Overview

Dataset statistics

Number of variables19
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory177.1 B

Variable types

Numeric18
Categorical1

Dataset

Description퇴직자 직종별(정무직, 별정직, 일반직, 경찰, 소방 등) 평균재직년수에 대한 데이터입니다. 2001년부터 시작되며 연 단위로 구분됩니다.
Author공무원연금공단
URLhttps://www.data.go.kr/data/15053006/fileData.do

Alerts

연도 is highly correlated with 정무직High correlation
증감평균재직연수 is highly correlated with 증감률(퍼센트)High correlation
증감률(퍼센트) is highly correlated with 증감평균재직연수High correlation
정무직 is highly correlated with 연도 and 1 other fieldsHigh correlation
별정직 is highly correlated with 일반직 and 5 other fieldsHigh correlation
일반직 is highly correlated with 별정직 and 10 other fieldsHigh correlation
경찰 is highly correlated with 일반직 and 10 other fieldsHigh correlation
소방 is highly correlated with 정무직High correlation
교육 is highly correlated with 일반직 and 9 other fieldsHigh correlation
법관검사 is highly correlated with 일반직 and 9 other fieldsHigh correlation
기능직 is highly correlated with 별정직 and 10 other fieldsHigh correlation
공안직 is highly correlated with 별정직 and 11 other fieldsHigh correlation
군무원 is highly correlated with 별정직 and 11 other fieldsHigh correlation
연구직 is highly correlated with 일반직 and 10 other fieldsHigh correlation
지도직 is highly correlated with 별정직 and 10 other fieldsHigh correlation
계약직 is highly correlated with 일반직 and 9 other fieldsHigh correlation
공중보건의 is highly correlated with 별정직 and 10 other fieldsHigh correlation
기타 is highly correlated with 경찰 and 3 other fieldsHigh correlation
연도 has unique values Unique
증감평균재직연수 has 2 (9.5%) zeros Zeros
증감률(퍼센트) has 2 (9.5%) zeros Zeros
정무직 has 6 (28.6%) zeros Zeros
별정직 has 6 (28.6%) zeros Zeros
일반직 has 6 (28.6%) zeros Zeros
경찰 has 6 (28.6%) zeros Zeros
소방 has 10 (47.6%) zeros Zeros
교육 has 6 (28.6%) zeros Zeros
법관검사 has 6 (28.6%) zeros Zeros
기능직 has 6 (28.6%) zeros Zeros
공안직 has 6 (28.6%) zeros Zeros
군무원 has 6 (28.6%) zeros Zeros
연구직 has 6 (28.6%) zeros Zeros
지도직 has 6 (28.6%) zeros Zeros
계약직 has 6 (28.6%) zeros Zeros
기타 has 6 (28.6%) zeros Zeros

Reproduction

Analysis started2022-11-19 09:36:20.220113
Analysis finished2022-11-19 09:36:58.175526
Duration37.96 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

연도
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011
Minimum2001
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:36:58.223910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2002
Q12006
median2011
Q32016
95-th percentile2020
Maximum2021
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.204836823
Coefficient of variation (CV)0.003085448445
Kurtosis-1.2
Mean2011
Median Absolute Deviation (MAD)5
Skewness0
Sum42231
Variance38.5
MonotonicityStrictly increasing
2022-11-19T18:36:58.339447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20011
 
4.8%
20121
 
4.8%
20211
 
4.8%
20031
 
4.8%
20041
 
4.8%
20051
 
4.8%
20061
 
4.8%
20071
 
4.8%
20081
 
4.8%
20091
 
4.8%
Other values (11)11
52.4%
ValueCountFrequency (%)
20011
4.8%
20021
4.8%
20031
4.8%
20041
4.8%
20051
4.8%
20061
4.8%
20071
4.8%
20081
4.8%
20091
4.8%
20101
4.8%
ValueCountFrequency (%)
20211
4.8%
20201
4.8%
20191
4.8%
20181
4.8%
20171
4.8%
20161
4.8%
20151
4.8%
20141
4.8%
20131
4.8%
20121
4.8%

퇴직자평균재직연수
Real number (ℝ≥0)

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23
Minimum16.9
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:36:58.455884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16.9
5-th percentile17.1
Q121.5
median22.7
Q326.1
95-th percentile27.3
Maximum27.7
Range10.8
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation3.243917385
Coefficient of variation (CV)0.1410398863
Kurtosis-0.7320836156
Mean23
Median Absolute Deviation (MAD)2.7
Skewness-0.2432076481
Sum483
Variance10.523
MonotonicityNot monotonic
2022-11-19T18:36:58.538000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
21.52
 
9.5%
16.91
 
4.8%
23.51
 
4.8%
19.31
 
4.8%
19.61
 
4.8%
221
 
4.8%
22.71
 
4.8%
20.71
 
4.8%
21.91
 
4.8%
22.21
 
4.8%
Other values (10)10
47.6%
ValueCountFrequency (%)
16.91
4.8%
17.11
4.8%
19.31
4.8%
19.61
4.8%
20.71
4.8%
21.52
9.5%
21.91
4.8%
221
4.8%
22.21
4.8%
22.71
4.8%
ValueCountFrequency (%)
27.71
4.8%
27.31
4.8%
27.11
4.8%
26.71
4.8%
26.61
4.8%
26.11
4.8%
25.41
4.8%
23.81
4.8%
23.51
4.8%
23.41
4.8%

증감평균재직연수
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3047619048
Minimum-2
Maximum3.5
Zeros2
Zeros (%)9.5%
Negative7
Negative (%)33.3%
Memory size317.0 B
2022-11-19T18:36:58.625062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1.9
Q1-0.2
median0.3
Q31
95-th percentile2.4
Maximum3.5
Range5.5
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.352950919
Coefficient of variation (CV)4.439370204
Kurtosis0.5636245684
Mean0.3047619048
Median Absolute Deviation (MAD)0.7
Skewness0.3088791254
Sum6.4
Variance1.83047619
MonotonicityNot monotonic
2022-11-19T18:36:58.702345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
02
 
9.5%
0.42
 
9.5%
1.22
 
9.5%
0.32
 
9.5%
-1.61
 
4.8%
-1.91
 
4.8%
-1.21
 
4.8%
-0.11
 
4.8%
-0.41
 
4.8%
11
 
4.8%
Other values (7)7
33.3%
ValueCountFrequency (%)
-21
4.8%
-1.91
4.8%
-1.61
4.8%
-1.21
4.8%
-0.41
4.8%
-0.21
4.8%
-0.11
4.8%
02
9.5%
0.32
9.5%
0.42
9.5%
ValueCountFrequency (%)
3.51
4.8%
2.41
4.8%
1.91
4.8%
1.22
9.5%
11
4.8%
0.71
4.8%
0.51
4.8%
0.42
9.5%
0.32
9.5%
02
9.5%

증감률(퍼센트)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.69047619
Minimum-8.8
Maximum14.7
Zeros2
Zeros (%)9.5%
Negative7
Negative (%)33.3%
Memory size317.0 B
2022-11-19T18:36:58.784949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-8.8
5-th percentile-7.6
Q1-1.2
median1.5
Q33.8
95-th percentile14.2
Maximum14.7
Range23.5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.112193122
Coefficient of variation (CV)3.615663537
Kurtosis0.4661239085
Mean1.69047619
Median Absolute Deviation (MAD)2.7
Skewness0.5124533889
Sum35.5
Variance37.35890476
MonotonicityNot monotonic
2022-11-19T18:36:58.870900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
02
 
9.5%
1.41
 
4.8%
-7.61
 
4.8%
14.21
 
4.8%
1.61
 
4.8%
9.71
 
4.8%
2.31
 
4.8%
3.21
 
4.8%
-8.81
 
4.8%
5.81
 
4.8%
Other values (10)10
47.6%
ValueCountFrequency (%)
-8.81
4.8%
-7.61
4.8%
-5.81
4.8%
-4.51
4.8%
-1.51
4.8%
-1.21
4.8%
-0.41
4.8%
02
9.5%
1.41
4.8%
1.51
4.8%
ValueCountFrequency (%)
14.71
4.8%
14.21
4.8%
9.71
4.8%
5.81
4.8%
5.41
4.8%
3.81
4.8%
3.21
4.8%
2.31
4.8%
1.71
4.8%
1.61
4.8%

정무직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.37142857
Minimum0
Maximum21
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:36:58.961780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.7
Q318.9
95-th percentile20.7
Maximum21
Range21
Interquartile range (IQR)18.9

Descriptive statistics

Standard deviation8.491474716
Coefficient of variation (CV)0.8187372316
Kurtosis-1.839889589
Mean10.37142857
Median Absolute Deviation (MAD)7.2
Skewness-0.09693585107
Sum217.8
Variance72.10514286
MonotonicityNot monotonic
2022-11-19T18:36:59.041527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
06
28.6%
18.92
 
9.5%
5.41
 
4.8%
5.61
 
4.8%
5.21
 
4.8%
51
 
4.8%
11.71
 
4.8%
15.81
 
4.8%
211
 
4.8%
20.71
 
4.8%
Other values (5)5
23.8%
ValueCountFrequency (%)
06
28.6%
51
 
4.8%
5.21
 
4.8%
5.41
 
4.8%
5.61
 
4.8%
11.71
 
4.8%
15.81
 
4.8%
161
 
4.8%
171
 
4.8%
17.21
 
4.8%
ValueCountFrequency (%)
211
4.8%
20.71
4.8%
20.41
4.8%
191
4.8%
18.92
9.5%
17.21
4.8%
171
4.8%
161
4.8%
15.81
4.8%
11.71
4.8%

별정직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.585714286
Minimum0
Maximum6.7
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:36:59.122752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.6
Q35.2
95-th percentile6.7
Maximum6.7
Range6.7
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation2.488229434
Coefficient of variation (CV)0.6939285273
Kurtosis-1.245874543
Mean3.585714286
Median Absolute Deviation (MAD)1.2
Skewness-0.5912978224
Sum75.3
Variance6.191285714
MonotonicityNot monotonic
2022-11-19T18:36:59.216165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
06
28.6%
4.93
14.3%
5.22
 
9.5%
6.72
 
9.5%
5.81
 
4.8%
6.11
 
4.8%
5.61
 
4.8%
3.21
 
4.8%
3.51
 
4.8%
3.81
 
4.8%
Other values (2)2
 
9.5%
ValueCountFrequency (%)
06
28.6%
3.21
 
4.8%
3.51
 
4.8%
3.81
 
4.8%
4.21
 
4.8%
4.61
 
4.8%
4.93
14.3%
5.22
 
9.5%
5.61
 
4.8%
5.81
 
4.8%
ValueCountFrequency (%)
6.72
9.5%
6.11
 
4.8%
5.81
 
4.8%
5.61
 
4.8%
5.22
9.5%
4.93
14.3%
4.61
 
4.8%
4.21
 
4.8%
3.81
 
4.8%
3.51
 
4.8%

일반직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.61904762
Minimum0
Maximum30.8
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:36:59.497325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27.2
Q330.2
95-th percentile30.7
Maximum30.8
Range30.8
Interquartile range (IQR)30.2

Descriptive statistics

Standard deviation13.43687535
Coefficient of variation (CV)0.6516729382
Kurtosis-1.085319832
Mean20.61904762
Median Absolute Deviation (MAD)3
Skewness-0.9844606681
Sum433
Variance180.549619
MonotonicityNot monotonic
2022-11-19T18:36:59.579758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
06
28.6%
29.42
 
9.5%
30.22
 
9.5%
271
 
4.8%
27.21
 
4.8%
26.21
 
4.8%
27.11
 
4.8%
27.61
 
4.8%
30.81
 
4.8%
30.61
 
4.8%
Other values (4)4
19.0%
ValueCountFrequency (%)
06
28.6%
26.21
 
4.8%
26.71
 
4.8%
271
 
4.8%
27.11
 
4.8%
27.21
 
4.8%
27.61
 
4.8%
29.42
 
9.5%
29.51
 
4.8%
30.22
 
9.5%
ValueCountFrequency (%)
30.81
4.8%
30.71
4.8%
30.61
4.8%
30.41
4.8%
30.22
9.5%
29.51
4.8%
29.42
9.5%
27.61
4.8%
27.21
4.8%
27.11
4.8%

경찰
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.13333333
Minimum0
Maximum33.9
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:36:59.685832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median29.5
Q332.2
95-th percentile33.9
Maximum33.9
Range33.9
Interquartile range (IQR)32.2

Descriptive statistics

Standard deviation14.52591936
Coefficient of variation (CV)0.6562915375
Kurtosis-1.117697541
Mean22.13333333
Median Absolute Deviation (MAD)4.2
Skewness-0.9387784238
Sum464.8
Variance211.0023333
MonotonicityNot monotonic
2022-11-19T18:36:59.804535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
06
28.6%
33.93
14.3%
28.72
 
9.5%
29.51
 
4.8%
24.81
 
4.8%
27.31
 
4.8%
31.81
 
4.8%
30.61
 
4.8%
33.71
 
4.8%
331
 
4.8%
Other values (3)3
14.3%
ValueCountFrequency (%)
06
28.6%
24.81
 
4.8%
27.31
 
4.8%
28.72
 
9.5%
29.51
 
4.8%
30.61
 
4.8%
30.71
 
4.8%
31.81
 
4.8%
32.11
 
4.8%
32.21
 
4.8%
ValueCountFrequency (%)
33.93
14.3%
33.71
 
4.8%
331
 
4.8%
32.21
 
4.8%
32.11
 
4.8%
31.81
 
4.8%
30.71
 
4.8%
30.61
 
4.8%
29.51
 
4.8%
28.72
9.5%

소방
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.73333333
Minimum0
Maximum30
Zeros10
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:36:59.966017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23.2
Q329.1
95-th percentile29.9
Maximum30
Range30
Interquartile range (IQR)29.1

Descriptive statistics

Standard deviation14.47416089
Coefficient of variation (CV)0.9824091099
Kurtosis-2.171759845
Mean14.73333333
Median Absolute Deviation (MAD)6.8
Skewness-0.06973829939
Sum309.4
Variance209.5013333
MonotonicityNot monotonic
2022-11-19T18:37:00.089144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
010
47.6%
29.12
 
9.5%
23.21
 
4.8%
26.91
 
4.8%
25.41
 
4.8%
301
 
4.8%
28.71
 
4.8%
29.71
 
4.8%
29.31
 
4.8%
29.91
 
4.8%
ValueCountFrequency (%)
010
47.6%
23.21
 
4.8%
25.41
 
4.8%
26.91
 
4.8%
28.11
 
4.8%
28.71
 
4.8%
29.12
 
9.5%
29.31
 
4.8%
29.71
 
4.8%
29.91
 
4.8%
ValueCountFrequency (%)
301
4.8%
29.91
4.8%
29.71
4.8%
29.31
4.8%
29.12
9.5%
28.71
4.8%
28.11
4.8%
26.91
4.8%
25.41
4.8%
23.21
4.8%

교육
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.72857143
Minimum0
Maximum31.6
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:37:00.187016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27.3
Q330.4
95-th percentile31.4
Maximum31.6
Range31.6
Interquartile range (IQR)30.4

Descriptive statistics

Standard deviation13.54880596
Coefficient of variation (CV)0.6536295086
Kurtosis-1.096963309
Mean20.72857143
Median Absolute Deviation (MAD)3.4
Skewness-0.9640045854
Sum435.3
Variance183.5701429
MonotonicityNot monotonic
2022-11-19T18:37:00.273950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
06
28.6%
26.22
 
9.5%
27.11
 
4.8%
25.51
 
4.8%
29.41
 
4.8%
27.31
 
4.8%
28.91
 
4.8%
30.71
 
4.8%
311
 
4.8%
28.51
 
4.8%
Other values (5)5
23.8%
ValueCountFrequency (%)
06
28.6%
25.51
 
4.8%
26.22
 
9.5%
27.11
 
4.8%
27.31
 
4.8%
28.51
 
4.8%
28.91
 
4.8%
29.41
 
4.8%
29.81
 
4.8%
30.41
 
4.8%
ValueCountFrequency (%)
31.61
4.8%
31.41
4.8%
31.31
4.8%
311
4.8%
30.71
4.8%
30.41
4.8%
29.81
4.8%
29.41
4.8%
28.91
4.8%
28.51
4.8%

법관검사
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.94285714
Minimum0
Maximum22.4
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:37:00.420258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18.1
Q320.8
95-th percentile22.3
Maximum22.4
Range22.4
Interquartile range (IQR)20.8

Descriptive statistics

Standard deviation9.226514587
Coefficient of variation (CV)0.6617377265
Kurtosis-1.138020331
Mean13.94285714
Median Absolute Deviation (MAD)3.2
Skewness-0.8809572862
Sum292.8
Variance85.12857143
MonotonicityNot monotonic
2022-11-19T18:37:00.550917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
06
28.6%
19.91
 
4.8%
221
 
4.8%
22.21
 
4.8%
22.31
 
4.8%
22.41
 
4.8%
21.31
 
4.8%
20.81
 
4.8%
18.51
 
4.8%
15.21
 
4.8%
Other values (6)6
28.6%
ValueCountFrequency (%)
06
28.6%
15.21
 
4.8%
17.21
 
4.8%
17.41
 
4.8%
17.91
 
4.8%
18.11
 
4.8%
18.51
 
4.8%
18.71
 
4.8%
18.91
 
4.8%
19.91
 
4.8%
ValueCountFrequency (%)
22.41
4.8%
22.31
4.8%
22.21
4.8%
221
4.8%
21.31
4.8%
20.81
4.8%
19.91
4.8%
18.91
4.8%
18.71
4.8%
18.51
4.8%

기능직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15
Minimum0
Maximum23
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:37:00.671366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20.1
Q321
95-th percentile22.4
Maximum23
Range23
Interquartile range (IQR)21

Descriptive statistics

Standard deviation9.76350347
Coefficient of variation (CV)0.6509002313
Kurtosis-1.07799096
Mean15
Median Absolute Deviation (MAD)1.8
Skewness-0.9917648323
Sum315
Variance95.326
MonotonicityNot monotonic
2022-11-19T18:37:00.786979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
06
28.6%
19.93
14.3%
22.42
 
9.5%
212
 
9.5%
20.12
 
9.5%
21.91
 
4.8%
22.21
 
4.8%
231
 
4.8%
20.31
 
4.8%
20.51
 
4.8%
ValueCountFrequency (%)
06
28.6%
19.93
14.3%
20.12
 
9.5%
20.31
 
4.8%
20.41
 
4.8%
20.51
 
4.8%
212
 
9.5%
21.91
 
4.8%
22.21
 
4.8%
22.42
 
9.5%
ValueCountFrequency (%)
231
 
4.8%
22.42
9.5%
22.21
 
4.8%
21.91
 
4.8%
212
9.5%
20.51
 
4.8%
20.41
 
4.8%
20.31
 
4.8%
20.12
9.5%
19.93
14.3%

공안직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.78571429
Minimum0
Maximum29.2
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:37:00.947877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25.8
Q326.5
95-th percentile28.4
Maximum29.2
Range29.2
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation12.25615297
Coefficient of variation (CV)0.6524187895
Kurtosis-1.090983108
Mean18.78571429
Median Absolute Deviation (MAD)1.4
Skewness-0.9772810156
Sum394.5
Variance150.2132857
MonotonicityNot monotonic
2022-11-19T18:37:01.096071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
06
28.6%
26.52
 
9.5%
26.41
 
4.8%
24.51
 
4.8%
22.11
 
4.8%
25.61
 
4.8%
25.11
 
4.8%
29.21
 
4.8%
28.41
 
4.8%
27.21
 
4.8%
Other values (5)5
23.8%
ValueCountFrequency (%)
06
28.6%
22.11
 
4.8%
24.51
 
4.8%
25.11
 
4.8%
25.61
 
4.8%
25.81
 
4.8%
26.11
 
4.8%
26.21
 
4.8%
26.41
 
4.8%
26.52
 
9.5%
ValueCountFrequency (%)
29.21
4.8%
28.41
4.8%
27.91
4.8%
27.21
4.8%
271
4.8%
26.52
9.5%
26.41
4.8%
26.21
4.8%
26.11
4.8%
25.81
4.8%

군무원
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.72380952
Minimum0
Maximum29.8
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:37:01.253984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27.8
Q328.2
95-th percentile29.8
Maximum29.8
Range29.8
Interquartile range (IQR)28.2

Descriptive statistics

Standard deviation12.90522781
Coefficient of variation (CV)0.6542969193
Kurtosis-1.11307039
Mean19.72380952
Median Absolute Deviation (MAD)1.7
Skewness-0.962238765
Sum414.2
Variance166.5449048
MonotonicityNot monotonic
2022-11-19T18:37:01.607123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
06
28.6%
283
14.3%
27.82
 
9.5%
29.82
 
9.5%
28.81
 
4.8%
261
 
4.8%
26.11
 
4.8%
28.91
 
4.8%
29.21
 
4.8%
28.21
 
4.8%
Other values (2)2
 
9.5%
ValueCountFrequency (%)
06
28.6%
21.21
 
4.8%
261
 
4.8%
26.11
 
4.8%
26.61
 
4.8%
27.82
 
9.5%
283
14.3%
28.21
 
4.8%
28.81
 
4.8%
28.91
 
4.8%
ValueCountFrequency (%)
29.82
9.5%
29.21
 
4.8%
28.91
 
4.8%
28.81
 
4.8%
28.21
 
4.8%
283
14.3%
27.82
9.5%
26.61
 
4.8%
26.11
 
4.8%
261
 
4.8%

연구직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.06190476
Minimum0
Maximum27.2
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:37:01.700628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22.2
Q324.9
95-th percentile27.1
Maximum27.2
Range27.2
Interquartile range (IQR)24.9

Descriptive statistics

Standard deviation11.22713125
Coefficient of variation (CV)0.6580233222
Kurtosis-1.117386488
Mean17.06190476
Median Absolute Deviation (MAD)3.7
Skewness-0.9174595569
Sum358.3
Variance126.0484762
MonotonicityNot monotonic
2022-11-19T18:37:01.821725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
06
28.6%
21.12
 
9.5%
21.51
 
4.8%
23.81
 
4.8%
20.21
 
4.8%
22.21
 
4.8%
231
 
4.8%
25.91
 
4.8%
24.51
 
4.8%
27.21
 
4.8%
Other values (5)5
23.8%
ValueCountFrequency (%)
06
28.6%
20.21
 
4.8%
21.12
 
9.5%
21.51
 
4.8%
22.21
 
4.8%
231
 
4.8%
23.51
 
4.8%
23.81
 
4.8%
24.51
 
4.8%
24.91
 
4.8%
ValueCountFrequency (%)
27.21
4.8%
27.11
4.8%
26.31
4.8%
261
4.8%
25.91
4.8%
24.91
4.8%
24.51
4.8%
23.81
4.8%
23.51
4.8%
231
4.8%

지도직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.46190476
Minimum0
Maximum35.4
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:37:01.947933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31.6
Q334.2
95-th percentile35.2
Maximum35.4
Range35.4
Interquartile range (IQR)34.2

Descriptive statistics

Standard deviation15.281933
Coefficient of variation (CV)0.6513509092
Kurtosis-1.081637139
Mean23.46190476
Median Absolute Deviation (MAD)3
Skewness-0.9872827851
Sum492.7
Variance233.5374762
MonotonicityNot monotonic
2022-11-19T18:37:02.067959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
06
28.6%
32.71
 
4.8%
30.31
 
4.8%
35.21
 
4.8%
34.81
 
4.8%
34.21
 
4.8%
35.41
 
4.8%
351
 
4.8%
34.61
 
4.8%
311
 
4.8%
Other values (6)6
28.6%
ValueCountFrequency (%)
06
28.6%
30.31
 
4.8%
30.61
 
4.8%
311
 
4.8%
31.21
 
4.8%
31.61
 
4.8%
31.71
 
4.8%
321
 
4.8%
32.41
 
4.8%
32.71
 
4.8%
ValueCountFrequency (%)
35.41
4.8%
35.21
4.8%
351
4.8%
34.81
4.8%
34.61
4.8%
34.21
4.8%
32.71
4.8%
32.41
4.8%
321
4.8%
31.71
4.8%

계약직
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.414285714
Minimum0
Maximum7.9
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:37:02.193213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.8
Q36.1
95-th percentile7.2
Maximum7.9
Range7.9
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation2.921007654
Coefficient of variation (CV)0.6617169443
Kurtosis-1.091845843
Mean4.414285714
Median Absolute Deviation (MAD)0.6
Skewness-0.8672266001
Sum92.7
Variance8.532285714
MonotonicityNot monotonic
2022-11-19T18:37:02.290679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
06
28.6%
5.73
14.3%
6.13
14.3%
5.82
 
9.5%
5.21
 
4.8%
61
 
4.8%
5.91
 
4.8%
7.91
 
4.8%
7.11
 
4.8%
7.21
 
4.8%
ValueCountFrequency (%)
06
28.6%
5.21
 
4.8%
5.73
14.3%
5.82
 
9.5%
5.91
 
4.8%
61
 
4.8%
6.13
14.3%
6.41
 
4.8%
7.11
 
4.8%
7.21
 
4.8%
ValueCountFrequency (%)
7.91
 
4.8%
7.21
 
4.8%
7.11
 
4.8%
6.41
 
4.8%
6.13
14.3%
61
 
4.8%
5.91
 
4.8%
5.82
9.5%
5.73
14.3%
5.21
 
4.8%

공중보건의
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size296.0 B
3.1
0.0
3.2
3.6
3.7

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
3.19
42.9%
0.06
28.6%
3.24
19.0%
3.61
 
4.8%
3.71
 
4.8%

Length

2022-11-19T18:37:02.421399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-19T18:37:02.568517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.19
42.9%
0.06
28.6%
3.24
19.0%
3.61
 
4.8%
3.71
 
4.8%

기타
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.1047619
Minimum0
Maximum20.7
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size317.0 B
2022-11-19T18:37:02.707878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.1
Q316.9
95-th percentile19
Maximum20.7
Range20.7
Interquartile range (IQR)16.9

Descriptive statistics

Standard deviation7.449394351
Coefficient of variation (CV)0.7372162175
Kurtosis-1.40127954
Mean10.1047619
Median Absolute Deviation (MAD)6.2
Skewness-0.3076746817
Sum212.2
Variance55.49347619
MonotonicityNot monotonic
2022-11-19T18:37:02.824848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
06
28.6%
191
 
4.8%
9.11
 
4.8%
11.11
 
4.8%
13.81
 
4.8%
18.31
 
4.8%
17.91
 
4.8%
15.91
 
4.8%
20.71
 
4.8%
17.31
 
4.8%
Other values (6)6
28.6%
ValueCountFrequency (%)
06
28.6%
7.11
 
4.8%
8.81
 
4.8%
9.11
 
4.8%
101
 
4.8%
11.11
 
4.8%
12.31
 
4.8%
13.81
 
4.8%
141
 
4.8%
15.91
 
4.8%
ValueCountFrequency (%)
20.71
4.8%
191
4.8%
18.31
4.8%
17.91
4.8%
17.31
4.8%
16.91
4.8%
15.91
4.8%
141
4.8%
13.81
4.8%
12.31
4.8%

Interactions

2022-11-19T18:36:55.441324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:20.651426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:22.809434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:25.455495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:27.272526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:29.025101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:31.049870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:32.772777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:34.137981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:36.066184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:38.559514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:40.632859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:42.799001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:44.964965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:47.218655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:49.397769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:51.546829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:53.602908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:55.581172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:20.817997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:23.116768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:25.555202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:27.362894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:29.125674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:31.151752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:32.851914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:34.222738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:36.219040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:38.664175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:40.887588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-19T18:36:50.795783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:53.115755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:54.883368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:56.994942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:22.235424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:24.602158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:26.662823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:28.402396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:30.589446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:32.384133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:33.799528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:35.613007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:37.977947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:40.197169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:42.097007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:44.229096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:46.425719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:48.868759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:50.948881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:53.199688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:54.984920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:57.095222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:22.331436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:24.736196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:26.752426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:28.648614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:30.698944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:32.465711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:33.867309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:35.697689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:38.095775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:40.299325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:42.206517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:44.340273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:46.547218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:48.991740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:51.092918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:53.278455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:55.068809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:57.198524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:22.431012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:24.863882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:27.008174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:28.735142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:30.794422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:32.541615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:33.933251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:35.779162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:38.206861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:40.374701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:42.306793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:44.446738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:46.839139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:49.093465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:51.203366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:53.352671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:55.150548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:57.301496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:22.556623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:25.005683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:27.091206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:28.836944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:30.894308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:32.618953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:34.003239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:35.866856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:38.329572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:40.461532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:42.415473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:44.565724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:46.963451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:49.222941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:51.320697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:53.434207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:55.244639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:57.405367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:22.690136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:25.373010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:27.186656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:28.950011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:30.978733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:32.702918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:34.075649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:35.962917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:38.437837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:40.553351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:42.529297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:44.864158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:47.082842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:49.313951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:51.443357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:53.520257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-19T18:36:55.343334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-19T18:37:02.952577image/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.
2022-11-19T18:37:03.219521image/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.
2022-11-19T18:37:03.729936image/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.
2022-11-19T18:37:04.006803image/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

2022-11-19T18:36:57.796196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-19T18:36:58.071225image/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

연도퇴직자평균재직연수증감평균재직연수증감률(퍼센트)정무직별정직일반직경찰소방교육법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
0200117.10.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
1200216.9-0.2-1.20.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
2200319.32.414.20.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
3200419.60.31.60.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
4200521.51.99.70.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
5200621.50.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
6200722.00.52.35.45.227.028.70.026.215.221.926.527.821.531.05.73.117.3
7200822.70.73.25.66.727.229.50.027.117.222.426.428.823.831.65.23.216.9
8200920.7-2.0-8.85.25.826.224.80.025.518.919.924.528.021.130.65.83.214.0
9201021.91.25.85.06.127.128.70.026.217.922.226.528.021.131.25.73.112.3

Last rows

연도퇴직자평균재직연수증감평균재직연수증감률(퍼센트)정무직별정직일반직경찰소방교육법관검사기능직공안직군무원연구직지도직계약직공중보건의기타
11201223.41.25.415.86.729.431.826.927.317.422.425.629.822.232.46.13.18.8
12201323.80.41.718.94.929.430.625.428.918.723.025.126.123.032.06.03.110.0
13201427.33.514.721.03.230.833.930.030.718.520.329.228.925.934.65.93.220.7
14201527.70.41.520.73.530.633.728.731.019.920.528.429.824.532.75.83.119.0
15201626.1-1.6-5.816.03.830.433.929.728.520.820.427.228.027.235.06.13.115.9
16201727.11.03.820.44.930.733.929.329.821.321.027.929.227.135.47.93.117.9
17201826.7-0.4-1.519.04.630.233.029.130.422.420.126.127.824.934.27.13.118.3
18201926.6-0.1-0.417.05.230.232.129.131.422.319.926.228.226.334.86.13.613.8
19202025.4-1.2-4.518.94.229.532.229.931.622.219.927.026.626.035.27.23.711.1
20202123.5-1.9-7.617.24.926.730.728.131.322.020.125.821.223.530.36.43.29.1