Overview

Dataset statistics

Number of variables6
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 KiB
Average record size in memory48.9 B

Variable types

Numeric5
Categorical1

Alerts

Id is highly overall correlated with SepalLengthCm and 3 other fieldsHigh correlation
SepalLengthCm is highly overall correlated with Id and 3 other fieldsHigh correlation
PetalLengthCm is highly overall correlated with Id and 3 other fieldsHigh correlation
PetalWidthCm is highly overall correlated with Id and 3 other fieldsHigh correlation
Species is highly overall correlated with Id and 3 other fieldsHigh correlation
Id is uniformly distributedUniform
Species is uniformly distributedUniform
Id has unique valuesUnique

Reproduction

Analysis started2023-09-22 19:16:58.600428
Analysis finished2023-09-22 19:17:03.166561
Duration4.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.5
Minimum1
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-09-22T12:17:03.320749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.45
Q138.25
median75.5
Q3112.75
95-th percentile142.55
Maximum150
Range149
Interquartile range (IQR)74.5

Descriptive statistics

Standard deviation43.445368
Coefficient of variation (CV)0.57543534
Kurtosis-1.2
Mean75.5
Median Absolute Deviation (MAD)37.5
Skewness0
Sum11325
Variance1887.5
MonotonicityStrictly increasing
2023-09-22T12:17:03.567640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
95 1
 
0.7%
97 1
 
0.7%
98 1
 
0.7%
99 1
 
0.7%
100 1
 
0.7%
101 1
 
0.7%
102 1
 
0.7%
103 1
 
0.7%
104 1
 
0.7%
Other values (140) 140
93.3%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
150 1
0.7%
149 1
0.7%
148 1
0.7%
147 1
0.7%
146 1
0.7%
145 1
0.7%
144 1
0.7%
143 1
0.7%
142 1
0.7%
141 1
0.7%

SepalLengthCm
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8433333
Minimum4.3
Maximum7.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-09-22T12:17:03.801925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile4.6
Q15.1
median5.8
Q36.4
95-th percentile7.255
Maximum7.9
Range3.6
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation0.82806613
Coefficient of variation (CV)0.14171126
Kurtosis-0.55206404
Mean5.8433333
Median Absolute Deviation (MAD)0.7
Skewness0.31491096
Sum876.5
Variance0.68569351
MonotonicityNot monotonic
2023-09-22T12:17:04.021860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
5 10
 
6.7%
5.1 9
 
6.0%
6.3 9
 
6.0%
5.7 8
 
5.3%
6.7 8
 
5.3%
5.8 7
 
4.7%
5.5 7
 
4.7%
6.4 7
 
4.7%
4.9 6
 
4.0%
5.4 6
 
4.0%
Other values (25) 73
48.7%
ValueCountFrequency (%)
4.3 1
 
0.7%
4.4 3
 
2.0%
4.5 1
 
0.7%
4.6 4
 
2.7%
4.7 2
 
1.3%
4.8 5
3.3%
4.9 6
4.0%
5 10
6.7%
5.1 9
6.0%
5.2 4
 
2.7%
ValueCountFrequency (%)
7.9 1
 
0.7%
7.7 4
2.7%
7.6 1
 
0.7%
7.4 1
 
0.7%
7.3 1
 
0.7%
7.2 3
2.0%
7.1 1
 
0.7%
7 1
 
0.7%
6.9 4
2.7%
6.8 3
2.0%

SepalWidthCm
Real number (ℝ)

Distinct23
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.054
Minimum2
Maximum4.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-09-22T12:17:04.234985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.345
Q12.8
median3
Q33.3
95-th percentile3.8
Maximum4.4
Range2.4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.43359431
Coefficient of variation (CV)0.14197587
Kurtosis0.29078106
Mean3.054
Median Absolute Deviation (MAD)0.25
Skewness0.33405266
Sum458.1
Variance0.18800403
MonotonicityNot monotonic
2023-09-22T12:17:04.441217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3 26
17.3%
2.8 14
9.3%
3.2 13
 
8.7%
3.1 12
 
8.0%
3.4 12
 
8.0%
2.9 10
 
6.7%
2.7 9
 
6.0%
2.5 8
 
5.3%
3.5 6
 
4.0%
3.3 6
 
4.0%
Other values (13) 34
22.7%
ValueCountFrequency (%)
2 1
 
0.7%
2.2 3
 
2.0%
2.3 4
 
2.7%
2.4 3
 
2.0%
2.5 8
 
5.3%
2.6 5
 
3.3%
2.7 9
 
6.0%
2.8 14
9.3%
2.9 10
 
6.7%
3 26
17.3%
ValueCountFrequency (%)
4.4 1
 
0.7%
4.2 1
 
0.7%
4.1 1
 
0.7%
4 1
 
0.7%
3.9 2
 
1.3%
3.8 6
4.0%
3.7 3
 
2.0%
3.6 3
 
2.0%
3.5 6
4.0%
3.4 12
8.0%

PetalLengthCm
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7586667
Minimum1
Maximum6.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-09-22T12:17:04.649750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.3
Q11.6
median4.35
Q35.1
95-th percentile6.1
Maximum6.9
Range5.9
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation1.7644204
Coefficient of variation (CV)0.46942721
Kurtosis-1.4019208
Mean3.7586667
Median Absolute Deviation (MAD)1.25
Skewness-0.27446425
Sum563.8
Variance3.1131794
MonotonicityNot monotonic
2023-09-22T12:17:04.883844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1.5 14
 
9.3%
1.4 12
 
8.0%
5.1 8
 
5.3%
4.5 8
 
5.3%
1.6 7
 
4.7%
1.3 7
 
4.7%
5.6 6
 
4.0%
4.7 5
 
3.3%
4.9 5
 
3.3%
4 5
 
3.3%
Other values (33) 73
48.7%
ValueCountFrequency (%)
1 1
 
0.7%
1.1 1
 
0.7%
1.2 2
 
1.3%
1.3 7
4.7%
1.4 12
8.0%
1.5 14
9.3%
1.6 7
4.7%
1.7 4
 
2.7%
1.9 2
 
1.3%
3 1
 
0.7%
ValueCountFrequency (%)
6.9 1
 
0.7%
6.7 2
1.3%
6.6 1
 
0.7%
6.4 1
 
0.7%
6.3 1
 
0.7%
6.1 3
2.0%
6 2
1.3%
5.9 2
1.3%
5.8 3
2.0%
5.7 3
2.0%

PetalWidthCm
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1986667
Minimum0.1
Maximum2.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-09-22T12:17:05.123311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.3
median1.3
Q31.8
95-th percentile2.3
Maximum2.5
Range2.4
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.76316074
Coefficient of variation (CV)0.6366747
Kurtosis-1.3397542
Mean1.1986667
Median Absolute Deviation (MAD)0.7
Skewness-0.10499656
Sum179.8
Variance0.58241432
MonotonicityNot monotonic
2023-09-22T12:17:05.343910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.2 28
18.7%
1.3 13
 
8.7%
1.8 12
 
8.0%
1.5 12
 
8.0%
1.4 8
 
5.3%
2.3 8
 
5.3%
1 7
 
4.7%
0.4 7
 
4.7%
0.3 7
 
4.7%
0.1 6
 
4.0%
Other values (12) 42
28.0%
ValueCountFrequency (%)
0.1 6
 
4.0%
0.2 28
18.7%
0.3 7
 
4.7%
0.4 7
 
4.7%
0.5 1
 
0.7%
0.6 1
 
0.7%
1 7
 
4.7%
1.1 3
 
2.0%
1.2 5
 
3.3%
1.3 13
8.7%
ValueCountFrequency (%)
2.5 3
 
2.0%
2.4 3
 
2.0%
2.3 8
5.3%
2.2 3
 
2.0%
2.1 6
4.0%
2 6
4.0%
1.9 5
3.3%
1.8 12
8.0%
1.7 2
 
1.3%
1.6 4
 
2.7%

Species
Categorical

HIGH CORRELATION  UNIFORM 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Iris-setosa
50 
Iris-versicolor
50 
Iris-virginica
50 

Length

Max length15
Median length14
Mean length13.333333
Min length11

Characters and Unicode

Total characters2000
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
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 rowIris-setosa
2nd rowIris-setosa
3rd rowIris-setosa
4th rowIris-setosa
5th rowIris-setosa

Common Values

ValueCountFrequency (%)
Iris-setosa 50
33.3%
Iris-versicolor 50
33.3%
Iris-virginica 50
33.3%

Length

2023-09-22T12:17:05.594202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-22T12:17:05.845171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
iris-setosa 50
33.3%
iris-versicolor 50
33.3%
iris-virginica 50
33.3%

Most occurring characters

ValueCountFrequency (%)
i 350
17.5%
r 300
15.0%
s 300
15.0%
I 150
7.5%
- 150
7.5%
o 150
7.5%
e 100
 
5.0%
a 100
 
5.0%
v 100
 
5.0%
c 100
 
5.0%
Other values (4) 200
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1700
85.0%
Uppercase Letter 150
 
7.5%
Dash Punctuation 150
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 350
20.6%
r 300
17.6%
s 300
17.6%
o 150
8.8%
e 100
 
5.9%
a 100
 
5.9%
v 100
 
5.9%
c 100
 
5.9%
t 50
 
2.9%
l 50
 
2.9%
Other values (2) 100
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
I 150
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1850
92.5%
Common 150
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 350
18.9%
r 300
16.2%
s 300
16.2%
I 150
8.1%
o 150
8.1%
e 100
 
5.4%
a 100
 
5.4%
v 100
 
5.4%
c 100
 
5.4%
t 50
 
2.7%
Other values (3) 150
8.1%
Common
ValueCountFrequency (%)
- 150
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 350
17.5%
r 300
15.0%
s 300
15.0%
I 150
7.5%
- 150
7.5%
o 150
7.5%
e 100
 
5.0%
a 100
 
5.0%
v 100
 
5.0%
c 100
 
5.0%
Other values (4) 200
10.0%

Interactions

2023-09-22T12:17:02.085712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:16:59.046148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:16:59.806622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:00.644839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:01.351955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:02.235150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:16:59.220772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:16:59.961495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:00.798280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:01.507388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:02.390925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:16:59.375561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:00.117059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:00.945682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:01.662320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:02.535306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:16:59.516675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:00.259120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:01.080329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:01.804617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:02.680400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:16:59.664113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:00.504073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:01.218984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-22T12:17:01.944166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-22T12:17:05.980066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
Id1.0000.734-0.4120.8680.8790.904
SepalLengthCm0.7341.000-0.1590.8810.8340.617
SepalWidthCm-0.412-0.1591.000-0.303-0.2780.437
PetalLengthCm0.8680.881-0.3031.0000.9360.890
PetalWidthCm0.8790.834-0.2780.9361.0000.924
Species0.9040.6170.4370.8900.9241.000

Missing values

2023-09-22T12:17:02.858641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-22T12:17:03.062393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
015.13.51.40.2Iris-setosa
124.93.01.40.2Iris-setosa
234.73.21.30.2Iris-setosa
344.63.11.50.2Iris-setosa
455.03.61.40.2Iris-setosa
565.43.91.70.4Iris-setosa
674.63.41.40.3Iris-setosa
785.03.41.50.2Iris-setosa
894.42.91.40.2Iris-setosa
9104.93.11.50.1Iris-setosa
IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
1401416.73.15.62.4Iris-virginica
1411426.93.15.12.3Iris-virginica
1421435.82.75.11.9Iris-virginica
1431446.83.25.92.3Iris-virginica
1441456.73.35.72.5Iris-virginica
1451466.73.05.22.3Iris-virginica
1461476.32.55.01.9Iris-virginica
1471486.53.05.22.0Iris-virginica
1481496.23.45.42.3Iris-virginica
1491505.93.05.11.8Iris-virginica