Overview

Dataset statistics

Number of variables10
Number of observations1591
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.6 KiB
Average record size in memory84.1 B

Variable types

Numeric4
Categorical3
Text3

Dataset

Description샘플 데이터
Author롯데멤버스
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=323

Alerts

통계청분류명(STAT_CLASS_NM) is highly overall correlated with 대분류코드(L_CLASS_CD) and 5 other fieldsHigh correlation
통계청분류코드(STAT_CLASS_CD) is highly overall correlated with 대분류코드(L_CLASS_CD) and 5 other fieldsHigh correlation
대분류코드(L_CLASS_CD) is highly overall correlated with 중분류코드(M_CLASS_CD) and 5 other fieldsHigh correlation
중분류코드(M_CLASS_CD) is highly overall correlated with 대분류코드(L_CLASS_CD) and 5 other fieldsHigh correlation
소분류코드(S_CLASS_CD) is highly overall correlated with 대분류코드(L_CLASS_CD) and 5 other fieldsHigh correlation
세부분류코드(DETAL_CLASS_CD) is highly overall correlated with 대분류코드(L_CLASS_CD) and 5 other fieldsHigh correlation
대분류명(L_CLASS_NM) is highly overall correlated with 대분류코드(L_CLASS_CD) and 5 other fieldsHigh correlation
세부분류코드(DETAL_CLASS_CD) has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:50:56.204298
Analysis finished2023-12-10 14:50:59.814843
Duration3.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류코드(L_CLASS_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1395349
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 KiB
2023-12-10T23:50:59.892601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q36
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.642543
Coefficient of variation (CV)0.63836713
Kurtosis-0.75570308
Mean4.1395349
Median Absolute Deviation (MAD)2
Skewness0.3494449
Sum6586
Variance6.9830335
MonotonicityIncreasing
2023-12-10T23:51:00.029212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 476
29.9%
6 331
20.8%
5 219
13.8%
4 181
 
11.4%
3 107
 
6.7%
9 68
 
4.3%
2 63
 
4.0%
10 61
 
3.8%
7 44
 
2.8%
8 41
 
2.6%
ValueCountFrequency (%)
1 476
29.9%
2 63
 
4.0%
3 107
 
6.7%
4 181
 
11.4%
5 219
13.8%
6 331
20.8%
7 44
 
2.8%
8 41
 
2.6%
9 68
 
4.3%
10 61
 
3.8%
ValueCountFrequency (%)
10 61
 
3.8%
9 68
 
4.3%
8 41
 
2.6%
7 44
 
2.8%
6 331
20.8%
5 219
13.8%
4 181
 
11.4%
3 107
 
6.7%
2 63
 
4.0%
1 476
29.9%

대분류명(L_CLASS_NM)
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
식품
476 
가전/디지털/컴퓨터
331 
주방/욕실/가정용품
219 
문화용품
181 
화장품
107 
Other values (5)
277 

Length

Max length10
Median length8
Mean length5.8837209
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row식품
2nd row식품
3rd row식품
4th row식품
5th row식품

Common Values

ValueCountFrequency (%)
식품 476
29.9%
가전/디지털/컴퓨터 331
20.8%
주방/욕실/가정용품 219
13.8%
문화용품 181
 
11.4%
화장품 107
 
6.7%
의류/패션잡화/신발 68
 
4.3%
일용품 63
 
4.0%
스포츠/레저용품 61
 
3.8%
내구소비재 44
 
2.8%
건강/의료용품 41
 
2.6%

Length

2023-12-10T23:51:00.198617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:51:00.370815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품 476
29.9%
가전/디지털/컴퓨터 331
20.8%
주방/욕실/가정용품 219
13.8%
문화용품 181
 
11.4%
화장품 107
 
6.7%
의류/패션잡화/신발 68
 
4.3%
일용품 63
 
4.0%
스포츠/레저용품 61
 
3.8%
내구소비재 44
 
2.8%
건강/의료용품 41
 
2.6%

중분류코드(M_CLASS_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean417.87681
Minimum101
Maximum1003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 KiB
2023-12-10T23:51:00.595806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1109
median403
Q3605
95-th percentile903
Maximum1003
Range902
Interquartile range (IQR)496

Descriptive statistics

Standard deviation263.99357
Coefficient of variation (CV)0.63174976
Kurtosis-0.7700956
Mean417.87681
Median Absolute Deviation (MAD)206
Skewness0.34702072
Sum664842
Variance69692.606
MonotonicityIncreasing
2023-12-10T23:51:00.770123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 181
 
11.4%
611 83
 
5.2%
401 73
 
4.6%
105 66
 
4.1%
501 66
 
4.1%
503 64
 
4.0%
301 59
 
3.7%
603 56
 
3.5%
112 51
 
3.2%
403 48
 
3.0%
Other values (41) 844
53.0%
ValueCountFrequency (%)
101 181
11.4%
102 46
 
2.9%
103 12
 
0.8%
104 9
 
0.6%
105 66
 
4.1%
106 26
 
1.6%
107 26
 
1.6%
108 19
 
1.2%
109 31
 
1.9%
110 2
 
0.1%
ValueCountFrequency (%)
1003 32
 
2.0%
1002 8
 
0.5%
1001 21
 
1.3%
903 21
 
1.3%
902 35
2.2%
901 12
 
0.8%
802 41
2.6%
702 5
 
0.3%
701 39
2.5%
611 83
5.2%

중분류명(M_CLASS_NM)
Text

Distinct51
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
2023-12-10T23:51:01.003801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.0534255
Min length2

Characters and Unicode

Total characters6449
Distinct characters104
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row가공식품
2nd row가공식품
3rd row가공식품
4th row가공식품
5th row가공식품
ValueCountFrequency (%)
가공식품 181
 
11.4%
컴퓨터 83
 
5.2%
문구/오피스 73
 
4.6%
주방용품 66
 
4.1%
음료 66
 
4.1%
가정용품 64
 
4.0%
화장품 59
 
3.7%
주방가전 56
 
3.5%
건강식품 51
 
3.2%
서적 48
 
3.0%
Other values (41) 844
53.0%
2023-12-10T23:51:01.426799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
품 810
 
12.6%
가 487
 
7.6%
용 430
 
6.7%
식 296
 
4.6%
/ 243
 
3.8%
공 181
 
2.8%
구 178
 
2.8%
전 165
 
2.6%
주 141
 
2.2%
스 134
 
2.1%
Other values (94) 3384
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6206
96.2%
Other Punctuation 243
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
품 810
 
13.1%
가 487
 
7.8%
용 430
 
6.9%
식 296
 
4.8%
공 181
 
2.9%
구 178
 
2.9%
전 165
 
2.7%
주 141
 
2.3%
스 134
 
2.2%
류 124
 
2.0%
Other values (93) 3260
52.5%
Other Punctuation
ValueCountFrequency (%)
/ 243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6206
96.2%
Common 243
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
품 810
 
13.1%
가 487
 
7.8%
용 430
 
6.9%
식 296
 
4.8%
공 181
 
2.9%
구 178
 
2.9%
전 165
 
2.7%
주 141
 
2.3%
스 134
 
2.2%
류 124
 
2.0%
Other values (93) 3260
52.5%
Common
ValueCountFrequency (%)
/ 243
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6206
96.2%
ASCII 243
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
품 810
 
13.1%
가 487
 
7.8%
용 430
 
6.9%
식 296
 
4.8%
공 181
 
2.9%
구 178
 
2.9%
전 165
 
2.7%
주 141
 
2.3%
스 134
 
2.2%
류 124
 
2.0%
Other values (93) 3260
52.5%
ASCII
ValueCountFrequency (%)
/ 243
100.0%

소분류코드(S_CLASS_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct301
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41801.924
Minimum10101
Maximum100399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 KiB
2023-12-10T23:51:01.585879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile10108
Q110901
median40306
Q360505
95-th percentile90301
Maximum100399
Range90298
Interquartile range (IQR)49604

Descriptive statistics

Standard deviation26399.811
Coefficient of variation (CV)0.63154536
Kurtosis-0.77002517
Mean41801.924
Median Absolute Deviation (MAD)20596
Skewness0.34706703
Sum66506861
Variance6.9695002 × 108
MonotonicityIncreasing
2023-12-10T23:51:01.734314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10901 22
 
1.4%
10506 18
 
1.1%
11202 18
 
1.1%
10107 17
 
1.1%
10801 17
 
1.1%
11204 16
 
1.0%
40103 15
 
0.9%
10105 15
 
0.9%
50308 15
 
0.9%
10102 15
 
0.9%
Other values (291) 1423
89.4%
ValueCountFrequency (%)
10101 5
 
0.3%
10102 15
0.9%
10103 8
0.5%
10104 10
0.6%
10105 15
0.9%
10106 8
0.5%
10107 17
1.1%
10108 5
 
0.3%
10109 5
 
0.3%
10110 7
0.4%
ValueCountFrequency (%)
100399 1
 
0.1%
100388 1
 
0.1%
100304 3
 
0.2%
100303 12
0.8%
100302 9
0.6%
100301 6
0.4%
100299 1
 
0.1%
100202 2
 
0.1%
100201 5
0.3%
100199 3
 
0.2%

소분류명(S_CLASS_NM)
Text

Distinct300
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
2023-12-10T23:51:02.012008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length5.5028284
Min length1

Characters and Unicode

Total characters8755
Distinct characters293
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)2.9%

Sample

1st row조미료
2nd row조미료
3rd row조미료
4th row조미료
5th row조미료
ValueCountFrequency (%)
티백/분말차 22
 
1.4%
차음료 18
 
1.1%
건강즙/환/분말 18
 
1.1%
면류 17
 
1.1%
주류 17
 
1.1%
건강보조식품 16
 
1.0%
사무용품 15
 
0.9%
식용유 15
 
0.9%
침구류/커튼 15
 
0.9%
조미식품 15
 
0.9%
Other values (292) 1437
89.5%
2023-12-10T23:51:02.471290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
품 548
 
6.3%
/ 522
 
6.0%
기 405
 
4.6%
용 399
 
4.6%
류 205
 
2.3%
식 186
 
2.1%
세 186
 
2.1%
가 153
 
1.7%
구 139
 
1.6%
트 138
 
1.6%
Other values (283) 5874
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8123
92.8%
Other Punctuation 522
 
6.0%
Uppercase Letter 96
 
1.1%
Space Separator 14
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
품 548
 
6.7%
기 405
 
5.0%
용 399
 
4.9%
류 205
 
2.5%
식 186
 
2.3%
세 186
 
2.3%
가 153
 
1.9%
구 139
 
1.7%
트 138
 
1.7%
리 122
 
1.5%
Other values (274) 5642
69.5%
Uppercase Letter
ValueCountFrequency (%)
C 20
20.8%
D 18
18.8%
P 17
17.7%
V 13
13.5%
T 10
10.4%
I 9
9.4%
Y 9
9.4%
Other Punctuation
ValueCountFrequency (%)
/ 522
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8123
92.8%
Common 536
 
6.1%
Latin 96
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
품 548
 
6.7%
기 405
 
5.0%
용 399
 
4.9%
류 205
 
2.5%
식 186
 
2.3%
세 186
 
2.3%
가 153
 
1.9%
구 139
 
1.7%
트 138
 
1.7%
리 122
 
1.5%
Other values (274) 5642
69.5%
Latin
ValueCountFrequency (%)
C 20
20.8%
D 18
18.8%
P 17
17.7%
V 13
13.5%
T 10
10.4%
I 9
9.4%
Y 9
9.4%
Common
ValueCountFrequency (%)
/ 522
97.4%
14
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8123
92.8%
ASCII 632
 
7.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
품 548
 
6.7%
기 405
 
5.0%
용 399
 
4.9%
류 205
 
2.5%
식 186
 
2.3%
세 186
 
2.3%
가 153
 
1.9%
구 139
 
1.7%
트 138
 
1.7%
리 122
 
1.5%
Other values (274) 5642
69.5%
ASCII
ValueCountFrequency (%)
/ 522
82.6%
C 20
 
3.2%
D 18
 
2.8%
P 17
 
2.7%
14
 
2.2%
V 13
 
2.1%
T 10
 
1.6%
I 9
 
1.4%
Y 9
 
1.4%

세부분류코드(DETAL_CLASS_CD)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1591
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4180210.8
Minimum1010101
Maximum10039999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 KiB
2023-12-10T23:51:02.624420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1010101
5-th percentile1010802.5
Q11090116.5
median4030606
Q36050504.5
95-th percentile9030102.5
Maximum10039999
Range9029898
Interquartile range (IQR)4960388

Descriptive statistics

Standard deviation2639981
Coefficient of variation (CV)0.63154256
Kurtosis-0.77002232
Mean4180210.8
Median Absolute Deviation (MAD)2059601
Skewness0.34706829
Sum6.6507153 × 109
Variance6.9694997 × 1012
MonotonicityStrictly increasing
2023-12-10T23:51:02.777279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1010101 1
 
0.1%
6020202 1
 
0.1%
6020106 1
 
0.1%
6020105 1
 
0.1%
6020104 1
 
0.1%
6020103 1
 
0.1%
6020102 1
 
0.1%
6020101 1
 
0.1%
6019999 1
 
0.1%
6010499 1
 
0.1%
Other values (1581) 1581
99.4%
ValueCountFrequency (%)
1010101 1
0.1%
1010102 1
0.1%
1010103 1
0.1%
1010104 1
0.1%
1010199 1
0.1%
1010201 1
0.1%
1010202 1
0.1%
1010203 1
0.1%
1010204 1
0.1%
1010205 1
0.1%
ValueCountFrequency (%)
10039999 1
0.1%
10038801 1
0.1%
10030499 1
0.1%
10030402 1
0.1%
10030401 1
0.1%
10030399 1
0.1%
10030311 1
0.1%
10030310 1
0.1%
10030309 1
0.1%
10030308 1
0.1%

세부분류명(DETAL_CLASS_NM)
Text

Distinct1586
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
2023-12-10T23:51:03.054879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length5.5694532
Min length1

Characters and Unicode

Total characters8861
Distinct characters637
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1581 ?
Unique (%)99.4%

Sample

1st row핵산조미료
2nd row종합조미료
3rd row육류양념장
4th row일반양념장
5th row기타조미료
ValueCountFrequency (%)
아이스크림 7
 
0.4%
유아동 6
 
0.4%
차량용 6
 
0.4%
tv 3
 
0.2%
병/통조림 3
 
0.2%
의료용 2
 
0.1%
애완동물용 2
 
0.1%
세정제 2
 
0.1%
기타화장품 2
 
0.1%
오미자차 2
 
0.1%
Other values (1585) 1588
97.8%
2023-12-10T23:51:03.567339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 489
 
5.5%
/ 366
 
4.1%
타 259
 
2.9%
품 219
 
2.5%
용 215
 
2.4%
스 173
 
2.0%
이 172
 
1.9%
트 156
 
1.8%
세 122
 
1.4%
류 121
 
1.4%
Other values (627) 6569
74.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8271
93.3%
Other Punctuation 369
 
4.2%
Uppercase Letter 142
 
1.6%
Space Separator 32
 
0.4%
Open Punctuation 12
 
0.1%
Close Punctuation 12
 
0.1%
Decimal Number 10
 
0.1%
Lowercase Letter 10
 
0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 489
 
5.9%
타 259
 
3.1%
품 219
 
2.6%
용 215
 
2.6%
스 173
 
2.1%
이 172
 
2.1%
트 156
 
1.9%
세 122
 
1.5%
류 121
 
1.5%
리 107
 
1.3%
Other values (587) 6238
75.4%
Uppercase Letter
ValueCountFrequency (%)
D 25
17.6%
C 17
12.0%
V 17
12.0%
T 15
10.6%
P 10
 
7.0%
S 8
 
5.6%
B 7
 
4.9%
X 6
 
4.2%
R 6
 
4.2%
O 6
 
4.2%
Other values (10) 25
17.6%
Lowercase Letter
ValueCountFrequency (%)
r 2
20.0%
i 2
20.0%
c 1
10.0%
o 1
10.0%
g 1
10.0%
u 1
10.0%
t 1
10.0%
a 1
10.0%
Decimal Number
ValueCountFrequency (%)
0 4
40.0%
1 2
20.0%
3 2
20.0%
4 1
 
10.0%
5 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
/ 366
99.2%
% 2
 
0.5%
& 1
 
0.3%
Space Separator
ValueCountFrequency (%)
32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8271
93.3%
Common 438
 
4.9%
Latin 152
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 489
 
5.9%
타 259
 
3.1%
품 219
 
2.6%
용 215
 
2.6%
스 173
 
2.1%
이 172
 
2.1%
트 156
 
1.9%
세 122
 
1.5%
류 121
 
1.5%
리 107
 
1.3%
Other values (587) 6238
75.4%
Latin
ValueCountFrequency (%)
D 25
16.4%
C 17
11.2%
V 17
11.2%
T 15
9.9%
P 10
 
6.6%
S 8
 
5.3%
B 7
 
4.6%
X 6
 
3.9%
R 6
 
3.9%
O 6
 
3.9%
Other values (18) 35
23.0%
Common
ValueCountFrequency (%)
/ 366
83.6%
32
 
7.3%
( 12
 
2.7%
) 12
 
2.7%
0 4
 
0.9%
- 3
 
0.7%
% 2
 
0.5%
1 2
 
0.5%
3 2
 
0.5%
4 1
 
0.2%
Other values (2) 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8271
93.3%
ASCII 590
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 489
 
5.9%
타 259
 
3.1%
품 219
 
2.6%
용 215
 
2.6%
스 173
 
2.1%
이 172
 
2.1%
트 156
 
1.9%
세 122
 
1.5%
류 121
 
1.5%
리 107
 
1.3%
Other values (587) 6238
75.4%
ASCII
ValueCountFrequency (%)
/ 366
62.0%
32
 
5.4%
D 25
 
4.2%
C 17
 
2.9%
V 17
 
2.9%
T 15
 
2.5%
( 12
 
2.0%
) 12
 
2.0%
P 10
 
1.7%
S 8
 
1.4%
Other values (30) 76
 
12.9%

통계청분류코드(STAT_CLASS_CD)
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
A
455 
E
439 
L
245 
I
162 
J
121 
Other values (4)
169 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 455
28.6%
E 439
27.6%
L 245
15.4%
I 162
 
10.2%
J 121
 
7.6%
C 68
 
4.3%
F 41
 
2.6%
G 39
 
2.5%
B 21
 
1.3%

Length

2023-12-10T23:51:03.737830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:51:03.902443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 455
28.6%
e 439
27.6%
l 245
15.4%
i 162
 
10.2%
j 121
 
7.6%
c 68
 
4.3%
f 41
 
2.6%
g 39
 
2.5%
b 21
 
1.3%

통계청분류명(STAT_CLASS_NM)
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
식료품 및 비주류음료
455 
가정용품 및 가사 서비스
439 
기타 상품 및 서비스
245 
오락 및 문화
162 
교육
121 
Other values (4)
169 

Length

Max length13
Median length11
Mean length9.7837838
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row식료품 및 비주류음료
2nd row식료품 및 비주류음료
3rd row식료품 및 비주류음료
4th row식료품 및 비주류음료
5th row식료품 및 비주류음료

Common Values

ValueCountFrequency (%)
식료품 및 비주류음료 455
28.6%
가정용품 및 가사 서비스 439
27.6%
기타 상품 및 서비스 245
15.4%
오락 및 문화 162
 
10.2%
교육 121
 
7.6%
의류 및 신발 68
 
4.3%
보건 41
 
2.6%
교통 39
 
2.5%
주류 및 담배 21
 
1.3%

Length

2023-12-10T23:51:04.061306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:51:04.224191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
및 1390
27.5%
서비스 684
13.5%
식료품 455
 
9.0%
비주류음료 455
 
9.0%
가정용품 439
 
8.7%
가사 439
 
8.7%
기타 245
 
4.8%
상품 245
 
4.8%
문화 162
 
3.2%
오락 162
 
3.2%
Other values (7) 379
 
7.5%

Interactions

2023-12-10T23:50:58.980244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:57.132523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:57.997246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:58.460593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:59.104394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:57.234095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:58.101066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:58.594493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:59.216312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:57.759845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:58.206337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:58.704464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:59.310528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:57.878411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:58.333073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:58.822363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:51:04.344722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류코드(L_CLASS_CD)대분류명(L_CLASS_NM)중분류코드(M_CLASS_CD)중분류명(M_CLASS_NM)소분류코드(S_CLASS_CD)세부분류코드(DETAL_CLASS_CD)통계청분류코드(STAT_CLASS_CD)통계청분류명(STAT_CLASS_NM)
대분류코드(L_CLASS_CD)1.0001.0001.0001.0001.0001.0000.9520.952
대분류명(L_CLASS_NM)1.0001.0001.0001.0001.0001.0000.9520.952
중분류코드(M_CLASS_CD)1.0001.0001.0001.0001.0001.0000.9520.952
중분류명(M_CLASS_NM)1.0001.0001.0001.0001.0001.0001.0001.000
소분류코드(S_CLASS_CD)1.0001.0001.0001.0001.0001.0000.9520.952
세부분류코드(DETAL_CLASS_CD)1.0001.0001.0001.0001.0001.0000.9520.952
통계청분류코드(STAT_CLASS_CD)0.9520.9520.9521.0000.9520.9521.0001.000
통계청분류명(STAT_CLASS_NM)0.9520.9520.9521.0000.9520.9521.0001.000
2023-12-10T23:51:04.507325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류명(L_CLASS_NM)통계청분류명(STAT_CLASS_NM)통계청분류코드(STAT_CLASS_CD)
대분류명(L_CLASS_NM)1.0000.8330.833
통계청분류명(STAT_CLASS_NM)0.8331.0001.000
통계청분류코드(STAT_CLASS_CD)0.8331.0001.000
2023-12-10T23:51:04.623130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류코드(L_CLASS_CD)중분류코드(M_CLASS_CD)소분류코드(S_CLASS_CD)세부분류코드(DETAL_CLASS_CD)대분류명(L_CLASS_NM)통계청분류코드(STAT_CLASS_CD)통계청분류명(STAT_CLASS_NM)
대분류코드(L_CLASS_CD)1.0000.9810.9800.9801.0000.8330.833
중분류코드(M_CLASS_CD)0.9811.0000.9990.9990.9970.8310.831
소분류코드(S_CLASS_CD)0.9800.9991.0001.0000.9970.8310.831
세부분류코드(DETAL_CLASS_CD)0.9800.9991.0001.0000.9970.8310.831
대분류명(L_CLASS_NM)1.0000.9970.9970.9971.0000.8330.833
통계청분류코드(STAT_CLASS_CD)0.8330.8310.8310.8310.8331.0001.000
통계청분류명(STAT_CLASS_NM)0.8330.8310.8310.8310.8331.0001.000

Missing values

2023-12-10T23:50:59.490658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:50:59.724559image/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

대분류코드(L_CLASS_CD)대분류명(L_CLASS_NM)중분류코드(M_CLASS_CD)중분류명(M_CLASS_NM)소분류코드(S_CLASS_CD)소분류명(S_CLASS_NM)세부분류코드(DETAL_CLASS_CD)세부분류명(DETAL_CLASS_NM)통계청분류코드(STAT_CLASS_CD)통계청분류명(STAT_CLASS_NM)
01식품101가공식품10101조미료1010101핵산조미료A식료품 및 비주류음료
11식품101가공식품10101조미료1010102종합조미료A식료품 및 비주류음료
21식품101가공식품10101조미료1010103육류양념장A식료품 및 비주류음료
31식품101가공식품10101조미료1010104일반양념장A식료품 및 비주류음료
41식품101가공식품10101조미료1010199기타조미료A식료품 및 비주류음료
51식품101가공식품10102조미식품1010201식초A식료품 및 비주류음료
61식품101가공식품10102조미식품1010202식염A식료품 및 비주류음료
71식품101가공식품10102조미식품1010203케첩A식료품 및 비주류음료
81식품101가공식품10102조미식품1010204마요네즈A식료품 및 비주류음료
91식품101가공식품10102조미식품1010205소스류A식료품 및 비주류음료
대분류코드(L_CLASS_CD)대분류명(L_CLASS_NM)중분류코드(M_CLASS_CD)중분류명(M_CLASS_NM)소분류코드(S_CLASS_CD)소분류명(S_CLASS_NM)세부분류코드(DETAL_CLASS_CD)세부분류명(DETAL_CLASS_NM)통계청분류코드(STAT_CLASS_CD)통계청분류명(STAT_CLASS_NM)
158110스포츠/레저용품1003헬스/요가/레저용품100303등산/캠핑용품10030308캠핑취사도구I오락 및 문화
158210스포츠/레저용품1003헬스/요가/레저용품100303등산/캠핑용품10030309레저테이블/의자I오락 및 문화
158310스포츠/레저용품1003헬스/요가/레저용품100303등산/캠핑용품10030310버너류I오락 및 문화
158410스포츠/레저용품1003헬스/요가/레저용품100303등산/캠핑용품10030311바베큐용품I오락 및 문화
158510스포츠/레저용품1003헬스/요가/레저용품100303등산/캠핑용품10030399기타등산/캠핑용품I오락 및 문화
158610스포츠/레저용품1003헬스/요가/레저용품100304낚시용품10030401낚시대및세트I오락 및 문화
158710스포츠/레저용품1003헬스/요가/레저용품100304낚시용품10030402낚시장비소품I오락 및 문화
158810스포츠/레저용품1003헬스/요가/레저용품100304낚시용품10030499기타낚시용품I오락 및 문화
158910스포츠/레저용품1003헬스/요가/레저용품100388헬스/요가/레저용품세트및균일가10038801헬스/요가/레저용품세트I오락 및 문화
159010스포츠/레저용품1003헬스/요가/레저용품100399기타헬스/요가/레저용품10039999기타헬스/요가/레저용품I오락 및 문화