Overview

Dataset statistics

Number of variables10
Number of observations30
Missing cells30
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory90.4 B

Variable types

DateTime1
Categorical2
Text2
Numeric4
Unsupported1

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://bigdata-region.kr/#/dataset/730fd7af-3d8f-4971-850e-7bd56b187585

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
1인당 소비액 표준편차 has constant value ""Constant
1인당 소비액 is highly overall correlated with 유동 인구 소비융합지수High correlation
유동 인구 소비융합지수 is highly overall correlated with 1인당 소비액High correlation
소비액 has 30 (100.0%) missing valuesMissing
행정동 코드 has unique valuesUnique
전체 인구 has unique valuesUnique
1인당 소비액 has unique valuesUnique
유동 인구 소비융합지수 has unique valuesUnique
소비액 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 13:49:55.928418
Analysis finished2023-12-10 13:50:01.315899
Duration5.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2019-01-01 00:00:00
Maximum2019-01-01 00:00:00
2023-12-10T22:50:01.465955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:50:01.702104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

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

2023-12-10T22:50:02.015693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:50:02.287754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:50:02.604106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.9666667
Min length3

Characters and Unicode

Total characters149
Distinct characters42
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

Unique17 ?
Unique (%)56.7%

Sample

1st row광명시
2nd row고양시 일산동구
3rd row구리시
4th row성남시 분당구
5th row군포시
ValueCountFrequency (%)
광명시 3
 
6.8%
고양시 3
 
6.8%
성남시 3
 
6.8%
파주시 2
 
4.5%
안산시 2
 
4.5%
상록구 2
 
4.5%
안성시 2
 
4.5%
일산동구 2
 
4.5%
평택시 2
 
4.5%
안양시 2
 
4.5%
Other values (19) 21
47.7%
2023-12-10T22:50:03.222124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
19.5%
구 15
 
10.1%
14
 
9.4%
안 9
 
6.0%
양 7
 
4.7%
산 6
 
4.0%
성 6
 
4.0%
광 3
 
2.0%
평 3
 
2.0%
인 3
 
2.0%
Other values (32) 54
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 135
90.6%
Space Separator 14
 
9.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
21.5%
구 15
 
11.1%
안 9
 
6.7%
양 7
 
5.2%
산 6
 
4.4%
성 6
 
4.4%
광 3
 
2.2%
평 3
 
2.2%
인 3
 
2.2%
원 3
 
2.2%
Other values (31) 51
37.8%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 135
90.6%
Common 14
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
21.5%
구 15
 
11.1%
안 9
 
6.7%
양 7
 
5.2%
산 6
 
4.4%
성 6
 
4.4%
광 3
 
2.2%
평 3
 
2.2%
인 3
 
2.2%
원 3
 
2.2%
Other values (31) 51
37.8%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 135
90.6%
ASCII 14
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
21.5%
구 15
 
11.1%
안 9
 
6.7%
양 7
 
5.2%
산 6
 
4.4%
성 6
 
4.4%
광 3
 
2.2%
평 3
 
2.2%
인 3
 
2.2%
원 3
 
2.2%
Other values (31) 51
37.8%
ASCII
ValueCountFrequency (%)
14
100.0%

행정동명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:50:03.531219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.4
Min length2

Characters and Unicode

Total characters102
Distinct characters53
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

Unique28 ?
Unique (%)93.3%

Sample

1st row광명6동
2nd row마두1동
3rd row동구동
4th row정자3동
5th row군포2동
ValueCountFrequency (%)
정자3동 2
 
6.7%
광명6동 1
 
3.3%
옥천면 1
 
3.3%
철산2동 1
 
3.3%
갈현동 1
 
3.3%
장항2동 1
 
3.3%
일산2동 1
 
3.3%
마도면 1
 
3.3%
청북읍 1
 
3.3%
송탄동 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T22:50:04.089612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
23.5%
2 7
 
6.9%
산 4
 
3.9%
면 4
 
3.9%
읍 3
 
2.9%
1 3
 
2.9%
북 2
 
2.0%
하 2
 
2.0%
마 2
 
2.0%
안 2
 
2.0%
Other values (43) 49
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
87.3%
Decimal Number 13
 
12.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
27.0%
산 4
 
4.5%
면 4
 
4.5%
읍 3
 
3.4%
북 2
 
2.2%
하 2
 
2.2%
마 2
 
2.2%
안 2
 
2.2%
자 2
 
2.2%
일 2
 
2.2%
Other values (39) 42
47.2%
Decimal Number
ValueCountFrequency (%)
2 7
53.8%
1 3
23.1%
3 2
 
15.4%
6 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
87.3%
Common 13
 
12.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
27.0%
산 4
 
4.5%
면 4
 
4.5%
읍 3
 
3.4%
북 2
 
2.2%
하 2
 
2.2%
마 2
 
2.2%
안 2
 
2.2%
자 2
 
2.2%
일 2
 
2.2%
Other values (39) 42
47.2%
Common
ValueCountFrequency (%)
2 7
53.8%
1 3
23.1%
3 2
 
15.4%
6 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
87.3%
ASCII 13
 
12.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
27.0%
산 4
 
4.5%
면 4
 
4.5%
읍 3
 
3.4%
북 2
 
2.2%
하 2
 
2.2%
마 2
 
2.2%
안 2
 
2.2%
자 2
 
2.2%
일 2
 
2.2%
Other values (39) 42
47.2%
ASCII
ValueCountFrequency (%)
2 7
53.8%
1 3
23.1%
3 2
 
15.4%
6 1
 
7.7%

행정동 코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1328838 × 109
Minimum4.1111573 × 109
Maximum4.183034 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:50:04.309668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1111573 × 109
5-th percentile4.1121768 × 109
Q14.1210578 × 109
median4.1285575 × 109
Q34.1463072 × 109
95-th percentile4.161244 × 109
Maximum4.183034 × 109
Range71876700
Interquartile range (IQR)25249450

Descriptive statistics

Standard deviation17956763
Coefficient of variation (CV)0.0043448507
Kurtosis0.48120401
Mean4.1328838 × 109
Median Absolute Deviation (MAD)11950000
Skewness0.939184
Sum1.2398651 × 1011
Variance3.2244534 × 1014
MonotonicityNot monotonic
2023-12-10T22:50:04.598521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4121057000 1
 
3.3%
4183034000 1
 
3.3%
4121063100 1
 
3.3%
4121060000 1
 
3.3%
4129052000 1
 
3.3%
4128559000 1
 
3.3%
4128752000 1
 
3.3%
4159033000 1
 
3.3%
4122025900 1
 
3.3%
4122053500 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4111157300 1
3.3%
4111370000 1
3.3%
4113163000 1
3.3%
4113356000 1
3.3%
4113557000 1
3.3%
4117152000 1
3.3%
4117352000 1
3.3%
4121057000 1
3.3%
4121060000 1
3.3%
4121063100 1
3.3%
ValueCountFrequency (%)
4183034000 1
3.3%
4163053000 1
3.3%
4159033000 1
3.3%
4155039000 1
3.3%
4155034000 1
3.3%
4148025600 1
3.3%
4148025000 1
3.3%
4146358600 1
3.3%
4146153000 1
3.3%
4141052000 1
3.3%

소비액
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

전체 인구
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62247.683
Minimum15670.78
Maximum150788.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:50:04.877913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15670.78
5-th percentile17737.696
Q130744.318
median55522.775
Q378752.28
95-th percentile139254.15
Maximum150788.79
Range135118.01
Interquartile range (IQR)48007.962

Descriptive statistics

Standard deviation39437.365
Coefficient of variation (CV)0.63355555
Kurtosis-0.13826935
Mean62247.683
Median Absolute Deviation (MAD)24807.27
Skewness0.88060417
Sum1867430.5
Variance1.5553058 × 109
MonotonicityNot monotonic
2023-12-10T22:50:05.205516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
21786.65 1
 
3.3%
24369.26 1
 
3.3%
50426.59 1
 
3.3%
19819.21 1
 
3.3%
44798.9 1
 
3.3%
127285.14 1
 
3.3%
35017.33 1
 
3.3%
67549.01 1
 
3.3%
149046.98 1
 
3.3%
80011.49 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
15670.78 1
3.3%
16034.64 1
3.3%
19819.21 1
3.3%
21786.65 1
3.3%
24369.26 1
3.3%
27270.11 1
3.3%
27845.34 1
3.3%
30396.95 1
3.3%
31786.42 1
3.3%
33382.19 1
3.3%
ValueCountFrequency (%)
150788.79 1
3.3%
149046.98 1
3.3%
127285.14 1
3.3%
117204.75 1
3.3%
113963.95 1
3.3%
111660.88 1
3.3%
84218.49 1
3.3%
80011.49 1
3.3%
74974.65 1
3.3%
72067.89 1
3.3%

1인당 소비액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.4743
Minimum552.45
Maximum16565.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:50:05.415846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum552.45
5-th percentile1056.3845
Q11984.7225
median4140.73
Q37321.58
95-th percentile15688.324
Maximum16565.71
Range16013.26
Interquartile range (IQR)5336.8575

Descriptive statistics

Standard deviation4573.5491
Coefficient of variation (CV)0.80755183
Kurtosis0.63599064
Mean5663.4743
Median Absolute Deviation (MAD)2476.165
Skewness1.1946364
Sum169904.23
Variance20917351
MonotonicityNot monotonic
2023-12-10T22:50:05.627803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5646.45 1
 
3.3%
3674.84 1
 
3.3%
7092.05 1
 
3.3%
4356.45 1
 
3.3%
1066.29 1
 
3.3%
12885.59 1
 
3.3%
7398.09 1
 
3.3%
1863.65 1
 
3.3%
1657.36 1
 
3.3%
3057.24 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
552.45 1
3.3%
1048.28 1
3.3%
1066.29 1
3.3%
1357.6 1
3.3%
1657.36 1
3.3%
1671.77 1
3.3%
1863.65 1
3.3%
1967.44 1
3.3%
2036.57 1
3.3%
3057.24 1
3.3%
ValueCountFrequency (%)
16565.71 1
3.3%
16449.09 1
3.3%
14758.5 1
3.3%
12885.59 1
3.3%
10027.62 1
3.3%
9373.7 1
3.3%
8409.11 1
3.3%
7398.09 1
3.3%
7092.05 1
3.3%
6720.23 1
3.3%

1인당 소비액 표준편차
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
4778.7
30 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4778.7
2nd row4778.7
3rd row4778.7
4th row4778.7
5th row4778.7

Common Values

ValueCountFrequency (%)
4778.7 30
100.0%

Length

2023-12-10T22:50:05.886428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:50:06.038752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4778.7 30
100.0%

유동 인구 소비융합지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.515
Minimum11.56
Maximum346.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:50:06.190155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.56
5-th percentile22.1065
Q141.5325
median86.65
Q3153.21
95-th percentile328.299
Maximum346.66
Range335.1
Interquartile range (IQR)111.6775

Descriptive statistics

Standard deviation95.70788
Coefficient of variation (CV)0.80755921
Kurtosis0.63602126
Mean118.515
Median Absolute Deviation (MAD)51.82
Skewness1.1946532
Sum3555.45
Variance9159.9982
MonotonicityNot monotonic
2023-12-10T22:50:06.424795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
118.16 1
 
3.3%
76.9 1
 
3.3%
148.41 1
 
3.3%
91.16 1
 
3.3%
22.31 1
 
3.3%
269.65 1
 
3.3%
154.81 1
 
3.3%
39.0 1
 
3.3%
34.68 1
 
3.3%
63.98 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
11.56 1
3.3%
21.94 1
3.3%
22.31 1
3.3%
28.41 1
3.3%
34.68 1
3.3%
34.98 1
3.3%
39.0 1
3.3%
41.17 1
3.3%
42.62 1
3.3%
63.98 1
3.3%
ValueCountFrequency (%)
346.66 1
3.3%
344.22 1
3.3%
308.84 1
3.3%
269.65 1
3.3%
209.84 1
3.3%
196.16 1
3.3%
175.97 1
3.3%
154.81 1
3.3%
148.41 1
3.3%
140.63 1
3.3%

Interactions

2023-12-10T22:49:59.877214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:56.570748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:57.722165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:58.722036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:50:00.139318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:56.801635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:58.019183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:59.056452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:50:00.348988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:56.969990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:58.197945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:59.267219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:50:00.608438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:57.486655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:58.513503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:59.645357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:50:06.633107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드전체 인구1인당 소비액유동 인구 소비융합지수
시군구명1.0000.9391.0000.2590.0000.558
행정동명0.9391.0001.0000.9550.0000.000
행정동 코드1.0001.0001.0000.0000.0000.264
전체 인구0.2590.9550.0001.0000.7160.736
1인당 소비액0.0000.0000.0000.7161.0001.000
유동 인구 소비융합지수0.5580.0000.2640.7361.0001.000
2023-12-10T22:50:06.841583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드전체 인구1인당 소비액유동 인구 소비융합지수
행정동 코드1.0000.239-0.332-0.332
전체 인구0.2391.000-0.226-0.226
1인당 소비액-0.332-0.2261.0001.000
유동 인구 소비융합지수-0.332-0.2261.0001.000

Missing values

2023-12-10T22:50:00.946449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:50:01.220676image/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

기준년월시도명시군구명행정동명행정동 코드소비액전체 인구1인당 소비액1인당 소비액 표준편차유동 인구 소비융합지수
02019-01경기도광명시광명6동4121057000<NA>21786.655646.454778.7118.16
12019-01경기도고양시 일산동구마두1동4128556000<NA>59320.758409.114778.7175.97
22019-01경기도구리시동구동4131052000<NA>150788.794871.514778.7101.94
32019-01경기도성남시 분당구정자3동4113557000<NA>30396.9516449.094778.7344.22
42019-01경기도군포시군포2동4141052000<NA>117204.753659.514778.776.58
52019-01경기도성남시 수정구신촌동4113163000<NA>15670.781048.284778.721.94
62019-01경기도성남시 중원구은행2동4113356000<NA>16034.649373.74778.7196.16
72019-01경기도수원시 권선구입북동4111370000<NA>58931.31967.444778.741.17
82019-01경기도수원시 장안구정자3동4111157300<NA>60902.8714758.54778.7308.84
92019-01경기도안산시 상록구반월동4127160000<NA>111660.881671.774778.734.98
기준년월시도명시군구명행정동명행정동 코드소비액전체 인구1인당 소비액1인당 소비액 표준편차유동 인구 소비융합지수
202019-01경기도파주시문산읍4148025000<NA>84218.495900.864778.7123.48
212019-01경기도파주시법원읍4148025600<NA>27845.343863.284778.780.84
222019-01경기도평택시송탄동4122053500<NA>80011.493057.244778.763.98
232019-01경기도평택시청북읍4122025900<NA>149046.981657.364778.734.68
242019-01경기도화성시마도면4159033000<NA>67549.011863.654778.739.0
252019-01경기도고양시 일산서구일산2동4128752000<NA>35017.337398.094778.7154.81
262019-01경기도고양시 일산동구장항2동4128559000<NA>127285.1412885.594778.7269.65
272019-01경기도과천시갈현동4129052000<NA>44798.91066.294778.722.31
282019-01경기도광명시철산2동4121060000<NA>19819.214356.454778.791.16
292019-01경기도광명시하안1동4121063100<NA>50426.597092.054778.7148.41