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=ZTO011TOTALCGIPURIF

Alerts

MT has constant value ""Constant
정수기 has constant value ""Constant
199 is highly overall correlated with 199.1 and 3 other fieldsHigh correlation
132 is highly overall correlated with 132.1High correlation
199.1 is highly overall correlated with 199 and 3 other fieldsHigh correlation
132.1 is highly overall correlated with 199 and 2 other fieldsHigh correlation
201901 is highly overall correlated with 199 and 1 other fieldsHigh correlation
청주시 is highly overall correlated with 199 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 06:36:41.141320
Analysis finished2023-12-10 06:36:45.609065
Duration4.47 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

2023-12-10T15:36:45.934739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:46.459291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mt 99
100.0%

201901
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
201901
56 
201902
30 
201903
13 

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 56
56.6%
201902 30
30.3%
201903 13
 
13.1%

Length

2023-12-10T15:36:46.793177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:47.010932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 56
56.6%
201902 30
30.3%
201903 13
 
13.1%

199
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.42424
Minimum20
Maximum422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:36:47.336889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile23
Q148
median69
Q3121
95-th percentile362
Maximum422
Range402
Interquartile range (IQR)73

Descriptive statistics

Standard deviation103.87709
Coefficient of variation (CV)0.91582798
Kurtosis1.2854794
Mean113.42424
Median Absolute Deviation (MAD)29
Skewness1.5320063
Sum11229
Variance10790.451
MonotonicityNot monotonic
2023-12-10T15:36:47.620259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
23 4
 
4.0%
59 4
 
4.0%
27 4
 
4.0%
111 3
 
3.0%
99 3
 
3.0%
61 3
 
3.0%
44 3
 
3.0%
362 3
 
3.0%
42 3
 
3.0%
254 3
 
3.0%
Other values (34) 66
66.7%
ValueCountFrequency (%)
20 2
2.0%
23 4
4.0%
24 2
2.0%
26 2
2.0%
27 4
4.0%
31 1
 
1.0%
35 1
 
1.0%
42 3
3.0%
43 2
2.0%
44 3
3.0%
ValueCountFrequency (%)
422 1
 
1.0%
405 1
 
1.0%
380 2
2.0%
362 3
3.0%
310 2
2.0%
300 2
2.0%
274 1
 
1.0%
254 3
3.0%
221 2
2.0%
199 2
2.0%

132
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.92929
Minimum13
Maximum1008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:36:47.976514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15.8
Q149
median87
Q3155
95-th percentile380
Maximum1008
Range995
Interquartile range (IQR)106

Descriptive statistics

Standard deviation163.46823
Coefficient of variation (CV)1.2205562
Kurtosis15.783493
Mean133.92929
Median Absolute Deviation (MAD)45
Skewness3.5593643
Sum13259
Variance26721.862
MonotonicityNot monotonic
2023-12-10T15:36:48.254897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
49 6
 
6.1%
87 5
 
5.1%
55 5
 
5.1%
56 5
 
5.1%
94 4
 
4.0%
13 3
 
3.0%
143 3
 
3.0%
274 3
 
3.0%
121 3
 
3.0%
40 3
 
3.0%
Other values (32) 59
59.6%
ValueCountFrequency (%)
13 3
3.0%
14 2
2.0%
16 2
2.0%
18 2
2.0%
21 1
 
1.0%
27 2
2.0%
35 1
 
1.0%
36 3
3.0%
40 3
3.0%
44 3
3.0%
ValueCountFrequency (%)
1008 2
2.0%
609 1
 
1.0%
416 1
 
1.0%
380 2
2.0%
372 1
 
1.0%
306 1
 
1.0%
279 2
2.0%
274 3
3.0%
242 3
3.0%
193 2
2.0%

199.1
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.42424
Minimum20
Maximum422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:36:48.506436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile23
Q148
median69
Q3121
95-th percentile362
Maximum422
Range402
Interquartile range (IQR)73

Descriptive statistics

Standard deviation103.87709
Coefficient of variation (CV)0.91582798
Kurtosis1.2854794
Mean113.42424
Median Absolute Deviation (MAD)29
Skewness1.5320063
Sum11229
Variance10790.451
MonotonicityNot monotonic
2023-12-10T15:36:48.769903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
23 4
 
4.0%
59 4
 
4.0%
27 4
 
4.0%
111 3
 
3.0%
99 3
 
3.0%
61 3
 
3.0%
44 3
 
3.0%
362 3
 
3.0%
42 3
 
3.0%
254 3
 
3.0%
Other values (34) 66
66.7%
ValueCountFrequency (%)
20 2
2.0%
23 4
4.0%
24 2
2.0%
26 2
2.0%
27 4
4.0%
31 1
 
1.0%
35 1
 
1.0%
42 3
3.0%
43 2
2.0%
44 3
3.0%
ValueCountFrequency (%)
422 1
 
1.0%
405 1
 
1.0%
380 2
2.0%
362 3
3.0%
310 2
2.0%
300 2
2.0%
274 1
 
1.0%
254 3
3.0%
221 2
2.0%
199 2
2.0%

정수기
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정수기 99
100.0%

Length

2023-12-10T15:36:49.012306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:49.186984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정수기 99
100.0%

전체
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
정수기
52 
전체
47 

Length

Max length3
Median length3
Mean length2.5252525
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정수기 52
52.5%
전체 47
47.5%

Length

2023-12-10T15:36:49.358364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:49.543033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정수기 52
52.5%
전체 47
47.5%

전체.1
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
84 
정수기
15 

Length

Max length3
Median length2
Mean length2.1515152
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 84
84.8%
정수기 15
 
15.2%

Length

2023-12-10T15:36:49.719708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:49.891964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 84
84.8%
정수기 15
 
15.2%

2
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2
64 
1
35 

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 64
64.6%
1 35
35.4%

Length

2023-12-10T15:36:50.098037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:50.354311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 64
64.6%
1 35
35.4%

20
Categorical

Distinct5
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
30
47 
40
19 
50
14 
20
10 
60
9 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
30 47
47.5%
40 19
19.2%
50 14
 
14.1%
20 10
 
10.1%
60 9
 
9.1%

Length

2023-12-10T15:36:50.554841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:50.750409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30 47
47.5%
40 19
19.2%
50 14
 
14.1%
20 10
 
10.1%
60 9
 
9.1%

충청북도
Categorical

Distinct10
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
경기도
30 
충청북도
18 
서울특별시
11 
경상북도
10 
대전광역시
8 
Other values (5)
22 

Length

Max length5
Median length4
Mean length4.0707071
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row서울특별시
3rd row인천광역시
4th row경기도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 30
30.3%
충청북도 18
18.2%
서울특별시 11
 
11.1%
경상북도 10
 
10.1%
대전광역시 8
 
8.1%
인천광역시 7
 
7.1%
부산광역시 7
 
7.1%
대구광역시 4
 
4.0%
경상남도 2
 
2.0%
전라북도 2
 
2.0%

Length

2023-12-10T15:36:50.976386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:51.210509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
30.3%
충청북도 18
18.2%
서울특별시 11
 
11.1%
경상북도 10
 
10.1%
대전광역시 8
 
8.1%
인천광역시 7
 
7.1%
부산광역시 7
 
7.1%
대구광역시 4
 
4.0%
경상남도 2
 
2.0%
전라북도 2
 
2.0%

청주시
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
64 
청주시
8 
수원시
 
6
성남시
 
4
마포구
 
3
Other values (7)
14 

Length

Max length3
Median length2
Mean length2.3535354
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row마포구
2nd row송파구
3rd row전체
4th row전체
5th row부천시

Common Values

ValueCountFrequency (%)
전체 64
64.6%
청주시 8
 
8.1%
수원시 6
 
6.1%
성남시 4
 
4.0%
마포구 3
 
3.0%
송파구 3
 
3.0%
금천구 3
 
3.0%
부천시 2
 
2.0%
김해시 2
 
2.0%
노원구 2
 
2.0%
Other values (2) 2
 
2.0%

Length

2023-12-10T15:36:51.482085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 64
64.6%
청주시 8
 
8.1%
수원시 6
 
6.1%
성남시 4
 
4.0%
마포구 3
 
3.0%
송파구 3
 
3.0%
금천구 3
 
3.0%
부천시 2
 
2.0%
김해시 2
 
2.0%
노원구 2
 
2.0%
Other values (2) 2
 
2.0%

132.1
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.929293
Minimum7
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:36:51.714014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile19.6
Q145
median67
Q396
95-th percentile183
Maximum215
Range208
Interquartile range (IQR)51

Descriptive statistics

Standard deviation45.158508
Coefficient of variation (CV)0.59474422
Kurtosis1.3177573
Mean75.929293
Median Absolute Deviation (MAD)25
Skewness1.1911869
Sum7517
Variance2039.2909
MonotonicityNot monotonic
2023-12-10T15:36:52.389536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
45 7
 
7.1%
56 6
 
6.1%
50 4
 
4.0%
72 4
 
4.0%
183 3
 
3.0%
74 3
 
3.0%
85 3
 
3.0%
105 3
 
3.0%
99 3
 
3.0%
94 3
 
3.0%
Other values (33) 60
60.6%
ValueCountFrequency (%)
7 2
2.0%
16 3
3.0%
20 1
 
1.0%
22 2
2.0%
25 1
 
1.0%
30 1
 
1.0%
31 2
2.0%
33 2
2.0%
35 2
2.0%
38 2
2.0%
ValueCountFrequency (%)
215 1
 
1.0%
208 1
 
1.0%
197 1
 
1.0%
183 3
3.0%
169 1
 
1.0%
165 2
2.0%
132 2
2.0%
128 2
2.0%
108 3
3.0%
106 2
2.0%

Interactions

2023-12-10T15:36:43.916956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:42.146470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:42.769225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:43.324613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:44.067378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:42.351767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:42.917472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:43.483093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:44.272824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:42.471331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:43.045407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:43.611747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:44.409698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:42.647340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:43.199577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:43.760943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:36:52.589673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
201901199132199.1전체전체.1220충청북도청주시132.1
2019011.0000.6740.3590.6740.2060.1930.0000.1930.5550.6880.629
1990.6741.0000.7511.0000.0000.0000.2850.5220.7430.8140.936
1320.3590.7511.0000.7510.0000.0000.1830.1560.4530.7460.796
199.10.6741.0000.7511.0000.0000.0000.2850.5220.7430.8140.936
전체0.2060.0000.0000.0001.0000.5380.0000.0000.0000.0000.000
전체.10.1930.0000.0000.0000.5381.0000.0000.0660.0000.0000.000
20.0000.2850.1830.2850.0000.0001.0000.0000.5790.5820.577
200.1930.5220.1560.5220.0000.0660.0001.0000.6530.4520.655
충청북도0.5550.7430.4530.7430.0000.0000.5790.6531.0000.7830.777
청주시0.6880.8140.7460.8140.0000.0000.5820.4520.7831.0000.772
132.10.6290.9360.7960.9360.0000.0000.5770.6550.7770.7721.000
2023-12-10T15:36:52.853839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20전체.1충청북도201901청주시2전체
201.0000.0770.3170.1450.2570.0000.000
전체.10.0771.0000.0000.3150.0000.0000.361
충청북도0.3170.0001.0000.3820.4710.4270.000
2019010.1450.3150.3821.0000.3890.0000.336
청주시0.2570.0000.4710.3891.0000.4300.000
20.0000.0000.4270.0000.4301.0000.000
전체0.0000.3610.0000.3360.0000.0001.000
2023-12-10T15:36:53.058997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
199132199.1132.1201901전체전체.1220충청북도청주시
1991.0000.4311.0000.6030.5060.0000.0000.2080.2340.3130.513
1320.4311.0000.4310.6600.2530.0000.0000.1890.0960.2430.473
199.11.0000.4311.0000.6030.5060.0000.0000.2080.2340.3130.513
132.10.6030.6600.6031.0000.4560.0000.0000.4260.3190.3420.457
2019010.5060.2530.5060.4561.0000.3360.3150.0000.1450.3820.389
전체0.0000.0000.0000.0000.3361.0000.3610.0000.0000.0000.000
전체.10.0000.0000.0000.0000.3150.3611.0000.0000.0770.0000.000
20.2080.1890.2080.4260.0000.0000.0001.0000.0000.4270.430
200.2340.0960.2340.3190.1450.0000.0770.0001.0000.3170.257
충청북도0.3130.2430.3130.3420.3820.0000.0000.4270.3171.0000.471
청주시0.5130.4730.5130.4570.3890.0000.0000.4300.2570.4711.000

Missing values

2023-12-10T15:36:44.608886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:36:45.282958image/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

MT201901199132199.1정수기전체전체.1220충청북도청주시132.1
0MT201901254242254정수기전체전체230서울특별시마포구183
1MT201901483648정수기전체전체230서울특별시송파구74
2MT201901697069정수기전체전체230인천광역시전체67
3MT201901985598정수기전체전체230경기도전체52
4MT2019012711327정수기전체전체230경기도부천시42
5MT201901549454정수기전체전체230충청북도청주시59
6MT201901245024정수기전체전체230경상남도김해시72
7MT201901201820정수기전체전체240인천광역시전체33
8MT201901668466정수기전체전체240경기도전체35
9MT201901751475정수기전체전체240충청북도청주시31
MT201901199132199.1정수기전체전체.1220충청북도청주시132.1
89MT2019033510935정수기전체전체230충청북도전체93
90MT201903689968정수기전체전체240경기도수원시50
91MT201903422372422정수기전체전체250경기도고양시197
92MT201903273527정수기전체전체250경상북도전체30
93MT201903274416274정수기전체전체130부산광역시수영구169
94MT201903312131정수기전체전체130경기도성남시20
95MT201903677567정수기전체전체130경상북도전체81
96MT201903194609194정수기전체전체140경상북도전체208
97MT201903405306405정수기전체전체160경상북도전체215
98MT201903300279300정수기정수기전체220대전광역시전체128