Overview

Dataset statistics

Number of variables16
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory134.4 B

Variable types

DateTime1
Categorical3
Numeric2
Text4
Boolean6

Dataset

Description샘플 데이터
Author경기신용보증재단
URLhttps://bigdata-region.kr/#/dataset/dbcba1db-fdc8-47be-8320-2fe4e402ad96

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
보증금10억원초과여부 has constant value ""Constant
상담수 is highly overall correlated with 중복방문구분명 and 1 other fieldsHigh correlation
중복방문구분명 is highly overall correlated with 상담수High correlation
최근1년방문여부 is highly overall correlated with 상담수High correlation
보증금1억원초과여부 is highly imbalanced (64.7%)Imbalance
사업자등록번호 has unique valuesUnique
기업번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:01:51.252987
Analysis finished2023-12-10 14:01:53.039355
Duration1.79 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-04-01 00:00:00
Maximum2023-04-01 00:00:00
2023-12-10T23:01:53.105166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:53.289177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

성별코드
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
M
22 
F
8 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 22
73.3%
F 8
 
26.7%

Length

2023-12-10T23:01:53.535447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:01:53.704366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 22
73.3%
f 8
 
26.7%

연령대코드
Real number (ℝ)

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.666667
Minimum20
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:53.914068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q140
median50
Q350
95-th percentile60
Maximum70
Range50
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.943353
Coefficient of variation (CV)0.26153327
Kurtosis-0.52189184
Mean45.666667
Median Absolute Deviation (MAD)10
Skewness-0.10370894
Sum1370
Variance142.64368
MonotonicityNot monotonic
2023-12-10T23:01:54.130913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
50 9
30.0%
40 8
26.7%
60 6
20.0%
30 5
16.7%
20 1
 
3.3%
70 1
 
3.3%
ValueCountFrequency (%)
20 1
 
3.3%
30 5
16.7%
40 8
26.7%
50 9
30.0%
60 6
20.0%
70 1
 
3.3%
ValueCountFrequency (%)
70 1
 
3.3%
60 6
20.0%
50 9
30.0%
40 8
26.7%
30 5
16.7%
20 1
 
3.3%

사업자등록번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:01:54.427710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters300
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

Unique30 ?
Unique (%)100.0%

Sample

1st row34410*****
2nd row20620*****
3rd row14009*****
4th row68736*****
5th row12591*****
ValueCountFrequency (%)
34410 1
 
3.3%
20620 1
 
3.3%
12932 1
 
3.3%
12402 1
 
3.3%
82586 1
 
3.3%
82002 1
 
3.3%
20630 1
 
3.3%
20354 1
 
3.3%
13525 1
 
3.3%
12735 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:01:54.889280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 150
50.0%
2 29
 
9.7%
1 26
 
8.7%
0 23
 
7.7%
3 15
 
5.0%
4 13
 
4.3%
6 12
 
4.0%
5 10
 
3.3%
9 8
 
2.7%
8 8
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 150
50.0%
Decimal Number 150
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29
19.3%
1 26
17.3%
0 23
15.3%
3 15
10.0%
4 13
8.7%
6 12
8.0%
5 10
 
6.7%
9 8
 
5.3%
8 8
 
5.3%
7 6
 
4.0%
Other Punctuation
ValueCountFrequency (%)
* 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 150
50.0%
2 29
 
9.7%
1 26
 
8.7%
0 23
 
7.7%
3 15
 
5.0%
4 13
 
4.3%
6 12
 
4.0%
5 10
 
3.3%
9 8
 
2.7%
8 8
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 150
50.0%
2 29
 
9.7%
1 26
 
8.7%
0 23
 
7.7%
3 15
 
5.0%
4 13
 
4.3%
6 12
 
4.0%
5 10
 
3.3%
9 8
 
2.7%
8 8
 
2.7%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T23:01:55.077554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:01:55.218410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:01:55.448426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.8333333
Min length3

Characters and Unicode

Total characters145
Distinct characters39
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

Unique14 ?
Unique (%)46.7%

Sample

1st row시흥시
2nd row남양주시
3rd row시흥시
4th row안양시 동안구
5th row안성시
ValueCountFrequency (%)
안양시 5
 
11.6%
시흥시 4
 
9.3%
수원시 4
 
9.3%
동안구 3
 
7.0%
김포시 3
 
7.0%
성남시 2
 
4.7%
분당구 2
 
4.7%
만안구 2
 
4.7%
남양주시 2
 
4.7%
하남시 1
 
2.3%
Other values (15) 15
34.9%
2023-12-10T23:01:56.020247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 33
22.8%
안 13
 
9.0%
13
 
9.0%
구 13
 
9.0%
양 7
 
4.8%
남 5
 
3.4%
포 4
 
2.8%
성 4
 
2.8%
원 4
 
2.8%
수 4
 
2.8%
Other values (29) 45
31.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 132
91.0%
Space Separator 13
 
9.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 33
25.0%
안 13
 
9.8%
구 13
 
9.8%
양 7
 
5.3%
남 5
 
3.8%
포 4
 
3.0%
성 4
 
3.0%
원 4
 
3.0%
수 4
 
3.0%
흥 4
 
3.0%
Other values (28) 41
31.1%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
91.0%
Common 13
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 33
25.0%
안 13
 
9.8%
구 13
 
9.8%
양 7
 
5.3%
남 5
 
3.8%
포 4
 
3.0%
성 4
 
3.0%
원 4
 
3.0%
수 4
 
3.0%
흥 4
 
3.0%
Other values (28) 41
31.1%
Common
ValueCountFrequency (%)
13
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 132
91.0%
ASCII 13
 
9.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 33
25.0%
안 13
 
9.8%
구 13
 
9.8%
양 7
 
5.3%
남 5
 
3.8%
포 4
 
3.0%
성 4
 
3.0%
원 4
 
3.0%
수 4
 
3.0%
흥 4
 
3.0%
Other values (28) 41
31.1%
ASCII
ValueCountFrequency (%)
13
100.0%

행정동명
Text

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:01:56.313171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9333333
Min length2

Characters and Unicode

Total characters88
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)70.0%

Sample

1st row은행동
2nd row오남읍
3rd row장곡동
4th row호계동
5th row서운면
ValueCountFrequency (%)
장기동 3
 
10.0%
정자동 2
 
6.7%
호계동 2
 
6.7%
안양동 2
 
6.7%
신천동 1
 
3.3%
은행동 1
 
3.3%
포곡읍 1
 
3.3%
서현동 1
 
3.3%
매탄동 1
 
3.3%
관양동 1
 
3.3%
Other values (15) 15
50.0%
2023-12-10T23:01:56.828051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
27.3%
장 5
 
5.7%
신 4
 
4.5%
면 4
 
4.5%
안 3
 
3.4%
양 3
 
3.4%
기 3
 
3.4%
서 3
 
3.4%
곡 3
 
3.4%
읍 2
 
2.3%
Other values (27) 34
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
27.3%
장 5
 
5.7%
신 4
 
4.5%
면 4
 
4.5%
안 3
 
3.4%
양 3
 
3.4%
기 3
 
3.4%
서 3
 
3.4%
곡 3
 
3.4%
읍 2
 
2.3%
Other values (27) 34
38.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
27.3%
장 5
 
5.7%
신 4
 
4.5%
면 4
 
4.5%
안 3
 
3.4%
양 3
 
3.4%
기 3
 
3.4%
서 3
 
3.4%
곡 3
 
3.4%
읍 2
 
2.3%
Other values (27) 34
38.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
27.3%
장 5
 
5.7%
신 4
 
4.5%
면 4
 
4.5%
안 3
 
3.4%
양 3
 
3.4%
기 3
 
3.4%
서 3
 
3.4%
곡 3
 
3.4%
읍 2
 
2.3%
Other values (27) 34
38.6%

보증금10억원초과여부
Boolean

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
30 
ValueCountFrequency (%)
False 30
100.0%
2023-12-10T23:01:56.997634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

보증금1억원초과여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
28 
True
 
2
ValueCountFrequency (%)
False 28
93.3%
True 2
 
6.7%
2023-12-10T23:01:57.110476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

보증금5천만원초과여부
Boolean

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
21 
True
9 
ValueCountFrequency (%)
False 21
70.0%
True 9
30.0%
2023-12-10T23:01:57.246269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

보증금2천만원초과여부
Boolean

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
True
19 
False
11 
ValueCountFrequency (%)
True 19
63.3%
False 11
36.7%
2023-12-10T23:01:57.375610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

보증금1천만원초과여부
Boolean

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
True
26 
False
4 
ValueCountFrequency (%)
True 26
86.7%
False 4
 
13.3%
2023-12-10T23:01:57.516348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

상담수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3333333
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:01:57.658942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6.65
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2180037
Coefficient of variation (CV)0.950573
Kurtosis4.8249504
Mean2.3333333
Median Absolute Deviation (MAD)0
Skewness2.16775
Sum70
Variance4.9195402
MonotonicityNot monotonic
2023-12-10T23:01:57.820038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 17
56.7%
2 4
 
13.3%
3 3
 
10.0%
4 2
 
6.7%
5 2
 
6.7%
8 1
 
3.3%
10 1
 
3.3%
ValueCountFrequency (%)
1 17
56.7%
2 4
 
13.3%
3 3
 
10.0%
4 2
 
6.7%
5 2
 
6.7%
8 1
 
3.3%
10 1
 
3.3%
ValueCountFrequency (%)
10 1
 
3.3%
8 1
 
3.3%
5 2
 
6.7%
4 2
 
6.7%
3 3
 
10.0%
2 4
 
13.3%
1 17
56.7%

중복방문구분명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
1회 상담
21 
2회 이상 중복상담
9 

Length

Max length10
Median length5
Mean length6.5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1회 상담
2nd row1회 상담
3rd row1회 상담
4th row2회 이상 중복상담
5th row1회 상담

Common Values

ValueCountFrequency (%)
1회 상담 21
70.0%
2회 이상 중복상담 9
30.0%

Length

2023-12-10T23:01:57.939458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:01:58.057412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1회 21
30.4%
상담 21
30.4%
2회 9
13.0%
이상 9
13.0%
중복상담 9
13.0%

최근1년방문여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
False
26 
True
4 
ValueCountFrequency (%)
False 26
86.7%
True 4
 
13.3%
2023-12-10T23:01:58.150923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

기업번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:01:58.390813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters720
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row++0PaNkjHWmbE3ZHrNtNTw==
2nd rowzzLuMwTUTTduEpiMOMfbFQ==
3rd row++7pX6ba6QPYRTr4neb3dA==
4th row++C8A/Rtzh48yTldVD9JrA==
5th row++K4pBqNH51Cspxu0DasbA==
ValueCountFrequency (%)
0pankjhwmbe3zhrntntw 1
 
3.3%
zzlumwtuttduepimomfbfq 1
 
3.3%
03dgwn1dnxcp+adtmgjva 1
 
3.3%
01+ba5z6s7arny0b/d2ra 1
 
3.3%
v0czmbdrxgjxtoo85kaw 1
 
3.3%
qpetflcwnvtjeiclsvbg 1
 
3.3%
gfqcud/0rg5ofnfsp+eq 1
 
3.3%
dtuaqtfldk5k/m3z5sua 1
 
3.3%
xeyfvjlsfybbr3o5dsjg 1
 
3.3%
umom2y0ndkhiraarwo/g 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:01:58.807532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 60
 
8.3%
+ 52
 
7.2%
/ 22
 
3.1%
w 18
 
2.5%
g 17
 
2.4%
A 17
 
2.4%
b 15
 
2.1%
z 15
 
2.1%
3 15
 
2.1%
0 14
 
1.9%
Other values (55) 475
66.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 267
37.1%
Uppercase Letter 232
32.2%
Math Symbol 112
15.6%
Decimal Number 87
 
12.1%
Other Punctuation 22
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 18
 
6.7%
g 17
 
6.4%
b 15
 
5.6%
z 15
 
5.6%
d 14
 
5.2%
t 13
 
4.9%
v 13
 
4.9%
u 13
 
4.9%
l 11
 
4.1%
p 11
 
4.1%
Other values (16) 127
47.6%
Uppercase Letter
ValueCountFrequency (%)
A 17
 
7.3%
Q 13
 
5.6%
T 13
 
5.6%
R 12
 
5.2%
F 12
 
5.2%
U 11
 
4.7%
J 11
 
4.7%
X 11
 
4.7%
E 11
 
4.7%
H 10
 
4.3%
Other values (16) 111
47.8%
Decimal Number
ValueCountFrequency (%)
3 15
17.2%
0 14
16.1%
5 14
16.1%
9 9
10.3%
4 8
9.2%
6 7
8.0%
8 6
 
6.9%
2 5
 
5.7%
7 5
 
5.7%
1 4
 
4.6%
Math Symbol
ValueCountFrequency (%)
= 60
53.6%
+ 52
46.4%
Other Punctuation
ValueCountFrequency (%)
/ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 499
69.3%
Common 221
30.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 18
 
3.6%
g 17
 
3.4%
A 17
 
3.4%
b 15
 
3.0%
z 15
 
3.0%
d 14
 
2.8%
Q 13
 
2.6%
t 13
 
2.6%
T 13
 
2.6%
v 13
 
2.6%
Other values (42) 351
70.3%
Common
ValueCountFrequency (%)
= 60
27.1%
+ 52
23.5%
/ 22
 
10.0%
3 15
 
6.8%
0 14
 
6.3%
5 14
 
6.3%
9 9
 
4.1%
4 8
 
3.6%
6 7
 
3.2%
8 6
 
2.7%
Other values (3) 14
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 60
 
8.3%
+ 52
 
7.2%
/ 22
 
3.1%
w 18
 
2.5%
g 17
 
2.4%
A 17
 
2.4%
b 15
 
2.1%
z 15
 
2.1%
3 15
 
2.1%
0 14
 
1.9%
Other values (55) 475
66.0%

Interactions

2023-12-10T23:01:52.326946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:52.084816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:52.435032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:01:52.205848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:01:58.918170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드연령대코드사업자등록번호시군구명행정동명보증금1억원초과여부보증금5천만원초과여부보증금2천만원초과여부보증금1천만원초과여부상담수중복방문구분명최근1년방문여부기업번호
성별코드1.0000.0001.0000.5950.4430.0000.0000.0000.0000.0000.0000.0001.000
연령대코드0.0001.0001.0000.0000.0000.0000.5200.5080.0000.0000.0000.2991.000
사업자등록번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구명0.5950.0001.0001.0000.9931.0000.8460.0000.0000.5190.5190.3621.000
행정동명0.4430.0001.0000.9931.0001.0001.0000.0000.6040.6901.0000.0001.000
보증금1억원초과여부0.0000.0001.0001.0001.0001.0000.2970.0000.0000.1750.0000.0001.000
보증금5천만원초과여부0.0000.5201.0000.8461.0000.2971.0000.5720.0000.3850.6030.3291.000
보증금2천만원초과여부0.0000.5081.0000.0000.0000.0000.5721.0000.5590.2300.5720.1171.000
보증금1천만원초과여부0.0000.0001.0000.0000.6040.0000.0000.5591.0000.0000.0000.0001.000
상담수0.0000.0001.0000.5190.6900.1750.3850.2300.0001.0001.0000.6431.000
중복방문구분명0.0000.0001.0000.5191.0000.0000.6030.5720.0001.0001.0000.3291.000
최근1년방문여부0.0000.2991.0000.3620.0000.0000.3290.1170.0000.6430.3291.0001.000
기업번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T23:01:59.353050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보증금5천만원초과여부보증금1억원초과여부보증금2천만원초과여부보증금1천만원초과여부최근1년방문여부성별코드중복방문구분명
보증금5천만원초과여부1.0000.1890.3860.0000.2110.0000.411
보증금1억원초과여부0.1891.0000.0000.0000.0000.0000.000
보증금2천만원초과여부0.3860.0001.0000.3760.0660.0000.386
보증금1천만원초과여부0.0000.0000.3761.0000.0000.0000.000
최근1년방문여부0.2110.0000.0660.0001.0000.0000.211
성별코드0.0000.0000.0000.0000.0001.0000.000
중복방문구분명0.4110.0000.3860.0000.2110.0001.000
2023-12-10T23:01:59.488385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드상담수성별코드보증금1억원초과여부보증금5천만원초과여부보증금2천만원초과여부보증금1천만원초과여부중복방문구분명최근1년방문여부
연령대코드1.0000.1550.0000.0000.3410.3320.0000.0000.184
상담수0.1551.0000.0000.1480.3660.2080.0000.9060.625
성별코드0.0000.0001.0000.0000.0000.0000.0000.0000.000
보증금1억원초과여부0.0000.1480.0001.0000.1890.0000.0000.0000.000
보증금5천만원초과여부0.3410.3660.0000.1891.0000.3860.0000.4110.211
보증금2천만원초과여부0.3320.2080.0000.0000.3861.0000.3760.3860.066
보증금1천만원초과여부0.0000.0000.0000.0000.0000.3761.0000.0000.000
중복방문구분명0.0000.9060.0000.0000.4110.3860.0001.0000.211
최근1년방문여부0.1840.6250.0000.0000.2110.0660.0000.2111.000

Missing values

2023-12-10T23:01:52.594652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:01:52.918941image/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

기준년월성별코드연령대코드사업자등록번호시도명시군구명행정동명보증금10억원초과여부보증금1억원초과여부보증금5천만원초과여부보증금2천만원초과여부보증금1천만원초과여부상담수중복방문구분명최근1년방문여부기업번호
02023-04M2034410*****경기도시흥시은행동NNNYY11회 상담N++0PaNkjHWmbE3ZHrNtNTw==
12023-04F6020620*****경기도남양주시오남읍NNYYY11회 상담NzzLuMwTUTTduEpiMOMfbFQ==
22023-04M6014009*****경기도시흥시장곡동NNNNY11회 상담N++7pX6ba6QPYRTr4neb3dA==
32023-04F4068736*****경기도안양시 동안구호계동NNYYY32회 이상 중복상담N++C8A/Rtzh48yTldVD9JrA==
42023-04M4012591*****경기도안성시서운면NNNYY11회 상담N++K4pBqNH51Cspxu0DasbA==
52023-04M3049508*****경기도포천시신북면NNNYY11회 상담N++LzmDark/eR45zuL/pwvg==
62023-04M5020617*****경기도의정부시신곡동NYYYY42회 이상 중복상담N++NqE2xfRX4bG3NcoyVV0w==
72023-04F4042392*****경기도부천시중동NNNYY32회 이상 중복상담NzzThi0EB5yY/0YqRvgxeKA==
82023-04M5014010*****경기도시흥시하중동NNNYY52회 이상 중복상담N++U95hMUxHGE2zN8HslPtw==
92023-04F6012418*****경기도수원시 권선구권선동NNNNY21회 상담N++XUUhwAUyPq73ugx9OXOQ==
기준년월성별코드연령대코드사업자등록번호시도명시군구명행정동명보증금10억원초과여부보증금1억원초과여부보증금5천만원초과여부보증금2천만원초과여부보증금1천만원초과여부상담수중복방문구분명최근1년방문여부기업번호
202023-04F5026617*****경기도수원시 팔달구우만동NNYYY52회 이상 중복상담Y+/Sdu1ZzksFK6v8dbMgegg==
212023-04M4012735*****경기도연천군신서면NNNNN11회 상담N+/UMoM2Y0NdkhirAaRWO/g==
222023-04M4013525*****경기도수원시 장안구정자동NNNNN11회 상담N+/XEYFvjLSFyBBr3O5DsJg==
232023-04M3020354*****경기도안양시 동안구관양동NNNNN11회 상담N+/dtUaqTFldK5K/m3z5suA==
242023-04M4020630*****경기도김포시장기동NNNNY11회 상담N+/gfqcuD/0Rg5ofnFSp+eQ==
252023-04M3082002*****경기도김포시장기동NNNYY21회 상담N+/qpeTFlcwnvTJEiclsVbg==
262023-04M4082586*****경기도김포시장기동NNNYY11회 상담N+/v0cZMbdrXgjXtOo85Kaw==
272023-04M5012402*****경기도수원시 영통구매탄동NNNNY11회 상담N+01+bA5Z6s7ARny0B/d2RA==
282023-04M5012932*****경기도성남시 분당구서현동NNYYY21회 상담N+03DgwN1dNXCP+adtmGJVA==
292023-04M6012439*****경기도화성시장안면NNNYY42회 이상 중복상담N+0FFKJyABfz9pZQFXk9jxw==