Overview

Dataset statistics

Number of variables26
Number of observations10000
Missing cells7009
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory231.0 B

Variable types

Numeric15
DateTime1
Categorical6
Text4

Alerts

parkng_at is highly imbalanced (93.8%)Imbalance
card_at is highly imbalanced (97.0%)Imbalance
rm has 6633 (66.3%) missing valuesMissing
skey has unique valuesUnique
unitprice has 108 (1.1%) zerosZeros
prices has 104 (1.0%) zerosZeros

Reproduction

Analysis started2024-04-21 09:05:42.784952
Analysis finished2024-04-21 09:05:44.004273
Duration1.22 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%
Mean317127.59
Minimum296149
Maximum338016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:44.200268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum296149
5-th percentile298344.6
Q1306502.75
median317083.5
Q3327639.25
95-th percentile335972.15
Maximum338016
Range41867
Interquartile range (IQR)21136.5

Descriptive statistics

Standard deviation12131.613
Coefficient of variation (CV)0.038254676
Kurtosis-1.2183224
Mean317127.59
Median Absolute Deviation (MAD)10574.5
Skewness-0.0013712682
Sum3.1712759 × 109
Variance1.4717604 × 108
MonotonicityNot monotonic
2024-04-21T18:05:44.619798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316419 1
 
< 0.1%
301022 1
 
< 0.1%
328272 1
 
< 0.1%
315468 1
 
< 0.1%
303531 1
 
< 0.1%
322965 1
 
< 0.1%
331520 1
 
< 0.1%
331484 1
 
< 0.1%
330791 1
 
< 0.1%
326971 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
296149 1
< 0.1%
296158 1
< 0.1%
296160 1
< 0.1%
296163 1
< 0.1%
296166 1
< 0.1%
296167 1
< 0.1%
296170 1
< 0.1%
296181 1
< 0.1%
296183 1
< 0.1%
296187 1
< 0.1%
ValueCountFrequency (%)
338016 1
< 0.1%
338012 1
< 0.1%
338008 1
< 0.1%
337996 1
< 0.1%
337994 1
< 0.1%
337992 1
< 0.1%
337985 1
< 0.1%
337984 1
< 0.1%
337983 1
< 0.1%
337982 1
< 0.1%

ccode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.361
Minimum77
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:45.007043image/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 deviation22.721979
Coefficient of variation (CV)0.23337865
Kurtosis2.5460427
Mean97.361
Median Absolute Deviation (MAD)9
Skewness1.8996538
Sum973610
Variance516.28831
MonotonicityNot monotonic
2024-04-21T18:05:45.289241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
79 422
 
4.2%
81 414
 
4.1%
88 407
 
4.1%
90 396
 
4.0%
101 394
 
3.9%
106 394
 
3.9%
78 390
 
3.9%
89 380
 
3.8%
80 378
 
3.8%
87 373
 
3.7%
Other values (22) 6052
60.5%
ValueCountFrequency (%)
77 371
3.7%
78 390
3.9%
79 422
4.2%
80 378
3.8%
81 414
4.1%
82 369
3.7%
83 372
3.7%
84 355
3.5%
85 237
2.4%
86 247
2.5%
ValueCountFrequency (%)
159 295
2.9%
158 314
3.1%
157 240
2.4%
156 248
2.5%
106 394
3.9%
105 230
2.3%
104 244
2.4%
103 231
2.3%
101 394
3.9%
100 261
2.6%

pcode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.2513
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:45.508732image/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 deviation22.431421
Coefficient of variation (CV)0.23305058
Kurtosis2.5056928
Mean96.2513
Median Absolute Deviation (MAD)9
Skewness1.8849879
Sum962513
Variance503.16867
MonotonicityNot monotonic
2024-04-21T18:05:45.746664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
78 422
 
4.2%
80 414
 
4.1%
87 407
 
4.1%
89 396
 
4.0%
100 394
 
3.9%
105 394
 
3.9%
77 390
 
3.9%
88 380
 
3.8%
79 378
 
3.8%
86 373
 
3.7%
Other values (22) 6052
60.5%
ValueCountFrequency (%)
76 371
3.7%
77 390
3.9%
78 422
4.2%
79 378
3.8%
80 414
4.1%
81 369
3.7%
82 372
3.7%
83 355
3.5%
84 237
2.4%
85 247
2.5%
ValueCountFrequency (%)
157 295
2.9%
156 314
3.1%
155 240
2.4%
154 248
2.5%
105 394
3.9%
104 230
2.3%
103 244
2.4%
102 231
2.3%
100 394
3.9%
99 261
2.6%

bssh_no
Real number (ℝ)

Distinct54
Distinct (%)0.5%
Missing31
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean406.38349
Minimum14
Maximum3198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:45.996699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile20
Q129
median39
Q359
95-th percentile2625
Maximum3198
Range3184
Interquartile range (IQR)30

Descriptive statistics

Standard deviation920.75245
Coefficient of variation (CV)2.2657231
Kurtosis2.6503963
Mean406.38349
Median Absolute Deviation (MAD)11
Skewness2.1362724
Sum4051237
Variance847785.07
MonotonicityNot monotonic
2024-04-21T18:05:46.237553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 329
 
3.3%
45 328
 
3.3%
3028 328
 
3.3%
26 327
 
3.3%
34 327
 
3.3%
20 324
 
3.2%
32 320
 
3.2%
30 319
 
3.2%
42 319
 
3.2%
44 315
 
3.1%
Other values (44) 6733
67.3%
ValueCountFrequency (%)
14 101
 
1.0%
17 83
 
0.8%
19 290
2.9%
20 324
3.2%
22 289
2.9%
23 184
1.8%
25 298
3.0%
26 327
3.3%
28 329
3.3%
29 295
2.9%
ValueCountFrequency (%)
3198 2
 
< 0.1%
3193 4
 
< 0.1%
3177 57
 
0.6%
3028 328
3.3%
2625 310
3.1%
2562 294
2.9%
2524 303
3.0%
2349 80
 
0.8%
87 88
 
0.9%
85 68
 
0.7%

search_no
Real number (ℝ)

Distinct1597
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500231.42
Minimum469120
Maximum513376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:46.490268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum469120
5-th percentile473687
Q1492977.75
median503984
Q3509349
95-th percentile512540.05
Maximum513376
Range44256
Interquartile range (IQR)16371.25

Descriptive statistics

Standard deviation12477.178
Coefficient of variation (CV)0.024942812
Kurtosis0.21876777
Mean500231.42
Median Absolute Deviation (MAD)5395.5
Skewness-1.238202
Sum5.0023142 × 109
Variance1.5567998 × 108
MonotonicityNot monotonic
2024-04-21T18:05:46.734172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
503237 14
 
0.1%
472935 14
 
0.1%
508628 14
 
0.1%
501119 13
 
0.1%
499539 13
 
0.1%
504972 13
 
0.1%
472920 13
 
0.1%
512513 13
 
0.1%
511745 13
 
0.1%
510946 13
 
0.1%
Other values (1587) 9867
98.7%
ValueCountFrequency (%)
469120 6
0.1%
469121 7
0.1%
472895 4
< 0.1%
472896 5
0.1%
472897 4
< 0.1%
472898 2
 
< 0.1%
472899 2
 
< 0.1%
472900 5
0.1%
472901 5
0.1%
472902 3
< 0.1%
ValueCountFrequency (%)
513376 5
 
0.1%
513375 8
0.1%
513372 7
0.1%
513370 8
0.1%
513369 4
 
< 0.1%
513357 2
 
< 0.1%
513354 10
0.1%
513353 13
0.1%
513352 8
0.1%
513351 9
0.1%

prices_no
Real number (ℝ)

Distinct1597
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500231.42
Minimum469120
Maximum513376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:47.189844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum469120
5-th percentile473687
Q1492977.75
median503984
Q3509349
95-th percentile512540.05
Maximum513376
Range44256
Interquartile range (IQR)16371.25

Descriptive statistics

Standard deviation12477.178
Coefficient of variation (CV)0.024942812
Kurtosis0.21876777
Mean500231.42
Median Absolute Deviation (MAD)5395.5
Skewness-1.238202
Sum5.0023142 × 109
Variance1.5567998 × 108
MonotonicityNot monotonic
2024-04-21T18:05:47.432941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
503237 14
 
0.1%
472935 14
 
0.1%
508628 14
 
0.1%
501119 13
 
0.1%
499539 13
 
0.1%
504972 13
 
0.1%
472920 13
 
0.1%
512513 13
 
0.1%
511745 13
 
0.1%
510946 13
 
0.1%
Other values (1587) 9867
98.7%
ValueCountFrequency (%)
469120 6
0.1%
469121 7
0.1%
472895 4
< 0.1%
472896 5
0.1%
472897 4
< 0.1%
472898 2
 
< 0.1%
472899 2
 
< 0.1%
472900 5
0.1%
472901 5
0.1%
472902 3
< 0.1%
ValueCountFrequency (%)
513376 5
 
0.1%
513375 8
0.1%
513372 7
0.1%
513370 8
0.1%
513369 4
 
< 0.1%
513357 2
 
< 0.1%
513354 10
0.1%
513353 13
0.1%
513352 8
0.1%
513351 9
0.1%

prdlst
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.2513
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:47.658217image/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 deviation22.431421
Coefficient of variation (CV)0.23305058
Kurtosis2.5056928
Mean96.2513
Median Absolute Deviation (MAD)9
Skewness1.8849879
Sum962513
Variance503.16867
MonotonicityNot monotonic
2024-04-21T18:05:47.896915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
78 422
 
4.2%
80 414
 
4.1%
87 407
 
4.1%
89 396
 
4.0%
100 394
 
3.9%
105 394
 
3.9%
77 390
 
3.9%
88 380
 
3.8%
79 378
 
3.8%
86 373
 
3.7%
Other values (22) 6052
60.5%
ValueCountFrequency (%)
76 371
3.7%
77 390
3.9%
78 422
4.2%
79 378
3.8%
80 414
4.1%
81 369
3.7%
82 372
3.7%
83 355
3.5%
84 237
2.4%
85 247
2.5%
ValueCountFrequency (%)
157 295
2.9%
156 314
3.1%
155 240
2.4%
154 248
2.5%
105 394
3.9%
104 230
2.3%
103 244
2.4%
102 231
2.3%
100 394
3.9%
99 261
2.6%

cl_no
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing31
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean409.2589
Minimum405
Maximum419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:48.102949image/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 deviation4.5343213
Coefficient of variation (CV)0.011079347
Kurtosis-0.88780289
Mean409.2589
Median Absolute Deviation (MAD)2
Skewness0.88322057
Sum4079902
Variance20.560069
MonotonicityNot monotonic
2024-04-21T18:05:48.278264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
406 2676
26.8%
417 2039
20.4%
405 1567
15.7%
407 1492
14.9%
411 630
 
6.3%
408 622
 
6.2%
410 618
 
6.2%
416 319
 
3.2%
419 6
 
0.1%
(Missing) 31
 
0.3%
ValueCountFrequency (%)
405 1567
15.7%
406 2676
26.8%
407 1492
14.9%
408 622
 
6.2%
410 618
 
6.2%
411 630
 
6.3%
416 319
 
3.2%
417 2039
20.4%
419 6
 
0.1%
ValueCountFrequency (%)
419 6
 
0.1%
417 2039
20.4%
416 319
 
3.2%
411 630
 
6.3%
410 618
 
6.2%
408 622
 
6.2%
407 1492
14.9%
406 2676
26.8%
405 1567
15.7%

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
2024-04-21T18:05:48.493669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:05:48.732773image/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%
Mean459.4938
Minimum456
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:48.940000image/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 deviation3.0364591
Coefficient of variation (CV)0.0066082699
Kurtosis-1.4094685
Mean459.4938
Median Absolute Deviation (MAD)3
Skewness0.24992585
Sum4594938
Variance9.2200836
MonotonicityNot monotonic
2024-04-21T18:05:49.138942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
456 3094
30.9%
464 1844
18.4%
461 1774
17.7%
458 1556
15.6%
459 980
 
9.8%
463 752
 
7.5%
ValueCountFrequency (%)
456 3094
30.9%
458 1556
15.6%
459 980
 
9.8%
461 1774
17.7%
463 752
 
7.5%
464 1844
18.4%
ValueCountFrequency (%)
464 1844
18.4%
463 752
 
7.5%
461 1774
17.7%
459 980
 
9.8%
458 1556
15.6%
456 3094
30.9%

pum_nm
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농산물
3094 
식료품
1844 
주류및음료,차
1774 
축산물
1556 
수산물
980 

Length

Max length7
Median length3
Mean length3.6344
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농산물 3094
30.9%
식료품 1844
18.4%
주류및음료,차 1774
17.7%
축산물 1556
15.6%
수산물 980
 
9.8%
세제 752
 
7.5%

Length

2024-04-21T18:05:49.363795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:05:49.551201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농산물 3094
30.9%
식료품 1844
18.4%
주류및음료,차 1774
17.7%
축산물 1556
15.6%
수산물 980
 
9.8%
세제 752
 
7.5%

gugun_cd
Real number (ℝ)

Distinct49
Distinct (%)0.5%
Missing33
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean188.96218
Minimum21
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:49.770962image/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 deviation102.76398
Coefficient of variation (CV)0.54383361
Kurtosis-0.97576401
Mean188.96218
Median Absolute Deviation (MAD)78
Skewness0.16302611
Sum1883386
Variance10560.436
MonotonicityNot monotonic
2024-04-21T18:05:50.010294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
189 643
 
6.4%
374 394
 
3.9%
111 373
 
3.7%
227 361
 
3.6%
145 329
 
3.3%
257 328
 
3.3%
95 328
 
3.3%
316 327
 
3.3%
92 327
 
3.3%
209 319
 
3.2%
Other values (39) 6238
62.4%
ValueCountFrequency (%)
21 315
3.1%
27 75
 
0.8%
31 184
1.8%
33 79
 
0.8%
48 290
2.9%
52 298
3.0%
53 84
 
0.8%
64 299
3.0%
66 79
 
0.8%
80 83
 
0.8%
ValueCountFrequency (%)
374 394
3.9%
373 4
 
< 0.1%
369 83
 
0.8%
365 310
3.1%
360 310
3.1%
333 90
 
0.9%
316 327
3.3%
314 82
 
0.8%
293 307
3.1%
275 68
 
0.7%

gugun_nm
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
해운대구
1101 
사상구
801 
수영구
788 
부산진구
785 
북구
782 
Other values (12)
5743 

Length

Max length4
Median length3
Mean length2.897
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해운대구
2nd row사상구
3rd row동구
4th row부산진구
5th row남구

Common Values

ValueCountFrequency (%)
해운대구 1101
11.0%
사상구 801
 
8.0%
수영구 788
 
7.9%
부산진구 785
 
7.8%
북구 782
 
7.8%
동래구 778
 
7.8%
사하구 761
 
7.6%
남구 760
 
7.6%
금정구 653
 
6.5%
중구 499
 
5.0%
Other values (7) 2292
22.9%

Length

2024-04-21T18:05:50.258479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해운대구 1101
11.0%
사상구 801
 
8.0%
수영구 788
 
7.9%
부산진구 785
 
7.8%
북구 782
 
7.8%
동래구 778
 
7.8%
사하구 761
 
7.6%
남구 760
 
7.6%
금정구 653
 
6.5%
중구 499
 
5.0%
Other values (7) 2292
22.9%

unit
Real number (ℝ)

Distinct376
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean671.06973
Minimum0.1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:50.503976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1
Q15
median420
Q3881.5
95-th percentile2000
Maximum10000
Range9999.9
Interquartile range (IQR)876.5

Descriptive statistics

Standard deviation1004.5047
Coefficient of variation (CV)1.4968708
Kurtosis14.448918
Mean671.06973
Median Absolute Deviation (MAD)416
Skewness3.2680931
Sum6710697.3
Variance1009029.6
MonotonicityNot monotonic
2024-04-21T18:05:50.751169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500.0 1306
 
13.1%
100.0 905
 
9.0%
1000.0 756
 
7.6%
1.0 719
 
7.2%
2000.0 581
 
5.8%
600.0 530
 
5.3%
1.8 475
 
4.8%
20.0 394
 
3.9%
320.0 265
 
2.6%
360.0 258
 
2.6%
Other values (366) 3811
38.1%
ValueCountFrequency (%)
0.1 2
 
< 0.1%
0.8 22
 
0.2%
0.9 214
 
2.1%
1.0 719
7.2%
1.05 2
 
< 0.1%
1.1 11
 
0.1%
1.2 19
 
0.2%
1.4 112
 
1.1%
1.5 230
 
2.3%
1.6 2
 
< 0.1%
ValueCountFrequency (%)
10000.0 1
 
< 0.1%
9000.0 6
 
0.1%
6000.0 154
1.5%
5000.0 16
 
0.2%
4500.0 2
 
< 0.1%
4000.0 3
 
< 0.1%
3560.0 1
 
< 0.1%
3500.0 15
 
0.1%
3400.0 2
 
< 0.1%
3190.0 3
 
< 0.1%

unitprice
Real number (ℝ)

ZEROS 

Distinct1866
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10435.494
Minimum0
Maximum100000
Zeros108
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:50.996981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile796
Q12500
median4788
Q310492
95-th percentile50000
Maximum100000
Range100000
Interquartile range (IQR)7992

Descriptive statistics

Standard deviation14415.54
Coefficient of variation (CV)1.3813951
Kurtosis4.7838528
Mean10435.494
Median Absolute Deviation (MAD)2972
Skewness2.3351846
Sum1.0435494 × 108
Variance2.0780778 × 108
MonotonicityNot monotonic
2024-04-21T18:05:51.247317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
676 181
 
1.8%
2500 162
 
1.6%
3500 156
 
1.6%
796 148
 
1.5%
1190 139
 
1.4%
5000 132
 
1.3%
3290 128
 
1.3%
2000 127
 
1.3%
4531 123
 
1.2%
4000 120
 
1.2%
Other values (1856) 8584
85.8%
ValueCountFrequency (%)
0 108
1.1%
215 1
 
< 0.1%
500 1
 
< 0.1%
541 1
 
< 0.1%
598 2
 
< 0.1%
676 181
1.8%
678 2
 
< 0.1%
680 22
 
0.2%
700 10
 
0.1%
730 9
 
0.1%
ValueCountFrequency (%)
100000 1
 
< 0.1%
79950 6
0.1%
74000 6
0.1%
72500 1
 
< 0.1%
71500 1
 
< 0.1%
70000 5
0.1%
69900 8
0.1%
68900 1
 
< 0.1%
68000 3
 
< 0.1%
67900 1
 
< 0.1%

prices
Real number (ℝ)

ZEROS 

Distinct1052
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8431.9116
Minimum0
Maximum100000
Zeros104
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:51.497805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q12450
median4000
Q39000
95-th percentile36917.85
Maximum100000
Range100000
Interquartile range (IQR)6550

Descriptive statistics

Standard deviation12071.653
Coefficient of variation (CV)1.4316627
Kurtosis9.8327676
Mean8431.9116
Median Absolute Deviation (MAD)2420
Skewness3.0937993
Sum84319116
Variance1.4572481 × 108
MonotonicityNot monotonic
2024-04-21T18:05:51.758963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9900 196
 
2.0%
3500 195
 
1.9%
2500 193
 
1.9%
5000 178
 
1.8%
3000 154
 
1.5%
3290 147
 
1.5%
4100 147
 
1.5%
4000 142
 
1.4%
1190 141
 
1.4%
2000 140
 
1.4%
Other values (1042) 8367
83.7%
ValueCountFrequency (%)
0 104
1.0%
139 1
 
< 0.1%
158 1
 
< 0.1%
183 1
 
< 0.1%
230 1
 
< 0.1%
240 1
 
< 0.1%
250 1
 
< 0.1%
257 1
 
< 0.1%
263 2
 
< 0.1%
273 1
 
< 0.1%
ValueCountFrequency (%)
100000 3
 
< 0.1%
70000 2
 
< 0.1%
69900 8
0.1%
68900 1
 
< 0.1%
68000 3
 
< 0.1%
67900 1
 
< 0.1%
67000 1
 
< 0.1%
66900 1
 
< 0.1%
65000 6
0.1%
64900 4
< 0.1%

rm
Text

MISSING 

Distinct930
Distinct (%)27.6%
Missing6633
Missing (%)66.3%
Memory size156.2 KiB
2024-04-21T18:05:52.694883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length33
Mean length6.4787645
Min length1

Characters and Unicode

Total characters21814
Distinct characters371
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

Unique482 ?
Unique (%)14.3%

Sample

1st row생물 손질된것
2nd row1kg
3rd row국산/손질생물
4th row크리넥스데코소프트(35*30)
5th row햇사과
ValueCountFrequency (%)
행사 147
 
3.7%
생물 108
 
2.7%
해동 94
 
2.4%
냉동 46
 
1.2%
품절 43
 
1.1%
하우스밀감 43
 
1.1%
이맛쌀 38
 
1.0%
고소한참기름 36
 
0.9%
곰표 34
 
0.9%
없음 32
 
0.8%
Other values (835) 3329
84.3%
2024-04-21T18:05:53.879569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1633
 
7.5%
0 918
 
4.2%
1 915
 
4.2%
/ 474
 
2.2%
2 455
 
2.1%
사 407
 
1.9%
3 366
 
1.7%
행 325
 
1.5%
원 287
 
1.3%
5 267
 
1.2%
Other values (361) 15767
72.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14363
65.8%
Decimal Number 3651
 
16.7%
Space Separator 1633
 
7.5%
Other Punctuation 782
 
3.6%
Lowercase Letter 413
 
1.9%
Math Symbol 352
 
1.6%
Uppercase Letter 279
 
1.3%
Close Punctuation 169
 
0.8%
Open Punctuation 162
 
0.7%
Dash Punctuation 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 407
 
2.8%
행 325
 
2.3%
원 287
 
2.0%
생 262
 
1.8%
기 247
 
1.7%
동 239
 
1.7%
한 234
 
1.6%
지 229
 
1.6%
고 226
 
1.6%
스 222
 
1.5%
Other values (314) 11685
81.4%
Lowercase Letter
ValueCountFrequency (%)
g 206
49.9%
l 56
 
13.6%
k 55
 
13.3%
m 45
 
10.9%
s 10
 
2.4%
u 6
 
1.5%
e 5
 
1.2%
j 5
 
1.2%
c 5
 
1.2%
i 4
 
1.0%
Other values (6) 16
 
3.9%
Decimal Number
ValueCountFrequency (%)
0 918
25.1%
1 915
25.1%
2 455
12.5%
3 366
 
10.0%
5 267
 
7.3%
9 246
 
6.7%
4 169
 
4.6%
8 158
 
4.3%
6 79
 
2.2%
7 78
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
L 78
28.0%
P 36
12.9%
C 36
12.9%
R 34
12.2%
G 27
 
9.7%
K 25
 
9.0%
T 24
 
8.6%
M 16
 
5.7%
A 2
 
0.7%
O 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
/ 474
60.6%
, 122
 
15.6%
* 76
 
9.7%
. 68
 
8.7%
% 42
 
5.4%
Math Symbol
ValueCountFrequency (%)
+ 206
58.5%
~ 146
41.5%
Space Separator
ValueCountFrequency (%)
1633
100.0%
Close Punctuation
ValueCountFrequency (%)
) 169
100.0%
Open Punctuation
ValueCountFrequency (%)
( 162
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14349
65.8%
Common 6759
31.0%
Latin 692
 
3.2%
Han 14
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 407
 
2.8%
행 325
 
2.3%
원 287
 
2.0%
생 262
 
1.8%
기 247
 
1.7%
동 239
 
1.7%
한 234
 
1.6%
지 229
 
1.6%
고 226
 
1.6%
스 222
 
1.5%
Other values (313) 11671
81.3%
Latin
ValueCountFrequency (%)
g 206
29.8%
L 78
 
11.3%
l 56
 
8.1%
k 55
 
7.9%
m 45
 
6.5%
P 36
 
5.2%
C 36
 
5.2%
R 34
 
4.9%
G 27
 
3.9%
K 25
 
3.6%
Other values (16) 94
13.6%
Common
ValueCountFrequency (%)
1633
24.2%
0 918
13.6%
1 915
13.5%
/ 474
 
7.0%
2 455
 
6.7%
3 366
 
5.4%
5 267
 
4.0%
9 246
 
3.6%
+ 206
 
3.0%
) 169
 
2.5%
Other values (11) 1110
16.4%
Han
ValueCountFrequency (%)
入 14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14348
65.8%
ASCII 7451
34.2%
CJK 14
 
0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1633
21.9%
0 918
12.3%
1 915
12.3%
/ 474
 
6.4%
2 455
 
6.1%
3 366
 
4.9%
5 267
 
3.6%
9 246
 
3.3%
g 206
 
2.8%
+ 206
 
2.8%
Other values (37) 1765
23.7%
Hangul
ValueCountFrequency (%)
사 407
 
2.8%
행 325
 
2.3%
원 287
 
2.0%
생 262
 
1.8%
기 247
 
1.7%
동 239
 
1.7%
한 234
 
1.6%
지 229
 
1.6%
고 226
 
1.6%
스 222
 
1.5%
Other values (312) 11670
81.3%
CJK
ValueCountFrequency (%)
入 14
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅣ 1
100.0%

bssh_nm
Text

Distinct54
Distinct (%)0.5%
Missing31
Missing (%)0.3%
Memory size156.2 KiB
2024-04-21T18:05:54.617486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.0956967
Min length4

Characters and Unicode

Total characters90675
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 row이마트(해운대점)
2nd row복이있는 덕포시장
3rd row탑마트(초량점)
4th row개금골목시장
5th row이마트(문현점)
ValueCountFrequency (%)
삼성홈플러스(서면점 329
 
3.0%
홈플러스 328
 
3.0%
익스플러스 328
 
3.0%
광안점 328
 
3.0%
메가마트(동래점 328
 
3.0%
탑마트(초량점 327
 
3.0%
농협하나로마트(자갈치점 327
 
3.0%
이마트(사상점 324
 
3.0%
이마트(해운대점 320
 
2.9%
뉴코아아울렛킴스클럽(괴정점 319
 
2.9%
Other values (49) 7722
70.3%
2024-04-21T18:05:55.574817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 7418
 
8.2%
( 7089
 
7.8%
) 7089
 
7.8%
마 4640
 
5.1%
트 4640
 
5.1%
스 3651
 
4.0%
플 3004
 
3.3%
러 3004
 
3.3%
홈 2676
 
3.0%
시 2261
 
2.5%
Other values (107) 45203
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75341
83.1%
Open Punctuation 7089
 
7.8%
Close Punctuation 7089
 
7.8%
Space Separator 1068
 
1.2%
Decimal Number 88
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 7418
 
9.8%
마 4640
 
6.2%
트 4640
 
6.2%
스 3651
 
4.8%
플 3004
 
4.0%
러 3004
 
4.0%
홈 2676
 
3.6%
시 2261
 
3.0%
성 2164
 
2.9%
삼 2164
 
2.9%
Other values (103) 39719
52.7%
Open Punctuation
ValueCountFrequency (%)
( 7089
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7089
100.0%
Space Separator
ValueCountFrequency (%)
1068
100.0%
Decimal Number
ValueCountFrequency (%)
1 88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75341
83.1%
Common 15334
 
16.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 7418
 
9.8%
마 4640
 
6.2%
트 4640
 
6.2%
스 3651
 
4.8%
플 3004
 
4.0%
러 3004
 
4.0%
홈 2676
 
3.6%
시 2261
 
3.0%
성 2164
 
2.9%
삼 2164
 
2.9%
Other values (103) 39719
52.7%
Common
ValueCountFrequency (%)
( 7089
46.2%
) 7089
46.2%
1068
 
7.0%
1 88
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75341
83.1%
ASCII 15334
 
16.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 7418
 
9.8%
마 4640
 
6.2%
트 4640
 
6.2%
스 3651
 
4.8%
플 3004
 
4.0%
러 3004
 
4.0%
홈 2676
 
3.6%
시 2261
 
3.0%
성 2164
 
2.9%
삼 2164
 
2.9%
Other values (103) 39719
52.7%
ASCII
ValueCountFrequency (%)
( 7089
46.2%
) 7089
46.2%
1068
 
7.0%
1 88
 
0.6%

la
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing94
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean35.163396
Minimum35.08484
Maximum35.323099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:55.818396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.05581197
Coefficient of variation (CV)0.0015872179
Kurtosis0.14378159
Mean35.163396
Median Absolute Deviation (MAD)0.0442
Skewness0.62746637
Sum348328.6
Variance0.0031149759
MonotonicityNot monotonic
2024-04-21T18:05:56.284681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.149452 329
 
3.3%
35.161668 328
 
3.3%
35.204113 328
 
3.3%
35.11677 327
 
3.3%
35.097233 327
 
3.3%
35.16423 324
 
3.2%
35.165993 320
 
3.2%
35.098934 319
 
3.2%
35.16483 319
 
3.2%
35.250107 315
 
3.1%
Other values (41) 6670
66.7%
ValueCountFrequency (%)
35.08484 284
2.8%
35.085472 289
2.9%
35.092663 291
2.9%
35.0931597 70
 
0.7%
35.095905 307
3.1%
35.0970155 82
 
0.8%
35.097233 327
3.3%
35.098934 319
3.2%
35.0996462 78
 
0.8%
35.099649 66
 
0.7%
ValueCountFrequency (%)
35.3230993 290
2.9%
35.250107 315
3.1%
35.250025 303
3.0%
35.2392 184
1.8%
35.234905 314
3.1%
35.2222318 78
 
0.8%
35.2159352 79
 
0.8%
35.2146331 81
 
0.8%
35.2119516 75
 
0.8%
35.211483 295
2.9%

lo
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing94
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean129.05633
Minimum128.89784
Maximum129.17634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:05:56.521746image/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 deviation0.062139142
Coefficient of variation (CV)0.00048148852
Kurtosis-0.0026655798
Mean129.05633
Median Absolute Deviation (MAD)0.03952
Skewness-0.23879792
Sum1278432
Variance0.003861273
MonotonicityNot monotonic
2024-04-21T18:05:56.762069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.06401 329
 
3.3%
129.113611 328
 
3.3%
129.08112 328
 
3.3%
129.0395 327
 
3.3%
129.02745 327
 
3.3%
128.97891 324
 
3.2%
129.16739 320
 
3.2%
128.99406 319
 
3.2%
128.97812 319
 
3.2%
129.09073 315
 
3.1%
Other values (41) 6670
66.7%
ValueCountFrequency (%)
128.89784 289
2.9%
128.9019892 80
 
0.8%
128.97157 284
2.8%
128.97812 319
3.2%
128.97891 324
3.2%
128.9813855 101
 
1.0%
128.9893459 78
 
0.8%
128.99406 319
3.2%
129.0016813 84
 
0.8%
129.00853 314
3.1%
ValueCountFrequency (%)
129.1763385 290
2.9%
129.1744835 83
 
0.8%
129.16739 320
3.2%
129.1623293 74
 
0.7%
129.1337096 310
3.1%
129.12317 310
3.1%
129.116693 83
 
0.8%
129.113611 328
3.3%
129.1114753 79
 
0.8%
129.1111015 294
2.9%

adres
Text

Distinct54
Distinct (%)0.5%
Missing31
Missing (%)0.3%
Memory size156.2 KiB
2024-04-21T18:05:57.984571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length21.916943
Min length14

Characters and Unicode

Total characters218490
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 해운대구 중1동 1767
2nd row(46950) 부산광역시 사상구 사상로293번길 15 (덕포동)
3rd row부산광역시 동구 초량동 393-1
4th row부산광역시 부산진구 개금동 가야대로 482번길 40
5th row부산광역시 남구 문현동 751
ValueCountFrequency (%)
부산광역시 9969
 
21.9%
해운대구 1411
 
3.1%
동래구 962
 
2.1%
사상구 801
 
1.8%
수영구 788
 
1.7%
부산진구 785
 
1.7%
북구 782
 
1.7%
사하구 761
 
1.7%
남구 760
 
1.7%
괘법동 643
 
1.4%
Other values (159) 27818
61.2%
2024-04-21T18:05:59.361405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35511
 
16.3%
동 12355
 
5.7%
부 11437
 
5.2%
산 11302
 
5.2%
구 10939
 
5.0%
1 10508
 
4.8%
광 10297
 
4.7%
시 10200
 
4.7%
역 9969
 
4.6%
2 6609
 
3.0%
Other values (106) 89363
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128442
58.8%
Decimal Number 45291
 
20.7%
Space Separator 35511
 
16.3%
Dash Punctuation 5060
 
2.3%
Open Punctuation 1926
 
0.9%
Close Punctuation 1926
 
0.9%
Other Punctuation 334
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12355
 
9.6%
부 11437
 
8.9%
산 11302
 
8.8%
구 10939
 
8.5%
광 10297
 
8.0%
시 10200
 
7.9%
역 9969
 
7.8%
로 2946
 
2.3%
남 2192
 
1.7%
대 2138
 
1.7%
Other values (91) 44667
34.8%
Decimal Number
ValueCountFrequency (%)
1 10508
23.2%
2 6609
14.6%
3 5728
12.6%
5 4436
9.8%
6 3508
 
7.7%
7 3383
 
7.5%
8 3186
 
7.0%
4 3058
 
6.8%
9 2607
 
5.8%
0 2268
 
5.0%
Space Separator
ValueCountFrequency (%)
35511
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5060
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1926
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1926
100.0%
Other Punctuation
ValueCountFrequency (%)
, 334
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128442
58.8%
Common 90048
41.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12355
 
9.6%
부 11437
 
8.9%
산 11302
 
8.8%
구 10939
 
8.5%
광 10297
 
8.0%
시 10200
 
7.9%
역 9969
 
7.8%
로 2946
 
2.3%
남 2192
 
1.7%
대 2138
 
1.7%
Other values (91) 44667
34.8%
Common
ValueCountFrequency (%)
35511
39.4%
1 10508
 
11.7%
2 6609
 
7.3%
3 5728
 
6.4%
- 5060
 
5.6%
5 4436
 
4.9%
6 3508
 
3.9%
7 3383
 
3.8%
8 3186
 
3.5%
4 3058
 
3.4%
Other values (5) 9061
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128442
58.8%
ASCII 90048
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35511
39.4%
1 10508
 
11.7%
2 6609
 
7.3%
3 5728
 
6.4%
- 5060
 
5.6%
5 4436
 
4.9%
6 3508
 
3.9%
7 3383
 
3.8%
8 3186
 
3.5%
4 3058
 
3.4%
Other values (5) 9061
 
10.1%
Hangul
ValueCountFrequency (%)
동 12355
 
9.6%
부 11437
 
8.9%
산 11302
 
8.8%
구 10939
 
8.5%
광 10297
 
8.0%
시 10200
 
7.9%
역 9969
 
7.8%
로 2946
 
2.3%
남 2192
 
1.7%
대 2138
 
1.7%
Other values (91) 44667
34.8%

telno
Text

Distinct54
Distinct (%)0.5%
Missing31
Missing (%)0.3%
Memory size156.2 KiB
2024-04-21T18:06:00.001105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.016652
Min length12

Characters and Unicode

Total characters119794
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row051-608-1234
2nd row051-303-8393
3rd row051-466-2112
4th row051-892-2606
5th row051-609-1234
ValueCountFrequency (%)
051-605-1000 329
 
3.3%
051-550-2000 328
 
3.3%
051-756-2277 328
 
3.3%
051-466-2112 327
 
3.3%
051-250-7711 327
 
3.3%
051-329-1234 324
 
3.3%
051-608-1234 320
 
3.2%
051-209-5061 319
 
3.2%
051-319-9157 319
 
3.2%
051-606-1234 315
 
3.2%
Other values (44) 6733
67.5%
2024-04-21T18:06:00.844280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27754
23.2%
- 19938
16.6%
1 17004
14.2%
5 16253
13.6%
2 10252
 
8.6%
6 6652
 
5.6%
3 5167
 
4.3%
9 4654
 
3.9%
4 4287
 
3.6%
8 4006
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99856
83.4%
Dash Punctuation 19938
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27754
27.8%
1 17004
17.0%
5 16253
16.3%
2 10252
 
10.3%
6 6652
 
6.7%
3 5167
 
5.2%
9 4654
 
4.7%
4 4287
 
4.3%
8 4006
 
4.0%
7 3827
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 19938
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119794
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27754
23.2%
- 19938
16.6%
1 17004
14.2%
5 16253
13.6%
2 10252
 
8.6%
6 6652
 
5.6%
3 5167
 
4.3%
9 4654
 
3.9%
4 4287
 
3.6%
8 4006
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119794
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27754
23.2%
- 19938
16.6%
1 17004
14.2%
5 16253
13.6%
2 10252
 
8.6%
6 6652
 
5.6%
3 5167
 
4.3%
9 4654
 
3.9%
4 4287
 
3.6%
8 4006
 
3.3%

parkng_at
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9887 
N
 
82
 
31

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 9887
98.9%
N 82
 
0.8%
31
 
0.3%

Length

2024-04-21T18:06:01.057895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:06:01.223835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9887
99.2%
n 82
 
0.8%

card_at
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9969 
 
31

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 9969
99.7%
31
 
0.3%

Length

2024-04-21T18:06:01.395053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:06:01.555697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9969
100.0%

item_name
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
배
 
422
양파
 
414
돼지고기
 
407
달걀
 
396
두부
 
394
Other values (27)
7967 

Length

Max length5
Median length4
Mean length2.4553
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row갈치
2nd row배
3rd row커피크림
4th row대파
5th row고등어

Common Values

ValueCountFrequency (%)
배 422
 
4.2%
양파 414
 
4.1%
돼지고기 407
 
4.1%
달걀 396
 
4.0%
두부 394
 
3.9%
쌀 394
 
3.9%
밀감 390
 
3.9%
쇠고기 380
 
3.8%
사과 378
 
3.8%
닭고기 373
 
3.7%
Other values (22) 6052
60.5%

Length

2024-04-21T18:06:01.743400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
배 422
 
4.2%
양파 414
 
4.1%
돼지고기 407
 
4.1%
달걀 396
 
4.0%
두부 394
 
3.9%
쌀 394
 
3.9%
밀감 390
 
3.9%
쇠고기 380
 
3.8%
사과 378
 
3.8%
닭고기 373
 
3.7%
Other values (22) 6052
60.5%

last_load_dttm
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-04-01 06:18:06
1984 
2021-04-01 06:18:04
1649 
2021-04-01 06:18:09
1574 
2021-04-01 06:18:08
1360 
2021-04-01 06:18:10
985 
Other values (4)
2448 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-01 06:18:07
2nd row2021-04-01 06:18:06
3rd row2021-04-01 06:18:06
4th row2021-04-01 06:18:04
5th row2021-04-01 06:18:07

Common Values

ValueCountFrequency (%)
2021-04-01 06:18:06 1984
19.8%
2021-04-01 06:18:04 1649
16.5%
2021-04-01 06:18:09 1574
15.7%
2021-04-01 06:18:08 1360
13.6%
2021-04-01 06:18:10 985
9.8%
2021-04-01 06:18:07 975
9.8%
2021-04-01 06:18:05 694
 
6.9%
2021-04-01 06:18:03 470
 
4.7%
2021-04-01 06:18:11 309
 
3.1%

Length

2024-04-21T18:06:01.959728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:06:02.159091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 10000
50.0%
06:18:06 1984
 
9.9%
06:18:04 1649
 
8.2%
06:18:09 1574
 
7.9%
06:18:08 1360
 
6.8%
06:18:10 985
 
4.9%
06:18:07 975
 
4.9%
06:18:05 694
 
3.5%
06:18:03 470
 
2.4%
06:18:11 309
 
1.5%

Sample

skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
21592316419159157325076785076781574052020-10-15459수산물374해운대구690.01101415200생물 손질된것이마트(해운대점)35.165993129.16739부산광역시 해운대구 중1동 1767051-608-1234YY갈치2021-04-01 06:18:07
1275332527879783177510166510166784172020-12-02456농산물192사상구1800.0300009000<NA>복이있는 덕포시장<NA><NA>(46950) 부산광역시 사상구 사상로293번길 15 (덕포동)051-303-8393YY배2021-04-01 06:18:06
16380321674979626508626508626964102020-11-05461주류및음료,차92동구1000.0235047001kg탑마트(초량점)35.11677129.0395부산광역시 동구 초량동 393-1051-466-2112YY커피크림2021-04-01 06:18:06
6494331529828166511793511793814172021-01-06456농산물120부산진구1000.040004000<NA>개금골목시장35.151289129.024543부산광역시 부산진구 개금동 가야대로 482번길 40051-892-2606YY대파2021-04-01 06:18:04
21888316144777637505948505948764052020-09-28459수산물64남구490.060815960국산/손질생물이마트(문현점)35.144615129.06464부산광역시 남구 문현동 751051-609-1234YY고등어2021-04-01 06:18:07
11929326069101100345102065102061004112020-12-03464식료품316중구420.032803280<NA>농협하나로마트(자갈치점)35.097233129.02745부산광역시 중구 남포동6가 1051-250-7711YY두부2021-04-01 06:18:06
35592302430989720472945472945974052019-03-28461주류및음료,차189사상구100.01480014800<NA>이마트(사상점)35.16423128.97891부산광역시 사상구 괘법동 531-2051-329-1234YY분말커피2021-04-01 06:18:09
23219314828888763506725506725874172020-10-07458축산물99동래구100.0125002500<NA>동래시장35.20381129.085949부산광역시 동래구 복천동 동래시장길 14051-552-1651YY돼지고기2021-04-01 06:18:07
32809305183797844502357502357784052020-07-30456농산물21금정구400.0298501990<NA>이마트(금정점)35.250107129.09073부산광역시 금정구 구서동 368051-606-1234YY배2021-04-01 06:18:09
30647307375828123503249503249814062020-08-13456농산물31금정구800.043503480<NA>홈플러스(동래점)35.2392129.0915(47710) 부산광역시 동래구 중앙대로 1523 (온천동)051-559-8000YY대파2021-04-01 06:18:09
skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
12992325024908985510144510144894172020-12-02458축산물275연제구600.026002600<NA>연일시장35.185184129.082459부산광역시 연제구 연산4동 636-13 등 (쌍미천로 141번길 양측)051-000-0000YY달걀2021-04-01 06:18:06
29843308186818028504983504983804062020-09-03456농산물145부산진구1622.022743690<NA>삼성홈플러스(서면점)35.149452129.06401부산광역시 부산진구 전포동 892-20051-605-1000YY양파2021-04-01 06:18:08
23778314262157155315058525058521554062020-09-17464식료품293영도구1.547883990<NA>삼성홈플러스(영도점)35.095905129.04424부산광역시 영도구 봉래동2가 151-1051-418-2000YY식용유2021-04-01 06:18:07
1154432644888872625510117510117874062020-11-26458축산물365해운대구100.0149502990한돈(홈플러스카드결재시 20%할인)삼성홈플러스(센텀시티점)35.170936129.13371부산광역시 해운대구 우동 해운대구 센텀동로6051-709-8000YY돼지고기2021-04-01 06:18:05
34217303797787720503174503174774052020-08-06456농산물189사상구100.09930993하우스이마트(사상점)35.16423128.97891부산광역시 사상구 괘법동 531-2051-329-1234YY밀감2021-04-01 06:18:09
1612132189991902524507751507751904112020-10-22461주류및음료,차174북구500.014601460<NA>농협하나로클럽마트35.250025129.011246부산광역시 북구 금곡동 북구금곡동1874-3051-330-9000YY맥주2021-04-01 06:18:06
6534331499807971511790511790794172021-01-06456농산물80동구300.0250002500<NA>수정시장35.12983129.045593부산광역시 동구 수정1동 중앙대로 371번길 50051-468-6176YY사과2021-04-01 06:18:04
8761329293101100595109885109881004172020-12-16464식료품53남구420.035003500<NA>못골골목시장35.135866129.089686부산광역시 남구 대연1동 못골로 66-1051-627-3203YY두부2021-04-01 06:18:04
1168832631778773028510112510112774062020-11-26456농산물257수영구1000.054605460<NA>홈플러스 익스플러스 광안점35.161668129.113611(48253) 부산광역시 수영구 수영로 625, 491-1 (광안동)051-756-2277YY밀감2021-04-01 06:18:06
32028306024888728503903503903874062020-08-20458축산물145부산진구100.0129502590<NA>삼성홈플러스(서면점)35.149452129.06401부산광역시 부산진구 전포동 892-20051-605-1000YY돼지고기2021-04-01 06:18:09