Overview

Dataset statistics

Number of variables34
Number of observations100
Missing cells627
Missing cells (%)18.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.6 KiB
Average record size in memory293.3 B

Variable types

Numeric12
Categorical14
Unsupported4
Text4

Alerts

pyrxia_room_at has constant value ""Constant
svc_id has constant value ""Constant
sfrnd_code is highly imbalanced (86.0%)Imbalance
sfrnd_code_nm is highly imbalanced (86.0%)Imbalance
data_updt_de is highly imbalanced (64.8%)Imbalance
engl_sttus is highly imbalanced (85.9%)Imbalance
detail_engl_sttus is highly imbalanced (85.9%)Imbalance
xcnts has 2 (2.0%) missing valuesMissing
ydnts has 2 (2.0%) missing valuesMissing
telno has 18 (18.0%) missing valuesMissing
buld_ground_floor_co has 2 (2.0%) missing valuesMissing
male_enfsn_co has 100 (100.0%) missing valuesMissing
use_end_ground_floor has 9 (9.0%) missing valuesMissing
use_begin_ground_floor has 7 (7.0%) missing valuesMissing
female_enfsn_co has 100 (100.0%) missing valuesMissing
bedd_co has 56 (56.0%) missing valuesMissing
rn_zip has 67 (67.0%) missing valuesMissing
rdnmadr has 63 (63.0%) missing valuesMissing
person_prmisn_de has 100 (100.0%) missing valuesMissing
clsbiz_de has 100 (100.0%) missing valuesMissing
skey has unique valuesUnique
person_prmisn_no has unique valuesUnique
male_enfsn_co is an unsupported type, check if it needs cleaning or further analysisUnsupported
female_enfsn_co is an unsupported type, check if it needs cleaning or further analysisUnsupported
person_prmisn_de is an unsupported type, check if it needs cleaning or further analysisUnsupported
clsbiz_de is an unsupported type, check if it needs cleaning or further analysisUnsupported
buld_ground_floor_co has 4 (4.0%) zerosZeros
use_end_ground_floor has 2 (2.0%) zerosZeros
use_begin_ground_floor has 2 (2.0%) zerosZeros
chair_co has 10 (10.0%) zerosZeros
bedd_co has 27 (27.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:17:47.390930
Analysis finished2023-12-10 10:17:48.428640
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean780.82
Minimum1
Maximum24359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:48.514681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum24359
Range24358
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation4167.3403
Coefficient of variation (CV)5.3371331
Kurtosis29.894764
Mean780.82
Median Absolute Deviation (MAD)25.5
Skewness5.5942408
Sum78082
Variance17366725
MonotonicityNot monotonic
2023-12-10T19:17:48.726518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
24359 1
1.0%
24358 1
1.0%
24357 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

xcnts
Real number (ℝ)

MISSING 

Distinct89
Distinct (%)90.8%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean385561.84
Minimum384415.09
Maximum401476.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:48.912082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum384415.09
5-th percentile384566.57
Q1384989.2
median385102.78
Q3385375.17
95-th percentile385666.59
Maximum401476.29
Range17061.2
Interquartile range (IQR)385.97253

Descriptive statistics

Standard deviation2504.7654
Coefficient of variation (CV)0.0064964037
Kurtosis29.808134
Mean385561.84
Median Absolute Deviation (MAD)188.18671
Skewness5.5042186
Sum37785060
Variance6273849.5
MonotonicityNot monotonic
2023-12-10T19:17:49.110337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
385037.963018366 3
 
3.0%
385091.885760201 2
 
2.0%
385007.017930066 2
 
2.0%
385279.431902627 2
 
2.0%
385077.985604846 2
 
2.0%
385290.402745953 2
 
2.0%
384645.673444747 2
 
2.0%
384566.572628899 2
 
2.0%
384679.630845683 1
 
1.0%
385086.045709807 1
 
1.0%
Other values (79) 79
79.0%
(Missing) 2
 
2.0%
ValueCountFrequency (%)
384415.0913224 1
1.0%
384495.148243884 1
1.0%
384538.088272228 1
1.0%
384560.530301413 1
1.0%
384566.572628899 2
2.0%
384591.613993499 1
1.0%
384599.392456657 1
1.0%
384608.762917794 1
1.0%
384645.673444747 2
2.0%
384679.630845683 1
1.0%
ValueCountFrequency (%)
401476.291625471 1
1.0%
398421.273686176 1
1.0%
398330.516530402 1
1.0%
385799.333219399 1
1.0%
385672.193658456 1
1.0%
385665.605645415 1
1.0%
385634.484656216 1
1.0%
385632.365526707 1
1.0%
385590.814676765 1
1.0%
385565.311665134 1
1.0%

ydnts
Real number (ℝ)

MISSING 

Distinct89
Distinct (%)90.8%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean180531.16
Minimum179506
Maximum204534.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:49.310533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum179506
5-th percentile179572.16
Q1179649.44
median179924.03
Q3180204.38
95-th percentile181201.7
Maximum204534.52
Range25028.518
Interquartile range (IQR)554.93991

Descriptive statistics

Standard deviation3132.6722
Coefficient of variation (CV)0.017352529
Kurtosis43.779871
Mean180531.16
Median Absolute Deviation (MAD)278.81854
Skewness6.4335728
Sum17692054
Variance9813635.2
MonotonicityNot monotonic
2023-12-10T19:17:49.547439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179957.737012606 3
 
3.0%
179711.262572397 2
 
2.0%
179584.983850781 2
 
2.0%
179598.179008617 2
 
2.0%
179999.046652795 2
 
2.0%
179589.945862549 2
 
2.0%
179574.211568157 2
 
2.0%
179924.031007563 2
 
2.0%
180117.137785364 1
 
1.0%
180104.678561261 1
 
1.0%
Other values (79) 79
79.0%
(Missing) 2
 
2.0%
ValueCountFrequency (%)
179506.001222169 1
1.0%
179553.867031936 1
1.0%
179561.77960176 1
1.0%
179562.815862117 1
1.0%
179563.399272841 1
1.0%
179573.706241829 1
1.0%
179574.211568157 2
2.0%
179584.27634744 1
1.0%
179584.913993246 1
1.0%
179584.983850781 2
2.0%
ValueCountFrequency (%)
204534.519237408 1
1.0%
197183.078261456 1
1.0%
187771.511373596 1
1.0%
181235.095994492 1
1.0%
181206.157156801 1
1.0%
181200.916935774 1
1.0%
181160.416309552 1
1.0%
181146.093577037 1
1.0%
181145.082616947 1
1.0%
181050.850707853 1
1.0%

last_updt_de
Real number (ℝ)

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0102549 × 1013
Minimum2.0030503 × 1013
Maximum2.0200331 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:49.771882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0030503 × 1013
5-th percentile2.0030697 × 1013
Q12.0050424 × 1013
median2.0081065 × 1013
Q32.0170803 × 1013
95-th percentile2.0200115 × 1013
Maximum2.0200331 × 1013
Range1.6982809 × 1011
Interquartile range (IQR)1.203794 × 1011

Descriptive statistics

Standard deviation6.1071004 × 1010
Coefficient of variation (CV)0.0030379732
Kurtosis-1.4437584
Mean2.0102549 × 1013
Median Absolute Deviation (MAD)4.5049581 × 1010
Skewness0.37694464
Sum2.0102549 × 1015
Variance3.7296675 × 1021
MonotonicityNot monotonic
2023-12-10T19:17:49.985100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030503000000 4
 
4.0%
20041126000000 2
 
2.0%
20060504000000 2
 
2.0%
20030701000000 2
 
2.0%
20031210000000 2
 
2.0%
20191230142451 1
 
1.0%
20170807140524 1
 
1.0%
20110615101016 1
 
1.0%
20200114113558 1
 
1.0%
20180105163527 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
20030503000000 4
4.0%
20030626000000 1
 
1.0%
20030701000000 2
2.0%
20030826000000 1
 
1.0%
20030827000000 1
 
1.0%
20030919000000 1
 
1.0%
20031104000000 1
 
1.0%
20031210000000 2
2.0%
20031224000000 1
 
1.0%
20040109000000 1
 
1.0%
ValueCountFrequency (%)
20200331092018 1
1.0%
20200207134611 1
1.0%
20200207134426 1
1.0%
20200131170529 1
1.0%
20200131135903 1
1.0%
20200114113558 1
1.0%
20191230142451 1
1.0%
20191226162703 1
1.0%
20191120163210 1
1.0%
20190917114737 1
1.0%

bizcnd_se_nm
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
일반미용업
70 
피부미용업
14 
네일아트업
9 
기타
 
5
메이크업업
 
2

Length

Max length5
Median length5
Mean length4.85
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row피부미용업
2nd row일반미용업
3rd row일반미용업
4th row일반미용업
5th row일반미용업

Common Values

ValueCountFrequency (%)
일반미용업 70
70.0%
피부미용업 14
 
14.0%
네일아트업 9
 
9.0%
기타 5
 
5.0%
메이크업업 2
 
2.0%

Length

2023-12-10T19:17:50.191546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:50.354694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 70
70.0%
피부미용업 14
 
14.0%
네일아트업 9
 
9.0%
기타 5
 
5.0%
메이크업업 2
 
2.0%

telno
Real number (ℝ)

MISSING 

Distinct80
Distinct (%)97.6%
Missing18
Missing (%)18.0%
Infinite0
Infinite (%)0.0%
Mean4.2002514 × 108
Minimum2310050
Maximum5.1923169 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:50.558045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2310050
5-th percentile2464682.6
Q15.1242156 × 108
median5.1247489 × 108
Q35.1306732 × 108
95-th percentile5.1627016 × 108
Maximum5.1923169 × 108
Range5.1692164 × 108
Interquartile range (IQR)645760.25

Descriptive statistics

Standard deviation1.9857655 × 108
Coefficient of variation (CV)0.472773
Kurtosis0.81118503
Mean4.2002514 × 108
Median Absolute Deviation (MAD)92246
Skewness-1.6707993
Sum3.4442062 × 1010
Variance3.9432645 × 1016
MonotonicityNot monotonic
2023-12-10T19:17:50.768361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
514645216 2
 
2.0%
512462279 2
 
2.0%
512423342 1
 
1.0%
514694790 1
 
1.0%
512432329 1
 
1.0%
512460497 1
 
1.0%
512456347 1
 
1.0%
512464613 1
 
1.0%
512534700 1
 
1.0%
519231688 1
 
1.0%
Other values (70) 70
70.0%
(Missing) 18
 
18.0%
ValueCountFrequency (%)
2310050 1
1.0%
2462279 1
1.0%
2462326 1
1.0%
2462362 1
1.0%
2464405 1
1.0%
2469958 1
1.0%
2486037 1
1.0%
2546515 1
1.0%
2551441 1
1.0%
2572025 1
1.0%
ValueCountFrequency (%)
519231688 1
1.0%
519079578 1
1.0%
518648855 1
1.0%
516784229 1
1.0%
516273530 1
1.0%
516206041 1
1.0%
515235757 1
1.0%
514696608 1
1.0%
514694790 1
1.0%
514680314 1
1.0%

buld_posesn_se_nm
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
임대
67 
<NA>
33 

Length

Max length4
Median length2
Mean length2.66
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
임대 67
67.0%
<NA> 33
33.0%

Length

2023-12-10T19:17:51.006183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:51.163334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임대 67
67.0%
na 33
33.0%

buld_ground_floor_co
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)13.3%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean4.2653061
Minimum0
Maximum16
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:51.296797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile10.15
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8485881
Coefficient of variation (CV)0.66785079
Kurtosis6.1742883
Mean4.2653061
Median Absolute Deviation (MAD)1
Skewness2.0996905
Sum418
Variance8.114454
MonotonicityNot monotonic
2023-12-10T19:17:51.506324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4 30
30.0%
3 19
19.0%
5 17
17.0%
2 8
 
8.0%
1 6
 
6.0%
0 4
 
4.0%
6 4
 
4.0%
8 3
 
3.0%
16 2
 
2.0%
13 2
 
2.0%
Other values (3) 3
 
3.0%
(Missing) 2
 
2.0%
ValueCountFrequency (%)
0 4
 
4.0%
1 6
 
6.0%
2 8
 
8.0%
3 19
19.0%
4 30
30.0%
5 17
17.0%
6 4
 
4.0%
7 1
 
1.0%
8 3
 
3.0%
10 1
 
1.0%
ValueCountFrequency (%)
16 2
 
2.0%
13 2
 
2.0%
11 1
 
1.0%
10 1
 
1.0%
8 3
 
3.0%
7 1
 
1.0%
6 4
 
4.0%
5 17
17.0%
4 30
30.0%
3 19
19.0%

buld_undgrnd_floor_co
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
42 
<NA>
30 
0
23 
2
 
3
4
 
1

Length

Max length4
Median length1
Mean length1.9
Min length1

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row1
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
1 42
42.0%
<NA> 30
30.0%
0 23
23.0%
2 3
 
3.0%
4 1
 
1.0%
6 1
 
1.0%

Length

2023-12-10T19:17:51.700719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:51.870154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 42
42.0%
na 30
30.0%
0 23
23.0%
2 3
 
3.0%
4 1
 
1.0%
6 1
 
1.0%

male_enfsn_co
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

pyrxia_room_at
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
- 100
100.0%

Length

2023-12-10T19:17:52.015380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:52.127489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100
100.0%

use_end_ground_floor
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)7.7%
Missing9
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean2.010989
Minimum0
Maximum9
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:52.228633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile3.5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.224695
Coefficient of variation (CV)0.60900135
Kurtosis11.518622
Mean2.010989
Median Absolute Deviation (MAD)1
Skewness2.4649465
Sum183
Variance1.4998779
MonotonicityNot monotonic
2023-12-10T19:17:52.373570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 34
34.0%
1 31
31.0%
3 19
19.0%
4 3
 
3.0%
0 2
 
2.0%
9 1
 
1.0%
6 1
 
1.0%
(Missing) 9
 
9.0%
ValueCountFrequency (%)
0 2
 
2.0%
1 31
31.0%
2 34
34.0%
3 19
19.0%
4 3
 
3.0%
6 1
 
1.0%
9 1
 
1.0%
ValueCountFrequency (%)
9 1
 
1.0%
6 1
 
1.0%
4 3
 
3.0%
3 19
19.0%
2 34
34.0%
1 31
31.0%
0 2
 
2.0%

use_end_undgrnd_floor
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
67 
0
30 
1
 
3

Length

Max length4
Median length4
Mean length3.01
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 67
67.0%
0 30
30.0%
1 3
 
3.0%

Length

2023-12-10T19:17:52.545012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:52.685352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 67
67.0%
0 30
30.0%
1 3
 
3.0%

use_begin_ground_floor
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)8.6%
Missing7
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean1.9892473
Minimum0
Maximum11
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:52.784748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3.4
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5215454
Coefficient of variation (CV)0.764885
Kurtosis17.206988
Mean1.9892473
Median Absolute Deviation (MAD)1
Skewness3.5378692
Sum185
Variance2.3151005
MonotonicityNot monotonic
2023-12-10T19:17:52.913017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 36
36.0%
2 35
35.0%
3 15
15.0%
4 2
 
2.0%
0 2
 
2.0%
9 1
 
1.0%
6 1
 
1.0%
11 1
 
1.0%
(Missing) 7
 
7.0%
ValueCountFrequency (%)
0 2
 
2.0%
1 36
36.0%
2 35
35.0%
3 15
15.0%
4 2
 
2.0%
6 1
 
1.0%
9 1
 
1.0%
11 1
 
1.0%
ValueCountFrequency (%)
11 1
 
1.0%
9 1
 
1.0%
6 1
 
1.0%
4 2
 
2.0%
3 15
15.0%
2 35
35.0%
1 36
36.0%
0 2
 
2.0%

use_begin_undgrnd_floor
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
67 
0
30 
1
 
3

Length

Max length4
Median length4
Mean length3.01
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 67
67.0%
0 30
30.0%
1 3
 
3.0%

Length

2023-12-10T19:17:53.095721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:53.243229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 67
67.0%
0 30
30.0%
1 3
 
3.0%

female_enfsn_co
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

snitat_biznd_nm
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
미용업
65 
미용업(종합)
15 
미용업(일반)
9 
미용업(피부)
8 
미용업(피부), 미용업(손톱ㆍ발톱)
 
2

Length

Max length22
Median length3
Mean length4.79
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row미용업(피부)
2nd row미용업(일반)
3rd row미용업
4th row미용업
5th row미용업

Common Values

ValueCountFrequency (%)
미용업 65
65.0%
미용업(종합) 15
 
15.0%
미용업(일반) 9
 
9.0%
미용업(피부) 8
 
8.0%
미용업(피부), 미용업(손톱ㆍ발톱) 2
 
2.0%
미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장) 1
 
1.0%

Length

2023-12-10T19:17:53.381727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:53.541710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업 65
63.1%
미용업(종합 15
 
14.6%
미용업(피부 10
 
9.7%
미용업(일반 9
 
8.7%
미용업(손톱ㆍ발톱 3
 
2.9%
미용업(화장ㆍ분장 1
 
1.0%

chair_co
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.89
Minimum0
Maximum27
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:53.689278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median3.5
Q36
95-th percentile13.05
Maximum27
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.5436047
Coefficient of variation (CV)0.92916251
Kurtosis8.2155802
Mean4.89
Median Absolute Deviation (MAD)1.5
Skewness2.5097202
Sum489
Variance20.644343
MonotonicityNot monotonic
2023-12-10T19:17:53.952251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 29
29.0%
4 12
12.0%
0 10
 
10.0%
5 8
 
8.0%
2 8
 
8.0%
7 7
 
7.0%
6 7
 
7.0%
8 5
 
5.0%
9 3
 
3.0%
1 3
 
3.0%
Other values (7) 8
 
8.0%
ValueCountFrequency (%)
0 10
 
10.0%
1 3
 
3.0%
2 8
 
8.0%
3 29
29.0%
4 12
12.0%
5 8
 
8.0%
6 7
 
7.0%
7 7
 
7.0%
8 5
 
5.0%
9 3
 
3.0%
ValueCountFrequency (%)
27 1
 
1.0%
23 1
 
1.0%
19 2
 
2.0%
14 1
 
1.0%
13 1
 
1.0%
12 1
 
1.0%
10 1
 
1.0%
9 3
3.0%
8 5
5.0%
7 7
7.0%

bedd_co
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)15.9%
Missing56
Missing (%)56.0%
Infinite0
Infinite (%)0.0%
Mean1.4318182
Minimum0
Maximum6
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:54.139949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile5.85
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1064434
Coefficient of variation (CV)1.4711668
Kurtosis-0.26869908
Mean1.4318182
Median Absolute Deviation (MAD)0
Skewness1.1320502
Sum63
Variance4.4371036
MonotonicityNot monotonic
2023-12-10T19:17:54.259103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 27
27.0%
5 4
 
4.0%
2 3
 
3.0%
6 3
 
3.0%
3 3
 
3.0%
4 2
 
2.0%
1 2
 
2.0%
(Missing) 56
56.0%
ValueCountFrequency (%)
0 27
27.0%
1 2
 
2.0%
2 3
 
3.0%
3 3
 
3.0%
4 2
 
2.0%
5 4
 
4.0%
6 3
 
3.0%
ValueCountFrequency (%)
6 3
 
3.0%
5 4
 
4.0%
4 2
 
2.0%
3 3
 
3.0%
2 3
 
3.0%
1 2
 
2.0%
0 27
27.0%

sfrnd_code
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3250000
97 
3400000
 
2
3330000
 
1

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row3250000
2nd row3400000
3rd row3250000
4th row3250000
5th row3250000

Common Values

ValueCountFrequency (%)
3250000 97
97.0%
3400000 2
 
2.0%
3330000 1
 
1.0%

Length

2023-12-10T19:17:54.437052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:54.582521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3250000 97
97.0%
3400000 2
 
2.0%
3330000 1
 
1.0%

sfrnd_code_nm
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
부산광역시 중구
97 
부산광역시 기장군
 
2
부산광역시 해운대구
 
1

Length

Max length10
Median length8
Mean length8.04
Min length8

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row부산광역시 중구
2nd row부산광역시 기장군
3rd row부산광역시 중구
4th row부산광역시 중구
5th row부산광역시 중구

Common Values

ValueCountFrequency (%)
부산광역시 중구 97
97.0%
부산광역시 기장군 2
 
2.0%
부산광역시 해운대구 1
 
1.0%

Length

2023-12-10T19:17:54.730600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:54.878466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 100
50.0%
중구 97
48.5%
기장군 2
 
1.0%
해운대구 1
 
0.5%

person_prmisn_no
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:17:55.170536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2200
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

Unique100 ?
Unique (%)100.0%

Sample

1st row3250000-212-2010-00001
2nd row3400000-211-2018-00017
3rd row3250000-204-1990-00328
4th row3250000-204-2003-00013
5th row3250000-204-1990-00323
ValueCountFrequency (%)
3250000-212-2010-00001 1
 
1.0%
3250000-213-2007-00017 1
 
1.0%
3250000-213-2015-00002 1
 
1.0%
3250000-213-2017-00006 1
 
1.0%
3250000-204-2009-00003 1
 
1.0%
3250000-213-2005-00013 1
 
1.0%
3250000-204-2000-00026 1
 
1.0%
3250000-204-2000-00025 1
 
1.0%
3250000-204-1998-00443 1
 
1.0%
3250000-204-1997-00445 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:17:55.735418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 958
43.5%
2 326
 
14.8%
- 300
 
13.6%
3 148
 
6.7%
1 137
 
6.2%
5 121
 
5.5%
4 104
 
4.7%
9 45
 
2.0%
8 21
 
1.0%
6 20
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1900
86.4%
Dash Punctuation 300
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 958
50.4%
2 326
 
17.2%
3 148
 
7.8%
1 137
 
7.2%
5 121
 
6.4%
4 104
 
5.5%
9 45
 
2.4%
8 21
 
1.1%
6 20
 
1.1%
7 20
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 958
43.5%
2 326
 
14.8%
- 300
 
13.6%
3 148
 
6.7%
1 137
 
6.2%
5 121
 
5.5%
4 104
 
4.7%
9 45
 
2.0%
8 21
 
1.0%
6 20
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 958
43.5%
2 326
 
14.8%
- 300
 
13.6%
3 148
 
6.7%
1 137
 
6.2%
5 121
 
5.5%
4 104
 
4.7%
9 45
 
2.0%
8 21
 
1.0%
6 20
 
0.9%

svc_id
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
05_18_01_P
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row05_18_01_P
2nd row05_18_01_P
3rd row05_18_01_P
4th row05_18_01_P
5th row05_18_01_P

Common Values

ValueCountFrequency (%)
05_18_01_P 100
100.0%

Length

2023-12-10T19:17:55.957143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:56.092770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
05_18_01_p 100
100.0%

data_updt_se
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
I
82 
U
18 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowU
3rd rowI
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
I 82
82.0%
U 18
 
18.0%

Length

2023-12-10T19:17:56.246973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:17:56.727365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 82
82.0%
u 18
 
18.0%

data_updt_de
Categorical

IMBALANCE 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2018-08-31 11:59:59.0
81 
2020-02-02 02:40:00.0
 
2
2020-02-09 02:40:00.0
 
2
2018-12-26 02:40:00.0
 
1
2018-12-28 02:40:00.0
 
1
Other values (13)
13 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique15 ?
Unique (%)15.0%

Sample

1st row2018-08-31 11:59:59.0
2nd row2018-12-26 02:40:00.0
3rd row2018-08-31 11:59:59.0
4th row2018-08-31 11:59:59.0
5th row2018-08-31 11:59:59.0

Common Values

ValueCountFrequency (%)
2018-08-31 11:59:59.0 81
81.0%
2020-02-02 02:40:00.0 2
 
2.0%
2020-02-09 02:40:00.0 2
 
2.0%
2018-12-26 02:40:00.0 1
 
1.0%
2018-12-28 02:40:00.0 1
 
1.0%
2019-02-01 02:21:07.0 1
 
1.0%
2019-11-22 02:40:00.0 1
 
1.0%
2018-12-19 02:40:00.0 1
 
1.0%
2020-01-01 02:40:00.0 1
 
1.0%
2019-12-28 02:40:00.0 1
 
1.0%
Other values (8) 8
 
8.0%

Length

2023-12-10T19:17:56.903901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-08-31 81
40.5%
11:59:59.0 81
40.5%
02:40:00.0 17
 
8.5%
2020-02-02 2
 
1.0%
2020-02-09 2
 
1.0%
2019-03-10 1
 
0.5%
2019-09-12 1
 
0.5%
02:36:40.0 1
 
0.5%
2018-10-20 1
 
0.5%
2019-06-12 1
 
0.5%
Other values (12) 12
 
6.0%

bplc_nm
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:17:57.352278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length5.86
Min length1

Characters and Unicode

Total characters586
Distinct characters207
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row킹덤스킨&바디
2nd row오니헤어
3rd row한미헤어클리닉
4th row카라헤어
5th row인어
ValueCountFrequency (%)
미용실 4
 
3.2%
베아띠미용실 2
 
1.6%
야시만들기 2
 
1.6%
뷰티 2
 
1.6%
설 2
 
1.6%
네일 2
 
1.6%
쁄라 1
 
0.8%
체리네일아트 1
 
0.8%
자미한 1
 
0.8%
남자다운 1
 
0.8%
Other values (107) 107
85.6%
2023-12-10T19:17:58.026999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 32
 
5.5%
헤 29
 
4.9%
미 29
 
4.9%
25
 
4.3%
용 23
 
3.9%
실 23
 
3.9%
스 17
 
2.9%
라 12
 
2.0%
리 10
 
1.7%
티 9
 
1.5%
Other values (197) 377
64.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 520
88.7%
Space Separator 25
 
4.3%
Lowercase Letter 19
 
3.2%
Uppercase Letter 10
 
1.7%
Open Punctuation 4
 
0.7%
Close Punctuation 4
 
0.7%
Other Punctuation 4
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 32
 
6.2%
헤 29
 
5.6%
미 29
 
5.6%
용 23
 
4.4%
실 23
 
4.4%
스 17
 
3.3%
라 12
 
2.3%
리 10
 
1.9%
티 9
 
1.7%
일 8
 
1.5%
Other values (172) 328
63.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
21.1%
a 3
15.8%
s 2
10.5%
u 2
10.5%
h 1
 
5.3%
b 1
 
5.3%
l 1
 
5.3%
c 1
 
5.3%
n 1
 
5.3%
m 1
 
5.3%
Other values (2) 2
10.5%
Uppercase Letter
ValueCountFrequency (%)
L 3
30.0%
K 2
20.0%
X 1
 
10.0%
U 1
 
10.0%
B 1
 
10.0%
G 1
 
10.0%
J 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
' 2
50.0%
& 1
25.0%
. 1
25.0%
Space Separator
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 520
88.7%
Common 37
 
6.3%
Latin 29
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 32
 
6.2%
헤 29
 
5.6%
미 29
 
5.6%
용 23
 
4.4%
실 23
 
4.4%
스 17
 
3.3%
라 12
 
2.3%
리 10
 
1.9%
티 9
 
1.7%
일 8
 
1.5%
Other values (172) 328
63.1%
Latin
ValueCountFrequency (%)
e 4
13.8%
L 3
 
10.3%
a 3
 
10.3%
K 2
 
6.9%
s 2
 
6.9%
u 2
 
6.9%
X 1
 
3.4%
U 1
 
3.4%
h 1
 
3.4%
B 1
 
3.4%
Other values (9) 9
31.0%
Common
ValueCountFrequency (%)
25
67.6%
( 4
 
10.8%
) 4
 
10.8%
' 2
 
5.4%
& 1
 
2.7%
. 1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 520
88.7%
ASCII 66
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 32
 
6.2%
헤 29
 
5.6%
미 29
 
5.6%
용 23
 
4.4%
실 23
 
4.4%
스 17
 
3.3%
라 12
 
2.3%
리 10
 
1.9%
티 9
 
1.7%
일 8
 
1.5%
Other values (172) 328
63.1%
ASCII
ValueCountFrequency (%)
25
37.9%
( 4
 
6.1%
) 4
 
6.1%
e 4
 
6.1%
L 3
 
4.5%
a 3
 
4.5%
K 2
 
3.0%
s 2
 
3.0%
' 2
 
3.0%
u 2
 
3.0%
Other values (15) 15
22.7%

lnm_zip
Real number (ℝ)

Distinct37
Distinct (%)37.4%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean600583.33
Minimum600011
Maximum619901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:58.244391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600011
5-th percentile600024.9
Q1600042
median600061
Q3600803
95-th percentile600816.2
Maximum619901
Range19890
Interquartile range (IQR)761

Descriptive statistics

Standard deviation2357.3238
Coefficient of variation (CV)0.0039250569
Kurtosis53.763779
Mean600583.33
Median Absolute Deviation (MAD)31
Skewness7.1876312
Sum59457750
Variance5556975.3
MonotonicityNot monotonic
2023-12-10T19:17:58.469071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
600042 10
 
10.0%
600061 8
 
8.0%
600092 8
 
8.0%
600051 7
 
7.0%
600110 6
 
6.0%
600031 6
 
6.0%
600032 4
 
4.0%
600808 4
 
4.0%
600811 3
 
3.0%
600025 3
 
3.0%
Other values (27) 40
40.0%
ValueCountFrequency (%)
600011 1
 
1.0%
600016 1
 
1.0%
600017 1
 
1.0%
600024 2
 
2.0%
600025 3
 
3.0%
600031 6
6.0%
600032 4
 
4.0%
600042 10
10.0%
600043 2
 
2.0%
600044 3
 
3.0%
ValueCountFrequency (%)
619901 1
 
1.0%
612842 1
 
1.0%
600819 1
 
1.0%
600818 2
2.0%
600816 1
 
1.0%
600815 1
 
1.0%
600814 1
 
1.0%
600813 1
 
1.0%
600811 3
3.0%
600810 2
2.0%

lnm_adres
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:17:58.801506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length33
Mean length23.5
Min length18

Characters and Unicode

Total characters2350
Distinct characters74
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)90.0%

Sample

1st row부산광역시 중구 부평동1가 31-1번지
2nd row부산광역시 기장군 기장읍 교리 324-16번지
3rd row부산광역시 중구 광복동1가 10번지
4th row부산광역시 중구 중앙동1가 22-19번지
5th row부산광역시 중구 창선동1가 38-6번지
ValueCountFrequency (%)
부산광역시 100
22.1%
중구 97
21.4%
영주동 12
 
2.6%
2층 11
 
2.4%
남포동2가 10
 
2.2%
대청동2가 8
 
1.8%
신창동1가 8
 
1.8%
창선동1가 7
 
1.5%
3층 7
 
1.5%
1층 6
 
1.3%
Other values (138) 187
41.3%
2023-12-10T19:17:59.289756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
357
 
15.2%
광 116
 
4.9%
2 113
 
4.8%
부 111
 
4.7%
1 108
 
4.6%
동 105
 
4.5%
지 105
 
4.5%
중 104
 
4.4%
번 100
 
4.3%
산 100
 
4.3%
Other values (64) 1031
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1378
58.6%
Decimal Number 454
 
19.3%
Space Separator 357
 
15.2%
Dash Punctuation 95
 
4.0%
Close Punctuation 28
 
1.2%
Open Punctuation 28
 
1.2%
Other Punctuation 10
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 116
 
8.4%
부 111
 
8.1%
동 105
 
7.6%
지 105
 
7.6%
중 104
 
7.5%
번 100
 
7.3%
산 100
 
7.3%
시 100
 
7.3%
역 100
 
7.3%
구 98
 
7.1%
Other values (49) 339
24.6%
Decimal Number
ValueCountFrequency (%)
2 113
24.9%
1 108
23.8%
3 56
12.3%
4 39
 
8.6%
5 30
 
6.6%
9 25
 
5.5%
6 21
 
4.6%
0 21
 
4.6%
7 21
 
4.6%
8 20
 
4.4%
Space Separator
ValueCountFrequency (%)
357
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1378
58.6%
Common 972
41.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 116
 
8.4%
부 111
 
8.1%
동 105
 
7.6%
지 105
 
7.6%
중 104
 
7.5%
번 100
 
7.3%
산 100
 
7.3%
시 100
 
7.3%
역 100
 
7.3%
구 98
 
7.1%
Other values (49) 339
24.6%
Common
ValueCountFrequency (%)
357
36.7%
2 113
 
11.6%
1 108
 
11.1%
- 95
 
9.8%
3 56
 
5.8%
4 39
 
4.0%
5 30
 
3.1%
) 28
 
2.9%
( 28
 
2.9%
9 25
 
2.6%
Other values (5) 93
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1378
58.6%
ASCII 972
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
357
36.7%
2 113
 
11.6%
1 108
 
11.1%
- 95
 
9.8%
3 56
 
5.8%
4 39
 
4.0%
5 30
 
3.1%
) 28
 
2.9%
( 28
 
2.9%
9 25
 
2.6%
Other values (5) 93
 
9.6%
Hangul
ValueCountFrequency (%)
광 116
 
8.4%
부 111
 
8.1%
동 105
 
7.6%
지 105
 
7.6%
중 104
 
7.5%
번 100
 
7.3%
산 100
 
7.3%
시 100
 
7.3%
역 100
 
7.3%
구 98
 
7.1%
Other values (49) 339
24.6%

rn_zip
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)57.6%
Missing67
Missing (%)67.0%
Infinite0
Infinite (%)0.0%
Mean48749.97
Minimum46023
Maximum48977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:17:59.479002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46023
5-th percentile47289.4
Q148946
median48948
Q348954
95-th percentile48976.4
Maximum48977
Range2954
Interquartile range (IQR)8

Descriptive statistics

Standard deviation714.29075
Coefficient of variation (CV)0.014652127
Kurtosis12.354448
Mean48749.97
Median Absolute Deviation (MAD)6
Skewness-3.6346291
Sum1608749
Variance510211.28
MonotonicityNot monotonic
2023-12-10T19:17:59.635434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
48947 6
 
6.0%
48948 2
 
2.0%
48954 2
 
2.0%
48977 2
 
2.0%
48953 2
 
2.0%
48946 2
 
2.0%
48959 2
 
2.0%
48976 2
 
2.0%
48949 2
 
2.0%
48952 2
 
2.0%
Other values (9) 9
 
9.0%
(Missing) 67
67.0%
ValueCountFrequency (%)
46023 1
 
1.0%
46057 1
 
1.0%
48111 1
 
1.0%
48907 1
 
1.0%
48932 1
 
1.0%
48933 1
 
1.0%
48938 1
 
1.0%
48946 2
 
2.0%
48947 6
6.0%
48948 2
 
2.0%
ValueCountFrequency (%)
48977 2
2.0%
48976 2
2.0%
48971 1
1.0%
48967 1
1.0%
48959 2
2.0%
48954 2
2.0%
48953 2
2.0%
48952 2
2.0%
48949 2
2.0%
48948 2
2.0%

rdnmadr
Text

MISSING 

Distinct36
Distinct (%)97.3%
Missing63
Missing (%)63.0%
Memory size932.0 B
2023-12-10T19:18:00.035850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length36
Mean length29.216216
Min length23

Characters and Unicode

Total characters1081
Distinct characters62
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)94.6%

Sample

1st row부산광역시 기장군 기장읍 차성로 405, 1층
2nd row부산광역시 기장군 정관읍 구연1로 11-4, 1층
3rd row부산광역시 중구 중구로40번길 20-2, 2층 (신창동1가)
4th row부산광역시 중구 광복로 63-2, 2층 (광복동2가)
5th row부산광역시 중구 구덕로 32, 1층 (남포동2가)
ValueCountFrequency (%)
부산광역시 37
 
16.7%
중구 34
 
15.4%
2층 11
 
5.0%
1층 8
 
3.6%
3층 7
 
3.2%
대청동2가 4
 
1.8%
남포동2가 4
 
1.8%
대청로 4
 
1.8%
광복동2가 4
 
1.8%
광복로 4
 
1.8%
Other values (80) 104
47.1%
2023-12-10T19:18:00.706212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
184
 
17.0%
광 58
 
5.4%
1 46
 
4.3%
중 42
 
3.9%
2 42
 
3.9%
부 41
 
3.8%
구 41
 
3.8%
동 40
 
3.7%
역 37
 
3.4%
시 37
 
3.4%
Other values (52) 513
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 592
54.8%
Decimal Number 187
 
17.3%
Space Separator 184
 
17.0%
Close Punctuation 35
 
3.2%
Open Punctuation 35
 
3.2%
Other Punctuation 33
 
3.1%
Dash Punctuation 14
 
1.3%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 58
 
9.8%
중 42
 
7.1%
부 41
 
6.9%
구 41
 
6.9%
동 40
 
6.8%
역 37
 
6.2%
시 37
 
6.2%
산 37
 
6.2%
가 33
 
5.6%
로 32
 
5.4%
Other values (36) 194
32.8%
Decimal Number
ValueCountFrequency (%)
1 46
24.6%
2 42
22.5%
3 29
15.5%
4 22
11.8%
0 10
 
5.3%
9 10
 
5.3%
7 9
 
4.8%
6 8
 
4.3%
8 6
 
3.2%
5 5
 
2.7%
Space Separator
ValueCountFrequency (%)
184
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Other Punctuation
ValueCountFrequency (%)
, 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 592
54.8%
Common 488
45.1%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 58
 
9.8%
중 42
 
7.1%
부 41
 
6.9%
구 41
 
6.9%
동 40
 
6.8%
역 37
 
6.2%
시 37
 
6.2%
산 37
 
6.2%
가 33
 
5.6%
로 32
 
5.4%
Other values (36) 194
32.8%
Common
ValueCountFrequency (%)
184
37.7%
1 46
 
9.4%
2 42
 
8.6%
) 35
 
7.2%
( 35
 
7.2%
, 33
 
6.8%
3 29
 
5.9%
4 22
 
4.5%
- 14
 
2.9%
0 10
 
2.0%
Other values (5) 38
 
7.8%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 592
54.8%
ASCII 489
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
184
37.6%
1 46
 
9.4%
2 42
 
8.6%
) 35
 
7.2%
( 35
 
7.2%
, 33
 
6.7%
3 29
 
5.9%
4 22
 
4.5%
- 14
 
2.9%
0 10
 
2.0%
Other values (6) 39
 
8.0%
Hangul
ValueCountFrequency (%)
광 58
 
9.8%
중 42
 
7.1%
부 41
 
6.9%
구 41
 
6.9%
동 40
 
6.8%
역 37
 
6.2%
시 37
 
6.2%
산 37
 
6.2%
가 33
 
5.6%
로 32
 
5.4%
Other values (36) 194
32.8%

person_prmisn_de
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

clsbiz_de
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

engl_sttus
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
폐업
98 
영업/정상
 
2

Length

Max length5
Median length2
Mean length2.06
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row영업/정상
3rd row폐업
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 98
98.0%
영업/정상 2
 
2.0%

Length

2023-12-10T19:18:00.922612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:18:01.051630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 98
98.0%
영업/정상 2
 
2.0%

detail_engl_sttus
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
폐업
98 
영업
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row영업
3rd row폐업
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 98
98.0%
영업 2
 
2.0%

Length

2023-12-10T19:18:01.190731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:18:01.320659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 98
98.0%
영업 2
 
2.0%

Sample

skeyxcntsydntslast_updt_debizcnd_se_nmtelnobuld_posesn_se_nmbuld_ground_floor_cobuld_undgrnd_floor_comale_enfsn_copyrxia_room_atuse_end_ground_flooruse_end_undgrnd_flooruse_begin_ground_flooruse_begin_undgrnd_floorfemale_enfsn_cosnitat_biznd_nmchair_cobedd_cosfrnd_codesfrnd_code_nmperson_prmisn_nosvc_iddata_updt_sedata_updt_debplc_nmlnm_ziplnm_adresrn_ziprdnmadrperson_prmisn_declsbiz_deengl_sttusdetail_engl_sttus
01384720.735033179776.50764220110615104600피부미용업512553755임대41<NA>-2020<NA>미용업(피부)053250000부산광역시 중구3250000-212-2010-0000105_18_01_PI2018-08-31 11:59:59.0킹덤스킨&바디600804부산광역시 중구 부평동1가 31-1번지<NA><NA><NA><NA>폐업폐업
124357401476.291625197183.07826120181224093008일반미용업515235757<NA>00<NA>-1010<NA>미용업(일반)303400000부산광역시 기장군3400000-211-2018-0001705_18_01_PU2018-12-26 02:40:00.0오니헤어619901부산광역시 기장군 기장읍 교리 324-16번지46057부산광역시 기장군 기장읍 차성로 405, 1층<NA><NA>영업/정상영업
23385365.029031179625.58794620031210000000일반미용업512457072임대3<NA><NA>-3<NA>3<NA><NA>미용업13<NA>3250000부산광역시 중구3250000-204-1990-0032805_18_01_PI2018-08-31 11:59:59.0한미헤어클리닉600031부산광역시 중구 광복동1가 10번지<NA><NA><NA><NA>폐업폐업
34385565.311665179885.32923320031210000000일반미용업2578555임대2<NA><NA>-<NA><NA>1<NA><NA>미용업3<NA>3250000부산광역시 중구3250000-204-2003-0001305_18_01_PI2018-08-31 11:59:59.0카라헤어600011부산광역시 중구 중앙동1가 22-19번지<NA><NA><NA><NA>폐업폐업
45385101.871082179668.14233520031224000000일반미용업2546515임대4<NA><NA>-3<NA>3<NA><NA>미용업7<NA>3250000부산광역시 중구3250000-204-1990-0032305_18_01_PI2018-08-31 11:59:59.0인어600051부산광역시 중구 창선동1가 38-6번지<NA><NA><NA><NA>폐업폐업
56385227.619169179563.39927320030919000000일반미용업2551441임대<NA><NA><NA>-<NA><NA><NA><NA><NA>미용업3<NA>3250000부산광역시 중구3250000-204-2002-0008505_18_01_PI2018-08-31 11:59:59.0올헤어클리닉 미용실600044부산광역시 중구 남포동4가 1-8번지<NA><NA><NA><NA>폐업폐업
67384972.157957179653.43092620040109000000일반미용업<NA>임대5<NA><NA>-3<NA>1<NA><NA>미용업23<NA>3250000부산광역시 중구3250000-204-2003-0001905_18_01_PI2018-08-31 11:59:59.0서진헤어600043부산광역시 중구 남포동3가 5번지<NA><NA><NA><NA>폐업폐업
724358398421.273686204534.51923720181226193531피부미용업<NA><NA>00<NA>-1010<NA>미용업(피부)023400000부산광역시 기장군3400000-212-2018-0001205_18_01_PU2018-12-28 02:40:00.0리뷰티(Lee Beauty)<NA>부산광역시 기장군 정관읍 매학리 766-3번지46023부산광역시 기장군 정관읍 구연1로 11-4, 1층<NA><NA>영업/정상영업
89385037.963018179957.73701320030826000000일반미용업2462279임대41<NA>-2<NA><NA><NA><NA>미용업5<NA>3250000부산광역시 중구3250000-204-2003-0001005_18_01_PI2018-08-31 11:59:59.0압구정600061부산광역시 중구 신창동1가 28번지<NA><NA><NA><NA>폐업폐업
910384645.673445179574.21156820030827000000일반미용업<NA>임대4<NA><NA>-<NA><NA>1<NA><NA>미용업3<NA>3250000부산광역시 중구3250000-204-2003-0001405_18_01_PI2018-08-31 11:59:59.0야시만들기600807부산광역시 중구 부평동2가 42-3번지<NA><NA><NA><NA>폐업폐업
skeyxcntsydntslast_updt_debizcnd_se_nmtelnobuld_posesn_se_nmbuld_ground_floor_cobuld_undgrnd_floor_comale_enfsn_copyrxia_room_atuse_end_ground_flooruse_end_undgrnd_flooruse_begin_ground_flooruse_begin_undgrnd_floorfemale_enfsn_cosnitat_biznd_nmchair_cobedd_cosfrnd_codesfrnd_code_nmperson_prmisn_nosvc_iddata_updt_sedata_updt_debplc_nmlnm_ziplnm_adresrn_ziprdnmadrperson_prmisn_declsbiz_deengl_sttusdetail_engl_sttus
9091384973.707671179705.75578320200131135903네일아트업512480797임대51<NA>-2010<NA>미용업(일반)703250000부산광역시 중구3250000-211-2012-0001005_18_01_PU2020-02-02 02:40:00.0네일카운티600818부산광역시 중구 창선동2가 7-1번지 (1,2층)48946부산광역시 중구 광복로39번길 3, 1-2층 (창선동2가)<NA><NA>폐업폐업
9192385126.535735179725.52067620200331092018네일아트업<NA><NA>00<NA>-<NA><NA><NA><NA><NA>미용업(종합)213250000부산광역시 중구3250000-213-2017-0000205_18_01_PU2020-04-02 02:40:00.0살롱드블룸600042부산광역시 중구 남포동2가 12-1번지48953부산광역시 중구 구덕로34번길 7, 4층 (남포동2가)<NA><NA>폐업폐업
9293384679.630846180117.13778520180503144352기타<NA><NA>40<NA>-2<NA>2<NA><NA>미용업(종합)303250000부산광역시 중구3250000-213-2017-0000305_18_01_PI2018-08-31 11:59:59.0다듬다 뷰티600803부산광역시 중구 보수동1가 125-7번지48967부산광역시 중구 대청로 57 (보수동1가)<NA><NA>폐업폐업
9394385290.402746179589.94586320190610111703피부미용업512478004<NA>30<NA>-3020<NA>미용업(피부), 미용업(손톱ㆍ발톱)063250000부산광역시 중구3250000-218-2015-0000105_18_01_PU2019-06-12 02:40:00.0뷰티싸롱 결600042부산광역시 중구 남포동2가 9-1번지48954부산광역시 중구 남포길 37 (남포동2가)<NA><NA>폐업폐업
9495385103.195299180361.88311620181018142723메이크업업<NA>임대111<NA>-<NA><NA>11<NA><NA>미용업(종합)113250000부산광역시 중구3250000-213-2017-0000505_18_01_PU2018-10-20 02:36:40.0뷰티트루베600800부산광역시 중구 대청동4가 34-2번지48971부산광역시 중구 중구로97번길 3, 11층 1106호 (대청동4가)<NA><NA>폐업폐업
9596384608.762918180072.68501720190530174228피부미용업512551044임대51<NA>-3030<NA>미용업(피부)053250000부산광역시 중구3250000-212-2009-0001005_18_01_PU2019-06-01 02:40:00.0정코스메틱600806부산광역시 중구 부평동2가 80-3번지 (3층)48977부산광역시 중구 흑교로 38, 3층 (부평동2가)<NA><NA>폐업폐업
9697385307.401516179597.82371420130212135759일반미용업512479522임대40<NA>-2010<NA>미용업1203250000부산광역시 중구3250000-204-1998-0044105_18_01_PI2018-08-31 11:59:59.0민요한헤어보그600032부산광역시 중구 광복동2가 32-4번지 (1,2층)48954부산광역시 중구 광복로 76, 1,2층 (광복동2가)<NA><NA>폐업폐업
9798385077.985605179999.04665320161230114448네일아트업512414182<NA>61<NA>-1010<NA>미용업703250000부산광역시 중구3250000-204-2010-0000205_18_01_PI2018-08-31 11:59:59.0엘프네일600061부산광역시 중구 신창동1가 2-1번지 (1층)48948부산광역시 중구 광복중앙로 31, 1층 (신창동1가)<NA><NA>폐업폐업
9899385632.365527181146.09357720101228155017일반미용업516273530임대61<NA>-1<NA>1<NA><NA>미용업303250000부산광역시 중구3250000-204-2010-0000405_18_01_PI2018-08-31 11:59:59.0스타일찾기600811부산광역시 중구 영주동 26-1번지<NA><NA><NA><NA>폐업폐업
99100385239.835341179685.85650320200131170529일반미용업513232326<NA>80<NA>-2<NA>2<NA><NA>미용업(일반)1903250000부산광역시 중구3250000-211-2017-0000405_18_01_PU2020-02-02 02:40:00.0라브리지600032부산광역시 중구 광복동2가 2-98번지 2층48952부산광역시 중구 광복로 63-2, 2층 (광복동2가)<NA><NA>폐업폐업