Overview

Dataset statistics

Number of variables8
Number of observations118
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory72.1 B

Variable types

Numeric7
Categorical1

Dataset

Description산재보험 장애급여 종류별 수급 집계현황
Author경기복지재단(경기도장애인복지종합지원센터)
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=9ZPAY1TL64U0OBSY1EIV26288827&infSeq=1

Alerts

기준년도 is highly overall correlated with 전체수급자수(명) and 1 other fieldsHigh correlation
전체수급자수(명) is highly overall correlated with 기준년도 and 5 other fieldsHigh correlation
전체금액(백만원) is highly overall correlated with 전체수급자수(명) and 4 other fieldsHigh correlation
연금수급자수(명) is highly overall correlated with 기준년도 and 5 other fieldsHigh correlation
연금금액(백만원) is highly overall correlated with 전체수급자수(명) and 4 other fieldsHigh correlation
일시금수급자수(명) is highly overall correlated with 전체수급자수(명) and 4 other fieldsHigh correlation
일시금금액(백만원) is highly overall correlated with 전체수급자수(명) and 4 other fieldsHigh correlation
전체수급자수(명) has 6 (5.1%) zerosZeros
전체금액(백만원) has 6 (5.1%) zerosZeros
연금수급자수(명) has 6 (5.1%) zerosZeros
연금금액(백만원) has 6 (5.1%) zerosZeros
일시금수급자수(명) has 6 (5.1%) zerosZeros
일시금금액(백만원) has 6 (5.1%) zerosZeros

Reproduction

Analysis started2024-03-12 23:00:06.346551
Analysis finished2024-03-12 23:00:10.559829
Duration4.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.9915
Minimum2012
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T08:00:10.602209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12015
median2018
Q32021
95-th percentile2023
Maximum2023
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4380867
Coefficient of variation (CV)0.0017037171
Kurtosis-1.1562015
Mean2017.9915
Median Absolute Deviation (MAD)3
Skewness-0.1813849
Sum238123
Variance11.82044
MonotonicityDecreasing
2024-03-13T08:00:10.683578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2023 12
10.2%
2022 12
10.2%
2021 11
9.3%
2020 11
9.3%
2019 10
8.5%
2018 10
8.5%
2017 10
8.5%
2016 10
8.5%
2015 8
6.8%
2014 8
6.8%
Other values (2) 16
13.6%
ValueCountFrequency (%)
2012 8
6.8%
2013 8
6.8%
2014 8
6.8%
2015 8
6.8%
2016 10
8.5%
2017 10
8.5%
2018 10
8.5%
2019 10
8.5%
2020 11
9.3%
2021 11
9.3%
ValueCountFrequency (%)
2023 12
10.2%
2022 12
10.2%
2021 11
9.3%
2020 11
9.3%
2019 10
8.5%
2018 10
8.5%
2017 10
8.5%
2016 10
8.5%
2015 8
6.8%
2014 8
6.8%

지사명
Categorical

Distinct12
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
고양지사
12 
부천지사
12 
성남지사
12 
수원지사
12 
안산지사
12 
Other values (7)
58 

Length

Max length5
Median length4
Mean length4.1355932
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고양지사
2nd row남양주지사
3rd row부천지사
4th row성남지사
5th row수원지사

Common Values

ValueCountFrequency (%)
고양지사 12
10.2%
부천지사 12
10.2%
성남지사 12
10.2%
수원지사 12
10.2%
안산지사 12
10.2%
안양지사 12
10.2%
의정부지사 12
10.2%
평택지사 12
10.2%
용인지사 8
6.8%
화성지사 8
6.8%
Other values (2) 6
5.1%

Length

2024-03-13T08:00:10.773674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양지사 12
10.2%
부천지사 12
10.2%
성남지사 12
10.2%
수원지사 12
10.2%
안산지사 12
10.2%
안양지사 12
10.2%
의정부지사 12
10.2%
평택지사 12
10.2%
용인지사 8
6.8%
화성지사 8
6.8%
Other values (2) 6
5.1%

전체수급자수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct108
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1199.1102
Minimum0
Maximum3449
Zeros6
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T08:00:10.865912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.35
Q1685
median953
Q31598.25
95-th percentile2800.75
Maximum3449
Range3449
Interquartile range (IQR)913.25

Descriptive statistics

Standard deviation770.68197
Coefficient of variation (CV)0.64271156
Kurtosis0.089853346
Mean1199.1102
Median Absolute Deviation (MAD)378
Skewness0.83992631
Sum141495
Variance593950.7
MonotonicityNot monotonic
2024-03-13T08:00:10.981147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.1%
748 2
 
1.7%
846 2
 
1.7%
1941 2
 
1.7%
654 2
 
1.7%
849 2
 
1.7%
2287 1
 
0.8%
2856 1
 
0.8%
2350 1
 
0.8%
1965 1
 
0.8%
Other values (98) 98
83.1%
ValueCountFrequency (%)
0 6
5.1%
91 1
 
0.8%
277 1
 
0.8%
416 1
 
0.8%
421 1
 
0.8%
449 1
 
0.8%
453 1
 
0.8%
461 1
 
0.8%
475 1
 
0.8%
501 1
 
0.8%
ValueCountFrequency (%)
3449 1
0.8%
3068 1
0.8%
3001 1
0.8%
2991 1
0.8%
2873 1
0.8%
2856 1
0.8%
2791 1
0.8%
2669 1
0.8%
2418 1
0.8%
2405 1
0.8%

전체금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct113
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19165.814
Minimum0
Maximum61757
Zeros6
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T08:00:11.101759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1252.05
Q19891.5
median14638.5
Q323006
95-th percentile45614.3
Maximum61757
Range61757
Interquartile range (IQR)13114.5

Descriptive statistics

Standard deviation13836.08
Coefficient of variation (CV)0.72191457
Kurtosis0.29322949
Mean19165.814
Median Absolute Deviation (MAD)5468.5
Skewness1.0607543
Sum2261566
Variance1.9143711 × 108
MonotonicityNot monotonic
2024-03-13T08:00:11.229098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.1%
11879 1
 
0.8%
7112 1
 
0.8%
40378 1
 
0.8%
32618 1
 
0.8%
22129 1
 
0.8%
19043 1
 
0.8%
7395 1
 
0.8%
9630 1
 
0.8%
16066 1
 
0.8%
Other values (103) 103
87.3%
ValueCountFrequency (%)
0 6
5.1%
1473 1
 
0.8%
4776 1
 
0.8%
5355 1
 
0.8%
5696 1
 
0.8%
7062 1
 
0.8%
7112 1
 
0.8%
7230 1
 
0.8%
7267 1
 
0.8%
7375 1
 
0.8%
ValueCountFrequency (%)
61757 1
0.8%
53649 1
0.8%
51573 1
0.8%
50686 1
0.8%
49388 1
0.8%
46891 1
0.8%
45389 1
0.8%
45224 1
0.8%
44590 1
0.8%
43701 1
0.8%

연금수급자수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348.60169
Minimum0
Maximum1717
Zeros6
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T08:00:11.358378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.85
Q126
median44
Q3681.75
95-th percentile1567.75
Maximum1717
Range1717
Interquartile range (IQR)655.75

Descriptive statistics

Standard deviation547.79685
Coefficient of variation (CV)1.5714119
Kurtosis0.40038944
Mean348.60169
Median Absolute Deviation (MAD)23.5
Skewness1.4091083
Sum41135
Variance300081.39
MonotonicityNot monotonic
2024-03-13T08:00:11.680304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.1%
22 5
 
4.2%
29 5
 
4.2%
44 5
 
4.2%
39 4
 
3.4%
37 2
 
1.7%
31 2
 
1.7%
26 2
 
1.7%
41 2
 
1.7%
65 2
 
1.7%
Other values (70) 83
70.3%
ValueCountFrequency (%)
0 6
5.1%
1 1
 
0.8%
3 1
 
0.8%
9 1
 
0.8%
10 1
 
0.8%
13 2
 
1.7%
14 1
 
0.8%
17 2
 
1.7%
18 1
 
0.8%
19 1
 
0.8%
ValueCountFrequency (%)
1717 1
0.8%
1703 1
0.8%
1661 1
0.8%
1659 1
0.8%
1632 1
0.8%
1572 1
0.8%
1567 1
0.8%
1552 1
0.8%
1532 1
0.8%
1530 1
0.8%

연금금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6971.5424
Minimum0
Maximum36191
Zeros6
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T08:00:11.794404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.4
Q1398.25
median759
Q312587.75
95-th percentile31824.7
Maximum36191
Range36191
Interquartile range (IQR)12189.5

Descriptive statistics

Standard deviation11242.619
Coefficient of variation (CV)1.6126444
Kurtosis0.38904422
Mean6971.5424
Median Absolute Deviation (MAD)445.5
Skewness1.4169011
Sum822642
Variance1.2639648 × 108
MonotonicityNot monotonic
2024-03-13T08:00:11.940129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.1%
449 2
 
1.7%
654 1
 
0.8%
417 1
 
0.8%
26426 1
 
0.8%
28804 1
 
0.8%
19881 1
 
0.8%
15069 1
 
0.8%
1411 1
 
0.8%
148 1
 
0.8%
Other values (102) 102
86.4%
ValueCountFrequency (%)
0 6
5.1%
24 1
 
0.8%
78 1
 
0.8%
148 1
 
0.8%
163 1
 
0.8%
168 1
 
0.8%
183 1
 
0.8%
225 1
 
0.8%
226 1
 
0.8%
229 1
 
0.8%
ValueCountFrequency (%)
36191 1
0.8%
35050 1
0.8%
33437 1
0.8%
33185 1
0.8%
32728 1
0.8%
32333 1
0.8%
31735 1
0.8%
31195 1
0.8%
29993 1
0.8%
29663 1
0.8%

일시금수급자수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct107
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean814.55085
Minimum0
Maximum1916
Zeros6
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T08:00:12.073647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile76.5
Q1578.25
median798.5
Q31050
95-th percentile1376.4
Maximum1916
Range1916
Interquartile range (IQR)471.75

Descriptive statistics

Standard deviation380.90061
Coefficient of variation (CV)0.46762042
Kurtosis1.0095598
Mean814.55085
Median Absolute Deviation (MAD)234
Skewness0.35240505
Sum96117
Variance145085.28
MonotonicityNot monotonic
2024-03-13T08:00:12.212975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.1%
1050 2
 
1.7%
462 2
 
1.7%
662 2
 
1.7%
688 2
 
1.7%
814 2
 
1.7%
575 2
 
1.7%
551 1
 
0.8%
1329 1
 
0.8%
499 1
 
0.8%
Other values (97) 97
82.2%
ValueCountFrequency (%)
0 6
5.1%
90 1
 
0.8%
274 1
 
0.8%
323 1
 
0.8%
358 1
 
0.8%
419 1
 
0.8%
425 1
 
0.8%
440 1
 
0.8%
458 1
 
0.8%
462 2
 
1.7%
ValueCountFrequency (%)
1916 1
0.8%
1905 1
0.8%
1875 1
0.8%
1817 1
0.8%
1559 1
0.8%
1441 1
0.8%
1365 1
0.8%
1342 1
0.8%
1330 1
0.8%
1329 1
0.8%

일시금금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12194.195
Minimum0
Maximum29290
Zeros6
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-13T08:00:12.336712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1096.5
Q19046.25
median11920.5
Q315293.25
95-th percentile20694.4
Maximum29290
Range29290
Interquartile range (IQR)6247

Descriptive statistics

Standard deviation5576.9999
Coefficient of variation (CV)0.45734876
Kurtosis1.3676846
Mean12194.195
Median Absolute Deviation (MAD)3250
Skewness0.3405907
Sum1438915
Variance31102928
MonotonicityNot monotonic
2024-03-13T08:00:12.460056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.1%
10991 2
 
1.7%
9396 2
 
1.7%
11225 1
 
0.8%
6645 1
 
0.8%
16533 1
 
0.8%
11574 1
 
0.8%
12737 1
 
0.8%
7060 1
 
0.8%
17632 1
 
0.8%
Other values (101) 101
85.6%
ValueCountFrequency (%)
0 6
5.1%
1290 1
 
0.8%
4752 1
 
0.8%
5175 1
 
0.8%
5192 1
 
0.8%
6452 1
 
0.8%
6540 1
 
0.8%
6645 1
 
0.8%
6829 1
 
0.8%
6944 1
 
0.8%
ValueCountFrequency (%)
29290 1
0.8%
28397 1
0.8%
28320 1
0.8%
27240 1
0.8%
22082 1
0.8%
21320 1
0.8%
20584 1
0.8%
20240 1
0.8%
18599 1
0.8%
18573 1
0.8%

Interactions

2024-03-13T08:00:09.917010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:06.568692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.091070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.822846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.314307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.831317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.424868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.981612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:06.633950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.175585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.891017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.379827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.915985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.491199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:10.058285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:06.708000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.261630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.959666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.464001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.008313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.563776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:10.124822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:06.786374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.329279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.030817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.540385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.093202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.630494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:10.200861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:06.867735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.407969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.100123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.615098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.192724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.701227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:10.267672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:06.936367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.482708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.172075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.688348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.284203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.770844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:10.339057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.010711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:07.752897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.247013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:08.760420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.357490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:00:09.844254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:00:12.558890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도지사명전체수급자수(명)전체금액(백만원)연금수급자수(명)연금금액(백만원)일시금수급자수(명)일시금금액(백만원)
기준년도1.0000.0000.4810.4630.6390.5520.3870.256
지사명0.0001.0000.6910.6800.6430.6570.7240.690
전체수급자수(명)0.4810.6911.0000.9750.8090.7410.9210.787
전체금액(백만원)0.4630.6800.9751.0000.8480.8660.9170.845
연금수급자수(명)0.6390.6430.8090.8481.0000.9800.3750.310
연금금액(백만원)0.5520.6570.7410.8660.9801.0000.2130.201
일시금수급자수(명)0.3870.7240.9210.9170.3750.2131.0000.927
일시금금액(백만원)0.2560.6900.7870.8450.3100.2010.9271.000
2024-03-13T08:00:12.671437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도전체수급자수(명)전체금액(백만원)연금수급자수(명)연금금액(백만원)일시금수급자수(명)일시금금액(백만원)지사명
기준년도1.000-0.571-0.487-0.520-0.486-0.244-0.0710.000
전체수급자수(명)-0.5711.0000.9660.8920.8400.8680.7780.373
전체금액(백만원)-0.4870.9661.0000.9280.8910.8180.7790.363
연금수급자수(명)-0.5200.8920.9281.0000.9710.6940.6350.329
연금금액(백만원)-0.4860.8400.8910.9711.0000.6270.5750.340
일시금수급자수(명)-0.2440.8680.8180.6940.6271.0000.9540.405
일시금금액(백만원)-0.0710.7780.7790.6350.5750.9541.0000.373
지사명0.0000.3730.3630.3290.3400.4050.3731.000

Missing values

2024-03-13T08:00:10.427898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:00:10.522277image/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

기준년도지사명전체수급자수(명)전체금액(백만원)연금수급자수(명)연금금액(백만원)일시금수급자수(명)일시금금액(백만원)
02023고양지사607118793765457011225
12023남양주지사579108002254255710258
22023부천지사12022097955739114820240
32023성남지사10541962970162698418003
42023수원지사835157264582279014905
52023안산지사19673020863917190529290
62023안양지사896157044997484714730
72023용인지사654113142032363410991
82023의정부지사10351957860121497518363
92023파주지사27747763242744752
기준년도지사명전체수급자수(명)전체금액(백만원)연금수급자수(명)연금금액(백만원)일시금수급자수(명)일시금금액(백만원)
1082013의정부지사279143701151527115127616586
1092013평택지사1496266816821208681414595
1102012고양지사119620009678135575186452
1112012부천지사19983220294818047105014155
1122012성남지사224042275119028353105013921
1132012수원지사19413687510922471484912160
1142012안산지사344961757163233437181728320
1152012안양지사24054538915303119587514193
1162012의정부지사266941650146226631120715019
1172012평택지사18563300968111689117521320