Overview

Dataset statistics

Number of variables26
Number of observations10000
Missing cells6871
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory231.0 B

Variable types

Numeric15
DateTime1
Categorical6
Text4

Alerts

parkng_at is highly imbalanced (95.0%)Imbalance
card_at is highly imbalanced (98.6%)Imbalance
rm has 6631 (66.3%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2023-10-09 07:51:52.434483
Analysis finished2023-10-09 07:51:54.719969
Duration2.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3576867.8
Minimum3556058
Maximum3597817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:51:54.906237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3556058
5-th percentile3558084.5
Q13566363.2
median3576924.5
Q33587211
95-th percentile3595862.1
Maximum3597817
Range41759
Interquartile range (IQR)20847.75

Descriptive statistics

Standard deviation12092.354
Coefficient of variation (CV)0.0033807103
Kurtosis-1.1943169
Mean3576867.8
Median Absolute Deviation (MAD)10416
Skewness0.012555762
Sum3.5768678 × 1010
Variance1.4622502 × 108
MonotonicityNot monotonic
2023-10-09T16:51:55.290279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3593367 1
 
< 0.1%
3596834 1
 
< 0.1%
3571512 1
 
< 0.1%
3578974 1
 
< 0.1%
3587035 1
 
< 0.1%
3584194 1
 
< 0.1%
3558718 1
 
< 0.1%
3583467 1
 
< 0.1%
3586890 1
 
< 0.1%
3586493 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
3556058 1
< 0.1%
3556059 1
< 0.1%
3556062 1
< 0.1%
3556077 1
< 0.1%
3556088 1
< 0.1%
3556091 1
< 0.1%
3556109 1
< 0.1%
3556110 1
< 0.1%
3556111 1
< 0.1%
3556113 1
< 0.1%
ValueCountFrequency (%)
3597817 1
< 0.1%
3597815 1
< 0.1%
3597805 1
< 0.1%
3597798 1
< 0.1%
3597792 1
< 0.1%
3597791 1
< 0.1%
3597789 1
< 0.1%
3597787 1
< 0.1%
3597781 1
< 0.1%
3597776 1
< 0.1%

ccode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.379
Minimum77
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:51:55.595114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile78
Q183
median91
Q3101
95-th percentile158
Maximum159
Range82
Interquartile range (IQR)18

Descriptive statistics

Standard deviation23.778219
Coefficient of variation (CV)0.24170015
Kurtosis1.9110436
Mean98.379
Median Absolute Deviation (MAD)9
Skewness1.7649299
Sum983790
Variance565.4037
MonotonicityNot monotonic
2023-10-09T16:51:55.914128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
158 396
 
4.0%
81 394
 
3.9%
77 394
 
3.9%
84 390
 
3.9%
79 383
 
3.8%
80 381
 
3.8%
101 379
 
3.8%
78 378
 
3.8%
89 377
 
3.8%
106 373
 
3.7%
Other values (22) 6155
61.6%
ValueCountFrequency (%)
77 394
3.9%
78 378
3.8%
79 383
3.8%
80 381
3.8%
81 394
3.9%
82 366
3.7%
83 367
3.7%
84 390
3.9%
85 261
2.6%
86 249
2.5%
ValueCountFrequency (%)
159 349
3.5%
158 396
4.0%
157 236
2.4%
156 255
2.5%
106 373
3.7%
105 252
2.5%
104 265
2.6%
103 249
2.5%
101 379
3.8%
100 260
2.6%

pcode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.2554
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:51:56.240321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile77
Q182
median90
Q3100
95-th percentile156
Maximum157
Range81
Interquartile range (IQR)18

Descriptive statistics

Standard deviation23.470229
Coefficient of variation (CV)0.24132572
Kurtosis1.8789102
Mean97.2554
Median Absolute Deviation (MAD)9
Skewness1.7519019
Sum972554
Variance550.85166
MonotonicityNot monotonic
2023-10-09T16:51:56.580383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
156 396
 
4.0%
80 394
 
3.9%
76 394
 
3.9%
83 390
 
3.9%
78 383
 
3.8%
79 381
 
3.8%
100 379
 
3.8%
77 378
 
3.8%
88 377
 
3.8%
105 373
 
3.7%
Other values (22) 6155
61.6%
ValueCountFrequency (%)
76 394
3.9%
77 378
3.8%
78 383
3.8%
79 381
3.8%
80 394
3.9%
81 366
3.7%
82 367
3.7%
83 390
3.9%
84 261
2.6%
85 249
2.5%
ValueCountFrequency (%)
157 349
3.5%
156 396
4.0%
155 236
2.4%
154 255
2.5%
105 373
3.7%
104 252
2.5%
103 265
2.6%
102 249
2.5%
100 379
3.8%
99 260
2.6%

bssh_no
Real number (ℝ)

Distinct54
Distinct (%)0.5%
Missing13
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean413.42175
Minimum14
Maximum3198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:51:56.985607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile20
Q129
median39
Q358
95-th percentile2625
Maximum3198
Range3184
Interquartile range (IQR)29

Descriptive statistics

Standard deviation928.84023
Coefficient of variation (CV)2.2467135
Kurtosis2.492741
Mean413.42175
Median Absolute Deviation (MAD)11
Skewness2.0993902
Sum4128843
Variance862744.17
MonotonicityNot monotonic
2023-10-09T16:51:57.399259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 347
 
3.5%
26 341
 
3.4%
31 339
 
3.4%
2524 332
 
3.3%
25 331
 
3.3%
3028 328
 
3.3%
23 328
 
3.3%
45 324
 
3.2%
43 324
 
3.2%
37 320
 
3.2%
Other values (44) 6673
66.7%
ValueCountFrequency (%)
14 64
 
0.6%
17 66
 
0.7%
19 301
3.0%
20 318
3.2%
22 319
3.2%
23 328
3.3%
25 331
3.3%
26 341
3.4%
28 347
3.5%
29 305
3.0%
ValueCountFrequency (%)
3198 2
 
< 0.1%
3193 3
 
< 0.1%
3177 62
 
0.6%
3028 328
3.3%
2625 294
2.9%
2562 316
3.2%
2524 332
3.3%
2349 74
 
0.7%
87 58
 
0.6%
85 68
 
0.7%

search_no
Real number (ℝ)

Distinct1541
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529121.82
Minimum482024
Maximum560618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:51:57.714575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum482024
5-th percentile483006
Q1525715
median530024
Q3553659.5
95-th percentile556853
Maximum560618
Range78594
Interquartile range (IQR)27944.5

Descriptive statistics

Standard deviation25904.785
Coefficient of variation (CV)0.048958074
Kurtosis-0.74393756
Mean529121.82
Median Absolute Deviation (MAD)22786
Skewness-0.73319353
Sum5.2912182 × 109
Variance6.7105791 × 108
MonotonicityNot monotonic
2023-10-09T16:51:58.039428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
556995 18
 
0.2%
485465 16
 
0.2%
525751 15
 
0.1%
529978 15
 
0.1%
527278 14
 
0.1%
553659 14
 
0.1%
529020 14
 
0.1%
551161 14
 
0.1%
531542 13
 
0.1%
485523 13
 
0.1%
Other values (1531) 9854
98.5%
ValueCountFrequency (%)
482024 2
 
< 0.1%
482025 8
0.1%
482026 4
 
< 0.1%
482850 9
0.1%
482851 6
0.1%
482852 9
0.1%
482853 2
 
< 0.1%
482854 8
0.1%
482856 9
0.1%
482913 10
0.1%
ValueCountFrequency (%)
560618 7
0.1%
560617 7
0.1%
560616 3
 
< 0.1%
560615 2
 
< 0.1%
560614 4
 
< 0.1%
560613 1
 
< 0.1%
560612 5
0.1%
557643 2
 
< 0.1%
557642 6
0.1%
557641 10
0.1%

prices_no
Real number (ℝ)

Distinct1541
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529121.82
Minimum482024
Maximum560618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:51:58.374594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum482024
5-th percentile483006
Q1525715
median530024
Q3553659.5
95-th percentile556853
Maximum560618
Range78594
Interquartile range (IQR)27944.5

Descriptive statistics

Standard deviation25904.785
Coefficient of variation (CV)0.048958074
Kurtosis-0.74393756
Mean529121.82
Median Absolute Deviation (MAD)22786
Skewness-0.73319353
Sum5.2912182 × 109
Variance6.7105791 × 108
MonotonicityNot monotonic
2023-10-09T16:51:58.707052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
556995 18
 
0.2%
485465 16
 
0.2%
525751 15
 
0.1%
529978 15
 
0.1%
527278 14
 
0.1%
553659 14
 
0.1%
529020 14
 
0.1%
551161 14
 
0.1%
531542 13
 
0.1%
485523 13
 
0.1%
Other values (1531) 9854
98.5%
ValueCountFrequency (%)
482024 2
 
< 0.1%
482025 8
0.1%
482026 4
 
< 0.1%
482850 9
0.1%
482851 6
0.1%
482852 9
0.1%
482853 2
 
< 0.1%
482854 8
0.1%
482856 9
0.1%
482913 10
0.1%
ValueCountFrequency (%)
560618 7
0.1%
560617 7
0.1%
560616 3
 
< 0.1%
560615 2
 
< 0.1%
560614 4
 
< 0.1%
560613 1
 
< 0.1%
560612 5
0.1%
557643 2
 
< 0.1%
557642 6
0.1%
557641 10
0.1%

prdlst
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.2554
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:51:59.017571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile77
Q182
median90
Q3100
95-th percentile156
Maximum157
Range81
Interquartile range (IQR)18

Descriptive statistics

Standard deviation23.470229
Coefficient of variation (CV)0.24132572
Kurtosis1.8789102
Mean97.2554
Median Absolute Deviation (MAD)9
Skewness1.7519019
Sum972554
Variance550.85166
MonotonicityNot monotonic
2023-10-09T16:51:59.531065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
156 396
 
4.0%
80 394
 
3.9%
76 394
 
3.9%
83 390
 
3.9%
78 383
 
3.8%
79 381
 
3.8%
100 379
 
3.8%
77 378
 
3.8%
88 377
 
3.8%
105 373
 
3.7%
Other values (22) 6155
61.6%
ValueCountFrequency (%)
76 394
3.9%
77 378
3.8%
78 383
3.8%
79 381
3.8%
80 394
3.9%
81 366
3.7%
82 367
3.7%
83 390
3.9%
84 261
2.6%
85 249
2.5%
ValueCountFrequency (%)
157 349
3.5%
156 396
4.0%
155 236
2.4%
154 255
2.5%
105 373
3.7%
104 252
2.5%
103 265
2.6%
102 249
2.5%
100 379
3.8%
99 260
2.6%

cl_no
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing13
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean408.9996
Minimum405
Maximum419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:51:59.827817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum405
5-th percentile405
Q1406
median407
Q3411
95-th percentile417
Maximum419
Range14
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.3494925
Coefficient of variation (CV)0.010634466
Kurtosis-0.54913727
Mean408.9996
Median Absolute Deviation (MAD)1
Skewness1.0302545
Sum4084679
Variance18.918085
MonotonicityNot monotonic
2023-10-09T16:52:00.179575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
406 2577
25.8%
407 1882
18.8%
417 1799
18.0%
405 1546
15.5%
410 643
 
6.4%
408 640
 
6.4%
411 607
 
6.1%
416 288
 
2.9%
419 5
 
0.1%
(Missing) 13
 
0.1%
ValueCountFrequency (%)
405 1546
15.5%
406 2577
25.8%
407 1882
18.8%
408 640
 
6.4%
410 643
 
6.4%
411 607
 
6.1%
416 288
 
2.9%
417 1799
18.0%
419 5
 
0.1%
ValueCountFrequency (%)
419 5
 
0.1%
417 1799
18.0%
416 288
 
2.9%
411 607
 
6.1%
410 643
 
6.4%
408 640
 
6.4%
407 1882
18.8%
406 2577
25.8%
405 1546
15.5%

examin_de
Date

Distinct73
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-08-29 00:00:00
Maximum2023-02-23 00:00:00
2023-10-09T16:52:00.509911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-09T16:52:00.924851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

pum_cd
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean459.5519
Minimum456
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:52:01.268865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum456
5-th percentile456
Q1456
median459
Q3463
95-th percentile464
Maximum464
Range8
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.0357253
Coefficient of variation (CV)0.0066058377
Kurtosis-1.4132827
Mean459.5519
Median Absolute Deviation (MAD)3
Skewness0.22072607
Sum4595519
Variance9.215628
MonotonicityNot monotonic
2023-10-09T16:52:01.571237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
456 3032
30.3%
464 1899
19.0%
461 1775
17.8%
458 1410
14.1%
459 1139
 
11.4%
463 745
 
7.4%
ValueCountFrequency (%)
456 3032
30.3%
458 1410
14.1%
459 1139
 
11.4%
461 1775
17.8%
463 745
 
7.4%
464 1899
19.0%
ValueCountFrequency (%)
464 1899
19.0%
463 745
 
7.4%
461 1775
17.8%
459 1139
 
11.4%
458 1410
14.1%
456 3032
30.3%

pum_nm
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농산물
3032 
식료품
1899 
주류및음료,차
1775 
축산물
1410 
수산물
1139 

Length

Max length7
Median length3
Mean length3.6355
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농산물
2nd row식료품
3rd row축산물
4th row식료품
5th row식료품

Common Values

ValueCountFrequency (%)
농산물 3032
30.3%
식료품 1899
19.0%
주류및음료,차 1775
17.8%
축산물 1410
14.1%
수산물 1139
 
11.4%
세제 745
 
7.4%

Length

2023-10-09T16:52:01.822824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T16:52:02.068848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농산물 3032
30.3%
식료품 1899
19.0%
주류및음료,차 1775
17.8%
축산물 1410
14.1%
수산물 1139
 
11.4%
세제 745
 
7.4%

gugun_cd
Real number (ℝ)

Distinct49
Distinct (%)0.5%
Missing15
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean184.72899
Minimum21
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:52:02.389235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile31
Q195
median186
Q3261
95-th percentile365
Maximum374
Range353
Interquartile range (IQR)166

Descriptive statistics

Standard deviation103.13537
Coefficient of variation (CV)0.55830633
Kurtosis-0.96481525
Mean184.72899
Median Absolute Deviation (MAD)79
Skewness0.19722211
Sum1844519
Variance10636.904
MonotonicityNot monotonic
2023-10-09T16:52:02.780129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
189 627
 
6.3%
111 376
 
3.8%
374 376
 
3.8%
227 372
 
3.7%
145 347
 
3.5%
92 341
 
3.4%
293 339
 
3.4%
174 332
 
3.3%
52 331
 
3.3%
257 328
 
3.3%
Other values (39) 6216
62.2%
ValueCountFrequency (%)
21 307
3.1%
27 77
 
0.8%
31 328
3.3%
33 61
 
0.6%
48 301
3.0%
52 331
3.3%
53 75
 
0.8%
64 320
3.2%
66 68
 
0.7%
80 58
 
0.6%
ValueCountFrequency (%)
374 376
3.8%
373 3
 
< 0.1%
369 78
 
0.8%
365 294
2.9%
360 313
3.1%
333 71
 
0.7%
316 275
2.8%
314 72
 
0.7%
293 339
3.4%
275 68
 
0.7%

gugun_nm
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
해운대구
1064 
부산진구
810 
남구
794 
북구
788 
수영구
783 
Other values (12)
5761 

Length

Max length4
Median length3
Mean length2.8977
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사하구
2nd row부산진구
3rd row영도구
4th row북구
5th row부산진구

Common Values

ValueCountFrequency (%)
해운대구 1064
10.6%
부산진구 810
 
8.1%
남구 794
 
7.9%
북구 788
 
7.9%
수영구 783
 
7.8%
금정구 773
 
7.7%
동래구 769
 
7.7%
사상구 753
 
7.5%
사하구 735
 
7.3%
동구 470
 
4.7%
Other values (7) 2261
22.6%

Length

2023-10-09T16:52:03.238748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해운대구 1064
10.6%
부산진구 810
 
8.1%
남구 794
 
7.9%
북구 788
 
7.9%
수영구 783
 
7.8%
금정구 773
 
7.7%
동래구 769
 
7.7%
사상구 753
 
7.5%
사하구 735
 
7.3%
동구 470
 
4.7%
Other values (7) 2261
22.6%

unit
Real number (ℝ)

Distinct364
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean660.53523
Minimum0.7
Maximum9000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:52:03.579835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1
Q15
median419
Q3840
95-th percentile2100
Maximum9000
Range8999.3
Interquartile range (IQR)835

Descriptive statistics

Standard deviation964.94821
Coefficient of variation (CV)1.460858
Kurtosis13.335558
Mean660.53523
Median Absolute Deviation (MAD)415
Skewness3.0889246
Sum6605352.3
Variance931125.05
MonotonicityNot monotonic
2023-10-09T16:52:04.029108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500.0 1417
 
14.2%
100.0 927
 
9.3%
1000.0 690
 
6.9%
1.0 687
 
6.9%
2000.0 601
 
6.0%
1.8 497
 
5.0%
600.0 455
 
4.5%
20.0 373
 
3.7%
1.5 265
 
2.6%
320.0 260
 
2.6%
Other values (354) 3828
38.3%
ValueCountFrequency (%)
0.7 1
 
< 0.1%
0.8 6
 
0.1%
0.86 1
 
< 0.1%
0.9 201
 
2.0%
1.0 687
6.9%
1.1 10
 
0.1%
1.18 1
 
< 0.1%
1.2 66
 
0.7%
1.4 112
 
1.1%
1.5 265
 
2.6%
ValueCountFrequency (%)
9000.0 4
 
< 0.1%
7500.0 2
 
< 0.1%
6400.0 1
 
< 0.1%
6000.0 125
1.2%
5000.0 6
 
0.1%
4500.0 1
 
< 0.1%
4450.0 1
 
< 0.1%
4000.0 1
 
< 0.1%
3744.0 1
 
< 0.1%
3690.0 1
 
< 0.1%

unitprice
Real number (ℝ)

Distinct2047
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10732.755
Minimum0
Maximum100000
Zeros80
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:52:04.384408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile873
Q12641.75
median5000
Q310974
95-th percentile49900
Maximum100000
Range100000
Interquartile range (IQR)8332.25

Descriptive statistics

Standard deviation14693.422
Coefficient of variation (CV)1.3690261
Kurtosis5.7047117
Mean10732.755
Median Absolute Deviation (MAD)3020
Skewness2.4494038
Sum1.0732755 × 108
Variance2.1589664 × 108
MonotonicityNot monotonic
2023-10-09T16:52:04.732284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 128
 
1.3%
3000 127
 
1.3%
2500 120
 
1.2%
4000 118
 
1.2%
3500 114
 
1.1%
5194 107
 
1.1%
2000 106
 
1.1%
1980 100
 
1.0%
6000 92
 
0.9%
820 91
 
0.9%
Other values (2037) 8897
89.0%
ValueCountFrequency (%)
0 80
0.8%
590 1
 
< 0.1%
642 1
 
< 0.1%
666 1
 
< 0.1%
676 50
0.5%
678 2
 
< 0.1%
680 9
 
0.1%
700 7
 
0.1%
730 3
 
< 0.1%
734 7
 
0.1%
ValueCountFrequency (%)
100000 2
< 0.1%
97950 1
 
< 0.1%
94450 3
< 0.1%
92950 1
 
< 0.1%
92550 1
 
< 0.1%
89950 2
< 0.1%
87950 3
< 0.1%
82500 4
< 0.1%
80000 1
 
< 0.1%
79450 2
< 0.1%

prices
Real number (ℝ)

Distinct1248
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8754.0641
Minimum0
Maximum120000
Zeros74
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:52:05.696516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile930
Q12550
median4530
Q39600
95-th percentile38779.3
Maximum120000
Range120000
Interquartile range (IQR)7050

Descriptive statistics

Standard deviation12188.6
Coefficient of variation (CV)1.3923362
Kurtosis11.872696
Mean8754.0641
Median Absolute Deviation (MAD)2684.5
Skewness3.2205066
Sum87540641
Variance1.4856197 × 108
MonotonicityNot monotonic
2023-10-09T16:52:06.039700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 178
 
1.8%
9900 176
 
1.8%
5000 158
 
1.6%
4000 152
 
1.5%
2500 145
 
1.5%
3500 130
 
1.3%
1980 123
 
1.2%
4700 120
 
1.2%
2000 117
 
1.2%
6000 104
 
1.0%
Other values (1238) 8597
86.0%
ValueCountFrequency (%)
0 74
0.7%
95 1
 
< 0.1%
109 1
 
< 0.1%
120 3
 
< 0.1%
150 1
 
< 0.1%
160 2
 
< 0.1%
180 1
 
< 0.1%
188 3
 
< 0.1%
199 1
 
< 0.1%
200 1
 
< 0.1%
ValueCountFrequency (%)
120000 4
 
< 0.1%
110000 1
 
< 0.1%
100000 2
 
< 0.1%
84200 1
 
< 0.1%
69900 4
 
< 0.1%
69000 1
 
< 0.1%
68000 2
 
< 0.1%
67900 2
 
< 0.1%
65900 3
 
< 0.1%
65000 20
0.2%

rm
Text

MISSING 

Distinct987
Distinct (%)29.3%
Missing6631
Missing (%)66.3%
Memory size156.2 KiB
2023-10-09T16:52:06.550781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length6.5262689
Min length1

Characters and Unicode

Total characters21987
Distinct characters382
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique507 ?
Unique (%)15.0%

Sample

1st row농할할인/4000원 11/4~10까지 행사
2nd row신선란
3rd row동서식물성프림
4th row황가
5th row곰표
ValueCountFrequency (%)
행사 125
 
3.1%
생물 123
 
3.1%
해동 82
 
2.1%
냉동 58
 
1.5%
곰표 45
 
1.1%
할인 43
 
1.1%
하우스밀감 37
 
0.9%
깨끗한나라 35
 
0.9%
햇배 35
 
0.9%
손질 35
 
0.9%
Other values (881) 3361
84.5%
2023-10-09T16:52:07.633363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1559
 
7.1%
1 987
 
4.5%
0 961
 
4.4%
/ 487
 
2.2%
2 396
 
1.8%
사 368
 
1.7%
생 318
 
1.4%
3 291
 
1.3%
9 288
 
1.3%
행 285
 
1.3%
Other values (372) 16047
73.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14693
66.8%
Decimal Number 3708
 
16.9%
Space Separator 1559
 
7.1%
Other Punctuation 816
 
3.7%
Lowercase Letter 391
 
1.8%
Math Symbol 325
 
1.5%
Close Punctuation 168
 
0.8%
Open Punctuation 160
 
0.7%
Uppercase Letter 160
 
0.7%
Dash Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 368
 
2.5%
생 318
 
2.2%
행 285
 
1.9%
스 276
 
1.9%
원 271
 
1.8%
한 259
 
1.8%
동 245
 
1.7%
기 243
 
1.7%
고 242
 
1.6%
인 224
 
1.5%
Other values (328) 11962
81.4%
Lowercase Letter
ValueCountFrequency (%)
g 199
50.9%
k 58
 
14.8%
l 55
 
14.1%
m 48
 
12.3%
s 10
 
2.6%
i 5
 
1.3%
p 5
 
1.3%
u 5
 
1.3%
c 1
 
0.3%
j 1
 
0.3%
Other values (4) 4
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 987
26.6%
0 961
25.9%
2 396
10.7%
3 291
 
7.8%
9 288
 
7.8%
5 264
 
7.1%
4 177
 
4.8%
8 167
 
4.5%
7 104
 
2.8%
6 73
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
L 41
25.6%
T 33
20.6%
R 24
15.0%
K 22
13.8%
G 19
11.9%
M 15
 
9.4%
P 3
 
1.9%
C 3
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/ 487
59.7%
, 131
 
16.1%
% 75
 
9.2%
* 70
 
8.6%
. 37
 
4.5%
& 16
 
2.0%
Math Symbol
ValueCountFrequency (%)
+ 171
52.6%
~ 154
47.4%
Space Separator
ValueCountFrequency (%)
1559
100.0%
Close Punctuation
ValueCountFrequency (%)
) 168
100.0%
Open Punctuation
ValueCountFrequency (%)
( 160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14687
66.8%
Common 6743
30.7%
Latin 551
 
2.5%
Han 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 368
 
2.5%
생 318
 
2.2%
행 285
 
1.9%
스 276
 
1.9%
원 271
 
1.8%
한 259
 
1.8%
동 245
 
1.7%
기 243
 
1.7%
고 242
 
1.6%
인 224
 
1.5%
Other values (327) 11956
81.4%
Common
ValueCountFrequency (%)
1559
23.1%
1 987
14.6%
0 961
14.3%
/ 487
 
7.2%
2 396
 
5.9%
3 291
 
4.3%
9 288
 
4.3%
5 264
 
3.9%
4 177
 
2.6%
+ 171
 
2.5%
Other values (12) 1162
17.2%
Latin
ValueCountFrequency (%)
g 199
36.1%
k 58
 
10.5%
l 55
 
10.0%
m 48
 
8.7%
L 41
 
7.4%
T 33
 
6.0%
R 24
 
4.4%
K 22
 
4.0%
G 19
 
3.4%
M 15
 
2.7%
Other values (12) 37
 
6.7%
Han
ValueCountFrequency (%)
入 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14680
66.8%
ASCII 7294
33.2%
Compat Jamo 7
 
< 0.1%
CJK 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1559
21.4%
1 987
13.5%
0 961
13.2%
/ 487
 
6.7%
2 396
 
5.4%
3 291
 
4.0%
9 288
 
3.9%
5 264
 
3.6%
g 199
 
2.7%
4 177
 
2.4%
Other values (34) 1685
23.1%
Hangul
ValueCountFrequency (%)
사 368
 
2.5%
생 318
 
2.2%
행 285
 
1.9%
스 276
 
1.9%
원 271
 
1.8%
한 259
 
1.8%
동 245
 
1.7%
기 243
 
1.7%
고 242
 
1.6%
인 224
 
1.5%
Other values (326) 11949
81.4%
Compat Jamo
ValueCountFrequency (%)
ㅣ 7
100.0%
CJK
ValueCountFrequency (%)
入 6
100.0%

bssh_nm
Text

Distinct54
Distinct (%)0.5%
Missing13
Missing (%)0.1%
Memory size156.2 KiB
2023-10-09T16:52:08.072516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length8.8441975
Min length4

Characters and Unicode

Total characters88327
Distinct characters115
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

Unique0 ?
Unique (%)0.0%

Sample

1st row롯데마트(사하점)
2nd row홈플러스(서면점)
3rd row홈플러스(영도점)
4th row정이있는구포시장
5th row홈플러스(서면점)
ValueCountFrequency (%)
홈플러스(서면점 347
 
3.1%
탑마트(초량점 341
 
3.1%
홈플러스(영도점 339
 
3.1%
농협하나로클럽마트(금곡점 332
 
3.0%
홈플러스(감만점 331
 
3.0%
홈플러스 328
 
3.0%
익스플러스 328
 
3.0%
광안점 328
 
3.0%
홈플러스(동래점 328
 
3.0%
메가마트(동래점 324
 
2.9%
Other values (49) 7698
69.8%
2023-10-09T16:52:08.704117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 7939
 
9.0%
( 7610
 
8.6%
) 7610
 
8.6%
트 4999
 
5.7%
마 4999
 
5.7%
스 3521
 
4.0%
러 2905
 
3.3%
플 2905
 
3.3%
홈 2577
 
2.9%
시 2035
 
2.3%
Other values (105) 41227
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71950
81.5%
Open Punctuation 7610
 
8.6%
Close Punctuation 7610
 
8.6%
Space Separator 1099
 
1.2%
Decimal Number 58
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 7939
 
11.0%
트 4999
 
6.9%
마 4999
 
6.9%
스 3521
 
4.9%
러 2905
 
4.0%
플 2905
 
4.0%
홈 2577
 
3.6%
시 2035
 
2.8%
장 1741
 
2.4%
이 1677
 
2.3%
Other values (101) 36652
50.9%
Open Punctuation
ValueCountFrequency (%)
( 7610
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7610
100.0%
Space Separator
ValueCountFrequency (%)
1099
100.0%
Decimal Number
ValueCountFrequency (%)
1 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71950
81.5%
Common 16377
 
18.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 7939
 
11.0%
트 4999
 
6.9%
마 4999
 
6.9%
스 3521
 
4.9%
러 2905
 
4.0%
플 2905
 
4.0%
홈 2577
 
3.6%
시 2035
 
2.8%
장 1741
 
2.4%
이 1677
 
2.3%
Other values (101) 36652
50.9%
Common
ValueCountFrequency (%)
( 7610
46.5%
) 7610
46.5%
1099
 
6.7%
1 58
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71950
81.5%
ASCII 16377
 
18.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 7939
 
11.0%
트 4999
 
6.9%
마 4999
 
6.9%
스 3521
 
4.9%
러 2905
 
4.0%
플 2905
 
4.0%
홈 2577
 
3.6%
시 2035
 
2.8%
장 1741
 
2.4%
이 1677
 
2.3%
Other values (101) 36652
50.9%
ASCII
ValueCountFrequency (%)
( 7610
46.5%
) 7610
46.5%
1099
 
6.7%
1 58
 
0.4%

la
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing80
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean35.164437
Minimum35.08484
Maximum35.323099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:52:08.962996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.08484
5-th percentile35.085472
Q135.11677
median35.161668
Q335.205868
95-th percentile35.250107
Maximum35.323099
Range0.2382593
Interquartile range (IQR)0.089098

Descriptive statistics

Standard deviation0.056839357
Coefficient of variation (CV)0.0016163875
Kurtosis0.0049356184
Mean35.164437
Median Absolute Deviation (MAD)0.0442
Skewness0.59866063
Sum348831.22
Variance0.0032307125
MonotonicityNot monotonic
2023-10-09T16:52:09.347674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.149452 347
 
3.5%
35.11677 341
 
3.4%
35.095905 339
 
3.4%
35.250025 332
 
3.3%
35.121605 331
 
3.3%
35.2392 328
 
3.3%
35.161668 328
 
3.3%
35.204113 324
 
3.2%
35.234905 324
 
3.2%
35.144615 320
 
3.2%
Other values (41) 6606
66.1%
ValueCountFrequency (%)
35.08484 302
3.0%
35.085472 319
3.2%
35.092663 302
3.0%
35.0931597 70
 
0.7%
35.095905 339
3.4%
35.0970155 72
 
0.7%
35.097233 275
2.8%
35.098934 288
2.9%
35.0996462 71
 
0.7%
35.099649 70
 
0.7%
ValueCountFrequency (%)
35.3230993 301
3.0%
35.250107 307
3.1%
35.250025 332
3.3%
35.2392 328
3.3%
35.234905 324
3.2%
35.2222318 71
 
0.7%
35.2159352 61
 
0.6%
35.2146331 63
 
0.6%
35.2119516 77
 
0.8%
35.211483 305
3.0%

lo
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing80
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean129.05653
Minimum128.89784
Maximum129.17634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-10-09T16:52:09.684411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.89784
5-th percentile128.97157
Q1129.0175
median129.06401
Q3129.0915
95-th percentile129.16739
Maximum129.17634
Range0.2784985
Interquartile range (IQR)0.0740041

Descriptive statistics

Standard deviation0.062173632
Coefficient of variation (CV)0.00048175504
Kurtosis0.071118945
Mean129.05653
Median Absolute Deviation (MAD)0.03952
Skewness-0.28791167
Sum1280240.7
Variance0.0038655605
MonotonicityNot monotonic
2023-10-09T16:52:09.945083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.06401 347
 
3.5%
129.0395 341
 
3.4%
129.04424 339
 
3.4%
129.0112455 332
 
3.3%
129.08223 331
 
3.3%
129.0915 328
 
3.3%
129.113611 328
 
3.3%
129.08112 324
 
3.2%
129.00853 324
 
3.2%
129.06464 320
 
3.2%
Other values (41) 6606
66.1%
ValueCountFrequency (%)
128.89784 319
3.2%
128.9019892 74
 
0.7%
128.97157 302
3.0%
128.97812 309
3.1%
128.97891 318
3.2%
128.9813855 64
 
0.6%
128.9893459 71
 
0.7%
128.99406 288
2.9%
129.0016813 69
 
0.7%
129.00853 324
3.2%
ValueCountFrequency (%)
129.1763385 301
3.0%
129.1744835 78
 
0.8%
129.16739 300
3.0%
129.1623293 76
 
0.8%
129.1337096 294
2.9%
129.12317 313
3.1%
129.116693 73
 
0.7%
129.113611 328
3.3%
129.1114753 68
 
0.7%
129.1111015 316
3.2%

adres
Text

Distinct54
Distinct (%)0.5%
Missing13
Missing (%)0.1%
Memory size156.2 KiB
2023-10-09T16:52:10.518323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length21.987083
Min length14

Characters and Unicode

Total characters219585
Distinct characters116
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

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 사하구 장림동 1033-2
2nd row부산광역시 부산진구 전포동 892-20
3rd row부산광역시 영도구 봉래동2가 151-1
4th row부산광역시 북구 구포1동 구포시장길 9
5th row부산광역시 부산진구 전포동 892-20
ValueCountFrequency (%)
부산광역시 9987
 
22.0%
해운대구 1358
 
3.0%
동래구 1097
 
2.4%
부산진구 810
 
1.8%
남구 794
 
1.7%
북구 788
 
1.7%
수영구 783
 
1.7%
사상구 753
 
1.7%
사하구 735
 
1.6%
온천동 704
 
1.5%
Other values (159) 27670
60.8%
2023-10-09T16:52:11.506788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35492
 
16.2%
동 12471
 
5.7%
부 11461
 
5.2%
산 11290
 
5.1%
구 10904
 
5.0%
1 10516
 
4.8%
광 10315
 
4.7%
시 10195
 
4.6%
역 9987
 
4.5%
2 6707
 
3.1%
Other values (106) 90247
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128123
58.3%
Decimal Number 46133
 
21.0%
Space Separator 35492
 
16.2%
Dash Punctuation 5108
 
2.3%
Close Punctuation 2198
 
1.0%
Open Punctuation 2198
 
1.0%
Other Punctuation 333
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12471
 
9.7%
부 11461
 
8.9%
산 11290
 
8.8%
구 10904
 
8.5%
광 10315
 
8.1%
시 10195
 
8.0%
역 9987
 
7.8%
로 2859
 
2.2%
남 2221
 
1.7%
대 2181
 
1.7%
Other values (91) 44239
34.5%
Decimal Number
ValueCountFrequency (%)
1 10516
22.8%
2 6707
14.5%
3 5904
12.8%
5 4612
10.0%
7 3655
 
7.9%
6 3392
 
7.4%
8 3217
 
7.0%
4 3153
 
6.8%
9 2594
 
5.6%
0 2383
 
5.2%
Space Separator
ValueCountFrequency (%)
35492
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2198
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2198
100.0%
Other Punctuation
ValueCountFrequency (%)
, 333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128123
58.3%
Common 91462
41.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12471
 
9.7%
부 11461
 
8.9%
산 11290
 
8.8%
구 10904
 
8.5%
광 10315
 
8.1%
시 10195
 
8.0%
역 9987
 
7.8%
로 2859
 
2.2%
남 2221
 
1.7%
대 2181
 
1.7%
Other values (91) 44239
34.5%
Common
ValueCountFrequency (%)
35492
38.8%
1 10516
 
11.5%
2 6707
 
7.3%
3 5904
 
6.5%
- 5108
 
5.6%
5 4612
 
5.0%
7 3655
 
4.0%
6 3392
 
3.7%
8 3217
 
3.5%
4 3153
 
3.4%
Other values (5) 9706
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128123
58.3%
ASCII 91462
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35492
38.8%
1 10516
 
11.5%
2 6707
 
7.3%
3 5904
 
6.5%
- 5108
 
5.6%
5 4612
 
5.0%
7 3655
 
4.0%
6 3392
 
3.7%
8 3217
 
3.5%
4 3153
 
3.4%
Other values (5) 9706
 
10.6%
Hangul
ValueCountFrequency (%)
동 12471
 
9.7%
부 11461
 
8.9%
산 11290
 
8.8%
구 10904
 
8.5%
광 10315
 
8.1%
시 10195
 
8.0%
역 9987
 
7.8%
로 2859
 
2.2%
남 2221
 
1.7%
대 2181
 
1.7%
Other values (91) 44239
34.5%

telno
Text

Distinct54
Distinct (%)0.5%
Missing13
Missing (%)0.1%
Memory size156.2 KiB
2023-10-09T16:52:11.907945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.012917
Min length12

Characters and Unicode

Total characters119973
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

Unique0 ?
Unique (%)0.0%

Sample

1st row051-603-2500
2nd row051-605-1000
3rd row051-418-2000
4th row051-333-9033
5th row051-605-1000
ValueCountFrequency (%)
051-605-1000 347
 
3.5%
051-466-2112 341
 
3.4%
051-418-2000 339
 
3.4%
051-330-9000 332
 
3.3%
051-609-8000 331
 
3.3%
051-756-2277 328
 
3.3%
051-559-8000 328
 
3.3%
051-550-2000 324
 
3.2%
051-604-2500 324
 
3.2%
051-609-1234 320
 
3.2%
Other values (44) 6673
66.8%
2023-10-09T16:52:12.693317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28727
23.9%
- 19974
16.6%
5 16750
14.0%
1 16714
13.9%
2 10166
 
8.5%
6 6961
 
5.8%
3 4644
 
3.9%
9 4416
 
3.7%
4 4204
 
3.5%
8 3851
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99999
83.4%
Dash Punctuation 19974
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28727
28.7%
5 16750
16.8%
1 16714
16.7%
2 10166
 
10.2%
6 6961
 
7.0%
3 4644
 
4.6%
9 4416
 
4.4%
4 4204
 
4.2%
8 3851
 
3.9%
7 3566
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 19974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28727
23.9%
- 19974
16.6%
5 16750
14.0%
1 16714
13.9%
2 10166
 
8.5%
6 6961
 
5.8%
3 4644
 
3.9%
9 4416
 
3.7%
4 4204
 
3.5%
8 3851
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28727
23.9%
- 19974
16.6%
5 16750
14.0%
1 16714
13.9%
2 10166
 
8.5%
6 6961
 
5.8%
3 4644
 
3.9%
9 4416
 
3.7%
4 4204
 
3.5%
8 3851
 
3.2%

parkng_at
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9911 
N
 
76
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Y 9911
99.1%
N 76
 
0.8%
13
 
0.1%

Length

2023-10-09T16:52:13.062037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T16:52:13.325389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9911
99.2%
n 76
 
0.8%

card_at
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9987 
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Y 9987
99.9%
13
 
0.1%

Length

2023-10-09T16:52:13.574577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T16:52:13.805316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9987
100.0%

item_name
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
오징어
 
396
양파
 
394
고등어
 
394
배추
 
390
배
 
383
Other values (27)
8043 

Length

Max length5
Median length4
Mean length2.4609
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양파
2nd row참기름
3rd row달걀
4th row두부
5th row두부

Common Values

ValueCountFrequency (%)
오징어 396
 
4.0%
양파 394
 
3.9%
고등어 394
 
3.9%
배추 390
 
3.9%
배 383
 
3.8%
사과 381
 
3.8%
두부 379
 
3.8%
밀감 378
 
3.8%
쇠고기 377
 
3.8%
쌀 373
 
3.7%
Other values (22) 6155
61.6%

Length

2023-10-09T16:52:14.066355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
오징어 396
 
4.0%
양파 394
 
3.9%
고등어 394
 
3.9%
배추 390
 
3.9%
배 383
 
3.8%
사과 381
 
3.8%
두부 379
 
3.8%
밀감 378
 
3.8%
쇠고기 377
 
3.8%
쌀 373
 
3.7%
Other values (22) 6155
61.6%

last_load_dttm
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-09-01 06:18:09
2086 
2023-09-01 06:18:11
1658 
2023-09-01 06:18:07
1505 
2023-09-01 06:18:12
1276 
2023-09-01 06:18:13
1133 
Other values (4)
2342 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-01 06:18:06
2nd row2023-09-01 06:18:11
3rd row2023-09-01 06:18:14
4th row2023-09-01 06:18:09
5th row2023-09-01 06:18:07

Common Values

ValueCountFrequency (%)
2023-09-01 06:18:09 2086
20.9%
2023-09-01 06:18:11 1658
16.6%
2023-09-01 06:18:07 1505
15.0%
2023-09-01 06:18:12 1276
12.8%
2023-09-01 06:18:13 1133
11.3%
2023-09-01 06:18:10 1098
11.0%
2023-09-01 06:18:08 668
 
6.7%
2023-09-01 06:18:14 439
 
4.4%
2023-09-01 06:18:06 137
 
1.4%

Length

2023-10-09T16:52:14.329377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-09T16:52:14.583214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-01 10000
50.0%
06:18:09 2086
 
10.4%
06:18:11 1658
 
8.3%
06:18:07 1505
 
7.5%
06:18:12 1276
 
6.4%
06:18:13 1133
 
5.7%
06:18:10 1098
 
5.5%
06:18:08 668
 
3.3%
06:18:14 439
 
2.2%
06:18:06 137
 
0.7%

Sample

skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
2273593367818041530747530747804072021-11-04456농산물223사하구3000.016634990농할할인/4000원 11/4~10까지 행사롯데마트(사하점)35.08484128.97157부산광역시 사하구 장림동 1033-2051-603-2500YY양파2023-09-01 06:18:06
2677335710611009928527359527359994062021-09-16464식료품145부산진구320.089908990<NA>홈플러스(서면점)35.149452129.06401부산광역시 부산진구 전포동 892-20051-605-1000YY참기름2023-09-01 06:18:11
416743556139908931552850552850894062022-11-24458축산물293영도구600.038903890신선란홈플러스(영도점)35.095905129.04424부산광역시 영도구 봉래동2가 151-1051-418-2000YY달걀2023-09-01 06:18:14
102183583369101100805561165561161004172023-02-01464식료품170북구420.035003500<NA>정이있는구포시장35.208355129.001681부산광역시 북구 구포1동 구포시장길 9051-333-9033YY두부2023-09-01 06:18:09
18843591720101100285316215316211004062021-11-25464식료품145부산진구380.051944700<NA>홈플러스(서면점)35.149452129.06401부산광역시 부산진구 전포동 892-20051-605-1000YY두부2023-09-01 06:18:07
395903558258979639553635553635964072022-12-01461주류및음료,차116부산진구500.027302730동서식물성프림롯데마트(부암점)35.152466129.02731부산광역시 부산진구 가야동 624-7051-608-2500YY커피크림2023-09-01 06:18:13
1261359235887862524530049530049864112021-10-28458축산물174북구1.099009900<NA>농협하나로클럽마트(금곡점)35.250025129.011246부산광역시 북구 금곡동 북구금곡동1874-3051-330-9000YY닭고기2023-09-01 06:18:07
414383556384999820552853552853984052022-11-24464식료품189사상구1.7926417500황가이마트(사상점)35.16423128.97891부산광역시 사상구 괘법동 531-2051-329-1234YY간장2023-09-01 06:18:14
233933574444105104204845134845131044052019-10-10464식료품189사상구2.530803080곰표이마트(사상점)35.16423128.97891부산광역시 사상구 괘법동 531-2051-329-1234YY밀가루2023-09-01 06:18:11
2579359101789882625531609531609884062021-11-25458축산물365해운대구100.094450188901++등급등심(홈플러스카드 결재시 30%할인)홈플러스(센텀시티점)35.170936129.13371부산광역시 해운대구 우동 해운대구 센텀동로6051-709-8000YY쇠고기2023-09-01 06:18:07
skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
19552359498515915731775291075291071574172021-10-13459수산물192사상구500.01000010000생물복이있는 덕포시장<NA><NA>(46950) 부산광역시 사상구 사상로293번길 15 (덕포동)051-303-8393YY갈치2023-09-01 06:18:10
62253587386106105545569485569481054172023-02-15456농산물369해운대구20.05800058000햅쌀좌동재래시장35.173034129.174484부산광역시 해운대구 좌1동 좌동로 91번길 20051-703-3337YY쌀2023-09-01 06:18:07
192133594646838240529042529042824052021-10-07456농산물273연제구2000.012201220<NA>이마트(연제점)35.17615129.08133부산광역시 연제구 연산2동 822-7051-860-1052YY무2023-09-01 06:18:10
91413584478908928554620554620894062023-01-05458축산물145부산진구600.040904090<NA>홈플러스(서면점)35.149452129.06401부산광역시 부산진구 전포동 892-20051-605-1000YY달걀2023-09-01 06:18:08
402423557593158156355511745511741564102022-10-27459수산물227서구500.000<NA>탑마트(서구)35.092663129.02449부산광역시 서구 남부민1동 685051-244-1221YY오징어2023-09-01 06:18:14
255703572257999838483779483779984072019-09-26464식료품360해운대구0.963506350<NA>탑마트(반여점)35.205868129.12317(48038) 부산광역시 해운대구 선수촌로 119 (반여동)051-525-0422YY간장2023-09-01 06:18:11
35820356202079783028554426554426784062022-12-15456농산물257수영구6000.03087030870<NA>홈플러스 익스플러스 광안점35.161668129.113611(48253) 부산광역시 수영구 수영로 625, 491-1 (광안동)051-756-2277YY배2023-09-01 06:18:13
16511357705715715525624854524854521554082019-10-17464식료품261수영구1.849004900<NA>메가마트 남천점35.137353129.111101부산광역시 수영구 남천동 남천동545-2051-608-6000YY식용유2023-09-01 06:18:09
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