Overview

Dataset statistics

Number of variables9
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory81.9 B

Variable types

Numeric5
Categorical4

Alerts

popltn_per_pblprfr_cas_co has constant value ""Constant
pblprfr_year is highly overall correlated with seq_no and 1 other fieldsHigh correlation
metrp_area_pblprfr_cas_co is highly overall correlated with seq_no and 1 other fieldsHigh correlation
seq_no is highly overall correlated with pblprfr_year and 1 other fieldsHigh correlation
ctprvn_cd is highly overall correlated with ctprvn_nmHigh correlation
all_popltn_co is highly overall correlated with ctprvn_accto_pblprfr_cas_co and 2 other fieldsHigh correlation
ctprvn_accto_pblprfr_cas_co is highly overall correlated with all_popltn_co and 1 other fieldsHigh correlation
metrp_nmetrp_area_pblprfr_rate is highly overall correlated with all_popltn_co and 1 other fieldsHigh correlation
ctprvn_nm is highly overall correlated with ctprvn_cd and 1 other fieldsHigh correlation
seq_no has unique valuesUnique
ctprvn_accto_pblprfr_cas_co has 1 (1.5%) zerosZeros
metrp_nmetrp_area_pblprfr_rate has 1 (1.5%) zerosZeros

Reproduction

Analysis started2023-12-10 09:40:53.970054
Analysis finished2023-12-10 09:40:59.608635
Duration5.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.5
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-10T18:40:59.724211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.35
Q117.75
median34.5
Q351.25
95-th percentile64.65
Maximum68
Range67
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation19.77372
Coefficient of variation (CV)0.5731513
Kurtosis-1.2
Mean34.5
Median Absolute Deviation (MAD)17
Skewness0
Sum2346
Variance391
MonotonicityStrictly increasing
2023-12-10T18:40:59.991202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
45 1
 
1.5%
51 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
44 1
 
1.5%
36 1
 
1.5%
Other values (58) 58
85.3%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
68 1
1.5%
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%

pblprfr_year
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
2017
17 
2018
17 
2019
17 
2020
17 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 17
25.0%
2018 17
25.0%
2019 17
25.0%
2020 17
25.0%

Length

2023-12-10T18:41:00.266788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:00.462617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 17
25.0%
2018 17
25.0%
2019 17
25.0%
2020 17
25.0%

ctprvn_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.176471
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-10T18:41:00.653105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile14.5
Q124
median31
Q335
95-th percentile38.65
Maximum39
Range28
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.3564697
Coefficient of variation (CV)0.25213707
Kurtosis0.029476192
Mean29.176471
Median Absolute Deviation (MAD)6
Skewness-0.70685758
Sum1984
Variance54.117647
MonotonicityNot monotonic
2023-12-10T18:41:00.846448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11 4
 
5.9%
21 4
 
5.9%
39 4
 
5.9%
38 4
 
5.9%
37 4
 
5.9%
36 4
 
5.9%
35 4
 
5.9%
34 4
 
5.9%
33 4
 
5.9%
32 4
 
5.9%
Other values (7) 28
41.2%
ValueCountFrequency (%)
11 4
5.9%
21 4
5.9%
22 4
5.9%
23 4
5.9%
24 4
5.9%
25 4
5.9%
26 4
5.9%
29 4
5.9%
31 4
5.9%
32 4
5.9%
ValueCountFrequency (%)
39 4
5.9%
38 4
5.9%
37 4
5.9%
36 4
5.9%
35 4
5.9%
34 4
5.9%
33 4
5.9%
32 4
5.9%
31 4
5.9%
29 4
5.9%

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
서울특별시
 
4
부산광역시
 
4
대구광역시
 
4
인천광역시
 
4
광주광역시
 
4
Other values (12)
48 

Length

Max length7
Median length5
Mean length4.6470588
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row부산광역시
3rd row대구광역시
4th row인천광역시
5th row광주광역시

Common Values

ValueCountFrequency (%)
서울특별시 4
 
5.9%
부산광역시 4
 
5.9%
대구광역시 4
 
5.9%
인천광역시 4
 
5.9%
광주광역시 4
 
5.9%
대전광역시 4
 
5.9%
울산광역시 4
 
5.9%
세종특별자치시 4
 
5.9%
경기도 4
 
5.9%
강원도 4
 
5.9%
Other values (7) 28
41.2%

Length

2023-12-10T18:41:01.123539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 4
 
5.9%
강원도 4
 
5.9%
경상남도 4
 
5.9%
경상북도 4
 
5.9%
전라남도 4
 
5.9%
전라북도 4
 
5.9%
충청남도 4
 
5.9%
충청북도 4
 
5.9%
경기도 4
 
5.9%
부산광역시 4
 
5.9%
Other values (7) 28
41.2%

all_popltn_co
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3049683.9
Minimum343788
Maximum13265377
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-10T18:41:01.384892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum343788
5-th percentile458268.8
Q11473125
median1861894
Q32954955
95-th percentile12030432
Maximum13265377
Range12921589
Interquartile range (IQR)1481830

Descriptive statistics

Standard deviation3275076.7
Coefficient of variation (CV)1.0739069
Kurtosis4.2486954
Mean3049683.9
Median Absolute Deviation (MAD)716184
Skewness2.2867153
Sum2.0737851 × 108
Variance1.0726128 × 1013
MonotonicityNot monotonic
2023-12-10T18:41:01.577878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
9736962 4
 
5.9%
3410925 4
 
5.9%
670876 4
 
5.9%
3358828 4
 
5.9%
2658956 4
 
5.9%
1861894 4
 
5.9%
1815112 4
 
5.9%
2120995 4
 
5.9%
1598599 4
 
5.9%
1539521 4
 
5.9%
Other values (7) 28
41.2%
ValueCountFrequency (%)
343788 4
5.9%
670876 4
5.9%
1145710 4
5.9%
1456121 4
5.9%
1473125 4
5.9%
1539521 4
5.9%
1598599 4
5.9%
1815112 4
5.9%
1861894 4
5.9%
2120995 4
5.9%
ValueCountFrequency (%)
13265377 4
5.9%
9736962 4
5.9%
3410925 4
5.9%
3358828 4
5.9%
2954955 4
5.9%
2658956 4
5.9%
2432883 4
5.9%
2120995 4
5.9%
1861894 4
5.9%
1815112 4
5.9%

ctprvn_accto_pblprfr_cas_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.5
Minimum0
Maximum1010
Zeros1
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-10T18:41:01.812283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.7
Q170.5
median166
Q3351.5
95-th percentile671.15
Maximum1010
Range1010
Interquartile range (IQR)281

Descriptive statistics

Standard deviation232.15265
Coefficient of variation (CV)0.93799049
Kurtosis0.98040794
Mean247.5
Median Absolute Deviation (MAD)113.5
Skewness1.2616687
Sum16830
Variance53894.851
MonotonicityNot monotonic
2023-12-10T18:41:02.047504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116 2
 
2.9%
114 2
 
2.9%
275 2
 
2.9%
32 2
 
2.9%
365 2
 
2.9%
344 1
 
1.5%
347 1
 
1.5%
42 1
 
1.5%
315 1
 
1.5%
437 1
 
1.5%
Other values (53) 53
77.9%
ValueCountFrequency (%)
0 1
1.5%
1 1
1.5%
20 1
1.5%
23 1
1.5%
25 1
1.5%
29 1
1.5%
32 2
2.9%
35 1
1.5%
42 1
1.5%
46 1
1.5%
ValueCountFrequency (%)
1010 1
1.5%
846 1
1.5%
733 1
1.5%
682 1
1.5%
651 1
1.5%
638 1
1.5%
635 1
1.5%
607 1
1.5%
589 1
1.5%
575 1
1.5%

popltn_per_pblprfr_cas_co
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
0
68 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 68
100.0%

Length

2023-12-10T18:41:02.305402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:02.484805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 68
100.0%

metrp_area_pblprfr_cas_co
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
1109
17 
1485
17 
1933
17 
736
17 

Length

Max length4
Median length4
Mean length3.75
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1109
2nd row1109
3rd row1109
4th row1109
5th row1109

Common Values

ValueCountFrequency (%)
1109 17
25.0%
1485 17
25.0%
1933 17
25.0%
736 17
25.0%

Length

2023-12-10T18:41:02.649460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:02.853005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1109 17
25.0%
1485 17
25.0%
1933 17
25.0%
736 17
25.0%

metrp_nmetrp_area_pblprfr_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.040441
Minimum0
Maximum88.45
Zeros1
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-10T18:41:03.125909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.157
Q16.5575
median13.09
Q327.835
95-th percentile55.619
Maximum88.45
Range88.45
Interquartile range (IQR)21.2775

Descriptive statistics

Standard deviation17.727941
Coefficient of variation (CV)0.93106776
Kurtosis2.8468031
Mean19.040441
Median Absolute Deviation (MAD)7.985
Skewness1.6102027
Sum1294.75
Variance314.27989
MonotonicityNot monotonic
2023-12-10T18:41:03.496865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.32 2
 
2.9%
31.02 1
 
1.5%
10.5 1
 
1.5%
2.17 1
 
1.5%
16.3 1
 
1.5%
22.61 1
 
1.5%
17.95 1
 
1.5%
29.38 1
 
1.5%
10.97 1
 
1.5%
31.4 1
 
1.5%
Other values (57) 57
83.8%
ValueCountFrequency (%)
0.0 1
1.5%
0.09 1
1.5%
1.68 1
1.5%
2.15 1
1.5%
2.17 1
1.5%
2.72 1
1.5%
2.89 1
1.5%
3.0 1
1.5%
3.13 1
1.5%
3.16 1
1.5%
ValueCountFrequency (%)
88.45 1
1.5%
62.09 1
1.5%
57.26 1
1.5%
56.97 1
1.5%
53.11 1
1.5%
52.25 1
1.5%
49.36 1
1.5%
42.96 1
1.5%
37.36 1
1.5%
35.28 1
1.5%

Interactions

2023-12-10T18:40:58.151493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:54.616299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:55.773910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:56.488755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:57.345167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:58.304381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:54.881259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:55.916016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:56.678863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:57.559640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:58.443323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:55.010313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:56.052050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:56.830993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:57.730597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:58.608152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:55.168808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:56.210318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:57.003381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:57.890884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:58.758239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:55.649894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:56.360210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:57.178626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:58.019088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:41:03.729576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nopblprfr_yearctprvn_cdctprvn_nmall_popltn_coctprvn_accto_pblprfr_cas_cometrp_area_pblprfr_cas_cometrp_nmetrp_area_pblprfr_rate
seq_no1.0000.9740.4780.0000.0000.0000.9740.000
pblprfr_year0.9741.0000.0000.0000.0000.0001.0000.000
ctprvn_cd0.4780.0001.0001.0000.8240.3870.0000.486
ctprvn_nm0.0000.0001.0001.0001.0000.4650.0000.628
all_popltn_co0.0000.0000.8241.0001.0000.7960.0000.615
ctprvn_accto_pblprfr_cas_co0.0000.0000.3870.4650.7961.0000.0000.844
metrp_area_pblprfr_cas_co0.9741.0000.0000.0000.0000.0001.0000.000
metrp_nmetrp_area_pblprfr_rate0.0000.0000.4860.6280.6150.8440.0001.000
2023-12-10T18:41:03.999027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
pblprfr_yearmetrp_area_pblprfr_cas_coctprvn_nm
pblprfr_year1.0001.0000.000
metrp_area_pblprfr_cas_co1.0001.0000.000
ctprvn_nm0.0000.0001.000
2023-12-10T18:41:04.867284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_noctprvn_cdall_popltn_coctprvn_accto_pblprfr_cas_cometrp_nmetrp_area_pblprfr_ratepblprfr_yearctprvn_nmmetrp_area_pblprfr_cas_co
seq_no1.0000.250-0.054-0.0830.0120.8750.0000.875
ctprvn_cd0.2501.000-0.218-0.034-0.0060.0000.9220.000
all_popltn_co-0.054-0.2181.0000.6450.6610.0000.9000.000
ctprvn_accto_pblprfr_cas_co-0.083-0.0340.6451.0000.9430.0000.1770.000
metrp_nmetrp_area_pblprfr_rate0.012-0.0060.6610.9431.0000.0000.2810.000
pblprfr_year0.8750.0000.0000.0000.0001.0000.0001.000
ctprvn_nm0.0000.9220.9000.1770.2810.0001.0000.000
metrp_area_pblprfr_cas_co0.8750.0000.0000.0000.0001.0000.0001.000

Missing values

2023-12-10T18:40:59.011981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:40:59.430664image/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

seq_nopblprfr_yearctprvn_cdctprvn_nmall_popltn_coctprvn_accto_pblprfr_cas_copopltn_per_pblprfr_cas_cometrp_area_pblprfr_cas_cometrp_nmetrp_area_pblprfr_rate
01201711서울특별시97369623440110931.02
12201721부산광역시34109251320110911.9
23201722대구광역시24328833030110927.32
34201723인천광역시29549551760110915.87
45201724광주광역시145612159011095.32
56201725대전광역시147312586011097.75
67201726울산광역시114571032011092.89
78201729세종특별자치시3437881011090.09
89201731경기도132653775890110953.11
910201732강원도15395211570110914.16
seq_nopblprfr_yearctprvn_cdctprvn_nmall_popltn_coctprvn_accto_pblprfr_cas_copopltn_per_pblprfr_cas_cometrp_area_pblprfr_cas_cometrp_nmetrp_area_pblprfr_rate
5859202029세종특별자치시3437882007362.72
5960202031경기도132653775607367.61
6061202032강원도1539521116073615.76
6162202033충청북도15985997107369.65
6263202034충청남도21209954607366.25
6364202035전라북도1815112457073662.09
6465202036전라남도1861894126073617.12
6566202037경상북도2658956275073637.36
6667202038경상남도3358828158073621.47
6768202039제주특별자치도6708764907366.66