Overview

Dataset statistics

Number of variables14
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory126.4 B

Variable types

Categorical3
Text2
Numeric9

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://bigdata-region.kr/#/dataset/aad91ccd-63c8-4d64-8349-72e87a28c029

Alerts

기준년 has constant value ""Constant
기준년월 has constant value ""Constant
시도명 has constant value ""Constant
해당년월 소비액 is highly overall correlated with 전년월소비액 and 2 other fieldsHigh correlation
전년월소비액 is highly overall correlated with 해당년월 소비액 and 4 other fieldsHigh correlation
년월 1인당 소비액 is highly overall correlated with 해당년월 소비액 and 2 other fieldsHigh correlation
전년월1인당 소비액 is highly overall correlated with 해당년월 소비액 and 4 other fieldsHigh correlation
소비 변화 지수 is highly overall correlated with 전년월소비액 and 4 other fieldsHigh correlation
소비 증감률 is highly overall correlated with 전년월소비액 and 4 other fieldsHigh correlation
1인당 소비변화지수 is highly overall correlated with 소비 변화 지수 and 2 other fieldsHigh correlation
1인당 소비증감률 is highly overall correlated with 소비 변화 지수 and 2 other fieldsHigh correlation
행정동명 has unique valuesUnique
행정동 코드 has unique valuesUnique
해당년월 소비액 has unique valuesUnique
전년월소비액 has unique valuesUnique
년월 1인당 소비액 has unique valuesUnique
전년월1인당 소비액 has unique valuesUnique
소비 변화 지수 has unique valuesUnique
소비 증감률 has unique valuesUnique
1인당 소비변화지수 has unique valuesUnique
1인당 소비증감률 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:54:30.138058
Analysis finished2023-12-10 13:54:45.947233
Duration15.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 30
100.0%

Length

2023-12-10T22:54:46.041616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:54:46.190376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 30
100.0%

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020-02
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-02
2nd row2020-02
3rd row2020-02
4th row2020-02
5th row2020-02

Common Values

ValueCountFrequency (%)
2020-02 30
100.0%

Length

2023-12-10T22:54:46.319312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:54:46.474286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-02 30
100.0%

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

2023-12-10T22:54:46.630732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:54:46.772815image/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
2023-12-10T22:54:47.000952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1
Min length3

Characters and Unicode

Total characters93
Distinct characters28
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

Unique11 ?
Unique (%)36.7%

Sample

1st row고양시
2nd row고양시
3rd row과천시
4th row구리시
5th row광명시
ValueCountFrequency (%)
부천시 3
 
10.0%
성남시 3
 
10.0%
수원시 3
 
10.0%
고양시 2
 
6.7%
이천시 2
 
6.7%
김포시 2
 
6.7%
남양주시 2
 
6.7%
용인시 2
 
6.7%
화성시 1
 
3.3%
가평군 1
 
3.3%
Other values (9) 9
30.0%
2023-12-10T22:54:47.664428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
31.2%
천 7
 
7.5%
남 6
 
6.5%
성 5
 
5.4%
양 5
 
5.4%
부 4
 
4.3%
수 3
 
3.2%
원 3
 
3.2%
포 3
 
3.2%
주 3
 
3.2%
Other values (18) 25
26.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
31.2%
천 7
 
7.5%
남 6
 
6.5%
성 5
 
5.4%
양 5
 
5.4%
부 4
 
4.3%
수 3
 
3.2%
원 3
 
3.2%
포 3
 
3.2%
주 3
 
3.2%
Other values (18) 25
26.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
31.2%
천 7
 
7.5%
남 6
 
6.5%
성 5
 
5.4%
양 5
 
5.4%
부 4
 
4.3%
수 3
 
3.2%
원 3
 
3.2%
포 3
 
3.2%
주 3
 
3.2%
Other values (18) 25
26.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
31.2%
천 7
 
7.5%
남 6
 
6.5%
성 5
 
5.4%
양 5
 
5.4%
부 4
 
4.3%
수 3
 
3.2%
원 3
 
3.2%
포 3
 
3.2%
주 3
 
3.2%
Other values (18) 25
26.9%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:54:47.988190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2333333
Min length2

Characters and Unicode

Total characters97
Distinct characters47
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 row흥도동
2nd row성사2동
3rd row문원동
4th row수택3동
5th row광명7동
ValueCountFrequency (%)
흥도동 1
 
3.3%
성사2동 1
 
3.3%
수동면 1
 
3.3%
장기본동 1
 
3.3%
광남동 1
 
3.3%
양촌읍 1
 
3.3%
청평면 1
 
3.3%
남양읍 1
 
3.3%
천현동 1
 
3.3%
창수면 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:54:48.576817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
23.7%
면 6
 
6.2%
읍 3
 
3.1%
2 3
 
3.1%
양 3
 
3.1%
수 3
 
3.1%
1 3
 
3.1%
흥 2
 
2.1%
남 2
 
2.1%
안 2
 
2.1%
Other values (37) 47
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
90.7%
Decimal Number 9
 
9.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
26.1%
면 6
 
6.8%
읍 3
 
3.4%
양 3
 
3.4%
수 3
 
3.4%
흥 2
 
2.3%
남 2
 
2.3%
안 2
 
2.3%
매 2
 
2.3%
도 2
 
2.3%
Other values (33) 40
45.5%
Decimal Number
ValueCountFrequency (%)
2 3
33.3%
1 3
33.3%
3 2
22.2%
7 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
90.7%
Common 9
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
26.1%
면 6
 
6.8%
읍 3
 
3.4%
양 3
 
3.4%
수 3
 
3.4%
흥 2
 
2.3%
남 2
 
2.3%
안 2
 
2.3%
매 2
 
2.3%
도 2
 
2.3%
Other values (33) 40
45.5%
Common
ValueCountFrequency (%)
2 3
33.3%
1 3
33.3%
3 2
22.2%
7 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
90.7%
ASCII 9
 
9.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
26.1%
면 6
 
6.8%
읍 3
 
3.4%
양 3
 
3.4%
수 3
 
3.4%
흥 2
 
2.3%
남 2
 
2.3%
안 2
 
2.3%
매 2
 
2.3%
도 2
 
2.3%
Other values (33) 40
45.5%
ASCII
ValueCountFrequency (%)
2 3
33.3%
1 3
33.3%
3 2
22.2%
7 1
 
11.1%

행정동 코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1349342 × 109
Minimum4.111368 × 109
Maximum4.1820325 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:48.801297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.111368 × 109
5-th percentile4.1116492 × 109
Q14.1176288 × 109
median4.1300575 × 109
Q34.1500368 × 109
95-th percentile4.1632431 × 109
Maximum4.1820325 × 109
Range70664500
Interquartile range (IQR)32408000

Descriptive statistics

Standard deviation19829346
Coefficient of variation (CV)0.004795565
Kurtosis-0.77203026
Mean4.1349342 × 109
Median Absolute Deviation (MAD)16392000
Skewness0.50820301
Sum1.2404802 × 1011
Variance3.9320295 × 1014
MonotonicityNot monotonic
2023-12-10T22:54:49.072534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4128153000 1
 
3.3%
4146352000 1
 
3.3%
4119062000 1
 
3.3%
4136034000 1
 
3.3%
4157052500 1
 
3.3%
4161053000 1
 
3.3%
4157025600 1
 
3.3%
4182032500 1
 
3.3%
4159026200 1
 
3.3%
4145051000 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4111368000 1
3.3%
4111566000 1
3.3%
4111751000 1
3.3%
4113367000 1
3.3%
4113567000 1
3.3%
4113568000 1
3.3%
4115061500 1
3.3%
4117151000 1
3.3%
4119062000 1
3.3%
4119068000 1
3.3%
ValueCountFrequency (%)
4182032500 1
3.3%
4165035000 1
3.3%
4161053000 1
3.3%
4159026200 1
3.3%
4157052500 1
3.3%
4157025600 1
3.3%
4155051000 1
3.3%
4150038000 1
3.3%
4150033000 1
3.3%
4146352000 1
3.3%

해당년월 소비액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2651441 × 108
Minimum8076675.6
Maximum1.8820968 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:49.308342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8076675.6
5-th percentile38150209
Q11.9028892 × 108
median3.8542294 × 108
Q37.6662162 × 108
95-th percentile1.2316641 × 109
Maximum1.8820968 × 109
Range1.8740201 × 109
Interquartile range (IQR)5.763327 × 108

Descriptive statistics

Standard deviation4.3321443 × 108
Coefficient of variation (CV)0.82279691
Kurtosis1.8619277
Mean5.2651441 × 108
Median Absolute Deviation (MAD)2.9898581 × 108
Skewness1.1986614
Sum1.5795432 × 1010
Variance1.8767474 × 1017
MonotonicityNot monotonic
2023-12-10T22:54:49.501166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
775000807.64 1
 
3.3%
741484057.45 1
 
3.3%
352636971.85 1
 
3.3%
85886487.12 1
 
3.3%
865025120.36 1
 
3.3%
1016480408.45 1
 
3.3%
807409461.65 1
 
3.3%
338917494.12 1
 
3.3%
1168311970.06 1
 
3.3%
340664312.33 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
8076675.59 1
3.3%
22473726.23 1
3.3%
57310353.81 1
3.3%
85886487.12 1
3.3%
86987771.12 1
3.3%
109447027.05 1
3.3%
120874687.89 1
3.3%
159640809.05 1
3.3%
282233263.39 1
3.3%
291969038.26 1
3.3%
ValueCountFrequency (%)
1882096792.43 1
3.3%
1283497721.24 1
3.3%
1168311970.06 1
3.3%
1016480408.45 1
3.3%
865025120.36 1
3.3%
824472412.3 1
3.3%
807409461.65 1
3.3%
775000807.64 1
3.3%
741484057.45 1
3.3%
706740148.89 1
3.3%

전년월소비액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2817867 × 108
Minimum7598514.4
Maximum1.8612623 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:49.740325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7598514.4
5-th percentile30842026
Q11.9475358 × 108
median3.6634317 × 108
Q38.4998714 × 108
95-th percentile1.2869549 × 109
Maximum1.8612623 × 109
Range1.8536637 × 109
Interquartile range (IQR)6.5523356 × 108

Descriptive statistics

Standard deviation4.5292614 × 108
Coefficient of variation (CV)0.85752448
Kurtosis1.0520814
Mean5.2817867 × 108
Median Absolute Deviation (MAD)2.9856706 × 108
Skewness1.0904613
Sum1.584536 × 1010
Variance2.0514209 × 1017
MonotonicityNot monotonic
2023-12-10T22:54:50.178632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1372031400.41 1
 
3.3%
757911145.66 1
 
3.3%
387964018.0 1
 
3.3%
69281989.74 1
 
3.3%
902749842.18 1
 
3.3%
989044839.47 1
 
3.3%
920305761.72 1
 
3.3%
344722313.45 1
 
3.3%
1182972591.9 1
 
3.3%
307382587.55 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
7598514.36 1
3.3%
18596913.28 1
3.3%
45808274.44 1
3.3%
69281989.74 1
3.3%
94703371.15 1
3.3%
100555858.64 1
3.3%
147802356.27 1
3.3%
187181142.16 1
3.3%
217470886.81 1
3.3%
233395956.47 1
3.3%
ValueCountFrequency (%)
1861262253.72 1
3.3%
1372031400.41 1
3.3%
1182972591.9 1
3.3%
989044839.47 1
3.3%
920305761.72 1
3.3%
902749842.18 1
3.3%
902366039.15 1
3.3%
878181651.83 1
3.3%
765403597.6 1
3.3%
758428546.62 1
3.3%

년월 1인당 소비액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8234.5893
Minimum934.62
Maximum28685.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:50.543560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum934.62
5-th percentile2089.7925
Q13884.8
median6408.365
Q39933.5625
95-th percentile19949.574
Maximum28685.67
Range27751.05
Interquartile range (IQR)6048.7625

Descriptive statistics

Standard deviation6322.0349
Coefficient of variation (CV)0.76774137
Kurtosis3.1183046
Mean8234.5893
Median Absolute Deviation (MAD)2796.855
Skewness1.7013006
Sum247037.68
Variance39968125
MonotonicityNot monotonic
2023-12-10T22:54:50.755976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6741.02 1
 
3.3%
10043.07 1
 
3.3%
7047.47 1
 
3.3%
2640.56 1
 
3.3%
16022.36 1
 
3.3%
6952.98 1
 
3.3%
5903.26 1
 
3.3%
7574.38 1
 
3.3%
5786.75 1
 
3.3%
3854.64 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
934.62 1
3.3%
1958.91 1
3.3%
2249.76 1
3.3%
2640.56 1
3.3%
3313.47 1
3.3%
3544.78 1
3.3%
3678.24 1
3.3%
3854.64 1
3.3%
3975.28 1
3.3%
5336.53 1
3.3%
ValueCountFrequency (%)
28685.67 1
3.3%
23162.75 1
3.3%
16022.36 1
3.3%
15589.29 1
3.3%
14193.94 1
3.3%
14157.09 1
3.3%
12351.1 1
3.3%
10043.07 1
3.3%
9605.04 1
3.3%
7694.83 1
3.3%

전년월1인당 소비액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6932.8287
Minimum729.78
Maximum21500.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:51.010447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum729.78
5-th percentile1450.6775
Q13275.555
median6135.815
Q39235.1925
95-th percentile14792.864
Maximum21500.46
Range20770.68
Interquartile range (IQR)5959.6375

Descriptive statistics

Standard deviation4768.0488
Coefficient of variation (CV)0.6877494
Kurtosis1.5653104
Mean6932.8287
Median Absolute Deviation (MAD)3045.92
Skewness1.1654315
Sum207984.86
Variance22734289
MonotonicityNot monotonic
2023-12-10T22:54:51.191561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
12067.15 1
 
3.3%
9304.57 1
 
3.3%
8114.56 1
 
3.3%
2679.74 1
 
3.3%
15053.05 1
 
3.3%
7993.27 1
 
3.3%
6075.16 1
 
3.3%
5404.17 1
 
3.3%
6196.47 1
 
3.3%
2856.7 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
729.78 1
3.3%
1254.32 1
3.3%
1690.67 1
3.3%
2370.01 1
3.3%
2679.74 1
3.3%
2851.94 1
3.3%
2856.7 1
3.3%
3212.73 1
3.3%
3464.03 1
3.3%
3736.7 1
3.3%
ValueCountFrequency (%)
21500.46 1
3.3%
15053.05 1
3.3%
14474.86 1
3.3%
12067.15 1
3.3%
11974.22 1
3.3%
11607.54 1
3.3%
9865.7 1
3.3%
9304.57 1
3.3%
9027.06 1
3.3%
8114.56 1
3.3%

소비 변화 지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.185
Minimum56.49
Maximum454.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:51.443404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56.49
5-th percentile66.507
Q191.01
median98.54
Q3114.64
95-th percentile162.07
Maximum454.47
Range397.98
Interquartile range (IQR)23.63

Descriptive statistics

Standard deviation68.85889
Coefficient of variation (CV)0.60837469
Kurtosis22.378755
Mean113.185
Median Absolute Deviation (MAD)11.55
Skewness4.4933776
Sum3395.55
Variance4741.5467
MonotonicityNot monotonic
2023-12-10T22:54:51.650282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
56.49 1
 
3.3%
97.83 1
 
3.3%
90.89 1
 
3.3%
123.97 1
 
3.3%
95.82 1
 
3.3%
102.77 1
 
3.3%
87.73 1
 
3.3%
98.32 1
 
3.3%
98.76 1
 
3.3%
110.83 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
56.49 1
3.3%
64.5 1
3.3%
68.96 1
3.3%
81.78 1
3.3%
84.25 1
3.3%
85.29 1
3.3%
87.73 1
3.3%
90.89 1
3.3%
91.37 1
3.3%
91.85 1
3.3%
ValueCountFrequency (%)
454.47 1
3.3%
192.31 1
3.3%
125.11 1
3.3%
125.1 1
3.3%
123.97 1
3.3%
120.85 1
3.3%
116.71 1
3.3%
115.91 1
3.3%
110.83 1
3.3%
108.84 1
3.3%

소비 증감률
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.185
Minimum-43.51
Maximum354.47
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)53.3%
Memory size402.0 B
2023-12-10T22:54:51.959632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-43.51
5-th percentile-33.493
Q1-8.99
median-1.46
Q314.64
95-th percentile62.07
Maximum354.47
Range397.98
Interquartile range (IQR)23.63

Descriptive statistics

Standard deviation68.85889
Coefficient of variation (CV)5.2225172
Kurtosis22.378755
Mean13.185
Median Absolute Deviation (MAD)11.55
Skewness4.4933776
Sum395.55
Variance4741.5467
MonotonicityNot monotonic
2023-12-10T22:54:52.306775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
-43.51 1
 
3.3%
-2.17 1
 
3.3%
-9.11 1
 
3.3%
23.97 1
 
3.3%
-4.18 1
 
3.3%
2.77 1
 
3.3%
-12.27 1
 
3.3%
-1.68 1
 
3.3%
-1.24 1
 
3.3%
10.83 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-43.51 1
3.3%
-35.5 1
3.3%
-31.04 1
3.3%
-18.22 1
3.3%
-15.75 1
3.3%
-14.71 1
3.3%
-12.27 1
3.3%
-9.11 1
3.3%
-8.63 1
3.3%
-8.15 1
3.3%
ValueCountFrequency (%)
354.47 1
3.3%
92.31 1
3.3%
25.11 1
3.3%
25.1 1
3.3%
23.97 1
3.3%
20.85 1
3.3%
16.71 1
3.3%
15.91 1
3.3%
10.83 1
3.3%
8.84 1
3.3%

1인당 소비변화지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.13433
Minimum55.86
Maximum555.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:52.692143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum55.86
5-th percentile71.618
Q195.49
median109.505
Q3138.7475
95-th percentile194.736
Maximum555.85
Range499.99
Interquartile range (IQR)43.2575

Descriptive statistics

Standard deviation86.638271
Coefficient of variation (CV)0.66068336
Kurtosis21.263557
Mean131.13433
Median Absolute Deviation (MAD)22.585
Skewness4.313942
Sum3934.03
Variance7506.1901
MonotonicityNot monotonic
2023-12-10T22:54:53.008954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
55.86 1
 
3.3%
107.94 1
 
3.3%
86.85 1
 
3.3%
98.54 1
 
3.3%
106.44 1
 
3.3%
86.99 1
 
3.3%
97.17 1
 
3.3%
140.16 1
 
3.3%
93.39 1
 
3.3%
134.93 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
55.86 1
3.3%
62.51 1
3.3%
82.75 1
3.3%
86.85 1
3.3%
86.99 1
3.3%
87.89 1
3.3%
93.39 1
3.3%
94.93 1
3.3%
97.17 1
3.3%
98.09 1
3.3%
ValueCountFrequency (%)
555.85 1
3.3%
217.56 1
3.3%
166.84 1
3.3%
156.17 1
3.3%
145.68 1
3.3%
143.87 1
3.3%
140.16 1
3.3%
139.39 1
3.3%
136.82 1
3.3%
134.93 1
3.3%

1인당 소비증감률
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.134333
Minimum-44.14
Maximum455.85
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)36.7%
Memory size402.0 B
2023-12-10T22:54:53.351173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-44.14
5-th percentile-28.382
Q1-4.51
median9.505
Q338.7475
95-th percentile94.736
Maximum455.85
Range499.99
Interquartile range (IQR)43.2575

Descriptive statistics

Standard deviation86.638271
Coefficient of variation (CV)2.7827245
Kurtosis21.263557
Mean31.134333
Median Absolute Deviation (MAD)22.585
Skewness4.313942
Sum934.03
Variance7506.1901
MonotonicityNot monotonic
2023-12-10T22:54:53.595548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
-44.14 1
 
3.3%
7.94 1
 
3.3%
-13.15 1
 
3.3%
-1.46 1
 
3.3%
6.44 1
 
3.3%
-13.01 1
 
3.3%
-2.83 1
 
3.3%
40.16 1
 
3.3%
-6.61 1
 
3.3%
34.93 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-44.14 1
3.3%
-37.49 1
3.3%
-17.25 1
3.3%
-13.15 1
3.3%
-13.01 1
3.3%
-12.11 1
3.3%
-6.61 1
3.3%
-5.07 1
3.3%
-2.83 1
3.3%
-1.91 1
3.3%
ValueCountFrequency (%)
455.85 1
3.3%
117.56 1
3.3%
66.84 1
3.3%
56.17 1
3.3%
45.68 1
3.3%
43.87 1
3.3%
40.16 1
3.3%
39.39 1
3.3%
36.82 1
3.3%
34.93 1
3.3%

Interactions

2023-12-10T22:54:43.786322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:30.652722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:32.388863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.287023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:36.049196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:37.351282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:38.656885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:40.387591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:42.395054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:44.002614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:30.833449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:32.609415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.469734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:36.212970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:37.550188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:38.820611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:40.673556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:42.538321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:44.158064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:31.065996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:32.891144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.650815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:36.358284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:37.714395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:39.018817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:40.970357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:42.680022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:44.314735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:31.222406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:33.262982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.863678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:36.504330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:37.874593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:39.186457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:41.193304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:42.831463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:44.483146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:31.391309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:33.563859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.015039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:36.638675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:38.008500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:39.339578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:41.407800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:42.991242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:44.622230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:31.726610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:33.682368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.146148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:36.760487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:38.127484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:39.468972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:41.632686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:43.134989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:44.743805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:31.891524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:33.839801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.300513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:36.918126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:38.261998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:39.694127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:41.801306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:43.276704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:44.881305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:32.105514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.004208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.443844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:37.093092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:38.411169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:39.949975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:42.103879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:43.436951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:45.013074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:32.251623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.143287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.893659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:37.231970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:38.533021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:40.215632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:42.258818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:43.576502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:54:53.771885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드해당년월 소비액전년월소비액년월 1인당 소비액전년월1인당 소비액소비 변화 지수소비 증감률1인당 소비변화지수1인당 소비증감률
시군구명1.0001.0001.0000.6730.5500.0000.0000.0000.0000.0000.000
행정동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
행정동 코드1.0001.0001.0000.4600.4610.0000.0000.0000.0000.0000.000
해당년월 소비액0.6731.0000.4601.0000.8900.7510.0840.9010.9010.6290.629
전년월소비액0.5501.0000.4610.8901.0000.6610.6590.0170.0170.0000.000
년월 1인당 소비액0.0001.0000.0000.7510.6611.0000.9500.8520.8520.5980.598
전년월1인당 소비액0.0001.0000.0000.0840.6590.9501.0000.5270.5270.0000.000
소비 변화 지수0.0001.0000.0000.9010.0170.8520.5271.0001.0000.8390.839
소비 증감률0.0001.0000.0000.9010.0170.8520.5271.0001.0000.8390.839
1인당 소비변화지수0.0001.0000.0000.6290.0000.5980.0000.8390.8391.0001.000
1인당 소비증감률0.0001.0000.0000.6290.0000.5980.0000.8390.8391.0001.000
2023-12-10T22:54:54.029204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드해당년월 소비액전년월소비액년월 1인당 소비액전년월1인당 소비액소비 변화 지수소비 증감률1인당 소비변화지수1인당 소비증감률
행정동 코드1.000-0.120-0.036-0.354-0.231-0.030-0.030-0.295-0.295
해당년월 소비액-0.1201.0000.9280.6360.625-0.294-0.294-0.287-0.287
전년월소비액-0.0360.9281.0000.6150.737-0.518-0.518-0.452-0.452
년월 1인당 소비액-0.3540.6360.6151.0000.868-0.373-0.3730.0080.008
전년월1인당 소비액-0.2310.6250.7370.8681.000-0.644-0.644-0.382-0.382
소비 변화 지수-0.030-0.294-0.518-0.373-0.6441.0001.0000.6070.607
소비 증감률-0.030-0.294-0.518-0.373-0.6441.0001.0000.6070.607
1인당 소비변화지수-0.295-0.287-0.4520.008-0.3820.6070.6071.0001.000
1인당 소비증감률-0.295-0.287-0.4520.008-0.3820.6070.6071.0001.000

Missing values

2023-12-10T22:54:45.555135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:54:45.829124image/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

기준년기준년월시도명시군구명행정동명행정동 코드해당년월 소비액전년월소비액년월 1인당 소비액전년월1인당 소비액소비 변화 지수소비 증감률1인당 소비변화지수1인당 소비증감률
020202020-02경기도고양시흥도동4128153000775000807.641372031400.416741.0212067.1556.49-43.5155.86-44.14
120202020-02경기도고양시성사2동412815500086987771.1294703371.155443.793736.791.85-8.15145.6845.68
220202020-02경기도과천시문원동412905600057310353.8145808274.441958.911254.32125.1125.11156.1756.17
320202020-02경기도구리시수택3동4131059000706740148.89765403597.66135.836255.2892.34-7.6698.09-1.91
420202020-02경기도광명시광명7동4121058000159640809.05187181142.167694.837081.1885.29-14.71108.678.67
520202020-02경기도남양주시별내면4136031000291969038.26233395956.475779.373464.03125.125.1166.8466.84
620202020-02경기도부천시송내2동4119074000305664068.55327382340.1414157.0911974.2293.37-6.63118.2318.23
720202020-02경기도부천시중3동4119068000697730154.62666416111.0728685.6721500.46104.74.7133.4233.42
820202020-02경기도성남시구미동41135670001882096792.43414134080.0523162.754167.05454.47354.47555.85455.85
920202020-02경기도성남시도촌동4113367000499720322.91431143408.825336.534074.18115.9115.91130.9830.98
기준년기준년월시도명시군구명행정동명행정동 코드해당년월 소비액전년월소비액년월 1인당 소비액전년월1인당 소비액소비 변화 지수소비 증감률1인당 소비변화지수1인당 소비증감률
2020202020-02경기도이천시호법면4150033000566455230.24878181651.836680.97601.5164.5-35.587.89-12.11
2120202020-02경기도포천시창수면41650350008076675.597598514.36934.62729.78106.296.29128.0728.07
2220202020-02경기도하남시천현동4145051000340664312.33307382587.553854.642856.7110.8310.83134.9334.93
2320202020-02경기도화성시남양읍41590262001168311970.061182972591.95786.756196.4798.76-1.2493.39-6.61
2420202020-02경기도가평군청평면4182032500338917494.12344722313.457574.385404.1798.32-1.68140.1640.16
2520202020-02경기도김포시양촌읍4157025600807409461.65920305761.725903.266075.1687.73-12.2797.17-2.83
2620202020-02경기도광주시광남동41610530001016480408.45989044839.476952.987993.27102.772.7786.99-13.01
2720202020-02경기도김포시장기본동4157052500865025120.36902749842.1816022.3615053.0595.82-4.18106.446.44
2820202020-02경기도남양주시수동면413603400085886487.1269281989.742640.562679.74123.9723.9798.54-1.46
2920202020-02경기도부천시상동4119062000352636971.85387964018.07047.478114.5690.89-9.1186.85-13.15