Overview

Dataset statistics

Number of variables9
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.5%
Total size in memory14.7 KiB
Average record size in memory75.7 B

Variable types

Numeric2
Text5
Categorical2

Alerts

Dataset has 1 (0.5%) duplicate rowsDuplicates
LA_DCMLPOINT_PRE_VALUE is highly overall correlated with BRTC_NMHigh correlation
BRTC_NM is highly overall correlated with LA_DCMLPOINT_PRE_VALUE and 1 other fieldsHigh correlation
LO_DCMLPOINT_PRE_VALUE is highly overall correlated with BRTC_NMHigh correlation

Reproduction

Analysis started2023-12-10 06:40:11.315434
Analysis finished2023-12-10 06:40:13.197479
Duration1.88 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

YEAR_AND_MONTH
Real number (ℝ)

Distinct13
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200522
Minimum20200506
Maximum20200526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:40:13.304424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200506
5-th percentile20200506
Q120200520
median20200526
Q320200526
95-th percentile20200526
Maximum20200526
Range20
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation6.8154555
Coefficient of variation (CV)3.3739007 × 10-7
Kurtosis0.38976149
Mean20200522
Median Absolute Deviation (MAD)0
Skewness-1.3924615
Sum4.0199039 × 109
Variance46.450434
MonotonicityIncreasing
2023-12-10T15:40:13.538950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20200526 134
67.3%
20200506 15
 
7.5%
20200508 8
 
4.0%
20200514 7
 
3.5%
20200515 6
 
3.0%
20200520 6
 
3.0%
20200519 5
 
2.5%
20200522 5
 
2.5%
20200511 4
 
2.0%
20200512 3
 
1.5%
Other values (3) 6
 
3.0%
ValueCountFrequency (%)
20200506 15
7.5%
20200508 8
4.0%
20200511 4
 
2.0%
20200512 3
 
1.5%
20200514 7
3.5%
20200515 6
 
3.0%
20200518 2
 
1.0%
20200519 5
 
2.5%
20200520 6
 
3.0%
20200521 2
 
1.0%
ValueCountFrequency (%)
20200526 134
67.3%
20200525 2
 
1.0%
20200522 5
 
2.5%
20200521 2
 
1.0%
20200520 6
 
3.0%
20200519 5
 
2.5%
20200518 2
 
1.0%
20200515 6
 
3.0%
20200514 7
 
3.5%
20200512 3
 
1.5%

INDUTY_NM
Text

Distinct146
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:13.970363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length4.9095477
Min length2

Characters and Unicode

Total characters977
Distinct characters215
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)59.3%

Sample

1st row창고
2nd row의류판매(종합)
3rd row치킨
4th row부동산
5th row반도체가공기계제조
ValueCountFrequency (%)
한식 9
 
4.5%
부동산 5
 
2.5%
주민센터 5
 
2.5%
족발.보쌈 4
 
2.0%
한의원 4
 
2.0%
여행사 3
 
1.5%
어린이집 3
 
1.5%
화장품.향수 3
 
1.5%
무역업(종합 3
 
1.5%
건설업(종합 3
 
1.5%
Other values (136) 157
78.9%
2023-12-10T15:40:14.681685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 32
 
3.3%
제 30
 
3.1%
( 27
 
2.8%
) 27
 
2.8%
합 25
 
2.6%
종 24
 
2.5%
기 22
 
2.3%
조 22
 
2.3%
매 19
 
1.9%
한 18
 
1.8%
Other values (205) 731
74.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 890
91.1%
Other Punctuation 32
 
3.3%
Open Punctuation 27
 
2.8%
Close Punctuation 27
 
2.8%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 30
 
3.4%
합 25
 
2.8%
종 24
 
2.7%
기 22
 
2.5%
조 22
 
2.5%
매 19
 
2.1%
한 18
 
2.0%
사 15
 
1.7%
품 15
 
1.7%
식 14
 
1.6%
Other values (201) 686
77.1%
Other Punctuation
ValueCountFrequency (%)
. 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 890
91.1%
Common 87
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 30
 
3.4%
합 25
 
2.8%
종 24
 
2.7%
기 22
 
2.5%
조 22
 
2.5%
매 19
 
2.1%
한 18
 
2.0%
사 15
 
1.7%
품 15
 
1.7%
식 14
 
1.6%
Other values (201) 686
77.1%
Common
ValueCountFrequency (%)
. 32
36.8%
( 27
31.0%
) 27
31.0%
1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 890
91.1%
ASCII 87
 
8.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 32
36.8%
( 27
31.0%
) 27
31.0%
1
 
1.1%
Hangul
ValueCountFrequency (%)
제 30
 
3.4%
합 25
 
2.8%
종 24
 
2.7%
기 22
 
2.5%
조 22
 
2.5%
매 19
 
2.1%
한 18
 
2.0%
사 15
 
1.7%
품 15
 
1.7%
식 14
 
1.6%
Other values (201) 686
77.1%

BRTC_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
서울
50 
경기
47 
부산
17 
경남
11 
충남
9 
Other values (12)
65 

Length

Max length3
Median length2
Mean length2.0150754
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row서울
2nd row서울
3rd row인천
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
서울 50
25.1%
경기 47
23.6%
부산 17
 
8.5%
경남 11
 
5.5%
충남 9
 
4.5%
인천 9
 
4.5%
전북 8
 
4.0%
강원 8
 
4.0%
대구 7
 
3.5%
경북 7
 
3.5%
Other values (7) 26
13.1%

Length

2023-12-10T15:40:15.379296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 50
25.1%
경기 47
23.6%
부산 17
 
8.5%
경남 11
 
5.5%
충남 9
 
4.5%
인천 9
 
4.5%
전북 8
 
4.0%
강원 8
 
4.0%
경북 7
 
3.5%
대구 7
 
3.5%
Other values (7) 26
13.1%

SIGNGU_NM
Text

Distinct112
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:15.898187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.3417085
Min length2

Characters and Unicode

Total characters864
Distinct characters109
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)34.7%

Sample

1st row강남구
2nd row강남구
3rd row연수구
4th row관악구
5th row마포구
ValueCountFrequency (%)
강남구 12
 
4.7%
동구 6
 
2.4%
중구 6
 
2.4%
성남시 5
 
2.0%
마포구 5
 
2.0%
서구 5
 
2.0%
수영구 5
 
2.0%
영등포구 5
 
2.0%
수원시 5
 
2.0%
청주시 4
 
1.6%
Other values (124) 197
77.3%
2023-12-10T15:40:16.655585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 129
 
14.9%
105
 
12.2%
시 98
 
11.3%
남 22
 
2.5%
산 22
 
2.5%
서 21
 
2.4%
동 20
 
2.3%
천 20
 
2.3%
원 18
 
2.1%
포 18
 
2.1%
Other values (99) 391
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 759
87.8%
Space Separator 105
 
12.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 129
 
17.0%
시 98
 
12.9%
남 22
 
2.9%
산 22
 
2.9%
서 21
 
2.8%
동 20
 
2.6%
천 20
 
2.6%
원 18
 
2.4%
포 18
 
2.4%
주 18
 
2.4%
Other values (98) 373
49.1%
Space Separator
ValueCountFrequency (%)
105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 759
87.8%
Common 105
 
12.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 129
 
17.0%
시 98
 
12.9%
남 22
 
2.9%
산 22
 
2.9%
서 21
 
2.8%
동 20
 
2.6%
천 20
 
2.6%
원 18
 
2.4%
포 18
 
2.4%
주 18
 
2.4%
Other values (98) 373
49.1%
Common
ValueCountFrequency (%)
105
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 759
87.8%
ASCII 105
 
12.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 129
 
17.0%
시 98
 
12.9%
남 22
 
2.9%
산 22
 
2.9%
서 21
 
2.8%
동 20
 
2.6%
천 20
 
2.6%
원 18
 
2.4%
포 18
 
2.4%
주 18
 
2.4%
Other values (98) 373
49.1%
ASCII
ValueCountFrequency (%)
105
100.0%

EMD
Text

Distinct177
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:17.113707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2261307
Min length2

Characters and Unicode

Total characters642
Distinct characters144
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159 ?
Unique (%)79.9%

Sample

1st row대치동
2nd row삼성동
3rd row옥련동
4th row신림동
5th row서교동
ValueCountFrequency (%)
여의도동 4
 
2.0%
삼성동 3
 
1.5%
역삼동 3
 
1.5%
천곡동 2
 
1.0%
죽변리 2
 
1.0%
중동 2
 
1.0%
산본동 2
 
1.0%
초지동 2
 
1.0%
야탑1동 2
 
1.0%
신천동 2
 
1.0%
Other values (167) 175
87.9%
2023-12-10T15:40:17.895035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 169
26.3%
리 29
 
4.5%
천 17
 
2.6%
2 16
 
2.5%
1 13
 
2.0%
곡 13
 
2.0%
가 12
 
1.9%
신 10
 
1.6%
양 9
 
1.4%
정 9
 
1.4%
Other values (134) 345
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 606
94.4%
Decimal Number 36
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 169
27.9%
리 29
 
4.8%
천 17
 
2.8%
곡 13
 
2.1%
가 12
 
2.0%
신 10
 
1.7%
양 9
 
1.5%
정 9
 
1.5%
삼 9
 
1.5%
성 8
 
1.3%
Other values (129) 321
53.0%
Decimal Number
ValueCountFrequency (%)
2 16
44.4%
1 13
36.1%
4 3
 
8.3%
3 3
 
8.3%
7 1
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 606
94.4%
Common 36
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 169
27.9%
리 29
 
4.8%
천 17
 
2.8%
곡 13
 
2.1%
가 12
 
2.0%
신 10
 
1.7%
양 9
 
1.5%
정 9
 
1.5%
삼 9
 
1.5%
성 8
 
1.3%
Other values (129) 321
53.0%
Common
ValueCountFrequency (%)
2 16
44.4%
1 13
36.1%
4 3
 
8.3%
3 3
 
8.3%
7 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 606
94.4%
ASCII 36
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 169
27.9%
리 29
 
4.8%
천 17
 
2.8%
곡 13
 
2.1%
가 12
 
2.0%
신 10
 
1.7%
양 9
 
1.5%
정 9
 
1.5%
삼 9
 
1.5%
성 8
 
1.3%
Other values (129) 321
53.0%
ASCII
ValueCountFrequency (%)
2 16
44.4%
1 13
36.1%
4 3
 
8.3%
3 3
 
8.3%
7 1
 
2.8%

LA_DCMLPOINT_PRE_VALUE
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.281407
Minimum33
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:40:18.106305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile35
Q135
median37
Q337
95-th percentile37
Maximum38
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.98021129
Coefficient of variation (CV)0.027016904
Kurtosis0.207246
Mean36.281407
Median Absolute Deviation (MAD)0
Skewness-1.0132059
Sum7220
Variance0.96081417
MonotonicityNot monotonic
2023-12-10T15:40:18.280185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
37 117
58.8%
35 50
25.1%
36 26
 
13.1%
33 3
 
1.5%
34 2
 
1.0%
38 1
 
0.5%
ValueCountFrequency (%)
33 3
 
1.5%
34 2
 
1.0%
35 50
25.1%
36 26
 
13.1%
37 117
58.8%
38 1
 
0.5%
ValueCountFrequency (%)
38 1
 
0.5%
37 117
58.8%
36 26
 
13.1%
35 50
25.1%
34 2
 
1.0%
33 3
 
1.5%

LA_DCMLPOINT_RR_VALUE
Text

Distinct172
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:18.844965image/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 scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)74.4%

Sample

1st row503***
2nd row508***
3rd row423***
4th row487***
5th row553***
ValueCountFrequency (%)
170 3
 
1.5%
520 3
 
1.5%
353 3
 
1.5%
553 2
 
1.0%
498 2
 
1.0%
239 2
 
1.0%
524 2
 
1.0%
540 2
 
1.0%
506 2
 
1.0%
877 2
 
1.0%
Other values (162) 176
88.4%
2023-12-10T15:40:19.658286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 597
50.0%
5 86
 
7.2%
1 72
 
6.0%
4 68
 
5.7%
3 66
 
5.5%
0 62
 
5.2%
2 55
 
4.6%
8 48
 
4.0%
9 48
 
4.0%
6 48
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 597
50.0%
Decimal Number 597
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 86
14.4%
1 72
12.1%
4 68
11.4%
3 66
11.1%
0 62
10.4%
2 55
9.2%
8 48
8.0%
9 48
8.0%
6 48
8.0%
7 44
7.4%
Other Punctuation
ValueCountFrequency (%)
* 597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 597
50.0%
5 86
 
7.2%
1 72
 
6.0%
4 68
 
5.7%
3 66
 
5.5%
0 62
 
5.2%
2 55
 
4.6%
8 48
 
4.0%
9 48
 
4.0%
6 48
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 597
50.0%
5 86
 
7.2%
1 72
 
6.0%
4 68
 
5.7%
3 66
 
5.5%
0 62
 
5.2%
2 55
 
4.6%
8 48
 
4.0%
9 48
 
4.0%
6 48
 
4.0%

LO_DCMLPOINT_PRE_VALUE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
126
80 
127
71 
128
27 
129
21 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row127
2nd row127
3rd row126
4th row126
5th row126

Common Values

ValueCountFrequency (%)
126 80
40.2%
127 71
35.7%
128 27
 
13.6%
129 21
 
10.6%

Length

2023-12-10T15:40:19.898406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:20.077008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
126 80
40.2%
127 71
35.7%
128 27
 
13.6%
129 21
 
10.6%

LO_DCMLPOINT_RR_VALUE
Text

Distinct173
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:20.607340image/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 scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)74.4%

Sample

1st row062***
2nd row059***
3rd row653***
4th row916***
5th row919***
ValueCountFrequency (%)
920 3
 
1.5%
032 2
 
1.0%
905 2
 
1.0%
938 2
 
1.0%
932 2
 
1.0%
060 2
 
1.0%
075 2
 
1.0%
929 2
 
1.0%
658 2
 
1.0%
602 2
 
1.0%
Other values (163) 178
89.4%
2023-12-10T15:40:21.361220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 597
50.0%
9 84
 
7.0%
0 80
 
6.7%
1 70
 
5.9%
2 63
 
5.3%
7 53
 
4.4%
4 53
 
4.4%
6 52
 
4.4%
8 50
 
4.2%
5 49
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 597
50.0%
Decimal Number 597
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 84
14.1%
0 80
13.4%
1 70
11.7%
2 63
10.6%
7 53
8.9%
4 53
8.9%
6 52
8.7%
8 50
8.4%
5 49
8.2%
3 43
7.2%
Other Punctuation
ValueCountFrequency (%)
* 597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 597
50.0%
9 84
 
7.0%
0 80
 
6.7%
1 70
 
5.9%
2 63
 
5.3%
7 53
 
4.4%
4 53
 
4.4%
6 52
 
4.4%
8 50
 
4.2%
5 49
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 597
50.0%
9 84
 
7.0%
0 80
 
6.7%
1 70
 
5.9%
2 63
 
5.3%
7 53
 
4.4%
4 53
 
4.4%
6 52
 
4.4%
8 50
 
4.2%
5 49
 
4.1%

Interactions

2023-12-10T15:40:12.480562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:12.108324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:12.661095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:12.311549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:40:21.540616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
YEAR_AND_MONTHBRTC_NMLA_DCMLPOINT_PRE_VALUELO_DCMLPOINT_PRE_VALUE
YEAR_AND_MONTH1.0000.4460.0000.533
BRTC_NM0.4461.0000.9630.882
LA_DCMLPOINT_PRE_VALUE0.0000.9631.0000.576
LO_DCMLPOINT_PRE_VALUE0.5330.8820.5761.000
2023-12-10T15:40:21.682900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BRTC_NMLO_DCMLPOINT_PRE_VALUE
BRTC_NM1.0000.694
LO_DCMLPOINT_PRE_VALUE0.6941.000
2023-12-10T15:40:21.806751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
YEAR_AND_MONTHLA_DCMLPOINT_PRE_VALUEBRTC_NMLO_DCMLPOINT_PRE_VALUE
YEAR_AND_MONTH1.000-0.0070.1630.166
LA_DCMLPOINT_PRE_VALUE-0.0071.0000.8400.407
BRTC_NM0.1630.8401.0000.694
LO_DCMLPOINT_PRE_VALUE0.1660.4070.6941.000

Missing values

2023-12-10T15:40:12.867910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:40:13.101715image/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

YEAR_AND_MONTHINDUTY_NMBRTC_NMSIGNGU_NMEMDLA_DCMLPOINT_PRE_VALUELA_DCMLPOINT_RR_VALUELO_DCMLPOINT_PRE_VALUELO_DCMLPOINT_RR_VALUE
020200506창고서울강남구대치동37503***127062***
120200506의류판매(종합)서울강남구삼성동37508***127059***
220200506치킨인천연수구옥련동37423***126653***
320200506부동산서울관악구신림동37487***126916***
420200506반도체가공기계제조서울마포구서교동37553***126919***
520200506치과서울강서구화곡7동37541***126839***
620200506안과서울강남구역삼동37499***127029***
720200506의류판매(종합)경기수원시 팔달구매산로1가37266***126999***
820200506건설업(종합)부산해운대구우동35173***129129***
920200506영농조합충북진천군 진천읍상계리36835***127385***
YEAR_AND_MONTHINDUTY_NMBRTC_NMSIGNGU_NMEMDLA_DCMLPOINT_PRE_VALUELA_DCMLPOINT_RR_VALUELO_DCMLPOINT_PRE_VALUELO_DCMLPOINT_RR_VALUE
18920200526치과광주북구매곡동35189***126893***
19020200526사진.스튜디오전북정읍시수성동35583***126857***
19120200526광고대행(종합)광주광산구신창동35198***126842***
19220200526수산물음식점서울영등포구여의도동37523***126925***
19320200526택시미터기제조전북김제시교동35796***126869***
19420200526떡.한과전북군산시 서수면금암리36005***126909***
19520200526만두전남순천시연향동34952***127519***
19620200526가정문제상담광주북구중흥2동35168***126905***
19720200526화물운송광주광산구복룡동35116***126779***
19820200526피아노학원전북전주시 완산구서서학동35803***127148***

Duplicate rows

Most frequently occurring

YEAR_AND_MONTHINDUTY_NMBRTC_NMSIGNGU_NMEMDLA_DCMLPOINT_PRE_VALUELA_DCMLPOINT_RR_VALUELO_DCMLPOINT_PRE_VALUELO_DCMLPOINT_RR_VALUE# duplicates
020200526대학교경기수원시 장안구천천동37293***126975***2