Overview

Dataset statistics

Number of variables15
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.8 KiB
Average record size in memory127.7 B

Variable types

Text5
Numeric8
Categorical2

Alerts

313303 is highly overall correlated with 559543High correlation
559543 is highly overall correlated with 313303 and 2 other fieldsHigh correlation
0 is highly overall correlated with -999High correlation
-999 is highly overall correlated with 0 and 1 other fieldsHigh correlation
1130510300004510104 is highly overall correlated with 559543 and 1 other fieldsHigh correlation
1130510300104510104002524 is highly overall correlated with 559543 and 1 other fieldsHigh correlation
약국 is highly overall correlated with -999High correlation
개인 is highly imbalanced (89.8%)Imbalance
h51337 has unique valuesUnique
서울특별시 강북구 덕릉로 17 (수유동) has unique valuesUnique
0 has 82 (41.2%) zerosZeros
-999 has 70 (35.2%) zerosZeros

Reproduction

Analysis started2023-12-10 06:30:54.540831
Analysis finished2023-12-10 06:31:29.971825
Duration35.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

h51337
Text

UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:31:30.430562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1194
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique199 ?
Unique (%)100.0%

Sample

1st rowh11628
2nd rowh68184
3rd rowh35384
4th rowh91465
5th rowh17259
ValueCountFrequency (%)
h11628 1
 
0.5%
h43917 1
 
0.5%
h55279 1
 
0.5%
h53857 1
 
0.5%
h06692 1
 
0.5%
h51851 1
 
0.5%
h32623 1
 
0.5%
h81022 1
 
0.5%
h73910 1
 
0.5%
h06748 1
 
0.5%
Other values (189) 189
95.0%
2023-12-10T15:31:31.421805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 199
16.7%
3 113
9.5%
8 112
9.4%
2 108
9.0%
0 106
8.9%
4 106
8.9%
5 104
8.7%
1 100
8.4%
6 91
7.6%
7 85
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 995
83.3%
Lowercase Letter 199
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 113
11.4%
8 112
11.3%
2 108
10.9%
0 106
10.7%
4 106
10.7%
5 104
10.5%
1 100
10.1%
6 91
9.1%
7 85
8.5%
9 70
7.0%
Lowercase Letter
ValueCountFrequency (%)
h 199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 995
83.3%
Latin 199
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
3 113
11.4%
8 112
11.3%
2 108
10.9%
0 106
10.7%
4 106
10.7%
5 104
10.5%
1 100
10.1%
6 91
9.1%
7 85
8.5%
9 70
7.0%
Latin
ValueCountFrequency (%)
h 199
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 199
16.7%
3 113
9.5%
8 112
9.4%
2 108
9.0%
0 106
8.9%
4 106
8.9%
5 104
8.7%
1 100
8.4%
6 91
7.6%
7 85
7.1%

호림프라자약국
Text

Distinct198
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:31:31.855055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length7.6582915
Min length3

Characters and Unicode

Total characters1524
Distinct characters262
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique197 ?
Unique (%)99.0%

Sample

1st row강북연세내과의원
2nd row고운얼굴치과의원
3rd row동명온누리약국
4th row연세브라이튼안과의원
5th row강남리더스미의원
ValueCountFrequency (%)
유림한의원 2
 
1.0%
라이프약국 1
 
0.5%
수명산약국 1
 
0.5%
뉴욕씨엔디치과의원 1
 
0.5%
압구정미약국 1
 
0.5%
압구정제이약국 1
 
0.5%
에스지정신건강의학과의원 1
 
0.5%
강서발산플란트치과의원 1
 
0.5%
강남제일약국 1
 
0.5%
하루의원 1
 
0.5%
Other values (191) 191
94.6%
2023-12-10T15:31:32.627009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
의 175
 
11.5%
원 166
 
10.9%
과 106
 
7.0%
치 39
 
2.6%
국 38
 
2.5%
이 37
 
2.4%
약 36
 
2.4%
한 32
 
2.1%
정 22
 
1.4%
강 21
 
1.4%
Other values (252) 852
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1512
99.2%
Lowercase Letter 5
 
0.3%
Space Separator 3
 
0.2%
Uppercase Letter 2
 
0.1%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 175
 
11.6%
원 166
 
11.0%
과 106
 
7.0%
치 39
 
2.6%
국 38
 
2.5%
이 37
 
2.4%
약 36
 
2.4%
한 32
 
2.1%
정 22
 
1.5%
강 21
 
1.4%
Other values (243) 840
55.6%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
i 1
20.0%
h 1
20.0%
n 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
50.0%
T 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1512
99.2%
Latin 7
 
0.5%
Common 5
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 175
 
11.6%
원 166
 
11.0%
과 106
 
7.0%
치 39
 
2.6%
국 38
 
2.5%
이 37
 
2.4%
약 36
 
2.4%
한 32
 
2.1%
정 22
 
1.5%
강 21
 
1.4%
Other values (243) 840
55.6%
Latin
ValueCountFrequency (%)
e 2
28.6%
i 1
14.3%
N 1
14.3%
h 1
14.3%
T 1
14.3%
n 1
14.3%
Common
ValueCountFrequency (%)
3
60.0%
) 1
 
20.0%
( 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1512
99.2%
ASCII 12
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
의 175
 
11.6%
원 166
 
11.0%
과 106
 
7.0%
치 39
 
2.6%
국 38
 
2.5%
이 37
 
2.4%
약 36
 
2.4%
한 32
 
2.1%
정 22
 
1.5%
강 21
 
1.4%
Other values (243) 840
55.6%
ASCII
ValueCountFrequency (%)
3
25.0%
e 2
16.7%
) 1
 
8.3%
i 1
 
8.3%
N 1
 
8.3%
h 1
 
8.3%
T 1
 
8.3%
( 1
 
8.3%
n 1
 
8.3%

서울특별시 강북구 수유동 451-104번지
Text

Distinct186
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:31:33.185431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length20.894472
Min length17

Characters and Unicode

Total characters4158
Distinct characters61
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)87.4%

Sample

1st row서울특별시 강북구 미아동 189-14번지
2nd row서울특별시 강남구 역삼동 708번지
3rd row서울특별시 강남구 대치동 922-23번지
4th row서울특별시 강남구 역삼동 814-5번지
5th row서울특별시 강남구 대치동 897-15번지
ValueCountFrequency (%)
서울특별시 199
25.0%
강남구 132
16.6%
강서구 44
 
5.5%
신사동 30
 
3.8%
논현동 27
 
3.4%
역삼동 25
 
3.1%
강북구 23
 
2.9%
대치동 16
 
2.0%
도곡동 12
 
1.5%
화곡동 12
 
1.5%
Other values (202) 276
34.7%
2023-12-10T15:31:33.939535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
597
 
14.4%
서 245
 
5.9%
번 204
 
4.9%
특 199
 
4.8%
별 199
 
4.8%
시 199
 
4.8%
강 199
 
4.8%
구 199
 
4.8%
울 199
 
4.8%
동 199
 
4.8%
Other values (51) 1719
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2589
62.3%
Decimal Number 814
 
19.6%
Space Separator 597
 
14.4%
Dash Punctuation 158
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 245
9.5%
번 204
 
7.9%
특 199
 
7.7%
별 199
 
7.7%
시 199
 
7.7%
강 199
 
7.7%
구 199
 
7.7%
울 199
 
7.7%
동 199
 
7.7%
지 199
 
7.7%
Other values (39) 548
21.2%
Decimal Number
ValueCountFrequency (%)
1 169
20.8%
6 90
11.1%
2 88
10.8%
5 81
10.0%
7 80
9.8%
3 69
8.5%
4 69
8.5%
8 59
 
7.2%
0 56
 
6.9%
9 53
 
6.5%
Space Separator
ValueCountFrequency (%)
597
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2589
62.3%
Common 1569
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 245
9.5%
번 204
 
7.9%
특 199
 
7.7%
별 199
 
7.7%
시 199
 
7.7%
강 199
 
7.7%
구 199
 
7.7%
울 199
 
7.7%
동 199
 
7.7%
지 199
 
7.7%
Other values (39) 548
21.2%
Common
ValueCountFrequency (%)
597
38.0%
1 169
 
10.8%
- 158
 
10.1%
6 90
 
5.7%
2 88
 
5.6%
5 81
 
5.2%
7 80
 
5.1%
3 69
 
4.4%
4 69
 
4.4%
8 59
 
3.8%
Other values (2) 109
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2589
62.3%
ASCII 1569
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
597
38.0%
1 169
 
10.8%
- 158
 
10.1%
6 90
 
5.7%
2 88
 
5.6%
5 81
 
5.2%
7 80
 
5.1%
3 69
 
4.4%
4 69
 
4.4%
8 59
 
3.8%
Other values (2) 109
 
6.9%
Hangul
ValueCountFrequency (%)
서 245
9.5%
번 204
 
7.9%
특 199
 
7.7%
별 199
 
7.7%
시 199
 
7.7%
강 199
 
7.7%
구 199
 
7.7%
울 199
 
7.7%
동 199
 
7.7%
지 199
 
7.7%
Other values (39) 548
21.2%

서울특별시 강북구 덕릉로 17
Text

Distinct186
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:31:34.628645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length18.125628
Min length16

Characters and Unicode

Total characters3607
Distinct characters88
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)87.4%

Sample

1st row서울특별시 강북구 도봉로 260
2nd row서울특별시 강남구 테헤란로 328
3rd row서울특별시 강남구 선릉로64길 5
4th row서울특별시 강남구 강남대로 442
5th row서울특별시 강남구 선릉로86길 12
ValueCountFrequency (%)
서울특별시 199
25.0%
강남구 132
 
16.6%
강서구 44
 
5.5%
강북구 23
 
2.9%
선릉로 14
 
1.8%
강남대로 14
 
1.8%
도봉로 10
 
1.3%
논현로 8
 
1.0%
테헤란로 8
 
1.0%
도산대로 8
 
1.0%
Other values (235) 336
42.2%
2023-12-10T15:31:35.553054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
597
16.6%
서 251
 
7.0%
강 225
 
6.2%
구 209
 
5.8%
로 200
 
5.5%
울 199
 
5.5%
시 199
 
5.5%
별 199
 
5.5%
특 199
 
5.5%
남 157
 
4.4%
Other values (78) 1172
32.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2341
64.9%
Decimal Number 658
 
18.2%
Space Separator 597
 
16.6%
Dash Punctuation 11
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 251
10.7%
강 225
9.6%
구 209
8.9%
로 200
8.5%
울 199
8.5%
시 199
8.5%
별 199
8.5%
특 199
8.5%
남 157
 
6.7%
길 60
 
2.6%
Other values (66) 443
18.9%
Decimal Number
ValueCountFrequency (%)
1 119
18.1%
2 91
13.8%
3 80
12.2%
4 73
11.1%
5 63
9.6%
6 61
9.3%
7 52
7.9%
0 46
 
7.0%
8 38
 
5.8%
9 35
 
5.3%
Space Separator
ValueCountFrequency (%)
597
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2341
64.9%
Common 1266
35.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 251
10.7%
강 225
9.6%
구 209
8.9%
로 200
8.5%
울 199
8.5%
시 199
8.5%
별 199
8.5%
특 199
8.5%
남 157
 
6.7%
길 60
 
2.6%
Other values (66) 443
18.9%
Common
ValueCountFrequency (%)
597
47.2%
1 119
 
9.4%
2 91
 
7.2%
3 80
 
6.3%
4 73
 
5.8%
5 63
 
5.0%
6 61
 
4.8%
7 52
 
4.1%
0 46
 
3.6%
8 38
 
3.0%
Other values (2) 46
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2341
64.9%
ASCII 1266
35.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
597
47.2%
1 119
 
9.4%
2 91
 
7.2%
3 80
 
6.3%
4 73
 
5.8%
5 63
 
5.0%
6 61
 
4.8%
7 52
 
4.1%
0 46
 
3.6%
8 38
 
3.0%
Other values (2) 46
 
3.6%
Hangul
ValueCountFrequency (%)
서 251
10.7%
강 225
9.6%
구 209
8.9%
로 200
8.5%
울 199
8.5%
시 199
8.5%
별 199
8.5%
특 199
8.5%
남 157
 
6.7%
길 60
 
2.6%
Other values (66) 443
18.9%

313303
Real number (ℝ)

HIGH CORRELATION 

Distinct184
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311255.91
Minimum294972
Maximum321001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:35.833914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum294972
5-th percentile296366.8
Q1313559
median314324
Q3315542.5
95-th percentile317137.6
Maximum321001
Range26029
Interquartile range (IQR)1983.5

Descriptive statistics

Standard deviation7431.0928
Coefficient of variation (CV)0.023874543
Kurtosis-0.073384481
Mean311255.91
Median Absolute Deviation (MAD)1118
Skewness-1.2980371
Sum61939927
Variance55221140
MonotonicityNot monotonic
2023-12-10T15:31:36.091802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317603 3
 
1.5%
314086 2
 
1.0%
295126 2
 
1.0%
313978 2
 
1.0%
314204 2
 
1.0%
314131 2
 
1.0%
313576 2
 
1.0%
315405 2
 
1.0%
313620 2
 
1.0%
314179 2
 
1.0%
Other values (174) 178
89.4%
ValueCountFrequency (%)
294972 1
0.5%
295126 2
1.0%
295167 1
0.5%
295174 1
0.5%
295175 1
0.5%
295184 1
0.5%
295254 1
0.5%
295320 1
0.5%
295330 1
0.5%
296482 1
0.5%
ValueCountFrequency (%)
321001 1
 
0.5%
319511 1
 
0.5%
319427 1
 
0.5%
319135 1
 
0.5%
318411 1
 
0.5%
317673 1
 
0.5%
317603 3
1.5%
317332 1
 
0.5%
317116 1
 
0.5%
317105 1
 
0.5%

559543
Real number (ℝ)

HIGH CORRELATION 

Distinct185
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean548376.57
Minimum542699
Maximum561829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:36.313808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum542699
5-th percentile543351.4
Q1544986
median547086
Q3550349.5
95-th percentile559711.4
Maximum561829
Range19130
Interquartile range (IQR)5363.5

Descriptive statistics

Standard deviation4671.1249
Coefficient of variation (CV)0.0085180972
Kurtosis0.94946993
Mean548376.57
Median Absolute Deviation (MAD)2301
Skewness1.3374029
Sum1.0912694 × 108
Variance21819408
MonotonicityNot monotonic
2023-12-10T15:31:36.579729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
544570 3
 
1.5%
546524 2
 
1.0%
544162 2
 
1.0%
544247 2
 
1.0%
551990 2
 
1.0%
546414 2
 
1.0%
547476 2
 
1.0%
551859 2
 
1.0%
544785 2
 
1.0%
546450 2
 
1.0%
Other values (175) 178
89.4%
ValueCountFrequency (%)
542699 1
0.5%
542972 1
0.5%
542974 1
0.5%
542985 1
0.5%
542995 1
0.5%
542997 1
0.5%
543054 1
0.5%
543095 1
0.5%
543142 1
0.5%
543310 1
0.5%
ValueCountFrequency (%)
561829 1
0.5%
560340 1
0.5%
560314 1
0.5%
560088 1
0.5%
560068 1
0.5%
560038 1
0.5%
559950 1
0.5%
559890 1
0.5%
559868 1
0.5%
559760 1
0.5%

221590
Real number (ℝ)

Distinct173
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170633.49
Minimum8216
Maximum518552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:36.840751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8216
5-th percentile14585.1
Q127899.5
median204374
Q3311237.5
95-th percentile414735
Maximum518552
Range510336
Interquartile range (IQR)283338

Descriptive statistics

Standard deviation152980.53
Coefficient of variation (CV)0.89654458
Kurtosis-1.4893618
Mean170633.49
Median Absolute Deviation (MAD)172817
Skewness0.30864474
Sum33956065
Variance2.3403044 × 1010
MonotonicityNot monotonic
2023-12-10T15:31:37.066098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29131 5
 
2.5%
31557 4
 
2.0%
269784 3
 
1.5%
28714 3
 
1.5%
24484 2
 
1.0%
28909 2
 
1.0%
28726 2
 
1.0%
13716 2
 
1.0%
29026 2
 
1.0%
419765 2
 
1.0%
Other values (163) 172
86.4%
ValueCountFrequency (%)
8216 1
0.5%
9421 1
0.5%
11636 1
0.5%
13547 1
0.5%
13716 2
1.0%
13735 1
0.5%
14417 1
0.5%
14542 1
0.5%
14577 1
0.5%
14586 1
0.5%
ValueCountFrequency (%)
518552 1
0.5%
509232 1
0.5%
420978 1
0.5%
419765 2
1.0%
417757 1
0.5%
416243 1
0.5%
415241 1
0.5%
415198 1
0.5%
415140 1
0.5%
414690 1
0.5%

서울특별시 강북구 덕릉로 17 (수유동)
Text

UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:31:37.630385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length45
Mean length32.929648
Min length23

Characters and Unicode

Total characters6553
Distinct characters243
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique199 ?
Unique (%)100.0%

Sample

1st row서울특별시 강북구 도봉로 260 4층 (미아동 운산빌딩)
2nd row서울특별시 강남구 테헤란로 328 (역삼동 동우빌딩 4층일부)
3rd row서울특별시 강남구 선릉로64길 5 (대치동 라이온스빌딩)
4th row서울특별시 강남구 강남대로 442 (역삼동 4층 일부)
5th row서울특별시 강남구 선릉로86길 12 3 6층 (대치동)
ValueCountFrequency (%)
서울특별시 199
 
14.7%
강남구 132
 
9.7%
강서구 44
 
3.2%
신사동 30
 
2.2%
2층 27
 
2.0%
논현동 27
 
2.0%
역삼동 25
 
1.8%
강북구 23
 
1.7%
1층 18
 
1.3%
3층 17
 
1.3%
Other values (463) 814
60.0%
2023-12-10T15:31:38.527658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1253
 
19.1%
서 257
 
3.9%
강 231
 
3.5%
동 213
 
3.3%
1 212
 
3.2%
구 211
 
3.2%
시 204
 
3.1%
로 203
 
3.1%
울 199
 
3.0%
( 199
 
3.0%
Other values (233) 3371
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3759
57.4%
Space Separator 1253
 
19.1%
Decimal Number 1060
 
16.2%
Open Punctuation 199
 
3.0%
Close Punctuation 199
 
3.0%
Uppercase Letter 48
 
0.7%
Dash Punctuation 23
 
0.4%
Lowercase Letter 7
 
0.1%
Math Symbol 4
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 257
 
6.8%
강 231
 
6.1%
동 213
 
5.7%
구 211
 
5.6%
시 204
 
5.4%
로 203
 
5.4%
울 199
 
5.3%
별 199
 
5.3%
특 199
 
5.3%
남 158
 
4.2%
Other values (196) 1685
44.8%
Uppercase Letter
ValueCountFrequency (%)
B 6
12.5%
O 5
10.4%
E 5
10.4%
K 4
8.3%
A 4
8.3%
R 4
8.3%
T 4
8.3%
S 3
6.2%
I 3
6.2%
W 3
6.2%
Other values (5) 7
14.6%
Decimal Number
ValueCountFrequency (%)
1 212
20.0%
2 176
16.6%
3 130
12.3%
0 117
11.0%
4 106
10.0%
5 89
8.4%
6 79
 
7.5%
7 65
 
6.1%
8 44
 
4.2%
9 42
 
4.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
28.6%
n 1
14.3%
v 1
14.3%
s 1
14.3%
t 1
14.3%
u 1
14.3%
Space Separator
ValueCountFrequency (%)
1253
100.0%
Open Punctuation
ValueCountFrequency (%)
( 199
100.0%
Close Punctuation
ValueCountFrequency (%)
) 199
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3759
57.4%
Common 2738
41.8%
Latin 56
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 257
 
6.8%
강 231
 
6.1%
동 213
 
5.7%
구 211
 
5.6%
시 204
 
5.4%
로 203
 
5.4%
울 199
 
5.3%
별 199
 
5.3%
특 199
 
5.3%
남 158
 
4.2%
Other values (196) 1685
44.8%
Latin
ValueCountFrequency (%)
B 6
 
10.7%
O 5
 
8.9%
E 5
 
8.9%
K 4
 
7.1%
A 4
 
7.1%
R 4
 
7.1%
T 4
 
7.1%
S 3
 
5.4%
I 3
 
5.4%
W 3
 
5.4%
Other values (12) 15
26.8%
Common
ValueCountFrequency (%)
1253
45.8%
1 212
 
7.7%
( 199
 
7.3%
) 199
 
7.3%
2 176
 
6.4%
3 130
 
4.7%
0 117
 
4.3%
4 106
 
3.9%
5 89
 
3.3%
6 79
 
2.9%
Other values (5) 178
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3759
57.4%
ASCII 2793
42.6%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1253
44.9%
1 212
 
7.6%
( 199
 
7.1%
) 199
 
7.1%
2 176
 
6.3%
3 130
 
4.7%
0 117
 
4.2%
4 106
 
3.8%
5 89
 
3.2%
6 79
 
2.8%
Other values (26) 233
 
8.3%
Hangul
ValueCountFrequency (%)
서 257
 
6.8%
강 231
 
6.1%
동 213
 
5.7%
구 211
 
5.6%
시 204
 
5.4%
로 203
 
5.4%
울 199
 
5.3%
별 199
 
5.3%
특 199
 
5.3%
남 158
 
4.2%
Other values (196) 1685
44.8%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
100.0%

약국
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
의원
98 
치과의원
36 
약국
35 
한의원
26 
일반병원
 
2
Other values (2)
 
2

Length

Max length4
Median length2
Mean length2.5326633
Min length2

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row의원
2nd row치과의원
3rd row약국
4th row의원
5th row의원

Common Values

ValueCountFrequency (%)
의원 98
49.2%
치과의원 36
 
18.1%
약국 35
 
17.6%
한의원 26
 
13.1%
일반병원 2
 
1.0%
한방병원 1
 
0.5%
치과병원 1
 
0.5%

Length

2023-12-10T15:31:38.772385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:31:39.006460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의원 98
49.2%
치과의원 36
 
18.1%
약국 35
 
17.6%
한의원 26
 
13.1%
일반병원 2
 
1.0%
한방병원 1
 
0.5%
치과병원 1
 
0.5%

개인
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
개인
195 
재단법인
 
2
소비자생활협동조합
 
2

Length

Max length9
Median length2
Mean length2.0904523
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 195
98.0%
재단법인 2
 
1.0%
소비자생활협동조합 2
 
1.0%

Length

2023-12-10T15:31:39.232484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:31:39.456806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 195
98.0%
재단법인 2
 
1.0%
소비자생활협동조합 2
 
1.0%

19700822
Real number (ℝ)

Distinct197
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20084932
Minimum19780223
Maximum20200914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:39.668650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19780223
5-th percentile19909228
Q120020720
median20110223
Q320161018
95-th percentile20192094
Maximum20200914
Range420691
Interquartile range (IQR)140297.5

Descriptive statistics

Standard deviation95628.893
Coefficient of variation (CV)0.0047612256
Kurtosis0.46893698
Mean20084932
Median Absolute Deviation (MAD)60579
Skewness-0.96131372
Sum3.9969015 × 109
Variance9.1448851 × 109
MonotonicityNot monotonic
2023-12-10T15:31:39.886497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120130 2
 
1.0%
20170623 2
 
1.0%
20180903 1
 
0.5%
20150810 1
 
0.5%
20200519 1
 
0.5%
20091110 1
 
0.5%
20060220 1
 
0.5%
20090804 1
 
0.5%
20190222 1
 
0.5%
20070910 1
 
0.5%
Other values (187) 187
94.0%
ValueCountFrequency (%)
19780223 1
0.5%
19800612 1
0.5%
19801120 1
0.5%
19801217 1
0.5%
19830604 1
0.5%
19861015 1
0.5%
19870929 1
0.5%
19880722 1
0.5%
19901129 1
0.5%
19901214 1
0.5%
ValueCountFrequency (%)
20200914 1
0.5%
20200814 1
0.5%
20200807 1
0.5%
20200708 1
0.5%
20200610 1
0.5%
20200519 1
0.5%
20200518 1
0.5%
20200416 1
0.5%
20200313 1
0.5%
20200108 1
0.5%

0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5376884
Minimum0
Maximum20
Zeros82
Zeros (%)41.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:40.118983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6.1
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4240663
Coefficient of variation (CV)1.5764353
Kurtosis17.70645
Mean1.5376884
Median Absolute Deviation (MAD)1
Skewness3.3672614
Sum306
Variance5.8760977
MonotonicityNot monotonic
2023-12-10T15:31:40.313489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 82
41.2%
1 57
28.6%
2 24
 
12.1%
3 11
 
5.5%
6 5
 
2.5%
5 5
 
2.5%
4 5
 
2.5%
8 4
 
2.0%
9 3
 
1.5%
7 2
 
1.0%
ValueCountFrequency (%)
0 82
41.2%
1 57
28.6%
2 24
 
12.1%
3 11
 
5.5%
4 5
 
2.5%
5 5
 
2.5%
6 5
 
2.5%
7 2
 
1.0%
8 4
 
2.0%
9 3
 
1.5%
ValueCountFrequency (%)
20 1
 
0.5%
9 3
 
1.5%
8 4
 
2.0%
7 2
 
1.0%
6 5
 
2.5%
5 5
 
2.5%
4 5
 
2.5%
3 11
 
5.5%
2 24
12.1%
1 57
28.6%

-999
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-174.84925
Minimum-999
Maximum17
Zeros70
Zeros (%)35.2%
Negative35
Negative (%)17.6%
Memory size1.9 KiB
2023-12-10T15:31:40.934319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q10
median0
Q31
95-th percentile3
Maximum17
Range1016
Interquartile range (IQR)1

Descriptive statistics

Standard deviation381.69551
Coefficient of variation (CV)-2.1829977
Kurtosis0.95277047
Mean-174.84925
Median Absolute Deviation (MAD)1
Skewness-1.715562
Sum-34795
Variance145691.46
MonotonicityNot monotonic
2023-12-10T15:31:41.129045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 70
35.2%
0 70
35.2%
-999 35
17.6%
3 8
 
4.0%
2 7
 
3.5%
4 4
 
2.0%
11 1
 
0.5%
7 1
 
0.5%
6 1
 
0.5%
5 1
 
0.5%
ValueCountFrequency (%)
-999 35
17.6%
0 70
35.2%
1 70
35.2%
2 7
 
3.5%
3 8
 
4.0%
4 4
 
2.0%
5 1
 
0.5%
6 1
 
0.5%
7 1
 
0.5%
11 1
 
0.5%
ValueCountFrequency (%)
17 1
 
0.5%
11 1
 
0.5%
7 1
 
0.5%
6 1
 
0.5%
5 1
 
0.5%
4 4
 
2.0%
3 8
 
4.0%
2 7
 
3.5%
1 70
35.2%
0 70
35.2%

1130510300004510104
Real number (ℝ)

HIGH CORRELATION 

Distinct186
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1596965 × 1018
Minimum1.1305101 × 1018
Maximum1.1680118 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:41.383039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305101 × 1018
5-th percentile1.1305102 × 1018
Q11.1500106 × 1018
median1.1680104 × 1018
Q31.1680107 × 1018
95-th percentile1.1680118 × 1018
Maximum1.1680118 × 1018
Range3.75017 × 1016
Interquartile range (IQR)1.800015 × 1016

Descriptive statistics

Standard deviation1.2879575 × 1016
Coefficient of variation (CV)0.011105988
Kurtosis0.30403128
Mean1.1596965 × 1018
Median Absolute Deviation (MAD)4.0000018 × 1011
Skewness-1.2797954
Sum-9.0280673 × 1018
Variance1.6588346 × 1032
MonotonicityNot monotonic
2023-12-10T15:31:41.599208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1168010600003160000 3
 
1.5%
1168010700005010000 2
 
1.0%
1168010800002410003 2
 
1.0%
1150010900006140034 2
 
1.0%
1168010800000010000 2
 
1.0%
1168010700005770005 2
 
1.0%
1150010400014790010 2
 
1.0%
1168011800005380001 2
 
1.0%
1168010100008150000 2
 
1.0%
1168010800001690000 2
 
1.0%
Other values (176) 178
89.4%
ValueCountFrequency (%)
1130510100000350020 1
0.5%
1130510100000490165 1
0.5%
1130510100001890014 1
0.5%
1130510100002070020 1
0.5%
1130510100003220001 1
0.5%
1130510100006910005 1
0.5%
1130510100008110009 1
0.5%
1130510100013480002 1
0.5%
1130510100013560000 1
0.5%
1130510200004120013 1
0.5%
ValueCountFrequency (%)
1168011800009570013 1
0.5%
1168011800009570011 1
0.5%
1168011800009530011 1
0.5%
1168011800009240005 1
0.5%
1168011800005440000 1
0.5%
1168011800005380001 2
1.0%
1168011800005270000 1
0.5%
1168011800004670007 1
0.5%
1168011800004190004 1
0.5%
1168011800004110017 1
0.5%

1130510300104510104002524
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1596965 × 1024
Minimum1.1305101 × 1024
Maximum1.1680118 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:31:41.911227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305101 × 1024
5-th percentile1.1305102 × 1024
Q11.1500106 × 1024
median1.1680104 × 1024
Q31.1680107 × 1024
95-th percentile1.1680118 × 1024
Maximum1.1680118 × 1024
Range3.75017 × 1022
Interquartile range (IQR)1.800015 × 1022

Descriptive statistics

Standard deviation1.2879575 × 1022
Coefficient of variation (CV)0.011105988
Kurtosis0.30403128
Mean1.1596965 × 1024
Median Absolute Deviation (MAD)4.0000018 × 1017
Skewness-1.2797954
Sum2.3077961 × 1026
Variance1.6588346 × 1044
MonotonicityNot monotonic
2023-12-10T15:31:42.196968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.16801080010001e+24 5
 
2.5%
1.16801080010241e+24 4
 
2.0%
1.16801060010316e+24 3
 
1.5%
1.15001090010614e+24 3
 
1.5%
1.16801070010577e+24 3
 
1.5%
1.16801070010574e+24 2
 
1.0%
1.16801010010815e+24 2
 
1.0%
1.16801070010501e+24 2
 
1.0%
1.13051030010229e+24 2
 
1.0%
1.15001060010677e+24 2
 
1.0%
Other values (154) 171
85.9%
ValueCountFrequency (%)
1.13051010010035e+24 1
0.5%
1.13051010010049e+24 1
0.5%
1.13051010010189e+24 1
0.5%
1.13051010010207e+24 1
0.5%
1.13051010010322e+24 1
0.5%
1.13051010010691e+24 1
0.5%
1.13051010010811e+24 1
0.5%
1.13051010011348e+24 1
0.5%
1.13051010011356e+24 1
0.5%
1.13051020010412e+24 1
0.5%
ValueCountFrequency (%)
1.16801180010957e+24 2
1.0%
1.16801180010953e+24 1
0.5%
1.16801180010924e+24 1
0.5%
1.16801180010544e+24 1
0.5%
1.16801180010538e+24 2
1.0%
1.16801180010527e+24 1
0.5%
1.16801180010467e+24 1
0.5%
1.16801180010419e+24 1
0.5%
1.16801180010411e+24 1
0.5%
1.1680118001018e+24 1
0.5%

Interactions

2023-12-10T15:31:17.401745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:30:55.805342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:00.036464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:03.152803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:05.732764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:08.840319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:11.622665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:14.150091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:18.538358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:30:56.053416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:00.288635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:03.311110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:05.903803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:08.992190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:11.756564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:14.305155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:19.662755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:30:56.323053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:00.524949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:03.452827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:06.050516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:09.159039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:11.912488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:14.499167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:20.889401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:30:56.564809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:00.669728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:03.585412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:06.203113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:09.303102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:12.076655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:14.666296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:22.391656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:30:56.807834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:00.853176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:03.737905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:06.393567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:09.458762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:12.232288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:14.846090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:23.725313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:30:57.112364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:01.029697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:03.900105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:06.573385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:09.651521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:12.404858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:15.043864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:25.171344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:30:57.405024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:01.197168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:04.044017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:06.731464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:09.837970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:12.537110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:15.615234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:26.499850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:30:57.727383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:01.401273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:04.207277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:06.901230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:10.036791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:12.701277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:15.805810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:31:42.402143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
313303559543221590약국개인197008220-99911305103000045101041130510300104510104002524
3133031.0000.6870.5560.0000.0000.1550.208NaN0.7850.785
5595430.6871.0000.8280.2010.0000.1820.336NaN1.0001.000
2215900.5560.8281.0000.0000.0000.0000.088NaN0.9460.946
약국0.0000.2010.0001.0000.5900.0000.417NaN0.1920.192
개인0.0000.0000.0000.5901.0000.0000.659NaN0.0000.000
197008220.1550.1820.0000.0000.0001.0000.000NaN0.0000.000
00.2080.3360.0880.4170.6590.0001.000NaN0.4510.451
-999NaNNaNNaNNaNNaNNaNNaN1.000NaNNaN
11305103000045101040.7851.0000.9460.1920.0000.0000.451NaN1.0001.000
11305103001045101040025240.7851.0000.9460.1920.0000.0000.451NaN1.0001.000
2023-12-10T15:31:42.608291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
약국개인
약국1.0000.477
개인0.4771.000
2023-12-10T15:31:42.731485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
313303559543221590197008220-99911305103000045101041130510300104510104002524약국개인
3133031.000-0.6960.4080.030-0.0600.0150.4870.4870.0000.000
559543-0.6961.000-0.308-0.0590.079-0.057-0.687-0.6870.1080.000
2215900.408-0.3081.000-0.008-0.096-0.051-0.056-0.0570.0000.000
197008220.030-0.059-0.0081.000-0.016-0.0390.1060.1060.0000.000
0-0.0600.079-0.096-0.0161.0000.6330.0090.0090.1790.330
-9990.015-0.057-0.051-0.0390.6331.0000.0450.0440.9870.000
11305103000045101040.487-0.687-0.0560.1060.0090.0451.0001.0000.1260.000
11305103001045101040025240.487-0.687-0.0570.1060.0090.0441.0001.0000.2040.258
약국0.0000.1080.0000.0000.1790.9870.1260.2041.0000.477
개인0.0000.0000.0000.0000.3300.0000.0000.2580.4771.000

Missing values

2023-12-10T15:31:29.452016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:31:29.824922image/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

h51337호림프라자약국서울특별시 강북구 수유동 451-104번지서울특별시 강북구 덕릉로 17313303559543221590서울특별시 강북구 덕릉로 17 (수유동)약국개인197008220-99911305103000045101041130510300104510104002524
0h11628강북연세내과의원서울특별시 강북구 미아동 189-14번지서울특별시 강북구 도봉로 260314086559267220454서울특별시 강북구 도봉로 260 4층 (미아동 운산빌딩)의원개인200904296111305101000018900141130510100101890014031435
1h68184고운얼굴치과의원서울특별시 강남구 역삼동 708번지서울특별시 강남구 테헤란로 328315995545040358801서울특별시 강남구 테헤란로 328 (역삼동 동우빌딩 4층일부)치과의원개인201303270111680101000070800001168010100107080001022754
2h35384동명온누리약국서울특별시 강남구 대치동 922-23번지서울특별시 강남구 선릉로64길 5316427544385353429서울특별시 강남구 선릉로64길 5 (대치동 라이온스빌딩)약국개인198610150-99911680106000092200231168010600109220023014106
3h91465연세브라이튼안과의원서울특별시 강남구 역삼동 814-5번지서울특별시 강남구 강남대로 44231408254487724578서울특별시 강남구 강남대로 442 (역삼동 4층 일부)의원개인200812241211680101000081400051168010100108140005023263
4h17259강남리더스미의원서울특별시 강남구 대치동 897-15번지서울특별시 강남구 선릉로86길 12316278544970204374서울특별시 강남구 선릉로86길 12 3 6층 (대치동)의원개인201710205011680106000089700151168010600108970015000001
5h59117고고성형외과의원서울특별시 강남구 신사동 570-12번지서울특별시 강남구 논현로167길 93142285473579421서울특별시 강남구 논현로167길 9 낙산프라자 5층층 (신사동)의원개인201802221111680107000057000121168010700105700012010363
6h27931김정숙산부인과의원서울특별시 강서구 화곡동 341-65번지서울특별시 강서구 강서로 5229826454843314542서울특별시 강서구 강서로 52 403호 (화곡동 화곡판타지아)의원개인199812301111500103000034100651150010300103410065012713
7h80895봄의언덕치과의원서울특별시 강서구 등촌동 717번지서울특별시 강서구 양천로 47029881655170915231서울특별시 강서구 양천로 470 2층 (등촌동 그레이스힐)치과의원개인201101111111500102000071700001150010200107170000000001
8h66327라미치과의원서울특별시 강남구 논현동 4-1번지서울특별시 강남구 도산대로 12431376554652828974서울특별시 강남구 도산대로 124 (논현동 대영빌딩)치과의원개인200710170011680108000000400011168010800100040001006019
9h66318에이티에이치과의원서울특별시 강남구 역삼동 820-9번지서울특별시 강남구 강남대로 406314217544529355358서울특별시 강남구 강남대로 406 GLASS TOWER 10층 (역삼동)치과의원개인201805010311680101000082000091168010100108200010023946
h51337호림프라자약국서울특별시 강북구 수유동 451-104번지서울특별시 강북구 덕릉로 17313303559543221590서울특별시 강북구 덕릉로 17 (수유동)약국개인197008220-99911305103000045101041130510300104510104002524
189h80955청담부부한의원서울특별시 강서구 화곡동 1159-1번지서울특별시 강서구 강서로 254297404550213518552서울특별시 강서구 강서로 254 301호 (화곡동 우장산아이파크이편한세상상가)한의원개인201410270111500103000115900011150010300110030009019260
190h67830미소드림치과의원서울특별시 강서구 염창동 252-16번지서울특별시 강서구 양천로 71330081155032814586서울특별시 강서구 양천로 713 (염창동)치과의원개인200410140011500101000025200161150010100102520016027840
191h35284유림한의원서울특별시 강서구 가양동 1460번지서울특별시 강서구 허준로 23298043552734282971서울특별시 강서구 허준로 23 206호 (가양동 한강타운아파트한강타운상가)한의원개인199310070011500104000146000001150010400114600000009726
192h80446탄탄의원서울특별시 강남구 논현동 111-13번지서울특별시 강남구 선릉로131길 831535754663031520서울특별시 강남구 선릉로131길 8 (논현동 노벨빌딩)의원개인201505011011680108000011100131168010800101110013005681
193h79082서울미소그린치과의원서울특별시 강남구 논현동 17번지서울특별시 강남구 강남대로 60031357354636429026서울특별시 강남구 강남대로 600 (논현동)치과의원개인200604140011680108000001700001168010800100170000006444
194h43885양재성모안과의원서울특별시 강남구 도곡동 953-11번지서울특별시 강남구 강남대로 238314831542972365234서울특별시 강남구 강남대로 238 4층 (도곡동)의원개인199505081211680118000095300111168011800109530011000001
195h06444새봄피부과의원서울특별시 강남구 청담동 22-11번지서울특별시 강남구 도산대로58길 12315456547138269340서울특별시 강남구 도산대로58길 12 4층 (청담동 K2빌딩)의원개인201606303111680104000002200111168010400100220011018268
196h64838서울뉴욕치과의원서울특별시 강북구 미아동 207-20번지서울특별시 강북구 도봉로 213314156558810220470서울특별시 강북구 도봉로 213 하당빌딩 5층 (미아동)치과의원개인201912030011305101000020700201130510100102070020031279
197h57962김정수여러분병원서울특별시 강남구 논현동 206-10번지서울특별시 강남구 봉은사로 17131466454544330994서울특별시 강남구 봉은사로 171 (논현동 지하1층일부 1-5층 6층일부)일반병원개인200506013311680108000020600101168010800102060010009130
198h58059오복치과의원서울특별시 강서구 화곡동 362-83번지서울특별시 강서구 가로공원로76길 6129752254865717148서울특별시 강서구 가로공원로76길 61 (화곡동)치과의원개인199309251011500103000036200831150010300103620083029758