Overview

Dataset statistics

Number of variables12
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory109.4 B

Variable types

Categorical1
Text2
Numeric9

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://bigdata-region.kr/#/dataset/83c8baa0-b422-45aa-919c-33f9abafdfab

Alerts

시도명 has constant value ""Constant
해당년 소비액 is highly overall correlated with 전년소비액 and 2 other fieldsHigh correlation
전년소비액 is highly overall correlated with 해당년 소비액 and 2 other fieldsHigh correlation
년 1인당 소비액 is highly overall correlated with 해당년 소비액 and 2 other fieldsHigh correlation
전년1인당 소비액 is highly overall correlated with 해당년 소비액 and 2 other fieldsHigh correlation
전체기간 소비변화지수 is highly overall correlated with 전체기간 소비증감률 and 2 other fieldsHigh correlation
전체기간 소비증감률 is highly overall correlated with 전체기간 소비변화지수 and 2 other fieldsHigh correlation
전체기간 1인당 소비변화지수 is highly overall correlated with 전체기간 소비변화지수 and 2 other fieldsHigh correlation
전체기간 1인당 소비증감률 is highly overall correlated with 전체기간 소비변화지수 and 2 other fieldsHigh correlation
행정동명 has unique valuesUnique
행정동 코드 has unique valuesUnique
해당년 소비액 has unique valuesUnique
전년소비액 has unique valuesUnique
년 1인당 소비액 has unique valuesUnique
전년1인당 소비액 has unique valuesUnique
전체기간 소비변화지수 has unique valuesUnique
전체기간 소비증감률 has unique valuesUnique
전체기간 1인당 소비변화지수 has unique valuesUnique
전체기간 1인당 소비증감률 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:49:15.470519
Analysis finished2023-12-10 13:49:30.568020
Duration15.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T22:49:30.689919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:30.838919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:49:31.066803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0333333
Min length3

Characters and Unicode

Total characters91
Distinct characters26
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

Unique12 ?
Unique (%)40.0%

Sample

1st row구리시
2nd row광명시
3rd row김포시
4th row부천시
5th row남양주시
ValueCountFrequency (%)
성남시 4
13.3%
수원시 4
13.3%
구리시 2
 
6.7%
부천시 2
 
6.7%
고양시 2
 
6.7%
하남시 2
 
6.7%
용인시 2
 
6.7%
양평군 1
 
3.3%
여주시 1
 
3.3%
가평군 1
 
3.3%
Other values (9) 9
30.0%
2023-12-10T22:49:31.624381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
31.9%
남 7
 
7.7%
성 5
 
5.5%
양 5
 
5.5%
수 4
 
4.4%
원 4
 
4.4%
평 3
 
3.3%
천 3
 
3.3%
주 3
 
3.3%
군 2
 
2.2%
Other values (16) 26
28.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
31.9%
남 7
 
7.7%
성 5
 
5.5%
양 5
 
5.5%
수 4
 
4.4%
원 4
 
4.4%
평 3
 
3.3%
천 3
 
3.3%
주 3
 
3.3%
군 2
 
2.2%
Other values (16) 26
28.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
31.9%
남 7
 
7.7%
성 5
 
5.5%
양 5
 
5.5%
수 4
 
4.4%
원 4
 
4.4%
평 3
 
3.3%
천 3
 
3.3%
주 3
 
3.3%
군 2
 
2.2%
Other values (16) 26
28.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
31.9%
남 7
 
7.7%
성 5
 
5.5%
양 5
 
5.5%
수 4
 
4.4%
원 4
 
4.4%
평 3
 
3.3%
천 3
 
3.3%
주 3
 
3.3%
군 2
 
2.2%
Other values (16) 26
28.6%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:49:31.954710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3333333
Min length3

Characters and Unicode

Total characters100
Distinct characters56
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row수택3동
2nd row철산3동
3rd row월곶면
4th row고강본동
5th row금곡동
ValueCountFrequency (%)
수택3동 1
 
3.3%
철산3동 1
 
3.3%
남종면 1
 
3.3%
행주동 1
 
3.3%
가평읍 1
 
3.3%
중산동 1
 
3.3%
초이동 1
 
3.3%
신장2동 1
 
3.3%
화현면 1
 
3.3%
통복동 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:49:32.573387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
23.0%
2 5
 
5.0%
면 5
 
5.0%
평 3
 
3.0%
수 3
 
3.0%
3 3
 
3.0%
산 2
 
2.0%
가 2
 
2.0%
남 2
 
2.0%
읍 2
 
2.0%
Other values (46) 50
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
91.0%
Decimal Number 9
 
9.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
25.3%
면 5
 
5.5%
평 3
 
3.3%
수 3
 
3.3%
산 2
 
2.2%
가 2
 
2.2%
남 2
 
2.2%
읍 2
 
2.2%
곡 2
 
2.2%
교 2
 
2.2%
Other values (43) 45
49.5%
Decimal Number
ValueCountFrequency (%)
2 5
55.6%
3 3
33.3%
4 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
91.0%
Common 9
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
25.3%
면 5
 
5.5%
평 3
 
3.3%
수 3
 
3.3%
산 2
 
2.2%
가 2
 
2.2%
남 2
 
2.2%
읍 2
 
2.2%
곡 2
 
2.2%
교 2
 
2.2%
Other values (43) 45
49.5%
Common
ValueCountFrequency (%)
2 5
55.6%
3 3
33.3%
4 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
91.0%
ASCII 9
 
9.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
25.3%
면 5
 
5.5%
평 3
 
3.3%
수 3
 
3.3%
산 2
 
2.2%
가 2
 
2.2%
남 2
 
2.2%
읍 2
 
2.2%
곡 2
 
2.2%
교 2
 
2.2%
Other values (43) 45
49.5%
ASCII
ValueCountFrequency (%)
2 5
55.6%
3 3
33.3%
4 1
 
11.1%

행정동 코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1348364 × 109
Minimum4.11116 × 109
Maximum4.1830395 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:32.812500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.11116 × 109
5-th percentile4.1114572 × 109
Q14.1145049 × 109
median4.1298031 × 109
Q34.1465042 × 109
95-th percentile4.175275 × 109
Maximum4.1830395 × 109
Range71879500
Interquartile range (IQR)31999350

Descriptive statistics

Standard deviation21879992
Coefficient of variation (CV)0.0052916223
Kurtosis-0.4625169
Mean4.1348364 × 109
Median Absolute Deviation (MAD)16400500
Skewness0.74499024
Sum1.2404509 × 1011
Variance4.7873406 × 1014
MonotonicityNot monotonic
2023-12-10T22:49:33.035968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4131059000 1
 
3.3%
4117357600 1
 
3.3%
4131054200 1
 
3.3%
4161035000 1
 
3.3%
4128163000 1
 
3.3%
4182025000 1
 
3.3%
4128552000 1
 
3.3%
4145060000 1
 
3.3%
4145053000 1
 
3.3%
4165041000 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4111160000 1
3.3%
4111369000 1
3.3%
4111565000 1
3.3%
4111752000 1
3.3%
4113158000 1
3.3%
4113164000 1
3.3%
4113551000 1
3.3%
4113554000 1
3.3%
4117357600 1
3.3%
4119065000 1
3.3%
ValueCountFrequency (%)
4183039500 1
3.3%
4182025000 1
3.3%
4167025000 1
3.3%
4165041000 1
3.3%
4161035000 1
3.3%
4157035000 1
3.3%
4155031000 1
3.3%
4146555000 1
3.3%
4146352000 1
3.3%
4145060000 1
3.3%

해당년 소비액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9587566 × 108
Minimum18104673
Maximum3.4771817 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:33.247462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18104673
5-th percentile76622111
Q11.5528933 × 108
median2.2741501 × 108
Q36.6595914 × 108
95-th percentile1.2539694 × 109
Maximum3.4771817 × 109
Range3.459077 × 109
Interquartile range (IQR)5.1066981 × 108

Descriptive statistics

Standard deviation6.6616344 × 108
Coefficient of variation (CV)1.3434082
Kurtosis13.928736
Mean4.9587566 × 108
Median Absolute Deviation (MAD)1.3971967 × 108
Skewness3.3667352
Sum1.487627 × 1010
Variance4.4377373 × 1017
MonotonicityNot monotonic
2023-12-10T22:49:33.435211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
711155430.12 1
 
3.3%
721770807.26 1
 
3.3%
81466543.41 1
 
3.3%
18104672.77 1
 
3.3%
470219097.13 1
 
3.3%
485639476.56 1
 
3.3%
856050426.09 1
 
3.3%
221138289.5 1
 
3.3%
3477181651.03 1
 
3.3%
114039767.78 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
18104672.77 1
3.3%
72658484.7 1
3.3%
81466543.41 1
3.3%
93924145.73 1
3.3%
96826727.09 1
3.3%
114039767.78 1
3.3%
116783410.67 1
3.3%
149050819.06 1
3.3%
174004867.92 1
3.3%
181041438.9 1
3.3%
ValueCountFrequency (%)
3477181651.03 1
3.3%
1340721540.37 1
3.3%
1147939062.55 1
3.3%
1118211418.57 1
3.3%
856050426.09 1
3.3%
788136813.88 1
3.3%
721770807.26 1
3.3%
711155430.12 1
3.3%
530370271.59 1
3.3%
485639476.56 1
3.3%

전년소비액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9018.117
Minimum792.48
Maximum35096.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:33.635717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum792.48
5-th percentile2245.385
Q13602.2
median6213.545
Q312031.127
95-th percentile24138.27
Maximum35096.14
Range34303.66
Interquartile range (IQR)8428.9275

Descriptive statistics

Standard deviation7765.0145
Coefficient of variation (CV)0.8610461
Kurtosis3.6679726
Mean9018.117
Median Absolute Deviation (MAD)3249.245
Skewness1.8226213
Sum270543.51
Variance60295449
MonotonicityNot monotonic
2023-12-10T22:49:33.831866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5569.86 1
 
3.3%
26427.56 1
 
3.3%
2679.28 1
 
3.3%
792.48 1
 
3.3%
5150.57 1
 
3.3%
6617.95 1
 
3.3%
12447.39 1
 
3.3%
4348.99 1
 
3.3%
35096.14 1
 
3.3%
6183.18 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
792.48 1
3.3%
1890.38 1
3.3%
2679.28 1
3.3%
2942.54 1
3.3%
2986.06 1
3.3%
3003.69 1
3.3%
3508.06 1
3.3%
3554.12 1
3.3%
3746.44 1
3.3%
4348.99 1
3.3%
ValueCountFrequency (%)
35096.14 1
3.3%
26427.56 1
3.3%
21340.25 1
3.3%
17006.56 1
3.3%
14916.8 1
3.3%
13818.77 1
3.3%
12447.39 1
3.3%
12206.98 1
3.3%
11503.57 1
3.3%
10528.82 1
3.3%

년 1인당 소비액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2843376 × 108
Minimum12058785
Maximum3.7425173 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:34.025761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12058785
5-th percentile74010455
Q11.7584655 × 108
median2.3491583 × 108
Q36.7318112 × 108
95-th percentile1.4228731 × 109
Maximum3.7425173 × 109
Range3.7304585 × 109
Interquartile range (IQR)4.9733457 × 108

Descriptive statistics

Standard deviation7.2477597 × 108
Coefficient of variation (CV)1.371555
Kurtosis13.370363
Mean5.2843376 × 108
Median Absolute Deviation (MAD)1.4220223 × 108
Skewness3.3086335
Sum1.5853013 × 1010
Variance5.2530021 × 1017
MonotonicityNot monotonic
2023-12-10T22:49:34.265833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
770164584.89 1
 
3.3%
706723551.3 1
 
3.3%
80777895.29 1
 
3.3%
12058785.38 1
 
3.3%
459409109.75 1
 
3.3%
526287798.63 1
 
3.3%
831463074.77 1
 
3.3%
233716065.69 1
 
3.3%
3742517278.58 1
 
3.3%
95107671.64 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
12058785.38 1
3.3%
68473458.99 1
3.3%
80777895.29 1
3.3%
90319527.97 1
3.3%
95107671.64 1
3.3%
95783623.3 1
3.3%
103591092.7 1
3.3%
174503671.86 1
3.3%
179875184.26 1
3.3%
204617469.82 1
3.3%
ValueCountFrequency (%)
3742517278.58 1
3.3%
1458024798.88 1
3.3%
1379909945.35 1
3.3%
1252261361.66 1
3.3%
832178760.26 1
3.3%
831463074.77 1
3.3%
770164584.89 1
3.3%
706723551.3 1
3.3%
572553835.08 1
3.3%
526287798.63 1
3.3%

전년1인당 소비액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8219.9507
Minimum655.91
Maximum33867.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:34.493261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum655.91
5-th percentile1932.6365
Q14118.9275
median6104.28
Q310857.972
95-th percentile19965.306
Maximum33867.05
Range33211.14
Interquartile range (IQR)6739.045

Descriptive statistics

Standard deviation6967.9127
Coefficient of variation (CV)0.84768303
Kurtosis5.4241442
Mean8219.9507
Median Absolute Deviation (MAD)2882.66
Skewness2.0712758
Sum246598.52
Variance48551807
MonotonicityNot monotonic
2023-12-10T22:49:34.699064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5483.92 1
 
3.3%
21227.84 1
 
3.3%
2678.79 1
 
3.3%
655.91 1
 
3.3%
4699.27 1
 
3.3%
6724.64 1
 
3.3%
11355.42 1
 
3.3%
4219.66 1
 
3.3%
33867.05 1
 
3.3%
5304.17 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
655.91 1
3.3%
1373.66 1
3.3%
2615.83 1
3.3%
2678.79 1
3.3%
2956.39 1
3.3%
3160.28 1
3.3%
3282.96 1
3.3%
4085.35 1
3.3%
4219.66 1
3.3%
4238.9 1
3.3%
ValueCountFrequency (%)
33867.05 1
3.3%
21227.84 1
3.3%
18422.21 1
3.3%
15104.75 1
3.3%
13670.38 1
3.3%
12793.85 1
3.3%
12511.91 1
3.3%
11355.42 1
3.3%
9365.63 1
3.3%
7976.3 1
3.3%

전체기간 소비변화지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.896667
Minimum82.86
Maximum150.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:34.894016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82.86
5-th percentile83.168
Q191.6875
median94.41
Q3103.7325
95-th percentile121.0155
Maximum150.14
Range67.28
Interquartile range (IQR)12.045

Descriptive statistics

Standard deviation13.691629
Coefficient of variation (CV)0.13844379
Kurtosis5.882597
Mean98.896667
Median Absolute Deviation (MAD)6.515
Skewness2.0307603
Sum2966.9
Variance187.46071
MonotonicityNot monotonic
2023-12-10T22:49:35.107134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
92.34 1
 
3.3%
102.13 1
 
3.3%
100.85 1
 
3.3%
150.14 1
 
3.3%
102.35 1
 
3.3%
92.28 1
 
3.3%
102.96 1
 
3.3%
94.62 1
 
3.3%
92.91 1
 
3.3%
119.91 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
82.86 1
3.3%
83.15 1
3.3%
83.19 1
3.3%
87.82 1
3.3%
88.56 1
3.3%
89.3 1
3.3%
91.3 1
3.3%
91.6 1
3.3%
91.95 1
3.3%
92.28 1
3.3%
ValueCountFrequency (%)
150.14 1
3.3%
121.92 1
3.3%
119.91 1
3.3%
110.13 1
3.3%
107.43 1
3.3%
106.11 1
3.3%
104.79 1
3.3%
103.99 1
3.3%
102.96 1
3.3%
102.35 1
3.3%

전체기간 소비증감률
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1033333
Minimum-17.14
Maximum50.14
Zeros0
Zeros (%)0.0%
Negative18
Negative (%)60.0%
Memory size402.0 B
2023-12-10T22:49:35.492994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-17.14
5-th percentile-16.832
Q1-8.3125
median-5.59
Q33.7325
95-th percentile21.0155
Maximum50.14
Range67.28
Interquartile range (IQR)12.045

Descriptive statistics

Standard deviation13.691629
Coefficient of variation (CV)-12.409331
Kurtosis5.882597
Mean-1.1033333
Median Absolute Deviation (MAD)6.515
Skewness2.0307603
Sum-33.1
Variance187.46071
MonotonicityNot monotonic
2023-12-10T22:49:35.708684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
-7.66 1
 
3.3%
2.13 1
 
3.3%
0.85 1
 
3.3%
50.14 1
 
3.3%
2.35 1
 
3.3%
-7.72 1
 
3.3%
2.96 1
 
3.3%
-5.38 1
 
3.3%
-7.09 1
 
3.3%
19.91 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-17.14 1
3.3%
-16.85 1
3.3%
-16.81 1
3.3%
-12.18 1
3.3%
-11.44 1
3.3%
-10.7 1
3.3%
-8.7 1
3.3%
-8.4 1
3.3%
-8.05 1
3.3%
-7.72 1
3.3%
ValueCountFrequency (%)
50.14 1
3.3%
21.92 1
3.3%
19.91 1
3.3%
10.13 1
3.3%
7.43 1
3.3%
6.11 1
3.3%
4.79 1
3.3%
3.99 1
3.3%
2.96 1
3.3%
2.35 1
3.3%

전체기간 1인당 소비변화지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.89367
Minimum82.76
Maximum137.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:35.908224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82.76
5-th percentile88.1725
Q198.9525
median108.93
Q3116.3875
95-th percentile135.471
Maximum137.62
Range54.86
Interquartile range (IQR)17.435

Descriptive statistics

Standard deviation14.288916
Coefficient of variation (CV)0.13121898
Kurtosis-0.38229943
Mean108.89367
Median Absolute Deviation (MAD)9.785
Skewness0.31473065
Sum3266.81
Variance204.17311
MonotonicityNot monotonic
2023-12-10T22:49:36.434616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
101.57 1
 
3.3%
124.49 1
 
3.3%
100.02 1
 
3.3%
120.82 1
 
3.3%
109.6 1
 
3.3%
98.41 1
 
3.3%
109.62 1
 
3.3%
103.06 1
 
3.3%
103.63 1
 
3.3%
116.57 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
82.76 1
3.3%
87.25 1
3.3%
89.3 1
3.3%
91.7 1
3.3%
95.04 1
3.3%
96.26 1
3.3%
98.41 1
3.3%
98.76 1
3.3%
99.53 1
3.3%
100.02 1
3.3%
ValueCountFrequency (%)
137.62 1
3.3%
137.55 1
3.3%
132.93 1
3.3%
124.91 1
3.3%
124.49 1
3.3%
122.83 1
3.3%
120.82 1
3.3%
116.57 1
3.3%
115.84 1
3.3%
114.43 1
3.3%

전체기간 1인당 소비증감률
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8936667
Minimum-17.24
Maximum37.62
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)30.0%
Memory size402.0 B
2023-12-10T22:49:36.636714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-17.24
5-th percentile-11.8275
Q1-1.0475
median8.93
Q316.3875
95-th percentile35.471
Maximum37.62
Range54.86
Interquartile range (IQR)17.435

Descriptive statistics

Standard deviation14.288916
Coefficient of variation (CV)1.6066395
Kurtosis-0.38229943
Mean8.8936667
Median Absolute Deviation (MAD)9.785
Skewness0.31473065
Sum266.81
Variance204.17311
MonotonicityNot monotonic
2023-12-10T22:49:36.835279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1.57 1
 
3.3%
24.49 1
 
3.3%
0.02 1
 
3.3%
20.82 1
 
3.3%
9.6 1
 
3.3%
-1.59 1
 
3.3%
9.62 1
 
3.3%
3.06 1
 
3.3%
3.63 1
 
3.3%
16.57 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-17.24 1
3.3%
-12.75 1
3.3%
-10.7 1
3.3%
-8.3 1
3.3%
-4.96 1
3.3%
-3.74 1
3.3%
-1.59 1
3.3%
-1.24 1
3.3%
-0.47 1
3.3%
0.02 1
3.3%
ValueCountFrequency (%)
37.62 1
3.3%
37.55 1
3.3%
32.93 1
3.3%
24.91 1
3.3%
24.49 1
3.3%
22.83 1
3.3%
20.82 1
3.3%
16.57 1
3.3%
15.84 1
3.3%
14.43 1
3.3%

Interactions

2023-12-10T22:49:28.458084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:16.129017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:17.967069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:19.610067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:21.108604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:22.611566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:24.011154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:25.472223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:27.259620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:28.635990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:16.298856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:18.179981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:19.776690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:21.291824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:22.759106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:24.186840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:25.724526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:27.380975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:28.806610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:16.544156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:18.366169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:19.952436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:21.492194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:22.896411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:24.344257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:25.881746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:27.540587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:28.946557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:16.725662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:18.564041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:20.103264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:21.683774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:23.053451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:24.498623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:26.021908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:27.679750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:29.124518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:16.880584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:18.735910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:20.279441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:21.833110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:23.221602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:24.659089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:26.168990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:27.790223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:29.296875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:17.027092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:18.875144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:20.460351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:22.015029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:23.411346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:24.827828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:26.325137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:27.904402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:29.477036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:17.176217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:19.087093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:20.639342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:22.168638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:23.570698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:24.990777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:26.837111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:28.042226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:29.646194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:17.689658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:19.254358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:20.814410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:22.312707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:23.717726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:25.163486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:26.994970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:28.199019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:29.916335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:17.823495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:19.417363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:20.967887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:22.475028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:23.868514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:25.316694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:27.124255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:28.319951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:49:37.033088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드해당년 소비액전년소비액년 1인당 소비액전년1인당 소비액전체기간 소비변화지수전체기간 소비증감률전체기간 1인당 소비변화지수전체기간 1인당 소비증감률
시군구명1.0001.0001.0000.0000.0000.1930.4810.9110.9110.8020.802
행정동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
행정동 코드1.0001.0001.0000.0000.0000.0000.0000.4320.4320.4510.451
해당년 소비액0.0001.0000.0001.0000.7050.9950.7520.0000.0000.0000.000
전년소비액0.0001.0000.0000.7051.0000.7100.9860.3690.3690.0000.000
년 1인당 소비액0.1931.0000.0000.9950.7101.0000.7490.0000.0000.0000.000
전년1인당 소비액0.4811.0000.0000.7520.9860.7491.0000.0000.0000.3060.306
전체기간 소비변화지수0.9111.0000.4320.0000.3690.0000.0001.0001.0000.6220.622
전체기간 소비증감률0.9111.0000.4320.0000.3690.0000.0001.0001.0000.6220.622
전체기간 1인당 소비변화지수0.8021.0000.4510.0000.0000.0000.3060.6220.6221.0001.000
전체기간 1인당 소비증감률0.8021.0000.4510.0000.0000.0000.3060.6220.6221.0001.000
2023-12-10T22:49:37.304294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드해당년 소비액전년소비액년 1인당 소비액전년1인당 소비액전체기간 소비변화지수전체기간 소비증감률전체기간 1인당 소비변화지수전체기간 1인당 소비증감률
행정동 코드1.000-0.079-0.307-0.037-0.3250.0340.034-0.229-0.229
해당년 소비액-0.0791.0000.7120.9870.726-0.340-0.340-0.039-0.039
전년소비액-0.3070.7121.0000.6360.988-0.138-0.1380.2390.239
년 1인당 소비액-0.0370.9870.6361.0000.660-0.439-0.439-0.136-0.136
전년1인당 소비액-0.3250.7260.9880.6601.000-0.198-0.1980.1520.152
전체기간 소비변화지수0.034-0.340-0.138-0.439-0.1981.0001.0000.6350.635
전체기간 소비증감률0.034-0.340-0.138-0.439-0.1981.0001.0000.6350.635
전체기간 1인당 소비변화지수-0.229-0.0390.239-0.1360.1520.6350.6351.0001.000
전체기간 1인당 소비증감률-0.229-0.0390.239-0.1360.1520.6350.6351.0001.000

Missing values

2023-12-10T22:49:30.115430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:49:30.449320image/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

시도명시군구명행정동명행정동 코드해당년 소비액전년소비액년 1인당 소비액전년1인당 소비액전체기간 소비변화지수전체기간 소비증감률전체기간 1인당 소비변화지수전체기간 1인당 소비증감률
0경기도구리시수택3동4131059000711155430.125569.86770164584.895483.9292.34-7.66101.571.57
1경기도광명시철산3동41210610001340721540.3714916.81458024798.8815104.7591.95-8.0598.76-1.24
2경기도김포시월곶면4157035000174004867.923746.44209276676.264085.3583.15-16.8591.7-8.3
3경기도부천시고강본동4119081000248426026.667890.28271195435.017026.8191.6-8.4112.2912.29
4경기도남양주시금곡동4136053000233691740.114795.81255972993.764981.9691.3-8.796.26-3.74
5경기도부천시약대동4119065000199090413.2211503.57204617469.829365.6397.3-2.7122.8322.83
6경기도성남시고등동4113164000116783410.671890.3895783623.31373.66121.9221.92137.6237.62
7경기도성남시분당동411355100093924145.732986.0690319527.972615.83103.993.99114.1514.15
8경기도성남시수내3동4113554000236124099.9121340.25225327247.2118422.21104.794.79115.8415.84
9경기도성남시수진2동4113158000374372683.6810528.82348487448.827654.46107.437.43137.5537.55
시도명시군구명행정동명행정동 코드해당년 소비액전년소비액년 1인당 소비액전년1인당 소비액전체기간 소비변화지수전체기간 소비증감률전체기간 1인당 소비변화지수전체기간 1인당 소비증감률
20경기도용인시죽전2동41465550001147939062.5513818.771379909945.3512511.9183.19-16.81110.4410.44
21경기도평택시통복동4122061000192187888.5317006.56174503671.8612793.85110.1310.13132.9332.93
22경기도포천시화현면4165041000114039767.786183.1895107671.645304.17119.9119.91116.5716.57
23경기도하남시신장2동41450530003477181651.0335096.143742517278.5833867.0592.91-7.09103.633.63
24경기도하남시초이동4145060000221138289.54348.99233716065.694219.6694.62-5.38103.063.06
25경기도고양시중산동4128552000856050426.0912447.39831463074.7711355.42102.962.96109.629.62
26경기도가평군가평읍4182025000485639476.566617.95526287798.636724.6492.28-7.7298.41-1.59
27경기도고양시행주동4128163000470219097.135150.57459409109.754699.27102.352.35109.69.6
28경기도광주시남종면416103500018104672.77792.4812058785.38655.91150.1450.14120.8220.82
29경기도구리시교문2동413105420081466543.412679.2880777895.292678.79100.850.85100.020.02