Overview

Dataset statistics

Number of variables6
Number of observations56
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory54.4 B

Variable types

Numeric2
Text1
Categorical3

Dataset

Description경기주택도시공사 GH주택청약센터의 은행정보로써 은행코드, 은행명, 사용여부, 등록일시, 수정일시 등의 정보를 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15119419/fileData.do

Alerts

사용여부 has constant value ""Constant
기타유의사항 has constant value ""Constant
구분 is highly overall correlated with 은행코드High correlation
은행코드 is highly overall correlated with 구분High correlation
협약여부 is highly imbalanced (50.9%)Imbalance
구분 has unique valuesUnique
은행코드 has unique valuesUnique
은행명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:13:31.113460
Analysis finished2023-12-12 05:13:31.835358
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.5
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T14:13:31.903812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.75
Q114.75
median28.5
Q342.25
95-th percentile53.25
Maximum56
Range55
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation16.309506
Coefficient of variation (CV)0.57226338
Kurtosis-1.2
Mean28.5
Median Absolute Deviation (MAD)14
Skewness0
Sum1596
Variance266
MonotonicityStrictly increasing
2023-12-12T14:13:32.029720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.8%
30 1
 
1.8%
32 1
 
1.8%
33 1
 
1.8%
34 1
 
1.8%
35 1
 
1.8%
36 1
 
1.8%
37 1
 
1.8%
38 1
 
1.8%
39 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
1 1
1.8%
2 1
1.8%
3 1
1.8%
4 1
1.8%
5 1
1.8%
6 1
1.8%
7 1
1.8%
8 1
1.8%
9 1
1.8%
10 1
1.8%
ValueCountFrequency (%)
56 1
1.8%
55 1
1.8%
54 1
1.8%
53 1
1.8%
52 1
1.8%
51 1
1.8%
50 1
1.8%
49 1
1.8%
48 1
1.8%
47 1
1.8%

은행코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.60714
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2023-12-12T14:13:32.165532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.25
Q138.5
median84.5
Q3264.25
95-th percentile291.25
Maximum999
Range997
Interquartile range (IQR)225.75

Descriptive statistics

Standard deviation159.90778
Coefficient of variation (CV)1.0342845
Kurtosis12.914773
Mean154.60714
Median Absolute Deviation (MAD)79
Skewness2.7074566
Sum8658
Variance25570.497
MonotonicityStrictly increasing
2023-12-12T14:13:32.301956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
1.8%
89 1
 
1.8%
209 1
 
1.8%
218 1
 
1.8%
227 1
 
1.8%
238 1
 
1.8%
240 1
 
1.8%
243 1
 
1.8%
247 1
 
1.8%
261 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
2 1
1.8%
3 1
1.8%
4 1
1.8%
7 1
1.8%
11 1
1.8%
12 1
1.8%
20 1
1.8%
23 1
1.8%
27 1
1.8%
31 1
1.8%
ValueCountFrequency (%)
999 1
1.8%
294 1
1.8%
292 1
1.8%
291 1
1.8%
290 1
1.8%
287 1
1.8%
280 1
1.8%
279 1
1.8%
278 1
1.8%
270 1
1.8%

은행명
Text

UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
2023-12-12T14:13:32.540160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.1964286
Min length3

Characters and Unicode

Total characters291
Distinct characters116
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)100.0%

Sample

1st row산업은행
2nd row기업은행
3rd row국민은행
4th row수협중앙회
5th row농협은행
ValueCountFrequency (%)
산업은행 1
 
1.8%
기업은행 1
 
1.8%
키움증권 1
 
1.8%
카카오뱅크 1
 
1.8%
유안타증권 1
 
1.8%
kb증권 1
 
1.8%
ktb투자증권 1
 
1.8%
미래에셋대우 1
 
1.8%
삼성증권 1
 
1.8%
한국투자증권 1
 
1.8%
Other values (46) 46
82.1%
2023-12-12T14:13:33.203985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
은 24
 
8.2%
행 24
 
8.2%
권 19
 
6.5%
증 19
 
6.5%
자 11
 
3.8%
투 11
 
3.8%
국 7
 
2.4%
이 6
 
2.1%
B 6
 
2.1%
한 5
 
1.7%
Other values (106) 159
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 265
91.1%
Uppercase Letter 25
 
8.6%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
은 24
 
9.1%
행 24
 
9.1%
권 19
 
7.2%
증 19
 
7.2%
자 11
 
4.2%
투 11
 
4.2%
국 7
 
2.6%
이 6
 
2.3%
한 5
 
1.9%
신 5
 
1.9%
Other values (93) 134
50.6%
Uppercase Letter
ValueCountFrequency (%)
B 6
24.0%
S 3
12.0%
K 3
12.0%
N 2
 
8.0%
P 2
 
8.0%
H 2
 
8.0%
C 2
 
8.0%
T 1
 
4.0%
D 1
 
4.0%
A 1
 
4.0%
Other values (2) 2
 
8.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 265
91.1%
Latin 25
 
8.6%
Common 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
은 24
 
9.1%
행 24
 
9.1%
권 19
 
7.2%
증 19
 
7.2%
자 11
 
4.2%
투 11
 
4.2%
국 7
 
2.6%
이 6
 
2.3%
한 5
 
1.9%
신 5
 
1.9%
Other values (93) 134
50.6%
Latin
ValueCountFrequency (%)
B 6
24.0%
S 3
12.0%
K 3
12.0%
N 2
 
8.0%
P 2
 
8.0%
H 2
 
8.0%
C 2
 
8.0%
T 1
 
4.0%
D 1
 
4.0%
A 1
 
4.0%
Other values (2) 2
 
8.0%
Common
ValueCountFrequency (%)
/ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 265
91.1%
ASCII 26
 
8.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
은 24
 
9.1%
행 24
 
9.1%
권 19
 
7.2%
증 19
 
7.2%
자 11
 
4.2%
투 11
 
4.2%
국 7
 
2.6%
이 6
 
2.3%
한 5
 
1.9%
신 5
 
1.9%
Other values (93) 134
50.6%
ASCII
ValueCountFrequency (%)
B 6
23.1%
S 3
11.5%
K 3
11.5%
N 2
 
7.7%
P 2
 
7.7%
H 2
 
7.7%
C 2
 
7.7%
T 1
 
3.8%
/ 1
 
3.8%
D 1
 
3.8%
Other values (3) 3
11.5%

사용여부
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
1
56 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 56
100.0%

Length

2023-12-12T14:13:33.354620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:13:33.458892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 56
100.0%

협약여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size580.0 B
1
50 
2
6 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 50
89.3%
2 6
 
10.7%

Length

2023-12-12T14:13:33.583201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:13:33.693464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
89.3%
2 6
 
10.7%

기타유의사항
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
공란은 데이터 미존재
56 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공란은 데이터 미존재
2nd row공란은 데이터 미존재
3rd row공란은 데이터 미존재
4th row공란은 데이터 미존재
5th row공란은 데이터 미존재

Common Values

ValueCountFrequency (%)
공란은 데이터 미존재 56
100.0%

Length

2023-12-12T14:13:33.799577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:13:33.924102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공란은 56
33.3%
데이터 56
33.3%
미존재 56
33.3%

Interactions

2023-12-12T14:13:31.448099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:31.286123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:31.539310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:31.373955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:13:33.990827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분은행코드은행명협약여부
구분1.0000.7681.0000.446
은행코드0.7681.0001.0000.153
은행명1.0001.0001.0001.000
협약여부0.4460.1531.0001.000
2023-12-12T14:13:34.106667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분은행코드협약여부
구분1.0001.0000.313
은행코드1.0001.0000.248
협약여부0.3130.2481.000

Missing values

2023-12-12T14:13:31.683943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:13:31.795724image/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

구분은행코드은행명사용여부협약여부기타유의사항
012산업은행11공란은 데이터 미존재
123기업은행12공란은 데이터 미존재
234국민은행12공란은 데이터 미존재
347수협중앙회11공란은 데이터 미존재
4511농협은행12공란은 데이터 미존재
5612지역농축협11공란은 데이터 미존재
6720우리은행12공란은 데이터 미존재
7823SC은행11공란은 데이터 미존재
8927한국씨티은행11공란은 데이터 미존재
91031대구은행11공란은 데이터 미존재
구분은행코드은행명사용여부협약여부기타유의사항
4647270하나금융투자11공란은 데이터 미존재
4748278신한금융투자11공란은 데이터 미존재
4849279DB금융투자11공란은 데이터 미존재
4950280유진투자증권11공란은 데이터 미존재
5051287메리츠종합금융증권11공란은 데이터 미존재
5152290부국증권11공란은 데이터 미존재
5253291신영증권11공란은 데이터 미존재
5354292케이프투자증권11공란은 데이터 미존재
5455294펀드온라인코리아11공란은 데이터 미존재
5556999캐피탈/보험사11공란은 데이터 미존재