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
설치갯수 is highly imbalanced (51.3%)Imbalance
사용빈도 has 20 (20.0%) zerosZeros
사용시간(초) has 20 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:53:58.150467
Analysis finished2023-12-10 10:54:08.922738
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
201804
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201804
2nd row201804
3rd row201804
4th row201804
5th row201804

Common Values

ValueCountFrequency (%)
201804 100
100.0%

Length

2023-12-10T19:54:09.058821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:09.198556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201804 100
100.0%

성별
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F
68 
M
32 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 68
68.0%
M 32
32.0%

Length

2023-12-10T19:54:09.359202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:09.521487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 68
68.0%
m 32
32.0%

나이
Real number (ℝ)

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.88
Minimum22
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:09.699851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile25
Q133
median37.5
Q344.25
95-th percentile54.15
Maximum61
Range39
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation8.8879444
Coefficient of variation (CV)0.22859939
Kurtosis-0.13764306
Mean38.88
Median Absolute Deviation (MAD)5.5
Skewness0.48597019
Sum3888
Variance78.995556
MonotonicityNot monotonic
2023-12-10T19:54:09.948339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
37 7
 
7.0%
38 7
 
7.0%
39 6
 
6.0%
35 6
 
6.0%
44 5
 
5.0%
32 5
 
5.0%
34 5
 
5.0%
36 5
 
5.0%
33 4
 
4.0%
50 4
 
4.0%
Other values (23) 46
46.0%
ValueCountFrequency (%)
22 2
 
2.0%
23 1
 
1.0%
25 3
3.0%
26 2
 
2.0%
28 2
 
2.0%
29 3
3.0%
30 2
 
2.0%
31 3
3.0%
32 5
5.0%
33 4
4.0%
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  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
83 
2
14 
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 row2

Common Values

ValueCountFrequency (%)
1 83
83.0%
2 14
 
14.0%
3 3
 
3.0%

Length

2023-12-10T19:54:10.243117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:10.416067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 83
83.0%
2 14
 
14.0%
3 3
 
3.0%

사용갯수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
67 
0
20 
2
11 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row2

Common Values

ValueCountFrequency (%)
1 67
67.0%
0 20
 
20.0%
2 11
 
11.0%
3 2
 
2.0%

Length

2023-12-10T19:54:10.604952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:54:10.800825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 67
67.0%
0 20
 
20.0%
2 11
 
11.0%
3 2
 
2.0%

사용빈도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.44
Minimum0
Maximum358
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:11.019118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q348.25
95-th percentile189.2
Maximum358
Range358
Interquartile range (IQR)46.25

Descriptive statistics

Standard deviation68.138054
Coefficient of variation (CV)1.644258
Kurtosis7.1280322
Mean41.44
Median Absolute Deviation (MAD)12
Skewness2.5747854
Sum4144
Variance4642.7943
MonotonicityNot monotonic
2023-12-10T19:54:11.285585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
20.0%
3 5
 
5.0%
4 4
 
4.0%
12 4
 
4.0%
2 4
 
4.0%
7 4
 
4.0%
21 3
 
3.0%
5 2
 
2.0%
19 2
 
2.0%
27 2
 
2.0%
Other values (44) 50
50.0%
ValueCountFrequency (%)
0 20
20.0%
1 2
 
2.0%
2 4
 
4.0%
3 5
 
5.0%
4 4
 
4.0%
5 2
 
2.0%
6 2
 
2.0%
7 4
 
4.0%
8 1
 
1.0%
9 2
 
2.0%
ValueCountFrequency (%)
358 1
1.0%
306 1
1.0%
261 1
1.0%
220 1
1.0%
212 1
1.0%
188 1
1.0%
182 1
1.0%
158 1
1.0%
145 1
1.0%
125 1
1.0%

사용시간(초)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean958.19
Minimum0
Maximum14073
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:11.554186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.75
median224.5
Q3755
95-th percentile4118.95
Maximum14073
Range14073
Interquartile range (IQR)740.25

Descriptive statistics

Standard deviation2146.2412
Coefficient of variation (CV)2.2398911
Kurtosis18.927462
Mean958.19
Median Absolute Deviation (MAD)224.5
Skewness4.0490973
Sum95819
Variance4606351.5
MonotonicityNot monotonic
2023-12-10T19:54:11.816529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
20.0%
59 2
 
2.0%
60 2
 
2.0%
197 2
 
2.0%
109 2
 
2.0%
232 1
 
1.0%
7282 1
 
1.0%
256 1
 
1.0%
2647 1
 
1.0%
492 1
 
1.0%
Other values (67) 67
67.0%
ValueCountFrequency (%)
0 20
20.0%
2 1
 
1.0%
8 1
 
1.0%
9 1
 
1.0%
11 1
 
1.0%
14 1
 
1.0%
15 1
 
1.0%
17 1
 
1.0%
19 1
 
1.0%
23 1
 
1.0%
ValueCountFrequency (%)
14073 1
1.0%
11055 1
1.0%
7282 1
1.0%
7084 1
1.0%
5030 1
1.0%
4071 1
1.0%
3786 1
1.0%
3597 1
1.0%
2907 1
1.0%
2647 1
1.0%

민감지수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.05
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:12.023381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1806078
Coefficient of variation (CV)0.53842169
Kurtosis-1.3954179
Mean4.05
Median Absolute Deviation (MAD)2
Skewness-0.023484312
Sum405
Variance4.7550505
MonotonicityNot monotonic
2023-12-10T19:54:12.196720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 20
20.0%
1 19
19.0%
4 16
16.0%
6 14
14.0%
3 12
12.0%
2 11
11.0%
5 8
 
8.0%
ValueCountFrequency (%)
1 19
19.0%
2 11
11.0%
3 12
12.0%
4 16
16.0%
5 8
 
8.0%
6 14
14.0%
7 20
20.0%
ValueCountFrequency (%)
7 20
20.0%
6 14
14.0%
5 8
 
8.0%
4 16
16.0%
3 12
12.0%
2 11
11.0%
1 19
19.0%

시도
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
41 
경기
24 
인천
8 
부산
6 
충남
 
4
Other values (9)
17 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 41
41.0%
경기 24
24.0%
인천 8
 
8.0%
부산 6
 
6.0%
충남 4
 
4.0%
대구 4
 
4.0%
대전 3
 
3.0%
충북 2
 
2.0%
경북 2
 
2.0%
전북 2
 
2.0%
Other values (4) 4
 
4.0%

Length

2023-12-10T19:54:12.438690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 41
41.0%
경기 24
24.0%
인천 8
 
8.0%
부산 6
 
6.0%
충남 4
 
4.0%
대구 4
 
4.0%
대전 3
 
3.0%
충북 2
 
2.0%
경북 2
 
2.0%
전북 2
 
2.0%
Other values (4) 4
 
4.0%

시군구
Text

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:54:12.890665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.82
Min length2

Characters and Unicode

Total characters382
Distinct characters71
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

Unique47 ?
Unique (%)47.0%

Sample

1st row유성구
2nd row평택시
3rd row서구
4th row고양시 일산동구
5th row강남구
ValueCountFrequency (%)
강서구 5
 
4.2%
중랑구 5
 
4.2%
강남구 4
 
3.3%
성북구 4
 
3.3%
성남시 4
 
3.3%
수지구 3
 
2.5%
천안시 3
 
2.5%
부평구 3
 
2.5%
영등포구 3
 
2.5%
용인시 3
 
2.5%
Other values (66) 83
69.2%
2023-12-10T19:54:13.606612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 87
22.8%
시 35
 
9.2%
20
 
5.2%
성 14
 
3.7%
강 13
 
3.4%
서 13
 
3.4%
남 13
 
3.4%
동 12
 
3.1%
수 8
 
2.1%
산 8
 
2.1%
Other values (61) 159
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 362
94.8%
Space Separator 20
 
5.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 87
24.0%
시 35
 
9.7%
성 14
 
3.9%
강 13
 
3.6%
서 13
 
3.6%
남 13
 
3.6%
동 12
 
3.3%
수 8
 
2.2%
산 8
 
2.2%
중 8
 
2.2%
Other values (60) 151
41.7%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 362
94.8%
Common 20
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 87
24.0%
시 35
 
9.7%
성 14
 
3.9%
강 13
 
3.6%
서 13
 
3.6%
남 13
 
3.6%
동 12
 
3.3%
수 8
 
2.2%
산 8
 
2.2%
중 8
 
2.2%
Other values (60) 151
41.7%
Common
ValueCountFrequency (%)
20
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 362
94.8%
ASCII 20
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 87
24.0%
시 35
 
9.7%
성 14
 
3.9%
강 13
 
3.6%
서 13
 
3.6%
남 13
 
3.6%
동 12
 
3.3%
수 8
 
2.2%
산 8
 
2.2%
중 8
 
2.2%
Other values (60) 151
41.7%
ASCII
ValueCountFrequency (%)
20
100.0%

읍면동
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:54:14.254833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.39
Min length2

Characters and Unicode

Total characters339
Distinct characters109
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

Unique92 ?
Unique (%)92.0%

Sample

1st row봉산동
2nd row안중읍
3rd row갈마동
4th row마두동
5th row압구정동
ValueCountFrequency (%)
중화2동 2
 
2.0%
청학동 2
 
2.0%
염창동 2
 
2.0%
압구정동 2
 
2.0%
화순읍 1
 
1.0%
상봉동 1
 
1.0%
엄궁동 1
 
1.0%
운서동 1
 
1.0%
장유면 1
 
1.0%
중동 1
 
1.0%
Other values (86) 86
86.0%
2023-12-10T19:54:15.471380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 92
27.1%
2 11
 
3.2%
1 10
 
2.9%
중 8
 
2.4%
가 8
 
2.4%
산 8
 
2.4%
화 7
 
2.1%
3 7
 
2.1%
정 6
 
1.8%
신 5
 
1.5%
Other values (99) 177
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 307
90.6%
Decimal Number 32
 
9.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 92
30.0%
중 8
 
2.6%
가 8
 
2.6%
산 8
 
2.6%
화 7
 
2.3%
정 6
 
2.0%
신 5
 
1.6%
장 5
 
1.6%
도 5
 
1.6%
읍 5
 
1.6%
Other values (93) 158
51.5%
Decimal Number
ValueCountFrequency (%)
2 11
34.4%
1 10
31.2%
3 7
21.9%
6 2
 
6.2%
5 1
 
3.1%
7 1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 307
90.6%
Common 32
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 92
30.0%
중 8
 
2.6%
가 8
 
2.6%
산 8
 
2.6%
화 7
 
2.3%
정 6
 
2.0%
신 5
 
1.6%
장 5
 
1.6%
도 5
 
1.6%
읍 5
 
1.6%
Other values (93) 158
51.5%
Common
ValueCountFrequency (%)
2 11
34.4%
1 10
31.2%
3 7
21.9%
6 2
 
6.2%
5 1
 
3.1%
7 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 307
90.6%
ASCII 32
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 92
30.0%
중 8
 
2.6%
가 8
 
2.6%
산 8
 
2.6%
화 7
 
2.3%
정 6
 
2.0%
신 5
 
1.6%
장 5
 
1.6%
도 5
 
1.6%
읍 5
 
1.6%
Other values (93) 158
51.5%
ASCII
ValueCountFrequency (%)
2 11
34.4%
1 10
31.2%
3 7
21.9%
6 2
 
6.2%
5 1
 
3.1%
7 1
 
3.1%

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.13
Minimum11
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:15.748818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median27
Q341
95-th percentile45
Maximum48
Range37
Interquartile range (IQR)30

Descriptive statistics

Standard deviation13.891102
Coefficient of variation (CV)0.5316151
Kurtosis-1.6776248
Mean26.13
Median Absolute Deviation (MAD)16
Skewness0.075477145
Sum2613
Variance192.96273
MonotonicityNot monotonic
2023-12-10T19:54:15.968858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11 41
41.0%
41 24
24.0%
28 8
 
8.0%
26 6
 
6.0%
44 4
 
4.0%
27 4
 
4.0%
30 3
 
3.0%
43 2
 
2.0%
47 2
 
2.0%
45 2
 
2.0%
Other values (4) 4
 
4.0%
ValueCountFrequency (%)
11 41
41.0%
26 6
 
6.0%
27 4
 
4.0%
28 8
 
8.0%
29 1
 
1.0%
30 3
 
3.0%
31 1
 
1.0%
41 24
24.0%
43 2
 
2.0%
44 4
 
4.0%
ValueCountFrequency (%)
48 1
 
1.0%
47 2
 
2.0%
46 1
 
1.0%
45 2
 
2.0%
44 4
 
4.0%
43 2
 
2.0%
41 24
24.0%
31 1
 
1.0%
30 3
 
3.0%
29 1
 
1.0%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26489.9
Minimum11140
Maximum48250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:16.273344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140
5-th percentile11258.5
Q111541.25
median27500
Q341273.5
95-th percentile45112.45
Maximum48250
Range37110
Interquartile range (IQR)29732.25

Descriptive statistics

Standard deviation13817.166
Coefficient of variation (CV)0.52160131
Kurtosis-1.6726607
Mean26489.9
Median Absolute Deviation (MAD)15760
Skewness0.081126087
Sum2648990
Variance1.9091409 × 108
MonotonicityNot monotonic
2023-12-10T19:54:16.640405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11260 5
 
5.0%
11500 5
 
5.0%
11680 4
 
4.0%
11290 4
 
4.0%
41135 3
 
3.0%
11740 3
 
3.0%
41465 3
 
3.0%
11560 3
 
3.0%
28237 3
 
3.0%
26530 2
 
2.0%
Other values (58) 65
65.0%
ValueCountFrequency (%)
11140 1
 
1.0%
11170 1
 
1.0%
11200 1
 
1.0%
11215 1
 
1.0%
11230 1
 
1.0%
11260 5
5.0%
11290 4
4.0%
11305 1
 
1.0%
11350 1
 
1.0%
11410 1
 
1.0%
ValueCountFrequency (%)
48250 1
1.0%
47850 1
1.0%
47190 1
1.0%
46790 1
1.0%
45140 1
1.0%
45111 1
1.0%
44200 1
1.0%
44133 2
2.0%
44131 1
1.0%
43130 1
1.0%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26490026
Minimum11140144
Maximum48250132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:54:16.928109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140144
5-th percentile11258602
Q111541353
median27500238
Q341273606
95-th percentile45112591
Maximum48250132
Range37109988
Interquartile range (IQR)29732253

Descriptive statistics

Standard deviation13817183
Coefficient of variation (CV)0.52159948
Kurtosis-1.6726595
Mean26490026
Median Absolute Deviation (MAD)15760130
Skewness0.081126998
Sum2.6490026 × 109
Variance1.9091456 × 1014
MonotonicityNot monotonic
2023-12-10T19:54:17.234923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11260103 3
 
3.0%
11680110 2
 
2.0%
28185104 2
 
2.0%
28237101 2
 
2.0%
11500101 2
 
2.0%
11740108 2
 
2.0%
11650101 2
 
2.0%
30200145 1
 
1.0%
46790250 1
 
1.0%
47190128 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
11140144 1
 
1.0%
11170101 1
 
1.0%
11200113 1
 
1.0%
11215104 1
 
1.0%
11230110 1
 
1.0%
11260102 1
 
1.0%
11260103 3
3.0%
11260104 1
 
1.0%
11290123 1
 
1.0%
11290125 1
 
1.0%
ValueCountFrequency (%)
48250132 1
1.0%
47850256 1
1.0%
47190128 1
1.0%
46790250 1
1.0%
45140390 1
1.0%
45111128 1
1.0%
44200111 1
1.0%
44133104 1
1.0%
44133101 1
1.0%
44131116 1
1.0%

Interactions

2023-12-10T19:54:07.244000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:59.338930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:00.652345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:01.810635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:03.149261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:04.403942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:06.006520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:07.401464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:59.520111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:00.841044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:01.964328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:03.325742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:04.972133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:06.181344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:07.576386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:59.698192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:01.004176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:02.138666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:03.474429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:05.151142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:06.356081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:07.722946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:59.883133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:01.153704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:02.310380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:03.644994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:05.311225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:06.546773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:07.863930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:00.058511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:01.314843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:02.482951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:03.828568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:05.471797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:06.721568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:07.985270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:00.235483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:01.492193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:02.655922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:04.025440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:05.640265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:06.897453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:08.117891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:00.433151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:01.662454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:02.945761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:04.199456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:05.814411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:54:07.060409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:54:17.462495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별나이설치갯수사용갯수사용빈도사용시간(초)민감지수시도시군구읍면동시도코드시군구코드읍면동코드
성별1.0000.0000.0000.0000.0000.1340.0850.0000.0000.0000.0000.0000.000
나이0.0001.0000.0000.0000.0000.0760.0000.0000.7550.9210.2720.2790.279
설치갯수0.0000.0001.0000.7530.6340.4470.2220.0000.0000.0000.0000.0000.000
사용갯수0.0000.0000.7531.0000.6290.4170.7060.0000.4900.9460.0000.0000.000
사용빈도0.0000.0000.6340.6291.0000.8930.5530.0000.0000.9350.0000.0000.000
사용시간(초)0.1340.0760.4470.4170.8931.0000.6890.0000.0000.9850.0000.0000.000
민감지수0.0850.0000.2220.7060.5530.6891.0000.3370.7010.7470.0000.0000.000
시도0.0000.0000.0000.0000.0000.0000.3371.0000.9981.0001.0001.0001.000
시군구0.0000.7550.0000.4900.0000.0000.7010.9981.0001.0000.9910.9940.994
읍면동0.0000.9210.0000.9460.9350.9850.7471.0001.0001.0001.0001.0001.000
시도코드0.0000.2720.0000.0000.0000.0000.0001.0000.9911.0001.0001.0001.000
시군구코드0.0000.2790.0000.0000.0000.0000.0001.0000.9941.0001.0001.0001.000
읍면동코드0.0000.2790.0000.0000.0000.0000.0001.0000.9941.0001.0001.0001.000
2023-12-10T19:54:17.741273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도설치갯수사용갯수성별
시도1.0000.0000.0000.000
설치갯수0.0001.0000.8000.000
사용갯수0.0000.8001.0000.000
성별0.0000.0000.0001.000
2023-12-10T19:54:17.925504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나이사용빈도사용시간(초)민감지수시도코드시군구코드읍면동코드성별설치갯수사용갯수시도
나이1.000-0.0530.072-0.0740.2430.2330.2330.0000.0000.0000.000
사용빈도-0.0531.0000.962-0.956-0.039-0.066-0.0640.0000.4620.4160.000
사용시간(초)0.0720.9621.000-0.991-0.018-0.049-0.0460.1380.3290.2930.000
민감지수-0.074-0.956-0.9911.0000.0110.0470.0440.0860.1470.5650.120
시도코드0.243-0.039-0.0180.0111.0000.9580.9570.0000.0000.0000.951
시군구코드0.233-0.066-0.0490.0470.9581.0001.0000.0000.0000.0000.951
읍면동코드0.233-0.064-0.0460.0440.9571.0001.0000.0000.0000.0000.951
성별0.0000.0000.1380.0860.0000.0000.0001.0000.0000.0000.000
설치갯수0.0000.4620.3290.1470.0000.0000.0000.0001.0000.8000.000
사용갯수0.0000.4160.2930.5650.0000.0000.0000.0000.8001.0000.000
시도0.0000.0000.0000.1200.9510.9510.9510.0000.0000.0001.000

Missing values

2023-12-10T19:54:08.325042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:54:08.737645image/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
0201804M481152324대전유성구봉산동303020030200145
1201804F3811193503경기평택시안중읍414122041220253
2201804F52114814181대전서구갈마동303017030170111
3201804F5110007경기고양시 일산동구마두동414128541285105
4201804M4122132874서울강남구압구정동111168011680110
5201804F41113196경기용인시 수지구신봉동414146541465105
6201804F3710007경기화성시봉담읍414159041590253
7201804F44112176서울영등포구도림동111156011560118
8201804F29113256인천서구가좌3동282826028260112
9201804F3211999142서울서대문구북가좌2동111141011410119
기준년월성별나이설치갯수사용갯수사용빈도사용시간(초)민감지수시도시군구읍면동시도코드시군구코드읍면동코드
90201804F2611709282부산사상구모라동262653026530102
91201804M5422121524서울강남구도곡동111168011680118
92201804F3610007서울영등포구당산동111156011560117
93201804M3211434783대구남구봉덕1동272720027200102
94201804F251121240711서울성북구종암동111129011290135
95201804F3422569872충북충주시연수동434313043130118
96201804F4721112534서울노원구중계1동111135011350106
97201804F3211886경기성남시 분당구분당동414113541135101
98201804F601115825611경기하남시신장동414145041450106
99201804F2211696경기용인시 수지구동천동414146541465103