Overview

Dataset statistics

Number of variables26
Number of observations10000
Missing cells7341
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory231.0 B

Variable types

Numeric15
DateTime2
Categorical5
Text4

Alerts

parkng_at is highly imbalanced (93.1%)Imbalance
card_at is highly imbalanced (94.7%)Imbalance
rm has 6861 (68.6%) missing valuesMissing
skey has unique valuesUnique
unitprice has 115 (1.1%) zerosZeros
prices has 114 (1.1%) zerosZeros

Reproduction

Analysis started2024-04-21 09:09:30.656386
Analysis finished2024-04-21 09:09:32.019930
Duration1.36 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%
Mean22781.464
Minimum1565
Maximum43614
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:32.218470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1565
5-th percentile3686.45
Q112202.25
median23093.5
Q333320.5
95-th percentile41435.25
Maximum43614
Range42049
Interquartile range (IQR)21118.25

Descriptive statistics

Standard deviation12154.817
Coefficient of variation (CV)0.53353976
Kurtosis-1.2087114
Mean22781.464
Median Absolute Deviation (MAD)10530
Skewness-0.034497015
Sum2.2781464 × 108
Variance1.4773957 × 108
MonotonicityNot monotonic
2024-04-21T18:09:32.633575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2546 1
 
< 0.1%
31152 1
 
< 0.1%
31972 1
 
< 0.1%
15581 1
 
< 0.1%
36220 1
 
< 0.1%
15623 1
 
< 0.1%
4028 1
 
< 0.1%
1573 1
 
< 0.1%
31280 1
 
< 0.1%
37609 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1565 1
< 0.1%
1570 1
< 0.1%
1571 1
< 0.1%
1572 1
< 0.1%
1573 1
< 0.1%
1575 1
< 0.1%
1578 1
< 0.1%
1580 1
< 0.1%
1590 1
< 0.1%
1592 1
< 0.1%
ValueCountFrequency (%)
43614 1
< 0.1%
43612 1
< 0.1%
43604 1
< 0.1%
43602 1
< 0.1%
43601 1
< 0.1%
43600 1
< 0.1%
43597 1
< 0.1%
43593 1
< 0.1%
43591 1
< 0.1%
43588 1
< 0.1%

ccode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.6966
Minimum77
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:33.020965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile78
Q183
median90
Q3100
95-th percentile156
Maximum159
Range82
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.977064
Coefficient of variation (CV)0.20039858
Kurtosis5.1403465
Mean94.6966
Median Absolute Deviation (MAD)8
Skewness2.304292
Sum946966
Variance360.12896
MonotonicityNot monotonic
2024-04-21T18:09:33.407468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
89 430
 
4.3%
82 418
 
4.2%
80 410
 
4.1%
87 404
 
4.0%
101 403
 
4.0%
106 401
 
4.0%
88 396
 
4.0%
78 395
 
4.0%
83 393
 
3.9%
77 393
 
3.9%
Other values (22) 5957
59.6%
ValueCountFrequency (%)
77 393
3.9%
78 395
4.0%
79 393
3.9%
80 410
4.1%
81 379
3.8%
82 418
4.2%
83 393
3.9%
84 383
3.8%
85 273
2.7%
86 260
2.6%
ValueCountFrequency (%)
159 82
 
0.8%
158 77
 
0.8%
157 258
2.6%
156 273
2.7%
106 401
4.0%
105 282
2.8%
104 259
2.6%
103 278
2.8%
101 403
4.0%
100 251
2.5%

pcode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.6276
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:33.783819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile77
Q182
median89
Q399
95-th percentile154
Maximum157
Range81
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.751023
Coefficient of variation (CV)0.20027239
Kurtosis5.0468898
Mean93.6276
Median Absolute Deviation (MAD)8
Skewness2.2798586
Sum936276
Variance351.60088
MonotonicityNot monotonic
2024-04-21T18:09:34.205573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
88 430
 
4.3%
81 418
 
4.2%
79 410
 
4.1%
86 404
 
4.0%
100 403
 
4.0%
105 401
 
4.0%
87 396
 
4.0%
77 395
 
4.0%
82 393
 
3.9%
76 393
 
3.9%
Other values (22) 5957
59.6%
ValueCountFrequency (%)
76 393
3.9%
77 395
4.0%
78 393
3.9%
79 410
4.1%
80 379
3.8%
81 418
4.2%
82 393
3.9%
83 383
3.8%
84 273
2.7%
85 260
2.6%
ValueCountFrequency (%)
157 82
 
0.8%
156 77
 
0.8%
155 258
2.6%
154 273
2.7%
105 401
4.0%
104 282
2.8%
103 259
2.6%
102 278
2.8%
100 403
4.0%
99 251
2.5%

bssh_no
Real number (ℝ)

Distinct52
Distinct (%)0.5%
Missing60
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean385.5168
Minimum14
Maximum3028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:34.636556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile19
Q129
median39
Q358
95-th percentile2625
Maximum3028
Range3014
Interquartile range (IQR)29

Descriptive statistics

Standard deviation887.98984
Coefficient of variation (CV)2.3033752
Kurtosis2.8565682
Mean385.5168
Median Absolute Deviation (MAD)11
Skewness2.1886498
Sum3832037
Variance788525.95
MonotonicityNot monotonic
2024-04-21T18:09:35.060291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 369
 
3.7%
41 345
 
3.5%
2524 341
 
3.4%
28 340
 
3.4%
2562 339
 
3.4%
31 336
 
3.4%
34 335
 
3.4%
38 333
 
3.3%
2625 332
 
3.3%
45 331
 
3.3%
Other values (42) 6539
65.4%
ValueCountFrequency (%)
14 75
 
0.8%
15 74
 
0.7%
17 68
 
0.7%
19 294
2.9%
20 280
2.8%
22 297
3.0%
23 256
2.6%
25 288
2.9%
26 319
3.2%
28 340
3.4%
ValueCountFrequency (%)
3028 246
2.5%
2625 332
3.3%
2562 339
3.4%
2524 341
3.4%
2349 65
 
0.7%
87 80
 
0.8%
85 59
 
0.6%
80 83
 
0.8%
79 72
 
0.7%
78 63
 
0.6%

search_no
Real number (ℝ)

Distinct1477
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473074.33
Minimum464693
Maximum481963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:35.465565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum464693
5-th percentile465537
Q1469022.75
median472952
Q3477025
95-th percentile480230
Maximum481963
Range17270
Interquartile range (IQR)8002.25

Descriptive statistics

Standard deviation4917.614
Coefficient of variation (CV)0.010395013
Kurtosis-1.1789209
Mean473074.33
Median Absolute Deviation (MAD)4033
Skewness-0.049938924
Sum4.7307433 × 109
Variance24182927
MonotonicityNot monotonic
2024-04-21T18:09:36.013678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
476984 35
 
0.4%
477037 30
 
0.3%
476985 28
 
0.3%
480234 27
 
0.3%
477045 27
 
0.3%
476983 27
 
0.3%
480235 27
 
0.3%
477151 26
 
0.3%
478640 26
 
0.3%
476974 24
 
0.2%
Other values (1467) 9723
97.2%
ValueCountFrequency (%)
464693 9
0.1%
464694 7
0.1%
464695 11
0.1%
464696 5
0.1%
464697 5
0.1%
464698 10
0.1%
464699 8
0.1%
464700 8
0.1%
464701 5
0.1%
464702 9
0.1%
ValueCountFrequency (%)
481963 4
< 0.1%
481962 6
0.1%
481956 4
< 0.1%
481955 3
< 0.1%
481954 3
< 0.1%
481953 1
 
< 0.1%
481951 1
 
< 0.1%
481949 3
< 0.1%
481945 6
0.1%
481935 7
0.1%

prices_no
Real number (ℝ)

Distinct1477
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473074.33
Minimum464693
Maximum481963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:36.267568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum464693
5-th percentile465537
Q1469022.75
median472952
Q3477025
95-th percentile480230
Maximum481963
Range17270
Interquartile range (IQR)8002.25

Descriptive statistics

Standard deviation4917.614
Coefficient of variation (CV)0.010395013
Kurtosis-1.1789209
Mean473074.33
Median Absolute Deviation (MAD)4033
Skewness-0.049938924
Sum4.7307433 × 109
Variance24182927
MonotonicityNot monotonic
2024-04-21T18:09:36.520699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
476984 35
 
0.4%
477037 30
 
0.3%
476985 28
 
0.3%
480234 27
 
0.3%
477045 27
 
0.3%
476983 27
 
0.3%
480235 27
 
0.3%
477151 26
 
0.3%
478640 26
 
0.3%
476974 24
 
0.2%
Other values (1467) 9723
97.2%
ValueCountFrequency (%)
464693 9
0.1%
464694 7
0.1%
464695 11
0.1%
464696 5
0.1%
464697 5
0.1%
464698 10
0.1%
464699 8
0.1%
464700 8
0.1%
464701 5
0.1%
464702 9
0.1%
ValueCountFrequency (%)
481963 4
< 0.1%
481962 6
0.1%
481956 4
< 0.1%
481955 3
< 0.1%
481954 3
< 0.1%
481953 1
 
< 0.1%
481951 1
 
< 0.1%
481949 3
< 0.1%
481945 6
0.1%
481935 7
0.1%

prdlst
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.6276
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:36.761786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile77
Q182
median89
Q399
95-th percentile154
Maximum157
Range81
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.751023
Coefficient of variation (CV)0.20027239
Kurtosis5.0468898
Mean93.6276
Median Absolute Deviation (MAD)8
Skewness2.2798586
Sum936276
Variance351.60088
MonotonicityNot monotonic
2024-04-21T18:09:36.999770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
88 430
 
4.3%
81 418
 
4.2%
79 410
 
4.1%
86 404
 
4.0%
100 403
 
4.0%
105 401
 
4.0%
87 396
 
4.0%
77 395
 
4.0%
82 393
 
3.9%
76 393
 
3.9%
Other values (22) 5957
59.6%
ValueCountFrequency (%)
76 393
3.9%
77 395
4.0%
78 393
3.9%
79 410
4.1%
80 379
3.8%
81 418
4.2%
82 393
3.9%
83 383
3.8%
84 273
2.7%
85 260
2.6%
ValueCountFrequency (%)
157 82
 
0.8%
156 77
 
0.8%
155 258
2.6%
154 273
2.7%
105 401
4.0%
104 282
2.8%
103 259
2.6%
102 278
2.8%
100 403
4.0%
99 251
2.5%

cl_no
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing60
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean409.11751
Minimum405
Maximum417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:37.213918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum405
5-th percentile405
Q1406
median407
Q3411
95-th percentile417
Maximum417
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.3815733
Coefficient of variation (CV)0.010709816
Kurtosis-0.66867013
Mean409.11751
Median Absolute Deviation (MAD)1
Skewness0.9735096
Sum4066628
Variance19.198184
MonotonicityNot monotonic
2024-04-21T18:09:37.403361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
406 2478
24.8%
407 1854
18.5%
417 1848
18.5%
405 1486
14.9%
411 676
 
6.8%
408 670
 
6.7%
410 618
 
6.2%
416 310
 
3.1%
(Missing) 60
 
0.6%
ValueCountFrequency (%)
405 1486
14.9%
406 2478
24.8%
407 1854
18.5%
408 670
 
6.7%
410 618
 
6.2%
411 676
 
6.8%
416 310
 
3.1%
417 1848
18.5%
ValueCountFrequency (%)
417 1848
18.5%
416 310
 
3.1%
411 676
 
6.8%
410 618
 
6.2%
408 670
 
6.7%
407 1854
18.5%
406 2478
24.8%
405 1486
14.9%

examin_de
Date

Distinct64
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-11-01 00:00:00
Maximum2019-08-28 00:00:00
2024-04-21T18:09:37.638689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:09:37.887551image/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%
Mean459.5888
Minimum456
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:38.088441image/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 deviation3.1174808
Coefficient of variation (CV)0.0067831957
Kurtosis-1.5034815
Mean459.5888
Median Absolute Deviation (MAD)3
Skewness0.18619139
Sum4595888
Variance9.7186864
MonotonicityNot monotonic
2024-04-21T18:09:38.286623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
456 3172
31.7%
464 2015
20.2%
461 1885
18.9%
458 1589
15.9%
463 787
 
7.9%
459 552
 
5.5%
ValueCountFrequency (%)
456 3172
31.7%
458 1589
15.9%
459 552
 
5.5%
461 1885
18.9%
463 787
 
7.9%
464 2015
20.2%
ValueCountFrequency (%)
464 2015
20.2%
463 787
 
7.9%
461 1885
18.9%
459 552
 
5.5%
458 1589
15.9%
456 3172
31.7%

pum_nm
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농산물
3172 
식료품
2015 
주류및음료,차
1885 
축산물
1589 
세제
787 

Length

Max length7
Median length3
Mean length3.6753
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농산물 3172
31.7%
식료품 2015
20.2%
주류및음료,차 1885
18.9%
축산물 1589
15.9%
세제 787
 
7.9%
수산물 552
 
5.5%

Length

2024-04-21T18:09:38.511036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:09:38.694546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농산물 3172
31.7%
식료품 2015
20.2%
주류및음료,차 1885
18.9%
축산물 1589
15.9%
세제 787
 
7.9%
수산물 552
 
5.5%

gugun_cd
Real number (ℝ)

Distinct47
Distinct (%)0.5%
Missing60
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean187.24718
Minimum21
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:38.910331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile31
Q195
median188
Q3261
95-th percentile365
Maximum374
Range353
Interquartile range (IQR)166

Descriptive statistics

Standard deviation102.68791
Coefficient of variation (CV)0.54840832
Kurtosis-0.96864109
Mean187.24718
Median Absolute Deviation (MAD)77
Skewness0.18722267
Sum1861237
Variance10544.807
MonotonicityNot monotonic
2024-04-21T18:09:39.152815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
189 553
 
5.5%
111 380
 
3.8%
227 378
 
3.8%
116 369
 
3.7%
374 361
 
3.6%
223 345
 
3.5%
174 341
 
3.4%
145 340
 
3.4%
261 339
 
3.4%
293 336
 
3.4%
Other values (37) 6198
62.0%
ValueCountFrequency (%)
21 304
3.0%
27 63
 
0.6%
31 256
2.6%
33 58
 
0.6%
48 294
2.9%
52 288
2.9%
53 82
 
0.8%
64 300
3.0%
66 85
 
0.9%
80 70
 
0.7%
ValueCountFrequency (%)
374 361
3.6%
369 69
 
0.7%
365 332
3.3%
360 333
3.3%
333 62
 
0.6%
316 335
3.4%
314 63
 
0.6%
293 336
3.4%
275 59
 
0.6%
273 300
3.0%

gugun_nm
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
해운대구
1095 
부산진구
858 
북구
818 
사하구
800 
동래구
772 
Other values (12)
5657 

Length

Max length4
Median length3
Mean length2.907
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 1095
10.9%
부산진구 858
 
8.6%
북구 818
 
8.2%
사하구 800
 
8.0%
동래구 772
 
7.7%
남구 755
 
7.5%
수영구 723
 
7.2%
사상구 702
 
7.0%
금정구 681
 
6.8%
동구 471
 
4.7%
Other values (7) 2325
23.2%

Length

2024-04-21T18:09:39.409857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해운대구 1095
10.9%
부산진구 858
 
8.6%
북구 818
 
8.2%
사하구 800
 
8.0%
동래구 772
 
7.7%
남구 755
 
7.5%
수영구 723
 
7.2%
사상구 702
 
7.0%
금정구 681
 
6.8%
동구 471
 
4.7%
Other values (7) 2325
23.2%

unit
Real number (ℝ)

Distinct243
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean680.18391
Minimum0
Maximum18000
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:39.643056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median380
Q3990
95-th percentile2396
Maximum18000
Range18000
Interquartile range (IQR)987

Descriptive statistics

Standard deviation1047.2836
Coefficient of variation (CV)1.5397066
Kurtosis25.420565
Mean680.18391
Median Absolute Deviation (MAD)377.5
Skewness3.6682746
Sum6801839.1
Variance1096803
MonotonicityNot monotonic
2024-04-21T18:09:39.892199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500.0 1180
 
11.8%
100.0 860
 
8.6%
1.0 852
 
8.5%
1000.0 720
 
7.2%
2000.0 616
 
6.2%
1.8 511
 
5.1%
600.0 489
 
4.9%
20.0 401
 
4.0%
360.0 267
 
2.7%
320.0 252
 
2.5%
Other values (233) 3852
38.5%
ValueCountFrequency (%)
0.0 8
 
0.1%
0.1 13
 
0.1%
0.8 21
 
0.2%
0.9 240
 
2.4%
1.0 852
8.5%
1.05 4
 
< 0.1%
1.1 10
 
0.1%
1.2 18
 
0.2%
1.4 136
 
1.4%
1.5 232
 
2.3%
ValueCountFrequency (%)
18000.0 2
 
< 0.1%
9000.0 4
 
< 0.1%
6000.0 157
1.6%
5000.0 8
 
0.1%
4500.0 3
 
< 0.1%
3800.0 2
 
< 0.1%
3744.0 8
 
0.1%
3700.0 4
 
< 0.1%
3600.0 2
 
< 0.1%
3560.0 2
 
< 0.1%

unitprice
Real number (ℝ)

ZEROS 

Distinct1616
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9611.0999
Minimum0
Maximum333460
Zeros115
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:40.140412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile796
Q12000
median3895
Q39400
95-th percentile47450
Maximum333460
Range333460
Interquartile range (IQR)7400

Descriptive statistics

Standard deviation13930.126
Coefficient of variation (CV)1.449379
Kurtosis32.845263
Mean9611.0999
Median Absolute Deviation (MAD)2485
Skewness3.3515992
Sum96110999
Variance1.9404842 × 108
MonotonicityNot monotonic
2024-04-21T18:09:40.393583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
676 202
 
2.0%
1190 185
 
1.8%
2500 182
 
1.8%
796 176
 
1.8%
2000 169
 
1.7%
3500 129
 
1.3%
3290 118
 
1.2%
0 115
 
1.1%
1500 115
 
1.1%
1410 113
 
1.1%
Other values (1606) 8496
85.0%
ValueCountFrequency (%)
0 115
1.1%
250 2
 
< 0.1%
279 1
 
< 0.1%
303 2
 
< 0.1%
350 1
 
< 0.1%
461 1
 
< 0.1%
518 1
 
< 0.1%
541 1
 
< 0.1%
590 1
 
< 0.1%
596 2
 
< 0.1%
ValueCountFrequency (%)
333460 1
 
< 0.1%
70000 5
 
0.1%
65000 6
 
0.1%
64000 1
 
< 0.1%
63000 6
 
0.1%
60800 1
 
< 0.1%
60500 9
 
0.1%
60000 24
0.2%
59900 23
0.2%
59800 3
 
< 0.1%

prices
Real number (ℝ)

ZEROS 

Distinct1093
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7968.5332
Minimum0
Maximum333460
Zeros114
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:40.649692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q12000
median3470
Q37990
95-th percentile39710
Maximum333460
Range333460
Interquartile range (IQR)5990

Descriptive statistics

Standard deviation12285.479
Coefficient of variation (CV)1.5417491
Kurtosis56.428795
Mean7968.5332
Median Absolute Deviation (MAD)2060
Skewness4.5352053
Sum79685332
Variance1.5093299 × 108
MonotonicityNot monotonic
2024-04-21T18:09:40.900854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2500 221
 
2.2%
1190 190
 
1.9%
2000 185
 
1.8%
5000 162
 
1.6%
3000 140
 
1.4%
3500 135
 
1.4%
3290 131
 
1.3%
1500 125
 
1.2%
676 117
 
1.2%
2450 117
 
1.2%
Other values (1083) 8477
84.8%
ValueCountFrequency (%)
0 114
1.1%
143 1
 
< 0.1%
157 1
 
< 0.1%
158 1
 
< 0.1%
192 1
 
< 0.1%
200 2
 
< 0.1%
225 1
 
< 0.1%
237 1
 
< 0.1%
239 1
 
< 0.1%
240 4
 
< 0.1%
ValueCountFrequency (%)
333460 1
 
< 0.1%
70000 5
 
0.1%
65000 6
 
0.1%
63000 4
 
< 0.1%
60800 1
 
< 0.1%
60500 9
 
0.1%
60000 24
0.2%
59900 23
0.2%
59800 3
 
< 0.1%
59000 11
0.1%

rm
Text

MISSING 

Distinct1019
Distinct (%)32.5%
Missing6861
Missing (%)68.6%
Memory size156.2 KiB
2024-04-21T18:09:41.688580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length31
Mean length7.3478815
Min length1

Characters and Unicode

Total characters23065
Distinct characters354
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

Unique553 ?
Unique (%)17.6%

Sample

1st row4200/1050g
2nd row내추럴소프트2겹35m*30
3rd row홈플러스카드결재시 10%할인
4th row산청산골
5th row 노지
ValueCountFrequency (%)
행사 196
 
5.1%
1등급 62
 
1.6%
없음 49
 
1.3%
하우스밀감 48
 
1.3%
샘표501 36
 
0.9%
품절 34
 
0.9%
햇양파 33
 
0.9%
하우스 33
 
0.9%
비트 32
 
0.8%
할인행사 28
 
0.7%
Other values (946) 3262
85.5%
2024-04-21T18:09:42.739531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1551
 
6.7%
1453
 
6.3%
1 1179
 
5.1%
/ 854
 
3.7%
2 688
 
3.0%
3 534
 
2.3%
사 477
 
2.1%
9 459
 
2.0%
g 438
 
1.9%
5 418
 
1.8%
Other values (344) 15014
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13006
56.4%
Decimal Number 5887
25.5%
Space Separator 1453
 
6.3%
Other Punctuation 1240
 
5.4%
Lowercase Letter 663
 
2.9%
Math Symbol 289
 
1.3%
Uppercase Letter 274
 
1.2%
Close Punctuation 107
 
0.5%
Open Punctuation 102
 
0.4%
Dash Punctuation 44
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 477
 
3.7%
행 391
 
3.0%
원 254
 
2.0%
지 230
 
1.8%
기 228
 
1.8%
고 220
 
1.7%
등 217
 
1.7%
스 214
 
1.6%
표 171
 
1.3%
하 169
 
1.3%
Other values (298) 10435
80.2%
Lowercase Letter
ValueCountFrequency (%)
g 438
66.1%
k 90
 
13.6%
m 49
 
7.4%
l 42
 
6.3%
p 11
 
1.7%
x 7
 
1.1%
a 6
 
0.9%
e 5
 
0.8%
u 4
 
0.6%
h 4
 
0.6%
Other values (4) 7
 
1.1%
Decimal Number
ValueCountFrequency (%)
0 1551
26.3%
1 1179
20.0%
2 688
11.7%
3 534
 
9.1%
9 459
 
7.8%
5 418
 
7.1%
4 347
 
5.9%
8 294
 
5.0%
6 214
 
3.6%
7 203
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
L 79
28.8%
G 36
13.1%
K 34
12.4%
P 31
 
11.3%
C 25
 
9.1%
T 25
 
9.1%
R 19
 
6.9%
M 16
 
5.8%
A 8
 
2.9%
N 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
/ 854
68.9%
. 156
 
12.6%
* 113
 
9.1%
, 82
 
6.6%
% 28
 
2.3%
& 7
 
0.6%
Math Symbol
ValueCountFrequency (%)
~ 194
67.1%
+ 95
32.9%
Space Separator
ValueCountFrequency (%)
1453
100.0%
Close Punctuation
ValueCountFrequency (%)
) 107
100.0%
Open Punctuation
ValueCountFrequency (%)
( 102
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12997
56.3%
Common 9122
39.5%
Latin 937
 
4.1%
Han 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 477
 
3.7%
행 391
 
3.0%
원 254
 
2.0%
지 230
 
1.8%
기 228
 
1.8%
고 220
 
1.7%
등 217
 
1.7%
스 214
 
1.6%
표 171
 
1.3%
하 169
 
1.3%
Other values (297) 10426
80.2%
Latin
ValueCountFrequency (%)
g 438
46.7%
k 90
 
9.6%
L 79
 
8.4%
m 49
 
5.2%
l 42
 
4.5%
G 36
 
3.8%
K 34
 
3.6%
P 31
 
3.3%
C 25
 
2.7%
T 25
 
2.7%
Other values (14) 88
 
9.4%
Common
ValueCountFrequency (%)
0 1551
17.0%
1453
15.9%
1 1179
12.9%
/ 854
9.4%
2 688
7.5%
3 534
 
5.9%
9 459
 
5.0%
5 418
 
4.6%
4 347
 
3.8%
8 294
 
3.2%
Other values (12) 1345
14.7%
Han
ValueCountFrequency (%)
入 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12993
56.3%
ASCII 10059
43.6%
CJK 9
 
< 0.1%
Compat Jamo 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1551
15.4%
1453
14.4%
1 1179
11.7%
/ 854
 
8.5%
2 688
 
6.8%
3 534
 
5.3%
9 459
 
4.6%
g 438
 
4.4%
5 418
 
4.2%
4 347
 
3.4%
Other values (36) 2138
21.3%
Hangul
ValueCountFrequency (%)
사 477
 
3.7%
행 391
 
3.0%
원 254
 
2.0%
지 230
 
1.8%
기 228
 
1.8%
고 220
 
1.7%
등 217
 
1.7%
스 214
 
1.6%
표 171
 
1.3%
하 169
 
1.3%
Other values (296) 10422
80.2%
CJK
ValueCountFrequency (%)
入 9
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅣ 4
100.0%

bssh_nm
Text

Distinct53
Distinct (%)0.5%
Missing60
Missing (%)0.6%
Memory size156.2 KiB
2024-04-21T18:09:43.614725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.1264588
Min length4

Characters and Unicode

Total characters90717
Distinct characters105
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 row신평골목시장
2nd row탑마트(반여점)
3rd row삼성홈플러스(서면점)
4th row만덕제일상가시장
5th row이마트(연제점)
ValueCountFrequency (%)
삼성홈플러스(가야점 369
 
3.4%
롯데마트(사하점 345
 
3.2%
농협하나로클럽마트 341
 
3.1%
삼성홈플러스(서면점 340
 
3.1%
메가마트 339
 
3.1%
남천점 339
 
3.1%
삼성홈플러스(영도점 336
 
3.1%
농협하나로마트(자갈치점 335
 
3.1%
탑마트(반여점 333
 
3.0%
삼성홈플러스(센텀시티점 332
 
3.0%
Other values (48) 7510
68.8%
2024-04-21T18:09:44.510952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 7571
 
8.3%
( 7166
 
7.9%
) 7166
 
7.9%
트 5007
 
5.5%
마 5007
 
5.5%
스 3280
 
3.6%
플 2724
 
3.0%
러 2724
 
3.0%
홈 2478
 
2.7%
삼 2232
 
2.5%
Other values (95) 45362
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75326
83.0%
Open Punctuation 7166
 
7.9%
Close Punctuation 7166
 
7.9%
Space Separator 979
 
1.1%
Decimal Number 80
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 7571
 
10.1%
트 5007
 
6.6%
마 5007
 
6.6%
스 3280
 
4.4%
플 2724
 
3.6%
러 2724
 
3.6%
홈 2478
 
3.3%
삼 2232
 
3.0%
성 2232
 
3.0%
시 2100
 
2.8%
Other values (91) 39971
53.1%
Open Punctuation
ValueCountFrequency (%)
( 7166
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7166
100.0%
Space Separator
ValueCountFrequency (%)
979
100.0%
Decimal Number
ValueCountFrequency (%)
1 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75326
83.0%
Common 15391
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 7571
 
10.1%
트 5007
 
6.6%
마 5007
 
6.6%
스 3280
 
4.4%
플 2724
 
3.6%
러 2724
 
3.6%
홈 2478
 
3.3%
삼 2232
 
3.0%
성 2232
 
3.0%
시 2100
 
2.8%
Other values (91) 39971
53.1%
Common
ValueCountFrequency (%)
( 7166
46.6%
) 7166
46.6%
979
 
6.4%
1 80
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75326
83.0%
ASCII 15391
 
17.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 7571
 
10.1%
트 5007
 
6.6%
마 5007
 
6.6%
스 3280
 
4.4%
플 2724
 
3.6%
러 2724
 
3.6%
홈 2478
 
3.3%
삼 2232
 
3.0%
성 2232
 
3.0%
시 2100
 
2.8%
Other values (91) 39971
53.1%
ASCII
ValueCountFrequency (%)
( 7166
46.6%
) 7166
46.6%
979
 
6.4%
1 80
 
0.5%

la
Real number (ℝ)

Distinct52
Distinct (%)0.5%
Missing60
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean35.16343
Minimum35.08484
Maximum35.323099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:44.749383image/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 deviation0.056709638
Coefficient of variation (CV)0.0016127447
Kurtosis0.027789346
Mean35.16343
Median Absolute Deviation (MAD)0.0442
Skewness0.60973912
Sum349524.5
Variance0.003215983
MonotonicityNot monotonic
2024-04-21T18:09:44.999877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.152466 369
 
3.7%
35.08484 345
 
3.5%
35.250025 341
 
3.4%
35.149452 340
 
3.4%
35.1373526 339
 
3.4%
35.095905 336
 
3.4%
35.097233 335
 
3.4%
35.205868 333
 
3.3%
35.1709359 332
 
3.3%
35.204113 331
 
3.3%
Other values (42) 6539
65.4%
ValueCountFrequency (%)
35.08484 345
3.5%
35.085472 297
3.0%
35.092663 299
3.0%
35.0931597 79
 
0.8%
35.095905 336
3.4%
35.0970155 63
 
0.6%
35.097233 335
3.4%
35.098934 310
3.1%
35.0996462 80
 
0.8%
35.099649 61
 
0.6%
ValueCountFrequency (%)
35.3230993 294
2.9%
35.250107 304
3.0%
35.250025 341
3.4%
35.2392 256
2.6%
35.234905 322
3.2%
35.2222318 79
 
0.8%
35.2159352 58
 
0.6%
35.2146331 72
 
0.7%
35.2119516 63
 
0.6%
35.211483 301
3.0%

lo
Real number (ℝ)

Distinct52
Distinct (%)0.5%
Missing60
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean129.05546
Minimum128.89784
Maximum129.17634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:09:45.239928image/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 deviation0.061689297
Coefficient of variation (CV)0.00047800609
Kurtosis0.0050960141
Mean129.05546
Median Absolute Deviation (MAD)0.03952
Skewness-0.22161957
Sum1282811.3
Variance0.0038055694
MonotonicityNot monotonic
2024-04-21T18:09:45.479963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.02731 369
 
3.7%
128.97157 345
 
3.5%
129.0112455 341
 
3.4%
129.06401 340
 
3.4%
129.1111015 339
 
3.4%
129.04424 336
 
3.4%
129.02745 335
 
3.4%
129.12317 333
 
3.3%
129.1337096 332
 
3.3%
129.08112 331
 
3.3%
Other values (42) 6539
65.4%
ValueCountFrequency (%)
128.89784 297
3.0%
128.9019892 65
 
0.7%
128.97157 345
3.5%
128.97812 273
2.7%
128.97891 280
2.8%
128.9813855 75
 
0.8%
128.9822804 74
 
0.7%
128.9893459 80
 
0.8%
128.99406 310
3.1%
129.0016813 83
 
0.8%
ValueCountFrequency (%)
129.1763385 294
2.9%
129.1744835 69
 
0.7%
129.16739 302
3.0%
129.1623293 59
 
0.6%
129.1337096 332
3.3%
129.12317 333
3.3%
129.116693 70
 
0.7%
129.113611 246
2.5%
129.1114753 85
 
0.9%
129.1111015 339
3.4%

adres
Text

Distinct52
Distinct (%)0.5%
Missing60
Missing (%)0.6%
Memory size156.2 KiB
2024-04-21T18:09:46.545883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length21.439135
Min length14

Characters and Unicode

Total characters213105
Distinct characters113
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 신평1동 다대로134번길18
2nd row(48038) 부산광역시 해운대구 선수촌로 119 (반여동)
3rd row부산광역시 부산진구 전포동 892-20
4th row부산광역시 북구 만덕2동 덕천로 304번길 23
5th row부산광역시 연제구 연산2동 822-7
ValueCountFrequency (%)
부산광역시 9940
 
22.3%
해운대구 1427
 
3.2%
부산진구 858
 
1.9%
북구 818
 
1.8%
사하구 800
 
1.8%
동래구 772
 
1.7%
남구 755
 
1.7%
수영구 723
 
1.6%
사상구 702
 
1.6%
금정구 681
 
1.5%
Other values (148) 27097
60.8%
2024-04-21T18:09:47.866444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34633
16.3%
동 12191
 
5.7%
부 11691
 
5.5%
산 11305
 
5.3%
구 10910
 
5.1%
광 10186
 
4.8%
시 10163
 
4.8%
1 10025
 
4.7%
역 9940
 
4.7%
2 6811
 
3.2%
Other values (103) 85250
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126886
59.5%
Decimal Number 43428
 
20.4%
Space Separator 34633
 
16.3%
Dash Punctuation 5318
 
2.5%
Open Punctuation 1297
 
0.6%
Close Punctuation 1297
 
0.6%
Other Punctuation 246
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12191
 
9.6%
부 11691
 
9.2%
산 11305
 
8.9%
구 10910
 
8.6%
광 10186
 
8.0%
시 10163
 
8.0%
역 9940
 
7.8%
로 2536
 
2.0%
남 2268
 
1.8%
대 1955
 
1.5%
Other values (88) 43741
34.5%
Decimal Number
ValueCountFrequency (%)
1 10025
23.1%
2 6811
15.7%
3 5783
13.3%
5 3980
 
9.2%
6 3391
 
7.8%
8 3160
 
7.3%
7 3074
 
7.1%
4 2674
 
6.2%
9 2448
 
5.6%
0 2082
 
4.8%
Space Separator
ValueCountFrequency (%)
34633
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5318
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1297
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1297
100.0%
Other Punctuation
ValueCountFrequency (%)
, 246
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 126886
59.5%
Common 86219
40.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12191
 
9.6%
부 11691
 
9.2%
산 11305
 
8.9%
구 10910
 
8.6%
광 10186
 
8.0%
시 10163
 
8.0%
역 9940
 
7.8%
로 2536
 
2.0%
남 2268
 
1.8%
대 1955
 
1.5%
Other values (88) 43741
34.5%
Common
ValueCountFrequency (%)
34633
40.2%
1 10025
 
11.6%
2 6811
 
7.9%
3 5783
 
6.7%
- 5318
 
6.2%
5 3980
 
4.6%
6 3391
 
3.9%
8 3160
 
3.7%
7 3074
 
3.6%
4 2674
 
3.1%
Other values (5) 7370
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 126886
59.5%
ASCII 86219
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34633
40.2%
1 10025
 
11.6%
2 6811
 
7.9%
3 5783
 
6.7%
- 5318
 
6.2%
5 3980
 
4.6%
6 3391
 
3.9%
8 3160
 
3.7%
7 3074
 
3.6%
4 2674
 
3.1%
Other values (5) 7370
 
8.5%
Hangul
ValueCountFrequency (%)
동 12191
 
9.6%
부 11691
 
9.2%
산 11305
 
8.9%
구 10910
 
8.6%
광 10186
 
8.0%
시 10163
 
8.0%
역 9940
 
7.8%
로 2536
 
2.0%
남 2268
 
1.8%
대 1955
 
1.5%
Other values (88) 43741
34.5%

telno
Text

Distinct52
Distinct (%)0.5%
Missing60
Missing (%)0.6%
Memory size156.2 KiB
2024-04-21T18:09:48.511493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.016097
Min length12

Characters and Unicode

Total characters119440
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 row010-485-6447
2nd row051-525-0422
3rd row051-605-1000
4th row051-334-7145
5th row051-860-1052
ValueCountFrequency (%)
051-890-8023 369
 
3.7%
051-603-2500 345
 
3.5%
051-330-9000 341
 
3.4%
051-605-1000 340
 
3.4%
051-608-6000 339
 
3.4%
051-418-2000 336
 
3.4%
051-250-7711 335
 
3.4%
051-525-0422 333
 
3.4%
051-709-8000 332
 
3.3%
051-550-2000 331
 
3.3%
Other values (42) 6539
65.8%
2024-04-21T18:09:49.354146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28843
24.1%
- 19880
16.6%
1 16666
14.0%
5 15792
13.2%
2 10082
 
8.4%
6 6663
 
5.6%
3 4836
 
4.0%
9 4312
 
3.6%
4 4267
 
3.6%
8 4135
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99560
83.4%
Dash Punctuation 19880
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28843
29.0%
1 16666
16.7%
5 15792
15.9%
2 10082
 
10.1%
6 6663
 
6.7%
3 4836
 
4.9%
9 4312
 
4.3%
4 4267
 
4.3%
8 4135
 
4.2%
7 3964
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 19880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28843
24.1%
- 19880
16.6%
1 16666
14.0%
5 15792
13.2%
2 10082
 
8.4%
6 6663
 
5.6%
3 4836
 
4.0%
9 4312
 
3.6%
4 4267
 
3.6%
8 4135
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28843
24.1%
- 19880
16.6%
1 16666
14.0%
5 15792
13.2%
2 10082
 
8.4%
6 6663
 
5.6%
3 4836
 
4.0%
9 4312
 
3.6%
4 4267
 
3.6%
8 4135
 
3.5%

parkng_at
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9875 
N
 
65
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Y 9875
98.8%
N 65
 
0.7%
60
 
0.6%

Length

2024-04-21T18:09:49.571735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:09:49.737876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9875
99.3%
n 65
 
0.7%

card_at
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9940 
 
60

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 9940
99.4%
60
 
0.6%

Length

2024-04-21T18:09:49.909687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:09:50.067697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9940
100.0%

item_name
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
쇠고기
 
430
대파
 
418
사과
 
410
닭고기
 
404
두부
 
403
Other values (27)
7935 

Length

Max length5
Median length4
Mean length2.4612
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row배추
2nd row닭고기
3rd row사이다
4th row두부
5th row우유

Common Values

ValueCountFrequency (%)
쇠고기 430
 
4.3%
대파 418
 
4.2%
사과 410
 
4.1%
닭고기 404
 
4.0%
두부 403
 
4.0%
쌀 401
 
4.0%
돼지고기 396
 
4.0%
밀감 395
 
4.0%
고등어 393
 
3.9%
배 393
 
3.9%
Other values (22) 5957
59.6%

Length

2024-04-21T18:09:50.257494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
쇠고기 430
 
4.3%
대파 418
 
4.2%
사과 410
 
4.1%
닭고기 404
 
4.0%
두부 403
 
4.0%
쌀 401
 
4.0%
돼지고기 396
 
4.0%
밀감 395
 
4.0%
고등어 393
 
3.9%
배 393
 
3.9%
Other values (22) 5957
59.6%

last_load_dttm
Date

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-02-01 06:18:03
Maximum2021-02-01 06:18:11
2024-04-21T18:09:50.444286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:09:50.647885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

Sample

skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
985254684832349464847464847834172018-11-07456농산물218사하구2000.020002000<NA>신평골목시장35.199374128.901989부산광역시 사하구 신평1동 다대로134번길18010-485-6447NY배추2021-02-01 06:18:03
2996531526878638476984476984864072019-05-30458축산물360해운대구1.0400040004200/1050g탑마트(반여점)35.205868129.12317(48038) 부산광역시 해운대구 선수촌로 119 (반여동)051-525-0422YY닭고기2021-02-01 06:18:08
37625323858428466315466315844062018-11-29461주류및음료,차145부산진구1.524502450<NA>삼성홈플러스(서면점)35.149452129.06401부산광역시 부산진구 전포동 892-20051-605-1000YY사이다2021-02-01 06:18:04
3385635417101100794786084786081004172019-07-03464식료품180북구420.020002000<NA>만덕제일상가시장35.214633129.039816부산광역시 북구 만덕2동 덕천로 304번길 23051-334-7145YY두부2021-02-01 06:18:09
1617117731949340470627470627934052019-02-14461주류및음료,차273연제구200.0796796<NA>이마트(연제점)35.17615129.08133부산광역시 연제구 연산2동 822-7051-860-1052YY우유2021-02-01 06:18:06
3872340284888765480178480178874172019-07-31458축산물135부산진구500.01000010000<NA>부전시장35.160843129.057283부산광역시 부산진구 부전1동 중앙대로 767051-818-1091YY돼지고기2021-02-01 06:18:10
2025221813898845472059472059884082019-03-07458축산물95동래구100.0295005900<NA>메가마트(동래점)35.204113129.08112부산광역시 동래구 명륜동 506-3051-550-2000YY쇠고기2021-02-01 06:18:07
1979121352156154354722024722021544102019-03-14463세제227서구840.01090010900내추럴소프트2겹35m*30탑마트(서구)35.092663129.02449부산광역시 서구 남부민1동 685051-244-1221YY화장지2021-02-01 06:18:06
1599217553969530470616470616954162019-02-14463세제209사하구4.5860012900<NA>뉴코아아울렛킴스클럽(괴정점)35.098934128.99406부산광역시 사하구 괴정3동 961-1051-209-5061YY가루비누2021-02-01 06:18:06
4076842329858431480235480235844062019-08-01461주류및음료,차293영도구1.818242190홈플러스카드결재시 10%할인삼성홈플러스(영도점)35.095905129.04424부산광역시 영도구 봉래동2가 151-1051-418-2000YY사이다2021-02-01 06:18:11
skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
3479336354797838478649478649784072019-07-04456농산물360해운대구6000.045947459474970/649g탑마트(반여점)35.205868129.12317(48038) 부산광역시 해운대구 선수촌로 119 (반여동)051-525-0422YY배2021-02-01 06:18:09
354403700193922562478541478541924082019-06-27461주류및음료,차261수영구1.831303130<NA>메가마트 남천점35.137353129.111101부산광역시 수영구 남천동 남천동545-2051-608-6000YY콜라2021-02-01 06:18:09
81399700919023467418467418904072018-12-20461주류및음료,차31금정구500.014601460<NA>롯데마트(금정점)35.2392129.0915부산광역시 금정구 부곡동 223-1051-080-7700YY맥주2021-02-01 06:18:04
2331024871838219473720473720824062019-04-11456농산물48기장군2000.0197019701490/1512g삼성홈플러스(정관점)35.323099129.176339부산광역시 기장군 정관면 매학리 712-1051-519-8200YY무2021-02-01 06:18:07
3149733058979623477762477762964072019-06-13461주류및음료,차31금정구500.025002500<NA>롯데마트(금정점)35.2392129.0915부산광역시 금정구 부곡동 223-1051-080-7700YY커피크림2021-02-01 06:18:09
58927451999838466438466438984072018-12-06464식료품360해운대구0.963506350<NA>탑마트(반여점)35.205868129.12317(48038) 부산광역시 해운대구 선수촌로 119 (반여동)051-525-0422YY간장2021-02-01 06:18:04
1445816019878643469895469895864072019-02-07458축산물186북구1.094309430<NA>롯데마트(화명점)35.234905129.00853부산광역시 북구 화명3동 1975051-604-2500YY닭고기2021-02-01 06:18:06
3122032781787745477753477753774082019-06-13456농산물95동래구1500.0993314900하우스밀감메가마트(동래점)35.204113129.08112부산광역시 동래구 명륜동 506-3051-550-2000YY밀감2021-02-01 06:18:09
342213578279782625478628478628784062019-07-04456농산물365해운대구3744.02241913990실속배삼성홈플러스(센텀시티점)35.170936129.13371부산광역시 해운대구 우동 해운대구 센텀동로6051-709-8000YY배2021-02-01 06:18:09
2917330734898830476296476296884162019-05-23458축산물209사하구100.03995079905/22~28까지 행사뉴코아아울렛킴스클럽(괴정점)35.098934128.99406부산광역시 사하구 괴정3동 961-1051-209-5061YY쇠고기2021-02-01 06:18:08