Overview

Dataset statistics

Number of variables13
Number of observations230
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.5 KiB
Average record size in memory113.6 B

Variable types

Categorical3
Text1
Numeric9

Dataset

Description전국 시군구별 DMB 매체의 주파수, 평균 전계강도, 최대 전계강도, 최소 전계강도, 측정지점수(양호, 보통, 불량) 및 수신율 측정 데이터 정보
Author과학기술정보통신부 중앙전파관리소
URLhttps://www.data.go.kr/data/15012496/fileData.do

Alerts

권역 is highly overall correlated with 주파수(MHz) and 1 other fieldsHigh correlation
광역시 is highly overall correlated with 주파수(MHz) and 1 other fieldsHigh correlation
주파수(MHz) is highly overall correlated with 권역 and 2 other fieldsHigh correlation
평균전계 is highly overall correlated with 최대전계 and 4 other fieldsHigh correlation
최대전계 is highly overall correlated with 평균전계 and 2 other fieldsHigh correlation
최소전계 is highly overall correlated with 평균전계 and 2 other fieldsHigh correlation
측정지점수 is highly overall correlated with 양호지점수 and 2 other fieldsHigh correlation
양호지점수 is highly overall correlated with 평균전계 and 3 other fieldsHigh correlation
보통지점수 is highly overall correlated with 측정지점수 and 2 other fieldsHigh correlation
불량지점수 is highly overall correlated with 평균전계 and 4 other fieldsHigh correlation
수신율(퍼센트) is highly overall correlated with 평균전계 and 3 other fieldsHigh correlation
매체 is highly overall correlated with 주파수(MHz)High correlation
양호지점수 has 19 (8.3%) zerosZeros
보통지점수 has 29 (12.6%) zerosZeros
불량지점수 has 44 (19.1%) zerosZeros
수신율(퍼센트) has 6 (2.6%) zerosZeros

Reproduction

Analysis started2023-12-12 05:15:38.109774
Analysis finished2023-12-12 05:15:47.962819
Duration9.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

권역
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경상권
92 
수도권
48 
전라권
37 
충청권
26 
강원권
17 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상권 92
40.0%
수도권 48
20.9%
전라권 37
16.1%
충청권 26
 
11.3%
강원권 17
 
7.4%
제주권 10
 
4.3%

Length

2023-12-12T14:15:48.046862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:15:48.188620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상권 92
40.0%
수도권 48
20.9%
전라권 37
16.1%
충청권 26
 
11.3%
강원권 17
 
7.4%
제주권 10
 
4.3%

광역시
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
40 
경상남도
34 
전라남도
33 
경상북도
30 
대구광역시
28 
Other values (6)
65 

Length

Max length5
Median length4
Mean length3.8652174
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도
2nd row경상북도
3rd row경상북도
4th row경상북도
5th row경상북도

Common Values

ValueCountFrequency (%)
경기도 40
17.4%
경상남도 34
14.8%
전라남도 33
14.3%
경상북도 30
13.0%
대구광역시 28
12.2%
충청북도 26
11.3%
강원도 17
7.4%
제주도 8
 
3.5%
서울특별시 8
 
3.5%
전라북도 4
 
1.7%

Length

2023-12-12T14:15:48.338874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 40
17.4%
경상남도 34
14.8%
전라남도 33
14.3%
경상북도 30
13.0%
대구광역시 28
12.2%
충청북도 26
11.3%
강원도 17
7.4%
제주도 8
 
3.5%
서울특별시 8
 
3.5%
전라북도 4
 
1.7%

시_군_구
Text

Distinct76
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T14:15:48.650065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1043478
Min length2

Characters and Unicode

Total characters714
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)3.5%

Sample

1st row창녕군
2nd row경산시
3rd row고령군
4th row구미시
5th row문경시
ValueCountFrequency (%)
문경시 6
 
2.6%
여수시 4
 
1.7%
서초구 4
 
1.7%
여주군 4
 
1.7%
용인시처인구 4
 
1.7%
진주시 4
 
1.7%
함안군 4
 
1.7%
진천군 4
 
1.7%
음성군 4
 
1.7%
경산시 4
 
1.7%
Other values (66) 188
81.7%
2023-12-12T14:15:49.140483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 105
 
14.7%
군 95
 
13.3%
구 52
 
7.3%
성 35
 
4.9%
주 26
 
3.6%
천 22
 
3.1%
서 16
 
2.2%
영 14
 
2.0%
수 12
 
1.7%
양 12
 
1.7%
Other values (70) 325
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 714
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 105
 
14.7%
군 95
 
13.3%
구 52
 
7.3%
성 35
 
4.9%
주 26
 
3.6%
천 22
 
3.1%
서 16
 
2.2%
영 14
 
2.0%
수 12
 
1.7%
양 12
 
1.7%
Other values (70) 325
45.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 714
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 105
 
14.7%
군 95
 
13.3%
구 52
 
7.3%
성 35
 
4.9%
주 26
 
3.6%
천 22
 
3.1%
서 16
 
2.2%
영 14
 
2.0%
수 12
 
1.7%
양 12
 
1.7%
Other values (70) 325
45.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 714
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 105
 
14.7%
군 95
 
13.3%
구 52
 
7.3%
성 35
 
4.9%
주 26
 
3.6%
천 22
 
3.1%
서 16
 
2.2%
영 14
 
2.0%
수 12
 
1.7%
양 12
 
1.7%
Other values (70) 325
45.5%

매체
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
MBC
114 
KBS
88 
SBS
14 
YTN
14 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
MBC 114
49.6%
KBS 88
38.3%
SBS 14
 
6.1%
YTN 14
 
6.1%

Length

2023-12-12T14:15:49.330689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:15:49.784663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mbc 114
49.6%
kbs 88
38.3%
sbs 14
 
6.1%
ytn 14
 
6.1%

주파수(MHz)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.36974
Minimum175.28
Maximum213.008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T14:15:49.907125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum175.28
5-th percentile175.28
Q1183.008
median189.008
Q3205.28
95-th percentile211.28
Maximum213.008
Range37.728
Interquartile range (IQR)22.272

Descriptive statistics

Standard deviation12.751181
Coefficient of variation (CV)0.065941967
Kurtosis-1.5015474
Mean193.36974
Median Absolute Deviation (MAD)12
Skewness0.0068859584
Sum44475.04
Variance162.59262
MonotonicityNot monotonic
2023-12-12T14:15:50.033965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
205.28 31
13.5%
189.008 26
11.3%
187.28 26
11.3%
175.28 25
10.9%
177.008 20
8.7%
207.008 18
7.8%
183.008 16
7.0%
208.736 14
6.1%
201.008 12
 
5.2%
199.28 12
 
5.2%
Other values (3) 30
13.0%
ValueCountFrequency (%)
175.28 25
10.9%
177.008 20
8.7%
181.28 11
 
4.8%
183.008 16
7.0%
187.28 26
11.3%
189.008 26
11.3%
199.28 12
 
5.2%
201.008 12
 
5.2%
205.28 31
13.5%
207.008 18
7.8%
ValueCountFrequency (%)
213.008 10
 
4.3%
211.28 9
 
3.9%
208.736 14
6.1%
207.008 18
7.8%
205.28 31
13.5%
201.008 12
 
5.2%
199.28 12
 
5.2%
189.008 26
11.3%
187.28 26
11.3%
183.008 16
7.0%

평균전계
Real number (ℝ)

HIGH CORRELATION 

Distinct229
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.133609
Minimum26.04
Maximum78.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T14:15:50.204398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.04
5-th percentile35.639
Q143.6875
median51.55
Q359.0975
95-th percentile72.7225
Maximum78.61
Range52.57
Interquartile range (IQR)15.41

Descriptive statistics

Standard deviation11.438604
Coefficient of variation (CV)0.2194094
Kurtosis-0.4790166
Mean52.133609
Median Absolute Deviation (MAD)7.67
Skewness0.28102822
Sum11990.73
Variance130.84166
MonotonicityNot monotonic
2023-12-12T14:15:50.402743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.28 2
 
0.9%
37.27 1
 
0.4%
47.05 1
 
0.4%
46.47 1
 
0.4%
50.51 1
 
0.4%
39.84 1
 
0.4%
43.36 1
 
0.4%
47.03 1
 
0.4%
52.6 1
 
0.4%
47.36 1
 
0.4%
Other values (219) 219
95.2%
ValueCountFrequency (%)
26.04 1
0.4%
27.18 1
0.4%
27.81 1
0.4%
29.09 1
0.4%
29.59 1
0.4%
33.37 1
0.4%
33.48 1
0.4%
34.12 1
0.4%
34.65 1
0.4%
35.15 1
0.4%
ValueCountFrequency (%)
78.61 1
0.4%
78.6 1
0.4%
78.32 1
0.4%
76.88 1
0.4%
75.46 1
0.4%
75.07 1
0.4%
73.94 1
0.4%
73.71 1
0.4%
73.4 1
0.4%
73.33 1
0.4%

최대전계
Real number (ℝ)

HIGH CORRELATION 

Distinct227
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.268217
Minimum36.83
Maximum126.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T14:15:50.605974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.83
5-th percentile46.9605
Q159.275
median70.675
Q386.7275
95-th percentile95.242
Maximum126.12
Range89.29
Interquartile range (IQR)27.4525

Descriptive statistics

Standard deviation16.600377
Coefficient of variation (CV)0.22970509
Kurtosis-0.077928002
Mean72.268217
Median Absolute Deviation (MAD)13.18
Skewness0.31387488
Sum16621.69
Variance275.57253
MonotonicityNot monotonic
2023-12-12T14:15:50.835409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.42 2
 
0.9%
88.92 2
 
0.9%
74.39 2
 
0.9%
47.01 1
 
0.4%
90.22 1
 
0.4%
47.08 1
 
0.4%
58.82 1
 
0.4%
54.59 1
 
0.4%
50.71 1
 
0.4%
46.07 1
 
0.4%
Other values (217) 217
94.3%
ValueCountFrequency (%)
36.83 1
0.4%
37.89 1
0.4%
41.14 1
0.4%
42.21 1
0.4%
42.64 1
0.4%
43.0 1
0.4%
45.17 1
0.4%
45.78 1
0.4%
46.01 1
0.4%
46.07 1
0.4%
ValueCountFrequency (%)
126.12 1
0.4%
124.71 1
0.4%
117.94 1
0.4%
117.09 1
0.4%
104.22 1
0.4%
103.14 1
0.4%
101.39 1
0.4%
97.9 1
0.4%
96.46 1
0.4%
95.9 1
0.4%

최소전계
Real number (ℝ)

HIGH CORRELATION 

Distinct217
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.745217
Minimum15.81
Maximum67.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T14:15:50.987432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.81
5-th percentile21.3105
Q129.605
median36.195
Q341.9425
95-th percentile59.2755
Maximum67.21
Range51.4
Interquartile range (IQR)12.3375

Descriptive statistics

Standard deviation10.373793
Coefficient of variation (CV)0.28231681
Kurtosis0.61257418
Mean36.745217
Median Absolute Deviation (MAD)6.465
Skewness0.66300641
Sum8451.4
Variance107.61557
MonotonicityNot monotonic
2023-12-12T14:15:51.172657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.27 2
 
0.9%
35.89 2
 
0.9%
46.55 2
 
0.9%
29.14 2
 
0.9%
32.25 2
 
0.9%
35.94 2
 
0.9%
42.8 2
 
0.9%
37.53 2
 
0.9%
39.06 2
 
0.9%
36.85 2
 
0.9%
Other values (207) 210
91.3%
ValueCountFrequency (%)
15.81 1
0.4%
16.03 1
0.4%
16.81 1
0.4%
18.28 1
0.4%
18.47 1
0.4%
18.93 1
0.4%
18.96 1
0.4%
19.06 1
0.4%
19.07 1
0.4%
19.4 1
0.4%
ValueCountFrequency (%)
67.21 1
0.4%
66.05 1
0.4%
65.9 1
0.4%
65.73 1
0.4%
65.1 1
0.4%
64.02 1
0.4%
63.39 1
0.4%
61.51 1
0.4%
60.91 1
0.4%
60.3 1
0.4%

측정지점수
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1068.1783
Minimum1
Maximum12814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T14:15:51.352813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q1179
median598
Q31276
95-th percentile2557.8
Maximum12814
Range12813
Interquartile range (IQR)1097

Descriptive statistics

Standard deviation1867.7289
Coefficient of variation (CV)1.748518
Kurtosis23.011314
Mean1068.1783
Median Absolute Deviation (MAD)491
Skewness4.4737905
Sum245681
Variance3488411.3
MonotonicityNot monotonic
2023-12-12T14:15:51.539330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 5
 
2.2%
3 5
 
2.2%
140 4
 
1.7%
274 4
 
1.7%
345 4
 
1.7%
16 4
 
1.7%
540 4
 
1.7%
335 4
 
1.7%
1281 4
 
1.7%
1290 4
 
1.7%
Other values (84) 188
81.7%
ValueCountFrequency (%)
1 4
1.7%
3 5
2.2%
5 5
2.2%
8 1
 
0.4%
9 4
1.7%
11 2
 
0.9%
14 2
 
0.9%
16 4
1.7%
18 1
 
0.4%
35 2
 
0.9%
ValueCountFrequency (%)
12814 2
0.9%
11871 2
0.9%
7082 4
1.7%
3104 2
0.9%
2592 2
0.9%
2516 4
1.7%
2447 1
 
0.4%
2376 1
 
0.4%
2357 4
1.7%
2312 4
1.7%

양호지점수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct164
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean570.94783
Minimum0
Maximum8274
Zeros19
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T14:15:51.706068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.25
median182
Q3688.75
95-th percentile2056.55
Maximum8274
Range8274
Interquartile range (IQR)671.5

Descriptive statistics

Standard deviation1091.1045
Coefficient of variation (CV)1.9110407
Kurtosis23.439715
Mean570.94783
Median Absolute Deviation (MAD)178
Skewness4.2993189
Sum131318
Variance1190509.1
MonotonicityNot monotonic
2023-12-12T14:15:51.916714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
8.3%
3 6
 
2.6%
5 6
 
2.6%
1 5
 
2.2%
4 5
 
2.2%
33 3
 
1.3%
229 3
 
1.3%
13 3
 
1.3%
8 3
 
1.3%
17 2
 
0.9%
Other values (154) 175
76.1%
ValueCountFrequency (%)
0 19
8.3%
1 5
 
2.2%
2 2
 
0.9%
3 6
 
2.6%
4 5
 
2.2%
5 6
 
2.6%
6 2
 
0.9%
8 3
 
1.3%
9 2
 
0.9%
11 1
 
0.4%
ValueCountFrequency (%)
8274 1
0.4%
8081 1
0.4%
5526 1
0.4%
4939 1
0.4%
4845 1
0.4%
4681 1
0.4%
2392 1
0.4%
2290 1
0.4%
2277 1
0.4%
2144 1
0.4%

보통지점수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct136
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.90435
Minimum0
Maximum2676
Zeros29
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T14:15:52.067716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median56.5
Q3161.75
95-th percentile678.95
Maximum2676
Range2676
Interquartile range (IQR)157.75

Descriptive statistics

Standard deviation393.26613
Coefficient of variation (CV)2.248464
Kurtosis23.504078
Mean174.90435
Median Absolute Deviation (MAD)55.5
Skewness4.6122502
Sum40228
Variance154658.25
MonotonicityNot monotonic
2023-12-12T14:15:52.257138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
12.6%
1 11
 
4.8%
4 8
 
3.5%
2 8
 
3.5%
8 5
 
2.2%
3 5
 
2.2%
52 3
 
1.3%
5 3
 
1.3%
49 3
 
1.3%
126 3
 
1.3%
Other values (126) 152
66.1%
ValueCountFrequency (%)
0 29
12.6%
1 11
 
4.8%
2 8
 
3.5%
3 5
 
2.2%
4 8
 
3.5%
5 3
 
1.3%
6 1
 
0.4%
7 1
 
0.4%
8 5
 
2.2%
10 2
 
0.9%
ValueCountFrequency (%)
2676 1
0.4%
2576 1
0.4%
2440 1
0.4%
2348 1
0.4%
2127 1
0.4%
1651 1
0.4%
971 1
0.4%
904 1
0.4%
730 1
0.4%
711 1
0.4%

불량지점수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct147
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.32609
Minimum0
Maximum5299
Zeros44
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T14:15:52.435803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.25
median76.5
Q3388.75
95-th percentile1171.85
Maximum5299
Range5299
Interquartile range (IQR)386.5

Descriptive statistics

Standard deviation657.21708
Coefficient of variation (CV)2.038982
Kurtosis27.678599
Mean322.32609
Median Absolute Deviation (MAD)76.5
Skewness4.6902523
Sum74135
Variance431934.29
MonotonicityNot monotonic
2023-12-12T14:15:52.601714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44
 
19.1%
1 10
 
4.3%
7 7
 
3.0%
2 4
 
1.7%
16 3
 
1.3%
3 3
 
1.3%
8 3
 
1.3%
18 3
 
1.3%
825 3
 
1.3%
330 2
 
0.9%
Other values (137) 148
64.3%
ValueCountFrequency (%)
0 44
19.1%
1 10
 
4.3%
2 4
 
1.7%
3 3
 
1.3%
4 2
 
0.9%
5 2
 
0.9%
6 2
 
0.9%
7 7
 
3.0%
8 3
 
1.3%
9 1
 
0.4%
ValueCountFrequency (%)
5299 1
0.4%
4612 1
0.4%
4234 1
0.4%
3165 1
0.4%
2004 1
0.4%
1815 1
0.4%
1725 1
0.4%
1519 1
0.4%
1442 1
0.4%
1430 1
0.4%

수신율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct175
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.002696
Minimum0
Maximum133.33
Zeros6
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T14:15:52.766798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.0025
Q138.565
median81.395
Q398.9775
95-th percentile100
Maximum133.33
Range133.33
Interquartile range (IQR)60.4125

Descriptive statistics

Standard deviation34.741667
Coefficient of variation (CV)0.51088662
Kurtosis-0.98632331
Mean68.002696
Median Absolute Deviation (MAD)18.605
Skewness-0.68128993
Sum15640.62
Variance1206.9834
MonotonicityNot monotonic
2023-12-12T14:15:52.957515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 42
 
18.3%
0.0 6
 
2.6%
97.22 4
 
1.7%
56.25 3
 
1.3%
98.13 2
 
0.9%
81.25 2
 
0.9%
78.38 2
 
0.9%
98.18 2
 
0.9%
99.24 1
 
0.4%
99.23 1
 
0.4%
Other values (165) 165
71.7%
ValueCountFrequency (%)
0.0 6
2.6%
0.24 1
 
0.4%
0.63 1
 
0.4%
0.67 1
 
0.4%
0.71 1
 
0.4%
0.98 1
 
0.4%
1.89 1
 
0.4%
2.14 1
 
0.4%
4.01 1
 
0.4%
4.49 1
 
0.4%
ValueCountFrequency (%)
133.33 1
 
0.4%
100.19 1
 
0.4%
100.0 42
18.3%
99.92 1
 
0.4%
99.88 1
 
0.4%
99.82 1
 
0.4%
99.76 1
 
0.4%
99.7 1
 
0.4%
99.56 1
 
0.4%
99.43 1
 
0.4%

Interactions

2023-12-12T14:15:46.551613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:38.792237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:39.828799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:40.781653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:41.970681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:42.895131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.780253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:44.676743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.570055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:46.686545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:38.900086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:39.946812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:40.909004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:42.062804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.020886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.876936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:44.797153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.694894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:46.788383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:39.002464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:40.039372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:41.024700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:42.193366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.112648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.983466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:44.900320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.790449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:46.894702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:39.152548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:40.140638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:41.106608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:42.288063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.204834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:44.086339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.001544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.868356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:46.992708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:39.262341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:40.246839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:41.202527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:42.389030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.298969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:44.196780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.092618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.951465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:47.101885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:39.389244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:40.362289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:41.297833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:42.499489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.397086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:44.300722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.196646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:46.072515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:47.197610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:39.492268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:40.462398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:41.396370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:42.582294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.478877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:44.391692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.288027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:46.177889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:47.295212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:39.603146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:40.575864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:41.774667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:42.680649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.591290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:44.493395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.379637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:46.311931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:47.408390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:39.731047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:40.671086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:41.867368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:42.779818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:43.674216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:44.591400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:45.463450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:46.433067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:15:53.107514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역광역시시_군_구매체주파수(MHz)평균전계최대전계최소전계측정지점수양호지점수보통지점수불량지점수수신율(퍼센트)
권역1.0001.0000.9990.5240.8490.3710.4710.4930.4350.4350.2800.1190.255
광역시1.0001.0001.0000.5150.8360.4560.4710.5460.5540.3360.3180.0950.346
시_군_구0.9991.0001.0000.0000.7910.9020.9070.9010.9980.9140.8380.7710.897
매체0.5240.5150.0001.0000.9600.0000.2640.3700.0000.0000.0000.0000.000
주파수(MHz)0.8490.8360.7910.9601.0000.4670.4010.4550.4130.2690.2960.1710.209
평균전계0.3710.4560.9020.0000.4671.0000.7500.7790.2940.3340.0950.2920.777
최대전계0.4710.4710.9070.2640.4010.7501.0000.4940.4180.4480.0000.0000.508
최소전계0.4930.5460.9010.3700.4550.7790.4941.0000.3020.2620.0000.1310.592
측정지점수0.4350.5540.9980.0000.4130.2940.4180.3021.0000.7630.8830.7420.256
양호지점수0.4350.3360.9140.0000.2690.3340.4480.2620.7631.0000.8040.7940.302
보통지점수0.2800.3180.8380.0000.2960.0950.0000.0000.8830.8041.0000.9620.373
불량지점수0.1190.0950.7710.0000.1710.2920.0000.1310.7420.7940.9621.0000.396
수신율(퍼센트)0.2550.3460.8970.0000.2090.7770.5080.5920.2560.3020.3730.3961.000
2023-12-12T14:15:53.329600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역매체광역시
권역1.0000.3630.989
매체0.3631.0000.332
광역시0.9890.3321.000
2023-12-12T14:15:53.435867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주파수(MHz)평균전계최대전계최소전계측정지점수양호지점수보통지점수불량지점수수신율(퍼센트)권역광역시매체
주파수(MHz)1.000-0.061-0.053-0.085-0.116-0.136-0.0600.002-0.0240.6710.5930.690
평균전계-0.0611.0000.7250.7640.0430.570-0.171-0.6780.9140.2030.2120.000
최대전계-0.0530.7251.0000.3190.4760.7880.255-0.1610.5060.2680.2210.158
최소전계-0.0850.7640.3191.000-0.3170.136-0.432-0.7920.8390.2830.2670.226
측정지점수-0.1160.0430.476-0.3171.0000.7800.7770.630-0.1640.3120.3400.000
양호지점수-0.1360.5700.7880.1360.7801.0000.6060.1150.3740.1700.1770.000
보통지점수-0.060-0.1710.255-0.4320.7770.6061.0000.705-0.2980.1580.1540.000
불량지점수0.002-0.678-0.161-0.7920.6300.1150.7051.000-0.8000.0650.0420.000
수신율(퍼센트)-0.0240.9140.5060.839-0.1640.374-0.298-0.8001.0000.1280.1630.000
권역0.6710.2030.2680.2830.3120.1700.1580.0650.1281.0000.9890.363
광역시0.5930.2120.2210.2670.3400.1770.1540.0420.1630.9891.0000.332
매체0.6900.0000.1580.2260.0000.0000.0000.0000.0000.3630.3321.000

Missing values

2023-12-12T14:15:47.569854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:15:47.853024image/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

권역광역시시_군_구매체주파수(MHz)평균전계최대전계최소전계측정지점수양호지점수보통지점수불량지점수수신율(퍼센트)
0경상권경상남도창녕군KBS177.00837.2747.0131.76140031372.14
1경상권경상북도경산시KBS177.00862.9987.7245.01540470700100.19
2경상권경상북도고령군KBS177.00851.2767.8735.51102256330715285.13
3경상권경상북도구미시KBS177.00850.5370.7436.0740217012610673.63
4경상권경상북도문경시KBS177.00843.8362.4727.541914318511108543.31
5경상권경상북도봉화군KBS177.00840.2858.127.382312146351181521.5
6경상권경상북도상주시KBS177.00843.566.3426.58708211971651423440.21
7경상권경상북도영주시KBS177.00836.5837.8935.8930030.0
8경상권대구광역시달서구KBS177.00873.485.9659.0522922900100.0
9경상권대구광역시달성군KBS177.00852.6989.7532.642516126770554478.38
권역광역시시_군_구매체주파수(MHz)평균전계최대전계최소전계측정지점수양호지점수보통지점수불량지점수수신율(퍼센트)
220수도권경기도여주군YTN183.00839.3760.3722.62129017418593127.83
221수도권경기도오산시YTN183.00848.1165.3818.96274109719465.69
222수도권경기도용인시처인구YTN183.00839.2860.4726.09669238156515.55
223수도권경기도이천시YTN183.00838.51117.0924.34491336938920.77
224수도권경기도평택시YTN183.00851.5963.1938.89224147611692.86
225수도권경기도화성시YTN183.00852.2471.6116.03131584727119785.02
226수도권서울특별시구로구YTN183.00846.6746.6746.671010100.0
227수도권서울특별시서초구YTN183.00858.0773.6636.3836332197.22
228충청권충청북도음성군YTN183.00833.4842.6423.08102001020.0
229충청권충청북도진천군YTN183.00826.0441.1418.47825008250.0