Overview

Dataset statistics

Number of variables26
Number of observations10000
Missing cells6955
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory231.0 B

Variable types

Numeric15
DateTime1
Categorical6
Text4

Alerts

parkng_at is highly imbalanced (94.3%)Imbalance
card_at is highly imbalanced (97.7%)Imbalance
rm has 6632 (66.3%) missing valuesMissing
skey has unique valuesUnique
unitprice has 119 (1.2%) zerosZeros
prices has 115 (1.1%) zerosZeros

Reproduction

Analysis started2024-04-21 09:04:27.665413
Analysis finished2024-04-21 09:04:28.836763
Duration1.17 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%
Mean316905.53
Minimum296249
Maximum338016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:28.972437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum296249
5-th percentile298176.65
Q1306365.25
median316775
Q3327450.75
95-th percentile335938.7
Maximum338016
Range41767
Interquartile range (IQR)21085.5

Descriptive statistics

Standard deviation12114.306
Coefficient of variation (CV)0.038226869
Kurtosis-1.2113895
Mean316905.53
Median Absolute Deviation (MAD)10522.5
Skewness0.017745282
Sum3.1690553 × 109
Variance1.4675641 × 108
MonotonicityNot monotonic
2024-04-21T18:04:29.215048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
333193 1
 
< 0.1%
317191 1
 
< 0.1%
332686 1
 
< 0.1%
312741 1
 
< 0.1%
313843 1
 
< 0.1%
304764 1
 
< 0.1%
308947 1
 
< 0.1%
335358 1
 
< 0.1%
326423 1
 
< 0.1%
301214 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
296249 1
< 0.1%
296251 1
< 0.1%
296259 1
< 0.1%
296261 1
< 0.1%
296274 1
< 0.1%
296275 1
< 0.1%
296277 1
< 0.1%
296280 1
< 0.1%
296285 1
< 0.1%
296294 1
< 0.1%
ValueCountFrequency (%)
338016 1
< 0.1%
338011 1
< 0.1%
338010 1
< 0.1%
338003 1
< 0.1%
338002 1
< 0.1%
337997 1
< 0.1%
337989 1
< 0.1%
337981 1
< 0.1%
337979 1
< 0.1%
337971 1
< 0.1%

ccode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.7961
Minimum77
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:29.440019image/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 deviation23.080529
Coefficient of variation (CV)0.23600664
Kurtosis2.2934108
Mean97.7961
Median Absolute Deviation (MAD)9
Skewness1.844425
Sum977961
Variance532.7108
MonotonicityNot monotonic
2024-04-21T18:04:29.654329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
78 402
 
4.0%
80 391
 
3.9%
81 387
 
3.9%
84 383
 
3.8%
89 379
 
3.8%
106 378
 
3.8%
77 375
 
3.8%
83 374
 
3.7%
79 372
 
3.7%
88 372
 
3.7%
Other values (22) 6187
61.9%
ValueCountFrequency (%)
77 375
3.8%
78 402
4.0%
79 372
3.7%
80 391
3.9%
81 387
3.9%
82 368
3.7%
83 374
3.7%
84 383
3.8%
85 246
2.5%
86 257
2.6%
ValueCountFrequency (%)
159 313
3.1%
158 320
3.2%
157 247
2.5%
156 266
2.7%
106 378
3.8%
105 252
2.5%
104 268
2.7%
103 274
2.7%
101 361
3.6%
100 264
2.6%

pcode
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.6815
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:29.873613image/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 deviation22.783884
Coefficient of variation (CV)0.23565919
Kurtosis2.2559217
Mean96.6815
Median Absolute Deviation (MAD)9
Skewness1.8302235
Sum966815
Variance519.10537
MonotonicityNot monotonic
2024-04-21T18:04:30.311951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
77 402
 
4.0%
79 391
 
3.9%
80 387
 
3.9%
83 383
 
3.8%
88 379
 
3.8%
105 378
 
3.8%
76 375
 
3.8%
82 374
 
3.7%
78 372
 
3.7%
87 372
 
3.7%
Other values (22) 6187
61.9%
ValueCountFrequency (%)
76 375
3.8%
77 402
4.0%
78 372
3.7%
79 391
3.9%
80 387
3.9%
81 368
3.7%
82 374
3.7%
83 383
3.8%
84 246
2.5%
85 257
2.6%
ValueCountFrequency (%)
157 313
3.1%
156 320
3.2%
155 247
2.5%
154 266
2.7%
105 378
3.8%
104 252
2.5%
103 268
2.7%
102 274
2.7%
100 361
3.6%
99 264
2.6%

bssh_no
Real number (ℝ)

Distinct54
Distinct (%)0.5%
Missing23
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean417.11146
Minimum14
Maximum3198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:30.562040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile20
Q130
median39
Q359
95-th percentile2625
Maximum3198
Range3184
Interquartile range (IQR)29

Descriptive statistics

Standard deviation931.61673
Coefficient of variation (CV)2.2334959
Kurtosis2.4345911
Mean417.11146
Median Absolute Deviation (MAD)11
Skewness2.0856534
Sum4161521
Variance867909.73
MonotonicityNot monotonic
2024-04-21T18:04:30.802556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 346
 
3.5%
32 336
 
3.4%
34 334
 
3.3%
39 329
 
3.3%
3028 327
 
3.3%
2524 324
 
3.2%
19 318
 
3.2%
43 318
 
3.2%
2562 316
 
3.2%
31 315
 
3.1%
Other values (44) 6714
67.1%
ValueCountFrequency (%)
14 80
 
0.8%
17 87
 
0.9%
19 318
3.2%
20 303
3.0%
22 294
2.9%
23 175
1.8%
25 293
2.9%
26 300
3.0%
28 287
2.9%
29 308
3.1%
ValueCountFrequency (%)
3198 3
 
< 0.1%
3193 1
 
< 0.1%
3177 64
 
0.6%
3028 327
3.3%
2625 310
3.1%
2562 316
3.2%
2524 324
3.2%
2349 76
 
0.8%
87 68
 
0.7%
85 83
 
0.8%

search_no
Real number (ℝ)

Distinct1591
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500333.35
Minimum469120
Maximum513376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:31.054301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum469120
5-th percentile473701.95
Q1492995
median503983
Q3509344
95-th percentile512539
Maximum513376
Range44256
Interquartile range (IQR)16349

Descriptive statistics

Standard deviation12373.848
Coefficient of variation (CV)0.024731207
Kurtosis0.3008108
Mean500333.35
Median Absolute Deviation (MAD)5368
Skewness-1.2663772
Sum5.0033335 × 109
Variance1.5311211 × 108
MonotonicityNot monotonic
2024-04-21T18:04:31.298108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
503902 15
 
0.1%
500412 15
 
0.1%
501145 15
 
0.1%
510135 14
 
0.1%
513345 14
 
0.1%
506087 14
 
0.1%
504990 14
 
0.1%
505852 14
 
0.1%
509419 13
 
0.1%
510952 13
 
0.1%
Other values (1581) 9859
98.6%
ValueCountFrequency (%)
469120 8
0.1%
469121 3
 
< 0.1%
472893 1
 
< 0.1%
472895 2
 
< 0.1%
472896 3
 
< 0.1%
472897 3
 
< 0.1%
472898 4
< 0.1%
472899 6
0.1%
472900 2
 
< 0.1%
472901 4
< 0.1%
ValueCountFrequency (%)
513376 9
0.1%
513375 4
 
< 0.1%
513372 10
0.1%
513370 7
0.1%
513369 5
0.1%
513357 3
 
< 0.1%
513354 6
0.1%
513353 9
0.1%
513352 10
0.1%
513351 7
0.1%

prices_no
Real number (ℝ)

Distinct1591
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500333.35
Minimum469120
Maximum513376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:31.544751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum469120
5-th percentile473701.95
Q1492995
median503983
Q3509344
95-th percentile512539
Maximum513376
Range44256
Interquartile range (IQR)16349

Descriptive statistics

Standard deviation12373.848
Coefficient of variation (CV)0.024731207
Kurtosis0.3008108
Mean500333.35
Median Absolute Deviation (MAD)5368
Skewness-1.2663772
Sum5.0033335 × 109
Variance1.5311211 × 108
MonotonicityNot monotonic
2024-04-21T18:04:31.788792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
503902 15
 
0.1%
500412 15
 
0.1%
501145 15
 
0.1%
510135 14
 
0.1%
513345 14
 
0.1%
506087 14
 
0.1%
504990 14
 
0.1%
505852 14
 
0.1%
509419 13
 
0.1%
510952 13
 
0.1%
Other values (1581) 9859
98.6%
ValueCountFrequency (%)
469120 8
0.1%
469121 3
 
< 0.1%
472893 1
 
< 0.1%
472895 2
 
< 0.1%
472896 3
 
< 0.1%
472897 3
 
< 0.1%
472898 4
< 0.1%
472899 6
0.1%
472900 2
 
< 0.1%
472901 4
< 0.1%
ValueCountFrequency (%)
513376 9
0.1%
513375 4
 
< 0.1%
513372 10
0.1%
513370 7
0.1%
513369 5
0.1%
513357 3
 
< 0.1%
513354 6
0.1%
513353 9
0.1%
513352 10
0.1%
513351 7
0.1%

prdlst
Real number (ℝ)

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.6815
Minimum76
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:32.013601image/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 deviation22.783884
Coefficient of variation (CV)0.23565919
Kurtosis2.2559217
Mean96.6815
Median Absolute Deviation (MAD)9
Skewness1.8302235
Sum966815
Variance519.10537
MonotonicityNot monotonic
2024-04-21T18:04:32.253452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
77 402
 
4.0%
79 391
 
3.9%
80 387
 
3.9%
83 383
 
3.8%
88 379
 
3.8%
105 378
 
3.8%
76 375
 
3.8%
82 374
 
3.7%
78 372
 
3.7%
87 372
 
3.7%
Other values (22) 6187
61.9%
ValueCountFrequency (%)
76 375
3.8%
77 402
4.0%
78 372
3.7%
79 391
3.9%
80 387
3.9%
81 368
3.7%
82 374
3.7%
83 383
3.8%
84 246
2.5%
85 257
2.6%
ValueCountFrequency (%)
157 313
3.1%
156 320
3.2%
155 247
2.5%
154 266
2.7%
105 378
3.8%
104 252
2.5%
103 268
2.7%
102 274
2.7%
100 361
3.6%
99 264
2.6%

cl_no
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing23
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean409.22381
Minimum405
Maximum419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:32.459039image/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 deviation4.4943297
Coefficient of variation (CV)0.010982571
Kurtosis-0.83582555
Mean409.22381
Median Absolute Deviation (MAD)2
Skewness0.90252601
Sum4082826
Variance20.199
MonotonicityNot monotonic
2024-04-21T18:04:32.634628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
406 2660
26.6%
417 1994
19.9%
405 1555
15.6%
407 1533
15.3%
411 658
 
6.6%
410 646
 
6.5%
408 618
 
6.2%
416 309
 
3.1%
419 4
 
< 0.1%
(Missing) 23
 
0.2%
ValueCountFrequency (%)
405 1555
15.6%
406 2660
26.6%
407 1533
15.3%
408 618
 
6.2%
410 646
 
6.5%
411 658
 
6.6%
416 309
 
3.1%
417 1994
19.9%
419 4
 
< 0.1%
ValueCountFrequency (%)
419 4
 
< 0.1%
417 1994
19.9%
416 309
 
3.1%
411 658
 
6.6%
410 646
 
6.5%
408 618
 
6.2%
407 1533
15.3%
406 2660
26.6%
405 1555
15.6%

examin_de
Date

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-01-17 00:00:00
Maximum2021-01-28 00:00:00
2024-04-21T18:04:32.850949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:04:33.093004image/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%
Mean459.5595
Minimum456
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:33.300970image/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 deviation3.0545364
Coefficient of variation (CV)0.0066466615
Kurtosis-1.4366793
Mean459.5595
Median Absolute Deviation (MAD)3
Skewness0.2149107
Sum4595595
Variance9.3301928
MonotonicityNot monotonic
2024-04-21T18:04:33.501178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
456 3055
30.6%
464 1913
19.1%
461 1763
17.6%
458 1475
14.8%
459 1008
 
10.1%
463 786
 
7.9%
ValueCountFrequency (%)
456 3055
30.6%
458 1475
14.8%
459 1008
 
10.1%
461 1763
17.6%
463 786
 
7.9%
464 1913
19.1%
ValueCountFrequency (%)
464 1913
19.1%
463 786
 
7.9%
461 1763
17.6%
459 1008
 
10.1%
458 1475
14.8%
456 3055
30.6%

pum_nm
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
농산물
3055 
식료품
1913 
주류및음료,차
1763 
축산물
1475 
수산물
1008 

Length

Max length7
Median length3
Mean length3.6266
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농산물 3055
30.6%
식료품 1913
19.1%
주류및음료,차 1763
17.6%
축산물 1475
14.8%
수산물 1008
 
10.1%
세제 786
 
7.9%

Length

2024-04-21T18:04:33.722675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:04:33.909281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농산물 3055
30.6%
식료품 1913
19.1%
주류및음료,차 1763
17.6%
축산물 1475
14.8%
수산물 1008
 
10.1%
세제 786
 
7.9%

gugun_cd
Real number (ℝ)

Distinct49
Distinct (%)0.5%
Missing26
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean190.4618
Minimum21
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:34.129972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile31
Q199
median189
Q3262
95-th percentile369
Maximum374
Range353
Interquartile range (IQR)163

Descriptive statistics

Standard deviation103.15898
Coefficient of variation (CV)0.54162558
Kurtosis-0.99119636
Mean190.4618
Median Absolute Deviation (MAD)78
Skewness0.13698723
Sum1899666
Variance10641.776
MonotonicityNot monotonic
2024-04-21T18:04:34.376100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
189 609
 
6.1%
227 421
 
4.2%
374 419
 
4.2%
111 389
 
3.9%
316 334
 
3.3%
116 329
 
3.3%
257 327
 
3.3%
174 324
 
3.2%
186 318
 
3.2%
48 318
 
3.2%
Other values (39) 6186
61.9%
ValueCountFrequency (%)
21 304
3.0%
27 77
 
0.8%
31 175
1.8%
33 92
 
0.9%
48 318
3.2%
52 293
2.9%
53 85
 
0.9%
64 305
3.0%
66 68
 
0.7%
80 63
 
0.6%
ValueCountFrequency (%)
374 419
4.2%
373 1
 
< 0.1%
369 90
 
0.9%
365 310
3.1%
360 305
3.0%
333 88
 
0.9%
316 334
3.3%
314 83
 
0.8%
293 315
3.1%
275 83
 
0.8%

gugun_nm
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
해운대구
1125 
수영구
817 
북구
775 
사하구
765 
동래구
760 
Other values (12)
5758 

Length

Max length4
Median length3
Mean length2.8944
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연제구
2nd row동구
3rd row북구
4th row중구
5th row부산진구

Common Values

ValueCountFrequency (%)
해운대구 1125
11.2%
수영구 817
 
8.2%
북구 775
 
7.8%
사하구 765
 
7.6%
동래구 760
 
7.6%
부산진구 757
 
7.6%
사상구 753
 
7.5%
남구 751
 
7.5%
금정구 648
 
6.5%
중구 505
 
5.1%
Other values (7) 2344
23.4%

Length

2024-04-21T18:04:34.632512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해운대구 1125
11.2%
수영구 817
 
8.2%
북구 775
 
7.8%
사하구 765
 
7.6%
동래구 760
 
7.6%
부산진구 757
 
7.6%
사상구 753
 
7.5%
남구 751
 
7.5%
금정구 648
 
6.5%
중구 505
 
5.1%
Other values (7) 2344
23.4%

unit
Real number (ℝ)

Distinct381
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean654.59662
Minimum0.1
Maximum9000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:34.873909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1
Q14
median400
Q3850
95-th percentile2000
Maximum9000
Range8999.9
Interquartile range (IQR)846

Descriptive statistics

Standard deviation955.47572
Coefficient of variation (CV)1.4596405
Kurtosis13.081138
Mean654.59662
Median Absolute Deviation (MAD)397
Skewness3.0715158
Sum6545966.2
Variance912933.85
MonotonicityNot monotonic
2024-04-21T18:04:35.110995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500.0 1355
 
13.6%
100.0 888
 
8.9%
1.0 753
 
7.5%
1000.0 723
 
7.2%
2000.0 581
 
5.8%
1.8 489
 
4.9%
600.0 471
 
4.7%
20.0 378
 
3.8%
320.0 275
 
2.8%
360.0 264
 
2.6%
Other values (371) 3823
38.2%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
0.8 22
 
0.2%
0.9 214
 
2.1%
0.95 1
 
< 0.1%
1.0 753
7.5%
1.1 10
 
0.1%
1.2 26
 
0.3%
1.4 142
 
1.4%
1.5 231
 
2.3%
1.6 4
 
< 0.1%
ValueCountFrequency (%)
9000.0 3
 
< 0.1%
6000.0 126
1.3%
5000.0 11
 
0.1%
4500.0 2
 
< 0.1%
4000.0 3
 
< 0.1%
3744.0 2
 
< 0.1%
3700.0 1
 
< 0.1%
3500.0 10
 
0.1%
3400.0 1
 
< 0.1%
3312.0 1
 
< 0.1%

unitprice
Real number (ℝ)

ZEROS 

Distinct1857
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10307.1
Minimum0
Maximum100000
Zeros119
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:35.353043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile796
Q12500
median4630.5
Q310425
95-th percentile49950
Maximum100000
Range100000
Interquartile range (IQR)7925

Descriptive statistics

Standard deviation14397.869
Coefficient of variation (CV)1.3968885
Kurtosis5.3162583
Mean10307.1
Median Absolute Deviation (MAD)2830.5
Skewness2.4159271
Sum1.03071 × 108
Variance2.0729864 × 108
MonotonicityNot monotonic
2024-04-21T18:04:35.747406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
676 226
 
2.3%
3500 171
 
1.7%
2500 154
 
1.5%
3290 146
 
1.5%
796 140
 
1.4%
5000 133
 
1.3%
1190 132
 
1.3%
2000 122
 
1.2%
3000 121
 
1.2%
4000 121
 
1.2%
Other values (1847) 8534
85.3%
ValueCountFrequency (%)
0 119
1.2%
215 1
 
< 0.1%
676 226
2.3%
678 2
 
< 0.1%
680 14
 
0.1%
700 7
 
0.1%
729 1
 
< 0.1%
730 9
 
0.1%
750 8
 
0.1%
790 9
 
0.1%
ValueCountFrequency (%)
100000 3
 
< 0.1%
79950 9
0.1%
74000 4
< 0.1%
70000 6
0.1%
69900 8
0.1%
68900 4
< 0.1%
68000 5
0.1%
67900 1
 
< 0.1%
67800 1
 
< 0.1%
67000 2
 
< 0.1%

prices
Real number (ℝ)

ZEROS 

Distinct1073
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8348.714
Minimum0
Maximum100000
Zeros115
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:36.166385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q12450
median4000
Q38990
95-th percentile34000
Maximum100000
Range100000
Interquartile range (IQR)6540

Descriptive statistics

Standard deviation11980.428
Coefficient of variation (CV)1.4350028
Kurtosis10.420123
Mean8348.714
Median Absolute Deviation (MAD)2380
Skewness3.1629481
Sum83487140
Variance1.4353065 × 108
MonotonicityNot monotonic
2024-04-21T18:04:36.828768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3500 215
 
2.1%
9900 198
 
2.0%
2500 182
 
1.8%
3290 168
 
1.7%
5000 161
 
1.6%
3000 158
 
1.6%
4000 157
 
1.6%
4100 143
 
1.4%
6000 141
 
1.4%
1190 135
 
1.4%
Other values (1063) 8342
83.4%
ValueCountFrequency (%)
0 115
1.1%
133 1
 
< 0.1%
143 1
 
< 0.1%
148 2
 
< 0.1%
166 1
 
< 0.1%
183 1
 
< 0.1%
199 1
 
< 0.1%
250 1
 
< 0.1%
257 2
 
< 0.1%
263 2
 
< 0.1%
ValueCountFrequency (%)
100000 3
 
< 0.1%
70000 2
 
< 0.1%
69900 8
0.1%
68900 4
< 0.1%
68000 5
0.1%
67900 1
 
< 0.1%
67800 1
 
< 0.1%
67000 2
 
< 0.1%
66900 2
 
< 0.1%
66540 1
 
< 0.1%

rm
Text

MISSING 

Distinct948
Distinct (%)28.1%
Missing6632
Missing (%)66.3%
Memory size156.2 KiB
2024-04-21T18:04:37.818402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length29
Mean length6.5647268
Min length1

Characters and Unicode

Total characters22110
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

Unique513 ?
Unique (%)15.2%

Sample

1st row부산우유
2nd row고당도사과/제휴카드행사
3rd row동진쌀(신한,삼성카드 결재시 54,900원)
4th row하우스밀감
5th row오뚜기고소한 참기름
ValueCountFrequency (%)
행사 158
 
4.0%
생물 121
 
3.1%
해동 107
 
2.7%
품절 54
 
1.4%
하우스밀감 51
 
1.3%
냉동 45
 
1.1%
고소한참기름 37
 
0.9%
없음 36
 
0.9%
참그린 30
 
0.8%
녹차 29
 
0.7%
Other values (865) 3274
83.1%
2024-04-21T18:04:39.287766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1772
 
8.0%
1 908
 
4.1%
0 887
 
4.0%
/ 458
 
2.1%
2 447
 
2.0%
사 441
 
2.0%
행 354
 
1.6%
3 353
 
1.6%
원 312
 
1.4%
5 301
 
1.4%
Other values (376) 15877
71.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14542
65.8%
Decimal Number 3594
 
16.3%
Space Separator 1772
 
8.0%
Other Punctuation 782
 
3.5%
Lowercase Letter 408
 
1.8%
Math Symbol 344
 
1.6%
Uppercase Letter 265
 
1.2%
Close Punctuation 195
 
0.9%
Open Punctuation 194
 
0.9%
Dash Punctuation 14
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 441
 
3.0%
행 354
 
2.4%
원 312
 
2.1%
생 291
 
2.0%
기 258
 
1.8%
동 238
 
1.6%
스 227
 
1.6%
고 224
 
1.5%
지 221
 
1.5%
한 213
 
1.5%
Other values (327) 11763
80.9%
Lowercase Letter
ValueCountFrequency (%)
g 205
50.2%
k 59
 
14.5%
l 50
 
12.3%
m 44
 
10.8%
s 13
 
3.2%
u 7
 
1.7%
i 6
 
1.5%
p 6
 
1.5%
c 3
 
0.7%
j 3
 
0.7%
Other values (7) 12
 
2.9%
Decimal Number
ValueCountFrequency (%)
1 908
25.3%
0 887
24.7%
2 447
12.4%
3 353
 
9.8%
5 301
 
8.4%
9 213
 
5.9%
4 154
 
4.3%
8 143
 
4.0%
7 116
 
3.2%
6 72
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
L 88
33.2%
C 33
 
12.5%
P 30
 
11.3%
G 30
 
11.3%
K 29
 
10.9%
R 24
 
9.1%
T 22
 
8.3%
M 7
 
2.6%
J 1
 
0.4%
A 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
/ 458
58.6%
, 115
 
14.7%
* 82
 
10.5%
. 75
 
9.6%
% 51
 
6.5%
! 1
 
0.1%
Math Symbol
ValueCountFrequency (%)
+ 202
58.7%
~ 142
41.3%
Space Separator
ValueCountFrequency (%)
1772
100.0%
Close Punctuation
ValueCountFrequency (%)
) 195
100.0%
Open Punctuation
ValueCountFrequency (%)
( 194
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14531
65.7%
Common 6895
31.2%
Latin 673
 
3.0%
Han 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 441
 
3.0%
행 354
 
2.4%
원 312
 
2.1%
생 291
 
2.0%
기 258
 
1.8%
동 238
 
1.6%
스 227
 
1.6%
고 224
 
1.5%
지 221
 
1.5%
한 213
 
1.5%
Other values (326) 11752
80.9%
Latin
ValueCountFrequency (%)
g 205
30.5%
L 88
13.1%
k 59
 
8.8%
l 50
 
7.4%
m 44
 
6.5%
C 33
 
4.9%
P 30
 
4.5%
G 30
 
4.5%
K 29
 
4.3%
R 24
 
3.6%
Other values (17) 81
 
12.0%
Common
ValueCountFrequency (%)
1772
25.7%
1 908
13.2%
0 887
12.9%
/ 458
 
6.6%
2 447
 
6.5%
3 353
 
5.1%
5 301
 
4.4%
9 213
 
3.1%
+ 202
 
2.9%
) 195
 
2.8%
Other values (12) 1159
16.8%
Han
ValueCountFrequency (%)
入 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14529
65.7%
ASCII 7568
34.2%
CJK 11
 
< 0.1%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1772
23.4%
1 908
12.0%
0 887
11.7%
/ 458
 
6.1%
2 447
 
5.9%
3 353
 
4.7%
5 301
 
4.0%
9 213
 
2.8%
g 205
 
2.7%
+ 202
 
2.7%
Other values (39) 1822
24.1%
Hangul
ValueCountFrequency (%)
사 441
 
3.0%
행 354
 
2.4%
원 312
 
2.1%
생 291
 
2.0%
기 258
 
1.8%
동 238
 
1.6%
스 227
 
1.6%
고 224
 
1.5%
지 221
 
1.5%
한 213
 
1.5%
Other values (324) 11750
80.9%
CJK
ValueCountFrequency (%)
入 11
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅣ 1
50.0%
ㅡ 1
50.0%

bssh_nm
Text

Distinct54
Distinct (%)0.5%
Missing23
Missing (%)0.2%
Memory size156.2 KiB
2024-04-21T18:04:40.082824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.0852962
Min length4

Characters and Unicode

Total characters90644
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row이마트(연제점)
2nd row탑마트(초량점)
3rd row농협하나로클럽마트
4th row자갈치시장
5th row삼성홈플러스(가야점)
ValueCountFrequency (%)
탑마트(서구 346
 
3.1%
이마트(해운대점 336
 
3.1%
농협하나로마트(자갈치점 334
 
3.0%
삼성홈플러스(가야점 329
 
3.0%
홈플러스 327
 
3.0%
익스플러스 327
 
3.0%
광안점 327
 
3.0%
농협하나로클럽마트 324
 
2.9%
롯데마트(화명점 318
 
2.9%
삼성홈플러스(정관점 318
 
2.9%
Other values (49) 7726
70.2%
2024-04-21T18:04:41.254137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 7377
 
8.1%
( 7100
 
7.8%
) 7100
 
7.8%
트 4716
 
5.2%
마 4716
 
5.2%
스 3623
 
4.0%
플 2987
 
3.3%
러 2987
 
3.3%
홈 2660
 
2.9%
시 2236
 
2.5%
Other values (107) 45142
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75277
83.0%
Open Punctuation 7100
 
7.8%
Close Punctuation 7100
 
7.8%
Space Separator 1099
 
1.2%
Decimal Number 68
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 7377
 
9.8%
트 4716
 
6.3%
마 4716
 
6.3%
스 3623
 
4.8%
플 2987
 
4.0%
러 2987
 
4.0%
홈 2660
 
3.5%
시 2236
 
3.0%
삼 2158
 
2.9%
성 2158
 
2.9%
Other values (103) 39659
52.7%
Open Punctuation
ValueCountFrequency (%)
( 7100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7100
100.0%
Space Separator
ValueCountFrequency (%)
1099
100.0%
Decimal Number
ValueCountFrequency (%)
1 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75277
83.0%
Common 15367
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 7377
 
9.8%
트 4716
 
6.3%
마 4716
 
6.3%
스 3623
 
4.8%
플 2987
 
4.0%
러 2987
 
4.0%
홈 2660
 
3.5%
시 2236
 
3.0%
삼 2158
 
2.9%
성 2158
 
2.9%
Other values (103) 39659
52.7%
Common
ValueCountFrequency (%)
( 7100
46.2%
) 7100
46.2%
1099
 
7.2%
1 68
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75277
83.0%
ASCII 15367
 
17.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 7377
 
9.8%
트 4716
 
6.3%
마 4716
 
6.3%
스 3623
 
4.8%
플 2987
 
4.0%
러 2987
 
4.0%
홈 2660
 
3.5%
시 2236
 
3.0%
삼 2158
 
2.9%
성 2158
 
2.9%
Other values (103) 39659
52.7%
ASCII
ValueCountFrequency (%)
( 7100
46.2%
) 7100
46.2%
1099
 
7.2%
1 68
 
0.4%

la
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing91
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean35.163252
Minimum35.08484
Maximum35.323099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:41.662496image/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 deviation0.056784042
Coefficient of variation (CV)0.0016148689
Kurtosis0.14348319
Mean35.163252
Median Absolute Deviation (MAD)0.0442
Skewness0.64922844
Sum348432.66
Variance0.0032244274
MonotonicityNot monotonic
2024-04-21T18:04:42.094419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.092663 346
 
3.5%
35.165993 336
 
3.4%
35.097233 334
 
3.3%
35.152466 329
 
3.3%
35.161668 327
 
3.3%
35.250025 324
 
3.2%
35.234905 318
 
3.2%
35.3230993 318
 
3.2%
35.1373526 316
 
3.2%
35.095905 315
 
3.1%
Other values (41) 6646
66.5%
ValueCountFrequency (%)
35.08484 308
3.1%
35.085472 294
2.9%
35.092663 346
3.5%
35.0931597 75
 
0.8%
35.095905 315
3.1%
35.0970155 83
 
0.8%
35.097233 334
3.3%
35.098934 309
3.1%
35.0996462 72
 
0.7%
35.099649 69
 
0.7%
ValueCountFrequency (%)
35.3230993 318
3.2%
35.250107 304
3.0%
35.250025 324
3.2%
35.2392 175
1.8%
35.234905 318
3.2%
35.2222318 81
 
0.8%
35.2159352 92
 
0.9%
35.2146331 66
 
0.7%
35.2119516 77
 
0.8%
35.211483 308
3.1%

lo
Real number (ℝ)

Distinct51
Distinct (%)0.5%
Missing91
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean129.057
Minimum128.89784
Maximum129.17634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:04:42.494440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.89784
5-th percentile128.97157
Q1129.0175
median129.06401
Q3129.09215
95-th percentile129.16739
Maximum129.17634
Range0.2784985
Interquartile range (IQR)0.0746545

Descriptive statistics

Standard deviation0.062692932
Coefficient of variation (CV)0.00048577706
Kurtosis-0.028577501
Mean129.057
Median Absolute Deviation (MAD)0.03952
Skewness-0.22359835
Sum1278825.8
Variance0.0039304037
MonotonicityNot monotonic
2024-04-21T18:04:42.913607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.02449 346
 
3.5%
129.16739 336
 
3.4%
129.02745 334
 
3.3%
129.02731 329
 
3.3%
129.113611 327
 
3.3%
129.0112455 324
 
3.2%
129.00853 318
 
3.2%
129.1763385 318
 
3.2%
129.1111015 316
 
3.2%
129.04424 315
 
3.1%
Other values (41) 6646
66.5%
ValueCountFrequency (%)
128.89784 294
2.9%
128.9019892 76
 
0.8%
128.97157 308
3.1%
128.97812 306
3.1%
128.97891 303
3.0%
128.9813855 80
 
0.8%
128.9893459 72
 
0.7%
128.99406 309
3.1%
129.0016813 67
 
0.7%
129.00853 318
3.2%
ValueCountFrequency (%)
129.1763385 318
3.2%
129.1744835 90
 
0.9%
129.16739 336
3.4%
129.1623293 83
 
0.8%
129.1337096 310
3.1%
129.12317 305
3.0%
129.116693 87
 
0.9%
129.113611 327
3.3%
129.1114753 68
 
0.7%
129.1111015 316
3.2%

adres
Text

Distinct54
Distinct (%)0.5%
Missing23
Missing (%)0.2%
Memory size156.2 KiB
2024-04-21T18:04:44.114465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length21.910795
Min length14

Characters and Unicode

Total characters218604
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row부산광역시 연제구 연산2동 822-7
2nd row부산광역시 동구 초량동 393-1
3rd row부산광역시 북구 금곡동 북구금곡동1874-3
4th row부산광역시 중구 남포동4가 자갈치해안로 52번길
5th row부산광역시 부산진구 가야동 624-7
ValueCountFrequency (%)
부산광역시 9977
 
21.9%
해운대구 1435
 
3.2%
동래구 935
 
2.1%
수영구 817
 
1.8%
북구 775
 
1.7%
사하구 765
 
1.7%
부산진구 757
 
1.7%
사상구 753
 
1.7%
남구 751
 
1.7%
괘법동 609
 
1.3%
Other values (159) 27893
61.3%
2024-04-21T18:04:45.723763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35490
 
16.2%
동 12286
 
5.6%
부 11476
 
5.2%
산 11279
 
5.2%
구 10902
 
5.0%
1 10591
 
4.8%
광 10304
 
4.7%
시 10188
 
4.7%
역 9977
 
4.6%
2 6597
 
3.0%
Other values (106) 89514
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128564
58.8%
Decimal Number 45347
 
20.7%
Space Separator 35490
 
16.2%
Dash Punctuation 5070
 
2.3%
Close Punctuation 1901
 
0.9%
Open Punctuation 1901
 
0.9%
Other Punctuation 331
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12286
 
9.6%
부 11476
 
8.9%
산 11279
 
8.8%
구 10902
 
8.5%
광 10304
 
8.0%
시 10188
 
7.9%
역 9977
 
7.8%
로 2904
 
2.3%
남 2304
 
1.8%
대 2130
 
1.7%
Other values (91) 44814
34.9%
Decimal Number
ValueCountFrequency (%)
1 10591
23.4%
2 6597
14.5%
3 5654
12.5%
5 4464
9.8%
6 3590
 
7.9%
7 3414
 
7.5%
8 3185
 
7.0%
4 3123
 
6.9%
9 2521
 
5.6%
0 2208
 
4.9%
Space Separator
ValueCountFrequency (%)
35490
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5070
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1901
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1901
100.0%
Other Punctuation
ValueCountFrequency (%)
, 331
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128564
58.8%
Common 90040
41.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12286
 
9.6%
부 11476
 
8.9%
산 11279
 
8.8%
구 10902
 
8.5%
광 10304
 
8.0%
시 10188
 
7.9%
역 9977
 
7.8%
로 2904
 
2.3%
남 2304
 
1.8%
대 2130
 
1.7%
Other values (91) 44814
34.9%
Common
ValueCountFrequency (%)
35490
39.4%
1 10591
 
11.8%
2 6597
 
7.3%
3 5654
 
6.3%
- 5070
 
5.6%
5 4464
 
5.0%
6 3590
 
4.0%
7 3414
 
3.8%
8 3185
 
3.5%
4 3123
 
3.5%
Other values (5) 8862
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128564
58.8%
ASCII 90040
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35490
39.4%
1 10591
 
11.8%
2 6597
 
7.3%
3 5654
 
6.3%
- 5070
 
5.6%
5 4464
 
5.0%
6 3590
 
4.0%
7 3414
 
3.8%
8 3185
 
3.5%
4 3123
 
3.5%
Other values (5) 8862
 
9.8%
Hangul
ValueCountFrequency (%)
동 12286
 
9.6%
부 11476
 
8.9%
산 11279
 
8.8%
구 10902
 
8.5%
광 10304
 
8.0%
시 10188
 
7.9%
역 9977
 
7.8%
로 2904
 
2.3%
남 2304
 
1.8%
대 2130
 
1.7%
Other values (91) 44814
34.9%

telno
Text

Distinct54
Distinct (%)0.5%
Missing23
Missing (%)0.2%
Memory size156.2 KiB
2024-04-21T18:04:46.460869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.014032
Min length12

Characters and Unicode

Total characters119864
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 row051-860-1052
2nd row051-466-2112
3rd row051-330-9000
4th row051-245-2594
5th row051-890-8023
ValueCountFrequency (%)
051-244-1221 346
 
3.5%
051-608-1234 336
 
3.4%
051-250-7711 334
 
3.3%
051-890-8023 329
 
3.3%
051-756-2277 327
 
3.3%
051-330-9000 324
 
3.2%
051-604-2500 318
 
3.2%
051-519-8200 318
 
3.2%
051-608-6000 316
 
3.2%
051-418-2000 315
 
3.2%
Other values (44) 6714
67.3%
2024-04-21T18:04:47.572549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27806
23.2%
- 19954
16.6%
1 16851
14.1%
5 16245
13.6%
2 10371
 
8.7%
6 6598
 
5.5%
3 5133
 
4.3%
9 4669
 
3.9%
4 4355
 
3.6%
8 4061
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99910
83.4%
Dash Punctuation 19954
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27806
27.8%
1 16851
16.9%
5 16245
16.3%
2 10371
 
10.4%
6 6598
 
6.6%
3 5133
 
5.1%
9 4669
 
4.7%
4 4355
 
4.4%
8 4061
 
4.1%
7 3821
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 19954
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119864
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27806
23.2%
- 19954
16.6%
1 16851
14.1%
5 16245
13.6%
2 10371
 
8.7%
6 6598
 
5.5%
3 5133
 
4.3%
9 4669
 
3.9%
4 4355
 
3.6%
8 4061
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27806
23.2%
- 19954
16.6%
1 16851
14.1%
5 16245
13.6%
2 10371
 
8.7%
6 6598
 
5.5%
3 5133
 
4.3%
9 4669
 
3.9%
4 4355
 
3.6%
8 4061
 
3.4%

parkng_at
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9898 
N
 
79
 
23

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 9898
99.0%
N 79
 
0.8%
23
 
0.2%

Length

2024-04-21T18:04:47.789752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:04:47.952010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9898
99.2%
n 79
 
0.8%

card_at
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9977 
 
23

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 9977
99.8%
23
 
0.2%

Length

2024-04-21T18:04:48.124657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:04:48.281608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9977
100.0%

item_name
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
밀감
 
402
사과
 
391
양파
 
387
배추
 
383
쇠고기
 
379
Other values (27)
8058 

Length

Max length5
Median length4
Mean length2.4687
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row밀가루
2nd row우유
3rd row식용유
4th row돼지고기
5th row사과

Common Values

ValueCountFrequency (%)
밀감 402
 
4.0%
사과 391
 
3.9%
양파 387
 
3.9%
배추 383
 
3.8%
쇠고기 379
 
3.8%
쌀 378
 
3.8%
고등어 375
 
3.8%
무 374
 
3.7%
돼지고기 372
 
3.7%
배 372
 
3.7%
Other values (22) 6187
61.9%

Length

2024-04-21T18:04:48.472224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
밀감 402
 
4.0%
사과 391
 
3.9%
양파 387
 
3.9%
배추 383
 
3.8%
쇠고기 379
 
3.8%
쌀 378
 
3.8%
고등어 375
 
3.8%
무 374
 
3.7%
돼지고기 372
 
3.7%
배 372
 
3.7%
Other values (22) 6187
61.9%

last_load_dttm
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-10-01 06:18:07
1976 
2022-10-01 06:18:11
1817 
2022-10-01 06:18:09
1794 
2022-10-01 06:18:05
1519 
2022-10-01 06:18:08
803 
Other values (4)
2091 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-10-01 06:18:05
2nd row2022-10-01 06:18:07
3rd row2022-10-01 06:18:09
4th row2022-10-01 06:18:11
5th row2022-10-01 06:18:11

Common Values

ValueCountFrequency (%)
2022-10-01 06:18:07 1976
19.8%
2022-10-01 06:18:11 1817
18.2%
2022-10-01 06:18:09 1794
17.9%
2022-10-01 06:18:05 1519
15.2%
2022-10-01 06:18:08 803
8.0%
2022-10-01 06:18:10 764
 
7.6%
2022-10-01 06:18:06 543
 
5.4%
2022-10-01 06:18:12 462
 
4.6%
2022-10-01 06:18:04 322
 
3.2%

Length

2024-04-21T18:04:48.683972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:04:48.882474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-10-01 10000
50.0%
06:18:07 1976
 
9.9%
06:18:11 1817
 
9.1%
06:18:09 1794
 
9.0%
06:18:05 1519
 
7.6%
06:18:08 803
 
4.0%
06:18:10 764
 
3.8%
06:18:06 543
 
2.7%
06:18:12 462
 
2.3%
06:18:04 322
 
1.6%

Sample

skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
4803333193105104405125465125461044052021-01-14464식료품273연제구2.536803680<NA>이마트(연제점)35.17615129.08133부산광역시 연제구 연산2동 822-7051-860-1052YY밀가루2022-10-01 06:18:05
12183325833949326510190510190934102020-12-03461주류및음료,차92동구200.0800800부산우유탑마트(초량점)35.11677129.0395부산광역시 동구 초량동 393-1051-466-2112YY우유2022-10-01 06:18:07
2791431011115715525244921854921851554112020-02-06464식료품174북구1.839803980<NA>농협하나로클럽마트35.250025129.011246부산광역시 북구 금곡동 북구금곡동1874-3051-330-9000YY식용유2022-10-01 06:18:09
33607304440888778503201503201874172020-08-12458축산물314중구100.0115002300<NA>자갈치시장35.097015129.02861부산광역시 중구 남포동4가 자갈치해안로 52번길051-245-2594YY돼지고기2022-10-01 06:18:11
33713304339807939503196503196794062020-08-06456농산물116부산진구1400.03426415990고당도사과/제휴카드행사삼성홈플러스(가야점)35.152466129.02731부산광역시 부산진구 가야동 624-7051-890-8023YY사과2022-10-01 06:18:11
1754336239878632475260475260864052019-05-02458축산물374해운대구1.057805780<NA>이마트(해운대점)35.165993129.16739부산광역시 해운대구 중1동 1767051-608-1234YY닭고기2022-10-01 06:18:05
292337724828143513346513346814072021-01-28456농산물186북구1000.071207120<NA>롯데마트(화명점)35.234905129.00853부산광역시 북구 화명3동 1975051-604-2500YY대파2022-10-01 06:18:04
17374320627157155355085605085601554102020-10-29464식료품227서구1.859805980<NA>탑마트(서구)35.092663129.02449부산광역시 서구 남부민1동 685051-244-1221YY식용유2022-10-01 06:18:07
41293296701106105314995334995331054062020-06-18456농산물293영도구20.05990059900동진쌀(신한,삼성카드 결재시 54,900원)삼성홈플러스(영도점)35.095905129.04424부산광역시 영도구 봉래동2가 151-1051-418-2000YY쌀2022-10-01 06:18:12
3676030127510310225625014305014301024082020-07-16464식료품261수영구1.017901790<NA>메가마트 남천점35.137353129.111101부산광역시 수영구 남천동 남천동545-2051-608-6000YY설탕2022-10-01 06:18:11
skeyccodepcodebssh_nosearch_noprices_noprdlstcl_noexamin_depum_cdpum_nmgugun_cdgugun_nmunitunitpricepricesrmbssh_nmlaloadrestelnoparkng_atcard_atitem_namelast_load_dttm
21020316973797872507708507708784172020-10-21456농산물88동구600.0400004000<NA>초량시장35.11817129.039547부산광역시 동구 초량1동 중앙대로 231번길 27051-467-5054YY배2022-10-01 06:18:08
15410322587838228508556508556824062020-10-29456농산물145부산진구1600.037372990<NA>삼성홈플러스(서면점)35.149452129.06401부산광역시 부산진구 전포동 892-20051-605-1000YY무2022-10-01 06:18:07
4835333153104103195117505117501034062020-12-24464식료품48기장군1.0676676<NA>삼성홈플러스(정관점)35.323099129.176339부산광역시 기장군 정관면 매학리 712-1051-519-8200YY라면2022-10-01 06:18:05
3431130368280792349473704473704794172019-04-10456농산물218사하구300.0120001200<NA>신평골목시장35.199374128.901989부산광역시 사하구 신평1동 다대로134번길18010-485-6447NY사과2022-10-01 06:18:11
31997305993878630503902503902864162020-08-20458축산물209사하구1.0599059908/19~25까지 행사뉴코아아울렛킴스클럽(괴정점)35.098934128.99406부산광역시 사하구 괴정3동 961-1051-209-5061YY닭고기2022-10-01 06:18:10
3255430548715615430285023685023681544062020-07-30463세제257수영구840.01690016900<NA>홈플러스 익스플러스 광안점35.161668129.113611(48253) 부산광역시 수영구 수영로 625, 491-1 (광안동)051-756-2277YY화장지2022-10-01 06:18:11
8167329829969539511019511019954062020-12-17463세제116부산진구4.01485019800<NA>삼성홈플러스(가야점)35.152466129.02731부산광역시 부산진구 가야동 624-7051-890-8023YY가루비누2022-10-01 06:18:06
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