Overview

Dataset statistics

Number of variables6
Number of observations44
Missing cells2
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory53.9 B

Variable types

Numeric3
Text3

Dataset

Description경상북도영천시현재운영중인약국현황에 대한 순번, 상호, 주소, 전화번호, 기타정보 등의 데이터를 제공하고자 합니다.
Author경상북도 영천시
URLhttps://www.data.go.kr/data/15044651/fileData.do

Alerts

위도 has 1 (2.3%) missing valuesMissing
경도 has 1 (2.3%) missing valuesMissing
순번 has unique valuesUnique
주소(도로명) has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 12:14:53.686977
Analysis finished2024-03-14 12:14:56.551263
Duration2.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.5
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-14T21:14:56.778442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.15
Q111.75
median22.5
Q333.25
95-th percentile41.85
Maximum44
Range43
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation12.845233
Coefficient of variation (CV)0.57089923
Kurtosis-1.2
Mean22.5
Median Absolute Deviation (MAD)11
Skewness0
Sum990
Variance165
MonotonicityStrictly increasing
2024-03-14T21:14:57.200757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 1
 
2.3%
24 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
33 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
44 1
2.3%
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%

상호
Text

Distinct43
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size480.0 B
2024-03-14T21:14:58.030560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.2727273
Min length3

Characters and Unicode

Total characters188
Distinct characters70
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

Unique42 ?
Unique (%)95.5%

Sample

1st row대구약국
2nd row정약국
3rd row혜동약국
4th row소원약국
5th row서강약국
ValueCountFrequency (%)
중앙약국 2
 
4.5%
장수약국 1
 
2.3%
예약국 1
 
2.3%
새영천약국 1
 
2.3%
계영약국 1
 
2.3%
굿모닝약국 1
 
2.3%
화평당약국 1
 
2.3%
현약국 1
 
2.3%
청통동산약국 1
 
2.3%
선린약국 1
 
2.3%
Other values (33) 33
75.0%
2024-03-14T21:14:59.005883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
약 44
23.4%
국 44
23.4%
동 6
 
3.2%
산 5
 
2.7%
중 3
 
1.6%
화 3
 
1.6%
영 3
 
1.6%
새 3
 
1.6%
천 2
 
1.1%
수 2
 
1.1%
Other values (60) 73
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 188
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
약 44
23.4%
국 44
23.4%
동 6
 
3.2%
산 5
 
2.7%
중 3
 
1.6%
화 3
 
1.6%
영 3
 
1.6%
새 3
 
1.6%
천 2
 
1.1%
수 2
 
1.1%
Other values (60) 73
38.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 188
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
약 44
23.4%
국 44
23.4%
동 6
 
3.2%
산 5
 
2.7%
중 3
 
1.6%
화 3
 
1.6%
영 3
 
1.6%
새 3
 
1.6%
천 2
 
1.1%
수 2
 
1.1%
Other values (60) 73
38.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 188
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
약 44
23.4%
국 44
23.4%
동 6
 
3.2%
산 5
 
2.7%
중 3
 
1.6%
화 3
 
1.6%
영 3
 
1.6%
새 3
 
1.6%
천 2
 
1.1%
수 2
 
1.1%
Other values (60) 73
38.8%

주소(도로명)
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size480.0 B
2024-03-14T21:14:59.877203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length23
Mean length20.181818
Min length13

Characters and Unicode

Total characters888
Distinct characters70
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row경상북도 영천시 대창면 금창로 692-1
2nd row경상북도 영천시 오수1길 5 (오수동)
3rd row경북 영천시 호국로 73
4th row경상북도 영천시 호국로 93 (야사동)
5th row경상북도 영천시 금호읍 금호로 129
ValueCountFrequency (%)
영천시 44
21.1%
경상북도 31
 
14.8%
경북 9
 
4.3%
완산동 9
 
4.3%
시장로 8
 
3.8%
완산로 5
 
2.4%
금호읍 5
 
2.4%
금노동 4
 
1.9%
금호로 4
 
1.9%
강변로 4
 
1.9%
Other values (73) 86
41.1%
2024-03-14T21:15:01.187004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
165
18.6%
시 53
 
6.0%
영 44
 
5.0%
천 44
 
5.0%
북 41
 
4.6%
경 40
 
4.5%
1 34
 
3.8%
로 33
 
3.7%
상 32
 
3.6%
도 31
 
3.5%
Other values (60) 371
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 532
59.9%
Space Separator 165
 
18.6%
Decimal Number 123
 
13.9%
Close Punctuation 27
 
3.0%
Open Punctuation 27
 
3.0%
Dash Punctuation 13
 
1.5%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 53
 
10.0%
영 44
 
8.3%
천 44
 
8.3%
북 41
 
7.7%
경 40
 
7.5%
로 33
 
6.2%
상 32
 
6.0%
도 31
 
5.8%
동 28
 
5.3%
완 18
 
3.4%
Other values (45) 168
31.6%
Decimal Number
ValueCountFrequency (%)
1 34
27.6%
5 14
11.4%
3 13
 
10.6%
7 12
 
9.8%
8 10
 
8.1%
4 10
 
8.1%
2 9
 
7.3%
0 9
 
7.3%
6 7
 
5.7%
9 5
 
4.1%
Space Separator
ValueCountFrequency (%)
165
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 532
59.9%
Common 356
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 53
 
10.0%
영 44
 
8.3%
천 44
 
8.3%
북 41
 
7.7%
경 40
 
7.5%
로 33
 
6.2%
상 32
 
6.0%
도 31
 
5.8%
동 28
 
5.3%
완 18
 
3.4%
Other values (45) 168
31.6%
Common
ValueCountFrequency (%)
165
46.3%
1 34
 
9.6%
) 27
 
7.6%
( 27
 
7.6%
5 14
 
3.9%
3 13
 
3.7%
- 13
 
3.7%
7 12
 
3.4%
8 10
 
2.8%
4 10
 
2.8%
Other values (5) 31
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 532
59.9%
ASCII 356
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
165
46.3%
1 34
 
9.6%
) 27
 
7.6%
( 27
 
7.6%
5 14
 
3.9%
3 13
 
3.7%
- 13
 
3.7%
7 12
 
3.4%
8 10
 
2.8%
4 10
 
2.8%
Other values (5) 31
 
8.7%
Hangul
ValueCountFrequency (%)
시 53
 
10.0%
영 44
 
8.3%
천 44
 
8.3%
북 41
 
7.7%
경 40
 
7.5%
로 33
 
6.2%
상 32
 
6.0%
도 31
 
5.8%
동 28
 
5.3%
완 18
 
3.4%
Other values (45) 168
31.6%

전화번호
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size480.0 B
2024-03-14T21:15:02.074785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters528
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row054-335-4035
2nd row054-334-0793
3rd row054-333-9032
4th row054-332-7003
5th row054-336-3737
ValueCountFrequency (%)
054-335-4035 1
 
2.3%
054-334-0793 1
 
2.3%
054-334-2111 1
 
2.3%
054-333-0030 1
 
2.3%
054-333-5787 1
 
2.3%
054-336-3949 1
 
2.3%
054-334-2155 1
 
2.3%
054-334-2575 1
 
2.3%
054-338-8131 1
 
2.3%
054-336-2091 1
 
2.3%
Other values (34) 34
77.3%
2024-03-14T21:15:03.087279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 118
22.3%
- 88
16.7%
5 67
12.7%
0 64
12.1%
4 63
11.9%
2 29
 
5.5%
7 26
 
4.9%
1 24
 
4.5%
6 17
 
3.2%
9 16
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 440
83.3%
Dash Punctuation 88
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 118
26.8%
5 67
15.2%
0 64
14.5%
4 63
14.3%
2 29
 
6.6%
7 26
 
5.9%
1 24
 
5.5%
6 17
 
3.9%
9 16
 
3.6%
8 16
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 528
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 118
22.3%
- 88
16.7%
5 67
12.7%
0 64
12.1%
4 63
11.9%
2 29
 
5.5%
7 26
 
4.9%
1 24
 
4.5%
6 17
 
3.2%
9 16
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 118
22.3%
- 88
16.7%
5 67
12.7%
0 64
12.1%
4 63
11.9%
2 29
 
5.5%
7 26
 
4.9%
1 24
 
4.5%
6 17
 
3.2%
9 16
 
3.0%

위도
Real number (ℝ)

MISSING 

Distinct43
Distinct (%)100.0%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean35.96527
Minimum35.874812
Maximum36.045097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-14T21:15:03.327534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.874812
5-th percentile35.930569
Q135.960541
median35.963703
Q335.971874
95-th percentile36.036335
Maximum36.045097
Range0.170285
Interquartile range (IQR)0.0113335

Descriptive statistics

Standard deviation0.029578767
Coefficient of variation (CV)0.00082242584
Kurtosis3.5278831
Mean35.96527
Median Absolute Deviation (MAD)0.005362
Skewness0.36671632
Sum1546.5066
Variance0.00087490347
MonotonicityNot monotonic
2024-03-14T21:15:03.579574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35.874812 1
 
2.3%
35.958341 1
 
2.3%
35.963657 1
 
2.3%
36.041695 1
 
2.3%
35.96804 1
 
2.3%
35.968116 1
 
2.3%
35.96314 1
 
2.3%
35.975224 1
 
2.3%
35.994505 1
 
2.3%
35.965259 1
 
2.3%
Other values (33) 33
75.0%
ValueCountFrequency (%)
35.874812 1
2.3%
35.915813 1
2.3%
35.930557 1
2.3%
35.930681 1
2.3%
35.931563 1
2.3%
35.932094 1
2.3%
35.932392 1
2.3%
35.957938 1
2.3%
35.958016 1
2.3%
35.958341 1
2.3%
ValueCountFrequency (%)
36.045097 1
2.3%
36.041695 1
2.3%
36.040983 1
2.3%
35.994505 1
2.3%
35.988341 1
2.3%
35.982584 1
2.3%
35.976329 1
2.3%
35.975224 1
2.3%
35.975036 1
2.3%
35.974214 1
2.3%

경도
Real number (ℝ)

MISSING 

Distinct42
Distinct (%)97.7%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean128.91689
Minimum128.7879
Maximum129.01051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-14T21:15:03.927135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.7879
5-th percentile128.80174
Q1128.91291
median128.9361
Q3128.93925
95-th percentile128.95123
Maximum129.01051
Range0.222612
Interquartile range (IQR)0.026344

Descriptive statistics

Standard deviation0.04645474
Coefficient of variation (CV)0.00036034643
Kurtosis2.2969491
Mean128.91689
Median Absolute Deviation (MAD)0.008643
Skewness-1.5039003
Sum5543.4262
Variance0.0021580429
MonotonicityNot monotonic
2024-03-14T21:15:04.167952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
128.9361 2
 
4.5%
128.89717 1
 
2.3%
128.9279 1
 
2.3%
128.7879 1
 
2.3%
128.932826 1
 
2.3%
128.932383 1
 
2.3%
128.937081 1
 
2.3%
128.947724 1
 
2.3%
128.820112 1
 
2.3%
128.939212 1
 
2.3%
Other values (32) 32
72.7%
ValueCountFrequency (%)
128.7879 1
2.3%
128.788056 1
2.3%
128.799694 1
2.3%
128.820112 1
2.3%
128.870316 1
2.3%
128.870561 1
2.3%
128.8719 1
2.3%
128.873264 1
2.3%
128.875741 1
2.3%
128.89717 1
2.3%
ValueCountFrequency (%)
129.010512 1
2.3%
128.955233 1
2.3%
128.95156 1
2.3%
128.948301 1
2.3%
128.947724 1
2.3%
128.947478 1
2.3%
128.945538 1
2.3%
128.944743 1
2.3%
128.939534 1
2.3%
128.93931 1
2.3%

Interactions

2024-03-14T21:14:55.220260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:14:54.003474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:14:54.666119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:14:55.453905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:14:54.230805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:14:54.809196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:14:55.746215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:14:54.496116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:14:54.974407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:15:04.327005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번상호주소(도로명)전화번호위도경도
순번1.0000.9331.0001.0000.0000.000
상호0.9331.0001.0001.0000.9630.963
주소(도로명)1.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000
위도0.0000.9631.0001.0001.0000.998
경도0.0000.9631.0001.0000.9981.000
2024-03-14T21:15:04.705704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번위도경도
순번1.0000.0990.036
위도0.0991.0000.318
경도0.0360.3181.000

Missing values

2024-03-14T21:14:55.926941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:14:56.135290image/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.
2024-03-14T21:14:56.420688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

순번상호주소(도로명)전화번호위도경도
01대구약국경상북도 영천시 대창면 금창로 692-1054-335-403535.874812128.89717
12정약국경상북도 영천시 오수1길 5 (오수동)054-334-079335.958341128.913024
23혜동약국경북 영천시 호국로 73054-333-903235.974214128.945538
34소원약국경상북도 영천시 호국로 93 (야사동)054-332-700335.975036128.947478
45서강약국경상북도 영천시 금호읍 금호로 129054-336-373735.932094128.875741
56대학당약국경상북도 영천시 시장로 57-1 (완산동)054-333-754135.963703128.936544
67장성약국경상북도 영천시 완산로 64 (완산동)054-331-496935.965863128.939286
78온누리유명약국경상북도 영천시 시장로 74-1 (완산동)054-334-496335.963328128.938402
89하나약국경상북도 영천시 강변로 38 (금노동)054-332-163935.96076128.926732
910휘명동산약국경상북도 영천시 신녕면 신화로 114-8054-335-021736.045097128.799694
순번상호주소(도로명)전화번호위도경도
3435중원약국경북 영천시 완산로 52054-333-633135.964827128.939207
3536신세계약국경북 영천시 시장로 70-1(완산동)054-333-207235.963287128.937925
3637라온약국경북 영천시 망정1길 200054-334-772235.982584128.95156
3738중앙약국경북 영천시 금호읍 금호로 105-1054-336-556635.931563128.873264
3839제일약국경상북도 영천시 시장로 47(완산동)054-334-251335.963577128.935401
3940삼화약국경상북도 영천시 오수1길 7 (오수동)054-331-827535.958016128.913097
4041소망약국영천시 신녕면 장수로 1673054-336-227136.040983128.788056
4142금호동산약국영천시 금호읍 교대길 14054-337-508835.932392128.8719
4243진약국영천시 망정로 7(망정동)054-332-4142<NA><NA>
4344수덕약국영천시 완산로 68, 1층054-338-877335.96638128.93931