Overview

Dataset statistics

Number of variables19
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory172.7 B

Variable types

Categorical1
Numeric18

Dataset

Description공무원이 근무하고 있는 지역(서울,부산, 대전,광주,울산,제주 등)별, 나이별(18세이상~65세이상) 공무원연금에 가입한 수 데이터
Author공무원연금공단
URLhttps://www.data.go.kr/data/15053032/fileData.do

Alerts

is highly correlated with 서울 and 16 other fieldsHigh correlation
서울 is highly correlated with and 16 other fieldsHigh correlation
부산 is highly correlated with and 16 other fieldsHigh correlation
대구 is highly correlated with and 15 other fieldsHigh correlation
인천 is highly correlated with and 16 other fieldsHigh correlation
광주 is highly correlated with and 15 other fieldsHigh correlation
대전 is highly correlated with and 14 other fieldsHigh correlation
세종 is highly correlated with and 12 other fieldsHigh correlation
울산 is highly correlated with and 15 other fieldsHigh correlation
경기 is highly correlated with and 16 other fieldsHigh correlation
강원 is highly correlated with and 16 other fieldsHigh correlation
충북 is highly correlated with and 16 other fieldsHigh correlation
충남 is highly correlated with and 12 other fieldsHigh correlation
경북 is highly correlated with and 15 other fieldsHigh correlation
경남 is highly correlated with and 16 other fieldsHigh correlation
전북 is highly correlated with and 15 other fieldsHigh correlation
전남 is highly correlated with and 13 other fieldsHigh correlation
제주 is highly correlated with and 16 other fieldsHigh correlation
구분 has unique values Unique
has unique values Unique
서울 has unique values Unique
부산 has unique values Unique
인천 has unique values Unique
세종 has unique values Unique
울산 has unique values Unique
경기 has unique values Unique
전북 has unique values Unique
부산 has 1 (2.1%) zeros Zeros
대구 has 1 (2.1%) zeros Zeros
인천 has 1 (2.1%) zeros Zeros
광주 has 1 (2.1%) zeros Zeros
대전 has 1 (2.1%) zeros Zeros
세종 has 1 (2.1%) zeros Zeros
울산 has 1 (2.1%) zeros Zeros
충남 has 1 (2.1%) zeros Zeros
경남 has 1 (2.1%) zeros Zeros
전북 has 1 (2.1%) zeros Zeros
제주 has 1 (2.1%) zeros Zeros

Reproduction

Analysis started2022-11-19 09:46:50.470193
Analysis finished2022-11-19 09:47:32.073798
Duration41.6 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

구분
Categorical

UNIQUE

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size512.0 B
18세이상
 
1
19세
 
1
29세
 
1
20세
 
1
21세
 
1
Other values (43)
43 

Length

Max length5
Median length3
Mean length3.083333333
Min length3

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row18세이상
2nd row19세
3rd row20세
4th row21세
5th row22세

Common Values

ValueCountFrequency (%)
18세이상1
 
2.1%
19세1
 
2.1%
29세1
 
2.1%
20세1
 
2.1%
21세1
 
2.1%
22세1
 
2.1%
23세1
 
2.1%
24세1
 
2.1%
25세1
 
2.1%
26세1
 
2.1%
Other values (38)38
79.2%

Length

2022-11-19T18:47:32.142082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18세이상1
 
2.1%
19세1
 
2.1%
53세1
 
2.1%
44세1
 
2.1%
45세1
 
2.1%
46세1
 
2.1%
47세1
 
2.1%
48세1
 
2.1%
49세1
 
2.1%
50세1
 
2.1%
Other values (38)38
79.2%


Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26279.60417
Minimum33
Maximum43870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:32.299027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile792.1
Q112069.75
median33734
Q337812.25
95-th percentile42701.4
Maximum43870
Range43837
Interquartile range (IQR)25742.5

Descriptive statistics

Standard deviation15192.88691
Coefficient of variation (CV)0.5781246482
Kurtosis-1.020241763
Mean26279.60417
Median Absolute Deviation (MAD)5142.5
Skewness-0.7898024613
Sum1261421
Variance230823812.8
MonotonicityNot monotonic
2022-11-19T18:47:32.460634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
331
 
2.1%
435811
 
2.1%
6921
 
2.1%
9781
 
2.1%
15441
 
2.1%
50371
 
2.1%
104431
 
2.1%
167081
 
2.1%
236941
 
2.1%
300381
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
331
2.1%
4711
2.1%
6921
2.1%
9781
2.1%
10001
2.1%
10261
2.1%
11041
2.1%
15441
2.1%
22481
2.1%
50371
2.1%
ValueCountFrequency (%)
438701
2.1%
435811
2.1%
427981
2.1%
425221
2.1%
399591
2.1%
393181
2.1%
384351
2.1%
383241
2.1%
381381
2.1%
381311
2.1%

서울
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4981.145833
Minimum19
Maximum8144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:32.630208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile166
Q12283.75
median6438
Q37309
95-th percentile7893.85
Maximum8144
Range8125
Interquartile range (IQR)5025.25

Descriptive statistics

Standard deviation2895.577104
Coefficient of variation (CV)0.5813074343
Kurtosis-1.085826647
Mean4981.145833
Median Absolute Deviation (MAD)1189.5
Skewness-0.7625534789
Sum239095
Variance8384366.766
MonotonicityNot monotonic
2022-11-19T18:47:32.804055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
191
 
2.1%
79111
 
2.1%
1451
 
2.1%
2211
 
2.1%
3381
 
2.1%
7731
 
2.1%
16561
 
2.1%
26881
 
2.1%
39841
 
2.1%
53881
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
191
2.1%
841
2.1%
1451
2.1%
2051
2.1%
2211
2.1%
2361
2.1%
3381
2.1%
4391
2.1%
5181
2.1%
7731
2.1%
ValueCountFrequency (%)
81441
2.1%
81291
2.1%
79111
2.1%
78621
2.1%
77851
2.1%
77541
2.1%
76641
2.1%
75911
2.1%
74741
2.1%
74611
2.1%

부산
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE
ZEROS

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1607.583333
Minimum0
Maximum2777
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:32.980382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26.45
Q1811.75
median2057
Q32272
95-th percentile2674.8
Maximum2777
Range2777
Interquartile range (IQR)1460.25

Descriptive statistics

Standard deviation935.981129
Coefficient of variation (CV)0.5822286842
Kurtosis-1.050500172
Mean1607.583333
Median Absolute Deviation (MAD)409.5
Skewness-0.7167452232
Sum77164
Variance876060.6738
MonotonicityNot monotonic
2022-11-19T18:47:33.343816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
01
 
2.1%
27771
 
2.1%
211
 
2.1%
311
 
2.1%
721
 
2.1%
3251
 
2.1%
6341
 
2.1%
8921
 
2.1%
12111
 
2.1%
13961
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
01
2.1%
211
2.1%
241
2.1%
311
2.1%
501
2.1%
721
2.1%
1031
2.1%
1131
2.1%
2001
2.1%
3251
2.1%
ValueCountFrequency (%)
27771
2.1%
26991
2.1%
26931
2.1%
26411
2.1%
26171
2.1%
24951
2.1%
24381
2.1%
24361
2.1%
24191
2.1%
24001
2.1%

대구
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1332.479167
Minimum0
Maximum2483
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:33.508055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.8
Q1516.25
median1653.5
Q31983.5
95-th percentile2388.1
Maximum2483
Range2483
Interquartile range (IQR)1467.25

Descriptive statistics

Standard deviation826.3341756
Coefficient of variation (CV)0.620147914
Kurtosis-1.196773568
Mean1332.479167
Median Absolute Deviation (MAD)447
Skewness-0.5418256362
Sum63959
Variance682828.1698
MonotonicityNot monotonic
2022-11-19T18:47:33.674720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
16492
 
4.2%
01
 
2.1%
24771
 
2.1%
201
 
2.1%
281
 
2.1%
511
 
2.1%
1841
 
2.1%
3971
 
2.1%
5561
 
2.1%
8461
 
2.1%
Other values (37)37
77.1%
ValueCountFrequency (%)
01
2.1%
151
2.1%
201
2.1%
281
2.1%
421
2.1%
511
2.1%
591
2.1%
661
2.1%
1161
2.1%
1841
2.1%
ValueCountFrequency (%)
24831
2.1%
24771
2.1%
24701
2.1%
22361
2.1%
21531
2.1%
21491
2.1%
20841
2.1%
20641
2.1%
20561
2.1%
20521
2.1%

인천
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE
ZEROS

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1319.645833
Minimum0
Maximum2425
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:33.825529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.35
Q1511.25
median1632
Q31916.25
95-th percentile2366.6
Maximum2425
Range2425
Interquartile range (IQR)1405

Descriptive statistics

Standard deviation805.9119716
Coefficient of variation (CV)0.610703229
Kurtosis-1.133852374
Mean1319.645833
Median Absolute Deviation (MAD)403.5
Skewness-0.5624730578
Sum63343
Variance649494.1059
MonotonicityNot monotonic
2022-11-19T18:47:33.991597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
01
 
2.1%
24111
 
2.1%
491
 
2.1%
381
 
2.1%
681
 
2.1%
2391
 
2.1%
4791
 
2.1%
8091
 
2.1%
11391
 
2.1%
15721
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
01
2.1%
211
2.1%
231
2.1%
241
2.1%
351
2.1%
381
2.1%
491
2.1%
681
2.1%
881
2.1%
2391
2.1%
ValueCountFrequency (%)
24251
2.1%
24111
2.1%
24101
2.1%
22861
2.1%
21781
2.1%
21141
2.1%
20401
2.1%
20311
2.1%
20091
2.1%
19971
2.1%

광주
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean855.9583333
Minimum0
Maximum1562
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:34.159662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.4
Q1326.75
median1028.5
Q31295.75
95-th percentile1486
Maximum1562
Range1562
Interquartile range (IQR)969

Descriptive statistics

Standard deviation526.9454313
Coefficient of variation (CV)0.6156204231
Kurtosis-1.25439208
Mean855.9583333
Median Absolute Deviation (MAD)327
Skewness-0.5223566665
Sum41086
Variance277671.4876
MonotonicityNot monotonic
2022-11-19T18:47:34.325454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
10142
 
4.2%
01
 
2.1%
15621
 
2.1%
351
 
2.1%
401
 
2.1%
451
 
2.1%
1151
 
2.1%
2451
 
2.1%
3821
 
2.1%
5711
 
2.1%
Other values (37)37
77.1%
ValueCountFrequency (%)
01
2.1%
301
2.1%
311
2.1%
351
2.1%
401
2.1%
451
2.1%
541
2.1%
701
2.1%
841
2.1%
1151
2.1%
ValueCountFrequency (%)
15621
2.1%
15271
2.1%
14931
2.1%
14731
2.1%
14161
2.1%
14001
2.1%
13641
2.1%
13591
2.1%
13541
2.1%
13401
2.1%

대전
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct45
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1088.958333
Minimum0
Maximum1961
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:34.508899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.7
Q1419.25
median1310
Q31695
95-th percentile1880.55
Maximum1961
Range1961
Interquartile range (IQR)1275.75

Descriptive statistics

Standard deviation680.6812594
Coefficient of variation (CV)0.6250755778
Kurtosis-1.273617931
Mean1088.958333
Median Absolute Deviation (MAD)467.5
Skewness-0.4942499216
Sum52270
Variance463326.977
MonotonicityNot monotonic
2022-11-19T18:47:34.674773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
16902
 
4.2%
18202
 
4.2%
402
 
4.2%
471
 
2.1%
10891
 
2.1%
13921
 
2.1%
381
 
2.1%
531
 
2.1%
1241
 
2.1%
3001
 
2.1%
Other values (35)35
72.9%
ValueCountFrequency (%)
01
2.1%
311
2.1%
381
2.1%
402
4.2%
471
2.1%
531
2.1%
661
2.1%
971
2.1%
1241
2.1%
1821
2.1%
ValueCountFrequency (%)
19611
2.1%
19071
2.1%
18971
2.1%
18501
2.1%
18211
2.1%
18202
4.2%
18101
2.1%
17911
2.1%
17641
2.1%
17271
2.1%

세종
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE
ZEROS

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean672.3125
Minimum0
Maximum1427
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:34.839161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.85
Q1157.75
median884
Q31046.5
95-th percentile1317.4
Maximum1427
Range1427
Interquartile range (IQR)888.75

Descriptive statistics

Standard deviation467.2104704
Coefficient of variation (CV)0.6949305128
Kurtosis-1.44885455
Mean672.3125
Median Absolute Deviation (MAD)297
Skewness-0.2619312222
Sum32271
Variance218285.6237
MonotonicityNot monotonic
2022-11-19T18:47:34.989486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
01
 
2.1%
14271
 
2.1%
191
 
2.1%
301
 
2.1%
211
 
2.1%
911
 
2.1%
1691
 
2.1%
3471
 
2.1%
4571
 
2.1%
7281
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
01
2.1%
41
2.1%
61
2.1%
171
2.1%
191
2.1%
201
2.1%
211
2.1%
231
2.1%
301
2.1%
411
2.1%
ValueCountFrequency (%)
14271
2.1%
13361
2.1%
13301
2.1%
12941
2.1%
11791
2.1%
11541
2.1%
11411
2.1%
11381
2.1%
11251
2.1%
10811
2.1%

울산
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE
ZEROS

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean488.1875
Minimum0
Maximum897
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:35.348838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.05
Q1252.5
median583
Q3728.5
95-th percentile843
Maximum897
Range897
Interquartile range (IQR)476

Descriptive statistics

Standard deviation296.5434672
Coefficient of variation (CV)0.6074376488
Kurtosis-1.113681677
Mean488.1875
Median Absolute Deviation (MAD)187.5
Skewness-0.557535598
Sum23433
Variance87938.02793
MonotonicityNot monotonic
2022-11-19T18:47:35.489164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
01
 
2.1%
8631
 
2.1%
111
 
2.1%
91
 
2.1%
151
 
2.1%
1321
 
2.1%
2671
 
2.1%
3641
 
2.1%
4091
 
2.1%
4611
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
01
2.1%
11
2.1%
41
2.1%
71
2.1%
81
2.1%
91
2.1%
111
2.1%
151
2.1%
311
2.1%
1111
2.1%
ValueCountFrequency (%)
8971
2.1%
8631
2.1%
8501
2.1%
8301
2.1%
8151
2.1%
8041
2.1%
7991
2.1%
7761
2.1%
7651
2.1%
7451
2.1%

경기
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4949.875
Minimum6
Maximum8791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:35.624080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile69.45
Q12046.5
median6252
Q37261
95-th percentile8529.1
Maximum8791
Range8785
Interquartile range (IQR)5214.5

Descriptive statistics

Standard deviation2998.00339
Coefficient of variation (CV)0.6056725452
Kurtosis-1.138365245
Mean4949.875
Median Absolute Deviation (MAD)1487
Skewness-0.6134766916
Sum237594
Variance8988024.324
MonotonicityNot monotonic
2022-11-19T18:47:35.761731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
61
 
2.1%
87661
 
2.1%
1031
 
2.1%
1551
 
2.1%
2611
 
2.1%
10031
 
2.1%
20841
 
2.1%
33291
 
2.1%
48171
 
2.1%
62221
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
61
2.1%
541
2.1%
671
2.1%
741
2.1%
1031
2.1%
1551
2.1%
1561
2.1%
2611
2.1%
2721
2.1%
9671
2.1%
ValueCountFrequency (%)
87911
2.1%
87661
2.1%
87161
2.1%
81821
2.1%
81721
2.1%
78291
2.1%
77291
2.1%
76521
2.1%
76381
2.1%
76101
2.1%

강원
Real number (ℝ≥0)

HIGH CORRELATION

Distinct46
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1138.125
Minimum3
Maximum1810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:35.882964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile29.25
Q1591.75
median1444
Q31635.5
95-th percentile1745.7
Maximum1810
Range1807
Interquartile range (IQR)1043.75

Descriptive statistics

Standard deviation638.1494731
Coefficient of variation (CV)0.5607024475
Kurtosis-0.9356286251
Mean1138.125
Median Absolute Deviation (MAD)247
Skewness-0.8547964591
Sum54630
Variance407234.75
MonotonicityNot monotonic
2022-11-19T18:47:36.001240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
14522
 
4.2%
15002
 
4.2%
241
 
2.1%
16771
 
2.1%
201
 
2.1%
16801
 
2.1%
391
 
2.1%
571
 
2.1%
981
 
2.1%
2631
 
2.1%
Other values (36)36
75.0%
ValueCountFrequency (%)
31
2.1%
201
2.1%
241
2.1%
391
2.1%
571
2.1%
631
2.1%
661
2.1%
981
2.1%
1431
2.1%
2561
2.1%
ValueCountFrequency (%)
18101
2.1%
17751
2.1%
17521
2.1%
17341
2.1%
17311
2.1%
17121
2.1%
16991
2.1%
16831
2.1%
16801
2.1%
16771
2.1%

충북
Real number (ℝ≥0)

HIGH CORRELATION

Distinct46
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean948.5416667
Minimum2
Maximum1557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:36.152666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile24.05
Q1425
median1178.5
Q31361
95-th percentile1484.45
Maximum1557
Range1555
Interquartile range (IQR)936

Descriptive statistics

Standard deviation539.2559081
Coefficient of variation (CV)0.5685105115
Kurtosis-0.9539553443
Mean948.5416667
Median Absolute Deviation (MAD)221
Skewness-0.8356299788
Sum45530
Variance290796.9344
MonotonicityNot monotonic
2022-11-19T18:47:36.301012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
522
 
4.2%
13612
 
4.2%
261
 
2.1%
15031
 
2.1%
231
 
2.1%
14471
 
2.1%
221
 
2.1%
341
 
2.1%
2131
 
2.1%
4341
 
2.1%
Other values (36)36
75.0%
ValueCountFrequency (%)
21
2.1%
221
2.1%
231
2.1%
261
2.1%
341
2.1%
522
4.2%
661
2.1%
931
2.1%
1871
2.1%
2131
2.1%
ValueCountFrequency (%)
15571
2.1%
15331
2.1%
15031
2.1%
14501
2.1%
14471
2.1%
14421
2.1%
14401
2.1%
14061
2.1%
13931
2.1%
13841
2.1%

충남
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct45
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1153.895833
Minimum0
Maximum1995
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:36.524787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.8
Q1607.25
median1407
Q31605.75
95-th percentile1817.9
Maximum1995
Range1995
Interquartile range (IQR)998.5

Descriptive statistics

Standard deviation650.7817636
Coefficient of variation (CV)0.563986579
Kurtosis-0.8318717801
Mean1153.895833
Median Absolute Deviation (MAD)241
Skewness-0.8540839966
Sum55387
Variance423516.9038
MonotonicityNot monotonic
2022-11-19T18:47:36.678782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
332
 
4.2%
15382
 
4.2%
16052
 
4.2%
251
 
2.1%
16251
 
2.1%
15561
 
2.1%
231
 
2.1%
541
 
2.1%
741
 
2.1%
2651
 
2.1%
Other values (35)35
72.9%
ValueCountFrequency (%)
01
2.1%
231
2.1%
251
2.1%
332
4.2%
441
2.1%
541
2.1%
721
2.1%
741
2.1%
2561
2.1%
2651
2.1%
ValueCountFrequency (%)
19951
2.1%
18641
2.1%
18201
2.1%
18141
2.1%
18131
2.1%
17641
2.1%
17631
2.1%
17541
2.1%
16501
2.1%
16431
2.1%

경북
Real number (ℝ≥0)

HIGH CORRELATION

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1344.770833
Minimum2
Maximum2127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:36.811520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile30.7
Q1765.5
median1646
Q31864
95-th percentile2044.95
Maximum2127
Range2125
Interquartile range (IQR)1098.5

Descriptive statistics

Standard deviation738.1634553
Coefficient of variation (CV)0.5489139391
Kurtosis-0.7266332668
Mean1344.770833
Median Absolute Deviation (MAD)255.5
Skewness-0.9567645729
Sum64549
Variance544885.2868
MonotonicityNot monotonic
2022-11-19T18:47:37.134736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
362
 
4.2%
19521
 
2.1%
20461
 
2.1%
301
 
2.1%
881
 
2.1%
1301
 
2.1%
4121
 
2.1%
7611
 
2.1%
11801
 
2.1%
15261
 
2.1%
Other values (37)37
77.1%
ValueCountFrequency (%)
21
2.1%
91
2.1%
301
2.1%
321
2.1%
362
4.2%
881
2.1%
911
2.1%
1301
2.1%
3041
2.1%
4121
2.1%
ValueCountFrequency (%)
21271
2.1%
20891
2.1%
20461
2.1%
20431
2.1%
20391
2.1%
20161
2.1%
20061
2.1%
19731
2.1%
19521
2.1%
19081
2.1%

경남
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1560.208333
Minimum0
Maximum2738
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:37.268981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.55
Q1793.25
median1985
Q32145
95-th percentile2621.95
Maximum2738
Range2738
Interquartile range (IQR)1351.75

Descriptive statistics

Standard deviation898.2458783
Coefficient of variation (CV)0.575721754
Kurtosis-0.9264069562
Mean1560.208333
Median Absolute Deviation (MAD)334.5
Skewness-0.7799048949
Sum74890
Variance806845.6578
MonotonicityNot monotonic
2022-11-19T18:47:37.440820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
19912
 
4.2%
01
 
2.1%
27381
 
2.1%
251
 
2.1%
381
 
2.1%
811
 
2.1%
2651
 
2.1%
6051
 
2.1%
10321
 
2.1%
14971
 
2.1%
Other values (37)37
77.1%
ValueCountFrequency (%)
01
2.1%
111
2.1%
251
2.1%
381
2.1%
401
2.1%
681
2.1%
801
2.1%
811
2.1%
1661
2.1%
2651
2.1%
ValueCountFrequency (%)
27381
2.1%
27191
2.1%
26511
2.1%
25681
2.1%
24291
2.1%
23331
2.1%
23061
2.1%
22751
2.1%
22291
2.1%
22031
2.1%

전북
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE
ZEROS

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.895833
Minimum0
Maximum1958
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:37.663703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.75
Q1693.25
median1423
Q31618.5
95-th percentile1768.95
Maximum1958
Range1958
Interquartile range (IQR)925.25

Descriptive statistics

Standard deviation644.1349548
Coefficient of variation (CV)0.5553386229
Kurtosis-0.8469070305
Mean1159.895833
Median Absolute Deviation (MAD)260.5
Skewness-0.8851330487
Sum55675
Variance414909.84
MonotonicityNot monotonic
2022-11-19T18:47:37.828374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
01
 
2.1%
16671
 
2.1%
401
 
2.1%
521
 
2.1%
681
 
2.1%
2051
 
2.1%
5321
 
2.1%
7471
 
2.1%
11131
 
2.1%
14251
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
01
2.1%
331
2.1%
351
2.1%
401
2.1%
521
2.1%
651
2.1%
681
2.1%
711
2.1%
1581
2.1%
2051
2.1%
ValueCountFrequency (%)
19581
2.1%
18241
2.1%
17701
2.1%
17671
2.1%
17651
2.1%
17341
2.1%
17331
2.1%
17301
2.1%
17001
2.1%
16671
2.1%

전남
Real number (ℝ≥0)

HIGH CORRELATION

Distinct46
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1263.479167
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:38.000576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27.9
Q1744
median1608
Q31743.5
95-th percentile1953.25
Maximum2068
Range2067
Interquartile range (IQR)999.5

Descriptive statistics

Standard deviation689.6950294
Coefficient of variation (CV)0.5458697283
Kurtosis-0.7155560837
Mean1263.479167
Median Absolute Deviation (MAD)191
Skewness-0.9585556006
Sum60647
Variance475679.2336
MonotonicityNot monotonic
2022-11-19T18:47:38.154306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
232
 
4.2%
16112
 
4.2%
371
 
2.1%
18021
 
2.1%
16321
 
2.1%
20681
 
2.1%
491
 
2.1%
581
 
2.1%
941
 
2.1%
3521
 
2.1%
Other values (36)36
75.0%
ValueCountFrequency (%)
11
2.1%
232
4.2%
371
2.1%
391
2.1%
491
2.1%
581
2.1%
941
2.1%
1371
2.1%
3521
2.1%
3861
2.1%
ValueCountFrequency (%)
20681
2.1%
20481
2.1%
20041
2.1%
18591
2.1%
18511
2.1%
18201
2.1%
18071
2.1%
18021
2.1%
17731
2.1%
17591
2.1%

제주
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct44
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean414.5416667
Minimum0
Maximum697
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2022-11-19T18:47:38.311282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.8
Q1197.25
median521
Q3585.25
95-th percentile656.65
Maximum697
Range697
Interquartile range (IQR)388

Descriptive statistics

Standard deviation235.0860375
Coefficient of variation (CV)0.5670986934
Kurtosis-0.9410294482
Mean414.5416667
Median Absolute Deviation (MAD)75
Skewness-0.8370571405
Sum19898
Variance55265.44504
MonotonicityNot monotonic
2022-11-19T18:47:38.473009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
5513
 
6.2%
5992
 
4.2%
5482
 
4.2%
191
 
2.1%
5861
 
2.1%
6421
 
2.1%
131
 
2.1%
51
 
2.1%
231
 
2.1%
761
 
2.1%
Other values (34)34
70.8%
ValueCountFrequency (%)
01
2.1%
21
2.1%
51
2.1%
131
2.1%
191
2.1%
231
2.1%
321
2.1%
371
2.1%
411
2.1%
761
2.1%
ValueCountFrequency (%)
6971
2.1%
6961
2.1%
6641
2.1%
6431
2.1%
6421
2.1%
6251
2.1%
5992
4.2%
5931
2.1%
5911
2.1%
5881
2.1%

Interactions

2022-11-19T18:47:29.286664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:50.991662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.862157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:55.850005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:58.030965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:00.440660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:02.654099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:05.164763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:07.082030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:09.435131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:11.815076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:13.949316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:15.962340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:18.217962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:20.673728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.822520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:24.946465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:27.066265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:29.391678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:51.109643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.999225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:55.932370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:58.136776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:00.577002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:02.823428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:05.250878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:07.210791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:09.535228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:12.063621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:14.057911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:16.047926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:18.328690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:20.799843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.914313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:25.032474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:27.315193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:29.516984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:51.278388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:53.158954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:56.033194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:58.260959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:00.741368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:02.963625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:05.352038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:07.382498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:09.834714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:12.160337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:14.178630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:16.151549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:18.482560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:20.943621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:23.040040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:25.134073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:27.416626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:29.636846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:51.412011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:53.489078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:56.146445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:58.370993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:00.878334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:03.069699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:05.443129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:07.515196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:09.940564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:12.245790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:14.273314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:16.241738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:18.606922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:21.074282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:23.137902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:25.225969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:27.523767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:29.756853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:51.499582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:53.633041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:56.253518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:58.516770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:00.992110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:03.179445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:05.537975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:07.779782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:10.067265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:12.341201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:14.355763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:16.337015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:18.736486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:21.207151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:23.246542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:25.493247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:27.632792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:29.863970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:51.740134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:53.784154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:56.360778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:58.604517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:01.103852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:03.293264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:05.641804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:07.892941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:10.208304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:12.431969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:14.438042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:16.439047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:18.865238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:21.336470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:23.344998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:25.587007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:27.734904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:29.995481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:51.832392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:53.946894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:56.480836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:58.714869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:01.229041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:03.430155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:05.745954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:07.987906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:10.354418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:12.547457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:14.558311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:16.551970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:18.982796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:21.486209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:23.644009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:25.684313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:27.829486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:30.114546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:51.922401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:54.115258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:56.599066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:58.831705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:01.351474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:03.565584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.049559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:08.092221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:10.484764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:12.674469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:14.675166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:16.686755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:19.087351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:21.828125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:23.770233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:25.778363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:27.919155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:30.255844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.010293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:54.269659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:56.717453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:58.974821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:01.471994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:03.681575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.151730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:08.244017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:10.618242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:12.792109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:14.768926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:16.815986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:19.190927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:21.930090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:23.890363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:25.920602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:28.004055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:30.378680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.087197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:54.392663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:56.823333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:59.062368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:01.577012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:03.824281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.225026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:08.359485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:10.753995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:12.874095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:14.847643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:16.931393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:19.457259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.035640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:23.997628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:26.032517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:28.081466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:30.495912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.163065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:54.501927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:56.930328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:59.200754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:01.678044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:04.172793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.307746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:08.475115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:10.889252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:12.956462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:14.927006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:17.044889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:19.577160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.115200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:24.092957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:26.143791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:28.160606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:30.620865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.251504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:54.649139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:57.050410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:59.320702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:01.785101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:04.297971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.400300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:08.597702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:11.043449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:13.066039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:15.019556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:17.171361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:19.704860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.208498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:24.194239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:26.261785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:28.249137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:30.746948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.342532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:54.813063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:57.173121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:59.457448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:02.061606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:04.432926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.492461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:08.721361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:11.203009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:13.178872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:15.113034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:17.489335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:19.840987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.303316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:24.294569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:26.397953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:28.340240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:30.862196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.428396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:54.990067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:57.295855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:59.587451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:02.159379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:04.557820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.585664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:08.853476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:11.359713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:13.289459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:15.211924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:17.621253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:20.012607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.395797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:24.431034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:26.533521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:28.438315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:30.953200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.503835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:55.111198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:57.399037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:59.892365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:02.247704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:04.679712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.668570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:08.970033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:11.459020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:13.382274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:15.297767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:17.738963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:20.136498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.474303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:24.526785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:26.631530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:28.536304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:31.223462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.582814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:55.250308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:57.505466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:00.013042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:02.337096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:04.802158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.763154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:09.069637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:11.546031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:13.480914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:15.581216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:17.864476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:20.260840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.553997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:24.618175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:26.742629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:28.641124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:31.336797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.677191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:55.419074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:57.809090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:00.177149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:02.434502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:04.943520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.870944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:09.183831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:11.641047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:13.591193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:15.738848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:17.978229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:20.401374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.645467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:24.718618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:26.873049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:28.786478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:31.437216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:52.756582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:55.566280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:46:57.913901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:00.294667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:02.536605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:05.067866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:06.957499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:09.312498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:11.719863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:13.678381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:15.848678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:18.093146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:20.520573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:22.725936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:24.823427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:26.963933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:47:28.925401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-19T18:47:38.655366image/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.
2022-11-19T18:47:39.128678image/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.
2022-11-19T18:47:39.394670image/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.
2022-11-19T18:47:39.663573image/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

2022-11-19T18:47:31.671596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-19T18:47:31.971600image/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

구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
018세이상33190000000632020010
119세47184241521303117474202323361133232
220세692145212049353819111033922333025404913
321세9782213128384040309155573454883852585
422세15443387251684553211526198527413081689423
523세503777332518423911512491132100326321326541226520535276
624세1044316566343974792453001692672084507434578761605532636159
725세16708268889255680938248734736433298217481039118010327471028259
826세2369439841211846113957176145740948171071101414671526149711131420391
927세30038538813961151157275896072846162221317113316251830191714251611544

Last rows

구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
3856세3046757012076164913121043124447448848471333121214761810191415891773526
3957세276995426182414301245882108034944145321283101214271627159114651606479
4058세2401947601615119010838218603023303874105184810631486154912831455449
4159세2386549201608118310687009622823683749102785011681445145712961372410
4260세1261224938716485223544591242091934620398617767856750780210
4361세5582100239922626915518241111967256187256304407326386108
4462세2248439200116888497203127214393729116615813741
4563세10262051036624706641676652443680713932
4664세10002361135923544767546366333268653737
4765세이상11045185042353140238156242625940352319