Overview

Dataset statistics

Number of variables6
Number of observations277
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.9 KiB
Average record size in memory51.5 B

Variable types

Numeric3
Categorical1
Text2

Dataset

Description경상북도 영천시 가로등 분전함 위치 입니다.정확한 가로등의 위치는 제공이 되지 않지만 분전함의 위치를 참고하시면 도움이 될꺼 같습니다.행정동, 주소, 분전함 이름, 위도, 경도 등 제공
Author경상북도 영천시
URLhttps://www.data.go.kr/data/15127533/fileData.do

Alerts

분전함 ID is highly overall correlated with 행정동High correlation
위도 is highly overall correlated with 행정동High correlation
경도 is highly overall correlated with 행정동High correlation
행정동 is highly overall correlated with 분전함 ID and 2 other fieldsHigh correlation
분전함 ID has unique valuesUnique
분전함 이름 has unique valuesUnique

Reproduction

Analysis started2024-04-13 11:45:09.411320
Analysis finished2024-04-13 11:45:14.780491
Duration5.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분전함 ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct277
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5017.7365
Minimum1
Maximum10175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-13T20:45:15.012994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.8
Q170
median139
Q310067
95-th percentile10142.4
Maximum10175
Range10174
Interquartile range (IQR)9997

Descriptive statistics

Standard deviation5010.3554
Coefficient of variation (CV)0.99852901
Kurtosis-2.0138037
Mean5017.7365
Median Absolute Deviation (MAD)137
Skewness0.02181431
Sum1389913
Variance25103662
MonotonicityStrictly increasing
2024-04-13T20:45:15.459716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
10043 1
 
0.4%
10049 1
 
0.4%
10048 1
 
0.4%
10047 1
 
0.4%
10046 1
 
0.4%
10045 1
 
0.4%
10044 1
 
0.4%
10042 1
 
0.4%
10034 1
 
0.4%
Other values (267) 267
96.4%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
10175 1
0.4%
10173 1
0.4%
10172 1
0.4%
10168 1
0.4%
10163 1
0.4%
10162 1
0.4%
10160 1
0.4%
10156 1
0.4%
10155 1
0.4%
10154 1
0.4%

행정동
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
금호읍
33 
완산동
26 
망정동
 
17
고경면
 
13
신녕면
 
13
Other values (29)
175 

Length

Max length3
Median length3
Mean length2.9711191
Min length2

Unique

Unique3 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
금호읍 33
 
11.9%
완산동 26
 
9.4%
망정동 17
 
6.1%
고경면 13
 
4.7%
신녕면 13
 
4.7%
성내동 13
 
4.7%
야사동 12
 
4.3%
화산면 10
 
3.6%
문외동 10
 
3.6%
조교동 10
 
3.6%
Other values (24) 120
43.3%

Length

2024-04-13T20:45:15.876148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금호읍 33
 
11.9%
완산동 26
 
9.4%
망정동 17
 
6.1%
고경면 13
 
4.7%
신녕면 13
 
4.7%
성내동 13
 
4.7%
야사동 12
 
4.3%
화산면 10
 
3.6%
문외동 10
 
3.6%
조교동 10
 
3.6%
Other values (24) 120
43.3%

주소
Text

Distinct267
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-13T20:45:17.267318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length19.411552
Min length15

Characters and Unicode

Total characters5377
Distinct characters101
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique258 ?
Unique (%)93.1%

Sample

1st row경상북도 영천시 화산면 삼부리 120-1
2nd row경상북도 영천시 청통면 호당리 931-1
3rd row경상북도 영천시 화산면 당곡리 62
4th row경상북도 영천시 화산면 당곡리 442-1
5th row경상북도 영천시 화산면 덕암리 357-4
ValueCountFrequency (%)
경상북도 277
22.4%
영천시 277
22.4%
금호읍 34
 
2.7%
완산동 27
 
2.2%
산 22
 
1.8%
망정동 18
 
1.5%
고경면 13
 
1.1%
신녕면 13
 
1.1%
성내동 13
 
1.1%
야사동 11
 
0.9%
Other values (338) 532
43.0%
2024-04-13T20:45:19.146215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
960
17.9%
경 290
 
5.4%
북 286
 
5.3%
도 286
 
5.3%
천 284
 
5.3%
상 279
 
5.2%
영 277
 
5.2%
시 277
 
5.2%
- 226
 
4.2%
1 203
 
3.8%
Other values (91) 2009
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3103
57.7%
Decimal Number 1088
 
20.2%
Space Separator 960
 
17.9%
Dash Punctuation 226
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 290
 
9.3%
북 286
 
9.2%
도 286
 
9.2%
천 284
 
9.2%
상 279
 
9.0%
영 277
 
8.9%
시 277
 
8.9%
동 170
 
5.5%
리 108
 
3.5%
면 73
 
2.4%
Other values (79) 773
24.9%
Decimal Number
ValueCountFrequency (%)
1 203
18.7%
2 148
13.6%
3 131
12.0%
4 118
10.8%
6 98
9.0%
5 94
8.6%
9 82
7.5%
0 76
 
7.0%
7 70
 
6.4%
8 68
 
6.2%
Space Separator
ValueCountFrequency (%)
960
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 226
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3103
57.7%
Common 2274
42.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 290
 
9.3%
북 286
 
9.2%
도 286
 
9.2%
천 284
 
9.2%
상 279
 
9.0%
영 277
 
8.9%
시 277
 
8.9%
동 170
 
5.5%
리 108
 
3.5%
면 73
 
2.4%
Other values (79) 773
24.9%
Common
ValueCountFrequency (%)
960
42.2%
- 226
 
9.9%
1 203
 
8.9%
2 148
 
6.5%
3 131
 
5.8%
4 118
 
5.2%
6 98
 
4.3%
5 94
 
4.1%
9 82
 
3.6%
0 76
 
3.3%
Other values (2) 138
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3103
57.7%
ASCII 2274
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
960
42.2%
- 226
 
9.9%
1 203
 
8.9%
2 148
 
6.5%
3 131
 
5.8%
4 118
 
5.2%
6 98
 
4.3%
5 94
 
4.1%
9 82
 
3.6%
0 76
 
3.3%
Other values (2) 138
 
6.1%
Hangul
ValueCountFrequency (%)
경 290
 
9.3%
북 286
 
9.2%
도 286
 
9.2%
천 284
 
9.2%
상 279
 
9.0%
영 277
 
8.9%
시 277
 
8.9%
동 170
 
5.5%
리 108
 
3.5%
면 73
 
2.4%
Other values (79) 773
24.9%

분전함 이름
Text

UNIQUE 

Distinct277
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-13T20:45:20.052325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.234657
Min length3

Characters and Unicode

Total characters1727
Distinct characters98
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277 ?
Unique (%)100.0%

Sample

1st row삼부교차로1
2nd row삼부교차로2
3rd row당곡교차로1
4th row당곡교차로2
5th row효정교차로1
ValueCountFrequency (%)
삼부교차로1 1
 
0.4%
청통면-100 1
 
0.4%
신녕면-105 1
 
0.4%
신녕면-104 1
 
0.4%
신녕면-103 1
 
0.4%
화남면-104 1
 
0.4%
화남면-103 1
 
0.4%
화남면-102 1
 
0.4%
화남면-100 1
 
0.4%
고경면-100 1
 
0.4%
Other values (267) 267
96.4%
2024-04-13T20:45:21.361403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 257
 
14.9%
1 215
 
12.4%
0 176
 
10.2%
동 158
 
9.1%
면 62
 
3.6%
2 58
 
3.4%
금 41
 
2.4%
3 41
 
2.4%
산 35
 
2.0%
호 32
 
1.9%
Other values (88) 652
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 871
50.4%
Decimal Number 588
34.0%
Dash Punctuation 257
 
14.9%
Close Punctuation 5
 
0.3%
Open Punctuation 5
 
0.3%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 158
 
18.1%
면 62
 
7.1%
금 41
 
4.7%
산 35
 
4.0%
호 32
 
3.7%
읍 32
 
3.7%
교 30
 
3.4%
완 26
 
3.0%
정 20
 
2.3%
고 19
 
2.2%
Other values (74) 416
47.8%
Decimal Number
ValueCountFrequency (%)
1 215
36.6%
0 176
29.9%
2 58
 
9.9%
3 41
 
7.0%
4 28
 
4.8%
5 23
 
3.9%
6 16
 
2.7%
7 13
 
2.2%
8 10
 
1.7%
9 8
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 257
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
x 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 871
50.4%
Common 855
49.5%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 158
 
18.1%
면 62
 
7.1%
금 41
 
4.7%
산 35
 
4.0%
호 32
 
3.7%
읍 32
 
3.7%
교 30
 
3.4%
완 26
 
3.0%
정 20
 
2.3%
고 19
 
2.2%
Other values (74) 416
47.8%
Common
ValueCountFrequency (%)
- 257
30.1%
1 215
25.1%
0 176
20.6%
2 58
 
6.8%
3 41
 
4.8%
4 28
 
3.3%
5 23
 
2.7%
6 16
 
1.9%
7 13
 
1.5%
8 10
 
1.2%
Other values (3) 18
 
2.1%
Latin
ValueCountFrequency (%)
x 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 871
50.4%
ASCII 856
49.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 257
30.0%
1 215
25.1%
0 176
20.6%
2 58
 
6.8%
3 41
 
4.8%
4 28
 
3.3%
5 23
 
2.7%
6 16
 
1.9%
7 13
 
1.5%
8 10
 
1.2%
Other values (4) 19
 
2.2%
Hangul
ValueCountFrequency (%)
동 158
 
18.1%
면 62
 
7.1%
금 41
 
4.7%
산 35
 
4.0%
호 32
 
3.7%
읍 32
 
3.7%
교 30
 
3.4%
완 26
 
3.0%
정 20
 
2.3%
고 19
 
2.2%
Other values (74) 416
47.8%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct267
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.977342
Minimum35.8724
Maximum36.138342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-13T20:45:21.607696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.8724
5-th percentile35.926976
Q135.959211
median35.970576
Q335.989927
95-th percentile36.06894
Maximum36.138342
Range0.2659421
Interquartile range (IQR)0.03071549

Descriptive statistics

Standard deviation0.040910564
Coefficient of variation (CV)0.0011371203
Kurtosis1.9107427
Mean35.977342
Median Absolute Deviation (MAD)0.01548414
Skewness1.1057516
Sum9965.7236
Variance0.0016736743
MonotonicityNot monotonic
2024-04-13T20:45:21.875376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07023456 3
 
1.1%
35.96039612 2
 
0.7%
35.97279122 2
 
0.7%
35.96373893 2
 
0.7%
35.96435731 2
 
0.7%
35.92959345 2
 
0.7%
35.90721252 2
 
0.7%
35.9638715 2
 
0.7%
35.9188162 2
 
0.7%
35.97827872 1
 
0.4%
Other values (257) 257
92.8%
ValueCountFrequency (%)
35.87240004 1
0.4%
35.900482 1
0.4%
35.90242484 1
0.4%
35.90721252 2
0.7%
35.91015483 1
0.4%
35.9102372 1
0.4%
35.91283415 1
0.4%
35.9188162 2
0.7%
35.91971363 1
0.4%
35.92048284 1
0.4%
ValueCountFrequency (%)
36.13834214 1
0.4%
36.13208942 1
0.4%
36.10707229 1
0.4%
36.10703187 1
0.4%
36.08348966 1
0.4%
36.07891539 1
0.4%
36.07814013 1
0.4%
36.07728749 1
0.4%
36.076073 1
0.4%
36.07572582 1
0.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct267
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.92724
Minimum128.75883
Maximum129.10667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-13T20:45:22.294387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.75883
5-th percentile128.80533
Q1128.91443
median128.9331
Q3128.9511
95-th percentile129.00996
Maximum129.10667
Range0.3478398
Interquartile range (IQR)0.0366621

Descriptive statistics

Standard deviation0.054137289
Coefficient of variation (CV)0.00041990575
Kurtosis1.5993599
Mean128.92724
Median Absolute Deviation (MAD)0.0180551
Skewness-0.50749038
Sum35712.845
Variance0.002930846
MonotonicityNot monotonic
2024-04-13T20:45:22.614592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0049878 3
 
1.1%
128.9220773 2
 
0.7%
128.9517372 2
 
0.7%
128.9430475 2
 
0.7%
128.9191412 2
 
0.7%
128.950487 2
 
0.7%
128.9473053 2
 
0.7%
128.9237143 2
 
0.7%
129.0086837 2
 
0.7%
128.9631799 1
 
0.4%
Other values (257) 257
92.8%
ValueCountFrequency (%)
128.7588333 1
0.4%
128.7595139 1
0.4%
128.7867881 1
0.4%
128.7873225 1
0.4%
128.788074 1
0.4%
128.7901953 1
0.4%
128.7908944 1
0.4%
128.7920275 1
0.4%
128.793273 1
0.4%
128.7956841 1
0.4%
ValueCountFrequency (%)
129.1066731 1
0.4%
129.0751282 1
0.4%
129.0683897 1
0.4%
129.0530756 1
0.4%
129.0465795 1
0.4%
129.0442287 1
0.4%
129.0424552 1
0.4%
129.0346563 1
0.4%
129.02981 1
0.4%
129.0166853 1
0.4%

Interactions

2024-04-13T20:45:13.417113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:45:11.889633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:45:12.658176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:45:13.668505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:45:12.143746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:45:12.906974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:45:13.925344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:45:12.394777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:45:13.153082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T20:45:22.894441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분전함 ID행정동위도경도
분전함 ID1.0000.6660.4970.511
행정동0.6661.0000.9180.931
위도0.4970.9181.0000.847
경도0.5110.9310.8471.000
2024-04-13T20:45:23.146388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분전함 ID위도경도행정동
분전함 ID1.0000.014-0.0380.506
위도0.0141.0000.1130.610
경도-0.0380.1131.0000.646
행정동0.5060.6100.6461.000

Missing values

2024-04-13T20:45:14.280777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T20:45:14.631562image/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

분전함 ID행정동주소분전함 이름위도경도
01화산면경상북도 영천시 화산면 삼부리 120-1삼부교차로136.005162128.885129
12화산면경상북도 영천시 청통면 호당리 931-1삼부교차로236.004479128.884878
23화산면경상북도 영천시 화산면 당곡리 62당곡교차로136.019584128.871445
34화산면경상북도 영천시 화산면 당곡리 442-1당곡교차로236.019537128.870535
45화산면경상북도 영천시 화산면 덕암리 357-4효정교차로136.037279128.829117
56화산면경상북도 영천시 화산면 덕암리 331-2효정교차로236.038011128.830157
67신녕면경상북도 영천시 신녕면 화성리 126-23연정교차로136.045745128.796978
78신녕면경상북도 영천시 신녕면 화성리 131-3연정교차로236.04624128.795684
89신녕면경상북도 영천시 신녕면 화남리 689-1갑현교차로136.076073128.759514
910신녕면경상북도 영천시 신녕면 화남리 686-2갑현교차로236.075726128.758833
분전함 ID행정동주소분전함 이름위도경도
26710154언하동경상북도 영천시 언하동 740-13언하동-10235.983298128.965481
26810155언하동경상북도 영천시 언하동 394-2언하동-10335.985938128.963406
26910156언하동경상북도 영천시 언하동 284-18언하동-10435.978279128.96318
27010160문외동경상북도 영천시 문외동 47-8문외동-10135.971075128.9385
27110162문외동경상북도 영천시 문외동 184-6문외동-10235.970837128.936679
27210163문외동경상북도 영천시 문외동 154-37문외동-10335.971338128.936009
27310168완산동경상북도 영천시 완산동 1464완산동-10335.966761128.94264
27410172완산동경상북도 영천시 완산동 1460완산동-10735.967445128.94165
27510173완산동경상북도 영천시 완산동 920-2완산동-10135.961419128.940115
27610175망정동경상북도 영천시 망정동 378-2망정동-10935.982811128.953133