Overview

Dataset statistics

Number of variables11
Number of observations170
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.1 KiB
Average record size in memory96.8 B

Variable types

Categorical2
Numeric8
Text1

Dataset

Description인천의 경제활동별 지역내 총부가가치 및 요소소득 정보(경제활동별,산출액, 중간소비액 등)를 제공합니다. * 시군구별 경제활동별 산출액(당해년가격) 중간소비 지역내총부가가치(당해년가격) 고정자본소모 지역내순생산 기타생산세(기타생산보조금 공제) 지역내요소소득 산출액(2015년 기준년연쇄가격) 지역내총부가가치(2015년 기준년연쇄가격)
Author인천광역시
URLhttps://www.incheon.go.kr/data/DATA010201/view?docId=15055888

Alerts

2020년_산출액(당해년가격) is highly overall correlated with 2020년_중간소비 and 6 other fieldsHigh correlation
2020년_중간소비 is highly overall correlated with 2020년_산출액(당해년가격) and 6 other fieldsHigh correlation
2020년_지역내총부가가치(당해년가격) is highly overall correlated with 2020년_산출액(당해년가격) and 6 other fieldsHigh correlation
2020년_고정자본소모 is highly overall correlated with 2020년_산출액(당해년가격) and 6 other fieldsHigh correlation
2020년_지역내순생산 is highly overall correlated with 2020년_산출액(당해년가격) and 6 other fieldsHigh correlation
2020년_지역내요소소득 is highly overall correlated with 2020년_산출액(당해년가격) and 6 other fieldsHigh correlation
2020년_산출액(2015년 기준년연쇄가격) is highly overall correlated with 2020년_산출액(당해년가격) and 6 other fieldsHigh correlation
2020년_지역내총부가가치(2015년 기준년연쇄가격) is highly overall correlated with 2020년_산출액(당해년가격) and 6 other fieldsHigh correlation
2020년_산출액(당해년가격) has 3 (1.8%) zerosZeros
2020년_중간소비 has 3 (1.8%) zerosZeros
2020년_지역내총부가가치(당해년가격) has 3 (1.8%) zerosZeros
2020년_고정자본소모 has 3 (1.8%) zerosZeros
2020년_지역내순생산 has 3 (1.8%) zerosZeros
2020년_지역내요소소득 has 3 (1.8%) zerosZeros
2020년_산출액(2015년 기준년연쇄가격) has 5 (2.9%) zerosZeros
2020년_지역내총부가가치(2015년 기준년연쇄가격) has 5 (2.9%) zerosZeros

Reproduction

Analysis started2024-01-28 06:44:05.100358
Analysis finished2024-01-28 06:44:10.603687
Duration5.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역(구,군)별
Categorical

Distinct10
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
강화군
17 
옹진군
17 
중구
17 
동구
17 
미추홀구
17 
Other values (5)
85 

Length

Max length4
Median length3
Mean length2.8
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강화군 17
10.0%
옹진군 17
10.0%
중구 17
10.0%
동구 17
10.0%
미추홀구 17
10.0%
연수구 17
10.0%
남동구 17
10.0%
부평구 17
10.0%
계양구 17
10.0%
서구 17
10.0%

Length

2024-01-28T15:44:10.683779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:44:10.845886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강화군 17
10.0%
옹진군 17
10.0%
중구 17
10.0%
동구 17
10.0%
미추홀구 17
10.0%
연수구 17
10.0%
남동구 17
10.0%
부평구 17
10.0%
계양구 17
10.0%
서구 17
10.0%

경제활동별
Categorical

Distinct17
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
총부가가치(기초가격)
 
10
농업, 임업 및 어업
 
10
광업
 
10
제조업
 
10
전기, 가스, 증기 및 공기 조절 공급업
 
10
Other values (12)
120 

Length

Max length22
Median length11
Mean length8.9411765
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row총부가가치(기초가격)
2nd row농업, 임업 및 어업
3rd row광업
4th row제조업
5th row전기, 가스, 증기 및 공기 조절 공급업

Common Values

ValueCountFrequency (%)
총부가가치(기초가격) 10
 
5.9%
농업, 임업 및 어업 10
 
5.9%
광업 10
 
5.9%
제조업 10
 
5.9%
전기, 가스, 증기 및 공기 조절 공급업 10
 
5.9%
건설업 10
 
5.9%
도매 및 소매업 10
 
5.9%
운수 및 창고업 10
 
5.9%
숙박 및 음식점업 10
 
5.9%
정보통신업 10
 
5.9%
Other values (7) 70
41.2%

Length

2024-01-28T15:44:11.048744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 90
 
20.0%
행정 20
 
4.4%
서비스업 20
 
4.4%
총부가가치(기초가격 10
 
2.2%
정보통신업 10
 
2.2%
금융 10
 
2.2%
보험업 10
 
2.2%
부동산업 10
 
2.2%
사업서비스업 10
 
2.2%
공공 10
 
2.2%
Other values (25) 250
55.6%

2020년_산출액(당해년가격)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct168
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2302768.8
Minimum0
Maximum47691470
Zeros3
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-28T15:44:11.215287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6123.2
Q1150407.25
median604696.5
Q31331936.2
95-th percentile11917592
Maximum47691470
Range47691470
Interquartile range (IQR)1181529

Descriptive statistics

Standard deviation6088906
Coefficient of variation (CV)2.6441673
Kurtosis25.525248
Mean2302768.8
Median Absolute Deviation (MAD)484858.5
Skewness4.71396
Sum3.914707 × 108
Variance3.7074776 × 1013
MonotonicityNot monotonic
2024-01-28T15:44:11.351443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
1.8%
1599185 1
 
0.6%
870594 1
 
0.6%
1262219 1
 
0.6%
432357 1
 
0.6%
1482245 1
 
0.6%
1827503 1
 
0.6%
1696161 1
 
0.6%
2842760 1
 
0.6%
1004383 1
 
0.6%
Other values (158) 158
92.9%
ValueCountFrequency (%)
0 3
1.8%
40 1
 
0.6%
153 1
 
0.6%
701 1
 
0.6%
2088 1
 
0.6%
3872 1
 
0.6%
4982 1
 
0.6%
7518 1
 
0.6%
9956 1
 
0.6%
14301 1
 
0.6%
ValueCountFrequency (%)
47691470 1
0.6%
34630287 1
0.6%
27523773 1
0.6%
23979873 1
0.6%
23573897 1
0.6%
21439520 1
0.6%
17584799 1
0.6%
14966158 1
0.6%
12847710 1
0.6%
10780782 1
0.6%

2020년_중간소비
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct168
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1346664.8
Minimum0
Maximum29965137
Zeros3
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-28T15:44:11.456461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3570.65
Q169843.5
median272201
Q3602746.25
95-th percentile8040502.6
Maximum29965137
Range29965137
Interquartile range (IQR)532902.75

Descriptive statistics

Standard deviation3779385.2
Coefficient of variation (CV)2.806478
Kurtosis25.473767
Mean1346664.8
Median Absolute Deviation (MAD)238413.5
Skewness4.6819018
Sum2.2893302 × 108
Variance1.4283752 × 1013
MonotonicityNot monotonic
2024-01-28T15:44:11.568835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
1.8%
723175 1
 
0.6%
553490 1
 
0.6%
917712 1
 
0.6%
194149 1
 
0.6%
698938 1
 
0.6%
510719 1
 
0.6%
723750 1
 
0.6%
604190 1
 
0.6%
280138 1
 
0.6%
Other values (158) 158
92.9%
ValueCountFrequency (%)
0 3
1.8%
20 1
 
0.6%
85 1
 
0.6%
391 1
 
0.6%
868 1
 
0.6%
1495 1
 
0.6%
3023 1
 
0.6%
4240 1
 
0.6%
5464 1
 
0.6%
7630 1
 
0.6%
ValueCountFrequency (%)
29965137 1
0.6%
19650929 1
0.6%
16515828 1
0.6%
16005482 1
0.6%
14320701 1
0.6%
12733601 1
0.6%
12270432 1
0.6%
8173245 1
0.6%
8149881 1
0.6%
7906818 1
0.6%

2020년_지역내총부가가치(당해년가격)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct168
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean956103.91
Minimum0
Maximum17726333
Zeros3
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-28T15:44:11.686719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2781.9
Q177624.75
median276378.5
Q3706604.5
95-th percentile4584472.6
Maximum17726333
Range17726333
Interquartile range (IQR)628979.75

Descriptive statistics

Standard deviation2367135.9
Coefficient of variation (CV)2.4758145
Kurtosis25.428092
Mean956103.91
Median Absolute Deviation (MAD)246554.5
Skewness4.7746848
Sum1.6253766 × 108
Variance5.6033323 × 1012
MonotonicityNot monotonic
2024-01-28T15:44:11.814173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
1.8%
876011 1
 
0.6%
317103 1
 
0.6%
344507 1
 
0.6%
238207 1
 
0.6%
783307 1
 
0.6%
1316785 1
 
0.6%
972410 1
 
0.6%
2238570 1
 
0.6%
724245 1
 
0.6%
Other values (158) 158
92.9%
ValueCountFrequency (%)
0 3
1.8%
20 1
 
0.6%
68 1
 
0.6%
310 1
 
0.6%
1220 1
 
0.6%
1960 1
 
0.6%
2376 1
 
0.6%
3278 1
 
0.6%
4492 1
 
0.6%
6320 1
 
0.6%
ValueCountFrequency (%)
17726333 1
0.6%
14979357 1
0.6%
11007945 1
0.6%
10840295 1
0.6%
9659172 1
0.6%
7059340 1
0.6%
5434039 1
0.6%
5314366 1
0.6%
4697829 1
0.6%
4445926 1
0.6%

2020년_고정자본소모
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct168
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241831.32
Minimum0
Maximum4944199
Zeros3
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-28T15:44:11.928149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile409.8
Q113174.75
median47244.5
Q3113814.5
95-th percentile1299611.6
Maximum4944199
Range4944199
Interquartile range (IQR)100639.75

Descriptive statistics

Standard deviation690560.25
Coefficient of variation (CV)2.8555451
Kurtosis25.215609
Mean241831.32
Median Absolute Deviation (MAD)39785.5
Skewness4.8120262
Sum41111325
Variance4.7687346 × 1011
MonotonicityNot monotonic
2024-01-28T15:44:12.035365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
1.8%
85522 1
 
0.6%
123945 1
 
0.6%
17502 1
 
0.6%
93602 1
 
0.6%
66711 1
 
0.6%
425119 1
 
0.6%
78357 1
 
0.6%
753536 1
 
0.6%
96503 1
 
0.6%
Other values (158) 158
92.9%
ValueCountFrequency (%)
0 3
1.8%
3 1
 
0.6%
13 1
 
0.6%
60 1
 
0.6%
160 1
 
0.6%
308 1
 
0.6%
327 1
 
0.6%
511 1
 
0.6%
686 1
 
0.6%
1222 1
 
0.6%
ValueCountFrequency (%)
4944199 1
0.6%
4396310 1
0.6%
3622602 1
0.6%
3387873 1
0.6%
2140391 1
0.6%
2109482 1
0.6%
1543211 1
0.6%
1391407 1
0.6%
1390058 1
0.6%
1189066 1
0.6%

2020년_지역내순생산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct168
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean714272.57
Minimum0
Maximum13330023
Zeros3
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-28T15:44:12.137553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2382.55
Q151978.75
median213128.5
Q3613988
95-th percentile3075052.9
Maximum13330023
Range13330023
Interquartile range (IQR)562009.25

Descriptive statistics

Standard deviation1754109.6
Coefficient of variation (CV)2.4557986
Kurtosis27.597295
Mean714272.57
Median Absolute Deviation (MAD)194587
Skewness4.9634852
Sum1.2142634 × 108
Variance3.0769005 × 1012
MonotonicityNot monotonic
2024-01-28T15:44:12.246089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
1.8%
790488 1
 
0.6%
193159 1
 
0.6%
327005 1
 
0.6%
144606 1
 
0.6%
716595 1
 
0.6%
891665 1
 
0.6%
894053 1
 
0.6%
1485034 1
 
0.6%
627742 1
 
0.6%
Other values (158) 158
92.9%
ValueCountFrequency (%)
0 3
1.8%
18 1
 
0.6%
55 1
 
0.6%
250 1
 
0.6%
1060 1
 
0.6%
1633 1
 
0.6%
2068 1
 
0.6%
2767 1
 
0.6%
3805 1
 
0.6%
5099 1
 
0.6%
ValueCountFrequency (%)
13330023 1
0.6%
11591485 1
0.6%
8730813 1
0.6%
7518781 1
0.6%
6063746 1
0.6%
5669282 1
0.6%
3922959 1
0.6%
3890828 1
0.6%
3571141 1
0.6%
2468723 1
0.6%

2020년_기타생산세(기타생산보조금 공제)
Text

Distinct162
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-01-28T15:44:12.511680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.5058824
Min length2

Characters and Unicode

Total characters766
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157 ?
Unique (%)92.4%

Sample

1st row15217
2nd row(6280)
3rd row25
4th row1955
5th row92
ValueCountFrequency (%)
0 5
 
2.9%
5 2
 
1.2%
10 2
 
1.2%
1 2
 
1.2%
8 2
 
1.2%
291 2
 
1.2%
80 2
 
1.2%
8627 1
 
0.6%
1565 1
 
0.6%
1492 1
 
0.6%
Other values (150) 150
88.2%
2024-01-28T15:44:12.893620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
163
21.3%
1 90
11.7%
2 83
10.8%
5 71
9.3%
8 61
 
8.0%
4 53
 
6.9%
0 52
 
6.8%
7 50
 
6.5%
3 48
 
6.3%
9 44
 
5.7%
Other values (3) 51
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 589
76.9%
Space Separator 163
 
21.3%
Open Punctuation 7
 
0.9%
Close Punctuation 7
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 90
15.3%
2 83
14.1%
5 71
12.1%
8 61
10.4%
4 53
9.0%
0 52
8.8%
7 50
8.5%
3 48
8.1%
9 44
7.5%
6 37
6.3%
Space Separator
ValueCountFrequency (%)
163
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
163
21.3%
1 90
11.7%
2 83
10.8%
5 71
9.3%
8 61
 
8.0%
4 53
 
6.9%
0 52
 
6.8%
7 50
 
6.5%
3 48
 
6.3%
9 44
 
5.7%
Other values (3) 51
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163
21.3%
1 90
11.7%
2 83
10.8%
5 71
9.3%
8 61
 
8.0%
4 53
 
6.9%
0 52
 
6.8%
7 50
 
6.5%
3 48
 
6.3%
9 44
 
5.7%
Other values (3) 51
 
6.7%

2020년_지역내요소소득
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct168
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701423.37
Minimum0
Maximum13093755
Zeros3
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-28T15:44:13.009474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2401.15
Q151661
median209654
Q3602486.5
95-th percentile3016524.5
Maximum13093755
Range13093755
Interquartile range (IQR)550825.5

Descriptive statistics

Standard deviation1723512.2
Coefficient of variation (CV)2.4571639
Kurtosis27.604885
Mean701423.37
Median Absolute Deviation (MAD)188063.5
Skewness4.9636738
Sum1.1924197 × 108
Variance2.9704942 × 1012
MonotonicityNot monotonic
2024-01-28T15:44:13.116526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
1.8%
789987 1
 
0.6%
191100 1
 
0.6%
322775 1
 
0.6%
144118 1
 
0.6%
707969 1
 
0.6%
794721 1
 
0.6%
891908 1
 
0.6%
1484998 1
 
0.6%
626934 1
 
0.6%
Other values (158) 158
92.9%
ValueCountFrequency (%)
0 3
1.8%
17 1
 
0.6%
54 1
 
0.6%
249 1
 
0.6%
1140 1
 
0.6%
1628 1
 
0.6%
2056 1
 
0.6%
2823 1
 
0.6%
3917 1
 
0.6%
5074 1
 
0.6%
ValueCountFrequency (%)
13093755 1
0.6%
11402358 1
0.6%
8576263 1
0.6%
7372886 1
0.6%
5946941 1
0.6%
5555463 1
0.6%
3878534 1
0.6%
3836704 1
0.6%
3497755 1
0.6%
2428354 1
0.6%

2020년_산출액(2015년 기준년연쇄가격)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct166
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2250639.8
Minimum0
Maximum49158208
Zeros5
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-28T15:44:13.221055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5575.7
Q1142502
median564983.5
Q31241730.5
95-th percentile11040669
Maximum49158208
Range49158208
Interquartile range (IQR)1099228.5

Descriptive statistics

Standard deviation6082495.4
Coefficient of variation (CV)2.7025628
Kurtosis27.571808
Mean2250639.8
Median Absolute Deviation (MAD)457541
Skewness4.8570876
Sum3.8260877 × 108
Variance3.6996751 × 1013
MonotonicityNot monotonic
2024-01-28T15:44:13.584101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
2.9%
3036081 1
 
0.6%
1125324 1
 
0.6%
444862 1
 
0.6%
1474749 1
 
0.6%
1727869 1
 
0.6%
1526076 1
 
0.6%
2542128 1
 
0.6%
928902 1
 
0.6%
1499470 1
 
0.6%
Other values (156) 156
91.8%
ValueCountFrequency (%)
0 5
2.9%
36 1
 
0.6%
1872 1
 
0.6%
3552 1
 
0.6%
4457 1
 
0.6%
6943 1
 
0.6%
9278 1
 
0.6%
12903 1
 
0.6%
19146 1
 
0.6%
19633 1
 
0.6%
ValueCountFrequency (%)
49158208 1
0.6%
34174830 1
0.6%
25760995 1
0.6%
23617529 1
0.6%
22982755 1
0.6%
22100002 1
0.6%
17691048 1
0.6%
14911219 1
0.6%
11309823 1
0.6%
10711704 1
0.6%

2020년_지역내총부가가치(2015년 기준년연쇄가격)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct166
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean900971.58
Minimum0
Maximum17268524
Zeros5
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-28T15:44:13.689419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2679.3
Q176169
median261014.5
Q3664640.25
95-th percentile4068090
Maximum17268524
Range17268524
Interquartile range (IQR)588471.25

Descriptive statistics

Standard deviation2243979.6
Coefficient of variation (CV)2.4906219
Kurtosis26.630698
Mean900971.58
Median Absolute Deviation (MAD)228067
Skewness4.8474771
Sum1.5316517 × 108
Variance5.0354444 × 1012
MonotonicityNot monotonic
2024-01-28T15:44:13.801330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
2.9%
1414662 1
 
0.6%
302184 1
 
0.6%
260523 1
 
0.6%
787421 1
 
0.6%
1247221 1
 
0.6%
840470 1
 
0.6%
1940276 1
 
0.6%
664667 1
 
0.6%
812993 1
 
0.6%
Other values (156) 156
91.8%
ValueCountFrequency (%)
0 5
2.9%
20 1
 
0.6%
987 1
 
0.6%
1989 1
 
0.6%
2124 1
 
0.6%
3358 1
 
0.6%
4342 1
 
0.6%
6364 1
 
0.6%
8539 1
 
0.6%
12158 1
 
0.6%
ValueCountFrequency (%)
17268524 1
0.6%
14202993 1
0.6%
10191209 1
0.6%
9377997 1
0.6%
9233397 1
0.6%
6729603 1
0.6%
5563486 1
0.6%
5143834 1
0.6%
4222998 1
0.6%
3878758 1
0.6%

Interactions

2024-01-28T15:44:09.692594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:05.416507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.020616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.618468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.188389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.755603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.349109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.122717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.762330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:05.487781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.104002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.685295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.249952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.839271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.417161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.184140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.832825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:05.558504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.177366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.754646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.315736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.909537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.496269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.249806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.908926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:05.631671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.261395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.825953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.385871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.980485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.581090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.321315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.974056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:05.696108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.324728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.895116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.445883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.044162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.647415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.386540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:10.045930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:05.765764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.390110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.965781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.515343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.110328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.713192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.470996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:10.115170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:05.833200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.467477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.038239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.586995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.182852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.779341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.555306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:10.186507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:05.922435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:06.547521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.107969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:07.664811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:08.274650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.050087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:09.623056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T15:44:13.877235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역(구,군)별경제활동별2020년_산출액(당해년가격)2020년_중간소비2020년_지역내총부가가치(당해년가격)2020년_고정자본소모2020년_지역내순생산2020년_지역내요소소득2020년_산출액(2015년 기준년연쇄가격)2020년_지역내총부가가치(2015년 기준년연쇄가격)
행정구역(구,군)별1.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
경제활동별0.0001.0000.4150.4260.3210.4180.4050.4050.3990.425
2020년_산출액(당해년가격)0.0000.4151.0000.9590.9790.9480.9790.9790.9970.927
2020년_중간소비0.0000.4260.9591.0000.9060.9440.9020.9020.9560.977
2020년_지역내총부가가치(당해년가격)0.0000.3210.9790.9061.0000.9430.9940.9940.9770.990
2020년_고정자본소모0.0000.4180.9480.9440.9431.0000.9350.9350.9680.949
2020년_지역내순생산0.0000.4050.9790.9020.9940.9351.0001.0000.9740.937
2020년_지역내요소소득0.0000.4050.9790.9020.9940.9351.0001.0000.9740.937
2020년_산출액(2015년 기준년연쇄가격)0.0000.3990.9970.9560.9770.9680.9740.9741.0000.921
2020년_지역내총부가가치(2015년 기준년연쇄가격)0.0000.4250.9270.9770.9900.9490.9370.9370.9211.000
2024-01-28T15:44:13.980153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경제활동별행정구역(구,군)별
경제활동별1.0000.000
행정구역(구,군)별0.0001.000
2024-01-28T15:44:14.052152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2020년_산출액(당해년가격)2020년_중간소비2020년_지역내총부가가치(당해년가격)2020년_고정자본소모2020년_지역내순생산2020년_지역내요소소득2020년_산출액(2015년 기준년연쇄가격)2020년_지역내총부가가치(2015년 기준년연쇄가격)행정구역(구,군)별경제활동별
2020년_산출액(당해년가격)1.0000.9700.9770.8570.9700.9700.9990.9770.0000.181
2020년_중간소비0.9701.0000.9050.7890.9010.9020.9700.9060.0000.200
2020년_지역내총부가가치(당해년가격)0.9770.9051.0000.8840.9930.9920.9760.9970.0000.134
2020년_고정자본소모0.8570.7890.8841.0000.8300.8260.8610.8900.0000.174
2020년_지역내순생산0.9700.9010.9930.8301.0001.0000.9670.9870.0000.176
2020년_지역내요소소득0.9700.9020.9920.8261.0001.0000.9670.9860.0000.176
2020년_산출액(2015년 기준년연쇄가격)0.9990.9700.9760.8610.9670.9671.0000.9780.0000.172
2020년_지역내총부가가치(2015년 기준년연쇄가격)0.9770.9060.9970.8900.9870.9860.9781.0000.0000.199
행정구역(구,군)별0.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
경제활동별0.1810.2000.1340.1740.1760.1760.1720.1990.0001.000

Missing values

2024-01-28T15:44:10.344325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T15:44:10.528386image/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

행정구역(구,군)별경제활동별2020년_산출액(당해년가격)2020년_중간소비2020년_지역내총부가가치(당해년가격)2020년_고정자본소모2020년_지역내순생산2020년_기타생산세(기타생산보조금 공제)2020년_지역내요소소득2020년_산출액(2015년 기준년연쇄가격)2020년_지역내총부가가치(2015년 기준년연쇄가격)
0강화군총부가가치(기초가격)314052516455791494946329348116559815217115038130360811414662
1강화군농업, 임업 및 어업2121749388011829315749102545(6280)108825192836109618
2강화군광업143017980632012225099255074129036364
3강화군제조업781508586543194964398891550751955153120750004175386
4강화군전기, 가스, 증기 및 공기 조절 공급업412502754913701706166409265494442417858
5강화군건설업243463144165992984816944834049407822170382042
6강화군도매 및 소매업188072905859748672549023218898834318609698643
7강화군운수 및 창고업119798666925310620896322102833192711339147721
8강화군숙박 및 음식점업2134651454966796913468545017155378519343562055
9강화군정보통신업42120193202280099671283344127904267324332
행정구역(구,군)별경제활동별2020년_산출액(당해년가격)2020년_중간소비2020년_지역내총부가가치(당해년가격)2020년_고정자본소모2020년_지역내순생산2020년_기타생산세(기타생산보조금 공제)2020년_지역내요소소득2020년_산출액(2015년 기준년연쇄가격)2020년_지역내총부가가치(2015년 기준년연쇄가격)
160서구운수 및 창고업125375781482943892816567427325429652702891204895402112
161서구숙박 및 음식점업1060394771924288470136572748143554271260944313251219
162서구정보통신업658070351545306525156138150387771149616677890336578
163서구금융 및 보험업611742198491413251337263795253553375971602149406291
164서구부동산업1983681572491141118946430494688610502584186018751181335118
165서구사업서비스업2266197902402136379415754112062542158120409620473601176889
166서구공공 행정, 국방 및 사회보장 행정89122328261460860917881742979110429781787007521258
167서구교육 서비스업102494028108974385097101646750708646042946155681392
168서구보건업 및 사회복지 서비스업1358655614810743845742276696184806691381277109691128
169서구문화 및 기타서비스업1454190799460654730137653517077193154977621218485471195