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 36 (36.0%) zerosZeros
사용시간(초) has 36 (36.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:53:39.174887
Analysis finished2023-12-10 10:53:48.657261
Duration9.48 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
201805
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201805
2nd row201805
3rd row201805
4th row201805
5th row201805

Common Values

ValueCountFrequency (%)
201805 100
100.0%

Length

2023-12-10T19:53:48.771509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:48.915829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201805 100
100.0%

성별
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F
67 
M
33 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 67
67.0%
M 33
33.0%

Length

2023-12-10T19:53:49.068698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:49.244365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 67
67.0%
m 33
33.0%

나이
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.52
Minimum22
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:49.389048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile27.9
Q132.75
median37
Q344
95-th percentile53.05
Maximum61
Range39
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation8.3840638
Coefficient of variation (CV)0.21765482
Kurtosis-0.042114051
Mean38.52
Median Absolute Deviation (MAD)5
Skewness0.59037165
Sum3852
Variance70.292525
MonotonicityNot monotonic
2023-12-10T19:53:49.596687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
38 7
 
7.0%
35 7
 
7.0%
39 6
 
6.0%
31 6
 
6.0%
36 6
 
6.0%
44 5
 
5.0%
29 5
 
5.0%
33 5
 
5.0%
37 5
 
5.0%
51 5
 
5.0%
Other values (21) 43
43.0%
ValueCountFrequency (%)
22 2
 
2.0%
25 2
 
2.0%
26 1
 
1.0%
28 2
 
2.0%
29 5
5.0%
30 2
 
2.0%
31 6
6.0%
32 5
5.0%
33 5
5.0%
34 3
3.0%
ValueCountFrequency (%)
61 1
 
1.0%
60 1
 
1.0%
57 1
 
1.0%
55 1
 
1.0%
54 1
 
1.0%
53 2
 
2.0%
52 2
 
2.0%
51 5
5.0%
50 2
 
2.0%
48 2
 
2.0%

설치갯수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
82 
2
15 
3
 
3

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 82
82.0%
2 15
 
15.0%
3 3
 
3.0%

Length

2023-12-10T19:53:49.805931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:49.957066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 82
82.0%
2 15
 
15.0%
3 3
 
3.0%

사용갯수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
54 
0
36 
2
9 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 54
54.0%
0 36
36.0%
2 9
 
9.0%
3 1
 
1.0%

Length

2023-12-10T19:53:50.128974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:50.270624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 54
54.0%
0 36
36.0%
2 9
 
9.0%
3 1
 
1.0%

사용빈도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.03
Minimum0
Maximum218
Zeros36
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:50.455376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q323.5
95-th percentile95.5
Maximum218
Range218
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation37.273885
Coefficient of variation (CV)1.8609029
Kurtosis10.519427
Mean20.03
Median Absolute Deviation (MAD)4
Skewness2.9931633
Sum2003
Variance1389.3425
MonotonicityNot monotonic
2023-12-10T19:53:50.650098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 36
36.0%
4 6
 
6.0%
1 6
 
6.0%
6 5
 
5.0%
2 4
 
4.0%
18 3
 
3.0%
8 2
 
2.0%
51 2
 
2.0%
25 2
 
2.0%
23 2
 
2.0%
Other values (27) 32
32.0%
ValueCountFrequency (%)
0 36
36.0%
1 6
 
6.0%
2 4
 
4.0%
3 2
 
2.0%
4 6
 
6.0%
5 2
 
2.0%
6 5
 
5.0%
7 1
 
1.0%
8 2
 
2.0%
9 1
 
1.0%
ValueCountFrequency (%)
218 1
1.0%
169 1
1.0%
140 1
1.0%
116 1
1.0%
105 1
1.0%
95 1
1.0%
85 1
1.0%
68 1
1.0%
67 1
1.0%
58 1
1.0%

사용시간(초)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.24
Minimum0
Maximum4488
Zeros36
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:50.835607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median34
Q3320.5
95-th percentile2821.15
Maximum4488
Range4488
Interquartile range (IQR)320.5

Descriptive statistics

Standard deviation928.37194
Coefficient of variation (CV)2.1232548
Kurtosis6.8692161
Mean437.24
Median Absolute Deviation (MAD)34
Skewness2.6982115
Sum43724
Variance861874.47
MonotonicityNot monotonic
2023-12-10T19:53:51.050473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
36.0%
62 2
 
2.0%
29 2
 
2.0%
11 2
 
2.0%
3835 1
 
1.0%
3778 1
 
1.0%
1236 1
 
1.0%
3108 1
 
1.0%
312 1
 
1.0%
92 1
 
1.0%
Other values (52) 52
52.0%
ValueCountFrequency (%)
0 36
36.0%
1 1
 
1.0%
3 1
 
1.0%
7 1
 
1.0%
10 1
 
1.0%
11 2
 
2.0%
15 1
 
1.0%
16 1
 
1.0%
20 1
 
1.0%
23 1
 
1.0%
ValueCountFrequency (%)
4488 1
1.0%
3835 1
1.0%
3778 1
1.0%
3108 1
1.0%
2995 1
1.0%
2812 1
1.0%
2233 1
1.0%
2146 1
1.0%
2093 1
1.0%
1840 1
1.0%

민감지수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.03
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:51.332902image/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 deviation2.1246747
Coefficient of variation (CV)0.42240053
Kurtosis-0.76502266
Mean5.03
Median Absolute Deviation (MAD)1
Skewness-0.7879496
Sum503
Variance4.5142424
MonotonicityNot monotonic
2023-12-10T19:53:51.536867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 36
36.0%
6 20
20.0%
1 13
 
13.0%
5 10
 
10.0%
3 10
 
10.0%
4 8
 
8.0%
2 3
 
3.0%
ValueCountFrequency (%)
1 13
 
13.0%
2 3
 
3.0%
3 10
 
10.0%
4 8
 
8.0%
5 10
 
10.0%
6 20
20.0%
7 36
36.0%
ValueCountFrequency (%)
7 36
36.0%
6 20
20.0%
5 10
 
10.0%
4 8
 
8.0%
3 10
 
10.0%
2 3
 
3.0%
1 13
 
13.0%

시도
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
41 
경기
21 
인천
9 
부산
6 
대전
 
4
Other values (9)
19 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 41
41.0%
경기 21
21.0%
인천 9
 
9.0%
부산 6
 
6.0%
대전 4
 
4.0%
충남 3
 
3.0%
경북 3
 
3.0%
대구 3
 
3.0%
경남 3
 
3.0%
전북 2
 
2.0%
Other values (4) 5
 
5.0%

Length

2023-12-10T19:53:51.761892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 41
41.0%
경기 21
21.0%
인천 9
 
9.0%
부산 6
 
6.0%
대전 4
 
4.0%
충남 3
 
3.0%
경북 3
 
3.0%
대구 3
 
3.0%
경남 3
 
3.0%
전북 2
 
2.0%
Other values (4) 5
 
5.0%

시군구
Text

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:53:52.109532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.85
Min length2

Characters and Unicode

Total characters385
Distinct characters74
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 row용인시 수지구
2nd row유성구
3rd row평택시
4th row구로구
5th row성남시 수정구
ValueCountFrequency (%)
서구 6
 
5.0%
강서구 5
 
4.1%
성북구 4
 
3.3%
중랑구 4
 
3.3%
용인시 4
 
3.3%
성남시 4
 
3.3%
수지구 3
 
2.5%
구로구 3
 
2.5%
서초구 3
 
2.5%
천안시 3
 
2.5%
Other values (62) 82
67.8%
2023-12-10T19:53:52.701693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 91
23.6%
시 34
 
8.8%
21
 
5.5%
서 18
 
4.7%
동 11
 
2.9%
성 11
 
2.9%
강 11
 
2.9%
남 9
 
2.3%
천 9
 
2.3%
북 8
 
2.1%
Other values (64) 162
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 364
94.5%
Space Separator 21
 
5.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 91
25.0%
시 34
 
9.3%
서 18
 
4.9%
동 11
 
3.0%
성 11
 
3.0%
강 11
 
3.0%
남 9
 
2.5%
천 9
 
2.5%
북 8
 
2.2%
수 8
 
2.2%
Other values (63) 154
42.3%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 364
94.5%
Common 21
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 91
25.0%
시 34
 
9.3%
서 18
 
4.9%
동 11
 
3.0%
성 11
 
3.0%
강 11
 
3.0%
남 9
 
2.5%
천 9
 
2.5%
북 8
 
2.2%
수 8
 
2.2%
Other values (63) 154
42.3%
Common
ValueCountFrequency (%)
21
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 364
94.5%
ASCII 21
 
5.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 91
25.0%
시 34
 
9.3%
서 18
 
4.9%
동 11
 
3.0%
성 11
 
3.0%
강 11
 
3.0%
남 9
 
2.5%
천 9
 
2.5%
북 8
 
2.2%
수 8
 
2.2%
Other values (63) 154
42.3%
ASCII
ValueCountFrequency (%)
21
100.0%

읍면동
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:53:53.124279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.34
Min length2

Characters and Unicode

Total characters334
Distinct characters113
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

Unique96 ?
Unique (%)96.0%

Sample

1st row동천동
2nd row봉산동
3rd row안중읍
4th row개봉1동
5th row상적동
ValueCountFrequency (%)
청학동 2
 
2.0%
장유면 2
 
2.0%
역촌동 1
 
1.0%
원당동 1
 
1.0%
동천동 1
 
1.0%
잠실3동 1
 
1.0%
화곡동 1
 
1.0%
염창동 1
 
1.0%
공항동 1
 
1.0%
도당동 1
 
1.0%
Other values (88) 88
88.0%
2023-12-10T19:53:54.187372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 92
27.5%
1 11
 
3.3%
2 9
 
2.7%
산 9
 
2.7%
가 9
 
2.7%
암 6
 
1.8%
읍 6
 
1.8%
정 6
 
1.8%
중 6
 
1.8%
3 6
 
1.8%
Other values (103) 174
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 303
90.7%
Decimal Number 31
 
9.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 92
30.4%
산 9
 
3.0%
가 9
 
3.0%
암 6
 
2.0%
읍 6
 
2.0%
정 6
 
2.0%
중 6
 
2.0%
화 5
 
1.7%
상 5
 
1.7%
방 5
 
1.7%
Other values (96) 154
50.8%
Decimal Number
ValueCountFrequency (%)
1 11
35.5%
2 9
29.0%
3 6
19.4%
6 2
 
6.5%
5 1
 
3.2%
7 1
 
3.2%
4 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 303
90.7%
Common 31
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 92
30.4%
산 9
 
3.0%
가 9
 
3.0%
암 6
 
2.0%
읍 6
 
2.0%
정 6
 
2.0%
중 6
 
2.0%
화 5
 
1.7%
상 5
 
1.7%
방 5
 
1.7%
Other values (96) 154
50.8%
Common
ValueCountFrequency (%)
1 11
35.5%
2 9
29.0%
3 6
19.4%
6 2
 
6.5%
5 1
 
3.2%
7 1
 
3.2%
4 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 303
90.7%
ASCII 31
 
9.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 92
30.4%
산 9
 
3.0%
가 9
 
3.0%
암 6
 
2.0%
읍 6
 
2.0%
정 6
 
2.0%
중 6
 
2.0%
화 5
 
1.7%
상 5
 
1.7%
방 5
 
1.7%
Other values (96) 154
50.8%
ASCII
ValueCountFrequency (%)
1 11
35.5%
2 9
29.0%
3 6
19.4%
6 2
 
6.5%
5 1
 
3.2%
7 1
 
3.2%
4 1
 
3.2%

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.23
Minimum11
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:54.407503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median27.5
Q341
95-th percentile47
Maximum48
Range37
Interquartile range (IQR)30

Descriptive statistics

Standard deviation14.084776
Coefficient of variation (CV)0.53697201
Kurtosis-1.6284393
Mean26.23
Median Absolute Deviation (MAD)16.5
Skewness0.11540284
Sum2623
Variance198.38091
MonotonicityNot monotonic
2023-12-10T19:53:54.608046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11 41
41.0%
41 21
21.0%
28 9
 
9.0%
26 6
 
6.0%
30 4
 
4.0%
44 3
 
3.0%
47 3
 
3.0%
27 3
 
3.0%
48 3
 
3.0%
45 2
 
2.0%
Other values (4) 5
 
5.0%
ValueCountFrequency (%)
11 41
41.0%
26 6
 
6.0%
27 3
 
3.0%
28 9
 
9.0%
29 1
 
1.0%
30 4
 
4.0%
31 1
 
1.0%
41 21
21.0%
43 1
 
1.0%
44 3
 
3.0%
ValueCountFrequency (%)
48 3
 
3.0%
47 3
 
3.0%
46 2
 
2.0%
45 2
 
2.0%
44 3
 
3.0%
43 1
 
1.0%
41 21
21.0%
31 1
 
1.0%
30 4
 
4.0%
29 1
 
1.0%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26572.49
Minimum11140
Maximum48250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:54.845623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140
5-th percentile11260
Q111530
median27700
Q341232.75
95-th percentile47133
Maximum48250
Range37110
Interquartile range (IQR)29702.75

Descriptive statistics

Standard deviation13998.042
Coefficient of variation (CV)0.526787
Kurtosis-1.6234956
Mean26572.49
Median Absolute Deviation (MAD)16020
Skewness0.12169051
Sum2657249
Variance1.9594519 × 108
MonotonicityNot monotonic
2023-12-10T19:53:55.127141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11500 5
 
5.0%
11260 4
 
4.0%
11290 4
 
4.0%
41465 3
 
3.0%
11530 3
 
3.0%
11650 3
 
3.0%
28260 3
 
3.0%
48250 2
 
2.0%
30200 2
 
2.0%
41135 2
 
2.0%
Other values (54) 69
69.0%
ValueCountFrequency (%)
11140 1
 
1.0%
11170 1
 
1.0%
11230 2
 
2.0%
11260 4
4.0%
11290 4
4.0%
11305 2
 
2.0%
11380 2
 
2.0%
11410 1
 
1.0%
11470 1
 
1.0%
11500 5
5.0%
ValueCountFrequency (%)
48250 2
2.0%
48127 1
1.0%
47850 1
1.0%
47190 1
1.0%
47130 1
1.0%
46790 1
1.0%
46150 1
1.0%
45140 1
1.0%
45113 1
1.0%
44133 2
2.0%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26572612
Minimum11140144
Maximum48250132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:55.403702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140144
5-th percentile11260103
Q111530107
median27700131
Q341232966
95-th percentile47133124
Maximum48250132
Range37109988
Interquartile range (IQR)29702860

Descriptive statistics

Standard deviation13998060
Coefficient of variation (CV)0.52678525
Kurtosis-1.6234941
Mean26572612
Median Absolute Deviation (MAD)16020024
Skewness0.12169203
Sum2.6572612 × 109
Variance1.9594569 × 1014
MonotonicityNot monotonic
2023-12-10T19:53:55.689099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11650101 3
 
3.0%
28185104 2
 
2.0%
11260103 2
 
2.0%
11305101 2
 
2.0%
48250132 2
 
2.0%
41465103 1
 
1.0%
43111131 1
 
1.0%
11500103 1
 
1.0%
11500101 1
 
1.0%
11500108 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
11140144 1
1.0%
11170101 1
1.0%
11230109 1
1.0%
11230110 1
1.0%
11260102 1
1.0%
11260103 2
2.0%
11260104 1
1.0%
11290103 1
1.0%
11290123 1
1.0%
11290125 1
1.0%
ValueCountFrequency (%)
48250132 2
2.0%
48127250 1
1.0%
47850256 1
1.0%
47190128 1
1.0%
47130124 1
1.0%
46790250 1
1.0%
46150108 1
1.0%
45140390 1
1.0%
45113132 1
1.0%
44133104 1
1.0%

Interactions

2023-12-10T19:53:46.899713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:40.117385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:41.237998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:42.285713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:43.342017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:44.638149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:45.703430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:47.069469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:40.257755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:41.376858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:42.456180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:43.486947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:44.790389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:45.861216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:47.219841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:40.391458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:41.510381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:42.609270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:43.609596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:44.935433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:45.997314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:47.374689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:40.561842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:41.654302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:42.741018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:43.735659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:45.073650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:46.149465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:47.584436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:40.679309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:41.817027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:42.876440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:43.871878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:45.223505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:46.354500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:47.790439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:40.822654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:41.967726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:43.016826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:44.017944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:45.379961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:46.512908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:47.989619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:40.998859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:42.117946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:43.174718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:44.494064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:45.548983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:46.725319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:53:55.897208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별나이설치갯수사용갯수사용빈도사용시간(초)민감지수시도시군구읍면동시도코드시군구코드읍면동코드
성별1.0000.0000.0000.0000.0000.3780.1520.0000.0000.6140.0000.0000.000
나이0.0001.0000.0000.0000.0000.0000.0610.4140.0000.0000.5220.5270.527
설치갯수0.0000.0001.0000.6040.6400.4680.2800.0000.0000.8180.0000.0300.030
사용갯수0.0000.0000.6041.0000.6260.5970.7130.0000.0000.9770.0000.0000.000
사용빈도0.0000.0000.6400.6261.0000.8310.6330.0000.0001.0000.0000.0000.000
사용시간(초)0.3780.0000.4680.5970.8311.0000.6790.0000.0001.0000.0000.0000.000
민감지수0.1520.0610.2800.7130.6330.6791.0000.0000.0000.9020.1760.1720.172
시도0.0000.4140.0000.0000.0000.0000.0001.0000.9941.0001.0001.0001.000
시군구0.0000.0000.0000.0000.0000.0000.0000.9941.0001.0000.9800.9830.983
읍면동0.6140.0000.8180.9771.0001.0000.9021.0001.0001.0001.0001.0001.000
시도코드0.0000.5220.0000.0000.0000.0000.1761.0000.9801.0001.0001.0001.000
시군구코드0.0000.5270.0300.0000.0000.0000.1721.0000.9831.0001.0001.0001.000
읍면동코드0.0000.5270.0300.0000.0000.0000.1721.0000.9831.0001.0001.0001.000
2023-12-10T19:53:56.127313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도설치갯수사용갯수성별
시도1.0000.0000.0000.000
설치갯수0.0001.0000.6160.000
사용갯수0.0000.6161.0000.000
성별0.0000.0000.0001.000
2023-12-10T19:53:56.301049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나이사용빈도사용시간(초)민감지수시도코드시군구코드읍면동코드성별설치갯수사용갯수시도
나이1.0000.0140.093-0.0790.2190.1950.1940.0000.0000.0000.172
사용빈도0.0141.0000.978-0.972-0.048-0.044-0.0400.0000.3450.4430.000
사용시간(초)0.0930.9781.000-0.993-0.015-0.019-0.0160.2740.3270.2940.000
민감지수-0.079-0.972-0.9931.0000.0170.0220.0180.1560.1900.5730.000
시도코드0.219-0.048-0.0150.0171.0000.9600.9600.0000.0000.0000.951
시군구코드0.195-0.044-0.0190.0220.9601.0001.0000.0000.0000.0000.951
읍면동코드0.194-0.040-0.0160.0180.9601.0001.0000.0000.0000.0000.951
성별0.0000.0000.2740.1560.0000.0000.0001.0000.0000.0000.000
설치갯수0.0000.3450.3270.1900.0000.0000.0000.0001.0000.6160.000
사용갯수0.0000.4430.2940.5730.0000.0000.0000.0000.6161.0000.000
시도0.1720.0000.0000.0000.9510.9510.9510.0000.0000.0001.000

Missing values

2023-12-10T19:53:48.239861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:53:48.540662image/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
0201805F22118356경기용인시 수지구동천동414146541465103
1201805M481161264대전유성구봉산동303020030200145
2201805F3810007경기평택시안중읍414122041220253
3201805F4210007서울구로구개봉1동111153011530107
4201805F32114166경기성남시 수정구상적동414113141131113
5201805F52114111822대전서구갈마동303017030170111
6201805F5110007경기고양시 일산동구마두동414128541285105
7201805F37112206서울서초구방배4동111165011650101
8201805M4122387292서울강남구압구정동111168011680110
9201805F41111116경기용인시 수지구신봉동414146541465105
기준년월성별나이설치갯수사용갯수사용빈도사용시간(초)민감지수시도시군구읍면동시도코드시군구코드읍면동코드
90201805F3510007광주광산구우산동292920029200109
91201805F5110007경남창원시 마산회원구내서읍484812748127250
92201805F29221162934서울동작구상도동111159011590102
93201805F5710007서울성북구보문동6가111129011290128
94201805F511141055서울성북구돈암1동111129011290103
95201805F391116929951경기용인시 수지구상현동414146541465107
96201805F3721176인천연수구청학동282818528185104
97201805M3810007인천연수구청학동282818528185104
98201805F3110007전남순천시석현동464615046150108
99201805F2910007전북전주시 덕진구장동454511345113132