Overview

Dataset statistics

Number of variables11
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory104.3 B

Variable types

Text1
Numeric10

Dataset

Description경상남도 시군별 지역내총생산에 대한 데이터로, 2010년 기준 행정구역(시군)별 당해년도 지역내총생산(시장가격)에 대한 정보를 제공합니다.
Author경상남도
URLhttps://www.data.go.kr/data/15113701/fileData.do

Alerts

2000년 가격 is highly overall correlated with 2001년 가격 and 8 other fieldsHigh correlation
2001년 가격 is highly overall correlated with 2000년 가격 and 8 other fieldsHigh correlation
2002년 가격 is highly overall correlated with 2000년 가격 and 8 other fieldsHigh correlation
2003년 가격 is highly overall correlated with 2000년 가격 and 8 other fieldsHigh correlation
2004년 가격 is highly overall correlated with 2000년 가격 and 8 other fieldsHigh correlation
2005년 가격 is highly overall correlated with 2000년 가격 and 8 other fieldsHigh correlation
2006년 가격 is highly overall correlated with 2000년 가격 and 8 other fieldsHigh correlation
2007년 가격 is highly overall correlated with 2000년 가격 and 8 other fieldsHigh correlation
2008년 가격 is highly overall correlated with 2000년 가격 and 8 other fieldsHigh correlation
2009년 가격 is highly overall correlated with 2000년 가격 and 8 other fieldsHigh correlation
행정구역(시군)별 has unique valuesUnique
2000년 가격 has unique valuesUnique
2001년 가격 has unique valuesUnique
2002년 가격 has unique valuesUnique
2003년 가격 has unique valuesUnique
2004년 가격 has unique valuesUnique
2005년 가격 has unique valuesUnique
2006년 가격 has unique valuesUnique
2007년 가격 has unique valuesUnique
2008년 가격 has unique valuesUnique
2009년 가격 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:35:24.692173
Analysis finished2023-12-12 13:35:35.823372
Duration11.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역(시군)별
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T22:35:35.982760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.047619
Min length3

Characters and Unicode

Total characters64
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row경상남도
2nd row창원시
3rd row마산시
4th row진주시
5th row진해시
ValueCountFrequency (%)
경상남도 1
 
4.8%
의령군 1
 
4.8%
거창군 1
 
4.8%
함양군 1
 
4.8%
산청군 1
 
4.8%
하동군 1
 
4.8%
남해군 1
 
4.8%
고성군 1
 
4.8%
창녕군 1
 
4.8%
함안군 1
 
4.8%
Other values (11) 11
52.4%
2023-12-12T22:35:36.324752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 10
15.6%
군 10
15.6%
창 3
 
4.7%
양 3
 
4.7%
산 3
 
4.7%
해 3
 
4.7%
남 2
 
3.1%
거 2
 
3.1%
진 2
 
3.1%
함 2
 
3.1%
Other values (23) 24
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 10
15.6%
군 10
15.6%
창 3
 
4.7%
양 3
 
4.7%
산 3
 
4.7%
해 3
 
4.7%
남 2
 
3.1%
거 2
 
3.1%
진 2
 
3.1%
함 2
 
3.1%
Other values (23) 24
37.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 10
15.6%
군 10
15.6%
창 3
 
4.7%
양 3
 
4.7%
산 3
 
4.7%
해 3
 
4.7%
남 2
 
3.1%
거 2
 
3.1%
진 2
 
3.1%
함 2
 
3.1%
Other values (23) 24
37.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 10
15.6%
군 10
15.6%
창 3
 
4.7%
양 3
 
4.7%
산 3
 
4.7%
해 3
 
4.7%
남 2
 
3.1%
거 2
 
3.1%
진 2
 
3.1%
함 2
 
3.1%
Other values (23) 24
37.5%

2000년 가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4085915.8
Minimum380756.2
Maximum42902116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T22:35:36.481172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum380756.2
5-th percentile382684.8
Q1616745.1
median1233472.2
Q33236350.6
95-th percentile10985790
Maximum42902116
Range42521360
Interquartile range (IQR)2619605.5

Descriptive statistics

Standard deviation9232913.3
Coefficient of variation (CV)2.2596925
Kurtosis17.627532
Mean4085915.8
Median Absolute Deviation (MAD)815009.5
Skewness4.0953492
Sum85804232
Variance8.5246689 × 1013
MonotonicityNot monotonic
2023-12-12T22:35:36.616512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
42902116.0 1
 
4.8%
10985790.3 1
 
4.8%
616745.1 1
 
4.8%
550339.4 1
 
4.8%
380756.2 1
 
4.8%
418462.7 1
 
4.8%
857974.1 1
 
4.8%
382684.8 1
 
4.8%
1160384.3 1
 
4.8%
878549.3 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
380756.2 1
4.8%
382684.8 1
4.8%
412785.6 1
4.8%
418462.7 1
4.8%
550339.4 1
4.8%
616745.1 1
4.8%
857974.1 1
4.8%
878549.3 1
4.8%
1066254.4 1
4.8%
1160384.3 1
4.8%
ValueCountFrequency (%)
42902116.0 1
4.8%
10985790.3 1
4.8%
5428372.9 1
4.8%
4345380.0 1
4.8%
3607132.8 1
4.8%
3236350.6 1
4.8%
3055867.3 1
4.8%
1707984.3 1
4.8%
1291376.3 1
4.8%
1285453.8 1
4.8%

2001년 가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4517889.1
Minimum438132.5
Maximum47437836
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T22:35:36.744503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum438132.5
5-th percentile443470.7
Q1682918.9
median1327547.9
Q33527599.5
95-th percentile12225209
Maximum47437836
Range46999704
Interquartile range (IQR)2844680.6

Descriptive statistics

Standard deviation10209267
Coefficient of variation (CV)2.2597426
Kurtosis17.626378
Mean4517889.1
Median Absolute Deviation (MAD)853709.3
Skewness4.0959194
Sum94875672
Variance1.0422913 × 1014
MonotonicityNot monotonic
2023-12-12T22:35:36.879500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
47437836.0 1
 
4.8%
12225208.7 1
 
4.8%
649890.2 1
 
4.8%
682918.9 1
 
4.8%
443470.7 1
 
4.8%
473838.6 1
 
4.8%
996961.2 1
 
4.8%
438132.5 1
 
4.8%
1257270.0 1
 
4.8%
942268.7 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
438132.5 1
4.8%
443470.7 1
4.8%
453465.8 1
4.8%
473838.6 1
4.8%
649890.2 1
4.8%
682918.9 1
4.8%
942268.7 1
4.8%
996961.2 1
4.8%
1198081.6 1
4.8%
1257270.0 1
4.8%
ValueCountFrequency (%)
47437836.0 1
4.8%
12225208.7 1
4.8%
5758189.5 1
4.8%
4927828.0 1
4.8%
3994742.4 1
4.8%
3527599.5 1
4.8%
3427844.5 1
4.8%
1717531.9 1
4.8%
1600246.4 1
4.8%
1394799.0 1
4.8%

2002년 가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4946106.6
Minimum452359.3
Maximum51934119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T22:35:37.028187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum452359.3
5-th percentile473124.2
Q1753194.5
median1481925.5
Q33902221.9
95-th percentile13677718
Maximum51934119
Range51481760
Interquartile range (IQR)3149027.4

Descriptive statistics

Standard deviation11191873
Coefficient of variation (CV)2.2627643
Kurtosis17.517188
Mean4946106.6
Median Absolute Deviation (MAD)1001309.2
Skewness4.08157
Sum1.0386824 × 108
Variance1.2525802 × 1014
MonotonicityNot monotonic
2023-12-12T22:35:37.195879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
51934119.0 1
 
4.8%
13677717.8 1
 
4.8%
627657.1 1
 
4.8%
753194.5 1
 
4.8%
480616.3 1
 
4.8%
478127.4 1
 
4.8%
1243016.7 1
 
4.8%
452359.3 1
 
4.8%
1365782.2 1
 
4.8%
872594.2 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
452359.3 1
4.8%
473124.2 1
4.8%
478127.4 1
4.8%
480616.3 1
4.8%
627657.1 1
4.8%
753194.5 1
4.8%
872594.2 1
4.8%
1243016.7 1
4.8%
1365782.2 1
4.8%
1393451.7 1
4.8%
ValueCountFrequency (%)
51934119.0 1
4.8%
13677717.8 1
4.8%
5941562.7 1
4.8%
5713296.4 1
4.8%
4505130.7 1
4.8%
3902221.9 1
4.8%
3557483.8 1
4.8%
1729928.3 1
4.8%
1664760.4 1
4.8%
1620167.8 1
4.8%

2003년 가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5271431.7
Minimum477460.8
Maximum55350033
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T22:35:37.327521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum477460.8
5-th percentile490647.8
Q1851357.5
median1646133.3
Q34409689.6
95-th percentile14242600
Maximum55350033
Range54872572
Interquartile range (IQR)3558332.1

Descriptive statistics

Standard deviation11910985
Coefficient of variation (CV)2.2595352
Kurtosis17.634103
Mean5271431.7
Median Absolute Deviation (MAD)1111536
Skewness4.0965827
Sum1.1070007 × 108
Variance1.4187157 × 1014
MonotonicityNot monotonic
2023-12-12T22:35:37.491776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
55350033.0 1
 
4.8%
14242599.6 1
 
4.8%
667661.9 1
 
4.8%
851357.5 1
 
4.8%
534597.3 1
 
4.8%
512363.3 1
 
4.8%
1294036.3 1
 
4.8%
490647.8 1
 
4.8%
1469297.3 1
 
4.8%
946143.6 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
477460.8 1
4.8%
490647.8 1
4.8%
512363.3 1
4.8%
534597.3 1
4.8%
667661.9 1
4.8%
851357.5 1
4.8%
946143.6 1
4.8%
1294036.3 1
4.8%
1368372.4 1
4.8%
1469297.3 1
4.8%
ValueCountFrequency (%)
55350033.0 1
4.8%
14242599.6 1
4.8%
6423426.0 1
4.8%
5840584.3 1
4.8%
4760776.6 1
4.8%
4409689.6 1
4.8%
3940134.9 1
4.8%
1873941.4 1
4.8%
1819683.9 1
4.8%
1781125.2 1
4.8%

2004년 가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5671504.7
Minimum489911.7
Maximum59550799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T22:35:37.668415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum489911.7
5-th percentile521476.1
Q1865441
median1637590.2
Q34615338.8
95-th percentile16386271
Maximum59550799
Range59060887
Interquartile range (IQR)3749897.8

Descriptive statistics

Standard deviation12868834
Coefficient of variation (CV)2.2690335
Kurtosis17.293878
Mean5671504.7
Median Absolute Deviation (MAD)1065745.4
Skewness4.0530686
Sum1.191016 × 108
Variance1.6560688 × 1014
MonotonicityNot monotonic
2023-12-12T22:35:37.816681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
59550799.0 1
 
4.8%
16386270.6 1
 
4.8%
695220.8 1
 
4.8%
865441.0 1
 
4.8%
489911.7 1
 
4.8%
521476.1 1
 
4.8%
1165110.1 1
 
4.8%
571844.8 1
 
4.8%
1452050.0 1
 
4.8%
1034871.4 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
489911.7 1
4.8%
521476.1 1
4.8%
541670.8 1
4.8%
571844.8 1
4.8%
695220.8 1
4.8%
865441.0 1
4.8%
1034871.4 1
4.8%
1165110.1 1
4.8%
1452050.0 1
4.8%
1543500.7 1
4.8%
ValueCountFrequency (%)
59550799.0 1
4.8%
16386270.6 1
4.8%
7153853.6 1
4.8%
5863899.6 1
4.8%
5013799.0 1
4.8%
4615338.8 1
4.8%
4194306.4 1
4.8%
2005595.0 1
4.8%
1989709.0 1
4.8%
1809339.3 1
4.8%

2005년 가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6038268.7
Minimum485406.9
Maximum63401821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T22:35:37.977737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum485406.9
5-th percentile498276.7
Q1912074.5
median1760007.5
Q35341768.8
95-th percentile17361846
Maximum63401821
Range62916414
Interquartile range (IQR)4429694.3

Descriptive statistics

Standard deviation13708547
Coefficient of variation (CV)2.2702777
Kurtosis17.247869
Mean6038268.7
Median Absolute Deviation (MAD)1238114.3
Skewness4.0451417
Sum1.2680364 × 108
Variance1.8792425 × 1014
MonotonicityNot monotonic
2023-12-12T22:35:38.139978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
63401821.0 1
 
4.8%
17361845.7 1
 
4.8%
717123.2 1
 
4.8%
912074.5 1
 
4.8%
504870.5 1
 
4.8%
498276.7 1
 
4.8%
1176909.1 1
 
4.8%
521893.2 1
 
4.8%
1392269.1 1
 
4.8%
1004095.9 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
485406.9 1
4.8%
498276.7 1
4.8%
504870.5 1
4.8%
521893.2 1
4.8%
717123.2 1
4.8%
912074.5 1
4.8%
1004095.9 1
4.8%
1176909.1 1
4.8%
1392269.1 1
4.8%
1714818.0 1
4.8%
ValueCountFrequency (%)
63401821.0 1
4.8%
17361845.7 1
4.8%
8239466.0 1
4.8%
5692045.4 1
4.8%
5473865.4 1
4.8%
5341768.8 1
4.8%
4220373.0 1
4.8%
2243562.6 1
4.8%
2102612.4 1
4.8%
2038537.1 1
4.8%

2006년 가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6353289.8
Minimum495314.7
Maximum66709543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T22:35:38.297334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum495314.7
5-th percentile516377.9
Q1895247.5
median1774145.6
Q35559910.6
95-th percentile18642275
Maximum66709543
Range66214228
Interquartile range (IQR)4664663.1

Descriptive statistics

Standard deviation14441897
Coefficient of variation (CV)2.2731369
Kurtosis17.147958
Mean6353289.8
Median Absolute Deviation (MAD)1190642.4
Skewness4.0325229
Sum1.3341909 × 108
Variance2.085684 × 1014
MonotonicityNot monotonic
2023-12-12T22:35:38.470913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
66709543.0 1
 
4.8%
18642275.2 1
 
4.8%
716798.3 1
 
4.8%
895247.5 1
 
4.8%
583503.2 1
 
4.8%
516377.9 1
 
4.8%
1189379.4 1
 
4.8%
537554.6 1
 
4.8%
1355137.6 1
 
4.8%
1026499.5 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
495314.7 1
4.8%
516377.9 1
4.8%
537554.6 1
4.8%
583503.2 1
4.8%
716798.3 1
4.8%
895247.5 1
4.8%
1026499.5 1
4.8%
1189379.4 1
4.8%
1355137.6 1
4.8%
1757693.3 1
4.8%
ValueCountFrequency (%)
66709543.0 1
4.8%
18642275.2 1
4.8%
8466880.7 1
4.8%
6209882.5 1
4.8%
5633732.3 1
4.8%
5559910.6 1
4.8%
4371123.1 1
4.8%
2549848.9 1
4.8%
2272026.7 1
4.8%
2156211.4 1
4.8%

2007년 가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6956562.9
Minimum521627.5
Maximum73043911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T22:35:38.647299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum521627.5
5-th percentile530789.1
Q1946426.6
median1921265.2
Q35862530.4
95-th percentile20178871
Maximum73043911
Range72522284
Interquartile range (IQR)4916103.8

Descriptive statistics

Standard deviation15809592
Coefficient of variation (CV)2.2726154
Kurtosis17.164749
Mean6956562.9
Median Absolute Deviation (MAD)1327781.6
Skewness4.0332668
Sum1.4608782 × 108
Variance2.4994319 × 1014
MonotonicityNot monotonic
2023-12-12T22:35:38.784072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
73043911.0 1
 
4.8%
20178870.6 1
 
4.8%
722582.0 1
 
4.8%
946426.6 1
 
4.8%
593483.6 1
 
4.8%
521627.5 1
 
4.8%
1290845.6 1
 
4.8%
556102.1 1
 
4.8%
1415782.3 1
 
4.8%
1143882.5 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
521627.5 1
4.8%
530789.1 1
4.8%
556102.1 1
4.8%
593483.6 1
4.8%
722582.0 1
4.8%
946426.6 1
4.8%
1143882.5 1
4.8%
1290845.6 1
4.8%
1415782.3 1
4.8%
1906426.6 1
4.8%
ValueCountFrequency (%)
73043911.0 1
4.8%
20178870.6 1
4.8%
9370408.5 1
4.8%
7624887.9 1
4.8%
5907455.9 1
4.8%
5862530.4 1
4.8%
4594303.8 1
4.8%
2993739.9 1
4.8%
2504971.1 1
4.8%
2457529.7 1
4.8%

2008년 가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7589865.2
Minimum588238.2
Maximum79693585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T22:35:38.944906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum588238.2
5-th percentile600396.9
Q11065469.2
median2431902.9
Q36077891.7
95-th percentile21933168
Maximum79693585
Range79105347
Interquartile range (IQR)5012422.5

Descriptive statistics

Standard deviation17246151
Coefficient of variation (CV)2.2722605
Kurtosis17.177064
Mean7589865.2
Median Absolute Deviation (MAD)1752275.8
Skewness4.0343464
Sum1.5938717 × 108
Variance2.9742973 × 1014
MonotonicityNot monotonic
2023-12-12T22:35:39.123268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
79693585.0 1
 
4.8%
21933168.3 1
 
4.8%
726279.3 1
 
4.8%
1065469.2 1
 
4.8%
679627.1 1
 
4.8%
600396.9 1
 
4.8%
1129539.0 1
 
4.8%
588238.2 1
 
4.8%
1319592.5 1
 
4.8%
1181107.7 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
588238.2 1
4.8%
600396.9 1
4.8%
619106.6 1
4.8%
679627.1 1
4.8%
726279.3 1
4.8%
1065469.2 1
4.8%
1129539.0 1
4.8%
1181107.7 1
4.8%
1319592.5 1
4.8%
1883073.4 1
4.8%
ValueCountFrequency (%)
79693585.0 1
4.8%
21933168.3 1
4.8%
9536320.6 1
4.8%
9322149.4 1
4.8%
6400195.1 1
4.8%
6077891.7 1
4.8%
4964298.3 1
4.8%
3508783.9 1
4.8%
2910992.9 1
4.8%
2815451.9 1
4.8%

2009년 가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7920258.7
Minimum617203.8
Maximum83162716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T22:35:39.574677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum617203.8
5-th percentile639367.2
Q11163979.1
median2384894.4
Q36383186.8
95-th percentile22377278
Maximum83162716
Range82545512
Interquartile range (IQR)5219207.7

Descriptive statistics

Standard deviation17962934
Coefficient of variation (CV)2.2679731
Kurtosis17.329193
Mean7920258.7
Median Absolute Deviation (MAD)1654077.1
Skewness4.0549829
Sum1.6632543 × 108
Variance3.2266699 × 1014
MonotonicityNot monotonic
2023-12-12T22:35:39.693103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
83162716.0 1
 
4.8%
22377277.5 1
 
4.8%
730817.3 1
 
4.8%
1163979.1 1
 
4.8%
742614.5 1
 
4.8%
639367.2 1
 
4.8%
1403707.7 1
 
4.8%
660006.1 1
 
4.8%
1865294.1 1
 
4.8%
1288662.1 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
617203.8 1
4.8%
639367.2 1
4.8%
660006.1 1
4.8%
730817.3 1
4.8%
742614.5 1
4.8%
1163979.1 1
4.8%
1288662.1 1
4.8%
1403707.7 1
4.8%
1848345.3 1
4.8%
1865294.1 1
4.8%
ValueCountFrequency (%)
83162716.0 1
4.8%
22377277.5 1
4.8%
10112845.7 1
4.8%
9492630.1 1
4.8%
6415558.2 1
4.8%
6383186.8 1
4.8%
5111089.7 1
4.8%
4017942.6 1
4.8%
3055728.0 1
4.8%
2851565.6 1
4.8%

Interactions

2023-12-12T22:35:34.662710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:25.267528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:26.267919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.194093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:28.213942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:29.305526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:30.369199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:31.287745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:32.513769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:33.625191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:34.736333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:25.352433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:26.349990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.282054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:28.313723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:29.404333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:30.454102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:31.359975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:32.623348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:33.732498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:34.823119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:25.430691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:26.437386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.374817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:28.404893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:29.499719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:30.546728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:31.433538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:32.720398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:33.846641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:34.901335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:25.529664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:26.521131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.497379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:28.522006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:29.605892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:30.652881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:31.507088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:32.823698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:33.942031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:34.972761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:25.624483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:26.610081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.596578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:28.637135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:29.719829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:30.740895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:31.591377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:32.983745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:34.054186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:35.051054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:25.763571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:26.707012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.701053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:28.768169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:29.839847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:30.822172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:31.677467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:33.075712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:34.156902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:35.138588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:25.875691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:26.830157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.808063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:28.877664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:29.948834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:30.908899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:32.091603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:33.183199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:34.247778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:35.227870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:25.978218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:26.929127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.890767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:29.005541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:30.044722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:31.002887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:32.195214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:33.289162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:34.351721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:35.344212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:26.076243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.027020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.978950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:29.104800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:30.151648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:31.110791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:32.308499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:33.402558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:34.441234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:35.446333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:26.177391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:27.116094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:28.102230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:29.218158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:30.265505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:31.219572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:32.417918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:33.519267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:35:34.555362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:35:39.784857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역(시군)별2000년 가격2001년 가격2002년 가격2003년 가격2004년 가격2005년 가격2006년 가격2007년 가격2008년 가격2009년 가격
행정구역(시군)별1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2000년 가격1.0001.0001.0000.9930.9730.9740.9730.9730.9730.9740.974
2001년 가격1.0001.0001.0000.9940.9730.9730.9730.9730.9730.9740.974
2002년 가격1.0000.9930.9941.0000.9930.9930.9930.9930.9930.9840.983
2003년 가격1.0000.9730.9730.9931.0001.0001.0001.0001.0000.9930.994
2004년 가격1.0000.9740.9730.9931.0001.0001.0001.0001.0000.9930.993
2005년 가격1.0000.9730.9730.9931.0001.0001.0001.0001.0000.9930.994
2006년 가격1.0000.9730.9730.9931.0001.0001.0001.0001.0000.9930.994
2007년 가격1.0000.9730.9730.9931.0001.0001.0001.0001.0000.9930.993
2008년 가격1.0000.9740.9740.9840.9930.9930.9930.9930.9931.0001.000
2009년 가격1.0000.9740.9740.9830.9940.9930.9940.9940.9931.0001.000
2023-12-12T22:35:39.913731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2000년 가격2001년 가격2002년 가격2003년 가격2004년 가격2005년 가격2006년 가격2007년 가격2008년 가격2009년 가격
2000년 가격1.0000.9960.9860.9810.9870.9810.9680.9680.9750.962
2001년 가격0.9961.0000.9920.9860.9860.9820.9710.9710.9790.969
2002년 가격0.9860.9921.0000.9950.9860.9900.9870.9840.9900.984
2003년 가격0.9810.9860.9951.0000.9860.9920.9910.9860.9860.987
2004년 가격0.9870.9860.9860.9861.0000.9940.9840.9860.9820.978
2005년 가격0.9810.9820.9900.9920.9941.0000.9950.9920.9870.991
2006년 가격0.9680.9710.9870.9910.9840.9951.0000.9960.9920.996
2007년 가격0.9680.9710.9840.9860.9860.9920.9961.0000.9940.995
2008년 가격0.9750.9790.9900.9860.9820.9870.9920.9941.0000.991
2009년 가격0.9620.9690.9840.9870.9780.9910.9960.9950.9911.000

Missing values

2023-12-12T22:35:35.570725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:35:35.749155image/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(시군)별2000년 가격2001년 가격2002년 가격2003년 가격2004년 가격2005년 가격2006년 가격2007년 가격2008년 가격2009년 가격
0경상남도42902116.047437836.051934119.055350033.059550799.063401821.066709543.073043911.079693585.083162716.0
1창원시10985790.312225208.713677717.814242599.616386270.617361845.718642275.220178870.621933168.322377277.5
2마산시5428372.95758189.55941562.75840584.35863899.65692045.45633732.35862530.46400195.16415558.2
3진주시3055867.33427844.53557483.83940134.94194306.44220373.04371123.14594303.84964298.35111089.7
4진해시1233472.21327547.91620167.81819683.91809339.32038537.12272026.72457529.72815451.92851565.6
5통영시1291376.31600246.41729928.31873941.42005595.02243562.62549848.92993739.93508783.94017942.6
6사천시1707984.31717531.91664760.41781125.21989709.02102612.42156211.42504971.12910992.93055728.0
7김해시4345380.04927828.05713296.46423426.07153853.68239466.08466880.79370408.59536320.610112845.7
8밀양시1285453.81394799.01481925.51646133.31637590.21760007.51757693.31906426.61883073.41848345.3
9거제시3236350.63527599.53902221.94409689.64615338.85473865.46209882.57624887.99322149.49492630.1
행정구역(시군)별2000년 가격2001년 가격2002년 가격2003년 가격2004년 가격2005년 가격2006년 가격2007년 가격2008년 가격2009년 가격
11의령군412785.6453465.8473124.2477460.8541670.8485406.9495314.7530789.1619106.6617203.8
12함안군1066254.41198081.61393451.71368372.41543500.71714818.01774145.61921265.22431902.92384894.4
13창녕군878549.3942268.7872594.2946143.61034871.41004095.91026499.51143882.51181107.71288662.1
14고성군1160384.31257270.01365782.21469297.31452050.01392269.11355137.61415782.31319592.51865294.1
15남해군382684.8438132.5452359.3490647.8571844.8521893.2537554.6556102.1588238.2660006.1
16하동군857974.1996961.21243016.71294036.31165110.11176909.11189379.41290845.61129539.01403707.7
17산청군418462.7473838.6478127.4512363.3521476.1498276.7516377.9521627.5600396.9639367.2
18함양군380756.2443470.7480616.3534597.3489911.7504870.5583503.2593483.6679627.1742614.5
19거창군550339.4682918.9753194.5851357.5865441.0912074.5895247.5946426.61065469.21163979.1
20합천군616745.1649890.2627657.1667661.9695220.8717123.2716798.3722582.0726279.3730817.3