Overview

Dataset statistics

Number of variables13
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory112.3 B

Variable types

Categorical9
Numeric4

Dataset

DescriptionSample
Author(주)제로투원파트너스
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ZTO011TOTALCGIHEALM

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
건강측정기기 has constant value ""Constant
체중계/체지방계 has constant value ""Constant
체중계/체지방계.1 has constant value ""Constant
95 is highly overall correlated with 169 and 2 other fieldsHigh correlation
169 is highly overall correlated with 95 and 2 other fieldsHigh correlation
95.1 is highly overall correlated with 95 and 2 other fieldsHigh correlation
143 is highly overall correlated with 95 and 2 other fieldsHigh correlation
2 is highly overall correlated with 40High correlation
40 is highly overall correlated with 2High correlation
경기도 is highly overall correlated with 전체High correlation
전체 is highly overall correlated with 경기도High correlation

Reproduction

Analysis started2023-12-10 06:46:30.662932
Analysis finished2023-12-10 06:46:33.498275
Duration2.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MT
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
MT
99 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMT
2nd rowMT
3rd rowMT
4th rowMT
5th rowMT

Common Values

ValueCountFrequency (%)
MT 99
100.0%

Length

2023-12-10T15:46:33.577173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:33.700156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mt 99
100.0%

201901
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
201901
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201901
2nd row201901
3rd row201901
4th row201901
5th row201901

Common Values

ValueCountFrequency (%)
201901 99
100.0%

Length

2023-12-10T15:46:33.829300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:33.949144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

95
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.151515
Minimum9
Maximum623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:46:34.065619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile20.7
Q146
median70
Q3103
95-th percentile228.2
Maximum623
Range614
Interquartile range (IQR)57

Descriptive statistics

Standard deviation78.760728
Coefficient of variation (CV)0.88344801
Kurtosis21.110777
Mean89.151515
Median Absolute Deviation (MAD)28
Skewness3.7093535
Sum8826
Variance6203.2523
MonotonicityNot monotonic
2023-12-10T15:46:34.206959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 4
 
4.0%
57 4
 
4.0%
74 3
 
3.0%
37 2
 
2.0%
79 2
 
2.0%
61 2
 
2.0%
59 2
 
2.0%
36 2
 
2.0%
73 2
 
2.0%
58 2
 
2.0%
Other values (66) 74
74.7%
ValueCountFrequency (%)
9 1
1.0%
13 1
1.0%
14 1
1.0%
17 1
1.0%
18 1
1.0%
21 1
1.0%
22 1
1.0%
23 1
1.0%
31 1
1.0%
32 1
1.0%
ValueCountFrequency (%)
623 1
1.0%
275 1
1.0%
260 1
1.0%
259 1
1.0%
230 1
1.0%
228 1
1.0%
219 1
1.0%
186 1
1.0%
183 1
1.0%
172 1
1.0%

169
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20238932
Minimum9
Maximum2.0036403 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:46:34.359208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile27.7
Q163
median107
Q3172
95-th percentile469.2
Maximum2.0036403 × 109
Range2.0036403 × 109
Interquartile range (IQR)109

Descriptive statistics

Standard deviation2.0137341 × 108
Coefficient of variation (CV)9.9498043
Kurtosis99
Mean20238932
Median Absolute Deviation (MAD)49
Skewness9.9498744
Sum2.0036543 × 109
Variance4.0551252 × 1016
MonotonicityNot monotonic
2023-12-10T15:46:34.828207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 3
 
3.0%
58 2
 
2.0%
54 2
 
2.0%
471 2
 
2.0%
125 2
 
2.0%
53 2
 
2.0%
75 2
 
2.0%
149 2
 
2.0%
77 2
 
2.0%
62 2
 
2.0%
Other values (72) 78
78.8%
ValueCountFrequency (%)
9 1
1.0%
17 1
1.0%
20 2
2.0%
25 1
1.0%
28 1
1.0%
35 1
1.0%
36 1
1.0%
40 1
1.0%
43 1
1.0%
48 1
1.0%
ValueCountFrequency (%)
2003640320 1
1.0%
668 1
1.0%
623 1
1.0%
471 2
2.0%
469 1
1.0%
458 1
1.0%
365 1
1.0%
358 1
1.0%
356 1
1.0%
281 1
1.0%

95.1
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.151515
Minimum9
Maximum623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:46:35.003406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile20.7
Q146
median70
Q3103
95-th percentile228.2
Maximum623
Range614
Interquartile range (IQR)57

Descriptive statistics

Standard deviation78.760728
Coefficient of variation (CV)0.88344801
Kurtosis21.110777
Mean89.151515
Median Absolute Deviation (MAD)28
Skewness3.7093535
Sum8826
Variance6203.2523
MonotonicityNot monotonic
2023-12-10T15:46:35.145046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 4
 
4.0%
57 4
 
4.0%
74 3
 
3.0%
37 2
 
2.0%
79 2
 
2.0%
61 2
 
2.0%
59 2
 
2.0%
36 2
 
2.0%
73 2
 
2.0%
58 2
 
2.0%
Other values (66) 74
74.7%
ValueCountFrequency (%)
9 1
1.0%
13 1
1.0%
14 1
1.0%
17 1
1.0%
18 1
1.0%
21 1
1.0%
22 1
1.0%
23 1
1.0%
31 1
1.0%
32 1
1.0%
ValueCountFrequency (%)
623 1
1.0%
275 1
1.0%
260 1
1.0%
259 1
1.0%
230 1
1.0%
228 1
1.0%
219 1
1.0%
186 1
1.0%
183 1
1.0%
172 1
1.0%

건강측정기기
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
건강측정기기
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건강측정기기
2nd row건강측정기기
3rd row건강측정기기
4th row건강측정기기
5th row건강측정기기

Common Values

ValueCountFrequency (%)
건강측정기기 99
100.0%

Length

2023-12-10T15:46:35.303081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:35.407037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건강측정기기 99
100.0%

체중계/체지방계
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
체중계/체지방계
99 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row체중계/체지방계
2nd row체중계/체지방계
3rd row체중계/체지방계
4th row체중계/체지방계
5th row체중계/체지방계

Common Values

ValueCountFrequency (%)
체중계/체지방계 99
100.0%

Length

2023-12-10T15:46:35.503849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:35.604012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체중계/체지방계 99
100.0%

체중계/체지방계.1
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
체중계/체지방계
99 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row체중계/체지방계
2nd row체중계/체지방계
3rd row체중계/체지방계
4th row체중계/체지방계
5th row체중계/체지방계

Common Values

ValueCountFrequency (%)
체중계/체지방계 99
100.0%

Length

2023-12-10T15:46:35.717711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:35.834445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체중계/체지방계 99
100.0%

2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2
55 
1
44 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 55
55.6%
1 44
44.4%

Length

2023-12-10T15:46:35.975930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:36.113108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 55
55.6%
1 44
44.4%

40
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
40
36 
50
34 
60
13 
30
12 
20
4 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row40
3rd row40
4th row40
5th row40

Common Values

ValueCountFrequency (%)
40 36
36.4%
50 34
34.3%
60 13
 
13.1%
30 12
 
12.1%
20 4
 
4.0%

Length

2023-12-10T15:46:36.258706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:36.419292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
40 36
36.4%
50 34
34.3%
60 13
 
13.1%
30 12
 
12.1%
20 4
 
4.0%

경기도
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
22 
전라북도
15 
서울특별시
14 
부산광역시
13 
경상남도
9 
Other values (5)
26 

Length

Max length5
Median length4
Mean length4.1717172
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row경기도
2nd row경기도
3rd row경기도
4th row경기도
5th row충청북도

Common Values

ValueCountFrequency (%)
경기도 22
22.2%
전라북도 15
15.2%
서울특별시 14
14.1%
부산광역시 13
13.1%
경상남도 9
9.1%
전라남도 8
 
8.1%
인천광역시 7
 
7.1%
대구광역시 6
 
6.1%
충청북도 4
 
4.0%
강원도 1
 
1.0%

Length

2023-12-10T15:46:36.619622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:36.812356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 22
22.2%
전라북도 15
15.2%
서울특별시 14
14.1%
부산광역시 13
13.1%
경상남도 9
9.1%
전라남도 8
 
8.1%
인천광역시 7
 
7.1%
대구광역시 6
 
6.1%
충청북도 4
 
4.0%
강원도 1
 
1.0%

전체
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
32 
송파구
5 
평택시
5 
노원구
 
4
사상구
 
4
Other values (21)
49 

Length

Max length4
Median length3
Mean length2.6969697
Min length2

Unique

Unique6 ?
Unique (%)6.1%

Sample

1st row의정부시
2nd row평택시
3rd row남양주시
4th row오산시
5th row청주시

Common Values

ValueCountFrequency (%)
전체 32
32.3%
송파구 5
 
5.1%
평택시 5
 
5.1%
노원구 4
 
4.0%
사상구 4
 
4.0%
남양주시 4
 
4.0%
청주시 4
 
4.0%
익산시 4
 
4.0%
양산시 4
 
4.0%
중구 4
 
4.0%
Other values (16) 29
29.3%

Length

2023-12-10T15:46:37.060657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 32
32.3%
평택시 5
 
5.1%
송파구 5
 
5.1%
노원구 4
 
4.0%
사상구 4
 
4.0%
남양주시 4
 
4.0%
청주시 4
 
4.0%
익산시 4
 
4.0%
양산시 4
 
4.0%
중구 4
 
4.0%
Other values (16) 29
29.3%

143
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.12121
Minimum12
Maximum287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:46:37.231557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile32.9
Q164.5
median93
Q3120.5
95-th percentile176.7
Maximum287
Range275
Interquartile range (IQR)56

Descriptive statistics

Standard deviation51.734462
Coefficient of variation (CV)0.51671829
Kurtosis2.1007113
Mean100.12121
Median Absolute Deviation (MAD)28
Skewness1.1726397
Sum9912
Variance2676.4545
MonotonicityNot monotonic
2023-12-10T15:46:37.407087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 3
 
3.0%
59 3
 
3.0%
98 3
 
3.0%
78 3
 
3.0%
107 2
 
2.0%
51 2
 
2.0%
33 2
 
2.0%
108 2
 
2.0%
109 2
 
2.0%
79 2
 
2.0%
Other values (69) 75
75.8%
ValueCountFrequency (%)
12 1
 
1.0%
19 1
 
1.0%
25 1
 
1.0%
29 1
 
1.0%
32 1
 
1.0%
33 2
2.0%
43 3
3.0%
44 2
2.0%
46 1
 
1.0%
50 1
 
1.0%
ValueCountFrequency (%)
287 1
1.0%
272 1
1.0%
248 1
1.0%
226 1
1.0%
183 1
1.0%
176 1
1.0%
175 1
1.0%
174 1
1.0%
172 1
1.0%
164 2
2.0%

Interactions

2023-12-10T15:46:32.615089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:31.171763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:31.649670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:32.132991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:32.766972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:31.296637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:31.777794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:32.255875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:32.901455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:31.442019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:31.898953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:32.391282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:33.017812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:31.548574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:32.003152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:32.486108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:46:37.531153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
9516995.1240경기도전체143
951.0000.0001.0000.0000.0000.3220.4530.414
1690.0001.0000.0000.0000.0000.0000.7030.000
95.11.0000.0001.0000.0000.0000.3220.4530.414
20.0000.0000.0001.0000.5380.0000.0000.329
400.0000.0000.0000.5381.0000.0000.0000.000
경기도0.3220.0000.3220.0000.0001.0000.9510.133
전체0.4530.7030.4530.0000.0000.9511.0000.470
1430.4140.0000.4140.3290.0000.1330.4701.000
2023-12-10T15:46:37.687406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2경기도전체40
21.0000.0000.0000.641
경기도0.0001.0000.6820.000
전체0.0000.6821.0000.000
400.6410.0000.0001.000
2023-12-10T15:46:37.819763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
9516995.1143240경기도전체
951.0000.5771.0000.6130.0000.0000.1690.190
1690.5771.0000.5770.5880.0000.0000.0000.492
95.11.0000.5771.0000.6130.0000.0000.1690.190
1430.6130.5880.6131.0000.2520.0000.0250.162
20.0000.0000.0000.2521.0000.6410.0000.000
400.0000.0000.0000.0000.6411.0000.0000.000
경기도0.1690.0000.1690.0250.0000.0001.0000.682
전체0.1900.4920.1900.1620.0000.0000.6821.000

Missing values

2023-12-10T15:46:33.203294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:46:33.418066image/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

MT2019019516995.1건강측정기기체중계/체지방계체중계/체지방계.1240경기도전체143
0MT201901376437건강측정기기체중계/체지방계체중계/체지방계240경기도의정부시68
1MT2019017914479건강측정기기체중계/체지방계체중계/체지방계240경기도평택시64
2MT201901102262102건강측정기기체중계/체지방계체중계/체지방계240경기도남양주시116
3MT201901495649건강측정기기체중계/체지방계체중계/체지방계240경기도오산시59
4MT20190110494104건강측정기기체중계/체지방계체중계/체지방계240충청북도청주시143
5MT2019017014470건강측정기기체중계/체지방계체중계/체지방계240전라북도전체105
6MT201901988298건강측정기기체중계/체지방계체중계/체지방계240전라북도익산시164
7MT20190115073150건강측정기기체중계/체지방계체중계/체지방계240전라북도남원시111
8MT201901416941건강측정기기체중계/체지방계체중계/체지방계240전라남도전체65
9MT201901436243건강측정기기체중계/체지방계체중계/체지방계240전라남도목포시44
MT2019019516995.1건강측정기기체중계/체지방계체중계/체지방계.1240경기도전체143
89MT201901172266172건강측정기기체중계/체지방계체중계/체지방계140경상남도김해시118
90MT201901186618건강측정기기체중계/체지방계체중계/체지방계140경상남도거제시93
91MT2019015218652건강측정기기체중계/체지방계체중계/체지방계150서울특별시노원구142
92MT201901415341건강측정기기체중계/체지방계체중계/체지방계150서울특별시송파구80
93MT201901238023건강측정기기체중계/체지방계체중계/체지방계150부산광역시전체19
94MT201901495449건강측정기기체중계/체지방계체중계/체지방계150부산광역시중구33
95MT201901329932건강측정기기체중계/체지방계체중계/체지방계150부산광역시사상구78
96MT201901126469126건강측정기기체중계/체지방계체중계/체지방계150대구광역시전체159
97MT2019016112761건강측정기기체중계/체지방계체중계/체지방계240경기도수원시129
98MT201901186213186건강측정기기체중계/체지방계체중계/체지방계240경기도성남시226