Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells11
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory117.0 B

Variable types

Text4
DateTime1
Categorical4
Numeric4

Dataset

Description공공거래위원회의 소비자 민원학습에 대한 데이터로, 온라인 상거래에서 제기된 소비자 민원에 대한 데이터를 보여줍니다. 이 데이터는 사건제목 등을 포함하고 있습니다.
Author공정거래위원회
URLhttps://www.data.go.kr/data/15098340/fileData.do

Alerts

성별코드(GENDER_CODE) is highly overall correlated with 성별(GENDER)High correlation
성별(GENDER) is highly overall correlated with 성별코드(GENDER_CODE)High correlation
연령대코드(AGE_GROUP_CODE) is highly overall correlated with 연령대(AGE_GROUP_NAME)High correlation
처리결과코드(PRCS_RESULT_CODE) is highly overall correlated with 처리결과(PRCS_RESULT)High correlation
연령대(AGE_GROUP_NAME) is highly overall correlated with 연령대코드(AGE_GROUP_CODE)High correlation
처리결과(PRCS_RESULT) is highly overall correlated with 처리결과코드(PRCS_RESULT_CODE)High correlation
사건번호(ACCIDENT_NO) has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:06:42.391220
Analysis finished2023-12-12 20:06:46.890024
Duration4.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사건번호(ACCIDENT_NO)
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:06:47.094282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters120000
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

Unique10000 ?
Unique (%)100.0%

Sample

1st row2020-0131705
2nd row2020-0101070
3rd row2020-0155396
4th row2020-0034813
5th row2020-0008947
ValueCountFrequency (%)
2020-0131705 1
 
< 0.1%
2020-0272301 1
 
< 0.1%
2020-0025539 1
 
< 0.1%
2020-0152985 1
 
< 0.1%
2020-0238035 1
 
< 0.1%
2020-0019432 1
 
< 0.1%
2020-0010679 1
 
< 0.1%
2020-0263163 1
 
< 0.1%
2020-0023329 1
 
< 0.1%
2020-0013588 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-13T05:06:47.728974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 38568
32.1%
2 27989
23.3%
- 10000
 
8.3%
1 8518
 
7.1%
6 5125
 
4.3%
7 5099
 
4.2%
5 5046
 
4.2%
4 5046
 
4.2%
3 5039
 
4.2%
8 4821
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 110000
91.7%
Dash Punctuation 10000
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38568
35.1%
2 27989
25.4%
1 8518
 
7.7%
6 5125
 
4.7%
7 5099
 
4.6%
5 5046
 
4.6%
4 5046
 
4.6%
3 5039
 
4.6%
8 4821
 
4.4%
9 4749
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38568
32.1%
2 27989
23.3%
- 10000
 
8.3%
1 8518
 
7.1%
6 5125
 
4.3%
7 5099
 
4.2%
5 5046
 
4.2%
4 5046
 
4.2%
3 5039
 
4.2%
8 4821
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38568
32.1%
2 27989
23.3%
- 10000
 
8.3%
1 8518
 
7.1%
6 5125
 
4.3%
7 5099
 
4.2%
5 5046
 
4.2%
4 5046
 
4.2%
3 5039
 
4.2%
8 4821
 
4.0%

접수일자(RCPT_YMD)
Date

Distinct139
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-01-01 00:00:00
Maximum2020-05-26 00:00:00
2023-12-13T05:06:47.896915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:48.059401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

성별코드(GENDER_CODE)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
6163 
1
3746 
<NA>
 
91

Length

Max length4
Median length1
Mean length1.0273
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 6163
61.6%
1 3746
37.5%
<NA> 91
 
0.9%

Length

2023-12-13T05:06:48.202337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:06:48.301844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6163
61.6%
1 3746
37.5%
na 91
 
0.9%

성별(GENDER)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
여성
6163 
남성
3746 
<NA>
 
91

Length

Max length4
Median length2
Mean length2.0182
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여성 6163
61.6%
남성 3746
37.5%
<NA> 91
 
0.9%

Length

2023-12-13T05:06:48.418648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:06:48.534266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여성 6163
61.6%
남성 3746
37.5%
na 91
 
0.9%

연령대코드(AGE_GROUP_CODE)
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing9
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.7984186
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:06:48.637076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median4
Q35
95-th percentile9
Maximum12
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7477575
Coefficient of variation (CV)0.36423614
Kurtosis5.9624647
Mean4.7984186
Median Absolute Deviation (MAD)1
Skewness2.2498014
Sum47941
Variance3.0546562
MonotonicityNot monotonic
2023-12-13T05:06:48.736760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 3694
36.9%
5 2700
27.0%
3 1512
15.1%
6 1452
 
14.5%
11 363
 
3.6%
12 89
 
0.9%
9 82
 
0.8%
8 62
 
0.6%
2 31
 
0.3%
10 6
 
0.1%
(Missing) 9
 
0.1%
ValueCountFrequency (%)
2 31
 
0.3%
3 1512
15.1%
4 3694
36.9%
5 2700
27.0%
6 1452
 
14.5%
8 62
 
0.6%
9 82
 
0.8%
10 6
 
0.1%
11 363
 
3.6%
12 89
 
0.9%
ValueCountFrequency (%)
12 89
 
0.9%
11 363
 
3.6%
10 6
 
0.1%
9 82
 
0.8%
8 62
 
0.6%
6 1452
 
14.5%
5 2700
27.0%
4 3694
36.9%
3 1512
15.1%
2 31
 
0.3%

연령대(AGE_GROUP_NAME)
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
30 - 39세
3694 
40 - 49세
2700 
20 - 29세
1512 
50 - 59세
1452 
60 - 64세
 
363
Other values (6)
 
279

Length

Max length8
Median length8
Mean length7.9454
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40 - 49세
2nd row40 - 49세
3rd row30 - 39세
4th row40 - 49세
5th row40 - 49세

Common Values

ValueCountFrequency (%)
30 - 39세 3694
36.9%
40 - 49세 2700
27.0%
20 - 29세 1512
15.1%
50 - 59세 1452
 
14.5%
60 - 64세 363
 
3.6%
65 - 69세 89
 
0.9%
불명 82
 
0.8%
70 - 79세 62
 
0.6%
10 - 19세 31
 
0.3%
<NA> 9
 
0.1%

Length

2023-12-13T05:06:48.846906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9903
33.2%
30 3694
 
12.4%
39세 3694
 
12.4%
40 2700
 
9.1%
49세 2700
 
9.1%
20 1512
 
5.1%
29세 1512
 
5.1%
50 1452
 
4.9%
59세 1452
 
4.9%
64세 363
 
1.2%
Other values (10) 824
 
2.8%

지역코드(AREA_CODE)
Real number (ℝ)

Distinct242
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean678.8802
Minimum100
Maximum9907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:06:48.982176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1200
median800
Q3820
95-th percentile1500.05
Maximum9907
Range9807
Interquartile range (IQR)620

Descriptive statistics

Standard deviation715.36457
Coefficient of variation (CV)1.053742
Kurtosis102.30092
Mean678.8802
Median Absolute Deviation (MAD)400
Skewness8.1607259
Sum6788802
Variance511746.46
MonotonicityNot monotonic
2023-12-13T05:06:49.142733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800 1606
 
16.1%
100 1175
 
11.8%
400 354
 
3.5%
200 284
 
2.8%
300 197
 
2.0%
810 185
 
1.8%
1100 165
 
1.7%
600 160
 
1.6%
801 157
 
1.6%
809 144
 
1.4%
Other values (232) 5573
55.7%
ValueCountFrequency (%)
100 1175
11.8%
101 118
 
1.2%
102 43
 
0.4%
103 22
 
0.2%
104 80
 
0.8%
105 67
 
0.7%
106 45
 
0.4%
107 51
 
0.5%
108 31
 
0.3%
109 55
 
0.5%
ValueCountFrequency (%)
9907 1
 
< 0.1%
9906 1
 
< 0.1%
9903 2
 
< 0.1%
9901 5
 
0.1%
9900 29
0.3%
1700 63
0.6%
1604 42
0.4%
1603 5
 
0.1%
1600 50
0.5%
1519 1
 
< 0.1%

지역명(AREA_NAME)
Text

Distinct220
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:06:49.497351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.5754
Min length2

Characters and Unicode

Total characters35754
Distinct characters142
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

Unique25 ?
Unique (%)0.2%

Sample

1st row충청남도
2nd row강서구
3rd row서초구
4th row경상북도
5th row성남시
ValueCountFrequency (%)
경기도 1606
 
16.0%
서울특별시 1175
 
11.7%
인천광역시 354
 
3.5%
부산광역시 284
 
2.8%
대구광역시 197
 
2.0%
수원시 185
 
1.8%
충청남도 165
 
1.6%
대전광역시 160
 
1.6%
고양시 157
 
1.6%
성남시 144
 
1.4%
Other values (211) 5631
56.0%
2023-12-13T05:06:49.963051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 5549
 
15.5%
구 2451
 
6.9%
도 2174
 
6.1%
경 1793
 
5.0%
기 1643
 
4.6%
광 1565
 
4.4%
서 1544
 
4.3%
울 1296
 
3.6%
특 1288
 
3.6%
별 1288
 
3.6%
Other values (132) 15163
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35696
99.8%
Space Separator 58
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5549
 
15.5%
구 2451
 
6.9%
도 2174
 
6.1%
경 1793
 
5.0%
기 1643
 
4.6%
광 1565
 
4.4%
서 1544
 
4.3%
울 1296
 
3.6%
특 1288
 
3.6%
별 1288
 
3.6%
Other values (131) 15105
42.3%
Space Separator
ValueCountFrequency (%)
58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35696
99.8%
Common 58
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5549
 
15.5%
구 2451
 
6.9%
도 2174
 
6.1%
경 1793
 
5.0%
기 1643
 
4.6%
광 1565
 
4.4%
서 1544
 
4.3%
울 1296
 
3.6%
특 1288
 
3.6%
별 1288
 
3.6%
Other values (131) 15105
42.3%
Common
ValueCountFrequency (%)
58
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35696
99.8%
ASCII 58
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 5549
 
15.5%
구 2451
 
6.9%
도 2174
 
6.1%
경 1793
 
5.0%
기 1643
 
4.6%
광 1565
 
4.4%
서 1544
 
4.3%
울 1296
 
3.6%
특 1288
 
3.6%
별 1288
 
3.6%
Other values (131) 15105
42.3%
ASCII
ValueCountFrequency (%)
58
100.0%

품목코드(ITEM_CODE)
Real number (ℝ)

Distinct683
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246256.61
Minimum110101
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:06:50.103537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110101
5-th percentile120202
Q1170399
median189999
Q3350202
95-th percentile490301
Maximum999999
Range889898
Interquartile range (IQR)179803

Descriptive statistics

Standard deviation106586.95
Coefficient of variation (CV)0.43282877
Kurtosis-0.09354488
Mean246256.61
Median Absolute Deviation (MAD)39890
Skewness0.85725163
Sum2.4625661 × 109
Variance1.1360777 × 1010
MonotonicityNot monotonic
2023-12-13T05:06:50.253626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189999 811
 
8.1%
350104 715
 
7.1%
370102 578
 
5.8%
179999 381
 
3.8%
370202 221
 
2.2%
170399 218
 
2.2%
299999 217
 
2.2%
170301 198
 
2.0%
170207 168
 
1.7%
170208 143
 
1.4%
Other values (673) 6350
63.5%
ValueCountFrequency (%)
110101 10
0.1%
110110 1
 
< 0.1%
110115 1
 
< 0.1%
110121 1
 
< 0.1%
110122 5
 
0.1%
110123 1
 
< 0.1%
110199 13
0.1%
110201 5
 
0.1%
110202 14
0.1%
110203 1
 
< 0.1%
ValueCountFrequency (%)
999999 2
 
< 0.1%
510302 3
 
< 0.1%
510112 2
 
< 0.1%
510109 1
 
< 0.1%
510105 1
 
< 0.1%
510104 1
 
< 0.1%
510102 1
 
< 0.1%
500799 4
 
< 0.1%
500707 3
 
< 0.1%
500706 57
0.6%

품목명(ITEM_NAME)
Text

Distinct682
Distinct (%)6.8%
Missing2
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-13T05:06:50.498494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length16
Mean length6.0141028
Min length1

Characters and Unicode

Total characters60129
Distinct characters461
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique184 ?
Unique (%)1.8%

Sample

1st row기타보건·위생용품
2nd row기타보건·위생용품
3rd row운동화
4th row블라인드
5th row인터넷교육서비스
ValueCountFrequency (%)
기타보건·위생용품 811
 
8.0%
항공여객운송서비스 715
 
7.1%
국외여행 578
 
5.7%
기타의류·섬유 381
 
3.8%
호텔 221
 
2.2%
기타간편복 218
 
2.2%
기타미분류물품 217
 
2.1%
점퍼·재킷류 198
 
2.0%
코트 168
 
1.7%
원피스 143
 
1.4%
Other values (674) 6459
63.9%
2023-12-13T05:06:50.881846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 4174
 
6.9%
타 3200
 
5.3%
· 2261
 
3.8%
스 2196
 
3.7%
품 1922
 
3.2%
용 1707
 
2.8%
서 1702
 
2.8%
비 1650
 
2.7%
여 1495
 
2.5%
류 1344
 
2.2%
Other values (451) 38478
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 56581
94.1%
Other Punctuation 2266
 
3.8%
Close Punctuation 411
 
0.7%
Open Punctuation 411
 
0.7%
Uppercase Letter 336
 
0.6%
Space Separator 111
 
0.2%
Decimal Number 7
 
< 0.1%
Lowercase Letter 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 4174
 
7.4%
타 3200
 
5.7%
스 2196
 
3.9%
품 1922
 
3.4%
용 1707
 
3.0%
서 1702
 
3.0%
비 1650
 
2.9%
여 1495
 
2.6%
류 1344
 
2.4%
보 1191
 
2.1%
Other values (425) 36000
63.6%
Uppercase Letter
ValueCountFrequency (%)
V 94
28.0%
T 87
25.9%
C 40
11.9%
P 37
 
11.0%
D 34
 
10.1%
L 14
 
4.2%
G 7
 
2.1%
S 6
 
1.8%
E 6
 
1.8%
W 5
 
1.5%
Other values (4) 6
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
r 1
16.7%
a 1
16.7%
t 1
16.7%
u 1
16.7%
i 1
16.7%
g 1
16.7%
Other Punctuation
ValueCountFrequency (%)
· 2261
99.8%
/ 5
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 411
100.0%
Open Punctuation
ValueCountFrequency (%)
( 411
100.0%
Space Separator
ValueCountFrequency (%)
111
100.0%
Decimal Number
ValueCountFrequency (%)
2 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 56581
94.1%
Common 3206
 
5.3%
Latin 342
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 4174
 
7.4%
타 3200
 
5.7%
스 2196
 
3.9%
품 1922
 
3.4%
용 1707
 
3.0%
서 1702
 
3.0%
비 1650
 
2.9%
여 1495
 
2.6%
류 1344
 
2.4%
보 1191
 
2.1%
Other values (425) 36000
63.6%
Latin
ValueCountFrequency (%)
V 94
27.5%
T 87
25.4%
C 40
11.7%
P 37
 
10.8%
D 34
 
9.9%
L 14
 
4.1%
G 7
 
2.0%
S 6
 
1.8%
E 6
 
1.8%
W 5
 
1.5%
Other values (10) 12
 
3.5%
Common
ValueCountFrequency (%)
· 2261
70.5%
) 411
 
12.8%
( 411
 
12.8%
111
 
3.5%
2 7
 
0.2%
/ 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 56581
94.1%
None 2261
 
3.8%
ASCII 1287
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 4174
 
7.4%
타 3200
 
5.7%
스 2196
 
3.9%
품 1922
 
3.4%
용 1707
 
3.0%
서 1702
 
3.0%
비 1650
 
2.9%
여 1495
 
2.6%
류 1344
 
2.4%
보 1191
 
2.1%
Other values (425) 36000
63.6%
None
ValueCountFrequency (%)
· 2261
100.0%
ASCII
ValueCountFrequency (%)
) 411
31.9%
( 411
31.9%
111
 
8.6%
V 94
 
7.3%
T 87
 
6.8%
C 40
 
3.1%
P 37
 
2.9%
D 34
 
2.6%
L 14
 
1.1%
G 7
 
0.5%
Other values (15) 41
 
3.2%

사건제목(ACCIDENT_TITLE)
Text

Distinct9629
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:06:51.240964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length233
Median length71
Mean length22.6212
Min length2

Characters and Unicode

Total characters226212
Distinct characters1075
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9507 ?
Unique (%)95.1%

Sample

1st row마스크 구입 후 사업자 일방적인 취소 부당하다
2nd row배송 받지 못한 마스크 피해
3rd row제품 불량에 대한 해당 사업자의 수선 및 환불거부
4th row블라인드 주문제작후 배송지연으로 취소요청
5th row광고와 다름
ValueCountFrequency (%)
문의 2071
 
3.7%
환불 1557
 
2.8%
요청 923
 
1.6%
후 883
 
1.6%
취소 845
 
1.5%
반품 630
 
1.1%
마스크 626
 
1.1%
인한 607
 
1.1%
건 570
 
1.0%
항공권 461
 
0.8%
Other values (13909) 47216
83.7%
2023-12-13T05:06:51.687048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49654
 
22.0%
불 4311
 
1.9%
의 4171
 
1.8%
문 3908
 
1.7%
환 3674
 
1.6%
구 3488
 
1.5%
로 3457
 
1.5%
한 3016
 
1.3%
지 2988
 
1.3%
이 2956
 
1.3%
Other values (1065) 144589
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 171492
75.8%
Space Separator 49654
 
22.0%
Decimal Number 1675
 
0.7%
Other Punctuation 1114
 
0.5%
Close Punctuation 699
 
0.3%
Uppercase Letter 661
 
0.3%
Open Punctuation 407
 
0.2%
Lowercase Letter 310
 
0.1%
Dash Punctuation 146
 
0.1%
Math Symbol 43
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
불 4311
 
2.5%
의 4171
 
2.4%
문 3908
 
2.3%
환 3674
 
2.1%
구 3488
 
2.0%
로 3457
 
2.0%
한 3016
 
1.8%
지 2988
 
1.7%
이 2956
 
1.7%
요 2883
 
1.7%
Other values (981) 136640
79.7%
Uppercase Letter
ValueCountFrequency (%)
T 94
14.2%
S 94
14.2%
A 79
12.0%
L 61
9.2%
V 53
 
8.0%
P 33
 
5.0%
X 33
 
5.0%
G 24
 
3.6%
C 24
 
3.6%
D 23
 
3.5%
Other values (15) 143
21.6%
Lowercase Letter
ValueCountFrequency (%)
s 48
15.5%
a 42
13.5%
t 30
 
9.7%
v 24
 
7.7%
c 18
 
5.8%
g 15
 
4.8%
k 15
 
4.8%
p 15
 
4.8%
e 14
 
4.5%
n 12
 
3.9%
Other values (14) 77
24.8%
Other Punctuation
ValueCountFrequency (%)
. 820
73.6%
/ 194
 
17.4%
% 35
 
3.1%
' 20
 
1.8%
* 16
 
1.4%
! 12
 
1.1%
‰ 7
 
0.6%
: 6
 
0.5%
# 1
 
0.1%
\ 1
 
0.1%
Other values (2) 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 540
32.2%
0 284
17.0%
2 230
13.7%
3 195
 
11.6%
9 144
 
8.6%
5 109
 
6.5%
4 78
 
4.7%
7 43
 
2.6%
8 29
 
1.7%
6 23
 
1.4%
Math Symbol
ValueCountFrequency (%)
+ 14
32.6%
> 13
30.2%
< 9
20.9%
~ 6
14.0%
= 1
 
2.3%
Close Punctuation
ValueCountFrequency (%)
) 559
80.0%
] 140
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 269
66.1%
[ 138
33.9%
Space Separator
ValueCountFrequency (%)
49654
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 146
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 171490
75.8%
Common 53749
 
23.8%
Latin 971
 
0.4%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
불 4311
 
2.5%
의 4171
 
2.4%
문 3908
 
2.3%
환 3674
 
2.1%
구 3488
 
2.0%
로 3457
 
2.0%
한 3016
 
1.8%
지 2988
 
1.7%
이 2956
 
1.7%
요 2883
 
1.7%
Other values (980) 136638
79.7%
Latin
ValueCountFrequency (%)
T 94
 
9.7%
S 94
 
9.7%
A 79
 
8.1%
L 61
 
6.3%
V 53
 
5.5%
s 48
 
4.9%
a 42
 
4.3%
P 33
 
3.4%
X 33
 
3.4%
t 30
 
3.1%
Other values (39) 404
41.6%
Common
ValueCountFrequency (%)
49654
92.4%
. 820
 
1.5%
) 559
 
1.0%
1 540
 
1.0%
0 284
 
0.5%
( 269
 
0.5%
2 230
 
0.4%
3 195
 
0.4%
/ 194
 
0.4%
- 146
 
0.3%
Other values (25) 858
 
1.6%
Han
ValueCountFrequency (%)
件 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 171470
75.8%
ASCII 54712
 
24.2%
Compat Jamo 20
 
< 0.1%
Punctuation 8
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49654
90.8%
. 820
 
1.5%
) 559
 
1.0%
1 540
 
1.0%
0 284
 
0.5%
( 269
 
0.5%
2 230
 
0.4%
3 195
 
0.4%
/ 194
 
0.4%
- 146
 
0.3%
Other values (72) 1821
 
3.3%
Hangul
ValueCountFrequency (%)
불 4311
 
2.5%
의 4171
 
2.4%
문 3908
 
2.3%
환 3674
 
2.1%
구 3488
 
2.0%
로 3457
 
2.0%
한 3016
 
1.8%
지 2988
 
1.7%
이 2956
 
1.7%
요 2883
 
1.7%
Other values (965) 136618
79.7%
Punctuation
ValueCountFrequency (%)
‰ 7
87.5%
’ 1
 
12.5%
Compat Jamo
ValueCountFrequency (%)
ㅡ 4
20.0%
ㅇ 2
 
10.0%
ㅏ 2
 
10.0%
ㅈ 1
 
5.0%
ㅜ 1
 
5.0%
ㅓ 1
 
5.0%
ㅣ 1
 
5.0%
ㅍ 1
 
5.0%
ㅂ 1
 
5.0%
ㅌ 1
 
5.0%
Other values (5) 5
25.0%
CJK
ValueCountFrequency (%)
件 2
100.0%

처리결과코드(PRCS_RESULT_CODE)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540.3453
Minimum401
Maximum612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:06:51.801084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum401
5-th percentile501
Q1502
median510
Q3603
95-th percentile610
Maximum612
Range211
Interquartile range (IQR)101

Descriptive statistics

Standard deviation45.774663
Coefficient of variation (CV)0.084713725
Kurtosis-1.3473183
Mean540.3453
Median Absolute Deviation (MAD)9
Skewness0.64725491
Sum5403453
Variance2095.3198
MonotonicityNot monotonic
2023-12-13T05:06:51.911439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
509 1611
16.1%
501 1530
15.3%
527 1468
14.7%
502 1336
13.4%
603 1263
12.6%
610 851
8.5%
604 290
 
2.9%
505 226
 
2.3%
605 205
 
2.1%
504 192
 
1.9%
Other values (16) 1028
10.3%
ValueCountFrequency (%)
401 11
 
0.1%
501 1530
15.3%
502 1336
13.4%
504 192
 
1.9%
505 226
 
2.3%
506 16
 
0.2%
507 74
 
0.7%
509 1611
16.1%
510 111
 
1.1%
511 80
 
0.8%
ValueCountFrequency (%)
612 10
 
0.1%
611 3
 
< 0.1%
610 851
8.5%
609 144
 
1.4%
608 123
 
1.2%
607 108
 
1.1%
606 72
 
0.7%
605 205
 
2.1%
604 290
 
2.9%
603 1263
12.6%

처리결과(PRCS_RESULT)
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타정보제공
1611 
분쟁해결기준설명
1530 
피해구제접수안내
1468 
법.제도설명
1336 
환급
1263 
Other values (21)
2792 

Length

Max length11
Median length9
Mean length5.9577
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row부당행위시정
2nd row합의불성립
3rd row기타정보제공
4th row분쟁해결기준설명
5th row법.제도설명

Common Values

ValueCountFrequency (%)
기타정보제공 1611
16.1%
분쟁해결기준설명 1530
15.3%
피해구제접수안내 1468
14.7%
법.제도설명 1336
13.4%
환급 1263
12.6%
합의불성립 851
8.5%
계약이행 290
 
2.9%
시장정보제공 226
 
2.3%
계약해제.해지 205
 
2.1%
상품정보제공 192
 
1.9%
Other values (16) 1028
10.3%

Length

2023-12-13T05:06:52.025524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타정보제공 1611
15.9%
분쟁해결기준설명 1530
15.1%
피해구제접수안내 1468
14.5%
법.제도설명 1336
13.2%
환급 1263
12.5%
합의불성립 851
8.4%
계약이행 290
 
2.9%
시장정보제공 226
 
2.2%
계약해제.해지 205
 
2.0%
상품정보제공 192
 
1.9%
Other values (18) 1140
11.3%

Interactions

2023-12-13T05:06:45.937390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.499420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.033351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.497674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.034041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.622674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.160856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.615461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.146136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.773998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.271144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.716330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.256862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.912102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.380540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.828197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:06:52.099338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드(GENDER_CODE)성별(GENDER)연령대코드(AGE_GROUP_CODE)연령대(AGE_GROUP_NAME)지역코드(AREA_CODE)품목코드(ITEM_CODE)처리결과코드(PRCS_RESULT_CODE)처리결과(PRCS_RESULT)
성별코드(GENDER_CODE)1.0001.0000.0890.1180.0000.2910.1020.104
성별(GENDER)1.0001.0000.0890.1180.0000.2910.1020.104
연령대코드(AGE_GROUP_CODE)0.0890.0891.0001.0000.2830.0780.1340.181
연령대(AGE_GROUP_NAME)0.1180.1181.0001.0000.2130.1060.1420.182
지역코드(AREA_CODE)0.0000.0000.2830.2131.0000.0860.0680.175
품목코드(ITEM_CODE)0.2910.2910.0780.1060.0861.0000.1940.280
처리결과코드(PRCS_RESULT_CODE)0.1020.1020.1340.1420.0680.1941.0001.000
처리결과(PRCS_RESULT)0.1040.1040.1810.1820.1750.2801.0001.000
2023-12-13T05:06:52.211356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드(GENDER_CODE)연령대(AGE_GROUP_NAME)처리결과(PRCS_RESULT)성별(GENDER)
성별코드(GENDER_CODE)1.0000.0900.0901.000
연령대(AGE_GROUP_NAME)0.0901.0000.0670.090
처리결과(PRCS_RESULT)0.0900.0671.0000.090
성별(GENDER)1.0000.0900.0901.000
2023-12-13T05:06:52.322746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드(AGE_GROUP_CODE)지역코드(AREA_CODE)품목코드(ITEM_CODE)처리결과코드(PRCS_RESULT_CODE)성별코드(GENDER_CODE)성별(GENDER)연령대(AGE_GROUP_NAME)처리결과(PRCS_RESULT)
연령대코드(AGE_GROUP_CODE)1.0000.060-0.0530.0640.0850.0851.0000.071
지역코드(AREA_CODE)0.0601.000-0.0170.0810.0000.0000.1290.089
품목코드(ITEM_CODE)-0.053-0.0171.000-0.1780.2090.2090.0440.129
처리결과코드(PRCS_RESULT_CODE)0.0640.081-0.1781.0000.0700.0700.0740.999
성별코드(GENDER_CODE)0.0850.0000.2090.0701.0001.0000.0900.090
성별(GENDER)0.0850.0000.2090.0701.0001.0000.0900.090
연령대(AGE_GROUP_NAME)1.0000.1290.0440.0740.0900.0901.0000.067
처리결과(PRCS_RESULT)0.0710.0890.1290.9990.0900.0900.0671.000

Missing values

2023-12-13T05:06:46.413799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:06:46.613755image/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.
2023-12-13T05:06:46.789762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

사건번호(ACCIDENT_NO)접수일자(RCPT_YMD)성별코드(GENDER_CODE)성별(GENDER)연령대코드(AGE_GROUP_CODE)연령대(AGE_GROUP_NAME)지역코드(AREA_CODE)지역명(AREA_NAME)품목코드(ITEM_CODE)품목명(ITEM_NAME)사건제목(ACCIDENT_TITLE)처리결과코드(PRCS_RESULT_CODE)처리결과(PRCS_RESULT)
335552020-01317052020-02-281남성540 - 49세1100충청남도189999기타보건·위생용품마스크 구입 후 사업자 일방적인 취소 부당하다607부당행위시정
250662020-01010702020-02-171남성540 - 49세104강서구189999기타보건·위생용품배송 받지 못한 마스크 피해610합의불성립
439042020-01553962020-03-101남성430 - 39세115서초구171004운동화제품 불량에 대한 해당 사업자의 수선 및 환불거부509기타정보제공
76942020-00348132020-01-201남성540 - 49세1400경상북도150606블라인드블라인드 주문제작후 배송지연으로 취소요청501분쟁해결기준설명
24652020-00089472020-01-061남성540 - 49세809성남시410302인터넷교육서비스광고와 다름502법.제도설명
37472020-00172432020-01-092여성430 - 39세800경기도140401침대네이버 침대 반품 요청 문의610합의불성립
657612020-02379462020-04-202여성1160 - 64세1423칠곡군170208원피스레몬트리)원피스 주문 후 배송지연으로 환불 요청610합의불성립
48672020-00237912020-01-142여성320 - 29세102강동구370206민박기타팬션 예약후 취소 거부527피해구제접수안내
128862020-00558582020-01-302여성650 - 59세800경기도370102국외여행코로나바이러스로 인한 국외여행 취소시 위약금509기타정보제공
562512020-01965072020-03-302여성650 - 59세303동구170303셔츠레몬트리)옷 주문 후 배송지연으로 환불 요청603환급
사건번호(ACCIDENT_NO)접수일자(RCPT_YMD)성별코드(GENDER_CODE)성별(GENDER)연령대코드(AGE_GROUP_CODE)연령대(AGE_GROUP_NAME)지역코드(AREA_CODE)지역명(AREA_NAME)품목코드(ITEM_CODE)품목명(ITEM_NAME)사건제목(ACCIDENT_TITLE)처리결과코드(PRCS_RESULT_CODE)처리결과(PRCS_RESULT)
643352020-02341462020-04-171남성540 - 49세205동래구360404인터넷정보이용서비스주식 리딩업체 계약해지527피해구제접수안내
266982020-01079902020-02-201남성1160 - 64세100서울특별시350206택배화물운송서비스택배 중 분실 - 보상501분쟁해결기준설명
538882020-02126822020-04-062여성430 - 39세1105아산시350104항공여객운송서비스항공권 결제수단 환불이 아닌 바우처 환불 강행603환급
554332020-02055752020-04-022여성1160 - 64세1319장성군210303복합기복합기 구매 후 사이즈 확인을 위해 개봉 시 반품 가능 여부501분쟁해결기준설명
456612020-01805172020-03-202여성650 - 59세831화성시299999기타미분류물품휴대폰 문자 스미싱 피해505시장정보제공
161832020-00691212020-02-042여성650 - 59세831화성시370102국외여행필리핀 해외여행 취소시 위약금 과다청구로 부당하여 환급 기준 문의501분쟁해결기준설명
172492020-00647062020-02-031남성430 - 39세824김포시189999기타보건·위생용품온라인구매 마스크 취소처리중으로 표시됨502법.제도설명
54002020-00252112020-01-142여성430 - 39세1506양산시370408헬스장운동프로그램 중도해지 환불문의527피해구제접수안내
508932020-01948422020-03-272여성540 - 49세100서울특별시350104항공여객운송서비스신종코로나로 인한 항공권 환불요청527피해구제접수안내
328832020-01336282020-03-022여성540 - 49세1007영동군500703복권복권구매 7일전 해지요청505시장정보제공