Overview

Dataset statistics

Number of variables4
Number of observations709
Missing cells13
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.7 KiB
Average record size in memory34.2 B

Variable types

Numeric2
Text1
Categorical1

Dataset

Description공정거래위원회의 소비자 민원학습에대한 데이터로, 소비자 민원을 처리하는 상담기관의 정보를 보여주는 데이터입니다. 이 데이터는 기관명, 상위기관을 포함하고 있습니다.
Author공정거래위원회
URLhttps://www.data.go.kr/data/15098353/fileData.do

Alerts

상위기관(PARENT_INSTITUTION) is highly overall correlated with 기관소재지(LOCATION_INSTITUTION)High correlation
기관소재지(LOCATION_INSTITUTION) is highly overall correlated with 상위기관(PARENT_INSTITUTION)High correlation
상위기관(PARENT_INSTITUTION) has 11 (1.6%) missing valuesMissing
기관코드(INSTITUTION_CODE) has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:11:03.749348
Analysis finished2023-12-12 11:11:05.181921
Duration1.43 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관코드(INSTITUTION_CODE)
Real number (ℝ)

UNIQUE 

Distinct709
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44768.271
Minimum10000
Maximum80000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-12-12T20:11:05.301695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile30204.4
Q131211
median40617
Q360201
95-th percentile61420.6
Maximum80000
Range70000
Interquartile range (IQR)28990

Descriptive statistics

Standard deviation13027.292
Coefficient of variation (CV)0.29099387
Kurtosis-1.4839668
Mean44768.271
Median Absolute Deviation (MAD)10314
Skewness0.1388662
Sum31740704
Variance1.6971034 × 108
MonotonicityNot monotonic
2023-12-12T20:11:05.522425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30810 1
 
0.1%
31602 1
 
0.1%
30302 1
 
0.1%
30305 1
 
0.1%
30306 1
 
0.1%
30403 1
 
0.1%
30406 1
 
0.1%
30408 1
 
0.1%
30500 1
 
0.1%
30504 1
 
0.1%
Other values (699) 699
98.6%
ValueCountFrequency (%)
10000 1
0.1%
10100 1
0.1%
20000 1
0.1%
20200 1
0.1%
30000 1
0.1%
30100 1
0.1%
30101 1
0.1%
30102 1
0.1%
30103 1
0.1%
30104 1
0.1%
ValueCountFrequency (%)
80000 1
0.1%
70300 1
0.1%
70200 1
0.1%
70100 1
0.1%
70001 1
0.1%
70000 1
0.1%
61604 1
0.1%
61603 1
0.1%
61602 1
0.1%
61601 1
0.1%

기관명(INSTITUTION_NAME)
Text

Distinct682
Distinct (%)96.5%
Missing2
Missing (%)0.3%
Memory size5.7 KiB
2023-12-12T20:11:06.028738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length7.9929279
Min length2

Characters and Unicode

Total characters5651
Distinct characters319
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

Unique671 ?
Unique (%)94.9%

Sample

1st row동두천시청
2nd row안산시청
3rd row안성시청
4th row여주군청
5th row오산시청
ValueCountFrequency (%)
농협중앙회 176
 
17.6%
한국여성소비자연합 31
 
3.1%
소비자교육중앙회 27
 
2.7%
녹색소비자연대 15
 
1.5%
소비자시민모임 11
 
1.1%
한국소비자연맹 10
 
1.0%
사)한국부인회 10
 
1.0%
사)소비자공익네트워크 9
 
0.9%
중구청 6
 
0.6%
동구청 6
 
0.6%
Other values (663) 697
69.8%
2023-12-12T20:11:06.706310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
292
 
5.2%
청 272
 
4.8%
부 261
 
4.6%
회 242
 
4.3%
지 242
 
4.3%
중 212
 
3.8%
앙 205
 
3.6%
군 196
 
3.5%
시 182
 
3.2%
협 180
 
3.2%
Other values (309) 3367
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4959
87.8%
Space Separator 292
 
5.2%
Uppercase Letter 144
 
2.5%
Close Punctuation 114
 
2.0%
Open Punctuation 114
 
2.0%
Lowercase Letter 17
 
0.3%
Other Punctuation 10
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 272
 
5.5%
부 261
 
5.3%
회 242
 
4.9%
지 242
 
4.9%
중 212
 
4.3%
앙 205
 
4.1%
군 196
 
4.0%
시 182
 
3.7%
협 180
 
3.6%
농 178
 
3.6%
Other values (274) 2789
56.2%
Uppercase Letter
ValueCountFrequency (%)
C 28
19.4%
Y 24
16.7%
A 24
16.7%
W 14
9.7%
M 10
 
6.9%
S 8
 
5.6%
G 8
 
5.6%
L 8
 
5.6%
K 7
 
4.9%
T 3
 
2.1%
Other values (7) 10
 
6.9%
Lowercase Letter
ValueCountFrequency (%)
n 4
23.5%
i 3
17.6%
y 2
11.8%
e 1
 
5.9%
l 1
 
5.9%
g 1
 
5.9%
r 1
 
5.9%
a 1
 
5.9%
o 1
 
5.9%
j 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
/ 7
70.0%
. 2
 
20.0%
& 1
 
10.0%
Space Separator
ValueCountFrequency (%)
292
100.0%
Close Punctuation
ValueCountFrequency (%)
) 114
100.0%
Open Punctuation
ValueCountFrequency (%)
( 114
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4959
87.8%
Common 531
 
9.4%
Latin 161
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 272
 
5.5%
부 261
 
5.3%
회 242
 
4.9%
지 242
 
4.9%
중 212
 
4.3%
앙 205
 
4.1%
군 196
 
4.0%
시 182
 
3.7%
협 180
 
3.6%
농 178
 
3.6%
Other values (274) 2789
56.2%
Latin
ValueCountFrequency (%)
C 28
17.4%
Y 24
14.9%
A 24
14.9%
W 14
8.7%
M 10
 
6.2%
S 8
 
5.0%
G 8
 
5.0%
L 8
 
5.0%
K 7
 
4.3%
n 4
 
2.5%
Other values (18) 26
16.1%
Common
ValueCountFrequency (%)
292
55.0%
) 114
 
21.5%
( 114
 
21.5%
/ 7
 
1.3%
. 2
 
0.4%
& 1
 
0.2%
- 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4959
87.8%
ASCII 692
 
12.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
292
42.2%
) 114
 
16.5%
( 114
 
16.5%
C 28
 
4.0%
Y 24
 
3.5%
A 24
 
3.5%
W 14
 
2.0%
M 10
 
1.4%
S 8
 
1.2%
G 8
 
1.2%
Other values (25) 56
 
8.1%
Hangul
ValueCountFrequency (%)
청 272
 
5.5%
부 261
 
5.3%
회 242
 
4.9%
지 242
 
4.9%
중 212
 
4.3%
앙 205
 
4.1%
군 196
 
4.0%
시 182
 
3.7%
협 180
 
3.6%
농 178
 
3.6%
Other values (274) 2789
56.2%

상위기관(PARENT_INSTITUTION)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)2.4%
Missing11
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean796.13181
Minimum100
Maximum1700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-12-12T20:11:06.892883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1200
median800
Q31200
95-th percentile1500
Maximum1700
Range1600
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation500.67323
Coefficient of variation (CV)0.62888233
Kurtosis-1.3436843
Mean796.13181
Median Absolute Deviation (MAD)500
Skewness-0.16196191
Sum555700
Variance250673.68
MonotonicityNot monotonic
2023-12-12T20:11:07.078244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100 158
22.3%
800 104
14.7%
1500 53
 
7.5%
1400 53
 
7.5%
1300 50
 
7.1%
1200 46
 
6.5%
1100 45
 
6.3%
900 42
 
5.9%
1000 33
 
4.7%
200 25
 
3.5%
Other values (7) 89
12.6%
ValueCountFrequency (%)
100 158
22.3%
200 25
 
3.5%
300 17
 
2.4%
400 17
 
2.4%
500 16
 
2.3%
600 15
 
2.1%
700 11
 
1.6%
800 104
14.7%
900 42
 
5.9%
1000 33
 
4.7%
ValueCountFrequency (%)
1700 1
 
0.1%
1600 12
 
1.7%
1500 53
7.5%
1400 53
7.5%
1300 50
7.1%
1200 46
6.5%
1100 45
6.3%
1000 33
 
4.7%
900 42
5.9%
800 104
14.7%

기관소재지(LOCATION_INSTITUTION)
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
서울특별시
158 
경기도
104 
경상북도
53 
경상남도
53 
전라남도
50 
Other values (13)
291 

Length

Max length7
Median length5
Mean length4.2143865
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 158
22.3%
경기도 104
14.7%
경상북도 53
 
7.5%
경상남도 53
 
7.5%
전라남도 50
 
7.1%
전라북도 46
 
6.5%
충청남도 45
 
6.3%
강원도 42
 
5.9%
충청북도 33
 
4.7%
부산광역시 25
 
3.5%
Other values (8) 100
14.1%

Length

2023-12-12T20:11:07.279788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 158
22.3%
경기도 104
14.7%
경상북도 53
 
7.5%
경상남도 53
 
7.5%
전라남도 50
 
7.1%
전라북도 46
 
6.5%
충청남도 45
 
6.3%
강원도 42
 
5.9%
충청북도 33
 
4.7%
부산광역시 25
 
3.5%
Other values (8) 100
14.1%

Interactions

2023-12-12T20:11:04.476103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:11:04.121467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:11:04.654683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:11:04.292240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:11:07.433416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관코드(INSTITUTION_CODE)상위기관(PARENT_INSTITUTION)기관소재지(LOCATION_INSTITUTION)
기관코드(INSTITUTION_CODE)1.0000.7360.786
상위기관(PARENT_INSTITUTION)0.7361.0001.000
기관소재지(LOCATION_INSTITUTION)0.7861.0001.000
2023-12-12T20:11:07.589925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관코드(INSTITUTION_CODE)상위기관(PARENT_INSTITUTION)기관소재지(LOCATION_INSTITUTION)
기관코드(INSTITUTION_CODE)1.0000.1370.455
상위기관(PARENT_INSTITUTION)0.1371.0000.995
기관소재지(LOCATION_INSTITUTION)0.4550.9951.000

Missing values

2023-12-12T20:11:04.841287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:11:04.962942image/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.
2023-12-12T20:11:05.089251image/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

기관코드(INSTITUTION_CODE)기관명(INSTITUTION_NAME)상위기관(PARENT_INSTITUTION)기관소재지(LOCATION_INSTITUTION)
030810동두천시청800경기도
130817안산시청800경기도
230818안성시청800경기도
330822여주군청800경기도
430824오산시청800경기도
530825옹진군청400인천광역시
630826용인시청800경기도
730833하남시청800경기도
830900강원도청900강원도
930902고성군청900강원도
기관코드(INSTITUTION_CODE)기관명(INSTITUTION_NAME)상위기관(PARENT_INSTITUTION)기관소재지(LOCATION_INSTITUTION)
69955582한국디지털위성방송(주)800경기도
70055583KT-PCS100서울특별시
70155584건국유업100서울특별시
70255585종근당건강(주)100서울특별시
70355586(주)현대택배100서울특별시
70455592(주)두루넷100서울특별시
70555588<NA><NA><NA>
70655589(주)엔시소프트100서울특별시
70755590청호나이스(주)800경기도
70855591(주)코리아홈쇼핑800경기도