Overview

Dataset statistics

Number of variables26
Number of observations10000
Missing cells6955
Missing cells (%)2.7%
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 (93.6%)Imbalance
card_at is highly imbalanced (97.7%)Imbalance
rm has 6653 (66.5%) missing valuesMissing
skey has unique valuesUnique
unitprice has 122 (1.2%) zerosZeros
prices has 118 (1.2%) zerosZeros

Reproduction

Analysis started2024-04-21 09:18:58.765869
Analysis finished2024-04-21 09:18:59.958119
Duration1.19 second
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%
Mean317099.76
Minimum296144
Maximum338022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:00.160741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum296144
5-th percentile298314.95
Q1306738.5
median317180
Q3327558.25
95-th percentile335884.05
Maximum338022
Range41878
Interquartile range (IQR)20819.75

Descriptive statistics

Standard deviation12053.632
Coefficient of variation (CV)0.038012113
Kurtosis-1.200992
Mean317099.76
Median Absolute Deviation (MAD)10407
Skewness-0.0026477668
Sum3.1709976 × 109
Variance1.4529004 × 108
MonotonicityNot monotonic
2024-04-21T18:19:00.578946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301512 1
 
< 0.1%
304817 1
 
< 0.1%
334658 1
 
< 0.1%
328975 1
 
< 0.1%
335375 1
 
< 0.1%
307211 1
 
< 0.1%
322560 1
 
< 0.1%
310370 1
 
< 0.1%
307933 1
 
< 0.1%
320243 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
296144 1
< 0.1%
296151 1
< 0.1%
296158 1
< 0.1%
296164 1
< 0.1%
296170 1
< 0.1%
296175 1
< 0.1%
296187 1
< 0.1%
296205 1
< 0.1%
296209 1
< 0.1%
296210 1
< 0.1%
ValueCountFrequency (%)
338022 1
< 0.1%
338014 1
< 0.1%
338009 1
< 0.1%
338001 1
< 0.1%
337999 1
< 0.1%
337996 1
< 0.1%
337995 1
< 0.1%
337990 1
< 0.1%
337989 1
< 0.1%
337985 1
< 0.1%

ccode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.6821
Minimum77
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:00.970274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile78
Q183
median90
Q3101
95-th percentile158
Maximum159
Range82
Interquartile range (IQR)18

Descriptive statistics

Standard deviation23.099208
Coefficient of variation (CV)0.23647329
Kurtosis2.3111818
Mean97.6821
Median Absolute Deviation (MAD)9
Skewness1.8513058
Sum976821
Variance533.5734
MonotonicityNot monotonic
2024-04-21T18:19:01.363001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
78 394
 
3.9%
80 391
 
3.9%
77 390
 
3.9%
106 388
 
3.9%
82 387
 
3.9%
81 383
 
3.8%
88 381
 
3.8%
89 380
 
3.8%
79 379
 
3.8%
87 377
 
3.8%
Other values (22) 6150
61.5%
ValueCountFrequency (%)
77 390
3.9%
78 394
3.9%
79 379
3.8%
80 391
3.9%
81 383
3.8%
82 387
3.9%
83 370
3.7%
84 374
3.7%
85 260
2.6%
86 274
2.7%
ValueCountFrequency (%)
159 301
3.0%
158 342
3.4%
157 258
2.6%
156 243
2.4%
106 388
3.9%
105 264
2.6%
104 238
2.4%
103 248
2.5%
101 369
3.7%
100 252
2.5%

pcode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.5677
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:01.740820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile77
Q182
median89
Q3100
95-th percentile156
Maximum157
Range81
Interquartile range (IQR)18

Descriptive statistics

Standard deviation22.802658
Coefficient of variation (CV)0.23613132
Kurtosis2.2737711
Mean96.5677
Median Absolute Deviation (MAD)9
Skewness1.8372268
Sum965677
Variance519.96121
MonotonicityNot monotonic
2024-04-21T18:19:02.166974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
77 394
 
3.9%
79 391
 
3.9%
76 390
 
3.9%
105 388
 
3.9%
81 387
 
3.9%
80 383
 
3.8%
87 381
 
3.8%
88 380
 
3.8%
78 379
 
3.8%
86 377
 
3.8%
Other values (22) 6150
61.5%
ValueCountFrequency (%)
76 390
3.9%
77 394
3.9%
78 379
3.8%
79 391
3.9%
80 383
3.8%
81 387
3.9%
82 370
3.7%
83 374
3.7%
84 260
2.6%
85 274
2.7%
ValueCountFrequency (%)
157 301
3.0%
156 342
3.4%
155 258
2.6%
154 243
2.4%
105 388
3.9%
104 264
2.6%
103 238
2.4%
102 248
2.5%
100 369
3.7%
99 252
2.5%

bssh_no
Real number (ℝ)

Distinct54
Distinct (%)0.5%
Missing22
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean412.83063
Minimum14
Maximum3198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:02.580469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile20
Q130
median40
Q359
95-th percentile2625
Maximum3198
Range3184
Interquartile range (IQR)29

Descriptive statistics

Standard deviation926.71287
Coefficient of variation (CV)2.2447774
Kurtosis2.5155176
Mean412.83063
Median Absolute Deviation (MAD)12
Skewness2.1044745
Sum4119224
Variance858796.75
MonotonicityNot monotonic
2024-04-21T18:19:03.004221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 352
 
3.5%
32 342
 
3.4%
3028 335
 
3.4%
19 328
 
3.3%
44 325
 
3.2%
22 323
 
3.2%
45 322
 
3.2%
37 318
 
3.2%
2562 317
 
3.2%
26 317
 
3.2%
Other values (44) 6699
67.0%
ValueCountFrequency (%)
14 68
 
0.7%
17 67
 
0.7%
19 328
3.3%
20 309
3.1%
22 323
3.2%
23 176
1.8%
25 293
2.9%
26 317
3.2%
28 307
3.1%
29 306
3.1%
ValueCountFrequency (%)
3198 4
 
< 0.1%
3193 2
 
< 0.1%
3177 55
 
0.5%
3028 335
3.4%
2625 285
2.9%
2562 317
3.2%
2524 316
3.2%
2349 92
 
0.9%
87 81
 
0.8%
85 84
 
0.8%

search_no
Real number (ℝ)

Distinct1597
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500453.29
Minimum469120
Maximum513376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:03.641361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum469120
5-th percentile473702.95
Q1492995
median504963
Q3509344
95-th percentile512541
Maximum513376
Range44256
Interquartile range (IQR)16349

Descriptive statistics

Standard deviation12315.581
Coefficient of variation (CV)0.024608852
Kurtosis0.35267661
Mean500453.29
Median Absolute Deviation (MAD)4540
Skewness-1.279944
Sum5.0045329 × 109
Variance1.5167353 × 108
MonotonicityNot monotonic
2024-04-21T18:19:04.061018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
492180 16
 
0.2%
507673 15
 
0.1%
508540 15
 
0.1%
508635 14
 
0.1%
503154 14
 
0.1%
492192 14
 
0.1%
508649 14
 
0.1%
512544 14
 
0.1%
503980 14
 
0.1%
502141 13
 
0.1%
Other values (1587) 9857
98.6%
ValueCountFrequency (%)
469120 7
0.1%
469121 6
0.1%
472895 2
 
< 0.1%
472896 1
 
< 0.1%
472897 6
0.1%
472898 3
< 0.1%
472899 4
< 0.1%
472900 2
 
< 0.1%
472901 3
< 0.1%
472902 3
< 0.1%
ValueCountFrequency (%)
513376 5
0.1%
513375 10
0.1%
513372 9
0.1%
513370 8
0.1%
513369 5
0.1%
513357 3
 
< 0.1%
513354 12
0.1%
513353 4
 
< 0.1%
513352 7
0.1%
513351 7
0.1%

prices_no
Real number (ℝ)

Distinct1597
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500453.29
Minimum469120
Maximum513376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:04.473053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum469120
5-th percentile473702.95
Q1492995
median504963
Q3509344
95-th percentile512541
Maximum513376
Range44256
Interquartile range (IQR)16349

Descriptive statistics

Standard deviation12315.581
Coefficient of variation (CV)0.024608852
Kurtosis0.35267661
Mean500453.29
Median Absolute Deviation (MAD)4540
Skewness-1.279944
Sum5.0045329 × 109
Variance1.5167353 × 108
MonotonicityNot monotonic
2024-04-21T18:19:04.901045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
492180 16
 
0.2%
507673 15
 
0.1%
508540 15
 
0.1%
508635 14
 
0.1%
503154 14
 
0.1%
492192 14
 
0.1%
508649 14
 
0.1%
512544 14
 
0.1%
503980 14
 
0.1%
502141 13
 
0.1%
Other values (1587) 9857
98.6%
ValueCountFrequency (%)
469120 7
0.1%
469121 6
0.1%
472895 2
 
< 0.1%
472896 1
 
< 0.1%
472897 6
0.1%
472898 3
< 0.1%
472899 4
< 0.1%
472900 2
 
< 0.1%
472901 3
< 0.1%
472902 3
< 0.1%
ValueCountFrequency (%)
513376 5
0.1%
513375 10
0.1%
513372 9
0.1%
513370 8
0.1%
513369 5
0.1%
513357 3
 
< 0.1%
513354 12
0.1%
513353 4
 
< 0.1%
513352 7
0.1%
513351 7
0.1%

prdlst
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.5677
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:05.291833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile77
Q182
median89
Q3100
95-th percentile156
Maximum157
Range81
Interquartile range (IQR)18

Descriptive statistics

Standard deviation22.802658
Coefficient of variation (CV)0.23613132
Kurtosis2.2737711
Mean96.5677
Median Absolute Deviation (MAD)9
Skewness1.8372268
Sum965677
Variance519.96121
MonotonicityNot monotonic
2024-04-21T18:19:05.715947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
77 394
 
3.9%
79 391
 
3.9%
76 390
 
3.9%
105 388
 
3.9%
81 387
 
3.9%
80 383
 
3.8%
87 381
 
3.8%
88 380
 
3.8%
78 379
 
3.8%
86 377
 
3.8%
Other values (22) 6150
61.5%
ValueCountFrequency (%)
76 390
3.9%
77 394
3.9%
78 379
3.8%
79 391
3.9%
80 383
3.8%
81 387
3.9%
82 370
3.7%
83 374
3.7%
84 260
2.6%
85 274
2.7%
ValueCountFrequency (%)
157 301
3.0%
156 342
3.4%
155 258
2.6%
154 243
2.4%
105 388
3.9%
104 264
2.6%
103 238
2.4%
102 248
2.5%
100 369
3.7%
99 252
2.5%

cl_no
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing22
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean409.15905
Minimum405
Maximum419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:06.069108image/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 deviation4.474204
Coefficient of variation (CV)0.010935122
Kurtosis-0.77565994
Mean409.15905
Median Absolute Deviation (MAD)2
Skewness0.93330518
Sum4082589
Variance20.018501
MonotonicityNot monotonic
2024-04-21T18:19:06.404341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
406 2616
26.2%
417 1953
19.5%
405 1609
16.1%
407 1595
16.0%
408 639
 
6.4%
410 629
 
6.3%
411 626
 
6.3%
416 305
 
3.0%
419 6
 
0.1%
(Missing) 22
 
0.2%
ValueCountFrequency (%)
405 1609
16.1%
406 2616
26.2%
407 1595
16.0%
408 639
 
6.4%
410 629
 
6.3%
411 626
 
6.3%
416 305
 
3.0%
417 1953
19.5%
419 6
 
0.1%
ValueCountFrequency (%)
419 6
 
0.1%
417 1953
19.5%
416 305
 
3.0%
411 626
 
6.3%
410 629
 
6.3%
408 639
 
6.4%
407 1595
16.0%
406 2616
26.2%
405 1609
16.1%

examin_de
Date

Distinct73
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-01-17 00:00:00
Maximum2021-01-28 00:00:00
2024-04-21T18:19:06.766061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:19:07.096087image/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%
Mean459.5288
Minimum456
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:07.304239image/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 deviation3.0427449
Coefficient of variation (CV)0.0066214454
Kurtosis-1.4196103
Mean459.5288
Median Absolute Deviation (MAD)3
Skewness0.23236264
Sum4595288
Variance9.2582964
MonotonicityNot monotonic
2024-04-21T18:19:07.501236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
456 3066
30.7%
464 1881
18.8%
461 1762
17.6%
458 1495
14.9%
459 1033
 
10.3%
463 763
 
7.6%
ValueCountFrequency (%)
456 3066
30.7%
458 1495
14.9%
459 1033
 
10.3%
461 1762
17.6%
463 763
 
7.6%
464 1881
18.8%
ValueCountFrequency (%)
464 1881
18.8%
463 763
 
7.6%
461 1762
17.6%
459 1033
 
10.3%
458 1495
14.9%
456 3066
30.7%

pum_nm
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농산물
3066 
식료품
1881 
주류및음료,차
1762 
축산물
1495 
수산물
1033 

Length

Max length7
Median length3
Mean length3.6285
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row축산물
2nd row농산물
3rd row농산물
4th row축산물
5th row주류및음료,차

Common Values

ValueCountFrequency (%)
농산물 3066
30.7%
식료품 1881
18.8%
주류및음료,차 1762
17.6%
축산물 1495
14.9%
수산물 1033
 
10.3%
세제 763
 
7.6%

Length

2024-04-21T18:19:07.725705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:19:07.909930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농산물 3066
30.7%
식료품 1881
18.8%
주류및음료,차 1762
17.6%
축산물 1495
14.9%
수산물 1033
 
10.3%
세제 763
 
7.6%

gugun_cd
Real number (ℝ)

Distinct49
Distinct (%)0.5%
Missing26
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean187.87106
Minimum21
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:08.130408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile31
Q195
median189
Q3261
95-th percentile365
Maximum374
Range353
Interquartile range (IQR)166

Descriptive statistics

Standard deviation102.55346
Coefficient of variation (CV)0.54587148
Kurtosis-0.9665017
Mean187.87106
Median Absolute Deviation (MAD)78
Skewness0.16432547
Sum1873826
Variance10517.211
MonotonicityNot monotonic
2024-04-21T18:19:08.375725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
189 615
 
6.2%
374 410
 
4.1%
227 377
 
3.8%
111 369
 
3.7%
186 352
 
3.5%
257 335
 
3.4%
48 328
 
3.3%
21 325
 
3.2%
155 323
 
3.2%
95 322
 
3.2%
Other values (39) 6218
62.2%
ValueCountFrequency (%)
21 325
3.2%
27 77
 
0.8%
31 176
1.8%
33 75
 
0.8%
48 328
3.3%
52 293
2.9%
53 79
 
0.8%
64 318
3.2%
66 97
 
1.0%
80 75
 
0.8%
ValueCountFrequency (%)
374 410
4.1%
373 2
 
< 0.1%
369 73
 
0.7%
365 285
2.9%
360 303
3.0%
333 82
 
0.8%
316 310
3.1%
314 76
 
0.8%
293 295
2.9%
275 84
 
0.8%

gugun_nm
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
해운대구
1073 
북구
819 
수영구
805 
남구
787 
사하구
778 
Other values (12)
5738 

Length

Max length4
Median length3
Mean length2.8836
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수영구
2nd row북구
3rd row수영구
4th row해운대구
5th row영도구

Common Values

ValueCountFrequency (%)
해운대구 1073
10.7%
북구 819
 
8.2%
수영구 805
 
8.1%
남구 787
 
7.9%
사하구 778
 
7.8%
동래구 772
 
7.7%
사상구 738
 
7.4%
부산진구 733
 
7.3%
금정구 653
 
6.5%
연제구 480
 
4.8%
Other values (7) 2362
23.6%

Length

2024-04-21T18:19:08.630857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해운대구 1073
10.7%
북구 819
 
8.2%
수영구 805
 
8.1%
남구 787
 
7.9%
사하구 778
 
7.8%
동래구 772
 
7.7%
사상구 738
 
7.4%
부산진구 733
 
7.3%
금정구 653
 
6.5%
연제구 480
 
4.8%
Other values (7) 2362
23.6%

unit
Real number (ℝ)

Distinct381
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean661.45532
Minimum0.1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:08.874039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1
Q14
median400
Q3870
95-th percentile2060.2
Maximum10000
Range9999.9
Interquartile range (IQR)866

Descriptive statistics

Standard deviation979.60349
Coefficient of variation (CV)1.4809821
Kurtosis13.872211
Mean661.45532
Median Absolute Deviation (MAD)397
Skewness3.1662765
Sum6614553.2
Variance959623.01
MonotonicityNot monotonic
2024-04-21T18:19:09.122946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500.0 1353
 
13.5%
100.0 880
 
8.8%
1000.0 750
 
7.5%
1.0 734
 
7.3%
2000.0 574
 
5.7%
1.8 465
 
4.7%
600.0 461
 
4.6%
20.0 388
 
3.9%
1.5 276
 
2.8%
360.0 268
 
2.7%
Other values (371) 3851
38.5%
ValueCountFrequency (%)
0.1 4
 
< 0.1%
0.8 18
 
0.2%
0.9 205
 
2.1%
0.95 1
 
< 0.1%
1.0 734
7.3%
1.05 1
 
< 0.1%
1.1 13
 
0.1%
1.2 28
 
0.3%
1.4 144
 
1.4%
1.5 276
 
2.8%
ValueCountFrequency (%)
10000.0 2
 
< 0.1%
9000.0 2
 
< 0.1%
7000.0 1
 
< 0.1%
6000.0 138
1.4%
5000.0 13
 
0.1%
4500.0 1
 
< 0.1%
4000.0 1
 
< 0.1%
3744.0 1
 
< 0.1%
3700.0 2
 
< 0.1%
3500.0 15
 
0.1%

unitprice
Real number (ℝ)

ZEROS 

Distinct1859
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10225.894
Minimum0
Maximum100000
Zeros122
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:09.370949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile796
Q12500
median4682
Q310000
95-th percentile49900
Maximum100000
Range100000
Interquartile range (IQR)7500

Descriptive statistics

Standard deviation14210.71
Coefficient of variation (CV)1.3896789
Kurtosis5.271924
Mean10225.894
Median Absolute Deviation (MAD)2862
Skewness2.4104029
Sum1.0225894 × 108
Variance2.0194427 × 108
MonotonicityNot monotonic
2024-04-21T18:19:09.622037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
676 185
 
1.8%
3500 183
 
1.8%
3290 147
 
1.5%
796 141
 
1.4%
1190 139
 
1.4%
2500 136
 
1.4%
5000 134
 
1.3%
4000 127
 
1.3%
0 122
 
1.2%
2000 118
 
1.2%
Other values (1849) 8568
85.7%
ValueCountFrequency (%)
0 122
1.2%
215 1
 
< 0.1%
246 2
 
< 0.1%
676 185
1.8%
678 4
 
< 0.1%
680 20
 
0.2%
700 8
 
0.1%
730 7
 
0.1%
733 1
 
< 0.1%
750 7
 
0.1%
ValueCountFrequency (%)
100000 2
 
< 0.1%
79950 8
0.1%
74000 8
0.1%
71500 1
 
< 0.1%
70000 5
0.1%
69900 7
0.1%
68900 2
 
< 0.1%
68000 3
 
< 0.1%
67900 2
 
< 0.1%
67000 2
 
< 0.1%

prices
Real number (ℝ)

ZEROS 

Distinct1054
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8301.7317
Minimum0
Maximum100000
Zeros118
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:09.873153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q12450
median4000
Q38990
95-th percentile34223
Maximum100000
Range100000
Interquartile range (IQR)6540

Descriptive statistics

Standard deviation11814.876
Coefficient of variation (CV)1.4231821
Kurtosis10.088147
Mean8301.7317
Median Absolute Deviation (MAD)2416
Skewness3.1285118
Sum83017317
Variance1.395913 × 108
MonotonicityNot monotonic
2024-04-21T18:19:10.137507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3500 231
 
2.3%
9900 182
 
1.8%
3290 169
 
1.7%
5000 165
 
1.7%
2500 164
 
1.6%
4000 154
 
1.5%
1190 142
 
1.4%
3000 138
 
1.4%
6000 137
 
1.4%
2000 127
 
1.3%
Other values (1044) 8391
83.9%
ValueCountFrequency (%)
0 118
1.2%
143 1
 
< 0.1%
199 1
 
< 0.1%
214 1
 
< 0.1%
230 2
 
< 0.1%
240 2
 
< 0.1%
249 1
 
< 0.1%
257 1
 
< 0.1%
269 1
 
< 0.1%
280 1
 
< 0.1%
ValueCountFrequency (%)
100000 2
 
< 0.1%
69900 7
0.1%
68900 2
 
< 0.1%
68000 3
< 0.1%
67900 2
 
< 0.1%
67000 2
 
< 0.1%
66900 2
 
< 0.1%
66540 1
 
< 0.1%
65000 6
0.1%
64900 5
0.1%

rm
Text

MISSING 

Distinct961
Distinct (%)28.7%
Missing6653
Missing (%)66.5%
Memory size156.2 KiB
2024-04-21T18:19:11.085601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length6.5318195
Min length1

Characters and Unicode

Total characters21862
Distinct characters383
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

Unique516 ?
Unique (%)15.4%

Sample

1st row1+
2nd row알뜰배6入
3rd row깨끗한나라
4th row9900/4kg
5th row하우스밀감 8/12~18까지 행사
ValueCountFrequency (%)
행사 173
 
4.4%
생물 122
 
3.1%
해동 117
 
3.0%
하우스밀감 53
 
1.3%
냉동 52
 
1.3%
고소한참기름 43
 
1.1%
품절 42
 
1.1%
이맛쌀 38
 
1.0%
할인행사 33
 
0.8%
1등급 32
 
0.8%
Other values (867) 3254
82.2%
2024-04-21T18:19:12.535105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1754
 
8.0%
1 905
 
4.1%
0 867
 
4.0%
사 480
 
2.2%
/ 465
 
2.1%
2 443
 
2.0%
행 381
 
1.7%
3 364
 
1.7%
원 309
 
1.4%
생 273
 
1.2%
Other values (373) 15621
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14415
65.9%
Decimal Number 3614
 
16.5%
Space Separator 1754
 
8.0%
Other Punctuation 804
 
3.7%
Lowercase Letter 388
 
1.8%
Math Symbol 329
 
1.5%
Uppercase Letter 239
 
1.1%
Close Punctuation 155
 
0.7%
Open Punctuation 146
 
0.7%
Dash Punctuation 18
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 480
 
3.3%
행 381
 
2.6%
원 309
 
2.1%
생 273
 
1.9%
기 257
 
1.8%
지 257
 
1.8%
동 251
 
1.7%
스 226
 
1.6%
한 207
 
1.4%
고 205
 
1.4%
Other values (326) 11569
80.3%
Lowercase Letter
ValueCountFrequency (%)
g 177
45.6%
l 51
 
13.1%
k 51
 
13.1%
m 48
 
12.4%
s 16
 
4.1%
p 9
 
2.3%
i 8
 
2.1%
u 8
 
2.1%
j 5
 
1.3%
c 5
 
1.3%
Other values (6) 10
 
2.6%
Decimal Number
ValueCountFrequency (%)
1 905
25.0%
0 867
24.0%
2 443
12.3%
3 364
10.1%
5 270
 
7.5%
9 233
 
6.4%
4 174
 
4.8%
8 152
 
4.2%
7 112
 
3.1%
6 94
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
L 77
32.2%
C 28
 
11.7%
P 26
 
10.9%
R 26
 
10.9%
G 24
 
10.0%
K 23
 
9.6%
T 20
 
8.4%
M 13
 
5.4%
A 1
 
0.4%
J 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
/ 465
57.8%
, 119
 
14.8%
. 92
 
11.4%
* 68
 
8.5%
% 60
 
7.5%
Math Symbol
ValueCountFrequency (%)
+ 178
54.1%
~ 151
45.9%
Space Separator
ValueCountFrequency (%)
1754
100.0%
Close Punctuation
ValueCountFrequency (%)
) 155
100.0%
Open Punctuation
ValueCountFrequency (%)
( 146
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14393
65.8%
Common 6820
31.2%
Latin 627
 
2.9%
Han 22
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 480
 
3.3%
행 381
 
2.6%
원 309
 
2.1%
생 273
 
1.9%
기 257
 
1.8%
지 257
 
1.8%
동 251
 
1.7%
스 226
 
1.6%
한 207
 
1.4%
고 205
 
1.4%
Other values (325) 11547
80.2%
Latin
ValueCountFrequency (%)
g 177
28.2%
L 77
12.3%
l 51
 
8.1%
k 51
 
8.1%
m 48
 
7.7%
C 28
 
4.5%
P 26
 
4.1%
R 26
 
4.1%
G 24
 
3.8%
K 23
 
3.7%
Other values (16) 96
15.3%
Common
ValueCountFrequency (%)
1754
25.7%
1 905
13.3%
0 867
12.7%
/ 465
 
6.8%
2 443
 
6.5%
3 364
 
5.3%
5 270
 
4.0%
9 233
 
3.4%
+ 178
 
2.6%
4 174
 
2.6%
Other values (11) 1167
17.1%
Han
ValueCountFrequency (%)
入 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14391
65.8%
ASCII 7447
34.1%
CJK 22
 
0.1%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1754
23.6%
1 905
12.2%
0 867
11.6%
/ 465
 
6.2%
2 443
 
5.9%
3 364
 
4.9%
5 270
 
3.6%
9 233
 
3.1%
+ 178
 
2.4%
g 177
 
2.4%
Other values (37) 1791
24.0%
Hangul
ValueCountFrequency (%)
사 480
 
3.3%
행 381
 
2.6%
원 309
 
2.1%
생 273
 
1.9%
기 257
 
1.8%
지 257
 
1.8%
동 251
 
1.7%
스 226
 
1.6%
한 207
 
1.4%
고 205
 
1.4%
Other values (323) 11545
80.2%
CJK
ValueCountFrequency (%)
入 22
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅡ 1
50.0%
ㅣ 1
50.0%

bssh_nm
Text

Distinct54
Distinct (%)0.5%
Missing22
Missing (%)0.2%
Memory size156.2 KiB
2024-04-21T18:19:13.296129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.0773702
Min length4

Characters and Unicode

Total characters90574
Distinct characters117
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 row메가마트 남천점
2nd row정이있는구포시장
3rd row홈플러스 익스플러스 광안점
4th row이마트(해운대점)
5th row삼성홈플러스(영도점)
ValueCountFrequency (%)
롯데마트(화명점 352
 
3.2%
이마트(해운대점 342
 
3.1%
홈플러스 335
 
3.0%
익스플러스 335
 
3.0%
광안점 335
 
3.0%
삼성홈플러스(정관점 328
 
3.0%
이마트(금정점 325
 
2.9%
롯데수퍼(명지점 323
 
2.9%
메가마트(동래점 322
 
2.9%
이마트(문현점 318
 
2.9%
Other values (49) 7707
69.9%
2024-04-21T18:19:14.467676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 7472
 
8.2%
( 7133
 
7.9%
) 7133
 
7.9%
마 4775
 
5.3%
트 4775
 
5.3%
스 3591
 
4.0%
플 2951
 
3.3%
러 2951
 
3.3%
홈 2616
 
2.9%
시 2157
 
2.4%
Other values (107) 45020
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75128
82.9%
Open Punctuation 7133
 
7.9%
Close Punctuation 7133
 
7.9%
Space Separator 1099
 
1.2%
Decimal Number 81
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 7472
 
9.9%
마 4775
 
6.4%
트 4775
 
6.4%
스 3591
 
4.8%
플 2951
 
3.9%
러 2951
 
3.9%
홈 2616
 
3.5%
시 2157
 
2.9%
성 2105
 
2.8%
삼 2105
 
2.8%
Other values (103) 39630
52.7%
Open Punctuation
ValueCountFrequency (%)
( 7133
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7133
100.0%
Space Separator
ValueCountFrequency (%)
1099
100.0%
Decimal Number
ValueCountFrequency (%)
1 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75128
82.9%
Common 15446
 
17.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 7472
 
9.9%
마 4775
 
6.4%
트 4775
 
6.4%
스 3591
 
4.8%
플 2951
 
3.9%
러 2951
 
3.9%
홈 2616
 
3.5%
시 2157
 
2.9%
성 2105
 
2.8%
삼 2105
 
2.8%
Other values (103) 39630
52.7%
Common
ValueCountFrequency (%)
( 7133
46.2%
) 7133
46.2%
1099
 
7.1%
1 81
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75128
82.9%
ASCII 15446
 
17.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 7472
 
9.9%
마 4775
 
6.4%
트 4775
 
6.4%
스 3591
 
4.8%
플 2951
 
3.9%
러 2951
 
3.9%
홈 2616
 
3.5%
시 2157
 
2.9%
성 2105
 
2.8%
삼 2105
 
2.8%
Other values (103) 39630
52.7%
ASCII
ValueCountFrequency (%)
( 7133
46.2%
) 7133
46.2%
1099
 
7.1%
1 81
 
0.5%

la
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing83
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean35.164044
Minimum35.08484
Maximum35.323099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:15.087636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.08484
5-th percentile35.085472
Q135.11677
median35.161668
Q335.204113
95-th percentile35.250107
Maximum35.323099
Range0.2382593
Interquartile range (IQR)0.087343

Descriptive statistics

Standard deviation0.057079692
Coefficient of variation (CV)0.0016232403
Kurtosis0.10890683
Mean35.164044
Median Absolute Deviation (MAD)0.0442
Skewness0.63634513
Sum348721.82
Variance0.0032580912
MonotonicityNot monotonic
2024-04-21T18:19:15.516976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.234905 352
 
3.5%
35.165993 342
 
3.4%
35.161668 335
 
3.4%
35.3230993 328
 
3.3%
35.250107 325
 
3.2%
35.085472 323
 
3.2%
35.204113 322
 
3.2%
35.144615 318
 
3.2%
35.11677 317
 
3.2%
35.1373526 317
 
3.2%
Other values (41) 6638
66.4%
ValueCountFrequency (%)
35.08484 311
3.1%
35.085472 323
3.2%
35.092663 312
3.1%
35.0931597 65
 
0.7%
35.095905 295
2.9%
35.0970155 76
 
0.8%
35.097233 310
3.1%
35.098934 305
3.0%
35.0996462 70
 
0.7%
35.099649 76
 
0.8%
ValueCountFrequency (%)
35.3230993 328
3.3%
35.250107 325
3.2%
35.250025 316
3.2%
35.2392 176
1.8%
35.234905 352
3.5%
35.2222318 63
 
0.6%
35.2159352 75
 
0.8%
35.2146331 69
 
0.7%
35.2119516 77
 
0.8%
35.211483 306
3.1%

lo
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing83
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean129.05632
Minimum128.89784
Maximum129.17634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:15.913459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.89784
5-th percentile128.97157
Q1129.01125
median129.06401
Q3129.0915
95-th percentile129.16739
Maximum129.17634
Range0.2784985
Interquartile range (IQR)0.0802545

Descriptive statistics

Standard deviation0.063317614
Coefficient of variation (CV)0.00049062001
Kurtosis-0.0013820402
Mean129.05632
Median Absolute Deviation (MAD)0.03952
Skewness-0.26606905
Sum1279851.5
Variance0.0040091203
MonotonicityNot monotonic
2024-04-21T18:19:16.340776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.00853 352
 
3.5%
129.16739 342
 
3.4%
129.113611 335
 
3.4%
129.1763385 328
 
3.3%
129.09073 325
 
3.2%
128.89784 323
 
3.2%
129.08112 322
 
3.2%
129.06464 318
 
3.2%
129.0395 317
 
3.2%
129.1111015 317
 
3.2%
Other values (41) 6638
66.4%
ValueCountFrequency (%)
128.89784 323
3.2%
128.9019892 92
 
0.9%
128.97157 311
3.1%
128.97812 306
3.1%
128.97891 309
3.1%
128.9813855 68
 
0.7%
128.9893459 70
 
0.7%
128.99406 305
3.0%
129.0016813 82
 
0.8%
129.00853 352
3.5%
ValueCountFrequency (%)
129.1763385 328
3.3%
129.1744835 73
 
0.7%
129.16739 342
3.4%
129.1623293 68
 
0.7%
129.1337096 285
2.9%
129.12317 303
3.0%
129.116693 86
 
0.9%
129.113611 335
3.4%
129.1114753 97
 
1.0%
129.1111015 317
3.2%

adres
Text

Distinct54
Distinct (%)0.5%
Missing22
Missing (%)0.2%
Memory size156.2 KiB
2024-04-21T18:19:17.565728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length21.877831
Min length14

Characters and Unicode

Total characters218297
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 수영구 남천동 남천동545-2
2nd row부산광역시 북구 구포1동 구포시장길 9
3rd row(48253) 부산광역시 수영구 수영로 625, 491-1 (광안동)
4th row부산광역시 해운대구 중1동 1767
5th row부산광역시 영도구 봉래동2가 151-1
ValueCountFrequency (%)
부산광역시 9978
 
22.0%
해운대구 1358
 
3.0%
동래구 948
 
2.1%
북구 819
 
1.8%
수영구 805
 
1.8%
남구 787
 
1.7%
사하구 778
 
1.7%
사상구 738
 
1.6%
부산진구 733
 
1.6%
괘법동 615
 
1.4%
Other values (159) 27866
61.3%
2024-04-21T18:19:18.935296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35447
 
16.2%
동 12290
 
5.6%
부 11394
 
5.2%
산 11258
 
5.2%
구 10890
 
5.0%
1 10542
 
4.8%
광 10313
 
4.7%
시 10206
 
4.7%
역 9978
 
4.6%
2 6599
 
3.0%
Other values (106) 89380
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128061
58.7%
Decimal Number 45551
 
20.9%
Space Separator 35447
 
16.2%
Dash Punctuation 5067
 
2.3%
Close Punctuation 1915
 
0.9%
Open Punctuation 1915
 
0.9%
Other Punctuation 341
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12290
 
9.6%
부 11394
 
8.9%
산 11258
 
8.8%
구 10890
 
8.5%
광 10313
 
8.1%
시 10206
 
8.0%
역 9978
 
7.8%
로 2830
 
2.2%
남 2252
 
1.8%
대 2066
 
1.6%
Other values (91) 44584
34.8%
Decimal Number
ValueCountFrequency (%)
1 10542
23.1%
2 6599
14.5%
3 5872
12.9%
5 4529
9.9%
7 3528
 
7.7%
6 3499
 
7.7%
8 3187
 
7.0%
4 3035
 
6.7%
9 2557
 
5.6%
0 2203
 
4.8%
Space Separator
ValueCountFrequency (%)
35447
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5067
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1915
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1915
100.0%
Other Punctuation
ValueCountFrequency (%)
, 341
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128061
58.7%
Common 90236
41.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12290
 
9.6%
부 11394
 
8.9%
산 11258
 
8.8%
구 10890
 
8.5%
광 10313
 
8.1%
시 10206
 
8.0%
역 9978
 
7.8%
로 2830
 
2.2%
남 2252
 
1.8%
대 2066
 
1.6%
Other values (91) 44584
34.8%
Common
ValueCountFrequency (%)
35447
39.3%
1 10542
 
11.7%
2 6599
 
7.3%
3 5872
 
6.5%
- 5067
 
5.6%
5 4529
 
5.0%
7 3528
 
3.9%
6 3499
 
3.9%
8 3187
 
3.5%
4 3035
 
3.4%
Other values (5) 8931
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128061
58.7%
ASCII 90236
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35447
39.3%
1 10542
 
11.7%
2 6599
 
7.3%
3 5872
 
6.5%
- 5067
 
5.6%
5 4529
 
5.0%
7 3528
 
3.9%
6 3499
 
3.9%
8 3187
 
3.5%
4 3035
 
3.4%
Other values (5) 8931
 
9.9%
Hangul
ValueCountFrequency (%)
동 12290
 
9.6%
부 11394
 
8.9%
산 11258
 
8.8%
구 10890
 
8.5%
광 10313
 
8.1%
시 10206
 
8.0%
역 9978
 
7.8%
로 2830
 
2.2%
남 2252
 
1.8%
대 2066
 
1.6%
Other values (91) 44584
34.8%

telno
Text

Distinct54
Distinct (%)0.5%
Missing22
Missing (%)0.2%
Memory size156.2 KiB
2024-04-21T18:19:19.688406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.015133
Min length12

Characters and Unicode

Total characters119887
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 row051-608-6000
2nd row051-333-9033
3rd row051-756-2277
4th row051-608-1234
5th row051-418-2000
ValueCountFrequency (%)
051-604-2500 352
 
3.5%
051-608-1234 342
 
3.4%
051-756-2277 335
 
3.4%
051-519-8200 328
 
3.3%
051-606-1234 325
 
3.3%
051-292-5602 323
 
3.2%
051-550-2000 322
 
3.2%
051-609-1234 318
 
3.2%
051-608-6000 317
 
3.2%
051-466-2112 317
 
3.2%
Other values (44) 6699
67.1%
2024-04-21T18:19:20.821757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27903
23.3%
- 19956
16.6%
1 16960
14.1%
5 16269
13.6%
2 10364
 
8.6%
6 6842
 
5.7%
3 4986
 
4.2%
9 4589
 
3.8%
4 4341
 
3.6%
8 3993
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99931
83.4%
Dash Punctuation 19956
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27903
27.9%
1 16960
17.0%
5 16269
16.3%
2 10364
 
10.4%
6 6842
 
6.8%
3 4986
 
5.0%
9 4589
 
4.6%
4 4341
 
4.3%
8 3993
 
4.0%
7 3684
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 19956
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119887
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27903
23.3%
- 19956
16.6%
1 16960
14.1%
5 16269
13.6%
2 10364
 
8.6%
6 6842
 
5.7%
3 4986
 
4.2%
9 4589
 
3.8%
4 4341
 
3.6%
8 3993
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119887
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27903
23.3%
- 19956
16.6%
1 16960
14.1%
5 16269
13.6%
2 10364
 
8.6%
6 6842
 
5.7%
3 4986
 
4.2%
9 4589
 
3.8%
4 4341
 
3.6%
8 3993
 
3.3%

parkng_at
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9882 
N
 
96
 
22

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 9882
98.8%
N 96
 
1.0%
22
 
0.2%

Length

2024-04-21T18:19:21.223395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:19:21.531209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9882
99.0%
n 96
 
1.0%

card_at
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9978 
 
22

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 9978
99.8%
22
 
0.2%

Length

2024-04-21T18:19:21.855682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:19:22.146695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9978
100.0%

item_name
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
밀감
 
394
사과
 
391
고등어
 
390
쌀
 
388
대파
 
387
Other values (27)
8050 

Length

Max length5
Median length4
Mean length2.4741
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row쇠고기
2nd row사과
3rd row쌀
4th row쇠고기
5th row소주

Common Values

ValueCountFrequency (%)
밀감 394
 
3.9%
사과 391
 
3.9%
고등어 390
 
3.9%
쌀 388
 
3.9%
대파 387
 
3.9%
양파 383
 
3.8%
돼지고기 381
 
3.8%
쇠고기 380
 
3.8%
배 379
 
3.8%
닭고기 377
 
3.8%
Other values (22) 6150
61.5%

Length

2024-04-21T18:19:22.501010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
밀감 394
 
3.9%
사과 391
 
3.9%
고등어 390
 
3.9%
쌀 388
 
3.9%
대파 387
 
3.9%
양파 383
 
3.8%
돼지고기 381
 
3.8%
쇠고기 380
 
3.8%
배 379
 
3.8%
닭고기 377
 
3.8%
Other values (22) 6150
61.5%

last_load_dttm
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03-01 06:18:09
1994 
2021-03-01 06:18:04
1710 
2021-03-01 06:18:06
1494 
2021-03-01 06:18:07
1340 
2021-03-01 06:18:08
1079 
Other values (3)
2383 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-01 06:18:09
2nd row2021-03-01 06:18:09
3rd row2021-03-01 06:18:06
4th row2021-03-01 06:18:06
5th row2021-03-01 06:18:08

Common Values

ValueCountFrequency (%)
2021-03-01 06:18:09 1994
19.9%
2021-03-01 06:18:04 1710
17.1%
2021-03-01 06:18:06 1494
14.9%
2021-03-01 06:18:07 1340
13.4%
2021-03-01 06:18:08 1079
10.8%
2021-03-01 06:18:05 908
9.1%
2021-03-01 06:18:10 799
8.0%
2021-03-01 06:18:03 676
 
6.8%

Length

2024-04-21T18:19:22.889907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:19:23.224857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-01 10000
50.0%
06:18:09 1994
 
10.0%
06:18:04 1710
 
8.6%
06:18:06 1494
 
7.5%
06:18:07 1340
 
6.7%
06:18:08 1079
 
5.4%
06:18:05 908
 
4.5%
06:18:10 799
 
4.0%
06:18:03 676
 
3.4%

Sample

skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
3650130151289882562502116502116884082020-07-23458축산물261수영구500.03800038000<NA>메가마트 남천점35.137353129.111101부산광역시 수영구 남천동 남천동545-2051-608-6000YY쇠고기2021-03-01 06:18:09
33376304627807980503217503217794172020-08-12456농산물170북구3000.02500025000<NA>정이있는구포시장35.208355129.001681부산광역시 북구 구포1동 구포시장길 9051-333-9033YY사과2021-03-01 06:18:09
1590332210310610530285077585077581054062020-10-22456농산물257수영구20.05290052900<NA>홈플러스 익스플러스 광안점35.161668129.113611(48253) 부산광역시 수영구 수영로 625, 491-1 (광안동)051-756-2277YY쌀2021-03-01 06:18:06
16152321859898832508632508632884052020-11-05458축산물374해운대구100.065000130001+이마트(해운대점)35.165993129.16739부산광역시 해운대구 중1동 1767051-608-1234YY쇠고기2021-03-01 06:18:06
27206310809929131492226492226914062020-02-13461주류및음료,차293영도구360.012501250<NA>삼성홈플러스(영도점)35.095905129.04424부산광역시 영도구 봉래동2가 151-1051-418-2000YY소주2021-03-01 06:18:08
13564324472797837509332509332784052020-11-12456농산물64남구3500.02554214900알뜰배6入이마트(문현점)35.144615129.06464부산광역시 남구 문현동 751051-609-1234YY배2021-03-01 06:18:05
21371316628101100225076855076851004072020-10-15464식료품155강서구420.029802980<NA>롯데수퍼(명지점)35.085472128.89784부산광역시 강서구 명지동 3231051-292-5602YY두부2021-03-01 06:18:07
26343311652156154204921604921601544052020-02-06463세제189사상구840.01390013900깨끗한나라이마트(사상점)35.16423128.97891부산광역시 사상구 괘법동 531-2051-329-1234YY화장지2021-03-01 06:18:08
35808302228969538472935472935954072019-03-28463세제360해운대구3.0742574259900/4kg탑마트(반여점)35.205868129.12317(48038) 부산광역시 해운대구 선수촌로 119 (반여동)051-525-0422YY가루비누2021-03-01 06:18:09
30801307251787730503244503244774162020-08-13456농산물209사하구1800.0772213900하우스밀감 8/12~18까지 행사뉴코아아울렛킴스클럽(괴정점)35.098934128.99406부산광역시 사하구 괴정3동 961-1051-209-5061YY밀감2021-03-01 06:18:09
skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
21849316176777639505949505949764062020-09-28459수산물116부산진구680.044045990국산/생물삼성홈플러스(가야점)35.152466129.02731부산광역시 부산진구 가야동 624-7051-890-8023YY고등어2021-03-01 06:18:07
7581330436787742510946510946774062020-12-10456농산물189사상구100.02570257조생삼성홈플러스(서부산점)35.16483128.97812부산광역시 사상구 괘법동 529-1051-319-9157YY밀감2021-03-01 06:18:04
35473302524929125472948472948914062019-03-28461주류및음료,차52남구360.011901190시원소주삼성홈플러스(감만점)35.121605129.08223부산광역시 남구 감만동 8051-609-8000YY소주2021-03-01 06:18:09
14739323264807979509392509392794172020-11-18456농산물180북구3000.02500025000<NA>만덕제일상가시장35.214633129.039816부산광역시 북구 만덕2동 덕천로 304번길 23051-334-7145YY사과2021-03-01 06:18:06
17636320394888775474451474451874172019-04-24458축산물227서구500.01395013950<NA>충무동새벽시장35.09316129.024782부산광역시 서구 남부민1동 해안새벽시장길 68051-242-7273YY돼지고기2021-03-01 06:18:06
984232820510410330284929984929981034062020-02-27464식료품257수영구1.0700700<NA>홈플러스 익스플러스 광안점35.161668129.113611(48253) 부산광역시 수영구 수영로 625, 491-1 (광안동)051-756-2277YY라면2021-03-01 06:18:04
6500331535898866511793511793884172021-01-06458축산물120부산진구100.059000118001+개금골목시장35.151289129.024543부산광역시 부산진구 개금동 가야대로 482번길 40051-892-2606YY쇠고기2021-03-01 06:18:04
6333331716777637511801511801764052021-01-07459수산물64남구756.046036960국산/생물/미판매이마트(문현점)35.144615129.06464부산광역시 남구 문현동 751051-609-1234YY고등어2021-03-01 06:18:04
40494297515848331501128501128834062020-07-09456농산물293영도구2100.032383400<NA>삼성홈플러스(영도점)35.095905129.04424부산광역시 영도구 봉래동2가 151-1051-418-2000YY배추2021-03-01 06:18:10
24247313744858428505049505049844062020-09-10461주류및음료,차145부산진구1.527902790<NA>삼성홈플러스(서면점)35.149452129.06401부산광역시 부산진구 전포동 892-20051-605-1000YY사이다2021-03-01 06:18:07