Overview

Dataset statistics

Number of variables19
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory175.6 B

Variable types

Categorical1
Numeric18

Dataset

Description2001년부터 현재까지 공무원이 거주하는 지역(서울,부산,대구,인천, 광주, 세종, 울산, 제주 등)에 대한 퇴직자 수 데이터로 현재기준 지역별 데이터가 포함되어 있습니다.
Author공무원연금공단
URLhttps://www.data.go.kr/data/15053011/fileData.do

Alerts

is highly correlated with 서울 and 11 other fieldsHigh correlation
서울 is highly correlated with High correlation
부산 is highly correlated with and 6 other fieldsHigh correlation
대구 is highly correlated with and 8 other fieldsHigh correlation
인천 is highly correlated with 울산 and 3 other fieldsHigh correlation
광주 is highly correlated with and 7 other fieldsHigh correlation
울산 is highly correlated with 인천 and 4 other fieldsHigh correlation
경기 is highly correlated with and 4 other fieldsHigh correlation
강원 is highly correlated with and 9 other fieldsHigh correlation
충북 is highly correlated with and 6 other fieldsHigh correlation
충남 is highly correlated with and 8 other fieldsHigh correlation
경북 is highly correlated with and 8 other fieldsHigh correlation
경남 is highly correlated with and 8 other fieldsHigh correlation
전북 is highly correlated with and 8 other fieldsHigh correlation
전남 is highly correlated with 광주 and 1 other fieldsHigh correlation
제주 is highly correlated with and 5 other fieldsHigh correlation
구분 has unique values Unique
has unique values Unique
서울 has unique values Unique
부산 has unique values Unique
대구 has unique values Unique
인천 has unique values Unique
광주 has unique values Unique
대전 has unique values Unique
경기 has unique values Unique
충북 has unique values Unique
충남 has unique values Unique
경북 has unique values Unique
경남 has unique values Unique
전북 has unique values Unique
전남 has unique values Unique
제주 has unique values Unique
세종 has 11 (47.8%) zeros Zeros

Reproduction

Analysis started2022-11-19 09:28:04.904124
Analysis finished2022-11-19 09:28:40.732440
Duration35.83 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

구분
Categorical

UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
2001
 
1
2002
 
1
2003
 
1
2004
 
1
2005
 
1
Other values (18)
18 

Length

Max length4
Median length4
Mean length3.739130435
Min length1

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row2001
2nd row2002
3rd row2003
4th row2004
5th row2005

Common Values

ValueCountFrequency (%)
20011
 
4.3%
20021
 
4.3%
20031
 
4.3%
20041
 
4.3%
20051
 
4.3%
20061
 
4.3%
20071
 
4.3%
20081
 
4.3%
20091
 
4.3%
20101
 
4.3%
Other values (13)13
56.5%

Length

2022-11-19T18:28:40.803491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20011
 
4.3%
20131
 
4.3%
1
 
4.3%
20211
 
4.3%
20201
 
4.3%
20191
 
4.3%
20181
 
4.3%
20171
 
4.3%
20161
 
4.3%
20151
 
4.3%
Other values (13)13
56.5%


Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32901.3913
Minimum16202
Maximum47319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:41.135818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16202
5-th percentile23213.5
Q127929
median30909
Q338054
95-th percentile44609.4
Maximum47319
Range31117
Interquartile range (IQR)10125

Descriptive statistics

Standard deviation7753.459221
Coefficient of variation (CV)0.2356574878
Kurtosis-0.3911132563
Mean32901.3913
Median Absolute Deviation (MAD)6025
Skewness-0.0297263312
Sum756732
Variance60116129.89
MonotonicityNot monotonic
2022-11-19T18:28:41.240612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
295091
 
4.3%
293641
 
4.3%
162021
 
4.3%
248991
 
4.3%
273841
 
4.3%
347621
 
4.3%
300211
 
4.3%
309091
 
4.3%
369341
 
4.3%
242801
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
162021
4.3%
230951
4.3%
242801
4.3%
248991
4.3%
261631
4.3%
273841
4.3%
284741
4.3%
293641
4.3%
295091
4.3%
300211
4.3%
ValueCountFrequency (%)
473191
4.3%
446761
4.3%
440101
4.3%
403401
4.3%
397811
4.3%
383981
4.3%
377101
4.3%
370591
4.3%
369341
4.3%
354081
4.3%

서울
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8522.913043
Minimum4068
Maximum11679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:41.392809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4068
5-th percentile6388.4
Q17051
median8438
Q39720.5
95-th percentile11455.4
Maximum11679
Range7611
Interquartile range (IQR)2669.5

Descriptive statistics

Standard deviation1842.412818
Coefficient of variation (CV)0.2161717254
Kurtosis0.09061372529
Mean8522.913043
Median Absolute Deviation (MAD)1298
Skewness-0.3343071538
Sum196027
Variance3394484.992
MonotonicityNot monotonic
2022-11-19T18:28:41.565571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
77801
 
4.3%
72541
 
4.3%
40681
 
4.3%
68481
 
4.3%
78201
 
4.3%
93791
 
4.3%
83201
 
4.3%
81721
 
4.3%
102161
 
4.3%
66081
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
40681
4.3%
63641
4.3%
66081
4.3%
66261
4.3%
68221
4.3%
68481
4.3%
72541
4.3%
77801
4.3%
78201
4.3%
81721
4.3%
ValueCountFrequency (%)
116791
4.3%
115401
4.3%
106941
4.3%
102161
4.3%
99121
4.3%
97361
4.3%
97051
4.3%
95771
4.3%
94361
4.3%
93791
4.3%

부산
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2175.956522
Minimum1267
Maximum3456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:41.699993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1267
5-th percentile1428
Q11646.5
median1987
Q32576.5
95-th percentile3086.9
Maximum3456
Range2189
Interquartile range (IQR)930

Descriptive statistics

Standard deviation602.0016972
Coefficient of variation (CV)0.2766607197
Kurtosis-0.7380614817
Mean2175.956522
Median Absolute Deviation (MAD)451
Skewness0.4232176943
Sum50047
Variance362406.0435
MonotonicityNot monotonic
2022-11-19T18:28:41.812234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
18291
 
4.3%
19871
 
4.3%
12671
 
4.3%
15791
 
4.3%
15381
 
4.3%
25131
 
4.3%
19491
 
4.3%
19351
 
4.3%
23601
 
4.3%
15361
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
12671
4.3%
14161
4.3%
15361
4.3%
15381
4.3%
15791
4.3%
15941
4.3%
16991
4.3%
18291
4.3%
19351
4.3%
19491
4.3%
ValueCountFrequency (%)
34561
4.3%
30921
4.3%
30411
4.3%
28611
4.3%
26861
4.3%
25921
4.3%
25611
4.3%
25131
4.3%
24781
4.3%
23601
4.3%

대구
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1453.347826
Minimum618
Maximum2151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:41.907628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum618
5-th percentile899.1
Q11103
median1447
Q31733.5
95-th percentile2084.6
Maximum2151
Range1533
Interquartile range (IQR)630.5

Descriptive statistics

Standard deviation410.7503786
Coefficient of variation (CV)0.2826235889
Kurtosis-0.6802319035
Mean1453.347826
Median Absolute Deviation (MAD)297
Skewness-0.08063313987
Sum33427
Variance168715.8735
MonotonicityNot monotonic
2022-11-19T18:28:42.032199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
11571
 
4.3%
12231
 
4.3%
6181
 
4.3%
10361
 
4.3%
10331
 
4.3%
17231
 
4.3%
13971
 
4.3%
14471
 
4.3%
16101
 
4.3%
10491
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
6181
4.3%
8881
4.3%
9991
4.3%
10331
4.3%
10361
4.3%
10491
4.3%
11571
4.3%
12231
4.3%
13461
4.3%
13971
4.3%
ValueCountFrequency (%)
21511
4.3%
20931
4.3%
20091
4.3%
19641
4.3%
17511
4.3%
17441
4.3%
17231
4.3%
16971
4.3%
16101
4.3%
15991
4.3%

인천
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1131.173913
Minimum735
Maximum1982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:42.180229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum735
5-th percentile750.6
Q1857
median1094
Q31325.5
95-th percentile1870.9
Maximum1982
Range1247
Interquartile range (IQR)468.5

Descriptive statistics

Standard deviation356.1896395
Coefficient of variation (CV)0.3148849486
Kurtosis0.4524006409
Mean1131.173913
Median Absolute Deviation (MAD)244
Skewness0.9857508589
Sum26017
Variance126871.0593
MonotonicityNot monotonic
2022-11-19T18:28:42.315032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
9001
 
4.3%
13011
 
4.3%
8281
 
4.3%
7901
 
4.3%
7711
 
4.3%
7351
 
4.3%
8871
 
4.3%
11031
 
4.3%
10941
 
4.3%
7491
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
7351
4.3%
7491
4.3%
7651
4.3%
7711
4.3%
7901
4.3%
8281
4.3%
8861
4.3%
8871
4.3%
8961
4.3%
9001
4.3%
ValueCountFrequency (%)
19821
4.3%
19001
4.3%
16091
4.3%
14041
4.3%
13571
4.3%
13381
4.3%
13131
4.3%
13011
4.3%
12031
4.3%
11541
4.3%

광주
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1000.173913
Minimum351
Maximum1400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:42.449712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum351
5-th percentile762
Q1847
median1025
Q31148
95-th percentile1299
Maximum1400
Range1049
Interquartile range (IQR)301

Descriptive statistics

Standard deviation231.5178478
Coefficient of variation (CV)0.2314775908
Kurtosis1.404534067
Mean1000.173913
Median Absolute Deviation (MAD)147
Skewness-0.774102094
Sum23004
Variance53600.51383
MonotonicityNot monotonic
2022-11-19T18:28:42.576952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10231
 
4.3%
8781
 
4.3%
3511
 
4.3%
7751
 
4.3%
8991
 
4.3%
10791
 
4.3%
10441
 
4.3%
9231
 
4.3%
12111
 
4.3%
7711
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
3511
4.3%
7611
4.3%
7711
4.3%
7751
4.3%
7821
4.3%
8161
4.3%
8781
4.3%
8991
4.3%
9231
4.3%
9811
4.3%
ValueCountFrequency (%)
14001
4.3%
13031
4.3%
12631
4.3%
12111
4.3%
12041
4.3%
11671
4.3%
11291
4.3%
11171
4.3%
11021
4.3%
10791
4.3%

대전
Real number (ℝ≥0)

UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1451.782609
Minimum548
Maximum2719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:42.707023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum548
5-th percentile898.2
Q11195
median1330
Q31771.5
95-th percentile2022.6
Maximum2719
Range2171
Interquartile range (IQR)576.5

Descriptive statistics

Standard deviation465.3762853
Coefficient of variation (CV)0.3205550766
Kurtosis1.270034534
Mean1451.782609
Median Absolute Deviation (MAD)307
Skewness0.6580010343
Sum33391
Variance216575.087
MonotonicityNot monotonic
2022-11-19T18:28:43.016149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
13591
 
4.3%
12291
 
4.3%
5481
 
4.3%
10171
 
4.3%
12461
 
4.3%
27191
 
4.3%
11901
 
4.3%
12911
 
4.3%
15151
 
4.3%
10231
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
5481
4.3%
8851
4.3%
10171
4.3%
10231
4.3%
10261
4.3%
11901
4.3%
12001
4.3%
12291
4.3%
12461
4.3%
12911
4.3%
ValueCountFrequency (%)
27191
4.3%
20311
4.3%
19471
4.3%
18791
4.3%
18011
4.3%
17951
4.3%
17481
4.3%
16661
4.3%
16341
4.3%
15151
4.3%

세종
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263.4782609
Minimum0
Maximum924
Zeros11
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:43.102909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q3536.5
95-th percentile785
Maximum924
Range924
Interquartile range (IQR)536.5

Descriptive statistics

Standard deviation316.2840883
Coefficient of variation (CV)1.200418157
Kurtosis-1.081070285
Mean263.4782609
Median Absolute Deviation (MAD)5
Skewness0.6713215353
Sum6060
Variance100035.6245
MonotonicityNot monotonic
2022-11-19T18:28:43.192137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
011
47.8%
51
 
4.3%
1641
 
4.3%
4651
 
4.3%
5261
 
4.3%
5191
 
4.3%
5711
 
4.3%
5471
 
4.3%
6171
 
4.3%
7981
 
4.3%
Other values (3)3
 
13.0%
ValueCountFrequency (%)
011
47.8%
51
 
4.3%
1641
 
4.3%
2561
 
4.3%
4651
 
4.3%
5191
 
4.3%
5261
 
4.3%
5471
 
4.3%
5711
 
4.3%
6171
 
4.3%
ValueCountFrequency (%)
9241
4.3%
7981
4.3%
6681
4.3%
6171
4.3%
5711
4.3%
5471
4.3%
5261
4.3%
5191
4.3%
4651
4.3%
2561
4.3%

울산
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean399.9130435
Minimum236
Maximum685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:43.293584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum236
5-th percentile249.6
Q1280.5
median359
Q3503.5
95-th percentile619.2
Maximum685
Range449
Interquartile range (IQR)223

Descriptive statistics

Standard deviation135.1971332
Coefficient of variation (CV)0.3380663256
Kurtosis-0.8869971795
Mean399.9130435
Median Absolute Deviation (MAD)98
Skewness0.5677789451
Sum9198
Variance18278.26482
MonotonicityNot monotonic
2022-11-19T18:28:43.402143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3592
 
8.7%
2881
 
4.3%
2641
 
4.3%
2781
 
4.3%
2491
 
4.3%
2551
 
4.3%
3271
 
4.3%
4221
 
4.3%
4871
 
4.3%
2361
 
4.3%
Other values (12)12
52.2%
ValueCountFrequency (%)
2361
4.3%
2491
4.3%
2551
4.3%
2641
4.3%
2681
4.3%
2781
4.3%
2831
4.3%
2881
4.3%
3271
4.3%
3331
4.3%
ValueCountFrequency (%)
6851
4.3%
6231
4.3%
5851
4.3%
5541
4.3%
5291
4.3%
5181
4.3%
4891
4.3%
4871
4.3%
4571
4.3%
4221
4.3%

경기
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4806.782609
Minimum2993
Maximum7427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:43.562728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2993
5-th percentile3448.8
Q14133
median4431
Q35386
95-th percentile7193.7
Maximum7427
Range4434
Interquartile range (IQR)1253

Descriptive statistics

Standard deviation1148.789915
Coefficient of variation (CV)0.238993524
Kurtosis0.3916653157
Mean4806.782609
Median Absolute Deviation (MAD)725
Skewness0.852712445
Sum110556
Variance1319718.269
MonotonicityNot monotonic
2022-11-19T18:28:43.694978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
42551
 
4.3%
44961
 
4.3%
29931
 
4.3%
36721
 
4.3%
36861
 
4.3%
40581
 
4.3%
39111
 
4.3%
44311
 
4.3%
48631
 
4.3%
42081
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
29931
4.3%
34241
4.3%
36721
4.3%
36861
4.3%
39111
4.3%
40581
4.3%
42081
4.3%
42551
4.3%
42901
4.3%
43061
4.3%
ValueCountFrequency (%)
74271
4.3%
72991
4.3%
62461
4.3%
60231
4.3%
56711
4.3%
55451
4.3%
52271
4.3%
51561
4.3%
50421
4.3%
48631
4.3%

강원
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1514.695652
Minimum642
Maximum2248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:43.839092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum642
5-th percentile1002.3
Q11203
median1436
Q31813.5
95-th percentile2112.2
Maximum2248
Range1606
Interquartile range (IQR)610.5

Descriptive statistics

Standard deviation412.7925337
Coefficient of variation (CV)0.2725250667
Kurtosis-0.5919426461
Mean1514.695652
Median Absolute Deviation (MAD)290
Skewness0.01373165553
Sum34838
Variance170397.6759
MonotonicityNot monotonic
2022-11-19T18:28:43.982485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
12032
 
8.7%
11641
 
4.3%
6421
 
4.3%
11661
 
4.3%
12881
 
4.3%
15501
 
4.3%
12951
 
4.3%
17261
 
4.3%
9911
 
4.3%
11041
 
4.3%
Other values (12)12
52.2%
ValueCountFrequency (%)
6421
4.3%
9911
4.3%
11041
4.3%
11641
4.3%
11661
4.3%
12032
8.7%
12761
4.3%
12881
4.3%
12951
4.3%
14261
4.3%
ValueCountFrequency (%)
22481
4.3%
21161
4.3%
20781
4.3%
20741
4.3%
18371
4.3%
18201
4.3%
18071
4.3%
17731
4.3%
17261
4.3%
16151
4.3%

충북
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1192.565217
Minimum512
Maximum1879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:44.123643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum512
5-th percentile814.4
Q11013.5
median1183
Q31367
95-th percentile1600.1
Maximum1879
Range1367
Interquartile range (IQR)353.5

Descriptive statistics

Standard deviation304.8772966
Coefficient of variation (CV)0.255648322
Kurtosis0.3993133734
Mean1192.565217
Median Absolute Deviation (MAD)175
Skewness0.06477021333
Sum27429
Variance92950.16601
MonotonicityNot monotonic
2022-11-19T18:28:44.205443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10081
 
4.3%
11491
 
4.3%
5121
 
4.3%
9191
 
4.3%
10191
 
4.3%
15321
 
4.3%
11501
 
4.3%
11981
 
4.3%
13581
 
4.3%
8271
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
5121
4.3%
8131
4.3%
8271
4.3%
9191
4.3%
9231
4.3%
10081
4.3%
10191
4.3%
10251
4.3%
10711
4.3%
11491
4.3%
ValueCountFrequency (%)
18791
4.3%
16021
4.3%
15831
4.3%
15321
4.3%
13861
4.3%
13761
4.3%
13581
4.3%
13461
4.3%
12921
4.3%
12781
4.3%

충남
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1324.434783
Minimum675
Maximum2041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:44.291076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum675
5-th percentile778.4
Q11148.5
median1244
Q31568.5
95-th percentile1951.6
Maximum2041
Range1366
Interquartile range (IQR)420

Descriptive statistics

Standard deviation369.8215572
Coefficient of variation (CV)0.2792297228
Kurtosis-0.4622123032
Mean1324.434783
Median Absolute Deviation (MAD)232
Skewness0.2257575049
Sum30462
Variance136767.9842
MonotonicityNot monotonic
2022-11-19T18:28:44.369326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
12011
 
4.3%
12441
 
4.3%
6751
 
4.3%
8451
 
4.3%
10401
 
4.3%
11441
 
4.3%
12141
 
4.3%
11851
 
4.3%
13231
 
4.3%
8691
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
6751
4.3%
7711
4.3%
8451
4.3%
8691
4.3%
10401
4.3%
11441
4.3%
11531
4.3%
11851
4.3%
11941
4.3%
12011
4.3%
ValueCountFrequency (%)
20411
4.3%
19611
4.3%
18671
4.3%
17281
4.3%
16481
4.3%
16061
4.3%
15311
4.3%
14761
4.3%
13801
4.3%
13661
4.3%

경북
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1809.652174
Minimum753
Maximum2684
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:44.599089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum753
5-th percentile1084.7
Q11518.5
median1690
Q32263.5
95-th percentile2416.4
Maximum2684
Range1931
Interquartile range (IQR)745

Descriptive statistics

Standard deviation515.647308
Coefficient of variation (CV)0.2849427727
Kurtosis-0.8360791326
Mean1809.652174
Median Absolute Deviation (MAD)499
Skewness-0.2335266006
Sum41622
Variance265892.1462
MonotonicityNot monotonic
2022-11-19T18:28:44.672194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
15761
 
4.3%
15781
 
4.3%
7531
 
4.3%
11611
 
4.3%
14741
 
4.3%
23241
 
4.3%
16171
 
4.3%
16901
 
4.3%
21111
 
4.3%
11091
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
7531
4.3%
10821
4.3%
11091
4.3%
11611
4.3%
14291
4.3%
14741
4.3%
15631
4.3%
15761
4.3%
15781
4.3%
16171
4.3%
ValueCountFrequency (%)
26841
4.3%
24181
4.3%
24021
4.3%
23241
4.3%
23101
4.3%
22811
4.3%
22461
4.3%
21891
4.3%
21841
4.3%
21111
4.3%

경남
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1851.086957
Minimum891
Maximum2621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:44.753785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum891
5-th percentile1313.5
Q11578
median1738
Q32159.5
95-th percentile2544.5
Maximum2621
Range1730
Interquartile range (IQR)581.5

Descriptive statistics

Standard deviation447.3816667
Coefficient of variation (CV)0.2416859268
Kurtosis-0.5377526376
Mean1851.086957
Median Absolute Deviation (MAD)337
Skewness0.01592013307
Sum42575
Variance200150.3557
MonotonicityNot monotonic
2022-11-19T18:28:44.834211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
16961
 
4.3%
16461
 
4.3%
8911
 
4.3%
14011
 
4.3%
16071
 
4.3%
16161
 
4.3%
18241
 
4.3%
18951
 
4.3%
24041
 
4.3%
13071
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
8911
4.3%
13071
4.3%
13721
4.3%
14011
4.3%
15321
4.3%
15631
4.3%
15931
4.3%
16071
4.3%
16161
4.3%
16461
4.3%
ValueCountFrequency (%)
26211
4.3%
25581
4.3%
24231
4.3%
24041
4.3%
23591
4.3%
21781
4.3%
21411
4.3%
21401
4.3%
20701
4.3%
18951
4.3%

전북
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1619.565217
Minimum647
Maximum2329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:44.918140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum647
5-th percentile1182.5
Q11382
median1626
Q31892.5
95-th percentile2206.9
Maximum2329
Range1682
Interquartile range (IQR)510.5

Descriptive statistics

Standard deviation382.3651071
Coefficient of variation (CV)0.236091207
Kurtosis0.637167649
Mean1619.565217
Median Absolute Deviation (MAD)254
Skewness-0.3154534886
Sum37250
Variance146203.0751
MonotonicityNot monotonic
2022-11-19T18:28:45.000069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
14541
 
4.3%
13761
 
4.3%
6471
 
4.3%
13621
 
4.3%
14221
 
4.3%
16261
 
4.3%
16001
 
4.3%
16491
 
4.3%
20711
 
4.3%
12231
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
6471
4.3%
11781
4.3%
12231
4.3%
12621
4.3%
13621
4.3%
13761
4.3%
13881
4.3%
14221
4.3%
14541
4.3%
15141
4.3%
ValueCountFrequency (%)
23291
4.3%
22221
4.3%
20711
4.3%
20351
4.3%
19271
4.3%
19051
4.3%
18801
4.3%
18141
4.3%
16881
4.3%
16781
4.3%

전남
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1901.434783
Minimum680
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:45.092616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum680
5-th percentile1455.2
Q11706.5
median1978
Q32140
95-th percentile2484.3
Maximum2500
Range1820
Interquartile range (IQR)433.5

Descriptive statistics

Standard deviation400.1569949
Coefficient of variation (CV)0.2104500236
Kurtosis2.750766876
Mean1901.434783
Median Absolute Deviation (MAD)177
Skewness-1.139375651
Sum43733
Variance160125.6206
MonotonicityNot monotonic
2022-11-19T18:28:45.173977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
19871
 
4.3%
17371
 
4.3%
6801
 
4.3%
16761
 
4.3%
18991
 
4.3%
21471
 
4.3%
19781
 
4.3%
18351
 
4.3%
21271
 
4.3%
14531
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
6801
4.3%
14531
4.3%
14751
4.3%
15061
4.3%
16131
4.3%
16761
4.3%
17371
4.3%
18131
4.3%
18351
4.3%
18571
4.3%
ValueCountFrequency (%)
25001
4.3%
24951
4.3%
23881
4.3%
21981
4.3%
21551
4.3%
21471
4.3%
21331
4.3%
21271
4.3%
20441
4.3%
20371
4.3%

제주
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean482.4347826
Minimum209
Maximum743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2022-11-19T18:28:45.264206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum209
5-th percentile324.3
Q1380
median458
Q3595
95-th percentile696
Maximum743
Range534
Interquartile range (IQR)215

Descriptive statistics

Standard deviation138.3851102
Coefficient of variation (CV)0.2868472905
Kurtosis-0.6213414043
Mean482.4347826
Median Absolute Deviation (MAD)96
Skewness0.2642844101
Sum11096
Variance19150.43874
MonotonicityNot monotonic
2022-11-19T18:28:45.349357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3861
 
4.3%
4761
 
4.3%
2091
 
4.3%
3741
 
4.3%
3931
 
4.3%
3621
 
4.3%
4101
 
4.3%
4381
 
4.3%
4581
 
4.3%
3211
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
2091
4.3%
3211
4.3%
3541
4.3%
3621
4.3%
3681
4.3%
3741
4.3%
3861
4.3%
3931
4.3%
4101
4.3%
4381
4.3%
ValueCountFrequency (%)
7431
4.3%
6971
4.3%
6871
4.3%
6581
4.3%
6191
4.3%
6071
4.3%
5831
4.3%
5621
4.3%
4881
4.3%
4761
4.3%

Interactions

2022-11-19T18:28:38.173098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:05.342655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:07.455886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:09.733178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:11.770806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:13.828828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:15.774795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:17.441352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:19.667003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:21.488644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:23.267759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:24.974927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:26.745184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:28.247921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:29.932956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:31.926706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:33.929368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:35.913729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:38.255604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:05.471449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:07.580261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:09.827852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:11.865149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:13.922220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:15.850435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:17.525175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:19.800812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:21.593122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:23.352592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:25.073812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:26.820312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:28.324913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:30.005885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:32.047535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:34.010879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:36.054572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:38.343043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:05.565528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:07.692307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:09.914903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:12.124192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:14.014268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:15.939120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:17.619374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:19.917748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:21.705791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:23.444850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:25.180413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:28:28.556127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:28:33.428462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:35.350986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:37.683525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:39.662592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:06.944685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:09.311329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:11.248932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:13.296365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:15.258104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:17.103389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:19.120135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:21.132313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:22.929632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:24.595521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:26.425882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:27.944229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:29.478219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:31.114647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:33.529431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:35.486882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:37.811831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:39.776053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:07.068140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:09.430863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:11.379193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:13.390811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:15.510805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:17.187407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:19.253398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:21.229914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:23.014553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:24.700751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:26.506181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:28.020878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:29.554701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:31.462302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:33.641108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:35.571876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:37.906355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:39.887398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:07.179985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:09.548259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:11.515532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:13.483909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:15.604203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:17.270283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:19.390241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:21.313050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:23.099641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:24.804777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:26.585773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:28.097325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:29.629394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:31.595957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:33.749452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:35.664222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:37.997470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:40.003361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:07.316077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:09.646996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:11.660506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:13.737993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:15.692792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:17.355287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:19.534259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:21.398539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:23.187105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:24.893422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:26.667906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:28.175783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:29.704766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:31.772509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:33.849216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:35.790793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:28:38.091205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-19T18:28:45.444930image/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.
2022-11-19T18:28:45.635887image/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.
2022-11-19T18:28:46.086399image/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.
2022-11-19T18:28:46.332978image/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

2022-11-19T18:28:40.282065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-19T18:28:40.631687image/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
020012950977801829115790010231359028342551615100812011576169614541987386
1200223095636414168887657618850268342411648137711082137212621506354
220032489968481579103679077510170278367211669198451161140113621676374
32004273847820153810337718991246024936861288101910401474160714221899393
420053476293792513172373510792719025540581550153211442324161616262147362
520063002183201949139788710441190032739111203115012141617182416001978410
620073090981721935144711039231291042244311295119811851690189516491835438
72008369341021623601610109412111515048748631726135813232111240420712127458
82009242806608153610497497711023023642089918278691109130712231453321
920103003584381963141988610251330033343271203102511941563156315141813439

Last rows

구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
1320144401099123456209314041400203146558562462248187918672418255823292500619
1420154034095773041200913131204179552651855452074137616482246235919272495687
1520163839897362686174413571263180151952952271773129214762281214018142198562
1620173705990332478169712031102187957145751561820127815312189214118802037607
1720183771094362561159913381117163454748956711807134616062184207016782044583
1820193978197052592175116091129166661755460231837138617282310217819052133658
19202047319116793092215119001303194779868574272116160219612684262122222388743
20202144676106942861196419821167174892462372992078158320412402242320352155697
21284746626159413461154816120066835943061436107113661649153213881475488
2216202406812676188283515482562642993642512675753891647680209