Overview

Dataset statistics

Number of variables12
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.8 KiB
Average record size in memory107.9 B

Variable types

Numeric11
Categorical1

Dataset

DescriptionSample
Author고려대학교 세종산학협력단
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KRUEECNMYACTPOPLTN01

Alerts

ATPT_CODE is highly overall correlated with EMPLYM_RATE and 3 other fieldsHigh correlation
EMPLYM_RATE is highly overall correlated with ATPT_CODE and 3 other fieldsHigh correlation
EMPLYM_RATE_15ABOVE_64BELOW is highly overall correlated with ATPT_CODE and 3 other fieldsHigh correlation
TOT_15ABOVE_POPLTN_CO is highly overall correlated with ECNMY_ACT_POPLTN_CO and 4 other fieldsHigh correlation
ECNMY_ACT_POPLTN_CO is highly overall correlated with TOT_15ABOVE_POPLTN_CO and 4 other fieldsHigh correlation
EMPYMN_MAN_CO is highly overall correlated with TOT_15ABOVE_POPLTN_CO and 4 other fieldsHigh correlation
UNEMPL_MAN_CO is highly overall correlated with TOT_15ABOVE_POPLTN_CO and 5 other fieldsHigh correlation
NON_ECNMY_ACT__POPLTN_CO is highly overall correlated with TOT_15ABOVE_POPLTN_CO and 5 other fieldsHigh correlation
ECNMY_ACT_PARTCPT_RATE is highly overall correlated with EMPLYM_RATE and 2 other fieldsHigh correlation
UNEMPL_RATE is highly overall correlated with ATPT_CODE and 2 other fieldsHigh correlation
ATPT_NM is highly overall correlated with ATPT_CODE and 8 other fieldsHigh correlation
EMPLYM_RATE has 9 (6.0%) zerosZeros
EMPLYM_RATE_15ABOVE_64BELOW has 9 (6.0%) zerosZeros
TOT_15ABOVE_POPLTN_CO has 9 (6.0%) zerosZeros
ECNMY_ACT_POPLTN_CO has 9 (6.0%) zerosZeros
EMPYMN_MAN_CO has 9 (6.0%) zerosZeros
UNEMPL_MAN_CO has 9 (6.0%) zerosZeros
NON_ECNMY_ACT__POPLTN_CO has 9 (6.0%) zerosZeros
ECNMY_ACT_PARTCPT_RATE has 9 (6.0%) zerosZeros
UNEMPL_RATE has 9 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:29:37.198212
Analysis finished2023-12-10 06:30:00.310086
Duration23.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDYY
Real number (ℝ)

Distinct9
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201004.92
Minimum201001
Maximum201009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:00.429341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201001
5-th percentile201001
Q1201003
median201005
Q3201007
95-th percentile201009
Maximum201009
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5528508
Coefficient of variation (CV)1.2700439 × 10-5
Kurtosis-1.2115961
Mean201004.92
Median Absolute Deviation (MAD)2
Skewness0.016814645
Sum30150738
Variance6.517047
MonotonicityIncreasing
2023-12-10T15:30:00.693201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
201001 17
11.3%
201002 17
11.3%
201003 17
11.3%
201004 17
11.3%
201005 17
11.3%
201006 17
11.3%
201007 17
11.3%
201008 17
11.3%
201009 14
9.3%
ValueCountFrequency (%)
201001 17
11.3%
201002 17
11.3%
201003 17
11.3%
201004 17
11.3%
201005 17
11.3%
201006 17
11.3%
201007 17
11.3%
201008 17
11.3%
201009 14
9.3%
ValueCountFrequency (%)
201009 14
9.3%
201008 17
11.3%
201007 17
11.3%
201006 17
11.3%
201005 17
11.3%
201004 17
11.3%
201003 17
11.3%
201002 17
11.3%
201001 17
11.3%

ATPT_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.473333
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:00.968132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q129
median41
Q345
95-th percentile49.1
Maximum50
Range39
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.254936
Coefficient of variation (CV)0.28116257
Kurtosis-0.057192817
Mean36.473333
Median Absolute Deviation (MAD)7
Skewness-0.73459667
Sum5471
Variance105.16371
MonotonicityNot monotonic
2023-12-10T15:30:01.200559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11 9
 
6.0%
41 9
 
6.0%
46 9
 
6.0%
45 9
 
6.0%
44 9
 
6.0%
43 9
 
6.0%
26 9
 
6.0%
42 9
 
6.0%
36 9
 
6.0%
31 9
 
6.0%
Other values (7) 60
40.0%
ValueCountFrequency (%)
11 9
6.0%
26 9
6.0%
27 9
6.0%
28 9
6.0%
29 9
6.0%
30 9
6.0%
31 9
6.0%
36 9
6.0%
41 9
6.0%
42 9
6.0%
ValueCountFrequency (%)
50 8
5.3%
48 8
5.3%
47 8
5.3%
46 9
6.0%
45 9
6.0%
44 9
6.0%
43 9
6.0%
42 9
6.0%
41 9
6.0%
36 9
6.0%

EMPLYM_RATE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.336667
Minimum0
Maximum65.8
Zeros9
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:01.452344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q156.7
median58.4
Q360.6
95-th percentile64.7
Maximum65.8
Range65.8
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation14.357181
Coefficient of variation (CV)0.2594515
Kurtosis10.84355
Mean55.336667
Median Absolute Deviation (MAD)2.15
Skewness-3.4577506
Sum8300.5
Variance206.12865
MonotonicityNot monotonic
2023-12-10T15:30:01.699880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
6.0%
59.5 5
 
3.3%
57.7 5
 
3.3%
57.6 4
 
2.7%
57.9 4
 
2.7%
59.9 4
 
2.7%
58.1 4
 
2.7%
61.3 4
 
2.7%
60.0 3
 
2.0%
62.2 3
 
2.0%
Other values (71) 105
70.0%
ValueCountFrequency (%)
0.0 9
6.0%
49.6 1
 
0.7%
49.7 1
 
0.7%
51.7 1
 
0.7%
53.4 1
 
0.7%
53.7 1
 
0.7%
53.9 3
 
2.0%
54.1 2
 
1.3%
54.3 1
 
0.7%
54.5 1
 
0.7%
ValueCountFrequency (%)
65.8 1
 
0.7%
65.5 1
 
0.7%
65.1 1
 
0.7%
65.0 1
 
0.7%
64.9 1
 
0.7%
64.8 1
 
0.7%
64.7 3
2.0%
64.5 1
 
0.7%
64.1 1
 
0.7%
63.5 1
 
0.7%

EMPLYM_RATE_15ABOVE_64BELOW
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.590667
Minimum0
Maximum69.4
Zeros9
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:02.004219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q161.1
median62.7
Q364.7
95-th percentile68.2
Maximum69.4
Range69.4
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation15.337975
Coefficient of variation (CV)0.25738888
Kurtosis11.286022
Mean59.590667
Median Absolute Deviation (MAD)1.95
Skewness-3.5512449
Sum8938.6
Variance235.25347
MonotonicityNot monotonic
2023-12-10T15:30:02.259945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
6.0%
62.6 6
 
4.0%
62.3 5
 
3.3%
61.5 4
 
2.7%
60.7 4
 
2.7%
66.6 4
 
2.7%
61.6 3
 
2.0%
62.2 3
 
2.0%
63.4 3
 
2.0%
60.3 3
 
2.0%
Other values (75) 106
70.7%
ValueCountFrequency (%)
0.0 9
6.0%
57.2 1
 
0.7%
57.7 1
 
0.7%
58.1 1
 
0.7%
58.8 1
 
0.7%
59.2 1
 
0.7%
59.4 1
 
0.7%
59.5 2
 
1.3%
59.6 1
 
0.7%
59.7 3
 
2.0%
ValueCountFrequency (%)
69.4 1
0.7%
69.2 1
0.7%
69.1 1
0.7%
69.0 1
0.7%
68.8 1
0.7%
68.6 1
0.7%
68.3 1
0.7%
68.2 2
1.3%
68.1 1
0.7%
68.0 1
0.7%

ATPT_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
서울
 
9
부산
 
9
대구
 
9
인천
 
9
광주
 
9
Other values (12)
105 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row부산
3rd row대구
4th row인천
5th row광주

Common Values

ValueCountFrequency (%)
서울 9
 
6.0%
부산 9
 
6.0%
대구 9
 
6.0%
인천 9
 
6.0%
광주 9
 
6.0%
대전 9
 
6.0%
울산 9
 
6.0%
세종 9
 
6.0%
경기 9
 
6.0%
강원 9
 
6.0%
Other values (7) 60
40.0%

Length

2023-12-10T15:30:02.508016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 9
 
6.0%
부산 9
 
6.0%
전남 9
 
6.0%
전북 9
 
6.0%
충남 9
 
6.0%
충북 9
 
6.0%
강원 9
 
6.0%
경기 9
 
6.0%
세종 9
 
6.0%
울산 9
 
6.0%
Other values (7) 60
40.0%

TOT_15ABOVE_POPLTN_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct121
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2410.2267
Minimum0
Maximum9432
Zeros9
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:02.730536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11216.25
median1470
Q32233.25
95-th percentile9283.45
Maximum9432
Range9432
Interquartile range (IQR)1017

Descriptive statistics

Standard deviation2528.238
Coefficient of variation (CV)1.0489628
Kurtosis2.7819321
Mean2410.2267
Median Absolute Deviation (MAD)585
Skewness2.0142402
Sum361534
Variance6391987.6
MonotonicityNot monotonic
2023-12-10T15:30:02.984817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
6.0%
2943 5
 
3.3%
1466 3
 
2.0%
434 3
 
2.0%
8536 3
 
2.0%
1462 3
 
2.0%
883 2
 
1.3%
1216 2
 
1.3%
1463 2
 
1.3%
2192 2
 
1.3%
Other values (111) 116
77.3%
ValueCountFrequency (%)
0 9
6.0%
433 1
 
0.7%
434 3
 
2.0%
435 2
 
1.3%
436 1
 
0.7%
437 1
 
0.7%
881 2
 
1.3%
882 1
 
0.7%
883 2
 
1.3%
884 1
 
0.7%
ValueCountFrequency (%)
9432 1
0.7%
9413 1
0.7%
9393 1
0.7%
9372 1
0.7%
9352 1
0.7%
9331 1
0.7%
9312 1
0.7%
9292 1
0.7%
9273 1
0.7%
8545 1
0.7%

ECNMY_ACT_POPLTN_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct129
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1473.4533
Minimum0
Maximum5889
Zeros9
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:03.217018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1708.75
median930.5
Q31448.25
95-th percentile5632.6
Maximum5889
Range5889
Interquartile range (IQR)739.5

Descriptive statistics

Standard deviation1568.4528
Coefficient of variation (CV)1.064474
Kurtosis2.8401047
Mean1473.4533
Median Absolute Deviation (MAD)387
Skewness2.0354136
Sum221018
Variance2460044.1
MonotonicityNot monotonic
2023-12-10T15:30:03.470870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
6.0%
287 4
 
2.7%
542 2
 
1.3%
931 2
 
1.3%
728 2
 
1.3%
289 2
 
1.3%
1406 2
 
1.3%
930 2
 
1.3%
714 2
 
1.3%
779 2
 
1.3%
Other values (119) 121
80.7%
ValueCountFrequency (%)
0 9
6.0%
283 1
 
0.7%
287 4
2.7%
289 2
 
1.3%
292 1
 
0.7%
527 1
 
0.7%
531 1
 
0.7%
535 1
 
0.7%
542 2
 
1.3%
543 1
 
0.7%
ValueCountFrequency (%)
5889 1
0.7%
5873 1
0.7%
5856 1
0.7%
5829 1
0.7%
5819 1
0.7%
5781 1
0.7%
5699 1
0.7%
5647 1
0.7%
5615 1
0.7%
5371 1
0.7%

EMPYMN_MAN_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct136
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1416.4133
Minimum0
Maximum5646
Zeros9
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:03.722059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1683.25
median910.5
Q31412.25
95-th percentile5330.4
Maximum5646
Range5646
Interquartile range (IQR)729

Descriptive statistics

Standard deviation1495.2064
Coefficient of variation (CV)1.0556286
Kurtosis2.8453531
Mean1416.4133
Median Absolute Deviation (MAD)370.5
Skewness2.0318513
Sum212462
Variance2235642.1
MonotonicityNot monotonic
2023-12-10T15:30:03.955504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
6.0%
525 2
 
1.3%
1618 2
 
1.3%
283 2
 
1.3%
1065 2
 
1.3%
1593 2
 
1.3%
1604 2
 
1.3%
704 1
 
0.7%
690 1
 
0.7%
1369 1
 
0.7%
Other values (126) 126
84.0%
ValueCountFrequency (%)
0 9
6.0%
275 1
 
0.7%
280 1
 
0.7%
281 1
 
0.7%
282 1
 
0.7%
283 2
 
1.3%
285 1
 
0.7%
286 1
 
0.7%
505 1
 
0.7%
508 1
 
0.7%
ValueCountFrequency (%)
5646 1
0.7%
5636 1
0.7%
5633 1
0.7%
5618 1
0.7%
5603 1
0.7%
5557 1
0.7%
5441 1
0.7%
5352 1
0.7%
5304 1
0.7%
5131 1
0.7%

UNEMPL_MAN_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.06
Minimum0
Maximum313
Zeros9
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:04.190811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median28.5
Q353
95-th percentile254.65
Maximum313
Range313
Interquartile range (IQR)35

Descriptive statistics

Standard deviation75.543865
Coefficient of variation (CV)1.3239374
Kurtosis3.6756885
Mean57.06
Median Absolute Deviation (MAD)14.5
Skewness2.204231
Sum8559
Variance5706.8756
MonotonicityNot monotonic
2023-12-10T15:30:04.426842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
6.0%
15 6
 
4.0%
18 6
 
4.0%
24 5
 
3.3%
16 4
 
2.7%
28 4
 
2.7%
17 4
 
2.7%
23 4
 
2.7%
26 4
 
2.7%
29 4
 
2.7%
Other values (61) 100
66.7%
ValueCountFrequency (%)
0 9
6.0%
4 2
 
1.3%
5 2
 
1.3%
6 1
 
0.7%
7 2
 
1.3%
8 1
 
0.7%
11 2
 
1.3%
13 1
 
0.7%
14 3
 
2.0%
15 6
4.0%
ValueCountFrequency (%)
313 1
0.7%
311 1
0.7%
305 1
0.7%
295 1
0.7%
273 1
0.7%
270 1
0.7%
258 1
0.7%
256 1
0.7%
253 1
0.7%
240 1
0.7%

NON_ECNMY_ACT__POPLTN_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct124
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean936.79333
Minimum0
Maximum3677
Zeros9
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:04.656864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1483
median605
Q3850.5
95-th percentile3502.3
Maximum3677
Range3677
Interquartile range (IQR)367.5

Descriptive statistics

Standard deviation962.39948
Coefficient of variation (CV)1.0273338
Kurtosis2.6631264
Mean936.79333
Median Absolute Deviation (MAD)219.5
Skewness1.9695051
Sum140519
Variance926212.76
MonotonicityNot monotonic
2023-12-10T15:30:04.919597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
6.0%
825 3
 
2.0%
531 3
 
2.0%
468 2
 
1.3%
517 2
 
1.3%
516 2
 
1.3%
820 2
 
1.3%
3236 2
 
1.3%
147 2
 
1.3%
341 2
 
1.3%
Other values (114) 121
80.7%
ValueCountFrequency (%)
0 9
6.0%
143 1
 
0.7%
144 1
 
0.7%
146 1
 
0.7%
147 2
 
1.3%
149 1
 
0.7%
150 1
 
0.7%
151 1
 
0.7%
336 1
 
0.7%
340 1
 
0.7%
ValueCountFrequency (%)
3677 1
0.7%
3626 1
0.7%
3613 1
0.7%
3595 1
0.7%
3576 1
0.7%
3550 1
0.7%
3523 1
0.7%
3505 1
0.7%
3499 1
0.7%
3307 1
0.7%

ECNMY_ACT_PARTCPT_RATE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.260667
Minimum0
Maximum67.1
Zeros9
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:05.187105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q158.625
median60.7
Q362.7
95-th percentile66.01
Maximum67.1
Range67.1
Interquartile range (IQR)4.075

Descriptive statistics

Standard deviation14.793613
Coefficient of variation (CV)0.25835559
Kurtosis11.07381
Mean57.260667
Median Absolute Deviation (MAD)2
Skewness-3.5099267
Sum8589.1
Variance218.85099
MonotonicityNot monotonic
2023-12-10T15:30:05.440609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
6.0%
62.1 4
 
2.7%
60.0 4
 
2.7%
62.4 4
 
2.7%
66.1 3
 
2.0%
63.6 3
 
2.0%
62.0 3
 
2.0%
59.7 3
 
2.0%
57.6 3
 
2.0%
60.4 3
 
2.0%
Other values (74) 111
74.0%
ValueCountFrequency (%)
0.0 9
6.0%
51.8 1
 
0.7%
52.4 1
 
0.7%
53.6 1
 
0.7%
54.9 1
 
0.7%
55.6 1
 
0.7%
55.7 1
 
0.7%
55.9 2
 
1.3%
56.3 1
 
0.7%
56.5 1
 
0.7%
ValueCountFrequency (%)
67.1 1
 
0.7%
66.8 1
 
0.7%
66.5 1
 
0.7%
66.4 1
 
0.7%
66.2 1
 
0.7%
66.1 3
2.0%
65.9 1
 
0.7%
65.7 1
 
0.7%
65.2 2
1.3%
65.0 1
 
0.7%

UNEMPL_RATE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.17
Minimum0
Maximum6.4
Zeros9
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:30:05.675406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.125
median3.3
Q34.2
95-th percentile5.2
Maximum6.4
Range6.4
Interquartile range (IQR)2.075

Descriptive statistics

Standard deviation1.3858707
Coefficient of variation (CV)0.43718318
Kurtosis0.041466692
Mean3.17
Median Absolute Deviation (MAD)1
Skewness-0.26113894
Sum475.5
Variance1.9206376
MonotonicityNot monotonic
2023-12-10T15:30:05.926313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4.3 9
 
6.0%
0.0 9
 
6.0%
3.6 7
 
4.7%
1.9 6
 
4.0%
2.0 6
 
4.0%
3.9 6
 
4.0%
4.2 5
 
3.3%
2.1 5
 
3.3%
1.8 5
 
3.3%
3.3 5
 
3.3%
Other values (33) 87
58.0%
ValueCountFrequency (%)
0.0 9
6.0%
1.4 2
 
1.3%
1.5 3
 
2.0%
1.6 1
 
0.7%
1.7 1
 
0.7%
1.8 5
3.3%
1.9 6
4.0%
2.0 6
4.0%
2.1 5
3.3%
2.2 4
2.7%
ValueCountFrequency (%)
6.4 1
 
0.7%
6.2 2
1.3%
6.0 1
 
0.7%
5.8 1
 
0.7%
5.5 2
1.3%
5.2 2
1.3%
5.1 1
 
0.7%
5.0 2
1.3%
4.9 2
1.3%
4.8 3
2.0%

Interactions

2023-12-10T15:29:57.429246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:37.911841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:40.013486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:42.306271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:44.060548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:45.877627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:47.566240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:49.446811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:51.578526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:53.712029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.645825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:57.610315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:38.117434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:40.219814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:42.478261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:44.231199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:46.073303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:47.765974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:49.959954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:51.773144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:53.931736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.817195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:57.784716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:38.318870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:40.429405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:42.658422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:44.416215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:46.219649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:47.934397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:50.104994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:51.947012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.141369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.984450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:57.928251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:38.500535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:40.987716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:42.813417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:44.589941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:46.370122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:48.096270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:50.276945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:52.092645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.324762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.128565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:58.088310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:38.681538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:41.174464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:42.978971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:44.750034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:46.528842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:48.308917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:50.473330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:52.275286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.509751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.306589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:58.265854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:38.851235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:41.335173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:43.117559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:44.897353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:46.669402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:48.452019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:50.637479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:52.421779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.657938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.469753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:58.429224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:39.025363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:41.506415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:43.265282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:45.066224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:46.804280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:48.616986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:50.789708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:52.585231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.833097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.623633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:58.588976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:39.234326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:41.669021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:43.417622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:45.237447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:46.948403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:48.772923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:50.920023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:52.960622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:54.991348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.773421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:58.827928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:39.438395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:41.822687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:43.574276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:45.399801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:47.090711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:48.940761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:51.075489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:53.151846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.151577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:56.928817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:59.006352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:39.643704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:41.988132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:43.740837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:45.571587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:47.245273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:49.122851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:51.258321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:53.345834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.310095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:57.086811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:59.156237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:39.833421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:42.153112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:43.892488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:45.713901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:47.405971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:49.275674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:51.418007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:53.543221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:55.494305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:29:57.263293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:30:06.109374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDYYATPT_CODEEMPLYM_RATEEMPLYM_RATE_15ABOVE_64BELOWATPT_NMTOT_15ABOVE_POPLTN_COECNMY_ACT_POPLTN_COEMPYMN_MAN_COUNEMPL_MAN_CONON_ECNMY_ACT__POPLTN_COECNMY_ACT_PARTCPT_RATEUNEMPL_RATE
STDYY1.0000.0000.2670.0860.0000.0000.0000.0000.0000.0000.0000.071
ATPT_CODE0.0001.0000.9340.8641.0000.9090.8380.8610.6730.9360.9340.722
EMPLYM_RATE0.2670.9341.0000.7860.8930.4140.4070.3970.2030.4950.9980.765
EMPLYM_RATE_15ABOVE_64BELOW0.0860.8640.7861.0000.9470.5170.4690.4580.2580.7700.7830.958
ATPT_NM0.0001.0000.8930.9471.0001.0000.9580.9740.8351.0000.9090.791
TOT_15ABOVE_POPLTN_CO0.0000.9090.4140.5171.0001.0000.9910.9910.7691.0000.4880.581
ECNMY_ACT_POPLTN_CO0.0000.8380.4070.4690.9580.9911.0000.9970.8060.9260.4470.658
EMPYMN_MAN_CO0.0000.8610.3970.4580.9740.9910.9971.0000.7840.9490.4470.600
UNEMPL_MAN_CO0.0000.6730.2030.2580.8350.7690.8060.7841.0000.7890.2030.662
NON_ECNMY_ACT__POPLTN_CO0.0000.9360.4950.7701.0001.0000.9260.9490.7891.0000.5770.604
ECNMY_ACT_PARTCPT_RATE0.0000.9340.9980.7830.9090.4880.4470.4470.2030.5771.0000.742
UNEMPL_RATE0.0710.7220.7650.9580.7910.5810.6580.6000.6620.6040.7421.000
2023-12-10T15:30:06.694571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDYYATPT_CODEEMPLYM_RATEEMPLYM_RATE_15ABOVE_64BELOWTOT_15ABOVE_POPLTN_COECNMY_ACT_POPLTN_COEMPYMN_MAN_COUNEMPL_MAN_CONON_ECNMY_ACT__POPLTN_COECNMY_ACT_PARTCPT_RATEUNEMPL_RATEATPT_NM
STDYY1.000-0.0450.2880.2160.0510.0530.064-0.155-0.0650.220-0.3520.000
ATPT_CODE-0.0451.0000.5010.549-0.234-0.220-0.214-0.470-0.2740.400-0.5880.968
EMPLYM_RATE0.2880.5011.0000.9610.1310.1860.190-0.075-0.0130.975-0.2650.705
EMPLYM_RATE_15ABOVE_64BELOW0.2160.5490.9611.0000.2040.2570.261-0.0290.0680.936-0.2610.841
TOT_15ABOVE_POPLTN_CO0.051-0.2340.1310.2041.0000.9900.9900.8690.9720.2040.4770.958
ECNMY_ACT_POPLTN_CO0.053-0.2200.1860.2570.9901.0000.9990.8580.9530.2570.4520.844
EMPYMN_MAN_CO0.064-0.2140.1900.2610.9900.9991.0000.8510.9530.2590.4390.888
UNEMPL_MAN_CO-0.155-0.470-0.075-0.0290.8690.8580.8511.0000.9120.0640.8240.555
NON_ECNMY_ACT__POPLTN_CO-0.065-0.274-0.0130.0680.9720.9530.9530.9121.0000.0720.5760.961
ECNMY_ACT_PARTCPT_RATE0.2200.4000.9750.9360.2040.2570.2590.0640.0721.000-0.1040.735
UNEMPL_RATE-0.352-0.588-0.265-0.2610.4770.4520.4390.8240.576-0.1041.0000.448
ATPT_NM0.0000.9680.7050.8410.9580.8440.8880.5550.9610.7350.4481.000

Missing values

2023-12-10T15:29:59.752555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:30:00.182774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

STDYYATPT_CODEEMPLYM_RATEEMPLYM_RATE_15ABOVE_64BELOWATPT_NMTOT_15ABOVE_POPLTN_COECNMY_ACT_POPLTN_COEMPYMN_MAN_COUNEMPL_MAN_CONON_ECNMY_ACT__POPLTN_COECNMY_ACT_PARTCPT_RATEUNEMPL_RATE
02010011157.662.1서울853752304918313330761.36.0
12010012654.159.7부산29431675159381126856.94.8
22010012755.660.9대구2035120711327582859.36.2
32010012857.962.8인천2216136712828584961.76.2
42010012955.760.0광주11866936613249358.44.7
52010013055.159.7대전12237086743451557.94.8
62010013157.760.7울산8815315082335060.34.3
7201001360.00.0세종000000.00.0
82010014157.762.8경기927356475352295362660.95.2
92010014249.757.7강원12156366043257952.45.0
STDYYATPT_CODEEMPLYM_RATEEMPLYM_RATE_15ABOVE_64BELOWATPT_NMTOT_15ABOVE_POPLTN_COECNMY_ACT_POPLTN_COEMPYMN_MAN_COUNEMPL_MAN_CONON_ECNMY_ACT__POPLTN_COECNMY_ACT_PARTCPT_RATEUNEMPL_RATE
1402010092957.161.0광주11997046842049558.72.9
1412010093056.760.7대전12337176991851658.22.5
1422010093159.362.2울산8895455271834461.33.3
143201009360.00.0세종000000.00.0
1442010094159.964.2경기943258565646210357662.13.6
1452010094258.162.6강원12237287111749559.52.4
1462010094360.865.7충북12547797631647562.12.0
1472010094462.066.6충남1717110110653661664.13.3
1482010094557.661.5전북14778698511860858.92.1
1492010094661.966.7전남14669259081754163.11.9