Overview

Dataset statistics

Number of variables12
Number of observations46
Missing cells4
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory108.9 B

Variable types

Categorical1
Text1
Numeric10

Dataset

Description시도별, 도시가스사별 전국 도시가스 보급률(%)현황을 연도별로 제공하여 지역에 따른 도시가스 보급 추이를 파악할 수 있는 데이터입니다.
Author한국가스안전공사
URLhttps://www.data.go.kr/data/15002175/fileData.do

Alerts

2013년 is highly overall correlated with 2014년 and 8 other fieldsHigh correlation
2014년 is highly overall correlated with 2013년 and 8 other fieldsHigh correlation
2015년 is highly overall correlated with 2013년 and 8 other fieldsHigh correlation
2016년 is highly overall correlated with 2013년 and 8 other fieldsHigh correlation
2017년 is highly overall correlated with 2013년 and 8 other fieldsHigh correlation
2018년 is highly overall correlated with 2013년 and 8 other fieldsHigh correlation
2019년 is highly overall correlated with 2013년 and 8 other fieldsHigh correlation
2020년 is highly overall correlated with 2013년 and 8 other fieldsHigh correlation
2021년 is highly overall correlated with 2013년 and 8 other fieldsHigh correlation
2022년 is highly overall correlated with 2013년 and 8 other fieldsHigh correlation
2013년 has 1 (2.2%) missing valuesMissing
2014년 has 1 (2.2%) missing valuesMissing
2015년 has 1 (2.2%) missing valuesMissing
2016년 has 1 (2.2%) missing valuesMissing
2017년 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:00:00.122572
Analysis finished2023-12-12 15:00:09.321455
Duration9.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도별
Categorical

Distinct17
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
경 기
6 
강 원
5 
경 북
5 
서 울
5 
전 남
4 
Other values (12)
21 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)13.0%

Sample

1st row서 울
2nd row서 울
3rd row서 울
4th row서 울
5th row서 울

Common Values

ValueCountFrequency (%)
경 기 6
13.0%
강 원 5
10.9%
경 북 5
10.9%
서 울 5
10.9%
전 남 4
8.7%
경 남 3
 
6.5%
전 북 3
 
6.5%
충 남 3
 
6.5%
인 천 2
 
4.3%
충 북 2
 
4.3%
Other values (7) 8
17.4%

Length

2023-12-13T00:00:09.372765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경 14
15.2%
북 10
10.9%
남 10
10.9%
전 8
8.7%
울 6
 
6.5%
기 6
 
6.5%
강 5
 
5.4%
원 5
 
5.4%
서 5
 
5.4%
충 5
 
5.4%
Other values (11) 18
19.6%

회사별
Text

Distinct34
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T00:00:09.543959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.7173913
Min length3

Characters and Unicode

Total characters217
Distinct characters57
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)47.8%

Sample

1st row코원ES
2nd row예스코
3rd row서 울
4th row귀뚜라미
5th row대 륜
ValueCountFrequency (%)
대 5
 
6.9%
성 3
 
4.2%
경 3
 
4.2%
서 3
 
4.2%
부 3
 
4.2%
해 3
 
4.2%
코원es 2
 
2.8%
cncity 2
 
2.8%
남 2
 
2.8%
전 2
 
2.8%
Other values (33) 44
61.1%
2023-12-13T00:00:09.827247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78
35.9%
E 7
 
3.2%
S 7
 
3.2%
대 6
 
2.8%
코 4
 
1.8%
천 4
 
1.8%
원 4
 
1.8%
서 4
 
1.8%
참 4
 
1.8%
빛 4
 
1.8%
Other values (47) 95
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109
50.2%
Space Separator 78
35.9%
Uppercase Letter 26
 
12.0%
Close Punctuation 2
 
0.9%
Open Punctuation 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 6
 
5.5%
코 4
 
3.7%
천 4
 
3.7%
원 4
 
3.7%
서 4
 
3.7%
참 4
 
3.7%
빛 4
 
3.7%
성 4
 
3.7%
남 4
 
3.7%
영 3
 
2.8%
Other values (37) 68
62.4%
Uppercase Letter
ValueCountFrequency (%)
E 7
26.9%
S 7
26.9%
C 4
15.4%
I 2
 
7.7%
T 2
 
7.7%
Y 2
 
7.7%
N 2
 
7.7%
Space Separator
ValueCountFrequency (%)
78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 109
50.2%
Common 82
37.8%
Latin 26
 
12.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 6
 
5.5%
코 4
 
3.7%
천 4
 
3.7%
원 4
 
3.7%
서 4
 
3.7%
참 4
 
3.7%
빛 4
 
3.7%
성 4
 
3.7%
남 4
 
3.7%
영 3
 
2.8%
Other values (37) 68
62.4%
Latin
ValueCountFrequency (%)
E 7
26.9%
S 7
26.9%
C 4
15.4%
I 2
 
7.7%
T 2
 
7.7%
Y 2
 
7.7%
N 2
 
7.7%
Common
ValueCountFrequency (%)
78
95.1%
) 2
 
2.4%
( 2
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 109
50.2%
ASCII 108
49.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
78
72.2%
E 7
 
6.5%
S 7
 
6.5%
C 4
 
3.7%
I 2
 
1.9%
) 2
 
1.9%
( 2
 
1.9%
T 2
 
1.9%
Y 2
 
1.9%
N 2
 
1.9%
Hangul
ValueCountFrequency (%)
대 6
 
5.5%
코 4
 
3.7%
천 4
 
3.7%
원 4
 
3.7%
서 4
 
3.7%
참 4
 
3.7%
빛 4
 
3.7%
성 4
 
3.7%
남 4
 
3.7%
영 3
 
2.8%
Other values (37) 68
62.4%

2013년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)95.6%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean66.437778
Minimum9.3
Maximum97.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T00:00:09.956880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile23.1
Q154.2
median65.9
Q388.6
95-th percentile96.54
Maximum97.5
Range88.2
Interquartile range (IQR)34.4

Descriptive statistics

Standard deviation23.810018
Coefficient of variation (CV)0.35838071
Kurtosis-0.52283425
Mean66.437778
Median Absolute Deviation (MAD)18.7
Skewness-0.50650703
Sum2989.7
Variance566.91695
MonotonicityNot monotonic
2023-12-13T00:00:10.071494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
59.1 2
 
4.3%
74.2 2
 
4.3%
48.1 1
 
2.2%
58.8 1
 
2.2%
94.3 1
 
2.2%
55.6 1
 
2.2%
41.0 1
 
2.2%
66.0 1
 
2.2%
55.2 1
 
2.2%
21.0 1
 
2.2%
Other values (33) 33
71.7%
ValueCountFrequency (%)
9.3 1
2.2%
21.0 1
2.2%
22.6 1
2.2%
25.1 1
2.2%
29.4 1
2.2%
33.1 1
2.2%
41.0 1
2.2%
47.0 1
2.2%
47.5 1
2.2%
48.1 1
2.2%
ValueCountFrequency (%)
97.5 1
2.2%
96.7 1
2.2%
96.6 1
2.2%
96.3 1
2.2%
95.1 1
2.2%
94.6 1
2.2%
94.3 1
2.2%
92.8 1
2.2%
91.5 1
2.2%
91.4 1
2.2%

2014년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean68.2
Minimum10.1
Maximum99.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T00:00:10.211136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile23.5
Q156.1
median68.5
Q389.3
95-th percentile96.48
Maximum99.7
Range89.6
Interquartile range (IQR)33.2

Descriptive statistics

Standard deviation23.3808
Coefficient of variation (CV)0.34282698
Kurtosis-0.28590398
Mean68.2
Median Absolute Deviation (MAD)18.4
Skewness-0.61479919
Sum3069
Variance546.66182
MonotonicityNot monotonic
2023-12-13T00:00:10.672262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
94.4 1
 
2.2%
49.7 1
 
2.2%
61.8 1
 
2.2%
95.7 1
 
2.2%
58.4 1
 
2.2%
43.4 1
 
2.2%
68.9 1
 
2.2%
61.3 1
 
2.2%
56.1 1
 
2.2%
22.2 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
10.1 1
2.2%
22.2 1
2.2%
23.2 1
2.2%
24.7 1
2.2%
30.6 1
2.2%
39.3 1
2.2%
43.4 1
2.2%
49.7 1
2.2%
50.1 1
2.2%
53.5 1
2.2%
ValueCountFrequency (%)
99.7 1
2.2%
98.1 1
2.2%
96.6 1
2.2%
96.0 1
2.2%
95.7 1
2.2%
94.6 1
2.2%
94.4 1
2.2%
93.4 1
2.2%
92.6 1
2.2%
92.3 1
2.2%

2015년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)95.6%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean69.897778
Minimum11.6
Maximum99.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T00:00:10.822170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile24.22
Q157.9
median69.4
Q390.7
95-th percentile97.52
Maximum99.2
Range87.6
Interquartile range (IQR)32.8

Descriptive statistics

Standard deviation22.829521
Coefficient of variation (CV)0.32661298
Kurtosis-0.051549855
Mean69.897778
Median Absolute Deviation (MAD)16
Skewness-0.74214812
Sum3145.4
Variance521.18704
MonotonicityNot monotonic
2023-12-13T00:00:10.954443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
63.4 2
 
4.3%
90.7 2
 
4.3%
63.5 1
 
2.2%
97.6 1
 
2.2%
61.3 1
 
2.2%
46.7 1
 
2.2%
70.5 1
 
2.2%
57.9 1
 
2.2%
22.2 1
 
2.2%
64.8 1
 
2.2%
Other values (33) 33
71.7%
ValueCountFrequency (%)
11.6 1
2.2%
22.2 1
2.2%
23.0 1
2.2%
29.1 1
2.2%
32.0 1
2.2%
41.1 1
2.2%
46.7 1
2.2%
51.8 1
2.2%
54.9 1
2.2%
55.5 1
2.2%
ValueCountFrequency (%)
99.2 1
2.2%
99.1 1
2.2%
97.6 1
2.2%
97.2 1
2.2%
95.8 1
2.2%
94.5 1
2.2%
94.1 1
2.2%
93.4 1
2.2%
92.5 1
2.2%
91.0 1
2.2%

2016년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)97.8%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean71.202222
Minimum12.6
Maximum102.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T00:00:11.082226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile28
Q159.4
median71.9
Q391.7
95-th percentile97.84
Maximum102.5
Range89.9
Interquartile range (IQR)32.3

Descriptive statistics

Standard deviation22.593015
Coefficient of variation (CV)0.31730772
Kurtosis-0.12164144
Mean71.202222
Median Absolute Deviation (MAD)17.8
Skewness-0.7111167
Sum3204.1
Variance510.44431
MonotonicityNot monotonic
2023-12-13T00:00:11.206061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
85.6 2
 
4.3%
95.2 1
 
2.2%
53.6 1
 
2.2%
67.8 1
 
2.2%
94.7 1
 
2.2%
64.4 1
 
2.2%
51.3 1
 
2.2%
72.9 1
 
2.2%
64.3 1
 
2.2%
59.4 1
 
2.2%
Other values (34) 34
73.9%
ValueCountFrequency (%)
12.6 1
2.2%
25.5 1
2.2%
26.8 1
2.2%
32.8 1
2.2%
33.9 1
2.2%
39.2 1
2.2%
51.3 1
2.2%
52.7 1
2.2%
53.6 1
2.2%
55.2 1
2.2%
ValueCountFrequency (%)
102.5 1
2.2%
100.6 1
2.2%
97.9 1
2.2%
97.6 1
2.2%
95.2 1
2.2%
94.7 1
2.2%
94.4 1
2.2%
94.3 1
2.2%
94.0 1
2.2%
93.2 1
2.2%

2017년
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.43913
Minimum3.8
Maximum103.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T00:00:11.327736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile27.95
Q159.55
median74.35
Q392.075
95-th percentile99.05
Maximum103.6
Range99.8
Interquartile range (IQR)32.525

Descriptive statistics

Standard deviation24.193603
Coefficient of variation (CV)0.33866038
Kurtosis0.67868386
Mean71.43913
Median Absolute Deviation (MAD)17.6
Skewness-0.98098629
Sum3286.2
Variance585.33043
MonotonicityNot monotonic
2023-12-13T00:00:11.459897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
95.4 1
 
2.2%
55.1 1
 
2.2%
70.5 1
 
2.2%
94.9 1
 
2.2%
67.7 1
 
2.2%
54.8 1
 
2.2%
74.5 1
 
2.2%
65.5 1
 
2.2%
58.9 1
 
2.2%
27.8 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
3.8 1
2.2%
9.2 1
2.2%
27.8 1
2.2%
28.4 1
2.2%
35.0 1
2.2%
37.1 1
2.2%
51.9 1
2.2%
54.8 1
2.2%
55.1 1
2.2%
55.6 1
2.2%
ValueCountFrequency (%)
103.6 1
2.2%
101.8 1
2.2%
99.3 1
2.2%
98.3 1
2.2%
95.7 1
2.2%
95.4 1
2.2%
94.9 1
2.2%
94.8 1
2.2%
94.6 1
2.2%
93.9 1
2.2%

2018년
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.006522
Minimum3.9
Maximum102.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T00:00:11.660650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile30.95
Q162.2
median75.9
Q392.25
95-th percentile99.5
Maximum102.8
Range98.9
Interquartile range (IQR)30.05

Descriptive statistics

Standard deviation23.212433
Coefficient of variation (CV)0.31795014
Kurtosis1.0253328
Mean73.006522
Median Absolute Deviation (MAD)16.1
Skewness-1.081374
Sum3358.3
Variance538.81707
MonotonicityNot monotonic
2023-12-13T00:00:11.803633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
75.9 2
 
4.3%
94.0 1
 
2.2%
71.3 1
 
2.2%
74.7 1
 
2.2%
94.9 1
 
2.2%
70.1 1
 
2.2%
58.7 1
 
2.2%
63.6 1
 
2.2%
61.8 1
 
2.2%
30.3 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
3.9 1
2.2%
14.0 1
2.2%
30.3 1
2.2%
32.9 1
2.2%
38.2 1
2.2%
39.9 1
2.2%
56.7 1
2.2%
57.7 1
2.2%
57.9 1
2.2%
58.7 1
2.2%
ValueCountFrequency (%)
102.8 1
2.2%
101.6 1
2.2%
99.9 1
2.2%
98.3 1
2.2%
96.4 1
2.2%
95.4 1
2.2%
94.9 1
2.2%
94.8 1
2.2%
94.4 1
2.2%
94.0 1
2.2%

2019년
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.926087
Minimum4
Maximum103.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T00:00:11.933988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile32.775
Q163.475
median78.45
Q393.35
95-th percentile99.725
Maximum103.5
Range99.5
Interquartile range (IQR)29.875

Descriptive statistics

Standard deviation23.064889
Coefficient of variation (CV)0.31199932
Kurtosis1.2747832
Mean73.926087
Median Absolute Deviation (MAD)15.2
Skewness-1.1796701
Sum3400.6
Variance531.98908
MonotonicityNot monotonic
2023-12-13T00:00:12.062457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
95.1 2
 
4.3%
93.7 1
 
2.2%
58.3 1
 
2.2%
77.8 1
 
2.2%
71.6 1
 
2.2%
59.9 1
 
2.2%
76.9 1
 
2.2%
64.0 1
 
2.2%
63.1 1
 
2.2%
32.2 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
4.0 1
2.2%
14.3 1
2.2%
32.2 1
2.2%
34.5 1
2.2%
35.0 1
2.2%
40.1 1
2.2%
58.3 1
2.2%
59.9 1
2.2%
60.2 1
2.2%
61.1 1
2.2%
ValueCountFrequency (%)
103.5 1
2.2%
101.8 1
2.2%
100.2 1
2.2%
98.3 1
2.2%
97.1 1
2.2%
95.2 1
2.2%
95.1 2
4.3%
95.0 1
2.2%
94.8 1
2.2%
94.0 1
2.2%

2020년
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.358696
Minimum5.7
Maximum103.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T00:00:12.180046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.7
5-th percentile34.175
Q165.4
median77.2
Q392.4
95-th percentile99.325
Maximum103.1
Range97.4
Interquartile range (IQR)27

Descriptive statistics

Standard deviation22.41851
Coefficient of variation (CV)0.30149144
Kurtosis1.4432102
Mean74.358696
Median Absolute Deviation (MAD)14.75
Skewness-1.2116398
Sum3420.5
Variance502.58959
MonotonicityNot monotonic
2023-12-13T00:00:12.302786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
95.1 2
 
4.3%
92.5 1
 
2.2%
76.9 1
 
2.2%
78.0 1
 
2.2%
72.3 1
 
2.2%
62.5 1
 
2.2%
77.3 1
 
2.2%
65.7 1
 
2.2%
63.5 1
 
2.2%
33.4 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
5.7 1
2.2%
15.1 1
2.2%
33.4 1
2.2%
36.5 1
2.2%
37.5 1
2.2%
42.0 1
2.2%
59.0 1
2.2%
60.7 1
2.2%
61.2 1
2.2%
62.5 1
2.2%
ValueCountFrequency (%)
103.1 1
2.2%
100.8 1
2.2%
99.7 1
2.2%
98.2 1
2.2%
97.3 1
2.2%
95.9 1
2.2%
95.5 1
2.2%
95.3 1
2.2%
95.1 2
4.3%
94.3 1
2.2%

2021년
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.797826
Minimum7.5
Maximum672.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T00:00:12.430845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.5
5-th percentile27.5
Q167.3
median79.25
Q392.35
95-th percentile101.275
Maximum672.9
Range665.4
Interquartile range (IQR)25.05

Descriptive statistics

Standard deviation90.964109
Coefficient of variation (CV)1.0360633
Kurtosis40.294966
Mean87.797826
Median Absolute Deviation (MAD)12.8
Skewness6.1338332
Sum4038.7
Variance8274.4691
MonotonicityNot monotonic
2023-12-13T00:00:12.550941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
81.9 2
 
4.3%
91.9 2
 
4.3%
73.3 2
 
4.3%
67.3 2
 
4.3%
672.9 1
 
2.2%
7.5 1
 
2.2%
67.5 1
 
2.2%
79.4 1
 
2.2%
96.0 1
 
2.2%
63.5 1
 
2.2%
Other values (32) 32
69.6%
ValueCountFrequency (%)
7.5 1
2.2%
16.8 1
2.2%
24.9 1
2.2%
35.3 1
2.2%
39.8 1
2.2%
44.0 1
2.2%
60.0 1
2.2%
62.1 1
2.2%
63.0 1
2.2%
63.5 1
2.2%
ValueCountFrequency (%)
672.9 1
2.2%
103.4 1
2.2%
101.7 1
2.2%
100.0 1
2.2%
99.7 1
2.2%
97.4 1
2.2%
96.8 1
2.2%
96.2 1
2.2%
96.0 1
2.2%
95.4 1
2.2%

2022년
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.793478
Minimum7.3
Maximum103.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T00:00:12.672208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile37.925
Q167.575
median79
Q391.75
95-th percentile100.85
Maximum103.8
Range96.5
Interquartile range (IQR)24.175

Descriptive statistics

Standard deviation21.262679
Coefficient of variation (CV)0.28053441
Kurtosis1.7840853
Mean75.793478
Median Absolute Deviation (MAD)12.2
Skewness-1.246265
Sum3486.5
Variance452.10151
MonotonicityNot monotonic
2023-12-13T00:00:12.791571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
85.6 2
 
4.3%
92.2 1
 
2.2%
61.5 1
 
2.2%
79.2 1
 
2.2%
94.6 1
 
2.2%
74.5 1
 
2.2%
66.9 1
 
2.2%
78.8 1
 
2.2%
69.0 1
 
2.2%
67.8 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
7.3 1
2.2%
20.9 1
2.2%
36.5 1
2.2%
42.2 1
2.2%
42.9 1
2.2%
45.5 1
2.2%
59.9 1
2.2%
61.5 1
2.2%
62.8 1
2.2%
66.7 1
2.2%
ValueCountFrequency (%)
103.8 1
2.2%
102.6 1
2.2%
101.0 1
2.2%
100.4 1
2.2%
97.5 1
2.2%
97.0 1
2.2%
96.9 1
2.2%
96.5 1
2.2%
95.5 1
2.2%
94.6 1
2.2%

Interactions

2023-12-13T00:00:08.229638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:00.553375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.354530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:02.262992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.191496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:04.073711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:05.212902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.026940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.775275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.455058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.309580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:00.626241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.451376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:02.358674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.265800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:04.151353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:05.302661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.100155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.844249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.528538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.379677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:00.698636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.535655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:02.456846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.340646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:04.238272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:05.381960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.162421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.908938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.606457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.445040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:00.780230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.623228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:02.538999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.424994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:04.323005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:05.466947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.229910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.981316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.680071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.512635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:00.866245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.702430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:02.631490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.508689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:04.396285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:05.537727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.300117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.045069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.756419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.587799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:00.944679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.781104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:02.716552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.598512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:04.479059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:05.618446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.384102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.108306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.831638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.669775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.016208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.866210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:02.818016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.684442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:04.556396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:05.687334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.475019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.182543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.902007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.743836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.090759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.965469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:02.927311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.761085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:04.639525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:05.765041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.554319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.251926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.986976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.808542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.162092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:02.059798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.026380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.847589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:04.718682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:05.855594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.624565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.317250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.061743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.894427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:01.250519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:02.171220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.115280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:03.953613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:04.809331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:05.950281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:06.706773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:07.389358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:00:08.154204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:00:12.905908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도별회사별2013년2014년2015년2016년2017년2018년2019년2020년2021년2022년
시도별1.0000.0000.6960.5430.5390.5380.2690.5150.5130.0000.4230.704
회사별0.0001.0000.8880.8760.8510.8490.7770.9040.8390.7300.9770.918
2013년0.6960.8881.0000.9900.9740.8940.8830.8810.8420.8340.6850.869
2014년0.5430.8760.9901.0000.9840.9100.9100.9150.9000.8920.7140.898
2015년0.5390.8510.9740.9841.0000.9320.9490.9400.9140.9170.6770.911
2016년0.5380.8490.8940.9100.9321.0000.9860.9560.9430.9230.8970.900
2017년0.2690.7770.8830.9100.9490.9861.0000.9910.9730.9160.8390.868
2018년0.5150.9040.8810.9150.9400.9560.9911.0000.9960.9440.8830.933
2019년0.5130.8390.8420.9000.9140.9430.9730.9961.0000.9840.9060.951
2020년0.0000.7300.8340.8920.9170.9230.9160.9440.9841.0000.7660.975
2021년0.4230.9770.6850.7140.6770.8970.8390.8830.9060.7661.0000.731
2022년0.7040.9180.8690.8980.9110.9000.8680.9330.9510.9750.7311.000
2023-12-13T00:00:13.052708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2013년2014년2015년2016년2017년2018년2019년2020년2021년2022년시도별
2013년1.0000.9710.9570.9520.9480.9280.9270.9190.8600.9300.306
2014년0.9711.0000.9910.9840.9810.9690.9660.9570.8790.9490.196
2015년0.9570.9911.0000.9910.9870.9780.9730.9630.8850.9560.194
2016년0.9520.9840.9911.0000.9970.9860.9810.9720.8930.9640.196
2017년0.9480.9810.9870.9971.0000.9910.9880.9790.9000.9690.016
2018년0.9280.9690.9780.9860.9911.0000.9970.9910.9110.9810.184
2019년0.9270.9660.9730.9810.9880.9971.0000.9940.9120.9830.183
2020년0.9190.9570.9630.9720.9790.9910.9941.0000.9180.9910.000
2021년0.8600.8790.8850.8930.9000.9110.9120.9181.0000.9270.189
2022년0.9300.9490.9560.9640.9690.9810.9830.9910.9271.0000.327
시도별0.3060.1960.1940.1960.0160.1840.1830.0000.1890.3271.000

Missing values

2023-12-13T00:00:09.004967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:00:09.162845image/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.
2023-12-13T00:00:09.271999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도별회사별2013년2014년2015년2016년2017년2018년2019년2020년2021년2022년
0서 울코원ES97.594.494.595.295.494.093.792.592.592.2
1서 울예스코96.396.099.1102.5103.6101.6101.8100.8101.7102.6
2서 울서 울95.196.697.297.998.398.398.398.299.7100.4
3서 울귀뚜라미96.798.199.2100.6101.8102.8103.5103.1103.4103.8
4서 울대 륜91.592.392.593.293.995.495.294.394.796.5
5인 천삼천리91.492.693.494.394.894.494.095.191.991.8
6인 천인 천88.689.390.190.891.792.191.790.889.889.6
7경 기코원ES85.283.083.084.184.685.783.581.480.680.8
8경 기예스코75.369.174.674.376.375.979.176.175.777.0
9경 기서 울81.783.484.185.687.487.287.386.686.187.3
시도별회사별2013년2014년2015년2016년2017년2018년2019년2020년2021년2022년
36전 남대 화61.465.269.471.268.971.374.576.979.181.4
37경 북대 성94.694.688.389.790.791.692.392.191.991.6
38경 북영남ES(구미)54.257.159.061.062.764.165.266.067.367.5
39경 북영남ES(포함)66.968.569.270.972.274.075.275.776.376.3
40경 북대성청정22.624.729.132.837.139.940.142.044.045.5
41경 북서라벌47.050.157.662.364.569.270.770.570.671.2
42경 남경 남64.064.367.369.274.277.681.481.781.982.4
43경 남경 동74.279.382.085.687.687.486.486.586.085.6
44경 남지에스이47.553.555.552.755.660.663.365.366.359.9
45제 주제 주9.310.111.612.69.214.014.315.116.820.9