Overview

Dataset statistics

Number of variables11
Number of observations234
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.5 KiB
Average record size in memory98.6 B

Variable types

Numeric10
Categorical1

Dataset

Description통계연도,자치구코드,자치구명,현원합계,현원_국공립,현원_사회복지법인,현원_법인단체등,현원_민간,현원_가정,현원_부모협동,현원_직장
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15459/S/1/datasetView.do

Alerts

자치구코드 is highly overall correlated with 자치구명High correlation
현원합계 is highly overall correlated with 현원_국공립 and 2 other fieldsHigh correlation
현원_국공립 is highly overall correlated with 현원합계High correlation
현원_민간 is highly overall correlated with 현원합계 and 1 other fieldsHigh correlation
현원_가정 is highly overall correlated with 현원합계 and 1 other fieldsHigh correlation
현원_부모협동 is highly overall correlated with 자치구명High correlation
현원_직장 is highly overall correlated with 자치구명High correlation
자치구명 is highly overall correlated with 자치구코드 and 2 other fieldsHigh correlation
현원_사회복지법인 has 92 (39.3%) zerosZeros
현원_법인단체등 has 17 (7.3%) zerosZeros
현원_부모협동 has 67 (28.6%) zerosZeros

Reproduction

Analysis started2024-04-06 12:22:04.629773
Analysis finished2024-04-06 12:22:27.361200
Duration22.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Real number (ℝ)

Distinct9
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-06T21:22:27.461952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32020
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5875237
Coefficient of variation (CV)0.0012822219
Kurtosis-1.2306062
Mean2018
Median Absolute Deviation (MAD)2
Skewness0
Sum472212
Variance6.695279
MonotonicityDecreasing
2024-04-06T21:22:27.680394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2022 26
11.1%
2021 26
11.1%
2020 26
11.1%
2019 26
11.1%
2018 26
11.1%
2017 26
11.1%
2016 26
11.1%
2015 26
11.1%
2014 26
11.1%
ValueCountFrequency (%)
2014 26
11.1%
2015 26
11.1%
2016 26
11.1%
2017 26
11.1%
2018 26
11.1%
2019 26
11.1%
2020 26
11.1%
2021 26
11.1%
2022 26
11.1%
ValueCountFrequency (%)
2022 26
11.1%
2021 26
11.1%
2020 26
11.1%
2019 26
11.1%
2018 26
11.1%
2017 26
11.1%
2016 26
11.1%
2015 26
11.1%
2014 26
11.1%

자치구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11400.577
Minimum11000
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-06T21:22:27.897709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11000
5-th percentile11110
Q111230
median11395
Q311560
95-th percentile11710
Maximum11740
Range740
Interquartile range (IQR)330

Descriptive statistics

Standard deviation199.94309
Coefficient of variation (CV)0.01753798
Kurtosis-1.0021198
Mean11400.577
Median Absolute Deviation (MAD)165
Skewness-0.047847509
Sum2667735
Variance39977.241
MonotonicityNot monotonic
2024-04-06T21:22:28.124960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
11000 9
 
3.8%
11380 9
 
3.8%
11110 9
 
3.8%
11140 9
 
3.8%
11170 9
 
3.8%
11200 9
 
3.8%
11215 9
 
3.8%
11230 9
 
3.8%
11260 9
 
3.8%
11290 9
 
3.8%
Other values (16) 144
61.5%
ValueCountFrequency (%)
11000 9
3.8%
11110 9
3.8%
11140 9
3.8%
11170 9
3.8%
11200 9
3.8%
11215 9
3.8%
11230 9
3.8%
11260 9
3.8%
11290 9
3.8%
11305 9
3.8%
ValueCountFrequency (%)
11740 9
3.8%
11710 9
3.8%
11680 9
3.8%
11650 9
3.8%
11620 9
3.8%
11590 9
3.8%
11560 9
3.8%
11545 9
3.8%
11530 9
3.8%
11500 9
3.8%

자치구명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
 
9
송파
 
9
강남
 
9
서초
 
9
관악
 
9
Other values (21)
189 

Length

Max length3
Median length2
Mean length2.0769231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row송파
3rd row강남
4th row서초
5th row관악

Common Values

ValueCountFrequency (%)
9
 
3.8%
송파 9
 
3.8%
강남 9
 
3.8%
서초 9
 
3.8%
관악 9
 
3.8%
동작 9
 
3.8%
영등포 9
 
3.8%
금천 9
 
3.8%
구로 9
 
3.8%
강서 9
 
3.8%
Other values (16) 144
61.5%

Length

2024-04-06T21:22:28.373118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9
 
3.8%
송파 9
 
3.8%
중구 9
 
3.8%
용산 9
 
3.8%
성동 9
 
3.8%
광진 9
 
3.8%
동대문 9
 
3.8%
중랑 9
 
3.8%
성북 9
 
3.8%
강북 9
 
3.8%
Other values (16) 144
61.5%

현원합계
Real number (ℝ)

HIGH CORRELATION 

Distinct233
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16615.077
Minimum2642
Maximum243432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-06T21:22:28.611431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2642
5-th percentile3556.85
Q16805
median8605
Q310755.25
95-th percentile15592.8
Maximum243432
Range240790
Interquartile range (IQR)3950.25

Descriptive statistics

Standard deviation40392.042
Coefficient of variation (CV)2.4310475
Kurtosis22.723171
Mean16615.077
Median Absolute Deviation (MAD)1982
Skewness4.9029962
Sum3887928
Variance1.631517 × 109
MonotonicityNot monotonic
2024-04-06T21:22:28.877599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7005 2
 
0.9%
167427 1
 
0.4%
10467 1
 
0.4%
8167 1
 
0.4%
7951 1
 
0.4%
7264 1
 
0.4%
4753 1
 
0.4%
3509 1
 
0.4%
3783 1
 
0.4%
6421 1
 
0.4%
Other values (223) 223
95.3%
ValueCountFrequency (%)
2642 1
0.4%
2712 1
0.4%
2976 1
0.4%
2992 1
0.4%
3240 1
0.4%
3263 1
0.4%
3410 1
0.4%
3446 1
0.4%
3476 1
0.4%
3500 1
0.4%
ValueCountFrequency (%)
243432 1
0.4%
238103 1
0.4%
236550 1
0.4%
234867 1
0.4%
226959 1
0.4%
217444 1
0.4%
196260 1
0.4%
182922 1
0.4%
167427 1
0.4%
15899 1
0.4%

현원_국공립
Real number (ℝ)

HIGH CORRELATION 

Distinct224
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5824.9744
Minimum1288
Maximum85925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-06T21:22:29.156656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1288
5-th percentile1586.2
Q12286.5
median3180
Q33854.5
95-th percentile5320.95
Maximum85925
Range84637
Interquartile range (IQR)1568

Descriptive statistics

Standard deviation14174.439
Coefficient of variation (CV)2.4333909
Kurtosis23.088919
Mean5824.9744
Median Absolute Deviation (MAD)761
Skewness4.9348546
Sum1363044
Variance2.0091474 × 108
MonotonicityNot monotonic
2024-04-06T21:22:29.870660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3512 2
 
0.9%
3791 2
 
0.9%
2948 2
 
0.9%
1737 2
 
0.9%
4307 2
 
0.9%
3268 2
 
0.9%
3667 2
 
0.9%
3838 2
 
0.9%
2383 2
 
0.9%
3566 2
 
0.9%
Other values (214) 214
91.5%
ValueCountFrequency (%)
1288 1
0.4%
1385 1
0.4%
1396 1
0.4%
1400 1
0.4%
1451 1
0.4%
1472 1
0.4%
1494 1
0.4%
1503 1
0.4%
1531 1
0.4%
1551 1
0.4%
ValueCountFrequency (%)
85925 1
0.4%
85501 1
0.4%
85465 1
0.4%
81969 1
0.4%
79732 1
0.4%
74098 1
0.4%
67240 1
0.4%
62556 1
0.4%
59036 1
0.4%
5823 1
0.4%

현원_사회복지법인
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.63248
Minimum0
Maximum2214
Zeros92
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-06T21:22:30.194696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median62.5
Q3102.5
95-th percentile281.4
Maximum2214
Range2214
Interquartile range (IQR)102.5

Descriptive statistics

Standard deviation328.40319
Coefficient of variation (CV)2.5530348
Kurtosis23.678029
Mean128.63248
Median Absolute Deviation (MAD)62.5
Skewness4.79936
Sum30100
Variance107848.65
MonotonicityNot monotonic
2024-04-06T21:22:30.456758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92
39.3%
77 6
 
2.6%
82 5
 
2.1%
78 3
 
1.3%
73 3
 
1.3%
92 3
 
1.3%
220 3
 
1.3%
89 3
 
1.3%
86 3
 
1.3%
24 3
 
1.3%
Other values (91) 110
47.0%
ValueCountFrequency (%)
0 92
39.3%
2 1
 
0.4%
9 1
 
0.4%
17 1
 
0.4%
19 1
 
0.4%
21 1
 
0.4%
22 2
 
0.9%
24 3
 
1.3%
26 2
 
0.9%
27 1
 
0.4%
ValueCountFrequency (%)
2214 1
0.4%
2088 1
0.4%
2005 1
0.4%
1878 1
0.4%
1714 1
0.4%
1580 1
0.4%
1393 1
0.4%
1187 1
0.4%
991 1
0.4%
337 1
0.4%

현원_법인단체등
Real number (ℝ)

ZEROS 

Distinct171
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean390.97436
Minimum0
Maximum6844
Zeros17
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-06T21:22:30.692870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q173
median161
Q3281
95-th percentile927.05
Maximum6844
Range6844
Interquartile range (IQR)208

Descriptive statistics

Standard deviation982.89646
Coefficient of variation (CV)2.5139665
Kurtosis24.16597
Mean390.97436
Median Absolute Deviation (MAD)93.5
Skewness4.8909765
Sum91488
Variance966085.45
MonotonicityNot monotonic
2024-04-06T21:22:31.056318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
7.3%
20 5
 
2.1%
65 3
 
1.3%
73 3
 
1.3%
67 3
 
1.3%
70 3
 
1.3%
147 3
 
1.3%
97 3
 
1.3%
40 3
 
1.3%
56 2
 
0.9%
Other values (161) 189
80.8%
ValueCountFrequency (%)
0 17
7.3%
16 1
 
0.4%
17 1
 
0.4%
20 5
 
2.1%
23 1
 
0.4%
29 1
 
0.4%
33 1
 
0.4%
36 2
 
0.9%
37 1
 
0.4%
38 1
 
0.4%
ValueCountFrequency (%)
6844 1
0.4%
6327 1
0.4%
5848 1
0.4%
5337 1
0.4%
4997 1
0.4%
4872 1
0.4%
4242 1
0.4%
3873 1
0.4%
3404 1
0.4%
1125 1
0.4%

현원_민간
Real number (ℝ)

HIGH CORRELATION 

Distinct231
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6377.1966
Minimum139
Maximum111653
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-06T21:22:31.321173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum139
5-th percentile418.25
Q11882
median3157
Q34658.25
95-th percentile8695.65
Maximum111653
Range111514
Interquartile range (IQR)2776.25

Descriptive statistics

Standard deviation16067.584
Coefficient of variation (CV)2.5195372
Kurtosis27.355899
Mean6377.1966
Median Absolute Deviation (MAD)1326.5
Skewness5.2349659
Sum1492264
Variance2.5816726 × 108
MonotonicityNot monotonic
2024-04-06T21:22:31.635795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1625 2
 
0.9%
5140 2
 
0.9%
1794 2
 
0.9%
47079 1
 
0.4%
4201 1
 
0.4%
4541 1
 
0.4%
1876 1
 
0.4%
1274 1
 
0.4%
364 1
 
0.4%
453 1
 
0.4%
Other values (221) 221
94.4%
ValueCountFrequency (%)
139 1
0.4%
174 1
0.4%
186 1
0.4%
203 1
0.4%
216 1
0.4%
254 1
0.4%
271 1
0.4%
294 1
0.4%
335 1
0.4%
364 1
0.4%
ValueCountFrequency (%)
111653 1
0.4%
105478 1
0.4%
102575 1
0.4%
97215 1
0.4%
87658 1
0.4%
77121 1
0.4%
62749 1
0.4%
54604 1
0.4%
47079 1
0.4%
9747 1
0.4%

현원_가정
Real number (ℝ)

HIGH CORRELATION 

Distinct223
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2870.0427
Minimum35
Maximum52890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-06T21:22:31.893620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile193.45
Q1828.5
median1342
Q32001.25
95-th percentile4758.8
Maximum52890
Range52855
Interquartile range (IQR)1172.75

Descriptive statistics

Standard deviation7298.792
Coefficient of variation (CV)2.5430952
Kurtosis28.616113
Mean2870.0427
Median Absolute Deviation (MAD)552
Skewness5.3074986
Sum671590
Variance53272365
MonotonicityNot monotonic
2024-04-06T21:22:32.294807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
889 2
 
0.9%
909 2
 
0.9%
1670 2
 
0.9%
2182 2
 
0.9%
987 2
 
0.9%
1634 2
 
0.9%
1504 2
 
0.9%
1280 2
 
0.9%
1103 2
 
0.9%
3886 2
 
0.9%
Other values (213) 214
91.5%
ValueCountFrequency (%)
35 1
0.4%
39 1
0.4%
43 1
0.4%
51 1
0.4%
57 1
0.4%
84 1
0.4%
89 1
0.4%
125 1
0.4%
130 1
0.4%
146 1
0.4%
ValueCountFrequency (%)
52890 1
0.4%
50061 1
0.4%
46284 1
0.4%
42804 1
0.4%
38323 1
0.4%
33223 1
0.4%
27106 1
0.4%
23846 1
0.4%
21258 1
0.4%
5874 1
0.4%

현원_부모협동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.726496
Minimum0
Maximum850
Zeros67
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-06T21:22:32.649149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q341.75
95-th percentile143.05
Maximum850
Range850
Interquartile range (IQR)41.75

Descriptive statistics

Standard deviation143.5929
Coefficient of variation (CV)2.4874695
Kurtosis19.944352
Mean57.726496
Median Absolute Deviation (MAD)24
Skewness4.5134474
Sum13508
Variance20618.921
MonotonicityNot monotonic
2024-04-06T21:22:32.964885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
28.6%
16 10
 
4.3%
29 8
 
3.4%
30 7
 
3.0%
23 6
 
2.6%
18 6
 
2.6%
27 6
 
2.6%
40 5
 
2.1%
24 5
 
2.1%
32 4
 
1.7%
Other values (68) 110
47.0%
ValueCountFrequency (%)
0 67
28.6%
6 2
 
0.9%
7 1
 
0.4%
8 2
 
0.9%
9 4
 
1.7%
10 1
 
0.4%
11 1
 
0.4%
15 2
 
0.9%
16 10
 
4.3%
17 4
 
1.7%
ValueCountFrequency (%)
850 1
 
0.4%
826 1
 
0.4%
798 1
 
0.4%
795 1
 
0.4%
760 1
 
0.4%
752 1
 
0.4%
715 1
 
0.4%
661 1
 
0.4%
597 1
 
0.4%
145 3
1.3%

현원_직장
Real number (ℝ)

HIGH CORRELATION 

Distinct209
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean965.52991
Minimum45
Maximum14333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-06T21:22:33.201197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile66
Q1177.25
median293
Q3857.25
95-th percentile2110.75
Maximum14333
Range14288
Interquartile range (IQR)680

Descriptive statistics

Standard deviation2385.967
Coefficient of variation (CV)2.4711476
Kurtosis20.83737
Mean965.52991
Median Absolute Deviation (MAD)176.5
Skewness4.630746
Sum225934
Variance5692838.4
MonotonicityNot monotonic
2024-04-06T21:22:33.491591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237 3
 
1.3%
66 3
 
1.3%
93 3
 
1.3%
245 2
 
0.9%
96 2
 
0.9%
229 2
 
0.9%
209 2
 
0.9%
214 2
 
0.9%
233 2
 
0.9%
151 2
 
0.9%
Other values (199) 211
90.2%
ValueCountFrequency (%)
45 1
0.4%
48 1
0.4%
49 2
0.9%
51 1
0.4%
56 1
0.4%
60 1
0.4%
62 1
0.4%
63 1
0.4%
64 1
0.4%
65 1
0.4%
ValueCountFrequency (%)
14333 1
0.4%
14085 1
0.4%
13709 1
0.4%
13250 1
0.4%
12740 1
0.4%
12129 1
0.4%
11800 1
0.4%
10841 1
0.4%
10080 1
0.4%
2300 1
0.4%

Interactions

2024-04-06T21:22:24.970951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:07.920532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:09.973703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:11.711797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:13.649482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:15.417953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:17.171235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:19.500477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:21.546587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:23.203203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:25.226957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:08.241130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:10.192264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:11.972558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:13.849517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:15.600026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:17.347036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:19.753660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:21.781427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:23.387767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:25.418917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:08.446031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:10.399690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:12.189818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:14.021726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:15.800735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:17.580650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:19.982372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:21.949782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:23.577169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:25.592974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:08.715868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:10.559558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:12.371789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:14.216574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:15.981052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:17.766968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:20.191931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:22.117811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:23.715809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:25.752775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:08.907639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:10.706299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:12.536811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:14.374078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:16.142097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:17.952292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:20.413023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:22.263633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:23.901587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:25.910621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:09.102189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:10.844522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:12.698506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:14.563441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:16.290874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:18.182028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:20.578007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:22.420506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:24.078653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:26.093031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:09.292417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:11.029080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:12.888033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:14.771869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:16.471386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:18.349706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:20.781106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:22.577528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:24.309723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:26.262620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:09.457335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:11.204982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:13.148913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:14.929545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:16.627221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:18.546828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:20.950904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:22.738762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:24.460337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:26.424003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:09.624063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:11.368449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:13.312079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:15.085350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:16.823760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:19.118761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:21.114713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:22.885024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:24.639548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:26.580138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:09.775260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:11.535248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:13.466142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:15.239731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:16.981620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:19.296355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:21.323779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:23.025570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:22:24.795540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T21:22:33.709096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도자치구코드자치구명현원합계현원_국공립현원_사회복지법인현원_법인단체등현원_민간현원_가정현원_부모협동현원_직장
통계연도1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
자치구코드0.0001.0001.000NaNNaN0.4380.373NaN0.1770.3960.406
자치구명0.0001.0001.0000.6730.6730.7150.7080.5310.5780.8530.870
현원합계0.000NaN0.6731.0000.9300.9640.9550.9090.9170.9560.943
현원_국공립0.000NaN0.6730.9301.0000.9170.8710.9090.8760.9210.962
현원_사회복지법인0.0000.4380.7150.9640.9171.0000.9910.9640.9820.9000.871
현원_법인단체등0.0000.3730.7080.9550.8710.9911.0000.9240.9820.9180.886
현원_민간0.000NaN0.5310.9090.9090.9640.9241.0000.9750.8750.829
현원_가정0.0000.1770.5780.9170.8760.9820.9820.9751.0000.9170.894
현원_부모협동0.0000.3960.8530.9560.9210.9000.9180.8750.9171.0000.929
현원_직장0.0000.4060.8700.9430.9620.8710.8860.8290.8940.9291.000
2024-04-06T21:22:33.992319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도자치구코드현원합계현원_국공립현원_사회복지법인현원_법인단체등현원_민간현원_가정현원_부모협동현원_직장자치구명
통계연도1.0000.000-0.3180.353-0.178-0.209-0.437-0.4360.0010.0820.000
자치구코드0.0001.0000.4030.3970.065-0.1770.2320.2680.1740.1340.964
현원합계-0.3180.4031.0000.5320.4910.1380.8680.8940.3750.0380.381
현원_국공립0.3530.3970.5321.0000.084-0.0340.2080.4300.3930.3170.381
현원_사회복지법인-0.1780.0650.4910.0841.0000.3500.4250.4840.1870.0820.366
현원_법인단체등-0.209-0.1770.138-0.0340.3501.0000.1480.1210.1290.1220.359
현원_민간-0.4370.2320.8680.2080.4250.1481.0000.7670.312-0.2520.247
현원_가정-0.4360.2680.8940.4300.4840.1210.7671.0000.3130.0120.261
현원_부모협동0.0010.1740.3750.3930.1870.1290.3120.3131.0000.1080.585
현원_직장0.0820.1340.0380.3170.0820.122-0.2520.0120.1081.0000.613
자치구명0.0000.9640.3810.3810.3660.3590.2470.2610.5850.6131.000

Missing values

2024-04-06T21:22:26.873169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T21:22:27.243630image/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

통계연도자치구코드자치구명현원합계현원_국공립현원_사회복지법인현원_법인단체등현원_민간현원_가정현원_부모협동현원_직장
0202211000167427819699913404470792125859712129
1202211710송파129905823599139451996301046
2202211680강남68463566001625570481037
3202211650서초7077407801741011585571172
4202211620관악63353545017015027200398
5202211590동작60223398063153381238178
6202211560영등포8726383871581681110101977
7202211545금천4726222913735715292950179
8202211530구로894243691732012499138550265
9202211500강서1072943071432903429185052658
통계연도자치구코드자치구명현원합계현원_국공립현원_사회복지법인현원_법인단체등현원_민간현원_가정현원_부모협동현원_직장
224201411380은평135721581120275974717602663
225201411350노원136322849221204447587429192
226201411320도봉956215901959525325423069
227201411305강북86171603114181536512792748
228201411290성북10877235513911254513261522108
229201411260중랑10930252889249596819580138
230201411230동대문862523980181393119060209
231201411215광진84371737092506013817493
232201411200성동716730980169213915040257
233201411170용산5038140075386178689324474