Overview

Dataset statistics

Number of variables14
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 KiB
Average record size in memory123.3 B

Variable types

Categorical5
Numeric7
Text2

Alerts

기준년월 has constant value ""Constant
사용빈도 is highly overall correlated with 사용시간(초) and 1 other fieldsHigh correlation
사용시간(초) is highly overall correlated with 사용빈도 and 1 other fieldsHigh correlation
민감지수 is highly overall correlated with 사용빈도 and 2 other fieldsHigh correlation
시도코드 is highly overall correlated with 시군구코드 and 2 other fieldsHigh correlation
시군구코드 is highly overall correlated with 시도코드 and 2 other fieldsHigh correlation
읍면동코드 is highly overall correlated with 시도코드 and 2 other fieldsHigh correlation
설치갯수 is highly overall correlated with 사용갯수High correlation
사용갯수 is highly overall correlated with 민감지수 and 1 other fieldsHigh correlation
시도 is highly overall correlated with 시도코드 and 2 other fieldsHigh correlation
사용빈도 has 31 (31.0%) zerosZeros
사용시간(초) has 31 (31.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:54:40.858120
Analysis finished2023-12-10 10:54:51.630493
Duration10.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
201802
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201802
2nd row201802
3rd row201802
4th row201802
5th row201802

Common Values

ValueCountFrequency (%)
201802 100
100.0%

Length

2023-12-10T19:54:51.772404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:51.979680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201802 100
100.0%

성별
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F
72 
M
28 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 72
72.0%
M 28
 
28.0%

Length

2023-12-10T19:54:52.186262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:52.372112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 72
72.0%
m 28
 
28.0%

나이
Real number (ℝ)

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.13
Minimum22
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:52.643079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile25
Q133.75
median38
Q345
95-th percentile54.15
Maximum61
Range39
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation8.9223983
Coefficient of variation (CV)0.22801938
Kurtosis-0.20334686
Mean39.13
Median Absolute Deviation (MAD)6
Skewness0.39195453
Sum3913
Variance79.609192
MonotonicityNot monotonic
2023-12-10T19:54:52.938006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
38 8
 
8.0%
35 7
 
7.0%
39 6
 
6.0%
37 6
 
6.0%
31 5
 
5.0%
44 5
 
5.0%
36 5
 
5.0%
50 4
 
4.0%
34 4
 
4.0%
25 4
 
4.0%
Other values (25) 46
46.0%
ValueCountFrequency (%)
22 2
 
2.0%
23 1
 
1.0%
25 4
4.0%
26 1
 
1.0%
27 1
 
1.0%
28 1
 
1.0%
29 3
3.0%
30 2
 
2.0%
31 5
5.0%
32 2
 
2.0%
ValueCountFrequency (%)
61 1
 
1.0%
60 2
2.0%
58 1
 
1.0%
57 1
 
1.0%
54 2
2.0%
53 1
 
1.0%
52 2
2.0%
51 2
2.0%
50 4
4.0%
48 3
3.0%

설치갯수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
78 
2
17 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 78
78.0%
2 17
 
17.0%
3 5
 
5.0%

Length

2023-12-10T19:54:53.234746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:53.427796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 78
78.0%
2 17
 
17.0%
3 5
 
5.0%

사용갯수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
58 
0
31 
2
10 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 58
58.0%
0 31
31.0%
2 10
 
10.0%
3 1
 
1.0%

Length

2023-12-10T19:54:53.643329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:53.905411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 58
58.0%
0 31
31.0%
2 10
 
10.0%
3 1
 
1.0%

사용빈도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.71
Minimum0
Maximum321
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:54.116565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5
Q319.25
95-th percentile84
Maximum321
Range321
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation48.575734
Coefficient of variation (CV)2.237482
Kurtosis18.394195
Mean21.71
Median Absolute Deviation (MAD)3.5
Skewness3.9976914
Sum2171
Variance2359.6019
MonotonicityNot monotonic
2023-12-10T19:54:54.386796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 31
31.0%
1 10
 
10.0%
4 6
 
6.0%
2 5
 
5.0%
3 4
 
4.0%
17 3
 
3.0%
9 3
 
3.0%
7 2
 
2.0%
82 2
 
2.0%
18 2
 
2.0%
Other values (31) 32
32.0%
ValueCountFrequency (%)
0 31
31.0%
1 10
 
10.0%
2 5
 
5.0%
3 4
 
4.0%
4 6
 
6.0%
6 2
 
2.0%
7 2
 
2.0%
9 3
 
3.0%
10 1
 
1.0%
11 1
 
1.0%
ValueCountFrequency (%)
321 1
1.0%
222 1
1.0%
192 1
1.0%
173 1
1.0%
122 1
1.0%
82 2
2.0%
69 1
1.0%
60 1
1.0%
59 1
1.0%
53 1
1.0%

사용시간(초)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1540.18
Minimum0
Maximum66971
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:54.691035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median32
Q3372.25
95-th percentile2026.5
Maximum66971
Range66971
Interquartile range (IQR)372.25

Descriptive statistics

Standard deviation7687.5015
Coefficient of variation (CV)4.9913007
Kurtosis56.925571
Mean1540.18
Median Absolute Deviation (MAD)32
Skewness7.257474
Sum154018
Variance59097679
MonotonicityNot monotonic
2023-12-10T19:54:54.977334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
31.0%
8 3
 
3.0%
234 2
 
2.0%
368 2
 
2.0%
23 2
 
2.0%
2 2
 
2.0%
25 1
 
1.0%
261 1
 
1.0%
39 1
 
1.0%
1840 1
 
1.0%
Other values (54) 54
54.0%
ValueCountFrequency (%)
0 31
31.0%
1 1
 
1.0%
2 2
 
2.0%
3 1
 
1.0%
5 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
8 3
 
3.0%
9 1
 
1.0%
11 1
 
1.0%
ValueCountFrequency (%)
66971 1
1.0%
33798 1
1.0%
20146 1
1.0%
4087 1
1.0%
2625 1
1.0%
1995 1
1.0%
1840 1
1.0%
1753 1
1.0%
1667 1
1.0%
1658 1
1.0%

민감지수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.82
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:55.212155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.162023
Coefficient of variation (CV)0.44855249
Kurtosis-1.0287197
Mean4.82
Median Absolute Deviation (MAD)1
Skewness-0.61349707
Sum482
Variance4.6743434
MonotonicityNot monotonic
2023-12-10T19:54:55.404679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 31
31.0%
6 23
23.0%
1 15
15.0%
4 13
13.0%
3 12
 
12.0%
5 4
 
4.0%
2 2
 
2.0%
ValueCountFrequency (%)
1 15
15.0%
2 2
 
2.0%
3 12
 
12.0%
4 13
13.0%
5 4
 
4.0%
6 23
23.0%
7 31
31.0%
ValueCountFrequency (%)
7 31
31.0%
6 23
23.0%
5 4
 
4.0%
4 13
13.0%
3 12
 
12.0%
2 2
 
2.0%
1 15
15.0%

시도
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
41 
경기
21 
인천
8 
부산
7 
대전
 
4
Other values (10)
19 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row대전
2nd row서울
3rd row대전
4th row경기
5th row경기

Common Values

ValueCountFrequency (%)
서울 41
41.0%
경기 21
21.0%
인천 8
 
8.0%
부산 7
 
7.0%
대전 4
 
4.0%
충남 3
 
3.0%
경북 3
 
3.0%
대구 2
 
2.0%
충북 2
 
2.0%
울산 2
 
2.0%
Other values (5) 7
 
7.0%

Length

2023-12-10T19:54:55.666476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 41
41.0%
경기 21
21.0%
인천 8
 
8.0%
부산 7
 
7.0%
대전 4
 
4.0%
충남 3
 
3.0%
경북 3
 
3.0%
대구 2
 
2.0%
충북 2
 
2.0%
울산 2
 
2.0%
Other values (5) 7
 
7.0%

시군구
Text

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:54:56.099024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.73
Min length2

Characters and Unicode

Total characters373
Distinct characters67
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

Unique36 ?
Unique (%)36.0%

Sample

1st row유성구
2nd row구로구
3rd row서구
4th row평택시
5th row고양시 일산동구
ValueCountFrequency (%)
강서구 5
 
4.2%
중랑구 5
 
4.2%
강동구 4
 
3.4%
성북구 4
 
3.4%
강남구 4
 
3.4%
구로구 4
 
3.4%
성남시 3
 
2.5%
천안시 3
 
2.5%
영등포구 3
 
2.5%
부평구 3
 
2.5%
Other values (59) 80
67.8%
2023-12-10T19:54:56.766932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 89
23.9%
시 34
 
9.1%
18
 
4.8%
동 15
 
4.0%
남 15
 
4.0%
강 14
 
3.8%
서 12
 
3.2%
성 10
 
2.7%
산 9
 
2.4%
북 8
 
2.1%
Other values (57) 149
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 355
95.2%
Space Separator 18
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 89
25.1%
시 34
 
9.6%
동 15
 
4.2%
남 15
 
4.2%
강 14
 
3.9%
서 12
 
3.4%
성 10
 
2.8%
산 9
 
2.5%
북 8
 
2.3%
주 8
 
2.3%
Other values (56) 141
39.7%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 355
95.2%
Common 18
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 89
25.1%
시 34
 
9.6%
동 15
 
4.2%
남 15
 
4.2%
강 14
 
3.9%
서 12
 
3.4%
성 10
 
2.8%
산 9
 
2.5%
북 8
 
2.3%
주 8
 
2.3%
Other values (56) 141
39.7%
Common
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 355
95.2%
ASCII 18
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 89
25.1%
시 34
 
9.6%
동 15
 
4.2%
남 15
 
4.2%
강 14
 
3.9%
서 12
 
3.4%
성 10
 
2.8%
산 9
 
2.5%
북 8
 
2.3%
주 8
 
2.3%
Other values (56) 141
39.7%
ASCII
ValueCountFrequency (%)
18
100.0%

읍면동
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:54:57.278642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.43
Min length2

Characters and Unicode

Total characters343
Distinct characters116
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

Unique90 ?
Unique (%)90.0%

Sample

1st row봉산동
2nd row개봉1동
3rd row갈마동
4th row비전동
5th row마두동
ValueCountFrequency (%)
염창동 2
 
2.0%
부평1동 2
 
2.0%
구월2동 2
 
2.0%
중화2동 2
 
2.0%
압구정동 2
 
2.0%
엄궁동 1
 
1.0%
옥계동 1
 
1.0%
부평2동 1
 
1.0%
북가좌2동 1
 
1.0%
덕정동 1
 
1.0%
Other values (85) 85
85.0%
2023-12-10T19:54:58.102989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 96
28.0%
2 12
 
3.5%
1 10
 
2.9%
가 10
 
2.9%
정 9
 
2.6%
3 7
 
2.0%
화 7
 
2.0%
구 6
 
1.7%
산 5
 
1.5%
도 5
 
1.5%
Other values (106) 176
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 309
90.1%
Decimal Number 34
 
9.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 96
31.1%
가 10
 
3.2%
정 9
 
2.9%
화 7
 
2.3%
구 6
 
1.9%
산 5
 
1.6%
도 5
 
1.6%
중 5
 
1.6%
암 4
 
1.3%
평 4
 
1.3%
Other values (100) 158
51.1%
Decimal Number
ValueCountFrequency (%)
2 12
35.3%
1 10
29.4%
3 7
20.6%
5 2
 
5.9%
6 2
 
5.9%
7 1
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 309
90.1%
Common 34
 
9.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 96
31.1%
가 10
 
3.2%
정 9
 
2.9%
화 7
 
2.3%
구 6
 
1.9%
산 5
 
1.6%
도 5
 
1.6%
중 5
 
1.6%
암 4
 
1.3%
평 4
 
1.3%
Other values (100) 158
51.1%
Common
ValueCountFrequency (%)
2 12
35.3%
1 10
29.4%
3 7
20.6%
5 2
 
5.9%
6 2
 
5.9%
7 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 309
90.1%
ASCII 34
 
9.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 96
31.1%
가 10
 
3.2%
정 9
 
2.9%
화 7
 
2.3%
구 6
 
1.9%
산 5
 
1.6%
도 5
 
1.6%
중 5
 
1.6%
암 4
 
1.3%
평 4
 
1.3%
Other values (100) 158
51.1%
ASCII
ValueCountFrequency (%)
2 12
35.3%
1 10
29.4%
3 7
20.6%
5 2
 
5.9%
6 2
 
5.9%
7 1
 
2.9%

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.14
Minimum11
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:58.757622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median27.5
Q341
95-th percentile46
Maximum48
Range37
Interquartile range (IQR)30

Descriptive statistics

Standard deviation13.940726
Coefficient of variation (CV)0.5333101
Kurtosis-1.6521625
Mean26.14
Median Absolute Deviation (MAD)16.5
Skewness0.09353005
Sum2614
Variance194.34384
MonotonicityNot monotonic
2023-12-10T19:54:58.952446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 41
41.0%
41 21
21.0%
28 8
 
8.0%
26 7
 
7.0%
30 4
 
4.0%
44 3
 
3.0%
47 3
 
3.0%
27 2
 
2.0%
43 2
 
2.0%
31 2
 
2.0%
Other values (5) 7
 
7.0%
ValueCountFrequency (%)
11 41
41.0%
26 7
 
7.0%
27 2
 
2.0%
28 8
 
8.0%
29 1
 
1.0%
30 4
 
4.0%
31 2
 
2.0%
41 21
21.0%
42 1
 
1.0%
43 2
 
2.0%
ValueCountFrequency (%)
48 1
 
1.0%
47 3
 
3.0%
46 2
 
2.0%
45 2
 
2.0%
44 3
 
3.0%
43 2
 
2.0%
42 1
 
1.0%
41 21
21.0%
31 2
 
2.0%
30 4
 
4.0%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26495.09
Minimum11140
Maximum48250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:59.165260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140
5-th percentile11260
Q111552.5
median27700
Q341285.5
95-th percentile46182
Maximum48250
Range37110
Interquartile range (IQR)29733

Descriptive statistics

Standard deviation13850.032
Coefficient of variation (CV)0.52273958
Kurtosis-1.6465729
Mean26495.09
Median Absolute Deviation (MAD)15960
Skewness0.10099574
Sum2649509
Variance1.9182339 × 108
MonotonicityNot monotonic
2023-12-10T19:54:59.361787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11260 5
 
5.0%
11500 5
 
5.0%
11530 4
 
4.0%
11680 4
 
4.0%
11290 4
 
4.0%
11740 4
 
4.0%
11560 3
 
3.0%
28237 3
 
3.0%
30200 2
 
2.0%
41360 2
 
2.0%
Other values (54) 64
64.0%
ValueCountFrequency (%)
11140 1
 
1.0%
11170 1
 
1.0%
11200 1
 
1.0%
11260 5
5.0%
11290 4
4.0%
11305 1
 
1.0%
11350 1
 
1.0%
11410 1
 
1.0%
11470 1
 
1.0%
11500 5
5.0%
ValueCountFrequency (%)
48250 1
1.0%
47850 1
1.0%
47190 1
1.0%
47170 1
1.0%
46790 1
1.0%
46150 1
1.0%
45111 2
2.0%
44133 2
2.0%
44131 1
1.0%
43130 1
1.0%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26495210
Minimum11140144
Maximum48250132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:59.599629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140144
5-th percentile11260103
Q111552614
median27700131
Q341285604
95-th percentile46182139
Maximum48250132
Range37109988
Interquartile range (IQR)29732990

Descriptive statistics

Standard deviation13850046
Coefficient of variation (CV)0.52273772
Kurtosis-1.646572
Mean26495210
Median Absolute Deviation (MAD)15960024
Skewness0.10099684
Sum2.649521 × 109
Variance1.9182377 × 1014
MonotonicityNot monotonic
2023-12-10T19:54:59.931000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11260103 3
 
3.0%
28237101 3
 
3.0%
11710101 2
 
2.0%
11530102 2
 
2.0%
28200101 2
 
2.0%
11740108 2
 
2.0%
11500101 2
 
2.0%
11680110 2
 
2.0%
41117104 1
 
1.0%
28110147 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
11140144 1
 
1.0%
11170101 1
 
1.0%
11200113 1
 
1.0%
11260102 1
 
1.0%
11260103 3
3.0%
11260104 1
 
1.0%
11290118 1
 
1.0%
11290125 1
 
1.0%
11290128 1
 
1.0%
11290135 1
 
1.0%
ValueCountFrequency (%)
48250132 1
1.0%
47850256 1
1.0%
47190128 1
1.0%
47170140 1
1.0%
46790250 1
1.0%
46150133 1
1.0%
45111142 1
1.0%
45111128 1
1.0%
44133104 1
1.0%
44133101 1
1.0%

Interactions

2023-12-10T19:54:49.658698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:41.949833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.149471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.310170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.443222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:46.612844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:48.209367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:49.860166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.114642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.315747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.458133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.619943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.134292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:48.438725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:50.044462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.275164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.473022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.628427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.818260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.299684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:48.636065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:50.226429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.450237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.636389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.779363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.970332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.463041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:48.851197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:50.408211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.615864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.791104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.953770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:46.104878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.644094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:49.031026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:50.592949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.779597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:43.963965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.104698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:46.246620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.805560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:49.215268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:50.789169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:42.969466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:44.144385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:45.290088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:46.423635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:47.988609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:49.409288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:55:00.155753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별나이설치갯수사용갯수사용빈도사용시간(초)민감지수시도시군구읍면동시도코드시군구코드읍면동코드
성별1.0000.0000.0380.0000.0000.2460.0000.0000.0000.0000.0000.0000.000
나이0.0001.0000.0000.2510.2730.0000.0000.0000.7560.8110.1430.1360.136
설치갯수0.0380.0001.0000.5250.1470.1560.3280.0000.6910.8530.0000.0000.000
사용갯수0.0000.2510.5251.0000.2880.1820.7450.0000.0000.9140.0000.0000.000
사용빈도0.0000.2730.1470.2881.0000.7170.6280.0000.7191.0000.0000.0000.000
사용시간(초)0.2460.0000.1560.1820.7171.0000.0000.5040.8771.0000.1390.1380.138
민감지수0.0000.0000.3280.7450.6280.0001.0000.0000.6470.8710.1380.1770.177
시도0.0000.0000.0000.0000.0000.5040.0001.0000.9981.0001.0001.0001.000
시군구0.0000.7560.6910.0000.7190.8770.6470.9981.0001.0000.9990.9990.999
읍면동0.0000.8110.8530.9141.0001.0000.8711.0001.0001.0001.0001.0001.000
시도코드0.0000.1430.0000.0000.0000.1390.1381.0000.9991.0001.0001.0001.000
시군구코드0.0000.1360.0000.0000.0000.1380.1771.0000.9991.0001.0001.0001.000
읍면동코드0.0000.1360.0000.0000.0000.1380.1771.0000.9991.0001.0001.0001.000
2023-12-10T19:55:00.361840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도설치갯수사용갯수성별
시도1.0000.0000.0000.000
설치갯수0.0001.0000.5250.062
사용갯수0.0000.5251.0000.000
성별0.0000.0620.0001.000
2023-12-10T19:55:00.516968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나이사용빈도사용시간(초)민감지수시도코드시군구코드읍면동코드성별설치갯수사용갯수시도
나이1.0000.0160.050-0.0640.2170.2200.2200.0000.0000.1450.000
사용빈도0.0161.0000.956-0.944-0.133-0.161-0.1600.0000.0940.1970.000
사용시간(초)0.0500.9561.000-0.990-0.133-0.157-0.1560.1610.1460.0700.289
민감지수-0.064-0.944-0.9901.0000.1180.1410.1400.0000.2270.6130.000
시도코드0.217-0.133-0.1330.1181.0000.9600.9600.0000.0000.0000.946
시군구코드0.220-0.161-0.1570.1410.9601.0001.0000.0000.0000.0000.946
읍면동코드0.220-0.160-0.1560.1400.9601.0001.0000.0000.0000.0000.946
성별0.0000.0000.1610.0000.0000.0000.0001.0000.0620.0000.000
설치갯수0.0000.0940.1460.2270.0000.0000.0000.0621.0000.5250.000
사용갯수0.1450.1970.0700.6130.0000.0000.0000.0000.5251.0000.000
시도0.0000.0000.2890.0000.9460.9460.9460.0000.0000.0001.000

Missing values

2023-12-10T19:54:51.102802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:54:51.486795image/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

기준년월성별나이설치갯수사용갯수사용빈도사용시간(초)민감지수시도시군구읍면동시도코드시군구코드읍면동코드
0201802M48111256대전유성구봉산동303020030200145
1201802F42114556서울구로구개봉1동111153011530107
2201802F52114316671대전서구갈마동303017030170111
3201802F3110007경기평택시비전동414122041220118
4201802F5110007경기고양시 일산동구마두동414128541285105
5201802M4111142794서울강남구압구정동111168011680110
6201802F4411136서울영등포구도림동111156011560118
7201802F291131434인천서구가좌3동282826028260112
8201802F3622161934서울관악구난향동111162011620102
9201802F6110007인천부평구부평1동282823728237101
기준년월성별나이설치갯수사용갯수사용빈도사용시간(초)민감지수시도시군구읍면동시도코드시군구코드읍면동코드
90201802F601112216581경기하남시신장동414145041450106
91201802F3410007서울구로구구로5동111153011530102
92201802M3820007경기남양주시퇴계원면414136041360370
93201802M462144433울산북구매곡동313120031200103
94201802F38222410722서울성북구동선동3가111129011290118
95201802F48117745경기수원시 영통구하동414111741117104
96201802M3510007부산부산진구양정동262623026230101
97201802F3732594993대전동구가양동303011030110114
98201802F4511533683전남순천시조례동464615046150133
99201802F2211116경기용인시 수지구동천동414146541465103