Overview

Dataset statistics

Number of variables18
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory165.2 B

Variable types

Numeric18

Dataset

Description공무원연금기금 연도별 기금 운용 데이터(공공자금기금,주택도시기금,국민투자기금,채권예금,주식,신탁상품 등)입니다.
Author공무원연금공단
URLhttps://www.data.go.kr/data/15052898/fileData.do

Alerts

구분 is highly correlated with 투자유가증권(계) and 1 other fieldsHigh correlation
기금총액 is highly correlated with 주식 and 3 other fieldsHigh correlation
공공금융(계) is highly correlated with 공공자금기금High correlation
공공자금기금 is highly correlated with 공공금융(계)High correlation
투자유가증권(계) is highly correlated with 구분 and 2 other fieldsHigh correlation
채권예금 is highly correlated with 투자유가증권(계)High correlation
주식 is highly correlated with 기금총액 and 1 other fieldsHigh correlation
is highly correlated with 기금총액 and 3 other fieldsHigh correlation
융자사업(연금대출) is highly correlated with 구분 and 2 other fieldsHigh correlation
주택사업 is highly correlated with 기금총액 and 2 other fieldsHigh correlation
시설운영사업 is highly correlated with 기금총액 and 3 other fieldsHigh correlation
구분 has unique values Unique
기금총액 has unique values Unique
투자유가증권(계) has unique values Unique
채권예금 has unique values Unique
주식 has unique values Unique
신탁상품 has unique values Unique
has unique values Unique
주택사업 has unique values Unique
시설운영사업 has unique values Unique
지불준비금 has unique values Unique
공공금융(계) has 3 (7.5%) zeros Zeros
공공자금기금 has 20 (50.0%) zeros Zeros
주택도시기금 has 3 (7.5%) zeros Zeros
국민투자기금 has 34 (85.0%) zeros Zeros
사회간접자본투자 has 20 (50.0%) zeros Zeros
융자사업(연금대출) has 19 (47.5%) zeros Zeros
융자사업(목적대출) has 19 (47.5%) zeros Zeros
학자금대출 has 31 (77.5%) zeros Zeros

Reproduction

Analysis started2022-11-19 08:53:40.666220
Analysis finished2022-11-19 08:54:22.910349
Duration42.24 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

구분
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.5
Minimum1982
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:23.000326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile1983.95
Q11991.75
median2001.5
Q32011.25
95-th percentile2019.05
Maximum2021
Range39
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.69045194
Coefficient of variation (CV)0.005840845338
Kurtosis-1.2
Mean2001.5
Median Absolute Deviation (MAD)10
Skewness0
Sum80060
Variance136.6666667
MonotonicityStrictly increasing
2022-11-19T17:54:23.349609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
19821
 
2.5%
20011
 
2.5%
19841
 
2.5%
19851
 
2.5%
19861
 
2.5%
19871
 
2.5%
19881
 
2.5%
19891
 
2.5%
19901
 
2.5%
19921
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
19821
2.5%
19831
2.5%
19841
2.5%
19851
2.5%
19861
2.5%
19871
2.5%
19881
2.5%
19891
2.5%
19901
2.5%
19911
2.5%
ValueCountFrequency (%)
20211
2.5%
20201
2.5%
20191
2.5%
20181
2.5%
20171
2.5%
20161
2.5%
20151
2.5%
20141
2.5%
20131
2.5%
20121
2.5%

기금총액
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53639.95
Minimum7704
Maximum151752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:23.507085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7704
5-th percentile14507.35
Q127738.75
median47352.5
Q362405.25
95-th percentile121061.9
Maximum151752
Range144048
Interquartile range (IQR)34666.5

Descriptive statistics

Standard deviation35027.86255
Coefficient of variation (CV)0.6530181805
Kurtosis0.7073264187
Mean53639.95
Median Absolute Deviation (MAD)18068.5
Skewness1.120069732
Sum2145598
Variance1226951155
MonotonicityNot monotonic
2022-11-19T17:54:23.661269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
77041
 
2.5%
208961
 
2.5%
146721
 
2.5%
178301
 
2.5%
209511
 
2.5%
244301
 
2.5%
278931
 
2.5%
317791
 
2.5%
357861
 
2.5%
449181
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
77041
2.5%
113791
2.5%
146721
2.5%
177521
2.5%
178301
2.5%
208961
2.5%
209511
2.5%
244301
2.5%
262901
2.5%
272761
2.5%
ValueCountFrequency (%)
1517521
2.5%
1330871
2.5%
1204291
2.5%
1095061
2.5%
1083791
2.5%
1032111
2.5%
875421
2.5%
852721
2.5%
836701
2.5%
635761
2.5%

공공금융(계)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4578.55
Minimum0
Maximum18585
Zeros3
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:23.805182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1100
median425
Q38032.75
95-th percentile17685.5
Maximum18585
Range18585
Interquartile range (IQR)7932.75

Descriptive statistics

Standard deviation6751.06098
Coefficient of variation (CV)1.474497599
Kurtosis-0.3471326969
Mean4578.55
Median Absolute Deviation (MAD)375
Skewness1.167469632
Sum183142
Variance45576824.36
MonotonicityNot monotonic
2022-11-19T17:54:23.913983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10016
40.0%
03
 
7.5%
6003
 
7.5%
153351
 
2.5%
171351
 
2.5%
13541
 
2.5%
15041
 
2.5%
43541
 
2.5%
73441
 
2.5%
98441
 
2.5%
Other values (11)11
27.5%
ValueCountFrequency (%)
03
 
7.5%
10016
40.0%
2501
 
2.5%
6003
 
7.5%
11571
 
2.5%
13541
 
2.5%
13791
 
2.5%
15041
 
2.5%
43541
 
2.5%
73441
 
2.5%
ValueCountFrequency (%)
185851
2.5%
185501
2.5%
176401
2.5%
171351
2.5%
165291
2.5%
161791
2.5%
153351
2.5%
133351
2.5%
118391
2.5%
98441
2.5%

공공자금기금
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4263.55
Minimum0
Maximum17985
Zeros20
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:24.032662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median156
Q37560.25
95-th percentile17133
Maximum17985
Range17985
Interquartile range (IQR)7560.25

Descriptive statistics

Standard deviation6586.272515
Coefficient of variation (CV)1.544786039
Kurtosis-0.3054429962
Mean4263.55
Median Absolute Deviation (MAD)156
Skewness1.190559577
Sum170542
Variance43378985.64
MonotonicityNot monotonic
2022-11-19T17:54:24.123192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
020
50.0%
5003
 
7.5%
147351
 
2.5%
6541
 
2.5%
9041
 
2.5%
37041
 
2.5%
65041
 
2.5%
93041
 
2.5%
113391
 
2.5%
129351
 
2.5%
Other values (9)9
22.5%
ValueCountFrequency (%)
020
50.0%
3121
 
2.5%
4541
 
2.5%
5003
 
7.5%
6541
 
2.5%
9041
 
2.5%
37041
 
2.5%
65041
 
2.5%
69791
 
2.5%
93041
 
2.5%
ValueCountFrequency (%)
179851
2.5%
179501
2.5%
170901
2.5%
165351
2.5%
159791
2.5%
156791
2.5%
147351
2.5%
129351
2.5%
113391
2.5%
93041
2.5%

주택도시기금
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.25
Minimum0
Maximum740
Zeros3
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:24.239885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1100
median100
Q3462.5
95-th percentile600
Maximum740
Range740
Interquartile range (IQR)362.5

Descriptive statistics

Standard deviation219.3023612
Coefficient of variation (CV)0.8267761027
Kurtosis-1.195862562
Mean265.25
Median Absolute Deviation (MAD)100
Skewness0.5851290656
Sum10610
Variance48093.52564
MonotonicityNot monotonic
2022-11-19T17:54:24.360960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10019
47.5%
6004
 
10.0%
03
 
7.5%
3002
 
5.0%
4502
 
5.0%
5002
 
5.0%
5502
 
5.0%
3451
 
2.5%
4351
 
2.5%
7401
 
2.5%
Other values (3)3
 
7.5%
ValueCountFrequency (%)
03
 
7.5%
10019
47.5%
2501
 
2.5%
3002
 
5.0%
3451
 
2.5%
4001
 
2.5%
4351
 
2.5%
4502
 
5.0%
5002
 
5.0%
5401
 
2.5%
ValueCountFrequency (%)
7401
 
2.5%
6004
10.0%
5502
5.0%
5401
 
2.5%
5002
5.0%
4502
5.0%
4351
 
2.5%
4001
 
2.5%
3451
 
2.5%
3002
5.0%

국민투자기금
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.75
Minimum0
Maximum500
Zeros34
Zeros (%)85.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:24.480689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile404.5
Maximum500
Range500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation133.1181758
Coefficient of variation (CV)2.675742226
Kurtosis6.122878389
Mean49.75
Median Absolute Deviation (MAD)0
Skewness2.684019968
Sum1990
Variance17720.44872
MonotonicityDecreasing
2022-11-19T17:54:24.580797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
034
85.0%
5001
 
2.5%
4901
 
2.5%
4001
 
2.5%
3001
 
2.5%
2001
 
2.5%
1001
 
2.5%
ValueCountFrequency (%)
034
85.0%
1001
 
2.5%
2001
 
2.5%
3001
 
2.5%
4001
 
2.5%
4901
 
2.5%
5001
 
2.5%
ValueCountFrequency (%)
5001
 
2.5%
4901
 
2.5%
4001
 
2.5%
3001
 
2.5%
2001
 
2.5%
1001
 
2.5%
034
85.0%

투자유가증권(계)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32060.725
Minimum4687
Maximum82100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:24.687797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4687
5-th percentile7978.35
Q112440.25
median32782.5
Q339920.25
95-th percentile77869.65
Maximum82100
Range77413
Interquartile range (IQR)27480

Descriptive statistics

Standard deviation21840.54976
Coefficient of variation (CV)0.68122445
Kurtosis0.02033692008
Mean32060.725
Median Absolute Deviation (MAD)16574
Skewness0.8549529368
Sum1282429
Variance477009613.6
MonotonicityNot monotonic
2022-11-19T17:54:24.799944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
46871
 
2.5%
350721
 
2.5%
93841
 
2.5%
107181
 
2.5%
103141
 
2.5%
89361
 
2.5%
88361
 
2.5%
79941
 
2.5%
100151
 
2.5%
124971
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
46871
2.5%
76811
2.5%
79941
2.5%
88361
2.5%
89361
2.5%
93841
2.5%
100151
2.5%
103141
2.5%
107181
2.5%
122701
2.5%
ValueCountFrequency (%)
821001
2.5%
785661
2.5%
778331
2.5%
731551
2.5%
725631
2.5%
581821
2.5%
458961
2.5%
428281
2.5%
423611
2.5%
414121
2.5%

채권예금
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16837.25
Minimum831
Maximum37518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:25.153743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum831
5-th percentile3405.7
Q16025.5
median18405.5
Q325705.5
95-th percentile33990
Maximum37518
Range36687
Interquartile range (IQR)19680

Descriptive statistics

Standard deviation11053.18435
Coefficient of variation (CV)0.6564720696
Kurtosis-1.32981569
Mean16837.25
Median Absolute Deviation (MAD)10475
Skewness0.2189205609
Sum673490
Variance122172884.3
MonotonicityNot monotonic
2022-11-19T17:54:25.303488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
44821
 
2.5%
192001
 
2.5%
83341
 
2.5%
90341
 
2.5%
82791
 
2.5%
63201
 
2.5%
42351
 
2.5%
34091
 
2.5%
33431
 
2.5%
42251
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
8311
2.5%
33431
2.5%
34091
2.5%
42251
2.5%
42351
2.5%
44631
2.5%
44821
2.5%
47531
2.5%
52361
2.5%
57991
2.5%
ValueCountFrequency (%)
375181
2.5%
365171
2.5%
338571
2.5%
321601
2.5%
313251
2.5%
298601
2.5%
290331
2.5%
287281
2.5%
270171
2.5%
258451
2.5%

주식
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6717.3
Minimum45
Maximum28052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:25.458627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile79.6
Q1598.5
median2842.5
Q37534.25
95-th percentile27394.55
Maximum28052
Range28007
Interquartile range (IQR)6935.75

Descriptive statistics

Standard deviation8884.561973
Coefficient of variation (CV)1.322638854
Kurtosis0.9140426738
Mean6717.3
Median Absolute Deviation (MAD)2386
Skewness1.501791751
Sum268692
Variance78935441.45
MonotonicityNot monotonic
2022-11-19T17:54:25.590281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
451
 
2.5%
4491
 
2.5%
801
 
2.5%
1141
 
2.5%
1101
 
2.5%
1251
 
2.5%
6101
 
2.5%
4641
 
2.5%
5641
 
2.5%
14811
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
451
2.5%
721
2.5%
801
2.5%
1101
2.5%
1141
2.5%
1251
2.5%
4491
2.5%
4641
2.5%
5501
2.5%
5641
2.5%
ValueCountFrequency (%)
280521
2.5%
274811
2.5%
273901
2.5%
261621
2.5%
232121
2.5%
205881
2.5%
160941
2.5%
138481
2.5%
114451
2.5%
103551
2.5%

신탁상품
Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7503.35
Minimum160
Maximum23550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:25.718320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile962.5
Q14108.75
median6632.5
Q39014.25
95-th percentile17030.45
Maximum23550
Range23390
Interquartile range (IQR)4905.5

Descriptive statistics

Standard deviation5047.950326
Coefficient of variation (CV)0.6727595442
Kurtosis1.674044028
Mean7503.35
Median Absolute Deviation (MAD)2536
Skewness1.188617859
Sum300134
Variance25481802.49
MonotonicityNot monotonic
2022-11-19T17:54:25.887692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1601
 
2.5%
154231
 
2.5%
9701
 
2.5%
15701
 
2.5%
19251
 
2.5%
24911
 
2.5%
39911
 
2.5%
41211
 
2.5%
61081
 
2.5%
67911
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
1601
2.5%
8201
2.5%
9701
2.5%
15701
2.5%
19251
2.5%
24911
2.5%
30361
2.5%
39481
2.5%
39911
2.5%
40721
2.5%
ValueCountFrequency (%)
235501
2.5%
182931
2.5%
169641
2.5%
154231
2.5%
144631
2.5%
131661
2.5%
122621
2.5%
119321
2.5%
97421
2.5%
93781
2.5%

사회간접자본투자
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1002.825
Minimum0
Maximum3300
Zeros20
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:26.049865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36
Q32072.75
95-th percentile2533.4
Maximum3300
Range3300
Interquartile range (IQR)2072.75

Descriptive statistics

Standard deviation1130.032763
Coefficient of variation (CV)1.126849413
Kurtosis-1.687383483
Mean1002.825
Median Absolute Deviation (MAD)36
Skewness0.3541863508
Sum40113
Variance1276974.046
MonotonicityNot monotonic
2022-11-19T17:54:26.175249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
020
50.0%
23991
 
2.5%
26171
 
2.5%
2611
 
2.5%
22131
 
2.5%
33001
 
2.5%
20901
 
2.5%
25291
 
2.5%
18611
 
2.5%
19441
 
2.5%
Other values (11)11
27.5%
ValueCountFrequency (%)
020
50.0%
721
 
2.5%
2611
 
2.5%
18611
 
2.5%
19241
 
2.5%
19381
 
2.5%
19441
 
2.5%
19611
 
2.5%
19881
 
2.5%
20551
 
2.5%
ValueCountFrequency (%)
33001
2.5%
26171
2.5%
25291
2.5%
23991
2.5%
23421
2.5%
22621
2.5%
22131
2.5%
21831
2.5%
21071
2.5%
20901
2.5%


Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15806.65
Minimum1155
Maximum55913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:26.321017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1155
5-th percentile3138.8
Q17614.25
median11119
Q318961.25
95-th percentile36213.25
Maximum55913
Range54758
Interquartile range (IQR)11347

Descriptive statistics

Standard deviation12477.39504
Coefficient of variation (CV)0.7893763095
Kurtosis1.490538844
Mean15806.65
Median Absolute Deviation (MAD)4956.5
Skewness1.358063321
Sum632266
Variance155685387.1
MonotonicityNot monotonic
2022-11-19T17:54:26.459050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
11551
 
2.5%
74001
 
2.5%
33251
 
2.5%
43941
 
2.5%
55651
 
2.5%
70601
 
2.5%
78951
 
2.5%
98781
 
2.5%
104711
 
2.5%
109401
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
11551
2.5%
19381
2.5%
32021
2.5%
33251
2.5%
42281
2.5%
43941
2.5%
55651
2.5%
70601
2.5%
74001
2.5%
74771
2.5%
ValueCountFrequency (%)
559131
2.5%
406071
2.5%
359821
2.5%
358161
2.5%
344071
2.5%
335021
2.5%
311171
2.5%
300211
2.5%
277111
2.5%
204981
2.5%

융자사업(연금대출)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5659
Minimum0
Maximum19376
Zeros19
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:26.572911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5369.5
Q39018
95-th percentile16084.3
Maximum19376
Range19376
Interquartile range (IQR)9018

Descriptive statistics

Standard deviation6137.462263
Coefficient of variation (CV)1.084548907
Kurtosis-1.016728788
Mean5659
Median Absolute Deviation (MAD)5369.5
Skewness0.5532439353
Sum226360
Variance37668443.03
MonotonicityNot monotonic
2022-11-19T17:54:26.873598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
019
47.5%
86321
 
2.5%
193761
 
2.5%
64971
 
2.5%
71801
 
2.5%
76011
 
2.5%
77111
 
2.5%
82411
 
2.5%
84561
 
2.5%
83961
 
2.5%
Other values (12)12
30.0%
ValueCountFrequency (%)
019
47.5%
42421
 
2.5%
64971
 
2.5%
71801
 
2.5%
76011
 
2.5%
77111
 
2.5%
82411
 
2.5%
83121
 
2.5%
83961
 
2.5%
84561
 
2.5%
ValueCountFrequency (%)
193761
2.5%
167171
2.5%
160511
2.5%
150941
2.5%
143131
2.5%
128471
2.5%
127281
2.5%
126701
2.5%
125811
2.5%
101761
2.5%

융자사업(목적대출)
Real number (ℝ≥0)

ZEROS

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1123.875
Minimum0
Maximum6643
Zeros19
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:27.024698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median204
Q31762.75
95-th percentile4651.55
Maximum6643
Range6643
Interquartile range (IQR)1762.75

Descriptive statistics

Standard deviation1712.80728
Coefficient of variation (CV)1.524019379
Kurtosis2.448860764
Mean1123.875
Median Absolute Deviation (MAD)204
Skewness1.747004025
Sum44955
Variance2933708.779
MonotonicityNot monotonic
2022-11-19T17:54:27.126817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
019
47.5%
23471
 
2.5%
20381
 
2.5%
16931
 
2.5%
25141
 
2.5%
33361
 
2.5%
46071
 
2.5%
54981
 
2.5%
66431
 
2.5%
44211
 
2.5%
Other values (12)12
30.0%
ValueCountFrequency (%)
019
47.5%
721
 
2.5%
3361
 
2.5%
4071
 
2.5%
5141
 
2.5%
5901
 
2.5%
6501
 
2.5%
7801
 
2.5%
8591
 
2.5%
8861
 
2.5%
ValueCountFrequency (%)
66431
2.5%
54981
2.5%
46071
2.5%
44211
2.5%
33361
2.5%
31081
2.5%
25141
2.5%
23471
2.5%
20381
2.5%
19721
2.5%

학자금대출
Real number (ℝ)

ZEROS

Distinct10
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean786.225
Minimum-11141
Maximum12227
Zeros31
Zeros (%)77.5%
Negative3
Negative (%)7.5%
Memory size488.0 B
2022-11-19T17:54:27.224000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-11141
5-th percentile-10304.75
Q10
median0
Q30
95-th percentile11172.65
Maximum12227
Range23368
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5050.839623
Coefficient of variation (CV)6.424165631
Kurtosis1.894841661
Mean786.225
Median Absolute Deviation (MAD)0
Skewness0.2874397952
Sum31449
Variance25510980.9
MonotonicityNot monotonic
2022-11-19T17:54:27.306723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
031
77.5%
88771
 
2.5%
93181
 
2.5%
100201
 
2.5%
111391
 
2.5%
122271
 
2.5%
118121
 
2.5%
-111411
 
2.5%
-105091
 
2.5%
-102941
 
2.5%
ValueCountFrequency (%)
-111411
 
2.5%
-105091
 
2.5%
-102941
 
2.5%
031
77.5%
88771
 
2.5%
93181
 
2.5%
100201
 
2.5%
111391
 
2.5%
118121
 
2.5%
122271
 
2.5%
ValueCountFrequency (%)
122271
 
2.5%
118121
 
2.5%
111391
 
2.5%
100201
 
2.5%
93181
 
2.5%
88771
 
2.5%
031
77.5%
-102941
 
2.5%
-105091
 
2.5%
-111411
 
2.5%

주택사업
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7538.875
Minimum151
Maximum34646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:27.415402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum151
5-th percentile331.15
Q11410.25
median4362
Q36904.5
95-th percentile23225.95
Maximum34646
Range34495
Interquartile range (IQR)5494.25

Descriptive statistics

Standard deviation8741.850233
Coefficient of variation (CV)1.159569595
Kurtosis1.233504594
Mean7538.875
Median Absolute Deviation (MAD)2914.5
Skewness1.461142126
Sum301555
Variance76419945.5
MonotonicityNot monotonic
2022-11-19T17:54:27.529758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3331
 
2.5%
1511
 
2.5%
7411
 
2.5%
9351
 
2.5%
12361
 
2.5%
14641
 
2.5%
12491
 
2.5%
17241
 
2.5%
43841
 
2.5%
67751
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
1511
2.5%
2961
2.5%
3331
2.5%
5101
2.5%
5481
2.5%
7411
2.5%
9351
2.5%
10441
2.5%
12361
2.5%
12491
2.5%
ValueCountFrequency (%)
346461
2.5%
237381
2.5%
231991
2.5%
228911
2.5%
218881
2.5%
213801
2.5%
196781
2.5%
173731
2.5%
173511
2.5%
72931
2.5%

시설운영사업
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3883.15
Minimum308
Maximum12185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:27.649906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum308
5-th percentile885.35
Q11780
median2761.5
Q33580.25
95-th percentile10130.3
Maximum12185
Range11877
Interquartile range (IQR)1800.25

Descriptive statistics

Standard deviation3264.215581
Coefficient of variation (CV)0.8406102214
Kurtosis0.3538131106
Mean3883.15
Median Absolute Deviation (MAD)1003
Skewness1.289548972
Sum155326
Variance10655103.36
MonotonicityNot monotonic
2022-11-19T17:54:27.760937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3081
 
2.5%
26711
 
2.5%
8911
 
2.5%
9451
 
2.5%
9931
 
2.5%
9891
 
2.5%
11481
 
2.5%
15111
 
2.5%
16661
 
2.5%
18181
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
3081
2.5%
7781
2.5%
8911
2.5%
9451
2.5%
9891
2.5%
9931
2.5%
11481
2.5%
15111
2.5%
16171
2.5%
16661
2.5%
ValueCountFrequency (%)
121851
2.5%
112001
2.5%
100741
2.5%
96921
2.5%
93661
2.5%
93531
2.5%
82051
2.5%
81081
2.5%
77101
2.5%
38241
2.5%

지불준비금
Real number (ℝ≥0)

UNIQUE

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7121.775
Minimum381
Maximum22787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2022-11-19T17:54:27.878698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum381
5-th percentile569.4
Q11864
median6289.5
Q310530.75
95-th percentile21631.4
Maximum22787
Range22406
Interquartile range (IQR)8666.75

Descriptive statistics

Standard deviation6308.437222
Coefficient of variation (CV)0.8857956368
Kurtosis0.5335260381
Mean7121.775
Median Absolute Deviation (MAD)4296
Skewness1.055230144
Sum284871
Variance39796380.18
MonotonicityNot monotonic
2022-11-19T17:54:27.990825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
7051
 
2.5%
215761
 
2.5%
6091
 
2.5%
12141
 
2.5%
7181
 
2.5%
10901
 
2.5%
13181
 
2.5%
20681
 
2.5%
19651
 
2.5%
43461
 
2.5%
Other values (30)30
75.0%
ValueCountFrequency (%)
3811
2.5%
4251
2.5%
5771
2.5%
6091
2.5%
7051
2.5%
7181
2.5%
10901
2.5%
12141
2.5%
13181
2.5%
15611
2.5%
ValueCountFrequency (%)
227871
2.5%
226841
2.5%
215761
2.5%
175651
2.5%
138351
2.5%
138141
2.5%
129441
2.5%
127311
2.5%
116781
2.5%
105571
2.5%

Interactions

2022-11-19T17:54:20.324465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:41.015272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:43.524449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:46.197990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:48.849333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:51.375025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:53.662895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:55.985348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:58.220566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:00.302797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:02.394636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:04.561235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:06.829936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:08.923716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:11.557089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:13.593413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:15.660132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:18.011682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:20.442230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:41.209788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:43.627692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:46.314200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:48.999510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:51.526212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:53.927208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:56.080422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:58.331573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:00.414858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:02.507306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:04.681963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:06.923562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:09.070379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:11.659905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:13.681715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:15.793234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:18.112267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:20.533621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:41.320154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:43.741951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:46.437825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:49.152263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:51.665231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:54.024248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:56.173311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:58.431412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:00.517326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:02.624545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:04.788208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:07.015094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:09.383226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:11.749186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:13.769242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:15.921643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T17:53:45.745109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:48.255139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:50.865771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:53.286216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:55.417106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:57.612076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:59.867411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:01.961970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:04.123029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:06.331125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:08.473754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:10.843930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:13.054446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:15.239256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:17.598802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:19.883206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:22.129869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:43.138163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:45.858445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:48.391648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:50.980880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:53.376477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:55.502830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:57.927078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:59.986065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:02.057743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:04.229554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:06.448210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:08.576174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:10.937300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:13.326300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:15.338957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:17.702756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:19.996093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:22.220638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:43.287690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:45.997535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:48.555937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:51.133373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:53.477345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:55.594823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:58.020817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:00.115118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:02.183135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:04.341229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:06.574559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:08.696676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:11.128707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:13.419293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:15.457647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:17.829172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:20.114790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:22.313007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:43.414973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:46.110394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:48.696657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:51.250731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:53.582342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:55.907538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:53:58.120902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:00.211252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:02.295496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:04.456341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:06.716966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:08.816333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:11.436583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:13.511019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:15.556978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:17.923784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T17:54:20.232364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-19T17:54:28.104728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-19T17:54:28.497521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-19T17:54:28.760072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-19T17:54:29.022192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-19T17:54:22.502530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-19T17:54:22.807998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

구분기금총액공공금융(계)공공자금기금주택도시기금국민투자기금투자유가증권(계)채권예금주식신탁상품사회간접자본투자융자사업(연금대출)융자사업(목적대출)학자금대출주택사업시설운영사업지불준비금
019827704115731234550046874482451600115505140333308705
1198311379137945443549076816789728200193806500510778381
21984146721354654300400938483348097003325016930741891609
319851783015049043003001071890341141570043940251409359451214
4198620951435437044502001031482791101925055650333601236993718
5198724430734465047401008936632012524910706004607014649891090
619882789398449304540088364235610399107895054980124911481318
71989317791183911339500079943409464412109878066430172415112068
8199035786133351293540001001533435646108010471044210438416661965
9199140436153351473560001227047538566661010418031080569316172413

Last rows

구분기금총액공공금융(계)공공자금기금주택도시기금국민투자기금투자유가증권(계)채권예금주식신탁상품사회간접자본투자융자사업(연금대출)융자사업(목적대출)학자금대출주택사업시설운영사업지불준비금
30201263576600500100038344225541035530362399204981017600649838244134
312013836706005001000380372030211445394823423581612847088772373881089217
32201485272100010003893919095138484072192433502127280931821888820512731
332015875421000100045896225841609451112107277111267001002017351771013835
34201610321110001000581822872820588660422623440712581011139228911007410522
352017109506100010007256333857273909378193831117143130122271967893535726
362018108379100010007783336517261621316619883002115094011812173739366425
3720191204291000100082100375182805214463206735982160510-111412138096922247
3820201330871000100078566321602748116964196140607167170-10509231991120013814
392021151752000073155290332321218293261755913193760-10294346461218522684