Overview

Dataset statistics

Number of variables11
Number of observations469
Missing cells390
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.2 KiB
Average record size in memory94.3 B

Variable types

Text3
Numeric6
Categorical2

Dataset

Description해당 파일 데이터는 신용보증기금의 공통일반부점사업장정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093128/fileData.do

Alerts

임대인사업자등록번호 is highly overall correlated with 자가임차구분코드High correlation
자가영업점건물총면적 is highly overall correlated with 자가영업점사용면적High correlation
자가영업점사용면적 is highly overall correlated with 자가영업점건물총면적High correlation
사업장개설일자 is highly overall correlated with 자가임차구분코드High correlation
자가임차구분코드 is highly overall correlated with 임대인사업자등록번호 and 1 other fieldsHigh correlation
임대인사업자등록번호 has 390 (83.2%) missing valuesMissing
임차영업점사용면적 is highly skewed (γ1 = 21.10883634)Skewed
임차영업점사용면적 has 383 (81.7%) zerosZeros
자가영업점건물총면적 has 429 (91.5%) zerosZeros
자가영업점사용면적 has 428 (91.3%) zerosZeros

Reproduction

Analysis started2023-12-13 01:02:47.083766
Analysis finished2023-12-13 01:02:50.182928
Duration3.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

부점코드
Text

Distinct240
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-13T10:02:50.441821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1407
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123 ?
Unique (%)26.2%

Sample

1st rowTRB
2nd rowTOJ
3rd rowTOJ
4th rowABE
5th rowABE
ValueCountFrequency (%)
vao 9
 
1.9%
tpd 7
 
1.5%
ncn 7
 
1.5%
vpa 7
 
1.5%
vam 6
 
1.3%
thn 6
 
1.3%
thi 5
 
1.1%
tbk 5
 
1.1%
tbr 5
 
1.1%
the 5
 
1.1%
Other values (230) 407
86.8%
2023-12-13T10:02:50.834473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 271
19.3%
A 204
14.5%
N 99
 
7.0%
H 90
 
6.4%
B 82
 
5.8%
V 71
 
5.0%
P 63
 
4.5%
J 58
 
4.1%
I 57
 
4.1%
O 50
 
3.6%
Other values (15) 362
25.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1407
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 271
19.3%
A 204
14.5%
N 99
 
7.0%
H 90
 
6.4%
B 82
 
5.8%
V 71
 
5.0%
P 63
 
4.5%
J 58
 
4.1%
I 57
 
4.1%
O 50
 
3.6%
Other values (15) 362
25.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 1407
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 271
19.3%
A 204
14.5%
N 99
 
7.0%
H 90
 
6.4%
B 82
 
5.8%
V 71
 
5.0%
P 63
 
4.5%
J 58
 
4.1%
I 57
 
4.1%
O 50
 
3.6%
Other values (15) 362
25.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 271
19.3%
A 204
14.5%
N 99
 
7.0%
H 90
 
6.4%
B 82
 
5.8%
V 71
 
5.0%
P 63
 
4.5%
J 58
 
4.1%
I 57
 
4.1%
O 50
 
3.6%
Other values (15) 362
25.7%

이력일련번호
Real number (ℝ)

Distinct9
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9381663
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T10:02:50.948873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4.6
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3116888
Coefficient of variation (CV)0.67676791
Kurtosis4.2575922
Mean1.9381663
Median Absolute Deviation (MAD)0
Skewness1.8774567
Sum909
Variance1.7205274
MonotonicityNot monotonic
2023-12-13T10:02:51.031995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 240
51.2%
2 117
24.9%
3 58
 
12.4%
4 30
 
6.4%
5 12
 
2.6%
6 6
 
1.3%
7 4
 
0.9%
9 1
 
0.2%
8 1
 
0.2%
ValueCountFrequency (%)
1 240
51.2%
2 117
24.9%
3 58
 
12.4%
4 30
 
6.4%
5 12
 
2.6%
6 6
 
1.3%
7 4
 
0.9%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
9 1
 
0.2%
8 1
 
0.2%
7 4
 
0.9%
6 6
 
1.3%
5 12
 
2.6%
4 30
 
6.4%
3 58
 
12.4%
2 117
24.9%
1 240
51.2%

사업장개설일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0001-01-01 00:00:00.000000
327 
00:00.0
142 

Length

Max length26
Median length26
Mean length20.247335
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row00:00.0
3rd row00:00.0
4th row0001-01-01 00:00:00.000000
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 327
69.7%
00:00.0 142
30.3%

Length

2023-12-13T10:02:51.128756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T10:02:51.202113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 327
41.1%
00:00:00.000000 327
41.1%
00:00.0 142
17.8%

사업장일련번호
Text

Distinct232
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-13T10:02:51.485652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.4392324
Min length1

Characters and Unicode

Total characters1144
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

Unique125 ?
Unique (%)26.7%

Sample

1st row162
2nd row38
3rd row38
4th row196
5th row196
ValueCountFrequency (%)
118 9
 
2.1%
22 7
 
1.6%
204 7
 
1.6%
121 7
 
1.6%
119 6
 
1.4%
115 6
 
1.4%
92 5
 
1.1%
112 5
 
1.1%
87 5
 
1.1%
4 5
 
1.1%
Other values (221) 377
85.9%
2023-12-13T10:02:51.911171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 300
26.2%
2 177
15.5%
9 86
 
7.5%
5 84
 
7.3%
3 84
 
7.3%
8 83
 
7.3%
4 82
 
7.2%
6 75
 
6.6%
0 72
 
6.3%
7 71
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1114
97.4%
Space Separator 30
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 300
26.9%
2 177
15.9%
9 86
 
7.7%
5 84
 
7.5%
3 84
 
7.5%
8 83
 
7.5%
4 82
 
7.4%
6 75
 
6.7%
0 72
 
6.5%
7 71
 
6.4%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 300
26.2%
2 177
15.5%
9 86
 
7.5%
5 84
 
7.3%
3 84
 
7.3%
8 83
 
7.3%
4 82
 
7.2%
6 75
 
6.6%
0 72
 
6.3%
7 71
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 300
26.2%
2 177
15.5%
9 86
 
7.5%
5 84
 
7.3%
3 84
 
7.3%
8 83
 
7.3%
4 82
 
7.2%
6 75
 
6.6%
0 72
 
6.3%
7 71
 
6.2%

자가임차구분코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
259 
2
125 
1
85 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
259
55.2%
2 125
26.7%
1 85
 
18.1%

Length

2023-12-13T10:02:52.014426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T10:02:52.081304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 125
59.5%
1 85
40.5%

임대인사업자등록번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct53
Distinct (%)67.1%
Missing390
Missing (%)83.2%
Infinite0
Infinite (%)0.0%
Mean2.7938058 × 109
Minimum4.1082474 × 108
Maximum6.1622046 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T10:02:52.163114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1082474 × 108
5-th percentile1.0461093 × 109
Q11.248101 × 109
median2.1027705 × 109
Q34.1085074 × 109
95-th percentile6.1311696 × 109
Maximum6.1622046 × 109
Range5.7513799 × 109
Interquartile range (IQR)2.8604064 × 109

Descriptive statistics

Standard deviation1.8661994 × 109
Coefficient of variation (CV)0.66797751
Kurtosis-0.96513081
Mean2.7938058 × 109
Median Absolute Deviation (MAD)9.5574846 × 108
Skewness0.74232574
Sum2.2071066 × 1011
Variance3.4827003 × 1018
MonotonicityNot monotonic
2023-12-13T10:02:52.488706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3128525444 4
 
0.9%
1258502740 3
 
0.6%
4108507361 3
 
0.6%
1028142945 3
 
0.6%
2102770454 3
 
0.6%
1248100998 3
 
0.6%
6068178187 3
 
0.6%
3048200500 2
 
0.4%
1098104258 2
 
0.4%
1148137557 2
 
0.4%
Other values (43) 51
 
10.9%
(Missing) 390
83.2%
ValueCountFrequency (%)
410824738 1
 
0.2%
1028142945 3
0.6%
1048105612 1
 
0.2%
1048118586 1
 
0.2%
1048608588 1
 
0.2%
1058206004 1
 
0.2%
1098104258 2
0.4%
1148137557 2
0.4%
1208197093 2
0.4%
1208779087 1
 
0.2%
ValueCountFrequency (%)
6162204609 2
0.4%
6158503617 2
0.4%
6128132527 1
 
0.2%
6088515740 1
 
0.2%
6088213829 1
 
0.2%
6068207597 1
 
0.2%
6068178187 3
0.6%
6051522220 1
 
0.2%
5148210527 1
 
0.2%
5138500876 1
 
0.2%

임차영업점사용면적
Real number (ℝ)

SKEWED  ZEROS 

Distinct62
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.77846
Minimum0
Maximum45298
Zeros383
Zeros (%)81.7%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T10:02:52.595540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile899
Maximum45298
Range45298
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2104.8204
Coefficient of variation (CV)10.431343
Kurtosis453.04333
Mean201.77846
Median Absolute Deviation (MAD)0
Skewness21.108836
Sum94634.1
Variance4430268.8
MonotonicityNot monotonic
2023-12-13T10:02:52.692128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 383
81.7%
39.0 4
 
0.9%
285.62 3
 
0.6%
465.59 3
 
0.6%
430.23 3
 
0.6%
92.0 3
 
0.6%
900.23 3
 
0.6%
264.46 2
 
0.4%
1018.15 2
 
0.4%
555.37 2
 
0.4%
Other values (52) 61
 
13.0%
ValueCountFrequency (%)
0.0 383
81.7%
39.0 4
 
0.9%
55.72 2
 
0.4%
92.0 3
 
0.6%
110.0 1
 
0.2%
110.54 1
 
0.2%
118.2 1
 
0.2%
124.87 1
 
0.2%
161.54 2
 
0.4%
207.65 1
 
0.2%
ValueCountFrequency (%)
45298.0 1
0.2%
1500.47 1
0.2%
1500.0 1
0.2%
1371.0 1
0.2%
1223.83 2
0.4%
1191.9 1
0.2%
1072.66 1
0.2%
1037.25 2
0.4%
1018.15 2
0.4%
1000.85 1
0.2%

자가영업점건물총면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean425.73751
Minimum0
Maximum18769.53
Zeros429
Zeros (%)91.5%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T10:02:52.785323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2889
Maximum18769.53
Range18769.53
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1903.2241
Coefficient of variation (CV)4.4704167
Kurtosis45.054683
Mean425.73751
Median Absolute Deviation (MAD)0
Skewness6.1703986
Sum199670.89
Variance3622261.8
MonotonicityNot monotonic
2023-12-13T10:02:52.875961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 429
91.5%
9870.79 7
 
1.5%
6001.1 4
 
0.9%
1371.0 3
 
0.6%
1213.0 3
 
0.6%
18769.53 2
 
0.4%
3703.0 2
 
0.4%
1956.99 2
 
0.4%
2889.0 2
 
0.4%
3399.1 2
 
0.4%
Other values (13) 13
 
2.8%
ValueCountFrequency (%)
0.0 429
91.5%
813.22 1
 
0.2%
873.92 1
 
0.2%
1018.0 1
 
0.2%
1191.9 1
 
0.2%
1213.0 3
 
0.6%
1260.25 1
 
0.2%
1371.0 3
 
0.6%
1582.43 1
 
0.2%
1956.99 2
 
0.4%
ValueCountFrequency (%)
18769.53 2
 
0.4%
9870.79 7
1.5%
8295.45 1
 
0.2%
6001.1 4
0.9%
5497.0 1
 
0.2%
4032.97 1
 
0.2%
3703.0 2
 
0.4%
3519.0 1
 
0.2%
3399.1 2
 
0.4%
3267.43 1
 
0.2%

자가영업점사용면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.701557
Minimum0
Maximum3595
Zeros428
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T10:02:52.968482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile763.65
Maximum3595
Range3595
Interquartile range (IQR)0

Descriptive statistics

Standard deviation386.07205
Coefficient of variation (CV)3.872277
Kurtosis30.710013
Mean99.701557
Median Absolute Deviation (MAD)0
Skewness5.0865323
Sum46760.03
Variance149051.63
MonotonicityNot monotonic
2023-12-13T10:02:53.062739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 428
91.3%
763.65 7
 
1.5%
1371.0 3
 
0.6%
1191.9 3
 
0.6%
739.0 2
 
0.4%
2615.0 2
 
0.4%
501.06 2
 
0.4%
849.0 2
 
0.4%
3595.0 1
 
0.2%
582.43 1
 
0.2%
Other values (18) 18
 
3.8%
ValueCountFrequency (%)
0.0 428
91.3%
330.58 1
 
0.2%
382.67 1
 
0.2%
441.32 1
 
0.2%
477.95 1
 
0.2%
501.06 2
 
0.4%
582.43 1
 
0.2%
608.76 1
 
0.2%
706.42 1
 
0.2%
730.0 1
 
0.2%
ValueCountFrequency (%)
3595.0 1
 
0.2%
2849.07 1
 
0.2%
2615.0 2
0.4%
2232.52 1
 
0.2%
1770.19 1
 
0.2%
1679.24 1
 
0.2%
1441.53 1
 
0.2%
1371.0 3
0.6%
1357.79 1
 
0.2%
1246.0 1
 
0.2%

최종수정수
Real number (ℝ)

Distinct9
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9381663
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T10:02:53.153909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4.6
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3116888
Coefficient of variation (CV)0.67676791
Kurtosis4.2575922
Mean1.9381663
Median Absolute Deviation (MAD)0
Skewness1.8774567
Sum909
Variance1.7205274
MonotonicityNot monotonic
2023-12-13T10:02:53.248696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 240
51.2%
2 117
24.9%
3 58
 
12.4%
4 30
 
6.4%
5 12
 
2.6%
6 6
 
1.3%
7 4
 
0.9%
9 1
 
0.2%
8 1
 
0.2%
ValueCountFrequency (%)
1 240
51.2%
2 117
24.9%
3 58
 
12.4%
4 30
 
6.4%
5 12
 
2.6%
6 6
 
1.3%
7 4
 
0.9%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
9 1
 
0.2%
8 1
 
0.2%
7 4
 
0.9%
6 6
 
1.3%
5 12
 
2.6%
4 30
 
6.4%
3 58
 
12.4%
2 117
24.9%
1 240
51.2%

처리직원번호
Text

Distinct311
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-13T10:02:53.531538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.2409382
Min length3

Characters and Unicode

Total characters1989
Distinct characters13
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

Unique206 ?
Unique (%)43.9%

Sample

1st row5450
2nd row6021
3rd row6021
4th row4454
5th row4454
ValueCountFrequency (%)
4800 8
 
1.7%
5366 6
 
1.3%
2421 5
 
1.1%
5424 5
 
1.1%
93818 5
 
1.1%
4282 5
 
1.1%
5434 4
 
0.9%
5202 4
 
0.9%
4165 4
 
0.9%
4853 4
 
0.9%
Other values (301) 419
89.3%
2023-12-13T10:02:53.935148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 324
16.3%
3 305
15.3%
9 244
12.3%
5 227
11.4%
2 176
8.8%
8 155
7.8%
1 144
7.2%
7 143
7.2%
0 137
6.9%
6 129
 
6.5%
Other values (3) 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1984
99.7%
Uppercase Letter 5
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 324
16.3%
3 305
15.4%
9 244
12.3%
5 227
11.4%
2 176
8.9%
8 155
7.8%
1 144
7.3%
7 143
7.2%
0 137
6.9%
6 129
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
E 2
40.0%
X 2
40.0%
A 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1984
99.7%
Latin 5
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
4 324
16.3%
3 305
15.4%
9 244
12.3%
5 227
11.4%
2 176
8.9%
8 155
7.8%
1 144
7.3%
7 143
7.2%
0 137
6.9%
6 129
 
6.5%
Latin
ValueCountFrequency (%)
E 2
40.0%
X 2
40.0%
A 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 324
16.3%
3 305
15.3%
9 244
12.3%
5 227
11.4%
2 176
8.8%
8 155
7.8%
1 144
7.2%
7 143
7.2%
0 137
6.9%
6 129
 
6.5%
Other values (3) 5
 
0.3%

Interactions

2023-12-13T10:02:49.541497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:47.411328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:47.851305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.226638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.637021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.077411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.624799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:47.488441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:47.921191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.297917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.726514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.163625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.693502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:47.559090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:47.979318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.356719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.788210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.239996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.756855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:47.627049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.036150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.413839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.854572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.302210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.830925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:47.698710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.098298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.485077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.926975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.372924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.914567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:47.774783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.158944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.551399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:48.999290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T10:02:49.445736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T10:02:54.020368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호사업장개설일자자가임차구분코드임대인사업자등록번호임차영업점사용면적자가영업점건물총면적자가영업점사용면적최종수정수
이력일련번호1.0000.1760.3250.0000.0000.0000.0000.964
사업장개설일자0.1761.0000.3390.1320.0000.2610.4730.253
자가임차구분코드0.3250.3391.000NaN0.0240.4760.5790.488
임대인사업자등록번호0.0000.132NaN1.0000.000NaNNaN0.000
임차영업점사용면적0.0000.0000.0240.0001.0000.0000.0000.000
자가영업점건물총면적0.0000.2610.476NaN0.0001.0000.8010.068
자가영업점사용면적0.0000.4730.579NaN0.0000.8011.0000.000
최종수정수0.9640.2530.4880.0000.0000.0680.0001.000
2023-12-13T10:02:54.125839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자가임차구분코드사업장개설일자
자가임차구분코드1.0000.542
사업장개설일자0.5421.000
2023-12-13T10:02:54.194158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호임대인사업자등록번호임차영업점사용면적자가영업점건물총면적자가영업점사용면적최종수정수사업장개설일자자가임차구분코드
이력일련번호1.000-0.033-0.0070.0420.0110.2330.1740.150
임대인사업자등록번호-0.0331.000-0.178NaNNaN0.0370.1031.000
임차영업점사용면적-0.007-0.1781.000-0.144-0.1450.1150.0000.040
자가영업점건물총면적0.042NaN-0.1441.0000.9550.1520.2770.362
자가영업점사용면적0.011NaN-0.1450.9551.0000.1560.3540.443
최종수정수0.2330.0370.1150.1520.1561.0000.2510.245
사업장개설일자0.1740.1030.0000.2770.3540.2511.0000.542
자가임차구분코드0.1501.0000.0400.3620.4430.2450.5421.000

Missing values

2023-12-13T10:02:50.013521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T10:02:50.132210image/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

부점코드이력일련번호사업장개설일자사업장일련번호자가임차구분코드임대인사업자등록번호임차영업점사용면적자가영업점건물총면적자가영업점사용면적최종수정수처리직원번호
0TRB10001-01-01 00:00:00.00000016226162204609161.540.00.035450
1TOJ100:00.03824108507361465.590.00.036021
2TOJ300:00.03824108507361465.590.00.026021
3ABE10001-01-01 00:00:00.000000196<NA>0.00.00.044454
4ABE40001-01-01 00:00:00.000000196<NA>0.00.00.034454
5ABE30001-01-01 00:00:00.000000196<NA>0.00.00.025228
6TAD10001-01-01 00:00:00.00000043212081970931037.250.00.035405
7VAM10001-01-01 00:00:00.0000001191<NA>0.00.00.062711
8VAM60001-01-01 00:00:00.0000001191<NA>0.00.00.052711
9QAE10001-01-01 00:00:00.0000001121<NA>0.00.00.014311
부점코드이력일련번호사업장개설일자사업장일련번호자가임차구분코드임대인사업자등록번호임차영업점사용면적자가영업점건물총면적자가영업점사용면적최종수정수처리직원번호
459TBK20001-01-01 00:00:00.00000016<NA>0.00.00.0193830
460THN300:00.012<NA>758.00.00.0293818
461TBA10001-01-01 00:00:00.00000041<NA>0.00.00.0293171
462THA300:00.0151<NA>0.03703.02615.024282
463THA200:00.051<NA>0.03703.02615.014282
464TBA20001-01-01 00:00:00.0000004<NA>0.00.00.012190
465VCB10001-01-01 00:00:00.00000031<NA>0.00.00.023224
466VCB20001-01-01 00:00:00.0000003<NA>0.00.00.013224
467TJD20001-01-01 00:00:00.0000002<NA>0.00.00.012451
468THN20001-01-01 00:00:00.0000001<NA>0.00.00.014451