Overview

Dataset statistics

Number of variables20
Number of observations114
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.1 KiB
Average record size in memory180.2 B

Variable types

Text1
Numeric9
Categorical10

Dataset

Description국민연금공단 노후준비상담 재무 서비스(지사명,합계,기초연금(지자체),장애인연금(지자체),희망키움통장(지자체),노인일자리사업(지자체),통합사례관리(지자체),긴급복지지원제도(지자체),가사·간병방문지원(지자체),주택연금(한국주택 금융공사),농지연금(한국농어촌공사),서민금융 지원제도(시민금융진흥원),소상공인지원 제도(소상공인 시장진흥공단),법률복지서비스(대한법률구조공단),여성취업지원서비스(여성새로일하기센터),귀농귀촌지원 제도(귀농귀촌종합센터),마이홈 주거상담(LH한국토지주택공사)
Author국민연금공단
URLhttps://www.data.go.kr/data/15073374/fileData.do

Alerts

직접입력 has constant value ""Constant
합계 is highly overall correlated with 기초연금(지자체)High correlation
기초연금(지자체) is highly overall correlated with 합계High correlation
희망키움통장(지자체) is highly overall correlated with 마이홈 주거상담(LH한국토지주택공사) and 4 other fieldsHigh correlation
노인일자리사업(지자체) is highly overall correlated with 가사_간병방문지원(지자체) and 2 other fieldsHigh correlation
주택연금(한국주택 금융공사) is highly overall correlated with 농지연금(한국농어촌공사) and 3 other fieldsHigh correlation
서민금융 지원제도(시민금융진흥원) is highly overall correlated with 마이홈 주거상담(LH한국토지주택공사) and 3 other fieldsHigh correlation
여성취업지원서비스(여성새로일하기센터) is highly overall correlated with 마이홈 주거상담(LH한국토지주택공사) and 5 other fieldsHigh correlation
마이홈 주거상담(LH한국토지주택공사) is highly overall correlated with 희망키움통장(지자체) and 6 other fieldsHigh correlation
노란우산공제(중소기업중앙회) is highly overall correlated with 희망키움통장(지자체) and 7 other fieldsHigh correlation
통합사례관리(지자체) is highly overall correlated with 농지연금(한국농어촌공사)High correlation
가사_간병방문지원(지자체) is highly overall correlated with 노인일자리사업(지자체)High correlation
농지연금(한국농어촌공사) is highly overall correlated with 희망키움통장(지자체) and 6 other fieldsHigh correlation
소상공인지원 제도(소상공인 시장진흥공단) is highly overall correlated with 희망키움통장(지자체) and 6 other fieldsHigh correlation
귀농귀촌지원 제도(귀농귀촌종합센터) is highly overall correlated with 희망키움통장(지자체) and 8 other fieldsHigh correlation
퇴직연금(근로복지공단) is highly overall correlated with 주택연금(한국주택 금융공사) and 5 other fieldsHigh correlation
장애인연금(지자체) is highly imbalanced (82.4%)Imbalance
통합사례관리(지자체) is highly imbalanced (87.4%)Imbalance
긴급복지지원제도(지자체) is highly imbalanced (73.6%)Imbalance
가사_간병방문지원(지자체) is highly imbalanced (87.3%)Imbalance
농지연금(한국농어촌공사) is highly imbalanced (65.6%)Imbalance
소상공인지원 제도(소상공인 시장진흥공단) is highly imbalanced (80.8%)Imbalance
법률복지서비스(대한법률구조공단) is highly imbalanced (92.7%)Imbalance
귀농귀촌지원 제도(귀농귀촌종합센터) is highly imbalanced (90.9%)Imbalance
퇴직연금(근로복지공단) is highly imbalanced (86.4%)Imbalance
지사명 has unique valuesUnique
합계 has 3 (2.6%) zerosZeros
기초연금(지자체) has 3 (2.6%) zerosZeros
희망키움통장(지자체) has 103 (90.4%) zerosZeros
노인일자리사업(지자체) has 84 (73.7%) zerosZeros
주택연금(한국주택 금융공사) has 20 (17.5%) zerosZeros
서민금융 지원제도(시민금융진흥원) has 94 (82.5%) zerosZeros
여성취업지원서비스(여성새로일하기센터) has 102 (89.5%) zerosZeros
마이홈 주거상담(LH한국토지주택공사) has 103 (90.4%) zerosZeros
노란우산공제(중소기업중앙회) has 106 (93.0%) zerosZeros

Reproduction

Analysis started2023-12-12 18:08:30.773874
Analysis finished2023-12-12 18:08:40.215452
Duration9.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지사명
Text

UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-13T03:08:40.425556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length3.2280702
Min length2

Characters and Unicode

Total characters368
Distinct characters106
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

Unique114 ?
Unique (%)100.0%

Sample

1st row종로중구
2nd row동대문중랑
3rd row성북강북
4th row도봉노원
5th row성동광진
ValueCountFrequency (%)
종로중구 1
 
0.9%
세종 1
 
0.9%
공주부여 1
 
0.9%
보령 1
 
0.9%
홍성 1
 
0.9%
천안 1
 
0.9%
서청주 1
 
0.9%
옥천 1
 
0.9%
충주 1
 
0.9%
동청주 1
 
0.9%
Other values (104) 104
91.2%
2023-12-13T03:08:40.800371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 21
 
5.7%
산 20
 
5.4%
주 20
 
5.4%
천 16
 
4.3%
동 13
 
3.5%
남 11
 
3.0%
대 11
 
3.0%
서 11
 
3.0%
양 10
 
2.7%
역 9
 
2.4%
Other values (96) 226
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 368
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 21
 
5.7%
산 20
 
5.4%
주 20
 
5.4%
천 16
 
4.3%
동 13
 
3.5%
남 11
 
3.0%
대 11
 
3.0%
서 11
 
3.0%
양 10
 
2.7%
역 9
 
2.4%
Other values (96) 226
61.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 368
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 21
 
5.7%
산 20
 
5.4%
주 20
 
5.4%
천 16
 
4.3%
동 13
 
3.5%
남 11
 
3.0%
대 11
 
3.0%
서 11
 
3.0%
양 10
 
2.7%
역 9
 
2.4%
Other values (96) 226
61.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 368
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 21
 
5.7%
산 20
 
5.4%
주 20
 
5.4%
천 16
 
4.3%
동 13
 
3.5%
남 11
 
3.0%
대 11
 
3.0%
서 11
 
3.0%
양 10
 
2.7%
역 9
 
2.4%
Other values (96) 226
61.4%

합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct104
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.25439
Minimum0
Maximum1194
Zeros3
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T03:08:40.928487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.65
Q165.5
median223.5
Q3348.25
95-th percentile645.15
Maximum1194
Range1194
Interquartile range (IQR)282.75

Descriptive statistics

Standard deviation214.11492
Coefficient of variation (CV)0.87660626
Kurtosis2.4093277
Mean244.25439
Median Absolute Deviation (MAD)152.5
Skewness1.2540596
Sum27845
Variance45845.2
MonotonicityNot monotonic
2023-12-13T03:08:41.072294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
548 3
 
2.6%
0 3
 
2.6%
238 3
 
2.6%
15 2
 
1.8%
115 2
 
1.8%
100 2
 
1.8%
18 2
 
1.8%
119 1
 
0.9%
187 1
 
0.9%
321 1
 
0.9%
Other values (94) 94
82.5%
ValueCountFrequency (%)
0 3
2.6%
4 1
 
0.9%
6 1
 
0.9%
8 1
 
0.9%
9 1
 
0.9%
10 1
 
0.9%
13 1
 
0.9%
14 1
 
0.9%
15 2
1.8%
18 2
1.8%
ValueCountFrequency (%)
1194 1
0.9%
755 1
0.9%
732 1
0.9%
715 1
0.9%
663 1
0.9%
651 1
0.9%
642 1
0.9%
582 1
0.9%
557 1
0.9%
553 1
0.9%

기초연금(지자체)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235.50877
Minimum0
Maximum1172
Zeros3
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T03:08:41.193960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q152.5
median209
Q3346.25
95-th percentile634.9
Maximum1172
Range1172
Interquartile range (IQR)293.75

Descriptive statistics

Standard deviation213.12439
Coefficient of variation (CV)0.90495307
Kurtosis2.2970878
Mean235.50877
Median Absolute Deviation (MAD)153.5
Skewness1.2408244
Sum26848
Variance45422.004
MonotonicityNot monotonic
2023-12-13T03:08:41.316552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 4
 
3.5%
0 3
 
2.6%
5 3
 
2.6%
12 2
 
1.8%
548 2
 
1.8%
520 2
 
1.8%
18 2
 
1.8%
368 2
 
1.8%
6 2
 
1.8%
37 2
 
1.8%
Other values (88) 90
78.9%
ValueCountFrequency (%)
0 3
2.6%
1 1
 
0.9%
5 3
2.6%
6 2
1.8%
7 1
 
0.9%
8 1
 
0.9%
11 1
 
0.9%
12 2
1.8%
13 1
 
0.9%
15 1
 
0.9%
ValueCountFrequency (%)
1172 1
0.9%
746 1
0.9%
729 1
0.9%
708 1
0.9%
651 1
0.9%
644 1
0.9%
630 1
0.9%
571 1
0.9%
550 1
0.9%
548 2
1.8%

장애인연금(지자체)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
111 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111
97.4%
1 3
 
2.6%

Length

2023-12-13T03:08:41.733114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:41.824846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 111
97.4%
1 3
 
2.6%

희망키움통장(지자체)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18421053
Minimum0
Maximum5
Zeros103
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T03:08:41.902815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.71104795
Coefficient of variation (CV)3.8599746
Kurtosis27.104027
Mean0.18421053
Median Absolute Deviation (MAD)0
Skewness4.9789947
Sum21
Variance0.50558919
MonotonicityNot monotonic
2023-12-13T03:08:42.026316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 103
90.4%
1 7
 
6.1%
4 1
 
0.9%
3 1
 
0.9%
2 1
 
0.9%
5 1
 
0.9%
ValueCountFrequency (%)
0 103
90.4%
1 7
 
6.1%
2 1
 
0.9%
3 1
 
0.9%
4 1
 
0.9%
5 1
 
0.9%
ValueCountFrequency (%)
5 1
 
0.9%
4 1
 
0.9%
3 1
 
0.9%
2 1
 
0.9%
1 7
 
6.1%
0 103
90.4%

노인일자리사업(지자체)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71929825
Minimum0
Maximum8
Zeros84
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T03:08:42.148798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6212569
Coefficient of variation (CV)2.2539425
Kurtosis8.7540904
Mean0.71929825
Median Absolute Deviation (MAD)0
Skewness2.901416
Sum82
Variance2.6284738
MonotonicityNot monotonic
2023-12-13T03:08:42.253406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 84
73.7%
1 11
 
9.6%
2 8
 
7.0%
3 3
 
2.6%
4 3
 
2.6%
8 2
 
1.8%
6 1
 
0.9%
7 1
 
0.9%
5 1
 
0.9%
ValueCountFrequency (%)
0 84
73.7%
1 11
 
9.6%
2 8
 
7.0%
3 3
 
2.6%
4 3
 
2.6%
5 1
 
0.9%
6 1
 
0.9%
7 1
 
0.9%
8 2
 
1.8%
ValueCountFrequency (%)
8 2
 
1.8%
7 1
 
0.9%
6 1
 
0.9%
5 1
 
0.9%
4 3
 
2.6%
3 3
 
2.6%
2 8
 
7.0%
1 11
 
9.6%
0 84
73.7%

통합사례관리(지자체)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
111 
2
 
2
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111
97.4%
2 2
 
1.8%
1 1
 
0.9%

Length

2023-12-13T03:08:42.405434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:42.505534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 111
97.4%
2 2
 
1.8%
1 1
 
0.9%

긴급복지지원제도(지자체)
Categorical

IMBALANCE 

Distinct5
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
102 
1
 
9
2
 
1
3
 
1
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)2.6%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 102
89.5%
1 9
 
7.9%
2 1
 
0.9%
3 1
 
0.9%
7 1
 
0.9%

Length

2023-12-13T03:08:42.604964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:42.696833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 102
89.5%
1 9
 
7.9%
2 1
 
0.9%
3 1
 
0.9%
7 1
 
0.9%

가사_간병방문지원(지자체)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
112 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 112
98.2%
1 2
 
1.8%

Length

2023-12-13T03:08:42.799065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:42.887666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 112
98.2%
1 2
 
1.8%

주택연금(한국주택 금융공사)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9824561
Minimum0
Maximum84
Zeros20
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T03:08:42.976145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile13.7
Maximum84
Range84
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.3949548
Coefficient of variation (CV)1.8856071
Kurtosis46.823579
Mean4.9824561
Median Absolute Deviation (MAD)2
Skewness6.1699191
Sum568
Variance88.265176
MonotonicityNot monotonic
2023-12-13T03:08:43.077307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 20
17.5%
3 19
16.7%
1 16
14.0%
4 15
13.2%
2 10
8.8%
7 8
 
7.0%
5 6
 
5.3%
8 5
 
4.4%
6 4
 
3.5%
11 2
 
1.8%
Other values (9) 9
7.9%
ValueCountFrequency (%)
0 20
17.5%
1 16
14.0%
2 10
8.8%
3 19
16.7%
4 15
13.2%
5 6
 
5.3%
6 4
 
3.5%
7 8
 
7.0%
8 5
 
4.4%
10 1
 
0.9%
ValueCountFrequency (%)
84 1
0.9%
44 1
0.9%
27 1
0.9%
21 1
0.9%
17 1
0.9%
15 1
0.9%
13 1
0.9%
12 1
0.9%
11 2
1.8%
10 1
0.9%

농지연금(한국농어촌공사)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
97 
1
12 
2
 
3
9
 
1
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)1.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 97
85.1%
1 12
 
10.5%
2 3
 
2.6%
9 1
 
0.9%
7 1
 
0.9%

Length

2023-12-13T03:08:43.206823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:43.311304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 97
85.1%
1 12
 
10.5%
2 3
 
2.6%
9 1
 
0.9%
7 1
 
0.9%

서민금융 지원제도(시민금융진흥원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39473684
Minimum0
Maximum5
Zeros94
Zeros (%)82.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T03:08:43.416607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0443139
Coefficient of variation (CV)2.6455952
Kurtosis8.9125508
Mean0.39473684
Median Absolute Deviation (MAD)0
Skewness3.0374058
Sum45
Variance1.0905915
MonotonicityNot monotonic
2023-12-13T03:08:43.529031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 94
82.5%
1 9
 
7.9%
2 4
 
3.5%
4 3
 
2.6%
5 2
 
1.8%
3 2
 
1.8%
ValueCountFrequency (%)
0 94
82.5%
1 9
 
7.9%
2 4
 
3.5%
3 2
 
1.8%
4 3
 
2.6%
5 2
 
1.8%
ValueCountFrequency (%)
5 2
 
1.8%
4 3
 
2.6%
3 2
 
1.8%
2 4
 
3.5%
1 9
 
7.9%
0 94
82.5%

소상공인지원 제도(소상공인 시장진흥공단)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
107 
1
 
3
2
 
2
3
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)1.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 107
93.9%
1 3
 
2.6%
2 2
 
1.8%
3 1
 
0.9%
5 1
 
0.9%

Length

2023-12-13T03:08:43.641414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:43.741547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 107
93.9%
1 3
 
2.6%
2 2
 
1.8%
3 1
 
0.9%
5 1
 
0.9%

법률복지서비스(대한법률구조공단)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
113 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 113
99.1%
1 1
 
0.9%

Length

2023-12-13T03:08:43.832498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:43.906756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 113
99.1%
1 1
 
0.9%

여성취업지원서비스(여성새로일하기센터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57017544
Minimum0
Maximum26
Zeros102
Zeros (%)89.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T03:08:43.981019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.35
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9448299
Coefficient of variation (CV)5.1647785
Kurtosis53.903052
Mean0.57017544
Median Absolute Deviation (MAD)0
Skewness7.0090935
Sum65
Variance8.672023
MonotonicityNot monotonic
2023-12-13T03:08:44.081054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 102
89.5%
1 6
 
5.3%
3 2
 
1.8%
13 1
 
0.9%
12 1
 
0.9%
2 1
 
0.9%
26 1
 
0.9%
ValueCountFrequency (%)
0 102
89.5%
1 6
 
5.3%
2 1
 
0.9%
3 2
 
1.8%
12 1
 
0.9%
13 1
 
0.9%
26 1
 
0.9%
ValueCountFrequency (%)
26 1
 
0.9%
13 1
 
0.9%
12 1
 
0.9%
3 2
 
1.8%
2 1
 
0.9%
1 6
 
5.3%
0 102
89.5%

귀농귀촌지원 제도(귀농귀촌종합센터)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
112 
1
 
1
11
 
1

Length

Max length2
Median length1
Mean length1.0087719
Min length1

Unique

Unique2 ?
Unique (%)1.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 112
98.2%
1 1
 
0.9%
11 1
 
0.9%

Length

2023-12-13T03:08:44.186332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:44.269671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 112
98.2%
1 1
 
0.9%
11 1
 
0.9%

마이홈 주거상담(LH한국토지주택공사)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6754386
Minimum0
Maximum21
Zeros103
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T03:08:44.345546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.35
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1412066
Coefficient of variation (CV)4.6506176
Kurtosis26.873962
Mean0.6754386
Median Absolute Deviation (MAD)0
Skewness5.2143835
Sum77
Variance9.867179
MonotonicityNot monotonic
2023-12-13T03:08:44.427194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 103
90.4%
1 5
 
4.4%
16 2
 
1.8%
2 1
 
0.9%
3 1
 
0.9%
21 1
 
0.9%
14 1
 
0.9%
ValueCountFrequency (%)
0 103
90.4%
1 5
 
4.4%
2 1
 
0.9%
3 1
 
0.9%
14 1
 
0.9%
16 2
 
1.8%
21 1
 
0.9%
ValueCountFrequency (%)
21 1
 
0.9%
16 2
 
1.8%
14 1
 
0.9%
3 1
 
0.9%
2 1
 
0.9%
1 5
 
4.4%
0 103
90.4%

퇴직연금(근로복지공단)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
110 
1
 
2
11
 
1
6
 
1

Length

Max length2
Median length1
Mean length1.0087719
Min length1

Unique

Unique2 ?
Unique (%)1.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 110
96.5%
1 2
 
1.8%
11 1
 
0.9%
6 1
 
0.9%

Length

2023-12-13T03:08:44.528553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:44.619284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 110
96.5%
1 2
 
1.8%
11 1
 
0.9%
6 1
 
0.9%

노란우산공제(중소기업중앙회)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23684211
Minimum0
Maximum7
Zeros106
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T03:08:44.695650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0070779
Coefficient of variation (CV)4.2521066
Kurtosis26.038004
Mean0.23684211
Median Absolute Deviation (MAD)0
Skewness4.956765
Sum27
Variance1.0142059
MonotonicityNot monotonic
2023-12-13T03:08:44.792170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 106
93.0%
2 3
 
2.6%
5 2
 
1.8%
3 1
 
0.9%
1 1
 
0.9%
7 1
 
0.9%
ValueCountFrequency (%)
0 106
93.0%
1 1
 
0.9%
2 3
 
2.6%
3 1
 
0.9%
5 2
 
1.8%
7 1
 
0.9%
ValueCountFrequency (%)
7 1
 
0.9%
5 2
 
1.8%
3 1
 
0.9%
2 3
 
2.6%
1 1
 
0.9%
0 106
93.0%

직접입력
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
114 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 114
100.0%

Length

2023-12-13T03:08:44.903762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:44.989208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 114
100.0%

Interactions

2023-12-13T03:08:38.968489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:31.855743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.705082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.493268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.297097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:35.264899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:36.475604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.335360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:38.107104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:39.062883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:31.964222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.800703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.587990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.411937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:35.365084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:36.574997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.430233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:38.192564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:39.156679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.055423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.881895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.674204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.507774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:35.468662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:36.669254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.512073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:38.319173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:39.263233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.154709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.964054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.759215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.619276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:35.896534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:36.755756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.592019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:38.427900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:39.368011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.261110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.064262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.856174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.729110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:35.995684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:36.857467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.677831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:38.529932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:39.446767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.340078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.137621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.937126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.812553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:36.087556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:36.954025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.770408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:38.618072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:39.529181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.422028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.217102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.026199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.912979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:36.180986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.049696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.860861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:38.706899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:39.626216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.503309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.304659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.125782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:35.038092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:36.283648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.149394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.943944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:38.782864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:39.731328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:32.593511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:33.387565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:34.207608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:35.158900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:36.366426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:37.229832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:38.019164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:08:38.861483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:08:45.067181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계기초연금(지자체)장애인연금(지자체)희망키움통장(지자체)노인일자리사업(지자체)통합사례관리(지자체)긴급복지지원제도(지자체)가사_간병방문지원(지자체)주택연금(한국주택 금융공사)농지연금(한국농어촌공사)서민금융 지원제도(시민금융진흥원)소상공인지원 제도(소상공인 시장진흥공단)법률복지서비스(대한법률구조공단)여성취업지원서비스(여성새로일하기센터)귀농귀촌지원 제도(귀농귀촌종합센터)마이홈 주거상담(LH한국토지주택공사)퇴직연금(근로복지공단)노란우산공제(중소기업중앙회)
합계1.0000.9980.1880.2860.5920.0000.3510.3350.2220.3440.0000.0000.0000.0000.0000.0000.0000.278
기초연금(지자체)0.9981.0000.0000.3740.6010.0000.4110.4380.2060.3540.0000.0000.0000.0000.0000.0000.0000.289
장애인연금(지자체)0.1880.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4020.0000.0000.0000.0000.000
희망키움통장(지자체)0.2860.3740.0001.0000.6470.7830.6350.0000.9320.6440.8090.8310.0000.8281.0000.7570.6420.855
노인일자리사업(지자체)0.5920.6010.0000.6471.0000.3790.1480.7100.7720.7660.4320.5810.1170.7860.8800.6700.5530.815
통합사례관리(지자체)0.0000.0000.0000.7830.3791.0000.3340.0000.7830.5640.4230.3670.0000.5310.8150.3660.0000.572
긴급복지지원제도(지자체)0.3510.4110.0000.6350.1480.3341.0000.0000.0000.3690.0000.6530.0000.5150.1740.0000.0000.215
가사_간병방문지원(지자체)0.3350.4380.0000.0000.7100.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
주택연금(한국주택 금융공사)0.2220.2060.0000.9320.7720.7830.0000.0001.0000.7630.7710.7350.0000.8511.0000.8220.6890.917
농지연금(한국농어촌공사)0.3440.3540.0000.6440.7660.5640.3690.0000.7631.0000.4950.6700.0000.9130.7090.7070.0000.742
서민금융 지원제도(시민금융진흥원)0.0000.0000.0000.8090.4320.4230.0000.0000.7710.4951.0000.6250.0000.6940.8520.7630.7460.891
소상공인지원 제도(소상공인 시장진흥공단)0.0000.0000.0000.8310.5810.3670.6530.0000.7350.6700.6251.0000.0000.9770.8290.9350.6250.834
법률복지서비스(대한법률구조공단)0.0000.0000.4020.0000.1170.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
여성취업지원서비스(여성새로일하기센터)0.0000.0000.0000.8280.7860.5310.5150.0000.8510.9130.6940.9770.0001.0001.0000.9770.7480.909
귀농귀촌지원 제도(귀농귀촌종합센터)0.0000.0000.0001.0000.8800.8150.1740.0001.0000.7090.8520.8290.0001.0001.0000.8290.4800.934
마이홈 주거상담(LH한국토지주택공사)0.0000.0000.0000.7570.6700.3660.0000.0000.8220.7070.7630.9350.0000.9770.8291.0000.9040.833
퇴직연금(근로복지공단)0.0000.0000.0000.6420.5530.0000.0000.0000.6890.0000.7460.6250.0000.7480.4800.9041.0000.875
노란우산공제(중소기업중앙회)0.2780.2890.0000.8550.8150.5720.2150.0000.9170.7420.8910.8340.0000.9090.9340.8330.8751.000
2023-12-13T03:08:45.280102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
긴급복지지원제도(지자체)퇴직연금(근로복지공단)법률복지서비스(대한법률구조공단)소상공인지원 제도(소상공인 시장진흥공단)가사_간병방문지원(지자체)통합사례관리(지자체)농지연금(한국농어촌공사)장애인연금(지자체)귀농귀촌지원 제도(귀농귀촌종합센터)
긴급복지지원제도(지자체)1.0000.0000.0000.2950.0000.2650.1440.0000.130
퇴직연금(근로복지공단)0.0001.0000.0000.5510.0000.0000.0000.0000.475
법률복지서비스(대한법률구조공단)0.0000.0001.0000.0000.0000.0000.0000.2630.000
소상공인지원 제도(소상공인 시장진흥공단)0.2950.5510.0001.0000.0000.2950.3070.0000.852
가사_간병방문지원(지자체)0.0000.0000.0000.0001.0000.0000.0000.0000.000
통합사례관리(지자체)0.2650.0000.0000.2950.0001.0000.5050.0000.484
농지연금(한국농어촌공사)0.1440.0000.0000.3070.0000.5051.0000.0000.688
장애인연금(지자체)0.0000.0000.2630.0000.0000.0000.0001.0000.000
귀농귀촌지원 제도(귀농귀촌종합센터)0.1300.4750.0000.8520.0000.4840.6880.0001.000
2023-12-13T03:08:45.451193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계기초연금(지자체)희망키움통장(지자체)노인일자리사업(지자체)주택연금(한국주택 금융공사)서민금융 지원제도(시민금융진흥원)여성취업지원서비스(여성새로일하기센터)마이홈 주거상담(LH한국토지주택공사)노란우산공제(중소기업중앙회)장애인연금(지자체)통합사례관리(지자체)긴급복지지원제도(지자체)가사_간병방문지원(지자체)농지연금(한국농어촌공사)소상공인지원 제도(소상공인 시장진흥공단)법률복지서비스(대한법률구조공단)귀농귀촌지원 제도(귀농귀촌종합센터)퇴직연금(근로복지공단)
합계1.0000.9920.0850.0810.170-0.100-0.0720.061-0.0290.1360.0000.2190.2430.2150.0000.0000.0000.000
기초연금(지자체)0.9921.0000.0090.0320.110-0.169-0.147-0.023-0.1230.0000.0000.2620.3190.2220.0000.0000.0000.000
희망키움통장(지자체)0.0850.0091.0000.2530.3160.4240.4920.5190.5110.0000.4560.4920.0000.5020.7290.0000.9860.467
노인일자리사업(지자체)0.0810.0320.2531.0000.1470.1800.3080.3410.3670.0000.1740.0800.6990.5680.3760.1100.5890.378
주택연금(한국주택 금융공사)0.1700.1100.3160.1471.0000.2820.2360.3880.3650.0000.4560.0000.0000.6370.6030.0000.9860.514
서민금융 지원제도(시민금융진흥원)-0.100-0.1690.4240.1800.2821.0000.4860.5790.6130.0000.1890.0000.0000.3610.4820.0000.5400.578
여성취업지원서비스(여성새로일하기센터)-0.072-0.1470.4920.3080.2360.4861.0000.5110.7200.0000.3100.2270.0000.5510.9050.0000.8570.559
마이홈 주거상담(LH한국토지주택공사)0.061-0.0230.5190.3410.3880.5790.5111.0000.7540.0000.2950.0000.0000.3350.6450.0000.8520.905
노란우산공제(중소기업중앙회)-0.029-0.1230.5110.3670.3650.6130.7200.7541.0000.0000.2790.1450.0000.6110.7330.0000.6810.742
장애인연금(지자체)0.1360.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.2630.0000.000
통합사례관리(지자체)0.0000.0000.4560.1740.4560.1890.3100.2950.2790.0001.0000.2650.0000.5050.2950.0000.4840.000
긴급복지지원제도(지자체)0.2190.2620.4920.0800.0000.0000.2270.0000.1450.0000.2651.0000.0000.1440.2950.0000.1300.000
가사_간병방문지원(지자체)0.2430.3190.0000.6990.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
농지연금(한국농어촌공사)0.2150.2220.5020.5680.6370.3610.5510.3350.6110.0000.5050.1440.0001.0000.3070.0000.6880.000
소상공인지원 제도(소상공인 시장진흥공단)0.0000.0000.7290.3760.6030.4820.9050.6450.7330.0000.2950.2950.0000.3071.0000.0000.8520.551
법률복지서비스(대한법률구조공단)0.0000.0000.0000.1100.0000.0000.0000.0000.0000.2630.0000.0000.0000.0000.0001.0000.0000.000
귀농귀촌지원 제도(귀농귀촌종합센터)0.0000.0000.9860.5890.9860.5400.8570.8520.6810.0000.4840.1300.0000.6880.8520.0001.0000.475
퇴직연금(근로복지공단)0.0000.0000.4670.3780.5140.5780.5590.9050.7420.0000.0000.0000.0000.0000.5510.0000.4751.000

Missing values

2023-12-13T03:08:39.880530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:08:40.113767image/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

지사명합계기초연금(지자체)장애인연금(지자체)희망키움통장(지자체)노인일자리사업(지자체)통합사례관리(지자체)긴급복지지원제도(지자체)가사_간병방문지원(지자체)주택연금(한국주택 금융공사)농지연금(한국농어촌공사)서민금융 지원제도(시민금융진흥원)소상공인지원 제도(소상공인 시장진흥공단)법률복지서비스(대한법률구조공단)여성취업지원서비스(여성새로일하기센터)귀농귀촌지원 제도(귀농귀촌종합센터)마이홈 주거상담(LH한국토지주택공사)퇴직연금(근로복지공단)노란우산공제(중소기업중앙회)직접입력
0종로중구151200002010000000000
1동대문중랑726700200020100000000
2성북강북18217800000040000000000
3도봉노원676600000010000000000
4성동광진979001100050000000000
5송파11941172003000122100002020
6강동하남73272900201000000000000
7서울남부지역본부47046001400040000100000
8서초29729400000030000000000
9관악66365100601050000000000
지사명합계기초연금(지자체)장애인연금(지자체)희망키움통장(지자체)노인일자리사업(지자체)통합사례관리(지자체)긴급복지지원제도(지자체)가사_간병방문지원(지자체)주택연금(한국주택 금융공사)농지연금(한국농어촌공사)서민금융 지원제도(시민금융진흥원)소상공인지원 제도(소상공인 시장진흥공단)법률복지서비스(대한법률구조공단)여성취업지원서비스(여성새로일하기센터)귀농귀촌지원 제도(귀농귀촌종합센터)마이홈 주거상담(LH한국토지주택공사)퇴직연금(근로복지공단)노란우산공제(중소기업중앙회)직접입력
104창원9245015010217100301070
105김해밀양55755000007000000000000
106통영27827400000040000000000
107진주11511200000030000000000
108마산20420200000020000000000
109거창403700000030000000000
110양산11111000000010000000000
111사천남해25525000000050000000000
112제주23823000001070000000000
113서귀포23923200000070000000000