Overview

Dataset statistics

Number of variables26
Number of observations10000
Missing cells7012
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
DateTime2
Categorical5
Text4

Alerts

parkng_at is highly imbalanced (94.8%)Imbalance
card_at is highly imbalanced (97.7%)Imbalance
rm has 6700 (67.0%) missing valuesMissing
skey has unique valuesUnique
unitprice has 128 (1.3%) zerosZeros
prices has 118 (1.2%) zerosZeros

Reproduction

Analysis started2024-04-21 09:15:58.080232
Analysis finished2024-04-21 09:15:59.463961
Duration1.38 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%
Mean317074.1
Minimum296143
Maximum338021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:15:59.659365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum296143
5-th percentile298255.75
Q1306711.5
median317069
Q3327549.75
95-th percentile335862.1
Maximum338021
Range41878
Interquartile range (IQR)20838.25

Descriptive statistics

Standard deviation12042.432
Coefficient of variation (CV)0.037979865
Kurtosis-1.1920634
Mean317074.1
Median Absolute Deviation (MAD)10405
Skewness-0.0010934831
Sum3.170741 × 109
Variance1.4502016 × 108
MonotonicityNot monotonic
2024-04-21T18:16:00.081437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299467 1
 
< 0.1%
321509 1
 
< 0.1%
329297 1
 
< 0.1%
331295 1
 
< 0.1%
321298 1
 
< 0.1%
317152 1
 
< 0.1%
325803 1
 
< 0.1%
300016 1
 
< 0.1%
302351 1
 
< 0.1%
304918 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
296143 1
< 0.1%
296144 1
< 0.1%
296151 1
< 0.1%
296157 1
< 0.1%
296183 1
< 0.1%
296188 1
< 0.1%
296189 1
< 0.1%
296195 1
< 0.1%
296197 1
< 0.1%
296205 1
< 0.1%
ValueCountFrequency (%)
338021 1
< 0.1%
338020 1
< 0.1%
338003 1
< 0.1%
337999 1
< 0.1%
337998 1
< 0.1%
337996 1
< 0.1%
337993 1
< 0.1%
337990 1
< 0.1%
337981 1
< 0.1%
337980 1
< 0.1%

ccode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.5518
Minimum77
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:00.471875image/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 deviation23.000322
Coefficient of variation (CV)0.23577548
Kurtosis2.3961636
Mean97.5518
Median Absolute Deviation (MAD)8
Skewness1.8772266
Sum975518
Variance529.01482
MonotonicityNot monotonic
2024-04-21T18:16:00.859453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
89 430
 
4.3%
84 425
 
4.2%
87 411
 
4.1%
81 407
 
4.1%
80 390
 
3.9%
88 386
 
3.9%
101 386
 
3.9%
82 383
 
3.8%
83 380
 
3.8%
106 373
 
3.7%
Other values (22) 6029
60.3%
ValueCountFrequency (%)
77 370
3.7%
78 362
3.6%
79 345
3.5%
80 390
3.9%
81 407
4.1%
82 383
3.8%
83 380
3.8%
84 425
4.2%
85 278
2.8%
86 248
2.5%
ValueCountFrequency (%)
159 323
3.2%
158 305
3.0%
157 233
2.3%
156 274
2.7%
106 373
3.7%
105 229
2.3%
104 224
2.2%
103 245
2.5%
101 386
3.9%
100 256
2.6%

pcode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.4383
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:01.240936image/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 deviation22.704271
Coefficient of variation (CV)0.23542795
Kurtosis2.3589625
Mean96.4383
Median Absolute Deviation (MAD)8
Skewness1.8633989
Sum964383
Variance515.48394
MonotonicityNot monotonic
2024-04-21T18:16:01.665718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
88 430
 
4.3%
83 425
 
4.2%
86 411
 
4.1%
80 407
 
4.1%
79 390
 
3.9%
87 386
 
3.9%
100 386
 
3.9%
81 383
 
3.8%
82 380
 
3.8%
105 373
 
3.7%
Other values (22) 6029
60.3%
ValueCountFrequency (%)
76 370
3.7%
77 362
3.6%
78 345
3.5%
79 390
3.9%
80 407
4.1%
81 383
3.8%
82 380
3.8%
83 425
4.2%
84 278
2.8%
85 248
2.5%
ValueCountFrequency (%)
157 323
3.2%
156 305
3.0%
155 233
2.3%
154 274
2.7%
105 373
3.7%
104 229
2.3%
103 224
2.2%
102 245
2.5%
100 386
3.9%
99 256
2.6%

bssh_no
Real number (ℝ)

Distinct54
Distinct (%)0.5%
Missing22
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean406.88535
Minimum14
Maximum3198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:02.078288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile20
Q129
median39
Q362
95-th percentile2625
Maximum3198
Range3184
Interquartile range (IQR)33

Descriptive statistics

Standard deviation920.67231
Coefficient of variation (CV)2.2627315
Kurtosis2.6311996
Mean406.88535
Median Absolute Deviation (MAD)11
Skewness2.1320599
Sum4059902
Variance847637.5
MonotonicityNot monotonic
2024-04-21T18:16:02.507453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 347
 
3.5%
34 337
 
3.4%
31 329
 
3.3%
19 329
 
3.3%
22 325
 
3.2%
25 323
 
3.2%
38 322
 
3.2%
20 321
 
3.2%
2625 318
 
3.2%
2524 315
 
3.1%
Other values (44) 6712
67.1%
ValueCountFrequency (%)
14 66
 
0.7%
17 69
 
0.7%
19 329
3.3%
20 321
3.2%
22 325
3.2%
23 183
1.8%
25 323
3.2%
26 305
3.0%
28 304
3.0%
29 310
3.1%
ValueCountFrequency (%)
3198 2
 
< 0.1%
3193 5
 
0.1%
3177 60
 
0.6%
3028 312
3.1%
2625 318
3.2%
2562 302
3.0%
2524 315
3.1%
2349 68
 
0.7%
87 67
 
0.7%
85 69
 
0.7%

search_no
Real number (ℝ)

Distinct1597
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500306.16
Minimum469120
Maximum513376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:02.925979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum469120
5-th percentile473693
Q1492989
median504964
Q3509341
95-th percentile512539
Maximum513376
Range44256
Interquartile range (IQR)16352

Descriptive statistics

Standard deviation12463.827
Coefficient of variation (CV)0.024912399
Kurtosis0.2578893
Mean500306.16
Median Absolute Deviation (MAD)4541
Skewness-1.257376
Sum5.0030616 × 109
Variance1.5534698 × 108
MonotonicityNot monotonic
2024-04-21T18:16:03.342482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
501145 15
 
0.1%
509328 15
 
0.1%
473684 14
 
0.1%
503257 14
 
0.1%
506015 14
 
0.1%
502358 13
 
0.1%
503178 13
 
0.1%
502115 13
 
0.1%
491379 13
 
0.1%
501407 13
 
0.1%
Other values (1587) 9863
98.6%
ValueCountFrequency (%)
469120 7
0.1%
469121 9
0.1%
472893 2
 
< 0.1%
472895 5
0.1%
472896 5
0.1%
472897 4
< 0.1%
472898 5
0.1%
472899 1
 
< 0.1%
472900 2
 
< 0.1%
472901 2
 
< 0.1%
ValueCountFrequency (%)
513376 7
0.1%
513375 8
0.1%
513372 12
0.1%
513370 6
0.1%
513369 4
 
< 0.1%
513357 2
 
< 0.1%
513354 8
0.1%
513353 8
0.1%
513352 5
0.1%
513351 11
0.1%

prices_no
Real number (ℝ)

Distinct1597
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500306.16
Minimum469120
Maximum513376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:03.749751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum469120
5-th percentile473693
Q1492989
median504964
Q3509341
95-th percentile512539
Maximum513376
Range44256
Interquartile range (IQR)16352

Descriptive statistics

Standard deviation12463.827
Coefficient of variation (CV)0.024912399
Kurtosis0.2578893
Mean500306.16
Median Absolute Deviation (MAD)4541
Skewness-1.257376
Sum5.0030616 × 109
Variance1.5534698 × 108
MonotonicityNot monotonic
2024-04-21T18:16:04.164333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
501145 15
 
0.1%
509328 15
 
0.1%
473684 14
 
0.1%
503257 14
 
0.1%
506015 14
 
0.1%
502358 13
 
0.1%
503178 13
 
0.1%
502115 13
 
0.1%
491379 13
 
0.1%
501407 13
 
0.1%
Other values (1587) 9863
98.6%
ValueCountFrequency (%)
469120 7
0.1%
469121 9
0.1%
472893 2
 
< 0.1%
472895 5
0.1%
472896 5
0.1%
472897 4
< 0.1%
472898 5
0.1%
472899 1
 
< 0.1%
472900 2
 
< 0.1%
472901 2
 
< 0.1%
ValueCountFrequency (%)
513376 7
0.1%
513375 8
0.1%
513372 12
0.1%
513370 6
0.1%
513369 4
 
< 0.1%
513357 2
 
< 0.1%
513354 8
0.1%
513353 8
0.1%
513352 5
0.1%
513351 11
0.1%

prdlst
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.4383
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:04.547717image/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 deviation22.704271
Coefficient of variation (CV)0.23542795
Kurtosis2.3589625
Mean96.4383
Median Absolute Deviation (MAD)8
Skewness1.8633989
Sum964383
Variance515.48394
MonotonicityNot monotonic
2024-04-21T18:16:04.969089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
88 430
 
4.3%
83 425
 
4.2%
86 411
 
4.1%
80 407
 
4.1%
79 390
 
3.9%
87 386
 
3.9%
100 386
 
3.9%
81 383
 
3.8%
82 380
 
3.8%
105 373
 
3.7%
Other values (22) 6029
60.3%
ValueCountFrequency (%)
76 370
3.7%
77 362
3.6%
78 345
3.5%
79 390
3.9%
80 407
4.1%
81 383
3.8%
82 380
3.8%
83 425
4.2%
84 278
2.8%
85 248
2.5%
ValueCountFrequency (%)
157 323
3.2%
156 305
3.0%
155 233
2.3%
154 274
2.7%
105 373
3.7%
104 229
2.3%
103 224
2.2%
102 245
2.5%
100 386
3.9%
99 256
2.6%

cl_no
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing22
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean409.24704
Minimum405
Maximum419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:05.319663image/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 deviation4.5060742
Coefficient of variation (CV)0.011010646
Kurtosis-0.85293581
Mean409.24704
Median Absolute Deviation (MAD)2
Skewness0.89597043
Sum4083467
Variance20.304705
MonotonicityNot monotonic
2024-04-21T18:16:05.650336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
406 2686
26.9%
417 2025
20.2%
407 1554
15.5%
405 1515
15.2%
410 652
 
6.5%
411 652
 
6.5%
408 593
 
5.9%
416 294
 
2.9%
419 7
 
0.1%
(Missing) 22
 
0.2%
ValueCountFrequency (%)
405 1515
15.2%
406 2686
26.9%
407 1554
15.5%
408 593
 
5.9%
410 652
 
6.5%
411 652
 
6.5%
416 294
 
2.9%
417 2025
20.2%
419 7
 
0.1%
ValueCountFrequency (%)
419 7
 
0.1%
417 2025
20.2%
416 294
 
2.9%
411 652
 
6.5%
410 652
 
6.5%
408 593
 
5.9%
407 1554
15.5%
406 2686
26.9%
405 1515
15.2%

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
2024-04-21T18:16:06.210259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:16:06.629275image/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%
Mean459.4852
Minimum456
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:06.993839image/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 deviation3.0230274
Coefficient of variation (CV)0.0065791617
Kurtosis-1.3955402
Mean459.4852
Median Absolute Deviation (MAD)3
Skewness0.25720036
Sum4594852
Variance9.1386948
MonotonicityNot monotonic
2024-04-21T18:16:07.364057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
456 3065
30.6%
464 1822
18.2%
461 1779
17.8%
458 1593
15.9%
459 998
 
10.0%
463 743
 
7.4%
ValueCountFrequency (%)
456 3065
30.6%
458 1593
15.9%
459 998
 
10.0%
461 1779
17.8%
463 743
 
7.4%
464 1822
18.2%
ValueCountFrequency (%)
464 1822
18.2%
463 743
 
7.4%
461 1779
17.8%
459 998
 
10.0%
458 1593
15.9%
456 3065
30.6%

pum_nm
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농산물
3065 
식료품
1822 
주류및음료,차
1779 
축산물
1593 
수산물
998 

Length

Max length7
Median length3
Mean length3.6373
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농산물 3065
30.6%
식료품 1822
18.2%
주류및음료,차 1779
17.8%
축산물 1593
15.9%
수산물 998
 
10.0%
세제 743
 
7.4%

Length

2024-04-21T18:16:07.766026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:16:08.092386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농산물 3065
30.6%
식료품 1822
18.2%
주류및음료,차 1779
17.8%
축산물 1593
15.9%
수산물 998
 
10.0%
세제 743
 
7.4%

gugun_cd
Real number (ℝ)

Distinct49
Distinct (%)0.5%
Missing24
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean188.44026
Minimum21
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:08.461222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile31
Q195
median189
Q3262
95-th percentile365
Maximum374
Range353
Interquartile range (IQR)167

Descriptive statistics

Standard deviation103.38717
Coefficient of variation (CV)0.54864691
Kurtosis-0.99716469
Mean188.44026
Median Absolute Deviation (MAD)83
Skewness0.1647034
Sum1879880
Variance10688.906
MonotonicityNot monotonic
2024-04-21T18:16:08.890732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
189 610
 
6.1%
227 437
 
4.4%
111 395
 
4.0%
374 386
 
3.9%
316 337
 
3.4%
293 329
 
3.3%
48 329
 
3.3%
155 325
 
3.2%
52 323
 
3.2%
360 322
 
3.2%
Other values (39) 6183
61.8%
ValueCountFrequency (%)
21 298
3.0%
27 86
 
0.9%
31 183
1.8%
33 88
 
0.9%
48 329
3.3%
52 323
3.2%
53 80
 
0.8%
64 310
3.1%
66 85
 
0.9%
80 86
 
0.9%
ValueCountFrequency (%)
374 386
3.9%
373 5
 
0.1%
369 75
 
0.8%
365 318
3.2%
360 322
3.2%
333 95
 
0.9%
316 337
3.4%
314 86
 
0.9%
293 329
3.3%
275 69
 
0.7%

gugun_nm
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
해운대구
1106 
남구
798 
북구
783 
동래구
759 
부산진구
751 
Other values (12)
5803 

Length

Max length4
Median length3
Mean length2.8783
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동구
2nd row중구
3rd row남구
4th row수영구
5th row수영구

Common Values

ValueCountFrequency (%)
해운대구 1106
11.1%
남구 798
 
8.0%
북구 783
 
7.8%
동래구 759
 
7.6%
부산진구 751
 
7.5%
수영구 746
 
7.5%
사상구 736
 
7.4%
사하구 722
 
7.2%
금정구 655
 
6.6%
서구 527
 
5.3%
Other values (7) 2417
24.2%

Length

2024-04-21T18:16:09.341053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해운대구 1106
11.1%
남구 798
 
8.0%
북구 783
 
7.8%
동래구 759
 
7.6%
부산진구 751
 
7.5%
수영구 746
 
7.5%
사상구 736
 
7.4%
사하구 722
 
7.2%
금정구 655
 
6.6%
서구 527
 
5.3%
Other values (7) 2417
24.2%

unit
Real number (ℝ)

Distinct421
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean667.16912
Minimum0.8
Maximum18000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:09.739583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1
Q14
median420
Q3900
95-th percentile2100
Maximum18000
Range17999.2
Interquartile range (IQR)896

Descriptive statistics

Standard deviation980.70771
Coefficient of variation (CV)1.4699537
Kurtosis22.332048
Mean667.16912
Median Absolute Deviation (MAD)417
Skewness3.5016188
Sum6671691.2
Variance961787.62
MonotonicityNot monotonic
2024-04-21T18:16:10.175473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500.0 1338
 
13.4%
100.0 913
 
9.1%
1.0 752
 
7.5%
1000.0 725
 
7.2%
2000.0 573
 
5.7%
1.8 465
 
4.7%
600.0 463
 
4.6%
20.0 373
 
3.7%
1.5 290
 
2.9%
320.0 262
 
2.6%
Other values (411) 3846
38.5%
ValueCountFrequency (%)
0.8 25
 
0.2%
0.9 211
 
2.1%
0.95 1
 
< 0.1%
1.0 752
7.5%
1.05 1
 
< 0.1%
1.1 12
 
0.1%
1.2 21
 
0.2%
1.4 137
 
1.4%
1.5 290
 
2.9%
1.7 47
 
0.5%
ValueCountFrequency (%)
18000.0 1
 
< 0.1%
10000.0 1
 
< 0.1%
9000.0 4
 
< 0.1%
6000.0 122
1.2%
5000.0 16
 
0.2%
4500.0 2
 
< 0.1%
4000.0 2
 
< 0.1%
3744.0 1
 
< 0.1%
3700.0 3
 
< 0.1%
3672.0 1
 
< 0.1%

unitprice
Real number (ℝ)

ZEROS 

Distinct1878
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10337.346
Minimum0
Maximum100000
Zeros128
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:10.605223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile798
Q12500
median4600
Q310000
95-th percentile50000
Maximum100000
Range100000
Interquartile range (IQR)7500

Descriptive statistics

Standard deviation14452.384
Coefficient of variation (CV)1.3980748
Kurtosis5.0460091
Mean10337.346
Median Absolute Deviation (MAD)2797.5
Skewness2.3824211
Sum1.0337346 × 108
Variance2.088714 × 108
MonotonicityNot monotonic
2024-04-21T18:16:11.040093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
676 169
 
1.7%
2500 167
 
1.7%
3500 156
 
1.6%
796 140
 
1.4%
1190 137
 
1.4%
3000 133
 
1.3%
5000 131
 
1.3%
2000 130
 
1.3%
4000 129
 
1.3%
0 128
 
1.3%
Other values (1868) 8580
85.8%
ValueCountFrequency (%)
0 128
1.3%
192 1
 
< 0.1%
215 2
 
< 0.1%
246 1
 
< 0.1%
598 1
 
< 0.1%
642 1
 
< 0.1%
676 169
1.7%
678 2
 
< 0.1%
680 18
 
0.2%
700 9
 
0.1%
ValueCountFrequency (%)
100000 1
 
< 0.1%
99950 1
 
< 0.1%
79950 7
0.1%
76533 1
 
< 0.1%
74000 7
0.1%
70000 5
0.1%
69950 1
 
< 0.1%
69900 8
0.1%
68900 3
 
< 0.1%
68000 2
 
< 0.1%

prices
Real number (ℝ)

ZEROS 

Distinct1039
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8296.1882
Minimum0
Maximum100000
Zeros118
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:11.457526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile830
Q12450
median4000
Q38990
95-th percentile33712
Maximum100000
Range100000
Interquartile range (IQR)6540

Descriptive statistics

Standard deviation11848.167
Coefficient of variation (CV)1.4281459
Kurtosis10.529605
Mean8296.1882
Median Absolute Deviation (MAD)2370
Skewness3.1754312
Sum82961882
Variance1.4037906 × 108
MonotonicityNot monotonic
2024-04-21T18:16:11.864694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3500 201
 
2.0%
2500 200
 
2.0%
3000 173
 
1.7%
5000 171
 
1.7%
9900 168
 
1.7%
4000 156
 
1.6%
3290 148
 
1.5%
2000 147
 
1.5%
1190 137
 
1.4%
6000 130
 
1.3%
Other values (1029) 8369
83.7%
ValueCountFrequency (%)
0 118
1.2%
139 2
 
< 0.1%
158 1
 
< 0.1%
183 1
 
< 0.1%
230 1
 
< 0.1%
250 2
 
< 0.1%
257 4
 
< 0.1%
263 1
 
< 0.1%
269 1
 
< 0.1%
273 2
 
< 0.1%
ValueCountFrequency (%)
100000 3
 
< 0.1%
76533 1
 
< 0.1%
70000 1
 
< 0.1%
69900 8
0.1%
68900 3
 
< 0.1%
68000 2
 
< 0.1%
67900 2
 
< 0.1%
65000 5
0.1%
64900 10
0.1%
63800 2
 
< 0.1%

rm
Text

MISSING 

Distinct945
Distinct (%)28.6%
Missing6700
Missing (%)67.0%
Memory size156.2 KiB
2024-04-21T18:16:12.678497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length6.5009091
Min length1

Characters and Unicode

Total characters21453
Distinct characters386
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

Unique503 ?
Unique (%)15.2%

Sample

1st row신고배
2nd row신동진
3rd row청표
4th row미손질대파
5th row
ValueCountFrequency (%)
행사 158
 
4.1%
해동 125
 
3.2%
생물 107
 
2.8%
품절 47
 
1.2%
하우스밀감 46
 
1.2%
냉동 45
 
1.2%
없음 42
 
1.1%
1등급 33
 
0.8%
고소한참기름 33
 
0.8%
할인행사 32
 
0.8%
Other values (863) 3216
82.8%
2024-04-21T18:16:13.720411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1724
 
8.0%
1 895
 
4.2%
0 879
 
4.1%
/ 477
 
2.2%
2 444
 
2.1%
사 433
 
2.0%
3 382
 
1.8%
행 337
 
1.6%
원 301
 
1.4%
5 274
 
1.3%
Other values (376) 15307
71.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14078
65.6%
Decimal Number 3572
 
16.7%
Space Separator 1724
 
8.0%
Other Punctuation 790
 
3.7%
Lowercase Letter 435
 
2.0%
Math Symbol 318
 
1.5%
Uppercase Letter 230
 
1.1%
Close Punctuation 149
 
0.7%
Open Punctuation 141
 
0.7%
Dash Punctuation 16
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 433
 
3.1%
행 337
 
2.4%
원 301
 
2.1%
동 261
 
1.9%
생 254
 
1.8%
스 241
 
1.7%
지 234
 
1.7%
한 229
 
1.6%
기 225
 
1.6%
등 219
 
1.6%
Other values (331) 11344
80.6%
Lowercase Letter
ValueCountFrequency (%)
g 204
46.9%
k 62
 
14.3%
l 53
 
12.2%
m 47
 
10.8%
s 18
 
4.1%
i 9
 
2.1%
p 9
 
2.1%
u 9
 
2.1%
j 5
 
1.1%
c 5
 
1.1%
Other values (5) 14
 
3.2%
Decimal Number
ValueCountFrequency (%)
1 895
25.1%
0 879
24.6%
2 444
12.4%
3 382
10.7%
5 274
 
7.7%
9 207
 
5.8%
4 163
 
4.6%
8 145
 
4.1%
7 108
 
3.0%
6 75
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
L 68
29.6%
R 29
12.6%
G 28
12.2%
C 26
 
11.3%
P 25
 
10.9%
K 22
 
9.6%
M 15
 
6.5%
T 13
 
5.7%
A 4
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/ 477
60.4%
, 105
 
13.3%
* 83
 
10.5%
. 65
 
8.2%
% 60
 
7.6%
Math Symbol
ValueCountFrequency (%)
+ 176
55.3%
~ 142
44.7%
Space Separator
ValueCountFrequency (%)
1724
100.0%
Close Punctuation
ValueCountFrequency (%)
) 149
100.0%
Open Punctuation
ValueCountFrequency (%)
( 141
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14065
65.6%
Common 6710
31.3%
Latin 665
 
3.1%
Han 13
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 433
 
3.1%
행 337
 
2.4%
원 301
 
2.1%
동 261
 
1.9%
생 254
 
1.8%
스 241
 
1.7%
지 234
 
1.7%
한 229
 
1.6%
기 225
 
1.6%
등 219
 
1.6%
Other values (330) 11331
80.6%
Latin
ValueCountFrequency (%)
g 204
30.7%
L 68
 
10.2%
k 62
 
9.3%
l 53
 
8.0%
m 47
 
7.1%
R 29
 
4.4%
G 28
 
4.2%
C 26
 
3.9%
P 25
 
3.8%
K 22
 
3.3%
Other values (14) 101
15.2%
Common
ValueCountFrequency (%)
1724
25.7%
1 895
13.3%
0 879
13.1%
/ 477
 
7.1%
2 444
 
6.6%
3 382
 
5.7%
5 274
 
4.1%
9 207
 
3.1%
+ 176
 
2.6%
4 163
 
2.4%
Other values (11) 1089
16.2%
Han
ValueCountFrequency (%)
入 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14061
65.5%
ASCII 7375
34.4%
CJK 13
 
0.1%
Compat Jamo 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1724
23.4%
1 895
12.1%
0 879
11.9%
/ 477
 
6.5%
2 444
 
6.0%
3 382
 
5.2%
5 274
 
3.7%
9 207
 
2.8%
g 204
 
2.8%
+ 176
 
2.4%
Other values (35) 1713
23.2%
Hangul
ValueCountFrequency (%)
사 433
 
3.1%
행 337
 
2.4%
원 301
 
2.1%
동 261
 
1.9%
생 254
 
1.8%
스 241
 
1.7%
지 234
 
1.7%
한 229
 
1.6%
기 225
 
1.6%
등 219
 
1.6%
Other values (328) 11327
80.6%
CJK
ValueCountFrequency (%)
入 13
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅡ 2
50.0%
ㅣ 2
50.0%

bssh_nm
Text

Distinct54
Distinct (%)0.5%
Missing22
Missing (%)0.2%
Memory size156.2 KiB
2024-04-21T18:16:14.388437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.0628382
Min length4

Characters and Unicode

Total characters90429
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 row탑마트(초량점)
2nd row농협하나로마트(자갈치점)
3rd row삼성홈플러스(감만점)
4th row홈플러스 익스플러스 광안점
5th row수영팔도시장
ValueCountFrequency (%)
탑마트(서구 347
 
3.2%
농협하나로마트(자갈치점 337
 
3.1%
삼성홈플러스(영도점 329
 
3.0%
삼성홈플러스(정관점 329
 
3.0%
롯데수퍼(명지점 325
 
3.0%
삼성홈플러스(감만점 323
 
2.9%
탑마트(반여점 322
 
2.9%
이마트(사상점 321
 
2.9%
삼성홈플러스(센텀시티점 318
 
2.9%
농협하나로클럽마트 315
 
2.9%
Other values (49) 7703
70.2%
2024-04-21T18:16:15.281761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 7351
 
8.1%
( 7112
 
7.9%
) 7112
 
7.9%
트 4641
 
5.1%
마 4641
 
5.1%
스 3604
 
4.0%
러 2998
 
3.3%
플 2998
 
3.3%
홈 2686
 
3.0%
시 2276
 
2.5%
Other values (107) 45010
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75087
83.0%
Open Punctuation 7112
 
7.9%
Close Punctuation 7112
 
7.9%
Space Separator 1051
 
1.2%
Decimal Number 67
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 7351
 
9.8%
트 4641
 
6.2%
마 4641
 
6.2%
스 3604
 
4.8%
러 2998
 
4.0%
플 2998
 
4.0%
홈 2686
 
3.6%
시 2276
 
3.0%
삼 2191
 
2.9%
성 2191
 
2.9%
Other values (103) 39510
52.6%
Open Punctuation
ValueCountFrequency (%)
( 7112
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7112
100.0%
Space Separator
ValueCountFrequency (%)
1051
100.0%
Decimal Number
ValueCountFrequency (%)
1 67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75087
83.0%
Common 15342
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 7351
 
9.8%
트 4641
 
6.2%
마 4641
 
6.2%
스 3604
 
4.8%
러 2998
 
4.0%
플 2998
 
4.0%
홈 2686
 
3.6%
시 2276
 
3.0%
삼 2191
 
2.9%
성 2191
 
2.9%
Other values (103) 39510
52.6%
Common
ValueCountFrequency (%)
( 7112
46.4%
) 7112
46.4%
1051
 
6.9%
1 67
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75087
83.0%
ASCII 15342
 
17.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 7351
 
9.8%
트 4641
 
6.2%
마 4641
 
6.2%
스 3604
 
4.8%
러 2998
 
4.0%
플 2998
 
4.0%
홈 2686
 
3.6%
시 2276
 
3.0%
삼 2191
 
2.9%
성 2191
 
2.9%
Other values (103) 39510
52.6%
ASCII
ValueCountFrequency (%)
( 7112
46.4%
) 7112
46.4%
1051
 
6.9%
1 67
 
0.4%

la
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing89
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean35.162727
Minimum35.08484
Maximum35.323099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:15.522115image/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 deviation0.057355919
Coefficient of variation (CV)0.0016311567
Kurtosis0.11984239
Mean35.162727
Median Absolute Deviation (MAD)0.0442
Skewness0.66649059
Sum348497.79
Variance0.0032897014
MonotonicityNot monotonic
2024-04-21T18:16:15.770406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.092663 347
 
3.5%
35.097233 337
 
3.4%
35.095905 329
 
3.3%
35.3230993 329
 
3.3%
35.085472 325
 
3.2%
35.121605 323
 
3.2%
35.205868 322
 
3.2%
35.16423 321
 
3.2%
35.1709359 318
 
3.2%
35.250025 315
 
3.1%
Other values (41) 6645
66.5%
ValueCountFrequency (%)
35.08484 285
2.9%
35.085472 325
3.2%
35.092663 347
3.5%
35.0931597 90
 
0.9%
35.095905 329
3.3%
35.0970155 86
 
0.9%
35.097233 337
3.4%
35.098934 294
2.9%
35.0996462 75
 
0.8%
35.099649 90
 
0.9%
ValueCountFrequency (%)
35.3230993 329
3.3%
35.250107 298
3.0%
35.250025 315
3.1%
35.2392 183
1.8%
35.234905 312
3.1%
35.2222318 85
 
0.9%
35.2159352 88
 
0.9%
35.2146331 87
 
0.9%
35.2119516 86
 
0.9%
35.211483 310
3.1%

lo
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing89
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean129.05634
Minimum128.89784
Maximum129.17634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:16:16.007130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.062530095
Coefficient of variation (CV)0.00048451782
Kurtosis0.043114717
Mean129.05634
Median Absolute Deviation (MAD)0.03952
Skewness-0.24456265
Sum1279077.3
Variance0.0039100128
MonotonicityNot monotonic
2024-04-21T18:16:16.253161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.02449 347
 
3.5%
129.02745 337
 
3.4%
129.04424 329
 
3.3%
129.1763385 329
 
3.3%
128.89784 325
 
3.2%
129.08223 323
 
3.2%
129.12317 322
 
3.2%
128.97891 321
 
3.2%
129.1337096 318
 
3.2%
129.0112455 315
 
3.1%
Other values (41) 6645
66.5%
ValueCountFrequency (%)
128.89784 325
3.2%
128.9019892 68
 
0.7%
128.97157 285
2.9%
128.97812 289
2.9%
128.97891 321
3.2%
128.9813855 66
 
0.7%
128.9893459 75
 
0.8%
128.99406 294
2.9%
129.0016813 69
 
0.7%
129.00853 312
3.1%
ValueCountFrequency (%)
129.1763385 329
3.3%
129.1744835 75
 
0.8%
129.16739 302
3.0%
129.1623293 84
 
0.8%
129.1337096 318
3.2%
129.12317 322
3.2%
129.116693 63
 
0.6%
129.113611 312
3.1%
129.1114753 85
 
0.9%
129.1111015 302
3.0%

adres
Text

Distinct54
Distinct (%)0.5%
Missing22
Missing (%)0.2%
Memory size156.2 KiB
2024-04-21T18:16:17.337138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length21.903187
Min length14

Characters and Unicode

Total characters218550
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 동구 초량동 393-1
2nd row부산광역시 중구 남포동6가 1
3rd row부산광역시 남구 감만동 8
4th row(48253) 부산광역시 수영구 수영로 625, 491-1 (광안동)
5th row부산광역시 수영구 민락동 수영로 725번길
ValueCountFrequency (%)
부산광역시 9978
 
21.9%
해운대구 1424
 
3.1%
동래구 942
 
2.1%
남구 798
 
1.8%
북구 783
 
1.7%
부산진구 751
 
1.6%
수영구 746
 
1.6%
사상구 736
 
1.6%
사하구 722
 
1.6%
괘법동 610
 
1.3%
Other values (159) 28055
61.6%
2024-04-21T18:16:18.765322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35567
 
16.3%
동 12288
 
5.6%
부 11507
 
5.3%
산 11218
 
5.1%
구 10897
 
5.0%
1 10572
 
4.8%
광 10290
 
4.7%
시 10210
 
4.7%
역 9978
 
4.6%
2 6589
 
3.0%
Other values (106) 89434
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128738
58.9%
Decimal Number 45161
 
20.7%
Space Separator 35567
 
16.3%
Dash Punctuation 4957
 
2.3%
Close Punctuation 1904
 
0.9%
Open Punctuation 1904
 
0.9%
Other Punctuation 319
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12288
 
9.5%
부 11507
 
8.9%
산 11218
 
8.7%
구 10897
 
8.5%
광 10290
 
8.0%
시 10210
 
7.9%
역 9978
 
7.8%
로 2935
 
2.3%
남 2346
 
1.8%
대 2147
 
1.7%
Other values (91) 44922
34.9%
Decimal Number
ValueCountFrequency (%)
1 10572
23.4%
2 6589
14.6%
3 5727
12.7%
5 4429
9.8%
6 3490
 
7.7%
7 3361
 
7.4%
8 3224
 
7.1%
4 3050
 
6.8%
9 2489
 
5.5%
0 2230
 
4.9%
Space Separator
ValueCountFrequency (%)
35567
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4957
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1904
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1904
100.0%
Other Punctuation
ValueCountFrequency (%)
, 319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128738
58.9%
Common 89812
41.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12288
 
9.5%
부 11507
 
8.9%
산 11218
 
8.7%
구 10897
 
8.5%
광 10290
 
8.0%
시 10210
 
7.9%
역 9978
 
7.8%
로 2935
 
2.3%
남 2346
 
1.8%
대 2147
 
1.7%
Other values (91) 44922
34.9%
Common
ValueCountFrequency (%)
35567
39.6%
1 10572
 
11.8%
2 6589
 
7.3%
3 5727
 
6.4%
- 4957
 
5.5%
5 4429
 
4.9%
6 3490
 
3.9%
7 3361
 
3.7%
8 3224
 
3.6%
4 3050
 
3.4%
Other values (5) 8846
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128738
58.9%
ASCII 89812
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35567
39.6%
1 10572
 
11.8%
2 6589
 
7.3%
3 5727
 
6.4%
- 4957
 
5.5%
5 4429
 
4.9%
6 3490
 
3.9%
7 3361
 
3.7%
8 3224
 
3.6%
4 3050
 
3.4%
Other values (5) 8846
 
9.8%
Hangul
ValueCountFrequency (%)
동 12288
 
9.5%
부 11507
 
8.9%
산 11218
 
8.7%
구 10897
 
8.5%
광 10290
 
8.0%
시 10210
 
7.9%
역 9978
 
7.8%
로 2935
 
2.3%
남 2346
 
1.8%
대 2147
 
1.7%
Other values (91) 44922
34.9%

telno
Text

Distinct54
Distinct (%)0.5%
Missing22
Missing (%)0.2%
Memory size156.2 KiB
2024-04-21T18:16:19.503582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.014231
Min length12

Characters and Unicode

Total characters119878
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 row051-466-2112
2nd row051-250-7711
3rd row051-609-8000
4th row051-756-2277
5th row051-751-6951
ValueCountFrequency (%)
051-244-1221 347
 
3.5%
051-250-7711 337
 
3.4%
051-418-2000 329
 
3.3%
051-519-8200 329
 
3.3%
051-292-5602 325
 
3.3%
051-609-8000 323
 
3.2%
051-525-0422 322
 
3.2%
051-329-1234 321
 
3.2%
051-709-8000 318
 
3.2%
051-330-9000 315
 
3.2%
Other values (44) 6712
67.3%
2024-04-21T18:16:20.621714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27677
23.1%
- 19956
16.6%
1 16933
14.1%
5 16240
13.5%
2 10508
 
8.8%
6 6587
 
5.5%
3 4984
 
4.2%
9 4695
 
3.9%
4 4419
 
3.7%
8 4078
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99922
83.4%
Dash Punctuation 19956
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27677
27.7%
1 16933
16.9%
5 16240
16.3%
2 10508
 
10.5%
6 6587
 
6.6%
3 4984
 
5.0%
9 4695
 
4.7%
4 4419
 
4.4%
8 4078
 
4.1%
7 3801
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 19956
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119878
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27677
23.1%
- 19956
16.6%
1 16933
14.1%
5 16240
13.5%
2 10508
 
8.8%
6 6587
 
5.5%
3 4984
 
4.2%
9 4695
 
3.9%
4 4419
 
3.7%
8 4078
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119878
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27677
23.1%
- 19956
16.6%
1 16933
14.1%
5 16240
13.5%
2 10508
 
8.8%
6 6587
 
5.5%
3 4984
 
4.2%
9 4695
 
3.9%
4 4419
 
3.7%
8 4078
 
3.4%

parkng_at
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9908 
N
 
70
 
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 9908
99.1%
N 70
 
0.7%
22
 
0.2%

Length

2024-04-21T18:16:21.018038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:16:21.534419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9908
99.3%
n 70
 
0.7%

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

2024-04-21T18:16:21.851732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:16:22.143447image/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
쇠고기
 
430
배추
 
425
닭고기
 
411
양파
 
407
사과
 
390
Other values (27)
7937 

Length

Max length5
Median length4
Mean length2.467
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row배
2nd row두부
3rd row사이다
4th row대파
5th row갈치

Common Values

ValueCountFrequency (%)
쇠고기 430
 
4.3%
배추 425
 
4.2%
닭고기 411
 
4.1%
양파 407
 
4.1%
사과 390
 
3.9%
돼지고기 386
 
3.9%
두부 386
 
3.9%
대파 383
 
3.8%
무 380
 
3.8%
쌀 373
 
3.7%
Other values (22) 6029
60.3%

Length

2024-04-21T18:16:22.492488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
쇠고기 430
 
4.3%
배추 425
 
4.2%
닭고기 411
 
4.1%
양파 407
 
4.1%
사과 390
 
3.9%
돼지고기 386
 
3.9%
두부 386
 
3.9%
대파 383
 
3.8%
무 380
 
3.8%
쌀 373
 
3.7%
Other values (22) 6029
60.3%

last_load_dttm
Date

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-05-01 06:18:03
Maximum2021-05-01 06:18:11
2024-04-21T18:16:22.826975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:16:23.183458image/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
38577299467797826501122501122784102020-07-09456농산물92동구2000.0209706990신고배탑마트(초량점)35.11677129.0395부산광역시 동구 초량동 393-1051-466-2112YY배2021-05-01 06:18:10
29713308339101100345049885049881004112020-09-03464식료품316중구420.035803580<NA>농협하나로마트(자갈치점)35.097233129.02745부산광역시 중구 남포동6가 1051-250-7711YY두부2021-05-01 06:18:08
38355299662858425502124502124844062020-07-23461주류및음료,차52남구1.526902690<NA>삼성홈플러스(감만점)35.121605129.08223부산광역시 남구 감만동 8051-609-8000YY사이다2021-05-01 06:18:10
2624431176182813028492165492165814062020-02-06456농산물257수영구1000.026602660<NA>홈플러스 익스플러스 광안점35.161668129.113611(48253) 부산광역시 수영구 수영로 625, 491-1 (광안동)051-756-2277YY대파2021-05-01 06:18:08
21124316867159157465076995076991574172020-10-21459수산물265수영구500.070007000<NA>수영팔도시장35.168651129.116693부산광역시 수영구 민락동 수영로 725번길051-751-6951YY갈치2021-05-01 06:18:07
30113307887828143503890503890814072020-08-20456농산물186북구1000.053505350<NA>롯데마트(화명점)35.234905129.00853부산광역시 북구 화명3동 1975051-604-2500YY대파2021-05-01 06:18:09
2227131576493922524505934505934924112020-09-24461주류및음료,차174북구1.833003300<NA>농협하나로클럽마트35.250025129.011246부산광역시 북구 금곡동 북구금곡동1874-3051-330-9000YY콜라2021-05-01 06:18:07
210933588996952562475241475241954082019-05-02463세제261수영구3.01050010500<NA>메가마트 남천점35.137353129.111101부산광역시 수영구 남천동 남천동545-2051-608-6000YY가루비누2021-05-01 06:18:03
19163318832106105424737284737281054062019-04-11456농산물189사상구20.05990059900신동진삼성홈플러스(서부산점)35.16483128.97812부산광역시 사상구 괘법동 529-1051-319-9157YY쌀2021-05-01 06:18:06
38221299805818023502129502129804062020-07-23456농산물31금정구1500.021863280<NA>홈플러스(동래점)35.2392129.0915(47710) 부산광역시 동래구 중앙대로 1523 (온천동)051-559-8000YY양파2021-05-01 06:18:10
skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
16933321098106105725085925085921054172020-11-04456농산물88동구20.06500065000신동진쌀초량시장35.11817129.039547부산광역시 동구 초량1동 중앙대로 231번길 27051-467-5054YY쌀2021-05-01 06:18:06
94913285491009925492895492895994062020-02-20464식료품52남구320.079807980오뚜기고소한 참기름삼성홈플러스(감만점)35.121605129.08223부산광역시 남구 감만동 8051-609-8000YY참기름2021-05-01 06:18:05
10037327979157155304929864929861554162020-02-27464식료품209사하구1.579086590<NA>뉴코아아울렛킴스클럽(괴정점)35.098934128.99406부산광역시 사하구 괴정3동 961-1051-209-5061YY식용유2021-05-01 06:18:05
30438307590105104385032575032571044072020-08-13464식료품360해운대구3.035164220탑마트(반여점)35.205868129.12317(48038) 부산광역시 해운대구 선수촌로 119 (반여동)051-525-0422YY밀가루2021-05-01 06:18:09
34809303187156154444736174736171544052019-04-04463세제21금정구990.01170913800이마트퓨어이마트(금정점)35.250107129.09073부산광역시 금정구 구서동 368051-606-1234YY화장지2021-05-01 06:18:09
26615311424105104394922514922511044062020-02-13464식료품116부산진구2.532903290<NA>삼성홈플러스(가야점)35.152466129.02731부산광역시 부산진구 가야동 624-7051-890-8023YY밀가루2021-05-01 06:18:08
29606308443807929504993504993794072020-09-03456농산물111동래구3000.01996019960홍로롯데마트(동래점)35.211483129.0776부산광역시 동래구 온천동 502-3051-668-2500YY사과2021-05-01 06:18:08
38040299972878637502134502134864052020-07-23458축산물64남구1.064806480올품/무항생제닭백숙용이마트(문현점)35.144615129.06464부산광역시 남구 문현동 751051-609-1234YY닭고기2021-05-01 06:18:10
19527318494797849473747473747784172019-04-10456농산물27금정구2000.04200014000<NA>부곡상가시장35.211952129.074353부산광역시 금정구 부곡1동 명륜로 663019-513-7978YY배2021-05-01 06:18:06
402602977681009937501135501135994052020-07-09464식료품64남구320.089808980<NA>이마트(문현점)35.144615129.06464부산광역시 남구 문현동 751051-609-1234YY참기름2021-05-01 06:18:10