244
Sum55407
Variance652.01932
MonotonicityNot monotonic
2025-06-20T09:27:15.845195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 67
 
8.4%
50 63
 
7.9%
70 57
 
7.1%
65 46
 
5.8%
75 43
 
5.4%
80 43
 
5.4%
45 38
 
4.8%
40 38
 
4.8%
55 37
 
4.6%
100 32
 
4.0%
Other values (84) 336
42.0%
ValueCountFrequency (%)
1 1
 
0.1%
10 1
 
0.1%
20 6
 
0.8%
25 2
 
0.2%
28 1
 
0.1%
30 13
1.6%
31 1
 
0.1%
35 15
1.9%
36 1
 
0.1%
37 1
 
0.1%
ValueCountFrequency (%)
255 1
 
0.1%
250 1
 
0.1%
190 1
 
0.1%
170 1
 
0.1%
165 1
 
0.1%
160 1
 
0.1%
150 4
0.5%
144 1
 
0.1%
140 1
 
0.1%
135 1
 
0.1%

Attack
Real number (ℝ)

High correlation 

Distinct111
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.00125
Minimum5
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2025-06-20T09:27:15.973405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30
Q155
median75
Q3100
95-th percentile136.2
Maximum190
Range185
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.457366
Coefficient of variation (CV)0.41084623
Kurtosis0.16971731
Mean79.00125
Median Absolute Deviation (MAD)20
Skewness0.55161375
Sum63201
Variance1053.4806
MonotonicityNot monotonic
2025-06-20T09:27:16.106618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 40
 
5.0%
65 39
 
4.9%
80 37
 
4.6%
50 37
 
4.6%
60 33
 
4.1%
85 33
 
4.1%
75 32
 
4.0%
70 31
 
3.9%
90 30
 
3.8%
55 30
 
3.8%
Other values (101) 458
57.2%
ValueCountFrequency (%)
5 2
 
0.2%
10 3
 
0.4%
15 1
 
0.1%
20 8
1.0%
22 1
 
0.1%
23 1
 
0.1%
24 1
 
0.1%
25 7
0.9%
27 1
 
0.1%
29 1
 
0.1%
ValueCountFrequency (%)
190 1
 
0.1%
185 1
 
0.1%
180 3
 
0.4%
170 2
 
0.2%
165 3
 
0.4%
164 1
 
0.1%
160 5
0.6%
155 2
 
0.2%
150 11
1.4%
147 1
 
0.1%

Defense
Real number (ℝ)

High correlation 

Distinct103
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.8425
Minimum5
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2025-06-20T09:27:16.248415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q150
median70
Q390
95-th percentile130
Maximum230
Range225
Interquartile range (IQR)40

Descriptive statistics

Standard deviation31.183501
Coefficient of variation (CV)0.42229747
Kurtosis2.7262604
Mean73.8425
Median Absolute Deviation (MAD)20
Skewness1.1559123
Sum59074
Variance972.41071
MonotonicityNot monotonic
2025-06-20T09:27:16.386433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 54
 
6.8%
50 49
 
6.1%
60 46
 
5.8%
80 39
 
4.9%
40 36
 
4.5%
65 36
 
4.5%
90 35
 
4.4%
100 33
 
4.1%
45 32
 
4.0%
55 32
 
4.0%
Other values (93) 408
51.0%
ValueCountFrequency (%)
5 2
 
0.2%
10 1
 
0.1%
15 4
 
0.5%
20 4
 
0.5%
23 1
 
0.1%
25 2
 
0.2%
28 1
 
0.1%
30 14
1.8%
32 2
 
0.2%
33 1
 
0.1%
ValueCountFrequency (%)
230 3
0.4%
200 2
 
0.2%
184 1
 
0.1%
180 3
0.4%
168 1
 
0.1%
160 3
0.4%
150 7
0.9%
145 2
 
0.2%
140 6
0.8%
135 2
 
0.2%

Sp. Atk
Real number (ℝ)

High correlation 

Distinct105
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.82
Minimum10
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2025-06-20T09:27:16.537228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q149.75
median65
Q395
95-th percentile131.05
Maximum194
Range184
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation32.722294
Coefficient of variation (CV)0.44935861
Kurtosis0.29789366
Mean72.82
Median Absolute Deviation (MAD)20
Skewness0.7446625
Sum58256
Variance1070.7485
MonotonicityNot monotonic
2025-06-20T09:27:16.694650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 51
 
6.4%
40 49
 
6.1%
65 44
 
5.5%
50 39
 
4.9%
55 35
 
4.4%
45 33
 
4.1%
70 30
 
3.8%
35 29
 
3.6%
100 27
 
3.4%
80 27
 
3.4%
Other values (95) 436
54.5%
ValueCountFrequency (%)
10 3
 
0.4%
15 4
 
0.5%
20 8
 
1.0%
23 1
 
0.1%
24 2
 
0.2%
25 11
1.4%
27 2
 
0.2%
29 1
 
0.1%
30 24
3.0%
31 1
 
0.1%
ValueCountFrequency (%)
194 1
 
0.1%
180 3
 
0.4%
175 1
 
0.1%
170 3
 
0.4%
165 2
 
0.2%
160 2
 
0.2%
159 1
 
0.1%
154 2
 
0.2%
150 9
1.1%
145 4
0.5%

Sp. Def
Real number (ℝ)

High correlation 

Distinct92
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.9025
Minimum20
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2025-06-20T09:27:16.838753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile32.95
Q150
median70
Q390
95-th percentile120
Maximum230
Range210
Interquartile range (IQR)40

Descriptive statistics

Standard deviation27.828916
Coefficient of variation (CV)0.38703683
Kurtosis1.6283941
Mean71.9025
Median Absolute Deviation (MAD)20
Skewness0.85401861
Sum57522
Variance774.44855
MonotonicityNot monotonic
2025-06-20T09:27:16.967804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 52
 
6.5%
50 50
 
6.2%
55 47
 
5.9%
65 44
 
5.5%
60 43
 
5.4%
75 40
 
5.0%
70 40
 
5.0%
90 36
 
4.5%
45 35
 
4.4%
40 30
 
3.8%
Other values (82) 383
47.9%
ValueCountFrequency (%)
20 6
 
0.8%
23 1
 
0.1%
25 11
1.4%
30 20
2.5%
31 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
34 1
 
0.1%
35 18
2.2%
36 1
 
0.1%
ValueCountFrequency (%)
230 1
 
0.1%
200 1
 
0.1%
160 2
 
0.2%
154 3
 
0.4%
150 7
0.9%
140 2
 
0.2%
138 1
 
0.1%
135 4
0.5%
130 9
1.1%
129 1
 
0.1%

Speed
Real number (ℝ)

High correlation 

Distinct108
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.2775
Minimum5
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2025-06-20T09:27:17.454483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q145
median65
Q390
95-th percentile115
Maximum180
Range175
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.060474
Coefficient of variation (CV)0.42562299
Kurtosis-0.23643667
Mean68.2775
Median Absolute Deviation (MAD)21
Skewness0.3579333
Sum54622
Variance844.51113
MonotonicityNot monotonic
2025-06-20T09:27:17.639652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 46
 
5.8%
60 44
 
5.5%
70 37
 
4.6%
65 36
 
4.5%
30 35
 
4.4%
80 33
 
4.1%
40 32
 
4.0%
100 31
 
3.9%
90 31
 
3.9%
55 30
 
3.8%
Other values (98) 445
55.6%
ValueCountFrequency (%)
5 2
 
0.2%
10 3
 
0.4%
15 9
1.1%
20 15
1.9%
22 1
 
0.1%
23 4
 
0.5%
24 1
 
0.1%
25 10
1.2%
28 4
 
0.5%
29 3
 
0.4%
ValueCountFrequency (%)
180 1
 
0.1%
160 1
 
0.1%
150 4
0.5%
145 3
0.4%
140 2
 
0.2%
135 2
 
0.2%
130 6
0.8%
128 1
 
0.1%
127 1
 
0.1%
126 1
 
0.1%

Generation
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.32375
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2025-06-20T09:27:17.750849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6612904
Coefficient of variation (CV)0.49982411
Kurtosis-1.2395758
Mean3.32375
Median Absolute Deviation (MAD)2
Skewness0.0142581
Sum2659
Variance2.7598858
MonotonicityIncreasing
2025-06-20T09:27:17.839201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 166
20.8%
5 165
20.6%
3 160
20.0%
4 121
15.1%
2 106
13.2%
6 82
10.2%
ValueCountFrequency (%)
1 166
20.8%
2 106
13.2%
3 160
20.0%
4 121
15.1%
5 165
20.6%
6 82
10.2%
ValueCountFrequency (%)
6 82
10.2%
5 165
20.6%
4 121
15.1%
3 160
20.0%
2 106
13.2%
1 166
20.8%

Legendary
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
False
735 
True
 
65
ValueCountFrequency (%)
False 735
91.9%
True 65
 
8.1%
2025-06-20T09:27:17.921481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Type 1 + Type 2
Text

Missing 

Distinct136
Distinct (%)32.9%
Missing386
Missing (%)48.2%
Memory size44.8 KiB
2025-06-20T09:27:18.183148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length14
Mean length11.642512
Min length8

Characters and Unicode

Total characters4820
Distinct characters29
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

Unique39 ?
Unique (%)9.4%

Sample

1st rowGrass+Poison
2nd rowGrass+Poison
3rd rowGrass+Poison
4th rowGrass+Poison
5th rowFire+Flying
ValueCountFrequency (%)
normal+flying 24
 
5.8%
grass+poison 15
 
3.6%
bug+flying 14
 
3.4%
bug+poison 12
 
2.9%
ghost+grass 10
 
2.4%
water+ground 10
 
2.4%
water+flying 7
 
1.7%
bug+steel 7
 
1.7%
steel+psychic 7
 
1.7%
fire+fighting 7
 
1.7%
Other values (126) 301
72.7%
2025-06-20T09:27:18.590002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 414
 
8.6%
r 388
 
8.0%
i 348
 
7.2%
o 313
 
6.5%
a 275
 
5.7%
n 272
 
5.6%
g 259
 
5.4%
s 259
 
5.4%
e 239
 
5.0%
c 224
 
4.6%
Other values (19) 1829
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
+ 414
 
8.6%
r 388
 
8.0%
i 348
 
7.2%
o 313
 
6.5%
a 275
 
5.7%
n 272
 
5.6%
g 259
 
5.4%
s 259
 
5.4%
e 239
 
5.0%
c 224
 
4.6%
Other values (19) 1829
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
+ 414
 
8.6%
r 388
 
8.0%
i 348
 
7.2%
o 313
 
6.5%
a 275
 
5.7%
n 272
 
5.6%
g 259
 
5.4%
s 259
 
5.4%
e 239
 
5.0%
c 224
 
4.6%
Other values (19) 1829
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
+ 414
 
8.6%
r 388
 
8.0%
i 348
 
7.2%
o 313
 
6.5%
a 275
 
5.7%
n 272
 
5.6%
g 259
 
5.4%
s 259
 
5.4%
e 239
 
5.0%
c 224
 
4.6%
Other values (19) 1829
37.9%

combine
Real number (ℝ)

High correlation 

Distinct162
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.82125
Minimum20
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2025-06-20T09:27:18.731740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile73.8
Q1111.75
median146.5
Q3182
95-th percentile250
Maximum360
Range340
Interquartile range (IQR)70.25

Descriptive statistics

Standard deviation54.462505
Coefficient of variation (CV)0.35872782
Kurtosis0.74122084
Mean151.82125
Median Absolute Deviation (MAD)35.5
Skewness0.60628324
Sum121457
Variance2966.1645
MonotonicityNot monotonic
2025-06-20T09:27:18.861436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170 28
 
3.5%
180 27
 
3.4%
120 27
 
3.4%
140 26
 
3.2%
150 26
 
3.2%
110 25
 
3.1%
130 22
 
2.8%
145 21
 
2.6%
135 20
 
2.5%
100 20
 
2.5%
Other values (152) 558
69.8%
ValueCountFrequency (%)
20 2
0.2%
25 2
0.2%
40 4
0.5%
45 1
 
0.1%
46 1
 
0.1%
48 1
 
0.1%
49 1
 
0.1%
50 3
0.4%
55 1
 
0.1%
58 1
 
0.1%
ValueCountFrequency (%)
360 2
 
0.2%
344 2
 
0.2%
330 3
0.4%
320 1
 
0.1%
300 3
0.4%
290 5
0.6%
285 1
 
0.1%
270 4
0.5%
265 3
0.4%
264 2
 
0.2%

Interactions

2025-06-20T09:27:13.230263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:01.616180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:02.796229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:04.147474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:05.212711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:06.354571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:07.643384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:08.979300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:10.490545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:11.871256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:13.351242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:01.743629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:02.910715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:04.262410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:05.328185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:06.467533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:07.755397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:09.135573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:10.665571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:11.977161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:13.495895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:01.858731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:03.032520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:04.369222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:05.439962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:06.575838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:07.874723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:09.298597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:10.852581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:12.082877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:13.607396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:01.979468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:03.148982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:04.475153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:05.556075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:06.671567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:07.988041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:09.447199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:11.019438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:12.187154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:13.718431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:02.096116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:03.266463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:04.586463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:05.670142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:06.769624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:08.100886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:09.595220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:11.177118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:12.300269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:13.820577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:02.205728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:03.382815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:04.680460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:05.779292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:06.869177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:08.240499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:09.727344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:11.294305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:12.414730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:13.927005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:02.322109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:03.504572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-20T09:27:05.888004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:06.976084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:08.352617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:09.871855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:11.428039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:12.807559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:14.024443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:02.445683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:03.604348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:04.884434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:05.997073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:07.068666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:08.459928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:10.002157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:11.534759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:12.904022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:14.137536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:02.565120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:03.718170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:04.986363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:06.127676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:07.202639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:08.650145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:10.153289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:11.646571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:13.008627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:14.249941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:02.672471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:04.035330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:05.109441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:06.232118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:07.537657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:08.817102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:10.325168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:11.756099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-20T09:27:13.117238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-20T09:27:18.974051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
#AttackDefenseGenerationHPLegendarySp. AtkSp. DefSpeedTotalType 1Type 2combine
#1.0000.1030.1170.9840.1200.2590.0880.0760.0190.1220.1620.2650.105
Attack0.1031.0000.5150.0540.5660.3660.3620.3210.3730.7200.1190.1050.817
Defense0.1170.5151.0000.0580.4330.2740.3140.5790.0930.6820.1510.1470.515
Generation0.9840.0540.0581.0000.0820.0780.0390.019-0.0140.0540.1580.2820.046
HP0.1200.5660.4330.0821.0000.3570.4710.4930.2660.7130.0770.1440.632
Legendary0.2590.3660.2740.0780.3571.0000.5020.3870.3420.7640.3030.1350.548
Sp. Atk0.0880.3620.3140.0390.4710.5021.0000.5720.4600.7300.1490.0700.810
Sp. Def0.0760.3210.5790.0190.4930.3870.5721.0000.3210.7570.0820.0970.551
Speed0.0190.3730.093-0.0140.2660.3420.4600.3211.0000.5680.1300.1390.508
Total0.1220.7200.6820.0540.7130.7640.7300.7570.5681.0000.1290.1190.892
Type 10.1620.1190.1510.1580.0770.3030.1490.0820.1300.1291.0000.2440.121
Type 20.2650.1050.1470.2820.1440.1350.0700.0970.1390.1190.2441.0000.096
combine0.1050.8170.5150.0460.6320.5480.8100.5510.5080.8920.1210.0961.000

Missing values

2025-06-20T09:27:14.432044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-20T09:27:14.588573image/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.
2025-06-20T09:27:14.729846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

#Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendaryType 1 + Type 2combine
Name
Bulbasaur1GrassPoison3184549496565451FalseGrass+Poison114
Ivysaur2GrassPoison4056062638080601FalseGrass+Poison142
Venusaur3GrassPoison525808283100100801FalseGrass+Poison182
VenusaurMega Venusaur3GrassPoison62580100123122120801FalseGrass+Poison222
Charmander4FireNaN3093952436050651FalseNaN112
Charmeleon5FireNaN4055864588065801FalseNaN144
Charizard6FireFlying534788478109851001FalseFire+Flying193
CharizardMega Charizard X6FireDragon63478130111130851001FalseFire+Dragon260
CharizardMega Charizard Y6FireFlying63478104781591151001FalseFire+Flying263
Squirtle7WaterNaN3144448655064431FalseNaN98
#Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendaryType 1 + Type 2combine
Name
Noibat714FlyingDragon2454030354540556FalseFlying+Dragon75
Noivern715FlyingDragon53585708097801236FalseFlying+Dragon167
Xerneas716FairyNaN6801261319513198996TrueNaN262
Yveltal717DarkFlying6801261319513198996TrueDark+Flying262
Zygarde50% Forme718DragonGround6001081001218195956TrueDragon+Ground181
Diancie719RockFairy60050100150100150506TrueRock+Fairy200
DiancieMega Diancie719RockFairy700501601101601101106TrueRock+Fairy320
HoopaHoopa Confined720PsychicGhost6008011060150130706TruePsychic+Ghost260
HoopaHoopa Unbound720PsychicDark6808016060170130806TruePsychic+Dark330
Volcanion721FireWater6008011012013090706TrueFire+Water240

Duplicate rows

Most frequently occurring

#Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendaryType 1 + Type 2combine# duplicates
0647WaterFighting580917290129901085FalseWater+Fighting2012
1678PsychicNaN46674487683811046FalseNaN1312