Overview

Dataset statistics

Number of variables10
Number of observations5065
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory415.6 KiB
Average record size in memory84.0 B

Variable types

Numeric4
Categorical2
Text4

Alerts

SIDO_NM is highly overall correlated with PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NO and 1 other fieldsHigh correlation
TNSHP_NM is highly overall correlated with PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NO and 2 other fieldsHigh correlation
PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NO is highly overall correlated with SIDO_NM and 1 other fieldsHigh correlation
LFSTS_CNTRCT_CASCNT is highly overall correlated with MTHT_CNTRCT_CASCNTHigh correlation
MTHT_CNTRCT_CASCNT is highly overall correlated with LFSTS_CNTRCT_CASCNTHigh correlation
PRSMP_LFSTS_PC_FLCTN_RATE is highly overall correlated with TNSHP_NMHigh correlation
TNSHP_NM is highly imbalanced (75.3%)Imbalance
PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NO has unique valuesUnique
LFSTS_CNTRCT_CASCNT has 2187 (43.2%) zerosZeros
MTHT_CNTRCT_CASCNT has 1976 (39.0%) zerosZeros
PRSMP_LFSTS_PC_FLCTN_RATE has 159 (3.1%) zerosZeros

Reproduction

Analysis started2023-12-11 22:31:21.818373
Analysis finished2023-12-11 22:31:25.250587
Duration3.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5065
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2533
Minimum1
Maximum5065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-12T07:31:25.308444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile254.2
Q11267
median2533
Q33799
95-th percentile4811.8
Maximum5065
Range5064
Interquartile range (IQR)2532

Descriptive statistics

Standard deviation1462.2839
Coefficient of variation (CV)0.57729328
Kurtosis-1.2
Mean2533
Median Absolute Deviation (MAD)1266
Skewness0
Sum12829645
Variance2138274.2
MonotonicityStrictly increasing
2023-12-12T07:31:25.414406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3375 1
 
< 0.1%
3382 1
 
< 0.1%
3381 1
 
< 0.1%
3380 1
 
< 0.1%
3379 1
 
< 0.1%
3378 1
 
< 0.1%
3377 1
 
< 0.1%
3376 1
 
< 0.1%
3374 1
 
< 0.1%
Other values (5055) 5055
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5065 1
< 0.1%
5064 1
< 0.1%
5063 1
< 0.1%
5062 1
< 0.1%
5061 1
< 0.1%
5060 1
< 0.1%
5059 1
< 0.1%
5058 1
< 0.1%
5057 1
< 0.1%
5056 1
< 0.1%

SIDO_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
경기도
747 
경상남도
547 
경상북도
533 
서울특별시
467 
전라남도
422 
Other values (12)
2349 

Length

Max length7
Median length5
Mean length4.1472853
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 747
14.7%
경상남도 547
10.8%
경상북도 533
10.5%
서울특별시 467
9.2%
전라남도 422
8.3%
전라북도 410
8.1%
강원도 298
 
5.9%
충청남도 285
 
5.6%
충청북도 238
 
4.7%
대구광역시 204
 
4.0%
Other values (7) 914
18.0%

Length

2023-12-12T07:31:25.524027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 747
14.7%
경상남도 547
10.8%
경상북도 533
10.5%
서울특별시 467
9.2%
전라남도 422
8.3%
전라북도 410
8.1%
강원도 298
 
5.9%
충청남도 285
 
5.6%
충청북도 238
 
4.7%
대구광역시 204
 
4.0%
Other values (7) 914
18.0%

SIGNGU_NM
Text

Distinct207
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2023-12-12T07:31:25.801115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.899309
Min length2

Characters and Unicode

Total characters14685
Distinct characters133
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

Unique0 ?
Unique (%)0.0%

Sample

1st row춘천시
2nd row춘천시
3rd row춘천시
4th row춘천시
5th row춘천시
ValueCountFrequency (%)
중구 268
 
5.3%
창원시 204
 
4.0%
동구 143
 
2.8%
북구 104
 
2.1%
서구 99
 
2.0%
청주시 95
 
1.9%
종로구 87
 
1.7%
전주시 83
 
1.6%
광산구 79
 
1.6%
목포시 64
 
1.3%
Other values (197) 3839
75.8%
2023-12-12T07:31:26.201455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 2830
19.3%
구 1504
 
10.2%
군 910
 
6.2%
주 734
 
5.0%
산 478
 
3.3%
천 451
 
3.1%
성 382
 
2.6%
원 342
 
2.3%
동 336
 
2.3%
중 274
 
1.9%
Other values (123) 6444
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14685
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2830
19.3%
구 1504
 
10.2%
군 910
 
6.2%
주 734
 
5.0%
산 478
 
3.3%
천 451
 
3.1%
성 382
 
2.6%
원 342
 
2.3%
동 336
 
2.3%
중 274
 
1.9%
Other values (123) 6444
43.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14685
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2830
19.3%
구 1504
 
10.2%
군 910
 
6.2%
주 734
 
5.0%
산 478
 
3.3%
천 451
 
3.1%
성 382
 
2.6%
원 342
 
2.3%
동 336
 
2.3%
중 274
 
1.9%
Other values (123) 6444
43.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14685
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 2830
19.3%
구 1504
 
10.2%
군 910
 
6.2%
주 734
 
5.0%
산 478
 
3.3%
천 451
 
3.1%
성 382
 
2.6%
원 342
 
2.3%
동 336
 
2.3%
중 274
 
1.9%
Other values (123) 6444
43.9%

TNSHP_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct33
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
<NA>
4353 
진해구
 
65
마산합포구
 
64
완산구
 
46
성산구
 
41
Other values (28)
496 

Length

Max length5
Median length4
Mean length3.88154
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 4353
85.9%
진해구 65
 
1.3%
마산합포구 64
 
1.3%
완산구 46
 
0.9%
성산구 41
 
0.8%
덕진구 37
 
0.7%
흥덕구 34
 
0.7%
덕양구 32
 
0.6%
상당구 31
 
0.6%
북구 31
 
0.6%
Other values (23) 331
 
6.5%

Length

2023-12-12T07:31:26.330678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4353
85.9%
진해구 65
 
1.3%
마산합포구 64
 
1.3%
완산구 46
 
0.9%
성산구 41
 
0.8%
덕진구 37
 
0.7%
흥덕구 34
 
0.7%
덕양구 32
 
0.6%
북구 31
 
0.6%
상당구 31
 
0.6%
Other values (23) 331
 
6.5%

EMD_NM
Text

Distinct3975
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2023-12-12T07:31:26.624884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1461007
Min length2

Characters and Unicode

Total characters15935
Distinct characters370
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

Unique3385 ?
Unique (%)66.8%

Sample

1st row봉의동
2nd row요선동
3rd row낙원동
4th row중앙로1가
5th row중앙로2가
ValueCountFrequency (%)
교동 18
 
0.4%
송정동 14
 
0.3%
중동 13
 
0.3%
남면 12
 
0.2%
중앙동 11
 
0.2%
금곡동 10
 
0.2%
신흥동 9
 
0.2%
내동 9
 
0.2%
서면 9
 
0.2%
신동 9
 
0.2%
Other values (3965) 4951
97.7%
2023-12-12T07:31:27.038816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 3760
23.6%
면 1183
 
7.4%
가 510
 
3.2%
산 376
 
2.4%
읍 246
 
1.5%
남 221
 
1.4%
성 214
 
1.3%
신 214
 
1.3%
대 199
 
1.2%
천 186
 
1.2%
Other values (360) 8826
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15500
97.3%
Decimal Number 435
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3760
24.3%
면 1183
 
7.6%
가 510
 
3.3%
산 376
 
2.4%
읍 246
 
1.6%
남 221
 
1.4%
성 214
 
1.4%
신 214
 
1.4%
대 199
 
1.3%
천 186
 
1.2%
Other values (352) 8391
54.1%
Decimal Number
ValueCountFrequency (%)
1 133
30.6%
2 132
30.3%
3 87
20.0%
4 40
 
9.2%
5 24
 
5.5%
6 12
 
2.8%
7 6
 
1.4%
8 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15500
97.3%
Common 435
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3760
24.3%
면 1183
 
7.6%
가 510
 
3.3%
산 376
 
2.4%
읍 246
 
1.6%
남 221
 
1.4%
성 214
 
1.4%
신 214
 
1.4%
대 199
 
1.3%
천 186
 
1.2%
Other values (352) 8391
54.1%
Common
ValueCountFrequency (%)
1 133
30.6%
2 132
30.3%
3 87
20.0%
4 40
 
9.2%
5 24
 
5.5%
6 12
 
2.8%
7 6
 
1.4%
8 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15500
97.3%
ASCII 435
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 3760
24.3%
면 1183
 
7.6%
가 510
 
3.3%
산 376
 
2.4%
읍 246
 
1.6%
남 221
 
1.4%
성 214
 
1.4%
신 214
 
1.4%
대 199
 
1.3%
천 186
 
1.2%
Other values (352) 8391
54.1%
ASCII
ValueCountFrequency (%)
1 133
30.6%
2 132
30.3%
3 87
20.0%
4 40
 
9.2%
5 24
 
5.5%
6 12
 
2.8%
7 6
 
1.4%
8 1
 
0.2%

LFSTS_CNTRCT_CASCNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct424
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.00849
Minimum0
Maximum2421
Zeros2187
Zeros (%)43.2%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-12T07:31:27.156278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q328
95-th percentile246.4
Maximum2421
Range2421
Interquartile range (IQR)28

Descriptive statistics

Standard deviation122.27172
Coefficient of variation (CV)2.7783666
Kurtosis65.088873
Mean44.00849
Median Absolute Deviation (MAD)1
Skewness6.3180179
Sum222903
Variance14950.373
MonotonicityNot monotonic
2023-12-12T07:31:27.267146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2187
43.2%
1 384
 
7.6%
2 199
 
3.9%
3 122
 
2.4%
4 104
 
2.1%
5 89
 
1.8%
6 68
 
1.3%
7 58
 
1.1%
10 54
 
1.1%
8 52
 
1.0%
Other values (414) 1748
34.5%
ValueCountFrequency (%)
0 2187
43.2%
1 384
 
7.6%
2 199
 
3.9%
3 122
 
2.4%
4 104
 
2.1%
5 89
 
1.8%
6 68
 
1.3%
7 58
 
1.1%
8 52
 
1.0%
9 42
 
0.8%
ValueCountFrequency (%)
2421 1
< 0.1%
1796 1
< 0.1%
1457 1
< 0.1%
1451 1
< 0.1%
1377 1
< 0.1%
1279 1
< 0.1%
1261 1
< 0.1%
1256 1
< 0.1%
1218 1
< 0.1%
1182 1
< 0.1%

MTHT_CNTRCT_CASCNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct442
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.11767
Minimum0
Maximum2742
Zeros1976
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-12T07:31:27.381974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q335
95-th percentile261.8
Maximum2742
Range2742
Interquartile range (IQR)35

Descriptive statistics

Standard deviation133.03682
Coefficient of variation (CV)2.7085328
Kurtosis84.9719
Mean49.11767
Median Absolute Deviation (MAD)2
Skewness6.8755555
Sum248781
Variance17698.795
MonotonicityNot monotonic
2023-12-12T07:31:27.496772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1976
39.0%
1 412
 
8.1%
2 181
 
3.6%
3 120
 
2.4%
4 111
 
2.2%
5 79
 
1.6%
6 73
 
1.4%
9 59
 
1.2%
10 54
 
1.1%
8 54
 
1.1%
Other values (432) 1946
38.4%
ValueCountFrequency (%)
0 1976
39.0%
1 412
 
8.1%
2 181
 
3.6%
3 120
 
2.4%
4 111
 
2.2%
5 79
 
1.6%
6 73
 
1.4%
7 53
 
1.0%
8 54
 
1.1%
9 59
 
1.2%
ValueCountFrequency (%)
2742 1
< 0.1%
2700 1
< 0.1%
1636 1
< 0.1%
1323 1
< 0.1%
1314 1
< 0.1%
1120 1
< 0.1%
1099 1
< 0.1%
1089 1
< 0.1%
1078 1
< 0.1%
1066 1
< 0.1%

LFSTS_CNTRCT_RLIMP
Text

Distinct93
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2023-12-12T07:31:27.663161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.604541
Min length2

Characters and Unicode

Total characters13192
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

Unique6 ?
Unique (%)0.1%

Sample

1st row50%
2nd row64%
3rd row33%
4th row0%
5th row0%
ValueCountFrequency (%)
0 2188
43.2%
100 221
 
4.4%
50 197
 
3.9%
33 102
 
2.0%
67 84
 
1.7%
40 75
 
1.5%
43 73
 
1.4%
44 70
 
1.4%
48 63
 
1.2%
38 63
 
1.2%
Other values (83) 1929
38.1%
2023-12-12T07:31:27.945504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 5065
38.4%
0 3075
23.3%
5 928
 
7.0%
4 829
 
6.3%
3 738
 
5.6%
1 577
 
4.4%
6 555
 
4.2%
2 520
 
3.9%
7 390
 
3.0%
8 298
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8127
61.6%
Other Punctuation 5065
38.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3075
37.8%
5 928
 
11.4%
4 829
 
10.2%
3 738
 
9.1%
1 577
 
7.1%
6 555
 
6.8%
2 520
 
6.4%
7 390
 
4.8%
8 298
 
3.7%
9 217
 
2.7%
Other Punctuation
ValueCountFrequency (%)
% 5065
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 5065
38.4%
0 3075
23.3%
5 928
 
7.0%
4 829
 
6.3%
3 738
 
5.6%
1 577
 
4.4%
6 555
 
4.2%
2 520
 
3.9%
7 390
 
3.0%
8 298
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 5065
38.4%
0 3075
23.3%
5 928
 
7.0%
4 829
 
6.3%
3 738
 
5.6%
1 577
 
4.4%
6 555
 
4.2%
2 520
 
3.9%
7 390
 
3.0%
8 298
 
2.3%

MTHT_CNTRCT_RLIMP
Text

Distinct93
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2023-12-12T07:31:28.135051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.6945706
Min length2

Characters and Unicode

Total characters13648
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

Unique6 ?
Unique (%)0.1%

Sample

1st row50%
2nd row36%
3rd row67%
4th row0%
5th row0%
ValueCountFrequency (%)
0 1976
39.0%
100 433
 
8.5%
50 197
 
3.9%
67 102
 
2.0%
33 84
 
1.7%
60 75
 
1.5%
57 74
 
1.5%
56 70
 
1.4%
62 63
 
1.2%
52 63
 
1.2%
Other values (83) 1928
38.1%
2023-12-12T07:31:28.437803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 5065
37.1%
0 3287
24.1%
5 986
 
7.2%
4 728
 
5.3%
6 716
 
5.2%
1 682
 
5.0%
3 593
 
4.3%
7 577
 
4.2%
8 421
 
3.1%
2 345
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8583
62.9%
Other Punctuation 5065
37.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3287
38.3%
5 986
 
11.5%
4 728
 
8.5%
6 716
 
8.3%
1 682
 
7.9%
3 593
 
6.9%
7 577
 
6.7%
8 421
 
4.9%
2 345
 
4.0%
9 248
 
2.9%
Other Punctuation
ValueCountFrequency (%)
% 5065
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13648
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 5065
37.1%
0 3287
24.1%
5 986
 
7.2%
4 728
 
5.3%
6 716
 
5.2%
1 682
 
5.0%
3 593
 
4.3%
7 577
 
4.2%
8 421
 
3.1%
2 345
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 5065
37.1%
0 3287
24.1%
5 986
 
7.2%
4 728
 
5.3%
6 716
 
5.2%
1 682
 
5.0%
3 593
 
4.3%
7 577
 
4.2%
8 421
 
3.1%
2 345
 
2.5%

PRSMP_LFSTS_PC_FLCTN_RATE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1458638
Minimum-4.36
Maximum0.09
Zeros159
Zeros (%)3.1%
Negative4883
Negative (%)96.4%
Memory size44.6 KiB
2023-12-12T07:31:28.699249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.36
5-th percentile-3.03
Q1-1.71
median-0.92
Q3-0.39
95-th percentile-0.02
Maximum0.09
Range4.45
Interquartile range (IQR)1.32

Descriptive statistics

Standard deviation0.92784849
Coefficient of variation (CV)-0.80973717
Kurtosis0.26125263
Mean-1.1458638
Median Absolute Deviation (MAD)0.58
Skewness-0.92010151
Sum-5803.8
Variance0.86090281
MonotonicityNot monotonic
2023-12-12T07:31:29.554301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.37 247
 
4.9%
-1.24 204
 
4.0%
-0.34 182
 
3.6%
-0.46 175
 
3.5%
0.0 159
 
3.1%
-0.23 154
 
3.0%
-0.91 126
 
2.5%
-0.58 121
 
2.4%
-0.75 106
 
2.1%
-2.52 95
 
1.9%
Other values (113) 3496
69.0%
ValueCountFrequency (%)
-4.36 6
 
0.1%
-3.94 31
0.6%
-3.84 15
 
0.3%
-3.81 24
0.5%
-3.69 22
0.4%
-3.63 7
 
0.1%
-3.3 53
1.0%
-3.18 13
 
0.3%
-3.17 24
0.5%
-3.05 20
 
0.4%
ValueCountFrequency (%)
0.09 23
 
0.5%
0.0 159
3.1%
-0.01 54
 
1.1%
-0.02 62
 
1.2%
-0.05 56
 
1.1%
-0.06 35
 
0.7%
-0.07 13
 
0.3%
-0.08 84
1.7%
-0.11 35
 
0.7%
-0.13 47
 
0.9%

Interactions

2023-12-12T07:31:24.776569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:23.874027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.213491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.490038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.849587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:23.991874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.284049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.560016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.917153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.071145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.351023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.631961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.986134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.145546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.419473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:24.706563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:31:29.656577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NOSIDO_NMTNSHP_NMLFSTS_CNTRCT_CASCNTMTHT_CNTRCT_CASCNTLFSTS_CNTRCT_RLIMPMTHT_CNTRCT_RLIMPPRSMP_LFSTS_PC_FLCTN_RATE
PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NO1.0000.9770.9950.1830.1890.3680.3770.808
SIDO_NM0.9771.0001.0000.2460.2590.4350.4450.807
TNSHP_NM0.9951.0001.0000.6080.5270.6380.6431.000
LFSTS_CNTRCT_CASCNT0.1830.2460.6081.0000.8050.5060.5060.243
MTHT_CNTRCT_CASCNT0.1890.2590.5270.8051.0000.4420.4420.220
LFSTS_CNTRCT_RLIMP0.3680.4350.6380.5060.4421.0001.0000.409
MTHT_CNTRCT_RLIMP0.3770.4450.6430.5060.4421.0001.0000.411
PRSMP_LFSTS_PC_FLCTN_RATE0.8080.8071.0000.2430.2200.4090.4111.000
2023-12-12T07:31:29.775018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMTNSHP_NM
SIDO_NM1.0000.981
TNSHP_NM0.9811.000
2023-12-12T07:31:29.867659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NOLFSTS_CNTRCT_CASCNTMTHT_CNTRCT_CASCNTPRSMP_LFSTS_PC_FLCTN_RATESIDO_NMTNSHP_NM
PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NO1.000-0.140-0.1420.2130.8890.954
LFSTS_CNTRCT_CASCNT-0.1401.0000.916-0.4070.1000.325
MTHT_CNTRCT_CASCNT-0.1420.9161.000-0.3810.1200.307
PRSMP_LFSTS_PC_FLCTN_RATE0.213-0.407-0.3811.0000.4800.981
SIDO_NM0.8890.1000.1200.4801.0000.981
TNSHP_NM0.9540.3250.3070.9810.9811.000

Missing values

2023-12-12T07:31:25.079026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:31:25.193220image/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

PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NOSIDO_NMSIGNGU_NMTNSHP_NMEMD_NMLFSTS_CNTRCT_CASCNTMTHT_CNTRCT_CASCNTLFSTS_CNTRCT_RLIMPMTHT_CNTRCT_RLIMPPRSMP_LFSTS_PC_FLCTN_RATE
01강원도춘천시<NA>봉의동1150%50%-0.79
12강원도춘천시<NA>요선동7464%36%-0.79
23강원도춘천시<NA>낙원동1233%67%-0.79
34강원도춘천시<NA>중앙로1가000%0%-0.79
45강원도춘천시<NA>중앙로2가000%0%-0.79
56강원도춘천시<NA>중앙로3가1420%80%-0.79
67강원도춘천시<NA>옥천동1517%83%-0.79
78강원도춘천시<NA>조양동1712%88%-0.79
89강원도춘천시<NA>죽림동1233%67%-0.79
910강원도춘천시<NA>운교동5645%55%-0.79
PRSMP_LFSTS_PC_FLCTS_ACCTO_MTRNT_RLIMP_INFO_NOSIDO_NMSIGNGU_NMTNSHP_NMEMD_NMLFSTS_CNTRCT_CASCNTMTHT_CNTRCT_CASCNTLFSTS_CNTRCT_RLIMPMTHT_CNTRCT_RLIMPPRSMP_LFSTS_PC_FLCTN_RATE
50555056충청북도보은군<NA>회남면000%0%-0.75
50565057충청북도진천군<NA>백곡면000%0%-0.75
50575058충청북도보은군<NA>회인면000%0%-0.75
50585059충청북도보은군<NA>내북면000%0%-0.75
50595060충청북도보은군<NA>산외면010%100%-0.75
50605061충청북도옥천군<NA>옥천읍303248%52%-0.75
50615062충청북도옥천군<NA>동이면030%100%-0.75
50625063충청북도옥천군<NA>안남면000%0%-0.75
50635064충청북도괴산군<NA>장연면000%0%-0.75
50645065충청북도진천군<NA>덕산읍10428627%73%-0.75