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 26 (26.0%) zerosZeros
사용시간(초) has 26 (26.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:53:20.134551
Analysis finished2023-12-10 10:53:29.380583
Duration9.25 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
201806
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201806
2nd row201806
3rd row201806
4th row201806
5th row201806

Common Values

ValueCountFrequency (%)
201806 100
100.0%

Length

2023-12-10T19:53:29.482337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:29.633239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201806 100
100.0%

성별
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F
66 
M
34 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 66
66.0%
M 34
34.0%

Length

2023-12-10T19:53:29.782864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:30.039745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 66
66.0%
m 34
34.0%

나이
Real number (ℝ)

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39
Minimum22
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:30.207367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile27.9
Q133
median38
Q344
95-th percentile54.05
Maximum61
Range39
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.4829002
Coefficient of variation (CV)0.21751026
Kurtosis-0.10842061
Mean39
Median Absolute Deviation (MAD)5.5
Skewness0.53704999
Sum3900
Variance71.959596
MonotonicityNot monotonic
2023-12-10T19:53:30.506273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
38 10
 
10.0%
39 7
 
7.0%
36 6
 
6.0%
35 6
 
6.0%
44 5
 
5.0%
33 5
 
5.0%
31 5
 
5.0%
51 5
 
5.0%
37 5
 
5.0%
29 4
 
4.0%
Other values (22) 42
42.0%
ValueCountFrequency (%)
22 2
 
2.0%
25 2
 
2.0%
26 1
 
1.0%
28 2
 
2.0%
29 4
4.0%
30 2
 
2.0%
31 5
5.0%
32 4
4.0%
33 5
5.0%
34 3
3.0%
ValueCountFrequency (%)
61 1
 
1.0%
60 1
 
1.0%
57 2
 
2.0%
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
81 
2
17 
3
 
2

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 81
81.0%
2 17
 
17.0%
3 2
 
2.0%

Length

2023-12-10T19:53:30.744702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:30.898946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 81
81.0%
2 17
 
17.0%
3 2
 
2.0%

사용갯수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
61 
0
26 
2
12 
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 row1

Common Values

ValueCountFrequency (%)
1 61
61.0%
0 26
26.0%
2 12
 
12.0%
3 1
 
1.0%

Length

2023-12-10T19:53:31.077139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:31.227103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 61
61.0%
0 26
26.0%
2 12
 
12.0%
3 1
 
1.0%

사용빈도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.62
Minimum0
Maximum579
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:31.394303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q322.5
95-th percentile183.2
Maximum579
Range579
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation76.934418
Coefficient of variation (CV)2.4330935
Kurtosis27.286012
Mean31.62
Median Absolute Deviation (MAD)7
Skewness4.6840617
Sum3162
Variance5918.9046
MonotonicityNot monotonic
2023-12-10T19:53:31.623731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 26
26.0%
2 7
 
7.0%
1 6
 
6.0%
9 4
 
4.0%
3 4
 
4.0%
8 3
 
3.0%
4 3
 
3.0%
17 3
 
3.0%
13 3
 
3.0%
7 3
 
3.0%
Other values (32) 38
38.0%
ValueCountFrequency (%)
0 26
26.0%
1 6
 
6.0%
2 7
 
7.0%
3 4
 
4.0%
4 3
 
3.0%
5 1
 
1.0%
6 1
 
1.0%
7 3
 
3.0%
8 3
 
3.0%
9 4
 
4.0%
ValueCountFrequency (%)
579 1
1.0%
280 1
1.0%
269 1
1.0%
206 1
1.0%
187 1
1.0%
183 1
1.0%
147 1
1.0%
130 1
1.0%
117 1
1.0%
78 1
1.0%

사용시간(초)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean842.51
Minimum0
Maximum33176
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:31.895529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median78
Q3381.25
95-th percentile3121.3
Maximum33176
Range33176
Interquartile range (IQR)381.25

Descriptive statistics

Standard deviation3479.0103
Coefficient of variation (CV)4.1293401
Kurtosis77.15398
Mean842.51
Median Absolute Deviation (MAD)78
Skewness8.3827264
Sum84251
Variance12103513
MonotonicityNot monotonic
2023-12-10T19:53:32.446545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
26.0%
2 2
 
2.0%
16 2
 
2.0%
1 2
 
2.0%
42 2
 
2.0%
13 2
 
2.0%
2516 1
 
1.0%
6512 1
 
1.0%
158 1
 
1.0%
385 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0 26
26.0%
1 2
 
2.0%
2 2
 
2.0%
5 1
 
1.0%
6 1
 
1.0%
7 1
 
1.0%
13 2
 
2.0%
16 2
 
2.0%
25 1
 
1.0%
27 1
 
1.0%
ValueCountFrequency (%)
33176 1
1.0%
6512 1
1.0%
6179 1
1.0%
5294 1
1.0%
3811 1
1.0%
3085 1
1.0%
2828 1
1.0%
2516 1
1.0%
2383 1
1.0%
2178 1
1.0%

민감지수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:32.642542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0474177
Coefficient of variation (CV)0.43562078
Kurtosis-1.0467672
Mean4.7
Median Absolute Deviation (MAD)2
Skewness-0.48493741
Sum470
Variance4.1919192
MonotonicityNot monotonic
2023-12-10T19:53:32.820748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 26
26.0%
6 20
20.0%
4 16
16.0%
1 11
11.0%
5 10
 
10.0%
3 9
 
9.0%
2 8
 
8.0%
ValueCountFrequency (%)
1 11
11.0%
2 8
 
8.0%
3 9
 
9.0%
4 16
16.0%
5 10
 
10.0%
6 20
20.0%
7 26
26.0%
ValueCountFrequency (%)
7 26
26.0%
6 20
20.0%
5 10
 
10.0%
4 16
16.0%
3 9
 
9.0%
2 8
 
8.0%
1 11
11.0%

시도
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
44 
경기
23 
인천
8 
부산
6 
대구
 
3
Other values (8)
16 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 44
44.0%
경기 23
23.0%
인천 8
 
8.0%
부산 6
 
6.0%
대구 3
 
3.0%
대전 3
 
3.0%
충남 3
 
3.0%
경북 3
 
3.0%
경남 3
 
3.0%
충북 1
 
1.0%
Other values (3) 3
 
3.0%

Length

2023-12-10T19:53:33.032021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 44
44.0%
경기 23
23.0%
인천 8
 
8.0%
부산 6
 
6.0%
대구 3
 
3.0%
대전 3
 
3.0%
충남 3
 
3.0%
경북 3
 
3.0%
경남 3
 
3.0%
충북 1
 
1.0%
Other values (3) 3
 
3.0%

시군구
Text

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:53:33.428124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.81
Min length2

Characters and Unicode

Total characters381
Distinct characters73
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

Unique37 ?
Unique (%)37.0%

Sample

1st row관악구
2nd row노원구
3rd row구로구
4th row북구
5th row유성구
ValueCountFrequency (%)
강서구 5
 
4.2%
용인시 5
 
4.2%
서구 5
 
4.2%
중랑구 5
 
4.2%
구로구 4
 
3.3%
성남시 3
 
2.5%
수지구 3
 
2.5%
천안시 3
 
2.5%
강남구 3
 
2.5%
관악구 3
 
2.5%
Other values (61) 81
67.5%
2023-12-10T19:53:34.080135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 92
24.1%
시 33
 
8.7%
20
 
5.2%
서 16
 
4.2%
동 13
 
3.4%
강 12
 
3.1%
남 10
 
2.6%
성 9
 
2.4%
북 9
 
2.4%
천 7
 
1.8%
Other values (63) 160
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 361
94.8%
Space Separator 20
 
5.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 92
25.5%
시 33
 
9.1%
서 16
 
4.4%
동 13
 
3.6%
강 12
 
3.3%
남 10
 
2.8%
성 9
 
2.5%
북 9
 
2.5%
천 7
 
1.9%
중 7
 
1.9%
Other values (62) 153
42.4%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 361
94.8%
Common 20
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 92
25.5%
시 33
 
9.1%
서 16
 
4.4%
동 13
 
3.6%
강 12
 
3.3%
남 10
 
2.8%
성 9
 
2.5%
북 9
 
2.5%
천 7
 
1.9%
중 7
 
1.9%
Other values (62) 153
42.4%
Common
ValueCountFrequency (%)
20
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 361
94.8%
ASCII 20
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 92
25.5%
시 33
 
9.1%
서 16
 
4.4%
동 13
 
3.6%
강 12
 
3.3%
남 10
 
2.8%
성 9
 
2.5%
북 9
 
2.5%
천 7
 
1.9%
중 7
 
1.9%
Other values (62) 153
42.4%
ASCII
ValueCountFrequency (%)
20
100.0%

읍면동
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:53:34.595523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.32
Min length2

Characters and Unicode

Total characters332
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 row인헌동
2nd row월계2동
3rd row구로2동
4th row구암동
5th row봉산동
ValueCountFrequency (%)
묵동 2
 
2.0%
장유면 2
 
2.0%
암사2동 1
 
1.0%
삼양동 1
 
1.0%
인헌동 1
 
1.0%
중동 1
 
1.0%
중리동 1
 
1.0%
내발산동 1
 
1.0%
괴안동 1
 
1.0%
화곡동 1
 
1.0%
Other values (88) 88
88.0%
2023-12-10T19:53:35.305359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 92
27.7%
2 12
 
3.6%
1 10
 
3.0%
산 9
 
2.7%
가 8
 
2.4%
암 6
 
1.8%
읍 5
 
1.5%
구 5
 
1.5%
중 5
 
1.5%
방 5
 
1.5%
Other values (103) 175
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 302
91.0%
Decimal Number 30
 
9.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 92
30.5%
산 9
 
3.0%
가 8
 
2.6%
암 6
 
2.0%
읍 5
 
1.7%
구 5
 
1.7%
중 5
 
1.7%
방 5
 
1.7%
정 5
 
1.7%
도 5
 
1.7%
Other values (97) 157
52.0%
Decimal Number
ValueCountFrequency (%)
2 12
40.0%
1 10
33.3%
3 4
 
13.3%
6 2
 
6.7%
5 1
 
3.3%
4 1
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 302
91.0%
Common 30
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 92
30.5%
산 9
 
3.0%
가 8
 
2.6%
암 6
 
2.0%
읍 5
 
1.7%
구 5
 
1.7%
중 5
 
1.7%
방 5
 
1.7%
정 5
 
1.7%
도 5
 
1.7%
Other values (97) 157
52.0%
Common
ValueCountFrequency (%)
2 12
40.0%
1 10
33.3%
3 4
 
13.3%
6 2
 
6.7%
5 1
 
3.3%
4 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 302
91.0%
ASCII 30
 
9.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 92
30.5%
산 9
 
3.0%
가 8
 
2.6%
암 6
 
2.0%
읍 5
 
1.7%
구 5
 
1.7%
중 5
 
1.7%
방 5
 
1.7%
정 5
 
1.7%
도 5
 
1.7%
Other values (97) 157
52.0%
ASCII
ValueCountFrequency (%)
2 12
40.0%
1 10
33.3%
3 4
 
13.3%
6 2
 
6.7%
5 1
 
3.3%
4 1
 
3.3%

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.43
Minimum11
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:35.519133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median26.5
Q341
95-th percentile47
Maximum48
Range37
Interquartile range (IQR)30

Descriptive statistics

Standard deviation14.05491
Coefficient of variation (CV)0.55269014
Kurtosis-1.6385155
Mean25.43
Median Absolute Deviation (MAD)15.5
Skewness0.19301183
Sum2543
Variance197.54051
MonotonicityNot monotonic
2023-12-10T19:53:35.697973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
11 44
44.0%
41 23
23.0%
28 8
 
8.0%
26 6
 
6.0%
27 3
 
3.0%
30 3
 
3.0%
44 3
 
3.0%
47 3
 
3.0%
48 3
 
3.0%
43 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
11 44
44.0%
26 6
 
6.0%
27 3
 
3.0%
28 8
 
8.0%
29 1
 
1.0%
30 3
 
3.0%
31 1
 
1.0%
41 23
23.0%
43 1
 
1.0%
44 3
 
3.0%
ValueCountFrequency (%)
48 3
 
3.0%
47 3
 
3.0%
45 1
 
1.0%
44 3
 
3.0%
43 1
 
1.0%
41 23
23.0%
31 1
 
1.0%
30 3
 
3.0%
29 1
 
1.0%
28 8
 
8.0%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25789.13
Minimum11170
Maximum48250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:35.917646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11170
5-th percentile11260
Q111530
median26850
Q341232.75
95-th percentile47133
Maximum48250
Range37080
Interquartile range (IQR)29702.75

Descriptive statistics

Standard deviation13969.984
Coefficient of variation (CV)0.54170049
Kurtosis-1.6346394
Mean25789.13
Median Absolute Deviation (MAD)15170
Skewness0.19826427
Sum2578913
Variance1.9516046 × 108
MonotonicityNot monotonic
2023-12-10T19:53:36.207559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11500 5
 
5.0%
11260 5
 
5.0%
11530 4
 
4.0%
11620 3
 
3.0%
11290 3
 
3.0%
11680 3
 
3.0%
41465 3
 
3.0%
28260 3
 
3.0%
26380 2
 
2.0%
48250 2
 
2.0%
Other values (54) 67
67.0%
ValueCountFrequency (%)
11170 1
 
1.0%
11200 1
 
1.0%
11230 2
 
2.0%
11260 5
5.0%
11290 3
3.0%
11305 2
 
2.0%
11350 1
 
1.0%
11380 2
 
2.0%
11410 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%
45140 1
1.0%
44133 2
2.0%
44131 1
1.0%
43111 1
1.0%
41670 1
1.0%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25789252
Minimum11170101
Maximum48250132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:36.480186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11170101
5-th percentile11260103
Q111530105
median26850106
Q341232966
95-th percentile47133124
Maximum48250132
Range37080031
Interquartile range (IQR)29702862

Descriptive statistics

Standard deviation13970003
Coefficient of variation (CV)0.54169865
Kurtosis-1.6346379
Mean25789252
Median Absolute Deviation (MAD)15170001
Skewness0.19826547
Sum2.5789252 × 109
Variance1.9516098 × 1014
MonotonicityNot monotonic
2023-12-10T19:53:36.746698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11620101 2
 
2.0%
11530102 2
 
2.0%
11260103 2
 
2.0%
11650101 2
 
2.0%
11590102 2
 
2.0%
11260104 2
 
2.0%
48250132 2
 
2.0%
11305101 2
 
2.0%
41133132 1
 
1.0%
11500106 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
11170101 1
1.0%
11200107 1
1.0%
11230109 1
1.0%
11230110 1
1.0%
11260102 1
1.0%
11260103 2
2.0%
11260104 2
2.0%
11290103 1
1.0%
11290125 1
1.0%
11290128 1
1.0%
ValueCountFrequency (%)
48250132 2
2.0%
48127250 1
1.0%
47850256 1
1.0%
47190128 1
1.0%
47130124 1
1.0%
45140390 1
1.0%
44133104 1
1.0%
44133101 1
1.0%
44131116 1
1.0%
43111131 1
1.0%

Interactions

2023-12-10T19:53:27.766317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:21.063313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:22.075486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:23.074285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:24.076099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:25.520299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:26.547184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.932436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:21.202138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:22.230959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:23.231158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:24.572856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:25.664191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:26.744668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.075836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:21.356476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:22.360255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:23.381436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:24.726161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:25.813528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:26.884898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.219559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:21.487319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:22.497064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:23.504296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:24.888921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:25.951635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.030446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.361078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:21.621655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:22.650892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:23.638383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:25.022497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:26.078358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.214269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.519024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:21.760547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:22.791819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:23.775468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:25.193218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:26.217499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.416324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:28.692310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:21.913699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:22.924479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:23.916950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:25.363849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:26.381316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:27.589042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:53:36.941034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별나이설치갯수사용갯수사용빈도사용시간(초)민감지수시도시군구읍면동시도코드시군구코드읍면동코드
성별1.0000.0000.0000.0000.1710.0870.1970.0000.0001.0000.0000.0000.000
나이0.0001.0000.0000.0000.2740.3860.0620.3580.0000.5300.5710.5740.574
설치갯수0.0000.0001.0000.7170.6100.0000.2330.0000.0000.8560.0000.0000.000
사용갯수0.0000.0000.7171.0000.5590.0000.7310.0000.0000.8960.0540.0550.055
사용빈도0.1710.2740.6100.5591.0000.9680.4750.0000.7791.0000.0000.0000.000
사용시간(초)0.0870.3860.0000.0000.9681.0000.5160.0000.7931.0000.1460.1710.171
민감지수0.1970.0620.2330.7310.4750.5161.0000.2310.0000.0000.0310.0000.000
시도0.0000.3580.0000.0000.0000.0000.2311.0000.9851.0001.0001.0001.000
시군구0.0000.0000.0000.0000.7790.7930.0000.9851.0001.0000.9900.9960.996
읍면동1.0000.5300.8560.8961.0001.0000.0001.0001.0001.0001.0001.0001.000
시도코드0.0000.5710.0000.0540.0000.1460.0311.0000.9901.0001.0001.0001.000
시군구코드0.0000.5740.0000.0550.0000.1710.0001.0000.9961.0001.0001.0001.000
읍면동코드0.0000.5740.0000.0550.0000.1710.0001.0000.9961.0001.0001.0001.000
2023-12-10T19:53:37.178477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도설치갯수사용갯수성별
시도1.0000.0000.0000.000
설치갯수0.0001.0000.7540.000
사용갯수0.0000.7541.0000.000
성별0.0000.0000.0001.000
2023-12-10T19:53:37.372550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
나이사용빈도사용시간(초)민감지수시도코드시군구코드읍면동코드성별설치갯수사용갯수시도
나이1.0000.0770.143-0.1350.2780.2490.2480.0000.0000.0000.149
사용빈도0.0771.0000.964-0.958-0.0530.0050.0070.1190.3050.3890.000
사용시간(초)0.1430.9641.000-0.992-0.0160.0320.0360.1420.0000.0000.000
민감지수-0.135-0.958-0.9921.0000.008-0.045-0.0480.2040.1550.5950.099
시도코드0.278-0.053-0.0160.0081.0000.9500.9500.0000.0000.0390.957
시군구코드0.2490.0050.032-0.0450.9501.0001.0000.0000.0000.0390.957
읍면동코드0.2480.0070.036-0.0480.9501.0001.0000.0000.0000.0390.957
성별0.0000.1190.1420.2040.0000.0000.0001.0000.0000.0000.000
설치갯수0.0000.3050.0000.1550.0000.0000.0000.0001.0000.7540.000
사용갯수0.0000.3890.0000.5950.0390.0390.0390.0000.7541.0000.000
시도0.1490.0000.0000.0990.9570.9570.9570.0000.0000.0001.000

Missing values

2023-12-10T19:53:28.969876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:53:29.274868image/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
0201806M391113795서울관악구인헌동111162011620101
1201806M46118765서울노원구월계2동111135011350102
2201806M3311782544서울구로구구로2동111153011530102
3201806F3410007대구북구구암동272723027230123
4201806M481172464대전유성구봉산동303020030200145
5201806F38113286경기평택시안중읍414122041220253
6201806F4211101334서울구로구개봉1동111153011530107
7201806F52113238111대전서구갈마동303017030170111
8201806F5110007경기고양시 일산동구마두동414128541285105
9201806F37112136서울서초구방배4동111165011650101
기준년월성별나이설치갯수사용갯수사용빈도사용시간(초)민감지수시도시군구읍면동시도코드시군구코드읍면동코드
90201806F5111166경남창원시 마산회원구내서읍484812748127250
91201806F3811223063광주북구양산동292917029170128
92201806F29211834143서울동작구상도동111159011590102
93201806F5710007서울성북구보문동6가111129011290128
94201806M5711579331761경기용인시 처인구유방동414146141461105
95201806F5111111794서울성북구돈암1동111129011290103
96201806F391111718651경기용인시 수지구상현동414146541465107
97201806M38112136인천연수구청학동282818528185104
98201806F451118730851서울동작구상도2동111159011590102
99201806F2211126경기용인시 수지구동천동414146541465103