Overview

Dataset statistics

Number of variables26
Number of observations10000
Missing cells7384
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
DateTime1
Categorical6
Text4

Alerts

parkng_at is highly imbalanced (92.9%)Imbalance
card_at is highly imbalanced (94.8%)Imbalance
rm has 6910 (69.1%) missing valuesMissing
skey has unique valuesUnique
unitprice has 131 (1.3%) zerosZeros
prices has 131 (1.3%) zerosZeros

Reproduction

Analysis started2024-04-21 09:07:20.408318
Analysis finished2024-04-21 09:07:21.459578
Duration1.05 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%
Mean22631.318
Minimum1561
Maximum43624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:21.863846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1561
5-th percentile3793.65
Q112036.25
median22656.5
Q333031
95-th percentile41479.25
Maximum43624
Range42063
Interquartile range (IQR)20994.75

Descriptive statistics

Standard deviation12120.005
Coefficient of variation (CV)0.53554128
Kurtosis-1.2009372
Mean22631.318
Median Absolute Deviation (MAD)10503.5
Skewness-0.005823637
Sum2.2631318 × 108
Variance1.4689453 × 108
MonotonicityNot monotonic
2024-04-21T18:07:22.274543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8959 1
 
< 0.1%
12196 1
 
< 0.1%
16774 1
 
< 0.1%
3074 1
 
< 0.1%
25599 1
 
< 0.1%
6726 1
 
< 0.1%
5087 1
 
< 0.1%
38029 1
 
< 0.1%
3207 1
 
< 0.1%
28873 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1561 1
< 0.1%
1562 1
< 0.1%
1569 1
< 0.1%
1572 1
< 0.1%
1578 1
< 0.1%
1580 1
< 0.1%
1585 1
< 0.1%
1587 1
< 0.1%
1588 1
< 0.1%
1589 1
< 0.1%
ValueCountFrequency (%)
43624 1
< 0.1%
43622 1
< 0.1%
43621 1
< 0.1%
43618 1
< 0.1%
43615 1
< 0.1%
43603 1
< 0.1%
43601 1
< 0.1%
43600 1
< 0.1%
43588 1
< 0.1%
43584 1
< 0.1%

ccode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.4493
Minimum77
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:22.658385image/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 deviation18.722748
Coefficient of variation (CV)0.19823067
Kurtosis5.4065517
Mean94.4493
Median Absolute Deviation (MAD)8
Skewness2.3443126
Sum944493
Variance350.54128
MonotonicityNot monotonic
2024-04-21T18:07:23.042193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
82 440
 
4.4%
83 419
 
4.2%
79 418
 
4.2%
106 415
 
4.2%
78 402
 
4.0%
81 401
 
4.0%
87 398
 
4.0%
80 395
 
4.0%
89 395
 
4.0%
88 394
 
3.9%
Other values (22) 5923
59.2%
ValueCountFrequency (%)
77 369
3.7%
78 402
4.0%
79 418
4.2%
80 395
4.0%
81 401
4.0%
82 440
4.4%
83 419
4.2%
84 379
3.8%
85 259
2.6%
86 266
2.7%
ValueCountFrequency (%)
159 82
 
0.8%
158 71
 
0.7%
157 258
2.6%
156 253
2.5%
106 415
4.2%
105 259
2.6%
104 253
2.5%
103 267
2.7%
101 394
3.9%
100 255
2.5%

pcode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.3829
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:23.418560image/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 deviation18.501333
Coefficient of variation (CV)0.19812335
Kurtosis5.3078367
Mean93.3829
Median Absolute Deviation (MAD)8
Skewness2.3191874
Sum933829
Variance342.29932
MonotonicityNot monotonic
2024-04-21T18:07:23.858304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
81 440
 
4.4%
82 419
 
4.2%
78 418
 
4.2%
105 415
 
4.2%
77 402
 
4.0%
80 401
 
4.0%
86 398
 
4.0%
79 395
 
4.0%
88 395
 
4.0%
87 394
 
3.9%
Other values (22) 5923
59.2%
ValueCountFrequency (%)
76 369
3.7%
77 402
4.0%
78 418
4.2%
79 395
4.0%
80 401
4.0%
81 440
4.4%
82 419
4.2%
83 379
3.8%
84 259
2.6%
85 266
2.7%
ValueCountFrequency (%)
157 82
 
0.8%
156 71
 
0.7%
155 258
2.6%
154 253
2.5%
105 415
4.2%
104 259
2.6%
103 253
2.5%
102 267
2.7%
100 394
3.9%
99 255
2.5%

bssh_no
Real number (ℝ)

Distinct53
Distinct (%)0.5%
Missing59
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean378.64038
Minimum14
Maximum3028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:24.266857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile20
Q129
median39
Q358
95-th percentile2625
Maximum3028
Range3014
Interquartile range (IQR)29

Descriptive statistics

Standard deviation881.44121
Coefficient of variation (CV)2.3279113
Kurtosis3.0308561
Mean378.64038
Median Absolute Deviation (MAD)11
Skewness2.2275251
Sum3764064
Variance776938.6
MonotonicityNot monotonic
2024-04-21T18:07:24.690016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2625 349
 
3.5%
28 343
 
3.4%
38 338
 
3.4%
43 330
 
3.3%
40 328
 
3.3%
26 319
 
3.2%
22 318
 
3.2%
31 317
 
3.2%
39 317
 
3.2%
41 315
 
3.1%
Other values (43) 6667
66.7%
ValueCountFrequency (%)
14 67
 
0.7%
15 72
 
0.7%
17 77
 
0.8%
19 270
2.7%
20 298
3.0%
22 318
3.2%
23 272
2.7%
25 309
3.1%
26 319
3.2%
28 343
3.4%
ValueCountFrequency (%)
3028 252
2.5%
2814 1
 
< 0.1%
2625 349
3.5%
2562 314
3.1%
2524 308
3.1%
2349 70
 
0.7%
87 79
 
0.8%
85 82
 
0.8%
80 89
 
0.9%
79 82
 
0.8%

search_no
Real number (ℝ)

Distinct1482
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473014.79
Minimum464693
Maximum481965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:25.094219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum464693
5-th percentile465541.9
Q1469018
median472930
Q3476997
95-th percentile480231
Maximum481965
Range17272
Interquartile range (IQR)7979

Descriptive statistics

Standard deviation4899.6311
Coefficient of variation (CV)0.010358304
Kurtosis-1.1713467
Mean473014.79
Median Absolute Deviation (MAD)4053
Skewness-0.020245746
Sum4.7301479 × 109
Variance24006385
MonotonicityNot monotonic
2024-04-21T18:07:25.532429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480233 32
 
0.3%
476984 31
 
0.3%
477037 30
 
0.3%
477045 28
 
0.3%
480234 27
 
0.3%
476983 26
 
0.3%
476981 25
 
0.2%
476980 23
 
0.2%
480235 23
 
0.2%
476982 23
 
0.2%
Other values (1472) 9732
97.3%
ValueCountFrequency (%)
464693 10
0.1%
464694 10
0.1%
464695 5
0.1%
464696 10
0.1%
464697 7
0.1%
464698 10
0.1%
464699 4
 
< 0.1%
464700 6
0.1%
464701 7
0.1%
464702 7
0.1%
ValueCountFrequency (%)
481965 4
< 0.1%
481963 2
< 0.1%
481962 3
< 0.1%
481956 2
< 0.1%
481955 4
< 0.1%
481954 4
< 0.1%
481951 1
 
< 0.1%
481949 3
< 0.1%
481945 3
< 0.1%
481935 4
< 0.1%

prices_no
Real number (ℝ)

Distinct1482
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473014.79
Minimum464693
Maximum481965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:25.944541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum464693
5-th percentile465541.9
Q1469018
median472930
Q3476997
95-th percentile480231
Maximum481965
Range17272
Interquartile range (IQR)7979

Descriptive statistics

Standard deviation4899.6311
Coefficient of variation (CV)0.010358304
Kurtosis-1.1713467
Mean473014.79
Median Absolute Deviation (MAD)4053
Skewness-0.020245746
Sum4.7301479 × 109
Variance24006385
MonotonicityNot monotonic
2024-04-21T18:07:26.380728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480233 32
 
0.3%
476984 31
 
0.3%
477037 30
 
0.3%
477045 28
 
0.3%
480234 27
 
0.3%
476983 26
 
0.3%
476981 25
 
0.2%
476980 23
 
0.2%
480235 23
 
0.2%
476982 23
 
0.2%
Other values (1472) 9732
97.3%
ValueCountFrequency (%)
464693 10
0.1%
464694 10
0.1%
464695 5
0.1%
464696 10
0.1%
464697 7
0.1%
464698 10
0.1%
464699 4
 
< 0.1%
464700 6
0.1%
464701 7
0.1%
464702 7
0.1%
ValueCountFrequency (%)
481965 4
< 0.1%
481963 2
< 0.1%
481962 3
< 0.1%
481956 2
< 0.1%
481955 4
< 0.1%
481954 4
< 0.1%
481951 1
 
< 0.1%
481949 3
< 0.1%
481945 3
< 0.1%
481935 4
< 0.1%

prdlst
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.3829
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:26.782410image/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 deviation18.501333
Coefficient of variation (CV)0.19812335
Kurtosis5.3078367
Mean93.3829
Median Absolute Deviation (MAD)8
Skewness2.3191874
Sum933829
Variance342.29932
MonotonicityNot monotonic
2024-04-21T18:07:27.202566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
81 440
 
4.4%
82 419
 
4.2%
78 418
 
4.2%
105 415
 
4.2%
77 402
 
4.0%
80 401
 
4.0%
86 398
 
4.0%
79 395
 
4.0%
88 395
 
4.0%
87 394
 
3.9%
Other values (22) 5923
59.2%
ValueCountFrequency (%)
76 369
3.7%
77 402
4.0%
78 418
4.2%
79 395
4.0%
80 401
4.0%
81 440
4.4%
82 419
4.2%
83 379
3.8%
84 259
2.6%
85 266
2.7%
ValueCountFrequency (%)
157 82
 
0.8%
156 71
 
0.7%
155 258
2.6%
154 253
2.5%
105 415
4.2%
104 259
2.6%
103 253
2.5%
102 267
2.7%
100 394
3.9%
99 255
2.5%

cl_no
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing59
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean409.13982
Minimum405
Maximum419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:27.556650image/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 deviation4.4264574
Coefficient of variation (CV)0.010818936
Kurtosis-0.71866753
Mean409.13982
Median Absolute Deviation (MAD)2
Skewness0.95863223
Sum4067259
Variance19.593525
MonotonicityNot monotonic
2024-04-21T18:07:27.882601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
406 2451
24.5%
417 1909
19.1%
407 1885
18.9%
405 1532
15.3%
408 621
 
6.2%
411 620
 
6.2%
410 619
 
6.2%
416 303
 
3.0%
419 1
 
< 0.1%
(Missing) 59
 
0.6%
ValueCountFrequency (%)
405 1532
15.3%
406 2451
24.5%
407 1885
18.9%
408 621
 
6.2%
410 619
 
6.2%
411 620
 
6.2%
416 303
 
3.0%
417 1909
19.1%
419 1
 
< 0.1%
ValueCountFrequency (%)
419 1
 
< 0.1%
417 1909
19.1%
416 303
 
3.0%
411 620
 
6.2%
410 619
 
6.2%
408 621
 
6.2%
407 1885
18.9%
406 2451
24.5%
405 1532
15.3%

examin_de
Date

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-11-01 00:00:00
Maximum2019-08-28 00:00:00
2024-04-21T18:07:28.262840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:07:28.690459image/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%
Mean459.5363
Minimum456
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:29.046234image/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 deviation3.1191433
Coefficient of variation (CV)0.0067875886
Kurtosis-1.5002092
Mean459.5363
Median Absolute Deviation (MAD)3
Skewness0.20620018
Sum4595363
Variance9.7290552
MonotonicityNot monotonic
2024-04-21T18:07:29.420244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
456 3269
32.7%
464 1956
19.6%
461 1891
18.9%
458 1568
15.7%
463 794
 
7.9%
459 522
 
5.2%
ValueCountFrequency (%)
456 3269
32.7%
458 1568
15.7%
459 522
 
5.2%
461 1891
18.9%
463 794
 
7.9%
464 1956
19.6%
ValueCountFrequency (%)
464 1956
19.6%
463 794
 
7.9%
461 1891
18.9%
459 522
 
5.2%
458 1568
15.7%
456 3269
32.7%

pum_nm
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농산물
3269 
식료품
1956 
주류및음료,차
1891 
축산물
1568 
세제
794 

Length

Max length7
Median length3
Mean length3.677
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농산물 3269
32.7%
식료품 1956
19.6%
주류및음료,차 1891
18.9%
축산물 1568
15.7%
세제 794
 
7.9%
수산물 522
 
5.2%

Length

2024-04-21T18:07:29.819761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:07:30.143624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농산물 3269
32.7%
식료품 1956
19.6%
주류및음료,차 1891
18.9%
축산물 1568
15.7%
세제 794
 
7.9%
수산물 522
 
5.2%

gugun_cd
Real number (ℝ)

Distinct47
Distinct (%)0.5%
Missing59
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean188.26778
Minimum21
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:30.502456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile31
Q195
median188
Q3262
95-th percentile365
Maximum374
Range353
Interquartile range (IQR)167

Descriptive statistics

Standard deviation103.16646
Coefficient of variation (CV)0.54797726
Kurtosis-0.97853862
Mean188.26778
Median Absolute Deviation (MAD)77
Skewness0.17712577
Sum1871570
Variance10643.319
MonotonicityNot monotonic
2024-04-21T18:07:30.926644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
189 592
 
5.9%
111 390
 
3.9%
374 375
 
3.8%
227 360
 
3.6%
365 349
 
3.5%
145 343
 
3.4%
360 338
 
3.4%
186 330
 
3.3%
273 328
 
3.3%
92 319
 
3.2%
Other values (37) 6217
62.2%
ValueCountFrequency (%)
21 285
2.9%
27 73
 
0.7%
31 272
2.7%
33 61
 
0.6%
48 271
2.7%
52 309
3.1%
53 74
 
0.7%
64 309
3.1%
66 85
 
0.9%
80 71
 
0.7%
ValueCountFrequency (%)
374 375
3.8%
369 74
 
0.7%
365 349
3.5%
360 338
3.4%
333 69
 
0.7%
316 312
3.1%
314 62
 
0.6%
293 317
3.2%
275 82
 
0.8%
273 328
3.3%

gugun_nm
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
해운대구
1136 
북구
809 
부산진구
804 
남구
777 
동래구
766 
Other values (12)
5708 

Length

Max length4
Median length3
Mean length2.9069
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중구
2nd row남구
3rd row영도구
4th row서구
5th row북구

Common Values

ValueCountFrequency (%)
해운대구 1136
11.4%
북구 809
 
8.1%
부산진구 804
 
8.0%
남구 777
 
7.8%
동래구 766
 
7.7%
사하구 761
 
7.6%
사상구 731
 
7.3%
수영구 727
 
7.3%
금정구 691
 
6.9%
연제구 489
 
4.9%
Other values (7) 2309
23.1%

Length

2024-04-21T18:07:31.374880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해운대구 1136
11.4%
북구 809
 
8.1%
부산진구 804
 
8.0%
남구 777
 
7.8%
동래구 766
 
7.7%
사하구 761
 
7.6%
사상구 731
 
7.3%
수영구 727
 
7.3%
금정구 691
 
6.9%
연제구 489
 
4.9%
Other values (7) 2309
23.1%

unit
Real number (ℝ)

Distinct260
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean683.67752
Minimum0
Maximum9000
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:31.924430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median380
Q31000
95-th percentile2202.4
Maximum9000
Range9000
Interquartile range (IQR)997

Descriptive statistics

Standard deviation1022.4866
Coefficient of variation (CV)1.4955685
Kurtosis12.46214
Mean683.67752
Median Absolute Deviation (MAD)378
Skewness3.0300207
Sum6836775.2
Variance1045478.8
MonotonicityNot monotonic
2024-04-21T18:07:32.160948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500.0 1097
 
11.0%
100.0 872
 
8.7%
1.0 805
 
8.1%
1000.0 772
 
7.7%
2000.0 642
 
6.4%
1.8 519
 
5.2%
600.0 498
 
5.0%
20.0 415
 
4.2%
360.0 290
 
2.9%
320.0 256
 
2.6%
Other values (250) 3834
38.3%
ValueCountFrequency (%)
0.0 4
 
< 0.1%
0.1 15
 
0.1%
0.7 3
 
< 0.1%
0.8 28
 
0.3%
0.9 226
 
2.3%
1.0 805
8.1%
1.005 1
 
< 0.1%
1.05 7
 
0.1%
1.1 13
 
0.1%
1.2 21
 
0.2%
ValueCountFrequency (%)
9000.0 6
 
0.1%
6000.0 156
1.6%
5000.0 6
 
0.1%
4500.0 1
 
< 0.1%
3800.0 3
 
< 0.1%
3744.0 13
 
0.1%
3700.0 5
 
0.1%
3600.0 1
 
< 0.1%
3500.0 11
 
0.1%
3400.0 4
 
< 0.1%

unitprice
Real number (ℝ)

ZEROS 

Distinct1654
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9483.068
Minimum0
Maximum70000
Zeros131
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:32.394093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile796
Q12000
median3800
Q38990
95-th percentile47450
Maximum70000
Range70000
Interquartile range (IQR)6990

Descriptive statistics

Standard deviation13466.415
Coefficient of variation (CV)1.4200483
Kurtosis4.4030086
Mean9483.068
Median Absolute Deviation (MAD)2450
Skewness2.2913135
Sum94830680
Variance1.8134432 × 108
MonotonicityNot monotonic
2024-04-21T18:07:32.641950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1190 211
 
2.1%
676 200
 
2.0%
2500 187
 
1.9%
2000 176
 
1.8%
796 152
 
1.5%
3500 145
 
1.5%
0 131
 
1.3%
1500 128
 
1.3%
7250 116
 
1.2%
3000 107
 
1.1%
Other values (1644) 8447
84.5%
ValueCountFrequency (%)
0 131
1.3%
250 2
 
< 0.1%
390 1
 
< 0.1%
531 1
 
< 0.1%
541 1
 
< 0.1%
590 1
 
< 0.1%
596 2
 
< 0.1%
664 1
 
< 0.1%
666 2
 
< 0.1%
676 200
2.0%
ValueCountFrequency (%)
70000 1
 
< 0.1%
65000 3
 
< 0.1%
64000 1
 
< 0.1%
63000 3
 
< 0.1%
60800 1
 
< 0.1%
60500 6
 
0.1%
60000 23
0.2%
59900 26
0.3%
59800 2
 
< 0.1%
59000 14
0.1%

prices
Real number (ℝ)

ZEROS 

Distinct1085
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7840.6123
Minimum0
Maximum70000
Zeros131
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:32.891757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q11990
median3430
Q37900
95-th percentile40000
Maximum70000
Range70000
Interquartile range (IQR)5910

Descriptive statistics

Standard deviation11741.399
Coefficient of variation (CV)1.4975105
Kurtosis8.7841853
Mean7840.6123
Median Absolute Deviation (MAD)2030
Skewness3.0026461
Sum78406123
Variance1.3786045 × 108
MonotonicityNot monotonic
2024-04-21T18:07:33.140186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1190 212
 
2.1%
2000 211
 
2.1%
2500 208
 
2.1%
3500 152
 
1.5%
5000 151
 
1.5%
1500 141
 
1.4%
3000 141
 
1.4%
0 131
 
1.3%
7250 116
 
1.2%
3290 112
 
1.1%
Other values (1075) 8425
84.2%
ValueCountFrequency (%)
0 131
1.3%
145 1
 
< 0.1%
199 2
 
< 0.1%
200 2
 
< 0.1%
213 1
 
< 0.1%
225 1
 
< 0.1%
229 1
 
< 0.1%
236 1
 
< 0.1%
237 1
 
< 0.1%
249 2
 
< 0.1%
ValueCountFrequency (%)
70000 1
 
< 0.1%
65000 3
 
< 0.1%
63000 1
 
< 0.1%
60800 1
 
< 0.1%
60500 6
 
0.1%
60000 23
0.2%
59900 26
0.3%
59800 2
 
< 0.1%
59000 11
0.1%
58000 11
0.1%

rm
Text

MISSING 

Distinct999
Distinct (%)32.3%
Missing6910
Missing (%)69.1%
Memory size156.2 KiB
2024-04-21T18:07:33.979432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length29
Mean length7.2255663
Min length1

Characters and Unicode

Total characters22327
Distinct characters349
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

Unique563 ?
Unique (%)18.2%

Sample

1st row행사
2nd row재주밀감
3rd row이마트
4th row100g/7490*
5th row맥심모카골드100개
ValueCountFrequency (%)
행사 154
 
4.1%
1등급 47
 
1.3%
없음 47
 
1.3%
하우스밀감 46
 
1.2%
이맛쌀 41
 
1.1%
햇양파 39
 
1.0%
샘표501 36
 
1.0%
흙대파 32
 
0.9%
곰표 32
 
0.9%
1+1 32
 
0.9%
Other values (928) 3228
86.4%
2024-04-21T18:07:35.076123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1601
 
7.2%
1461
 
6.5%
1 1090
 
4.9%
/ 790
 
3.5%
2 588
 
2.6%
3 449
 
2.0%
g 444
 
2.0%
사 439
 
2.0%
9 417
 
1.9%
5 410
 
1.8%
Other values (339) 14638
65.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12600
56.4%
Decimal Number 5592
25.0%
Space Separator 1461
 
6.5%
Other Punctuation 1189
 
5.3%
Lowercase Letter 661
 
3.0%
Uppercase Letter 293
 
1.3%
Math Symbol 275
 
1.2%
Close Punctuation 110
 
0.5%
Open Punctuation 104
 
0.5%
Dash Punctuation 42
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 439
 
3.5%
행 348
 
2.8%
원 248
 
2.0%
기 206
 
1.6%
지 203
 
1.6%
스 198
 
1.6%
이 193
 
1.5%
생 188
 
1.5%
고 183
 
1.5%
쌀 182
 
1.4%
Other values (293) 10212
81.0%
Lowercase Letter
ValueCountFrequency (%)
g 444
67.2%
k 86
 
13.0%
l 44
 
6.7%
m 40
 
6.1%
p 9
 
1.4%
a 8
 
1.2%
x 6
 
0.9%
e 6
 
0.9%
s 5
 
0.8%
u 5
 
0.8%
Other values (4) 8
 
1.2%
Decimal Number
ValueCountFrequency (%)
0 1601
28.6%
1 1090
19.5%
2 588
 
10.5%
3 449
 
8.0%
9 417
 
7.5%
5 410
 
7.3%
4 343
 
6.1%
8 271
 
4.8%
7 228
 
4.1%
6 195
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
L 81
27.6%
G 46
15.7%
K 40
13.7%
P 36
12.3%
C 27
 
9.2%
T 22
 
7.5%
R 18
 
6.1%
M 12
 
4.1%
A 10
 
3.4%
B 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 790
66.4%
. 165
 
13.9%
* 116
 
9.8%
, 87
 
7.3%
% 26
 
2.2%
& 5
 
0.4%
Math Symbol
ValueCountFrequency (%)
~ 173
62.9%
+ 102
37.1%
Space Separator
ValueCountFrequency (%)
1461
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12591
56.4%
Common 8773
39.3%
Latin 954
 
4.3%
Han 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 439
 
3.5%
행 348
 
2.8%
원 248
 
2.0%
기 206
 
1.6%
지 203
 
1.6%
스 198
 
1.6%
이 193
 
1.5%
생 188
 
1.5%
고 183
 
1.5%
쌀 182
 
1.4%
Other values (292) 10203
81.0%
Latin
ValueCountFrequency (%)
g 444
46.5%
k 86
 
9.0%
L 81
 
8.5%
G 46
 
4.8%
l 44
 
4.6%
m 40
 
4.2%
K 40
 
4.2%
P 36
 
3.8%
C 27
 
2.8%
T 22
 
2.3%
Other values (14) 88
 
9.2%
Common
ValueCountFrequency (%)
0 1601
18.2%
1461
16.7%
1 1090
12.4%
/ 790
9.0%
2 588
 
6.7%
3 449
 
5.1%
9 417
 
4.8%
5 410
 
4.7%
4 343
 
3.9%
8 271
 
3.1%
Other values (12) 1353
15.4%
Han
ValueCountFrequency (%)
入 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12585
56.4%
ASCII 9727
43.6%
CJK 9
 
< 0.1%
Compat Jamo 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1601
16.5%
1461
15.0%
1 1090
11.2%
/ 790
 
8.1%
2 588
 
6.0%
3 449
 
4.6%
g 444
 
4.6%
9 417
 
4.3%
5 410
 
4.2%
4 343
 
3.5%
Other values (36) 2134
21.9%
Hangul
ValueCountFrequency (%)
사 439
 
3.5%
행 348
 
2.8%
원 248
 
2.0%
기 206
 
1.6%
지 203
 
1.6%
스 198
 
1.6%
이 193
 
1.5%
생 188
 
1.5%
고 183
 
1.5%
쌀 182
 
1.4%
Other values (291) 10197
81.0%
CJK
ValueCountFrequency (%)
入 9
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅣ 6
100.0%

bssh_nm
Text

Distinct54
Distinct (%)0.5%
Missing59
Missing (%)0.6%
Memory size156.2 KiB
2024-04-21T18:07:35.884399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length9.095765
Min length4

Characters and Unicode

Total characters90421
Distinct characters108
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row농협하나로마트(자갈치점)
2nd row이마트(문현점)
3rd row삼성홈플러스(영도점)
4th row새벽해안시장
5th row정이있는구포시장
ValueCountFrequency (%)
삼성홈플러스(센텀시티점 349
 
3.2%
삼성홈플러스(서면점 343
 
3.1%
탑마트(반여점 338
 
3.1%
롯데마트(화명점 330
 
3.0%
이마트(연제점 328
 
3.0%
탑마트(초량점 319
 
2.9%
롯데수퍼(명지점 318
 
2.9%
삼성홈플러스(영도점 317
 
2.9%
삼성홈플러스(가야점 317
 
2.9%
롯데마트(사하점 315
 
2.9%
Other values (49) 7629
70.0%
2024-04-21T18:07:36.925366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 7560
 
8.4%
( 7157
 
7.9%
) 7157
 
7.9%
마 4959
 
5.5%
트 4959
 
5.5%
스 3258
 
3.6%
플 2703
 
3.0%
러 2703
 
3.0%
홈 2451
 
2.7%
성 2199
 
2.4%
Other values (98) 45315
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75066
83.0%
Open Punctuation 7157
 
7.9%
Close Punctuation 7157
 
7.9%
Space Separator 962
 
1.1%
Decimal Number 79
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 7560
 
10.1%
마 4959
 
6.6%
트 4959
 
6.6%
스 3258
 
4.3%
플 2703
 
3.6%
러 2703
 
3.6%
홈 2451
 
3.3%
성 2199
 
2.9%
삼 2199
 
2.9%
시 2179
 
2.9%
Other values (94) 39896
53.1%
Open Punctuation
ValueCountFrequency (%)
( 7157
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7157
100.0%
Space Separator
ValueCountFrequency (%)
962
100.0%
Decimal Number
ValueCountFrequency (%)
1 79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75066
83.0%
Common 15355
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 7560
 
10.1%
마 4959
 
6.6%
트 4959
 
6.6%
스 3258
 
4.3%
플 2703
 
3.6%
러 2703
 
3.6%
홈 2451
 
3.3%
성 2199
 
2.9%
삼 2199
 
2.9%
시 2179
 
2.9%
Other values (94) 39896
53.1%
Common
ValueCountFrequency (%)
( 7157
46.6%
) 7157
46.6%
962
 
6.3%
1 79
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75066
83.0%
ASCII 15355
 
17.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 7560
 
10.1%
마 4959
 
6.6%
트 4959
 
6.6%
스 3258
 
4.3%
플 2703
 
3.6%
러 2703
 
3.6%
홈 2451
 
3.3%
성 2199
 
2.9%
삼 2199
 
2.9%
시 2179
 
2.9%
Other values (94) 39896
53.1%
ASCII
ValueCountFrequency (%)
( 7157
46.6%
) 7157
46.6%
962
 
6.3%
1 79
 
0.5%

la
Real number (ℝ)

Distinct52
Distinct (%)0.5%
Missing60
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean35.163587
Minimum35.08484
Maximum35.323099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:37.167091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.08484
5-th percentile35.085472
Q135.11677
median35.16423
Q335.205868
95-th percentile35.250107
Maximum35.323099
Range0.2382593
Interquartile range (IQR)0.089098

Descriptive statistics

Standard deviation0.05573488
Coefficient of variation (CV)0.0015850169
Kurtosis0.038897052
Mean35.163587
Median Absolute Deviation (MAD)0.042625
Skewness0.57852267
Sum349526.05
Variance0.0031063769
MonotonicityNot monotonic
2024-04-21T18:07:37.415193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1709359 349
 
3.5%
35.149452 343
 
3.4%
35.205868 338
 
3.4%
35.234905 330
 
3.3%
35.17615 328
 
3.3%
35.11677 319
 
3.2%
35.085472 318
 
3.2%
35.095905 317
 
3.2%
35.152466 317
 
3.2%
35.08484 315
 
3.1%
Other values (42) 6666
66.7%
ValueCountFrequency (%)
35.08484 315
3.1%
35.085472 318
3.2%
35.092663 300
3.0%
35.0931597 60
 
0.6%
35.095905 317
3.2%
35.0970155 62
 
0.6%
35.097233 312
3.1%
35.098934 303
3.0%
35.0996462 73
 
0.7%
35.099649 72
 
0.7%
ValueCountFrequency (%)
35.3230993 270
2.7%
35.250107 285
2.9%
35.250025 308
3.1%
35.2392 272
2.7%
35.234905 330
3.3%
35.2222318 78
 
0.8%
35.2159352 61
 
0.6%
35.2146331 82
 
0.8%
35.2119516 73
 
0.7%
35.211483 312
3.1%

lo
Real number (ℝ)

Distinct52
Distinct (%)0.5%
Missing60
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean129.05579
Minimum128.89784
Maximum129.17634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:07:37.655211image/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 deviation0.062123815
Coefficient of variation (CV)0.00048137178
Kurtosis0.021004683
Mean129.05579
Median Absolute Deviation (MAD)0.03952
Skewness-0.27173001
Sum1282814.5
Variance0.0038593684
MonotonicityNot monotonic
2024-04-21T18:07:37.892115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.1337096 349
 
3.5%
129.06401 343
 
3.4%
129.12317 338
 
3.4%
129.00853 330
 
3.3%
129.08133 328
 
3.3%
129.0395 319
 
3.2%
128.89784 318
 
3.2%
129.04424 317
 
3.2%
129.02731 317
 
3.2%
128.97157 315
 
3.1%
Other values (42) 6666
66.7%
ValueCountFrequency (%)
128.89784 318
3.2%
128.9019892 70
 
0.7%
128.97157 315
3.1%
128.97812 294
2.9%
128.97891 298
3.0%
128.9813855 67
 
0.7%
128.9822804 72
 
0.7%
128.9893459 73
 
0.7%
128.99406 303
3.0%
129.0016813 89
 
0.9%
ValueCountFrequency (%)
129.1763385 270
2.7%
129.1744835 74
 
0.7%
129.16739 312
3.1%
129.1623293 63
 
0.6%
129.1337096 349
3.5%
129.12317 338
3.4%
129.116693 84
 
0.8%
129.113611 252
2.5%
129.1114753 85
 
0.9%
129.1111015 314
3.1%

adres
Text

Distinct53
Distinct (%)0.5%
Missing59
Missing (%)0.6%
Memory size156.2 KiB
2024-04-21T18:07:39.055444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length21.478121
Min length14

Characters and Unicode

Total characters213514
Distinct characters114
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row부산광역시 중구 남포동6가 1
2nd row부산광역시 남구 문현동 751
3rd row부산광역시 영도구 봉래동2가 151-1
4th row부산광역시 서구 남부민1동 해안새벽시장길 68
5th row부산광역시 북구 구포1동 구포시장길 9
ValueCountFrequency (%)
부산광역시 9941
 
22.2%
해운대구 1485
 
3.3%
북구 809
 
1.8%
부산진구 804
 
1.8%
남구 777
 
1.7%
동래구 766
 
1.7%
사하구 761
 
1.7%
사상구 731
 
1.6%
수영구 727
 
1.6%
금정구 691
 
1.5%
Other values (152) 27236
60.9%
2024-04-21T18:07:40.439156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34787
16.3%
동 12171
 
5.7%
부 11660
 
5.5%
산 11311
 
5.3%
구 10922
 
5.1%
광 10193
 
4.8%
시 10159
 
4.8%
1 10062
 
4.7%
역 9941
 
4.7%
2 6923
 
3.2%
Other values (104) 85385
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127048
59.5%
Decimal Number 43515
 
20.4%
Space Separator 34787
 
16.3%
Dash Punctuation 5228
 
2.4%
Open Punctuation 1342
 
0.6%
Close Punctuation 1342
 
0.6%
Other Punctuation 252
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12171
 
9.6%
부 11660
 
9.2%
산 11311
 
8.9%
구 10922
 
8.6%
광 10193
 
8.0%
시 10159
 
8.0%
역 9941
 
7.8%
로 2631
 
2.1%
남 2202
 
1.7%
대 1996
 
1.6%
Other values (89) 43862
34.5%
Decimal Number
ValueCountFrequency (%)
1 10062
23.1%
2 6923
15.9%
3 5826
13.4%
5 3981
 
9.1%
6 3336
 
7.7%
8 3153
 
7.2%
7 3050
 
7.0%
4 2634
 
6.1%
9 2492
 
5.7%
0 2058
 
4.7%
Space Separator
ValueCountFrequency (%)
34787
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5228
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1342
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1342
100.0%
Other Punctuation
ValueCountFrequency (%)
, 252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 127048
59.5%
Common 86466
40.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12171
 
9.6%
부 11660
 
9.2%
산 11311
 
8.9%
구 10922
 
8.6%
광 10193
 
8.0%
시 10159
 
8.0%
역 9941
 
7.8%
로 2631
 
2.1%
남 2202
 
1.7%
대 1996
 
1.6%
Other values (89) 43862
34.5%
Common
ValueCountFrequency (%)
34787
40.2%
1 10062
 
11.6%
2 6923
 
8.0%
3 5826
 
6.7%
- 5228
 
6.0%
5 3981
 
4.6%
6 3336
 
3.9%
8 3153
 
3.6%
7 3050
 
3.5%
4 2634
 
3.0%
Other values (5) 7486
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 127048
59.5%
ASCII 86466
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34787
40.2%
1 10062
 
11.6%
2 6923
 
8.0%
3 5826
 
6.7%
- 5228
 
6.0%
5 3981
 
4.6%
6 3336
 
3.9%
8 3153
 
3.6%
7 3050
 
3.5%
4 2634
 
3.0%
Other values (5) 7486
 
8.7%
Hangul
ValueCountFrequency (%)
동 12171
 
9.6%
부 11660
 
9.2%
산 11311
 
8.9%
구 10922
 
8.6%
광 10193
 
8.0%
시 10159
 
8.0%
역 9941
 
7.8%
로 2631
 
2.1%
남 2202
 
1.7%
대 1996
 
1.6%
Other values (89) 43862
34.5%

telno
Text

Distinct53
Distinct (%)0.5%
Missing59
Missing (%)0.6%
Memory size156.2 KiB
2024-04-21T18:07:41.093165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.01529
Min length12

Characters and Unicode

Total characters119444
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row051-250-7711
2nd row051-609-1234
3rd row051-418-2000
4th row051-242-7273
5th row051-333-9033
ValueCountFrequency (%)
051-709-8000 349
 
3.5%
051-605-1000 343
 
3.5%
051-525-0422 338
 
3.4%
051-604-2500 330
 
3.3%
051-860-1052 328
 
3.3%
051-466-2112 319
 
3.2%
051-292-5602 318
 
3.2%
051-418-2000 317
 
3.2%
051-890-8023 317
 
3.2%
051-603-2500 315
 
3.2%
Other values (43) 6667
67.1%
2024-04-21T18:07:41.947254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28691
24.0%
- 19882
16.6%
1 16765
14.0%
5 15848
13.3%
2 9999
 
8.4%
6 6678
 
5.6%
3 4800
 
4.0%
9 4361
 
3.7%
4 4309
 
3.6%
8 4087
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99562
83.4%
Dash Punctuation 19882
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28691
28.8%
1 16765
16.8%
5 15848
15.9%
2 9999
 
10.0%
6 6678
 
6.7%
3 4800
 
4.8%
9 4361
 
4.4%
4 4309
 
4.3%
8 4087
 
4.1%
7 4024
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 19882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119444
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28691
24.0%
- 19882
16.6%
1 16765
14.0%
5 15848
13.3%
2 9999
 
8.4%
6 6678
 
5.6%
3 4800
 
4.0%
9 4361
 
3.7%
4 4309
 
3.6%
8 4087
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28691
24.0%
- 19882
16.6%
1 16765
14.0%
5 15848
13.3%
2 9999
 
8.4%
6 6678
 
5.6%
3 4800
 
4.0%
9 4361
 
3.7%
4 4309
 
3.6%
8 4087
 
3.4%

parkng_at
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9870 
N
 
71
 
59

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 9870
98.7%
N 71
 
0.7%
59
 
0.6%

Length

2024-04-21T18:07:42.167575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:07:42.329254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9870
99.3%
n 71
 
0.7%

card_at
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9941 
 
59

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 9941
99.4%
59
 
0.6%

Length

2024-04-21T18:07:42.501480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:07:42.658932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9941
100.0%

item_name
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대파
 
440
무
 
419
배
 
418
쌀
 
415
밀감
 
402
Other values (27)
7906 

Length

Max length5
Median length4
Mean length2.4463
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row콜라
2nd row부엌용세제
3rd row우유
4th row고등어
5th row돼지고기

Common Values

ValueCountFrequency (%)
대파 440
 
4.4%
무 419
 
4.2%
배 418
 
4.2%
쌀 415
 
4.2%
밀감 402
 
4.0%
양파 401
 
4.0%
닭고기 398
 
4.0%
사과 395
 
4.0%
쇠고기 395
 
4.0%
돼지고기 394
 
3.9%
Other values (22) 5923
59.2%

Length

2024-04-21T18:07:42.891903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대파 440
 
4.4%
무 419
 
4.2%
배 418
 
4.2%
쌀 415
 
4.2%
밀감 402
 
4.0%
양파 401
 
4.0%
닭고기 398
 
4.0%
사과 395
 
4.0%
쇠고기 395
 
4.0%
돼지고기 394
 
3.9%
Other values (22) 5923
59.2%

last_load_dttm
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-12-22 18:55:05
1877 
2020-12-22 18:55:03
1644 
2020-12-22 18:55:06
1311 
2020-12-22 18:55:04
1252 
2020-12-22 18:55:02
1247 
Other values (3)
2669 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-22 18:55:02
2nd row2020-12-22 18:55:04
3rd row2020-12-22 18:55:06
4th row2020-12-22 18:55:07
5th row2020-12-22 18:55:03

Common Values

ValueCountFrequency (%)
2020-12-22 18:55:05 1877
18.8%
2020-12-22 18:55:03 1644
16.4%
2020-12-22 18:55:06 1311
13.1%
2020-12-22 18:55:04 1252
12.5%
2020-12-22 18:55:02 1247
12.5%
2020-12-22 18:55:07 1176
11.8%
2020-12-22 18:55:01 1128
11.3%
2020-12-22 18:55:08 365
 
3.6%

Length

2024-04-21T18:07:43.114913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:07:43.317292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-22 10000
50.0%
18:55:05 1877
 
9.4%
18:55:03 1644
 
8.2%
18:55:06 1311
 
6.6%
18:55:04 1252
 
6.3%
18:55:02 1247
 
6.2%
18:55:07 1176
 
5.9%
18:55:01 1128
 
5.6%
18:55:08 365
 
1.8%

Sample

skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
73988959939234467321467321924112018-12-13461주류및음료,차316중구1.825002500행사농협하나로마트(자갈치점)35.097233129.02745부산광역시 중구 남포동6가 1051-250-7711YY콜라2020-12-22 18:55:02
1720618766868537472931472931854052019-03-28463세제64남구1.435003500<NA>이마트(문현점)35.144615129.06464부산광역시 남구 문현동 751051-609-1234YY부엌용세제2020-12-22 18:55:04
3004731608949331476279476279934062019-05-23461주류및음료,차293영도구200.0790790<NA>삼성홈플러스(영도점)35.095905129.04424부산광역시 영도구 봉래동2가 151-1051-418-2000YY우유2020-12-22 18:55:06
3584237402777675479346479346764172019-07-17459수산물227서구500.020002000<NA>새벽해안시장35.09316129.024782부산광역시 서구 남부민1동 해안새벽시장길 68051-242-7273YY고등어2020-12-22 18:55:07
1122312784888780469851469851874172019-01-30458축산물170북구500.092509250<NA>정이있는구포시장35.208355129.001681부산광역시 북구 구포1동 구포시장길 9051-333-9033YY돼지고기2020-12-22 18:55:03
1148813049101100794691094691091004172019-01-16464식료품180북구420.020002000<NA>만덕제일상가시장35.214633129.039816부산광역시 북구 만덕2동 덕천로 304번길 23051-334-7145YY두부2020-12-22 18:55:03
2081222373103102224720814720811024072019-03-07464식료품155강서구1.017901790<NA>롯데수퍼(명지점)35.085472128.89784부산광역시 강서구 명지동 3231051-292-5602YY설탕2020-12-22 18:55:04
3476236323787740478648478648774052019-07-04456농산물273연제구1000.01192011920재주밀감이마트(연제점)35.17615129.08133부산광역시 연제구 연산2동 822-7051-860-1052YY밀감2020-12-22 18:55:06
1369115251106105204698674698671054052019-01-31456농산물189사상구20.05199051990이마트이마트(사상점)35.16423128.97891부산광역시 사상구 괘법동 531-2051-329-1234YY쌀2020-12-22 18:55:03
1306314624898877470572470572884172019-02-13458축산물333중구100.0490009800<NA>부평시장35.100146129.026287부산광역시 중구 부평동2가 중구로 42번길051-243-1128YY쇠고기2020-12-22 18:55:03
skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
40713422741009929480233480233994072019-08-01464식료품111동래구320.072507250<NA>롯데마트(동래점)35.211483129.0776부산광역시 동래구 온천동 502-3051-668-2500YY참기름2020-12-22 18:55:08
2964931210848320476961476961834052019-05-30456농산물189사상구1890.026242480<NA>이마트(사상점)35.16423128.97891부산광역시 사상구 괘법동 531-2051-329-1234YY배추2020-12-22 18:55:05
2140122962157155434737224737221554072019-04-11464식료품186북구1.869506950<NA>롯데마트(화명점)35.234905129.00853부산광역시 북구 화명3동 1975051-604-2500YY식용유2020-12-22 18:55:04
922910789807962469092469092794172019-01-16456농산물111동래구3000.01428014280부사온천시장35.222232129.082545부산광역시 동래구 온천동 온천장로 119번길 48051-555-8231YY사과2020-12-22 18:55:02
2437225933878644474362474362864052019-04-18458축산물21금정구1.059505950신세계포인트카드24일까지20%할인이마트(금정점)35.250107129.09073부산광역시 금정구 구서동 368051-606-1234YY닭고기2020-12-22 18:55:05
3761839179103102434793624793621024072019-07-18464식료품186북구1.015801580<NA>롯데마트(화명점)35.234905129.00853부산광역시 북구 화명3동 1975051-604-2500YY설탕2020-12-22 18:55:07
3732338883818075479346479346804172019-07-17456농산물227서구1000.020002000<NA>새벽해안시장35.09316129.024782부산광역시 서구 남부민1동 해안새벽시장길 68051-242-7273YY양파2020-12-22 18:55:07
1820319764103102314728724728721024062019-03-21464식료품293영도구1.016801680<NA>삼성홈플러스(영도점)35.095905129.04424부산광역시 영도구 봉래동2가 151-1051-418-2000YY설탕2020-12-22 18:55:04
77099270908937467404467404894052018-12-20458축산물64남구600.0268026801등급특란이마트(문현점)35.144615129.06464부산광역시 남구 문현동 751051-609-1234YY달걀2020-12-22 18:55:02
3582937390838275479346479346824172019-07-17456농산물227서구2000.015001500<NA>새벽해안시장35.09316129.024782부산광역시 서구 남부민1동 해안새벽시장길 68051-242-7273YY무2020-12-22 18:55:07