Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells3502
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory126.0 B

Variable types

Numeric5
Text4
DateTime2
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1170/S/1/datasetView.do

Alerts

시장유형 구분(시장/마트) 코드 is highly overall correlated with 시장유형 구분(시장/마트) 이름High correlation
시장유형 구분(시장/마트) 이름 is highly overall correlated with 시장유형 구분(시장/마트) 코드High correlation
시장/마트 번호 is highly overall correlated with 자치구 코드 and 1 other fieldsHigh correlation
자치구 코드 is highly overall correlated with 시장/마트 번호 and 1 other fieldsHigh correlation
자치구 이름 is highly overall correlated with 시장/마트 번호 and 1 other fieldsHigh correlation
비고 has 3502 (35.0%) missing valuesMissing
일련번호 has unique valuesUnique
가격(원) has 108 (1.1%) zerosZeros

Reproduction

Analysis started2024-05-11 06:35:58.972393
Analysis finished2024-05-11 06:36:05.806035
Duration6.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1479965
Minimum1437941
Maximum1507081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:36:05.906552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1437941
5-th percentile1455028.3
Q11466241.8
median1480303
Q31493495.2
95-th percentile1504394.1
Maximum1507081
Range69140
Interquartile range (IQR)27253.5

Descriptive statistics

Standard deviation15909.675
Coefficient of variation (CV)0.010750035
Kurtosis-1.1346502
Mean1479965
Median Absolute Deviation (MAD)13615
Skewness-0.066507821
Sum1.479965 × 1010
Variance2.5311777 × 108
MonotonicityNot monotonic
2024-05-11T15:36:06.148602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1486704 1
 
< 0.1%
1499622 1
 
< 0.1%
1471875 1
 
< 0.1%
1461262 1
 
< 0.1%
1463942 1
 
< 0.1%
1491172 1
 
< 0.1%
1492690 1
 
< 0.1%
1462239 1
 
< 0.1%
1494200 1
 
< 0.1%
1476143 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1437941 1
< 0.1%
1438382 1
< 0.1%
1438383 1
< 0.1%
1438390 1
< 0.1%
1438422 1
< 0.1%
1438727 1
< 0.1%
1439067 1
< 0.1%
1439602 1
< 0.1%
1440059 1
< 0.1%
1440064 1
< 0.1%
ValueCountFrequency (%)
1507081 1
< 0.1%
1507079 1
< 0.1%
1507071 1
< 0.1%
1507058 1
< 0.1%
1507055 1
< 0.1%
1507048 1
< 0.1%
1507047 1
< 0.1%
1507042 1
< 0.1%
1507015 1
< 0.1%
1507014 1
< 0.1%

시장/마트 번호
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.1881
Minimum1
Maximum226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:36:06.564371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q144
median92
Q3147
95-th percentile220
Maximum226
Range225
Interquartile range (IQR)103

Descriptive statistics

Standard deviation68.971787
Coefficient of variation (CV)0.66840835
Kurtosis-1.0071393
Mean103.1881
Median Absolute Deviation (MAD)52
Skewness0.3831757
Sum1031881
Variance4757.1074
MonotonicityNot monotonic
2024-05-11T15:36:06.840043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154 118
 
1.2%
121 118
 
1.2%
15 117
 
1.2%
78 116
 
1.2%
14 115
 
1.1%
213 114
 
1.1%
27 113
 
1.1%
127 113
 
1.1%
6 112
 
1.1%
199 112
 
1.1%
Other values (90) 8852
88.5%
ValueCountFrequency (%)
1 106
1.1%
2 102
1.0%
6 112
1.1%
8 106
1.1%
10 103
1.0%
11 101
1.0%
13 107
1.1%
14 115
1.1%
15 117
1.2%
16 107
1.1%
ValueCountFrequency (%)
226 103
1.0%
224 101
1.0%
223 97
1.0%
222 82
0.8%
221 107
1.1%
220 90
0.9%
219 106
1.1%
218 93
0.9%
217 102
1.0%
216 90
0.9%

시장/마트 이름
Text

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:36:07.207589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.7685
Min length4

Characters and Unicode

Total characters67685
Distinct characters139
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 row롯데백화점 잠실점
2nd row송화시장
3rd row뉴코아아울렛 강남점
4th row롯데마트 강변점
5th row신창시장
ValueCountFrequency (%)
이마트 1497
 
10.1%
홈플러스 1157
 
7.8%
롯데백화점 793
 
5.3%
미아점 440
 
3.0%
롯데마트 304
 
2.0%
강남점 287
 
1.9%
용산점 217
 
1.5%
현대백화점 207
 
1.4%
목동점 207
 
1.4%
하나로클럽 202
 
1.4%
Other values (92) 9553
64.3%
2024-05-11T15:36:07.777411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 5868
 
8.7%
장 5140
 
7.6%
시 5019
 
7.4%
4864
 
7.2%
마 2304
 
3.4%
트 2102
 
3.1%
이 1683
 
2.5%
화 1520
 
2.2%
동 1350
 
2.0%
백 1302
 
1.9%
Other values (129) 36533
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 61712
91.2%
Space Separator 4864
 
7.2%
Decimal Number 701
 
1.0%
Close Punctuation 204
 
0.3%
Open Punctuation 204
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 5868
 
9.5%
장 5140
 
8.3%
시 5019
 
8.1%
마 2304
 
3.7%
트 2102
 
3.4%
이 1683
 
2.7%
화 1520
 
2.5%
동 1350
 
2.2%
백 1302
 
2.1%
데 1187
 
1.9%
Other values (121) 34237
55.5%
Decimal Number
ValueCountFrequency (%)
1 203
29.0%
0 202
28.8%
4 102
14.6%
2 101
14.4%
3 93
13.3%
Space Separator
ValueCountFrequency (%)
4864
100.0%
Close Punctuation
ValueCountFrequency (%)
) 204
100.0%
Open Punctuation
ValueCountFrequency (%)
( 204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 61712
91.2%
Common 5973
 
8.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 5868
 
9.5%
장 5140
 
8.3%
시 5019
 
8.1%
마 2304
 
3.7%
트 2102
 
3.4%
이 1683
 
2.7%
화 1520
 
2.5%
동 1350
 
2.2%
백 1302
 
2.1%
데 1187
 
1.9%
Other values (121) 34237
55.5%
Common
ValueCountFrequency (%)
4864
81.4%
) 204
 
3.4%
( 204
 
3.4%
1 203
 
3.4%
0 202
 
3.4%
4 102
 
1.7%
2 101
 
1.7%
3 93
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 61712
91.2%
ASCII 5973
 
8.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 5868
 
9.5%
장 5140
 
8.3%
시 5019
 
8.1%
마 2304
 
3.7%
트 2102
 
3.4%
이 1683
 
2.7%
화 1520
 
2.5%
동 1350
 
2.2%
백 1302
 
2.1%
데 1187
 
1.9%
Other values (121) 34237
55.5%
ASCII
ValueCountFrequency (%)
4864
81.4%
) 204
 
3.4%
( 204
 
3.4%
1 203
 
3.4%
0 202
 
3.4%
4 102
 
1.7%
2 101
 
1.7%
3 93
 
1.6%

품목 번호
Real number (ℝ)

Distinct79
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.4309
Minimum13
Maximum321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:36:08.016557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile23
Q1136
median266
Q3308
95-th percentile320
Maximum321
Range308
Interquartile range (IQR)172

Descriptive statistics

Standard deviation104.26528
Coefficient of variation (CV)0.47516225
Kurtosis-0.90930781
Mean219.4309
Median Absolute Deviation (MAD)45
Skewness-0.78820027
Sum2194309
Variance10871.249
MonotonicityNot monotonic
2024-05-11T15:36:08.228079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171 598
 
6.0%
320 580
 
5.8%
311 523
 
5.2%
58 462
 
4.6%
307 435
 
4.3%
266 432
 
4.3%
306 398
 
4.0%
283 392
 
3.9%
305 378
 
3.8%
309 375
 
3.8%
Other values (69) 5427
54.3%
ValueCountFrequency (%)
13 34
 
0.3%
17 45
 
0.4%
18 119
1.2%
22 79
 
0.8%
23 249
2.5%
24 184
1.8%
25 120
1.2%
26 65
 
0.7%
27 69
 
0.7%
28 129
1.3%
ValueCountFrequency (%)
321 1
 
< 0.1%
320 580
5.8%
318 4
 
< 0.1%
316 20
 
0.2%
315 22
 
0.2%
314 14
 
0.1%
312 346
3.5%
311 523
5.2%
310 333
3.3%
309 375
3.8%

품목 이름
Text

Distinct74
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:36:08.610733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.8935
Min length1

Characters and Unicode

Total characters78935
Distinct characters86
Distinct categories8 ?
Distinct scripts3 ?
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 row쇠고기(한우,불고기)
2nd row달걀(10개)
3rd row달걀(30개)
4th row달걀(30개)
5th row오이(다다기)
ValueCountFrequency (%)
달걀(10개 598
 
5.5%
달걀(30개 580
 
5.4%
오이(다다기 523
 
4.8%
배(신고 481
 
4.5%
쇠고기(한우,불고기 462
 
4.3%
배추(2.5~3kg 435
 
4.0%
고등어(생물,국산 432
 
4.0%
돼지고기(생삼겹살 430
 
4.0%
사과(부사 413
 
3.8%
600g 398
 
3.7%
Other values (64) 6033
55.9%
2024-05-11T15:36:09.255432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 8365
 
10.6%
) 8365
 
10.6%
, 3478
 
4.4%
0 3434
 
4.4%
기 3357
 
4.3%
고 3275
 
4.1%
g 2288
 
2.9%
산 1902
 
2.4%
1 1746
 
2.2%
생 1431
 
1.8%
Other values (76) 41294
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44804
56.8%
Open Punctuation 8365
 
10.6%
Close Punctuation 8365
 
10.6%
Decimal Number 8306
 
10.5%
Other Punctuation 4288
 
5.4%
Lowercase Letter 3587
 
4.5%
Space Separator 785
 
1.0%
Math Symbol 435
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 3357
 
7.5%
고 3275
 
7.3%
산 1902
 
4.2%
생 1431
 
3.2%
국 1305
 
2.9%
걀 1244
 
2.8%
달 1244
 
2.8%
개 1179
 
2.6%
오 1172
 
2.6%
동 1170
 
2.6%
Other values (59) 27525
61.4%
Decimal Number
ValueCountFrequency (%)
0 3434
41.3%
1 1746
21.0%
3 1417
17.1%
5 833
 
10.0%
2 456
 
5.5%
6 398
 
4.8%
4 22
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
g 2288
63.8%
k 1179
32.9%
m 60
 
1.7%
c 60
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 3478
81.1%
. 810
 
18.9%
Open Punctuation
ValueCountFrequency (%)
( 8365
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8365
100.0%
Space Separator
ValueCountFrequency (%)
785
100.0%
Math Symbol
ValueCountFrequency (%)
~ 435
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44804
56.8%
Common 30544
38.7%
Latin 3587
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 3357
 
7.5%
고 3275
 
7.3%
산 1902
 
4.2%
생 1431
 
3.2%
국 1305
 
2.9%
걀 1244
 
2.8%
달 1244
 
2.8%
개 1179
 
2.6%
오 1172
 
2.6%
동 1170
 
2.6%
Other values (59) 27525
61.4%
Common
ValueCountFrequency (%)
( 8365
27.4%
) 8365
27.4%
, 3478
11.4%
0 3434
11.2%
1 1746
 
5.7%
3 1417
 
4.6%
5 833
 
2.7%
. 810
 
2.7%
785
 
2.6%
2 456
 
1.5%
Other values (3) 855
 
2.8%
Latin
ValueCountFrequency (%)
g 2288
63.8%
k 1179
32.9%
m 60
 
1.7%
c 60
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44804
56.8%
ASCII 34131
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 8365
24.5%
) 8365
24.5%
, 3478
10.2%
0 3434
10.1%
g 2288
 
6.7%
1 1746
 
5.1%
3 1417
 
4.2%
k 1179
 
3.5%
5 833
 
2.4%
. 810
 
2.4%
Other values (7) 2216
 
6.5%
Hangul
ValueCountFrequency (%)
기 3357
 
7.5%
고 3275
 
7.3%
산 1902
 
4.2%
생 1431
 
3.2%
국 1305
 
2.9%
걀 1244
 
2.8%
달 1244
 
2.8%
개 1179
 
2.6%
오 1172
 
2.6%
동 1170
 
2.6%
Other values (59) 27525
61.4%

실판매규격
Text

Distinct339
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:36:09.750700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length3.9844
Min length1

Characters and Unicode

Total characters39844
Distinct characters69
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)1.3%

Sample

1st row600g1+급
2nd row10개
3rd row30개
4th row30개
5th row1개
ValueCountFrequency (%)
1개 2621
25.2%
1마리 1685
16.2%
600g 964
 
9.3%
10개 501
 
4.8%
30개 495
 
4.8%
100g 457
 
4.4%
1포기 435
 
4.2%
1망 257
 
2.5%
1마리(25cm 231
 
2.2%
1kg 209
 
2.0%
Other values (263) 2563
24.6%
2024-05-11T15:36:10.489951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8664
21.7%
0 5238
13.1%
개 3946
9.9%
리 2662
 
6.7%
마 2661
 
6.7%
g 2608
 
6.5%
( 1314
 
3.3%
) 1276
 
3.2%
6 1208
 
3.0%
1187
 
3.0%
Other values (59) 9080
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18199
45.7%
Other Letter 11742
29.5%
Lowercase Letter 5545
 
13.9%
Open Punctuation 1368
 
3.4%
Close Punctuation 1330
 
3.3%
Space Separator 1187
 
3.0%
Other Punctuation 389
 
1.0%
Math Symbol 69
 
0.2%
Dash Punctuation 12
 
< 0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
개 3946
33.6%
리 2662
22.7%
마 2661
22.7%
기 455
 
3.9%
포 454
 
3.9%
망 376
 
3.2%
란 227
 
1.9%
특 164
 
1.4%
급 132
 
1.1%
근 119
 
1.0%
Other values (30) 546
 
4.6%
Decimal Number
ValueCountFrequency (%)
1 8664
47.6%
0 5238
28.8%
6 1208
 
6.6%
3 985
 
5.4%
5 815
 
4.5%
2 788
 
4.3%
4 218
 
1.2%
8 202
 
1.1%
9 48
 
0.3%
7 33
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
g 2608
47.0%
m 1108
20.0%
c 1107
20.0%
k 717
 
12.9%
f 2
 
< 0.1%
l 2
 
< 0.1%
a 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 1
33.3%
C 1
33.3%
G 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 1314
96.1%
[ 54
 
3.9%
Close Punctuation
ValueCountFrequency (%)
) 1276
95.9%
] 54
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 310
79.7%
, 79
 
20.3%
Space Separator
ValueCountFrequency (%)
1187
100.0%
Math Symbol
ValueCountFrequency (%)
+ 69
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22554
56.6%
Hangul 11742
29.5%
Latin 5548
 
13.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
개 3946
33.6%
리 2662
22.7%
마 2661
22.7%
기 455
 
3.9%
포 454
 
3.9%
망 376
 
3.2%
란 227
 
1.9%
특 164
 
1.4%
급 132
 
1.1%
근 119
 
1.0%
Other values (30) 546
 
4.6%
Common
ValueCountFrequency (%)
1 8664
38.4%
0 5238
23.2%
( 1314
 
5.8%
) 1276
 
5.7%
6 1208
 
5.4%
1187
 
5.3%
3 985
 
4.4%
5 815
 
3.6%
2 788
 
3.5%
. 310
 
1.4%
Other values (9) 769
 
3.4%
Latin
ValueCountFrequency (%)
g 2608
47.0%
m 1108
20.0%
c 1107
20.0%
k 717
 
12.9%
f 2
 
< 0.1%
l 2
 
< 0.1%
M 1
 
< 0.1%
C 1
 
< 0.1%
G 1
 
< 0.1%
a 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28102
70.5%
Hangul 11742
29.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8664
30.8%
0 5238
18.6%
g 2608
 
9.3%
( 1314
 
4.7%
) 1276
 
4.5%
6 1208
 
4.3%
1187
 
4.2%
m 1108
 
3.9%
c 1107
 
3.9%
3 985
 
3.5%
Other values (19) 3407
 
12.1%
Hangul
ValueCountFrequency (%)
개 3946
33.6%
리 2662
22.7%
마 2661
22.7%
기 455
 
3.9%
포 454
 
3.9%
망 376
 
3.2%
란 227
 
1.9%
특 164
 
1.4%
급 132
 
1.1%
근 119
 
1.0%
Other values (30) 546
 
4.6%

가격(원)
Real number (ℝ)

ZEROS 

Distinct824
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5333.6298
Minimum0
Maximum57000
Zeros108
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:36:10.733841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q11900
median3000
Q35500
95-th percentile24000
Maximum57000
Range57000
Interquartile range (IQR)3600

Descriptive statistics

Standard deviation6922.7221
Coefficient of variation (CV)1.2979382
Kurtosis10.266042
Mean5333.6298
Median Absolute Deviation (MAD)1510
Skewness3.0719743
Sum53336298
Variance47924081
MonotonicityNot monotonic
2024-05-11T15:36:10.967649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 537
 
5.4%
3000 461
 
4.6%
2500 458
 
4.6%
1000 343
 
3.4%
4000 338
 
3.4%
5000 325
 
3.2%
1500 271
 
2.7%
3500 234
 
2.3%
6000 221
 
2.2%
4500 134
 
1.3%
Other values (814) 6678
66.8%
ValueCountFrequency (%)
0 108
1.1%
119 1
 
< 0.1%
300 1
 
< 0.1%
330 3
 
< 0.1%
333 1
 
< 0.1%
334 10
 
0.1%
357 1
 
< 0.1%
400 16
 
0.2%
434 1
 
< 0.1%
450 2
 
< 0.1%
ValueCountFrequency (%)
57000 1
 
< 0.1%
53400 1
 
< 0.1%
51000 6
0.1%
49200 1
 
< 0.1%
47400 1
 
< 0.1%
45600 7
0.1%
45000 10
0.1%
43800 2
 
< 0.1%
42600 2
 
< 0.1%
42000 1
 
< 0.1%

년도-월
Date

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-08-01 00:00:00
Maximum2019-12-01 00:00:00
2024-05-11T15:36:11.197688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:11.369554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

비고
Text

MISSING 

Distinct1855
Distinct (%)28.5%
Missing3502
Missing (%)35.0%
Memory size156.2 KiB
2024-05-11T15:36:11.824334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length29
Mean length5.6983687
Min length1

Characters and Unicode

Total characters37028
Distinct characters341
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1214 ?
Unique (%)18.7%

Sample

1st row용인(30개6000원)
2nd row1등급란특란
3rd row경기도
4th row경북,8/8990원
5th row부산(2마리6560원)
ValueCountFrequency (%)
국산 913
 
11.2%
국내산 696
 
8.5%
국내 287
 
3.5%
100g 181
 
2.2%
러시아 120
 
1.5%
1팩 119
 
1.5%
특란 111
 
1.4%
무안 93
 
1.1%
3개 91
 
1.1%
하림 87
 
1.1%
Other values (1638) 5466
67.0%
2024-05-11T15:36:12.710435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4673
 
12.6%
2566
 
6.9%
국 2309
 
6.2%
산 2174
 
5.9%
1 1550
 
4.2%
원 1214
 
3.3%
내 1037
 
2.8%
2 932
 
2.5%
, 791
 
2.1%
9 761
 
2.1%
Other values (331) 19021
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20766
56.1%
Decimal Number 10727
29.0%
Space Separator 2566
 
6.9%
Other Punctuation 1133
 
3.1%
Lowercase Letter 1005
 
2.7%
Close Punctuation 357
 
1.0%
Open Punctuation 354
 
1.0%
Dash Punctuation 51
 
0.1%
Math Symbol 42
 
0.1%
Uppercase Letter 27
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 2309
 
11.1%
산 2174
 
10.5%
원 1214
 
5.8%
내 1037
 
5.0%
란 599
 
2.9%
개 514
 
2.5%
마 417
 
2.0%
리 412
 
2.0%
기 374
 
1.8%
부 331
 
1.6%
Other values (294) 11385
54.8%
Decimal Number
ValueCountFrequency (%)
0 4673
43.6%
1 1550
 
14.4%
2 932
 
8.7%
9 761
 
7.1%
3 619
 
5.8%
5 602
 
5.6%
8 576
 
5.4%
4 453
 
4.2%
6 303
 
2.8%
7 258
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
g 621
61.8%
c 141
 
14.0%
m 132
 
13.1%
k 77
 
7.7%
p 18
 
1.8%
l 10
 
1.0%
a 4
 
0.4%
r 1
 
0.1%
o 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 791
69.8%
. 211
 
18.6%
/ 126
 
11.1%
% 3
 
0.3%
& 1
 
0.1%
; 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
L 11
40.7%
C 6
22.2%
M 3
 
11.1%
P 3
 
11.1%
F 3
 
11.1%
G 1
 
3.7%
Math Symbol
ValueCountFrequency (%)
+ 37
88.1%
~ 5
 
11.9%
Space Separator
ValueCountFrequency (%)
2566
100.0%
Close Punctuation
ValueCountFrequency (%)
) 357
100.0%
Open Punctuation
ValueCountFrequency (%)
( 354
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20766
56.1%
Common 15230
41.1%
Latin 1032
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 2309
 
11.1%
산 2174
 
10.5%
원 1214
 
5.8%
내 1037
 
5.0%
란 599
 
2.9%
개 514
 
2.5%
마 417
 
2.0%
리 412
 
2.0%
기 374
 
1.8%
부 331
 
1.6%
Other values (294) 11385
54.8%
Common
ValueCountFrequency (%)
0 4673
30.7%
2566
16.8%
1 1550
 
10.2%
2 932
 
6.1%
, 791
 
5.2%
9 761
 
5.0%
3 619
 
4.1%
5 602
 
4.0%
8 576
 
3.8%
4 453
 
3.0%
Other values (12) 1707
 
11.2%
Latin
ValueCountFrequency (%)
g 621
60.2%
c 141
 
13.7%
m 132
 
12.8%
k 77
 
7.5%
p 18
 
1.7%
L 11
 
1.1%
l 10
 
1.0%
C 6
 
0.6%
a 4
 
0.4%
M 3
 
0.3%
Other values (5) 9
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20764
56.1%
ASCII 16262
43.9%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4673
28.7%
2566
15.8%
1 1550
 
9.5%
2 932
 
5.7%
, 791
 
4.9%
9 761
 
4.7%
g 621
 
3.8%
3 619
 
3.8%
5 602
 
3.7%
8 576
 
3.5%
Other values (27) 2571
15.8%
Hangul
ValueCountFrequency (%)
국 2309
 
11.1%
산 2174
 
10.5%
원 1214
 
5.8%
내 1037
 
5.0%
란 599
 
2.9%
개 514
 
2.5%
마 417
 
2.0%
리 412
 
2.0%
기 374
 
1.8%
부 331
 
1.6%
Other values (292) 11383
54.8%
Compat Jamo
ValueCountFrequency (%)
ㄷ 1
50.0%
ㅇ 1
50.0%

시장유형 구분(시장/마트) 코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5063 
1
4937 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 5063
50.6%
1 4937
49.4%

Length

2024-05-11T15:36:12.976185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:36:13.149575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5063
50.6%
1 4937
49.4%

시장유형 구분(시장/마트) 이름
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대형마트
5063 
전통시장
4937 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대형마트
2nd row전통시장
3rd row대형마트
4th row대형마트
5th row전통시장

Common Values

ValueCountFrequency (%)
대형마트 5063
50.6%
전통시장 4937
49.4%

Length

2024-05-11T15:36:13.344161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:36:13.540404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대형마트 5063
50.6%
전통시장 4937
49.4%

자치구 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean413991.5
Minimum110000
Maximum740000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:36:13.705374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110000
5-th percentile140000
Q1260000
median410000
Q3560000
95-th percentile710000
Maximum740000
Range630000
Interquartile range (IQR)300000

Descriptive statistics

Standard deviation184651.67
Coefficient of variation (CV)0.44602769
Kurtosis-1.1984209
Mean413991.5
Median Absolute Deviation (MAD)150000
Skewness0.11860166
Sum4.139915 × 109
Variance3.409624 × 1010
MonotonicityNot monotonic
2024-05-11T15:36:13.917670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
140000 615
 
6.2%
620000 508
 
5.1%
560000 471
 
4.7%
290000 446
 
4.5%
440000 421
 
4.2%
170000 421
 
4.2%
305000 415
 
4.2%
470000 411
 
4.1%
500000 411
 
4.1%
320000 407
 
4.1%
Other values (15) 5474
54.7%
ValueCountFrequency (%)
110000 194
 
1.9%
140000 615
6.2%
170000 421
4.2%
200000 392
3.9%
215000 406
4.1%
230000 403
4.0%
260000 386
3.9%
290000 446
4.5%
305000 415
4.2%
320000 407
4.1%
ValueCountFrequency (%)
740000 404
4.0%
710000 372
3.7%
680000 357
3.6%
650000 397
4.0%
620000 508
5.1%
590000 220
2.2%
560000 471
4.7%
545000 364
3.6%
530000 401
4.0%
500000 411
4.1%

자치구 이름
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
중구
 
615
관악구
 
508
영등포구
 
471
성북구
 
446
용산구
 
421
Other values (20)
7539 

Length

Max length4
Median length3
Mean length3.0666
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송파구
2nd row강서구
3rd row서초구
4th row광진구
5th row도봉구

Common Values

ValueCountFrequency (%)
중구 615
 
6.2%
관악구 508
 
5.1%
영등포구 471
 
4.7%
성북구 446
 
4.5%
용산구 421
 
4.2%
마포구 421
 
4.2%
강북구 415
 
4.2%
강서구 411
 
4.1%
양천구 411
 
4.1%
도봉구 407
 
4.1%
Other values (15) 5474
54.7%

Length

2024-05-11T15:36:14.176086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중구 615
 
6.2%
관악구 508
 
5.1%
영등포구 471
 
4.7%
성북구 446
 
4.5%
용산구 421
 
4.2%
마포구 421
 
4.2%
강북구 415
 
4.2%
강서구 411
 
4.1%
양천구 411
 
4.1%
도봉구 407
 
4.1%
Other values (15) 5474
54.7%

점검일자
Date

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-08-29 00:00:00
Maximum2019-12-30 00:00:00
2024-05-11T15:36:14.367345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:14.632299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

Interactions

2024-05-11T15:36:04.094939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:01.363696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:02.002218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:02.622121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:03.436088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:04.571253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:01.510723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:02.141411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:02.792578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:03.607196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:04.705737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:01.623744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:02.248933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:02.928144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:03.717296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:04.913000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:01.749575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:02.374808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:03.069284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:03.839555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:05.097393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:01.873032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:02.501040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:03.232222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:36:03.959866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:36:14.832832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호시장/마트 번호시장/마트 이름품목 번호품목 이름가격(원)년도-월시장유형 구분(시장/마트) 코드시장유형 구분(시장/마트) 이름자치구 코드자치구 이름점검일자
일련번호1.0000.0720.2780.0000.0000.0361.0000.0380.0380.1340.1800.906
시장/마트 번호0.0721.0001.0000.1490.5280.0790.0740.1370.1370.8290.9520.247
시장/마트 이름0.2781.0001.0000.4930.8160.4360.2881.0001.0001.0001.0000.697
품목 번호0.0000.1490.4931.0000.9950.7650.0000.1070.1070.2800.4200.043
품목 이름0.0000.5280.8160.9951.0000.8260.0000.2100.2100.6290.7840.137
가격(원)0.0360.0790.4360.7650.8261.0000.0400.2030.2030.1410.2170.033
년도-월1.0000.0740.2880.0000.0000.0401.0000.0150.0150.1000.1771.000
시장유형 구분(시장/마트) 코드0.0380.1371.0000.1070.2100.2030.0151.0001.0000.0920.2260.068
시장유형 구분(시장/마트) 이름0.0380.1371.0000.1070.2100.2030.0151.0001.0000.0920.2260.068
자치구 코드0.1340.8291.0000.2800.6290.1410.1000.0920.0921.0001.0000.304
자치구 이름0.1800.9521.0000.4200.7840.2170.1770.2260.2261.0001.0000.591
점검일자0.9060.2470.6970.0430.1370.0331.0000.0680.0680.3040.5911.000
2024-05-11T15:36:15.104974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시장유형 구분(시장/마트) 코드시장유형 구분(시장/마트) 이름자치구 이름
시장유형 구분(시장/마트) 코드1.0001.0000.195
시장유형 구분(시장/마트) 이름1.0001.0000.195
자치구 이름0.1950.1951.000
2024-05-11T15:36:15.706407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호시장/마트 번호품목 번호가격(원)자치구 코드시장유형 구분(시장/마트) 코드시장유형 구분(시장/마트) 이름자치구 이름
일련번호1.0000.019-0.0010.0100.0420.0290.0290.064
시장/마트 번호0.0191.0000.043-0.0070.5130.1350.1350.769
품목 번호-0.0010.0431.000-0.222-0.0370.0820.0820.161
가격(원)0.010-0.007-0.2221.0000.0100.1560.1560.078
자치구 코드0.0420.513-0.0370.0101.0000.0420.0420.999
시장유형 구분(시장/마트) 코드0.0290.1350.0820.1560.0421.0001.0000.195
시장유형 구분(시장/마트) 이름0.0290.1350.0820.1560.0421.0001.0000.195
자치구 이름0.0640.7690.1610.0780.9990.1950.1951.000

Missing values

2024-05-11T15:36:05.328358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:36:05.671406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

일련번호시장/마트 번호시장/마트 이름품목 번호품목 이름실판매규격가격(원)년도-월비고시장유형 구분(시장/마트) 코드시장유형 구분(시장/마트) 이름자치구 코드자치구 이름점검일자
263721486704147롯데백화점 잠실점58쇠고기(한우,불고기)600g1+급450002019-11<NA>2대형마트710000송파구2019-11-28
37597147926638송화시장171달걀(10개)10개20002019-10용인(30개6000원)1전통시장500000강서구2019-10-31
148561484839200뉴코아아울렛 강남점320달걀(30개)30개43902019-11<NA>2대형마트650000서초구2019-11-28
37992147863882롯데마트 강변점320달걀(30개)30개69802019-101등급란특란2대형마트215000광진구2019-10-31
13971148068826신창시장311오이(다다기)1개6002019-11경기도1전통시장320000도봉구2019-11-28
290361478436199서울중앙시장260조기(냉동,수입산)1마리35002019-10<NA>1전통시장140000중구2019-10-31
11093149751828홈플러스 방학점305사과(부사, 300g)251g11242019-12경북,8/8990원2대형마트320000도봉구2019-12-26
32426147611142이마트 가양점266고등어(생물,국산)1마리(30cm)32802019-10부산(2마리6560원)2대형마트500000강서구2019-10-31
502261465181205둔촌역전통시장171달걀(10개)특란20002019-09<NA>1전통시장740000강동구2019-09-30
2263149475222이마트 여의도점26배추1포기38802019-12<NA>2대형마트560000영등포구2019-12-26
일련번호시장/마트 번호시장/마트 이름품목 번호품목 이름실판매규격가격(원)년도-월비고시장유형 구분(시장/마트) 코드시장유형 구분(시장/마트) 이름자치구 코드자치구 이름점검일자
314911478254145마천중앙시장307배추(2.5~3kg)2.5kg45002019-10<NA>1전통시장710000송파구2019-10-31
273291476970110이마트 신도림점202돼지고기(생삼겹살)600g112802019-10100g1880원2대형마트530000구로구2019-10-31
186911483037207관악신사시장(신림4동)283닭고기(육계)1kg50002019-11하림1전통시장620000관악구2019-11-28
53292146001045롯데백화점 강남점310상추(100g)100g39802019-09국내산2대형마트680000강남구2019-09-29
257651489471110이마트 신도림점310상추(100g)100g6902019-11200g1380원(행사)2대형마트530000구로구2019-11-28
276711476769213화곡본동시장320달걀(30개)30개60002019-10안양(왕란)1전통시장500000강서구2019-10-31
109621499500223홈플러스 목동점171달걀(10개)10개26902019-12<NA>2대형마트470000양천구2019-12-26
2510149369674이마트 왕십리점28사과1개14962019-12<NA>2대형마트200000성동구2019-12-26
453111464944136세이브 마트171달걀(10개)10개23802019-09참한위생란2대형마트620000관악구2019-09-30
39651147031062후암시장58쇠고기(한우,불고기)1근 600g (1+)270002019-10100g 45001전통시장170000용산구2019-10-31