Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells43130
Missing cells (%)71.9%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory546.9 KiB
Average record size in memory56.0 B

Variable types

Numeric1
Text4
DateTime1

Dataset

Description인천광역시 서구 통신판매업 현황 정보 (관리번호, 법인 또는 상호명, 소재지주소(도로명,지번),취급품목 등) 에 관한 데이터를 제공합니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15039509&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
번호 has 8626 (86.3%) missing valuesMissing
관리번호 has 8626 (86.3%) missing valuesMissing
법인또는상호 has 8626 (86.3%) missing valuesMissing
소재지주소 has 8626 (86.3%) missing valuesMissing
취급품목 has 8626 (86.3%) missing valuesMissing

Reproduction

Analysis started2024-03-18 02:00:26.125100
Analysis finished2024-03-18 02:00:33.767246
Duration7.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

MISSING 

Distinct1374
Distinct (%)100.0%
Missing8626
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean6936.0044
Minimum8
Maximum13732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T11:00:33.820428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile658.9
Q13378.25
median6895.5
Q310632.75
95-th percentile13119.7
Maximum13732
Range13724
Interquartile range (IQR)7254.5

Descriptive statistics

Standard deviation4050.0434
Coefficient of variation (CV)0.58391592
Kurtosis-1.2476868
Mean6936.0044
Median Absolute Deviation (MAD)3610
Skewness-0.0073913279
Sum9530070
Variance16402851
MonotonicityNot monotonic
2024-03-18T11:00:33.929752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10612 1
 
< 0.1%
7697 1
 
< 0.1%
9959 1
 
< 0.1%
3843 1
 
< 0.1%
2908 1
 
< 0.1%
9888 1
 
< 0.1%
10275 1
 
< 0.1%
12797 1
 
< 0.1%
1978 1
 
< 0.1%
10635 1
 
< 0.1%
Other values (1364) 1364
 
13.6%
(Missing) 8626
86.3%
ValueCountFrequency (%)
8 1
< 0.1%
10 1
< 0.1%
15 1
< 0.1%
25 1
< 0.1%
37 1
< 0.1%
41 1
< 0.1%
42 1
< 0.1%
43 1
< 0.1%
55 1
< 0.1%
72 1
< 0.1%
ValueCountFrequency (%)
13732 1
< 0.1%
13731 1
< 0.1%
13727 1
< 0.1%
13715 1
< 0.1%
13701 1
< 0.1%
13696 1
< 0.1%
13684 1
< 0.1%
13668 1
< 0.1%
13647 1
< 0.1%
13618 1
< 0.1%

관리번호
Text

MISSING 

Distinct1374
Distinct (%)100.0%
Missing8626
Missing (%)86.3%
Memory size156.2 KiB
2024-03-18T11:00:34.108012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.968705
Min length7

Characters and Unicode

Total characters19193
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1374 ?
Unique (%)100.0%

Sample

1st row2016-인천서구-0312
2nd row2020-인천서구-2667
3rd row2021-인천서구-0920
4th row2020-인천서구-3338
5th row2016-인천서구-1141
ValueCountFrequency (%)
2018-인천서구-0431 1
 
0.1%
2022-인천서구-2305 1
 
0.1%
2018-인천서구-1335 1
 
0.1%
2021-인천서구-1739 1
 
0.1%
2021-인천서구-2810 1
 
0.1%
2018-인천서구-1425 1
 
0.1%
2018-인천서구-0926 1
 
0.1%
2014-인천서구-0777 1
 
0.1%
2022-인천서구-0507 1
 
0.1%
2018-인천서구-0448 1
 
0.1%
Other values (1364) 1364
99.3%
2024-03-18T11:00:34.410149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3079
16.0%
0 2758
14.4%
- 2738
14.3%
1 1765
9.2%
서 1366
7.1%
구 1366
7.1%
인 1364
7.1%
천 1364
7.1%
8 560
 
2.9%
9 525
 
2.7%
Other values (5) 2308
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10995
57.3%
Other Letter 5460
28.4%
Dash Punctuation 2738
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3079
28.0%
0 2758
25.1%
1 1765
16.1%
8 560
 
5.1%
9 525
 
4.8%
3 495
 
4.5%
4 476
 
4.3%
7 454
 
4.1%
6 447
 
4.1%
5 436
 
4.0%
Other Letter
ValueCountFrequency (%)
서 1366
25.0%
구 1366
25.0%
인 1364
25.0%
천 1364
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 2738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13733
71.6%
Hangul 5460
 
28.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3079
22.4%
0 2758
20.1%
- 2738
19.9%
1 1765
12.9%
8 560
 
4.1%
9 525
 
3.8%
3 495
 
3.6%
4 476
 
3.5%
7 454
 
3.3%
6 447
 
3.3%
Hangul
ValueCountFrequency (%)
서 1366
25.0%
구 1366
25.0%
인 1364
25.0%
천 1364
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13733
71.6%
Hangul 5460
 
28.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3079
22.4%
0 2758
20.1%
- 2738
19.9%
1 1765
12.9%
8 560
 
4.1%
9 525
 
3.8%
3 495
 
3.6%
4 476
 
3.5%
7 454
 
3.3%
6 447
 
3.3%
Hangul
ValueCountFrequency (%)
서 1366
25.0%
구 1366
25.0%
인 1364
25.0%
천 1364
25.0%

법인또는상호
Text

MISSING 

Distinct1371
Distinct (%)99.8%
Missing8626
Missing (%)86.3%
Memory size156.2 KiB
2024-03-18T11:00:34.661635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length7.0211063
Min length1

Characters and Unicode

Total characters9647
Distinct characters700
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1368 ?
Unique (%)99.6%

Sample

1st row셀레네(selene)
2nd row크레딧굿즈
3rd row셀러N제니스 컴퍼니
4th row이에스홈
5th row주식회사 코릴 (KOREEL Co. Ltd)
ValueCountFrequency (%)
주식회사 135
 
7.3%
주 26
 
1.4%
ltd 13
 
0.7%
co 10
 
0.5%
korea 5
 
0.3%
company 5
 
0.3%
스튜디오 4
 
0.2%
더 4
 
0.2%
컴퍼니 4
 
0.2%
유한회사 3
 
0.2%
Other values (1610) 1645
88.7%
2024-03-18T11:00:35.031284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
486
 
5.0%
이 374
 
3.9%
) 273
 
2.8%
( 272
 
2.8%
스 255
 
2.6%
주 230
 
2.4%
사 193
 
2.0%
회 157
 
1.6%
식 155
 
1.6%
리 125
 
1.3%
Other values (690) 7127
73.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6925
71.8%
Uppercase Letter 813
 
8.4%
Lowercase Letter 765
 
7.9%
Space Separator 486
 
5.0%
Close Punctuation 273
 
2.8%
Open Punctuation 272
 
2.8%
Decimal Number 55
 
0.6%
Other Punctuation 43
 
0.4%
Other Symbol 6
 
0.1%
Dash Punctuation 5
 
0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 374
 
5.4%
스 255
 
3.7%
주 230
 
3.3%
사 193
 
2.8%
회 157
 
2.3%
식 155
 
2.2%
리 125
 
1.8%
아 109
 
1.6%
에 104
 
1.5%
인 91
 
1.3%
Other values (618) 5132
74.1%
Uppercase Letter
ValueCountFrequency (%)
O 67
 
8.2%
E 64
 
7.9%
A 62
 
7.6%
C 60
 
7.4%
S 54
 
6.6%
L 51
 
6.3%
R 42
 
5.2%
T 39
 
4.8%
M 38
 
4.7%
I 37
 
4.6%
Other values (16) 299
36.8%
Lowercase Letter
ValueCountFrequency (%)
o 84
11.0%
e 82
10.7%
a 81
10.6%
r 55
 
7.2%
n 50
 
6.5%
t 49
 
6.4%
i 47
 
6.1%
l 47
 
6.1%
d 36
 
4.7%
s 34
 
4.4%
Other values (15) 200
26.1%
Decimal Number
ValueCountFrequency (%)
2 12
21.8%
1 12
21.8%
0 7
12.7%
8 6
10.9%
6 5
9.1%
9 4
 
7.3%
3 3
 
5.5%
4 3
 
5.5%
7 2
 
3.6%
5 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 25
58.1%
& 14
32.6%
' 3
 
7.0%
/ 1
 
2.3%
Space Separator
ValueCountFrequency (%)
486
100.0%
Close Punctuation
ValueCountFrequency (%)
) 273
100.0%
Open Punctuation
ValueCountFrequency (%)
( 272
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6929
71.8%
Latin 1578
 
16.4%
Common 1138
 
11.8%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 374
 
5.4%
스 255
 
3.7%
주 230
 
3.3%
사 193
 
2.8%
회 157
 
2.3%
식 155
 
2.2%
리 125
 
1.8%
아 109
 
1.6%
에 104
 
1.5%
인 91
 
1.3%
Other values (617) 5136
74.1%
Latin
ValueCountFrequency (%)
o 84
 
5.3%
e 82
 
5.2%
a 81
 
5.1%
O 67
 
4.2%
E 64
 
4.1%
A 62
 
3.9%
C 60
 
3.8%
r 55
 
3.5%
S 54
 
3.4%
L 51
 
3.2%
Other values (41) 918
58.2%
Common
ValueCountFrequency (%)
486
42.7%
) 273
24.0%
( 272
23.9%
. 25
 
2.2%
& 14
 
1.2%
2 12
 
1.1%
1 12
 
1.1%
0 7
 
0.6%
8 6
 
0.5%
6 5
 
0.4%
Other values (10) 26
 
2.3%
Han
ValueCountFrequency (%)
脈 1
50.0%
木 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6923
71.8%
ASCII 2715
 
28.1%
None 7
 
0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
486
17.9%
) 273
 
10.1%
( 272
 
10.0%
o 84
 
3.1%
e 82
 
3.0%
a 81
 
3.0%
O 67
 
2.5%
E 64
 
2.4%
A 62
 
2.3%
C 60
 
2.2%
Other values (60) 1184
43.6%
Hangul
ValueCountFrequency (%)
이 374
 
5.4%
스 255
 
3.7%
주 230
 
3.3%
사 193
 
2.8%
회 157
 
2.3%
식 155
 
2.2%
리 125
 
1.8%
아 109
 
1.6%
에 104
 
1.5%
인 91
 
1.3%
Other values (616) 5130
74.1%
None
ValueCountFrequency (%)
㈜ 6
85.7%
´ 1
 
14.3%
CJK
ValueCountFrequency (%)
脈 1
50.0%
木 1
50.0%

소재지주소
Text

MISSING 

Distinct1367
Distinct (%)99.5%
Missing8626
Missing (%)86.3%
Memory size156.2 KiB
2024-03-18T11:00:35.271340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length51
Mean length39.034934
Min length13

Characters and Unicode

Total characters53634
Distinct characters399
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1360 ?
Unique (%)99.0%

Sample

1st row인천광역시 서구 검단로 836， 117동 1603호 (불로동， 월드아파트)
2nd row인천광역시 서구 청마로134번길 12， 미나 403호 (당하동)
3rd row인천광역시 서구 청라대로 127， 청라 리베라움 더레이크 1동 12층 1205호 (청라동)
4th row인천광역시 서구 연희로33번길 16， B동 101호 (연희동， 청아빌라)
5th row인천광역시 서구 가석로 48 (가좌동)
ValueCountFrequency (%)
인천광역시 1374
 
13.5%
서구 1374
 
13.5%
가좌동 139
 
1.4%
청라동， 137
 
1.3%
청라동 123
 
1.2%
1층 99
 
1.0%
2층 72
 
0.7%
마전동， 72
 
0.7%
당하동， 72
 
0.7%
석남동 64
 
0.6%
Other values (2179) 6629
65.3%
2024-03-18T11:00:35.744061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8881
 
16.6%
1 2432
 
4.5%
동 2151
 
4.0%
， 1934
 
3.6%
0 1744
 
3.3%
2 1595
 
3.0%
서 1537
 
2.9%
인 1419
 
2.6%
시 1418
 
2.6%
로 1409
 
2.6%
Other values (389) 29114
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28811
53.7%
Decimal Number 10745
 
20.0%
Space Separator 8881
 
16.6%
Other Punctuation 1945
 
3.6%
Close Punctuation 1354
 
2.5%
Open Punctuation 1354
 
2.5%
Dash Punctuation 294
 
0.5%
Uppercase Letter 202
 
0.4%
Lowercase Letter 43
 
0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2151
 
7.5%
서 1537
 
5.3%
인 1419
 
4.9%
시 1418
 
4.9%
로 1409
 
4.9%
광 1395
 
4.8%
천 1394
 
4.8%
구 1380
 
4.8%
역 1374
 
4.8%
호 1118
 
3.9%
Other values (344) 14216
49.3%
Uppercase Letter
ValueCountFrequency (%)
B 32
15.8%
A 29
14.4%
K 20
9.9%
C 20
9.9%
S 16
7.9%
E 15
7.4%
I 14
6.9%
W 12
 
5.9%
V 12
 
5.9%
L 8
 
4.0%
Other values (7) 24
11.9%
Decimal Number
ValueCountFrequency (%)
1 2432
22.6%
0 1744
16.2%
2 1595
14.8%
3 1130
10.5%
4 944
 
8.8%
5 711
 
6.6%
6 630
 
5.9%
8 577
 
5.4%
7 547
 
5.1%
9 435
 
4.0%
Other Punctuation
ValueCountFrequency (%)
， 1934
99.4%
' 5
 
0.3%
. 3
 
0.2%
& 1
 
0.1%
@ 1
 
0.1%
/ 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 21
48.8%
a 6
 
14.0%
s 5
 
11.6%
r 5
 
11.6%
d 5
 
11.6%
b 1
 
2.3%
Space Separator
ValueCountFrequency (%)
8881
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1354
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1354
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 294
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28811
53.7%
Common 24574
45.8%
Latin 249
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2151
 
7.5%
서 1537
 
5.3%
인 1419
 
4.9%
시 1418
 
4.9%
로 1409
 
4.9%
광 1395
 
4.8%
천 1394
 
4.8%
구 1380
 
4.8%
역 1374
 
4.8%
호 1118
 
3.9%
Other values (344) 14216
49.3%
Latin
ValueCountFrequency (%)
B 32
12.9%
A 29
11.6%
e 21
 
8.4%
K 20
 
8.0%
C 20
 
8.0%
S 16
 
6.4%
E 15
 
6.0%
I 14
 
5.6%
W 12
 
4.8%
V 12
 
4.8%
Other values (14) 58
23.3%
Common
ValueCountFrequency (%)
8881
36.1%
1 2432
 
9.9%
， 1934
 
7.9%
0 1744
 
7.1%
2 1595
 
6.5%
) 1354
 
5.5%
( 1354
 
5.5%
3 1130
 
4.6%
4 944
 
3.8%
5 711
 
2.9%
Other values (11) 2495
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28811
53.7%
ASCII 22885
42.7%
None 1934
 
3.6%
Number Forms 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8881
38.8%
1 2432
 
10.6%
0 1744
 
7.6%
2 1595
 
7.0%
) 1354
 
5.9%
( 1354
 
5.9%
3 1130
 
4.9%
4 944
 
4.1%
5 711
 
3.1%
6 630
 
2.8%
Other values (33) 2110
 
9.2%
Hangul
ValueCountFrequency (%)
동 2151
 
7.5%
서 1537
 
5.3%
인 1419
 
4.9%
시 1418
 
4.9%
로 1409
 
4.9%
광 1395
 
4.8%
천 1394
 
4.8%
구 1380
 
4.8%
역 1374
 
4.8%
호 1118
 
3.9%
Other values (344) 14216
49.3%
None
ValueCountFrequency (%)
， 1934
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 4
100.0%

취급품목
Text

MISSING 

Distinct145
Distinct (%)10.6%
Missing8626
Missing (%)86.3%
Memory size156.2 KiB
2024-03-18T11:00:35.887038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length84
Median length79
Mean length8.9024745
Min length1

Characters and Unicode

Total characters12232
Distinct characters51
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

Unique94 ?
Unique (%)6.8%

Sample

1st row의류/패션/잡화/뷰티
2nd row종합몰
3rd row종합몰
4th row종합몰
5th row기타
ValueCountFrequency (%)
종합몰 541
27.5%
의류/패션/잡화/뷰티 458
23.2%
기타 325
16.5%
건강/식품 175
 
8.9%
가구/수납용품 109
 
5.5%
교육/도서/완구/오락 86
 
4.4%
가전 76
 
3.9%
컴퓨터/사무용품 73
 
3.7%
자동차/자동차용품 64
 
3.2%
레져/여행/공연 42
 
2.1%
Other values (3) 21
 
1.1%
2024-03-18T11:00:36.155906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 2146
 
17.5%
596
 
4.9%
합 541
 
4.4%
종 541
 
4.4%
몰 541
 
4.4%
류 458
 
3.7%
의 458
 
3.7%
패 458
 
3.7%
션 458
 
3.7%
잡 458
 
3.7%
Other values (41) 5577
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9487
77.6%
Other Punctuation 2146
 
17.5%
Space Separator 596
 
4.9%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
합 541
 
5.7%
종 541
 
5.7%
몰 541
 
5.7%
류 458
 
4.8%
의 458
 
4.8%
패 458
 
4.8%
션 458
 
4.8%
잡 458
 
4.8%
뷰 458
 
4.8%
티 458
 
4.8%
Other values (38) 4658
49.1%
Other Punctuation
ValueCountFrequency (%)
/ 2146
100.0%
Space Separator
ValueCountFrequency (%)
596
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9487
77.6%
Common 2745
 
22.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
합 541
 
5.7%
종 541
 
5.7%
몰 541
 
5.7%
류 458
 
4.8%
의 458
 
4.8%
패 458
 
4.8%
션 458
 
4.8%
잡 458
 
4.8%
뷰 458
 
4.8%
티 458
 
4.8%
Other values (38) 4658
49.1%
Common
ValueCountFrequency (%)
/ 2146
78.2%
596
 
21.7%
- 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9487
77.6%
ASCII 2745
 
22.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 2146
78.2%
596
 
21.7%
- 3
 
0.1%
Hangul
ValueCountFrequency (%)
합 541
 
5.7%
종 541
 
5.7%
몰 541
 
5.7%
류 458
 
4.8%
의 458
 
4.8%
패 458
 
4.8%
션 458
 
4.8%
잡 458
 
4.8%
뷰 458
 
4.8%
티 458
 
4.8%
Other values (38) 4658
49.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-09-01 00:00:00
Maximum2022-09-01 00:00:00
2024-03-18T11:00:36.256346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:00:36.343075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-18T11:00:26.727020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-03-18T11:00:33.532286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:00:33.619901image/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.
2024-03-18T11:00:33.707742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

번호관리번호법인또는상호소재지주소취급품목데이터기준일자
24192<NA><NA><NA><NA><NA>2022-09-01
48435<NA><NA><NA><NA><NA>2022-09-01
93762<NA><NA><NA><NA><NA>2022-09-01
21530<NA><NA><NA><NA><NA>2022-09-01
12222122222016-인천서구-0312셀레네(selene)인천광역시 서구 검단로 836， 117동 1603호 (불로동， 월드아파트)의류/패션/잡화/뷰티2022-09-01
21686<NA><NA><NA><NA><NA>2022-09-01
62430<NA><NA><NA><NA><NA>2022-09-01
24973<NA><NA><NA><NA><NA>2022-09-01
39247<NA><NA><NA><NA><NA>2022-09-01
31001<NA><NA><NA><NA><NA>2022-09-01
번호관리번호법인또는상호소재지주소취급품목데이터기준일자
60583<NA><NA><NA><NA><NA>2022-09-01
29879<NA><NA><NA><NA><NA>2022-09-01
32272<NA><NA><NA><NA><NA>2022-09-01
53238<NA><NA><NA><NA><NA>2022-09-01
86775<NA><NA><NA><NA><NA>2022-09-01
97066<NA><NA><NA><NA><NA>2022-09-01
39506<NA><NA><NA><NA><NA>2022-09-01
52697<NA><NA><NA><NA><NA>2022-09-01
32204<NA><NA><NA><NA><NA>2022-09-01
86936<NA><NA><NA><NA><NA>2022-09-01

Duplicate rows

Most frequently occurring

번호관리번호법인또는상호소재지주소취급품목데이터기준일자# duplicates
0<NA><NA><NA><NA><NA>2022-09-018626