Overview

Dataset statistics

Number of variables6
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory53.4 B

Variable types

Text3
Categorical1
Numeric2

Dataset

Description전북특별자치도 김제시 보건지소 및 진료소 현황입니다.보건지소 및 진료소명, 주소, 전화번호 등을 포함하고 있습니다.
Author전북특별자치도 김제시
URLhttps://www.data.go.kr/data/3039624/fileData.do

Alerts

주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2024-03-23 06:39:53.269707
Analysis finished2024-03-23 06:39:57.819401
Duration4.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

명칭
Text

Distinct38
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-03-23T06:39:58.286994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters78
Distinct characters50
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

Unique37 ?
Unique (%)94.9%

Sample

1st row만경
2nd row죽산
3rd row용지
4th row백구
5th row부량
ValueCountFrequency (%)
성덕 2
 
5.1%
심포 1
 
2.6%
백학 1
 
2.6%
회성 1
 
2.6%
부용 1
 
2.6%
신두 1
 
2.6%
회룡 1
 
2.6%
관상 1
 
2.6%
성동 1
 
2.6%
불로 1
 
2.6%
Other values (28) 28
71.8%
2024-03-23T06:39:59.652662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 5
 
6.4%
성 4
 
5.1%
동 4
 
5.1%
부 4
 
5.1%
덕 3
 
3.8%
용 3
 
3.8%
봉 2
 
2.6%
상 2
 
2.6%
창 2
 
2.6%
회 2
 
2.6%
Other values (40) 47
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 5
 
6.4%
성 4
 
5.1%
동 4
 
5.1%
부 4
 
5.1%
덕 3
 
3.8%
용 3
 
3.8%
봉 2
 
2.6%
상 2
 
2.6%
창 2
 
2.6%
회 2
 
2.6%
Other values (40) 47
60.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 5
 
6.4%
성 4
 
5.1%
동 4
 
5.1%
부 4
 
5.1%
덕 3
 
3.8%
용 3
 
3.8%
봉 2
 
2.6%
상 2
 
2.6%
창 2
 
2.6%
회 2
 
2.6%
Other values (40) 47
60.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 5
 
6.4%
성 4
 
5.1%
동 4
 
5.1%
부 4
 
5.1%
덕 3
 
3.8%
용 3
 
3.8%
봉 2
 
2.6%
상 2
 
2.6%
창 2
 
2.6%
회 2
 
2.6%
Other values (40) 47
60.3%

구분
Categorical

Distinct2
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size444.0 B
보건진료소
25 
보건지소
14 

Length

Max length5
Median length5
Mean length4.6410256
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
보건진료소 25
64.1%
보건지소 14
35.9%

Length

2024-03-23T06:40:00.362998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T06:40:00.749139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보건진료소 25
64.1%
보건지소 14
35.9%

주소
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-03-23T06:40:01.532636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length40
Mean length37.615385
Min length33

Characters and Unicode

Total characters1467
Distinct characters106
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

Unique39 ?
Unique (%)100.0%

Sample

1st row전북특별자치도 김제시 만경읍 만경3길 20 (만경리, 만경보건지소)
2nd row전북특별자치도 김제시 죽산면 해학로 35 (죽산리, 죽산보건지소)
3rd row전북특별자치도 김제시 용지면 용지로 475 (구암리, 용지보건지소)
4th row전북특별자치도 김제시 백구면 황토로 1267-21 (반월리, 백구보건지소)
5th row전북특별자치도 김제시 부량면 벽골제로 208 (대평리, 부량보건지소)
ValueCountFrequency (%)
전북특별자치도 39
 
14.4%
김제시 39
 
14.4%
용지면 3
 
1.1%
광활면 3
 
1.1%
금산면 3
 
1.1%
성덕면 3
 
1.1%
백산면 3
 
1.1%
만경읍 3
 
1.1%
백구면 2
 
0.7%
청하면 2
 
0.7%
Other values (157) 170
63.0%
2024-03-23T06:40:03.002748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
231
 
15.7%
제 43
 
2.9%
전 40
 
2.7%
시 40
 
2.7%
, 39
 
2.7%
( 39
 
2.7%
소 39
 
2.7%
) 39
 
2.7%
북 39
 
2.7%
건 39
 
2.7%
Other values (96) 879
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 994
67.8%
Space Separator 231
 
15.7%
Decimal Number 119
 
8.1%
Other Punctuation 39
 
2.7%
Open Punctuation 39
 
2.7%
Close Punctuation 39
 
2.7%
Dash Punctuation 6
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 43
 
4.3%
전 40
 
4.0%
시 40
 
4.0%
소 39
 
3.9%
북 39
 
3.9%
건 39
 
3.9%
보 39
 
3.9%
김 39
 
3.9%
도 39
 
3.9%
치 39
 
3.9%
Other values (81) 598
60.2%
Decimal Number
ValueCountFrequency (%)
1 24
20.2%
3 18
15.1%
4 17
14.3%
2 14
11.8%
6 11
9.2%
8 10
8.4%
5 8
 
6.7%
9 6
 
5.0%
0 6
 
5.0%
7 5
 
4.2%
Space Separator
ValueCountFrequency (%)
231
100.0%
Other Punctuation
ValueCountFrequency (%)
, 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 994
67.8%
Common 473
32.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 43
 
4.3%
전 40
 
4.0%
시 40
 
4.0%
소 39
 
3.9%
북 39
 
3.9%
건 39
 
3.9%
보 39
 
3.9%
김 39
 
3.9%
도 39
 
3.9%
치 39
 
3.9%
Other values (81) 598
60.2%
Common
ValueCountFrequency (%)
231
48.8%
, 39
 
8.2%
( 39
 
8.2%
) 39
 
8.2%
1 24
 
5.1%
3 18
 
3.8%
4 17
 
3.6%
2 14
 
3.0%
6 11
 
2.3%
8 10
 
2.1%
Other values (5) 31
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 994
67.8%
ASCII 473
32.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
231
48.8%
, 39
 
8.2%
( 39
 
8.2%
) 39
 
8.2%
1 24
 
5.1%
3 18
 
3.8%
4 17
 
3.6%
2 14
 
3.0%
6 11
 
2.3%
8 10
 
2.1%
Other values (5) 31
 
6.6%
Hangul
ValueCountFrequency (%)
제 43
 
4.3%
전 40
 
4.0%
시 40
 
4.0%
소 39
 
3.9%
북 39
 
3.9%
건 39
 
3.9%
보 39
 
3.9%
김 39
 
3.9%
도 39
 
3.9%
치 39
 
3.9%
Other values (81) 598
60.2%

위도
Real number (ℝ)

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.816103
Minimum35.686847
Maximum35.904029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-03-23T06:40:03.583102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.686847
5-th percentile35.71837
Q135.779256
median35.829504
Q335.854576
95-th percentile35.890598
Maximum35.904029
Range0.217182
Interquartile range (IQR)0.07532

Descriptive statistics

Standard deviation0.056461519
Coefficient of variation (CV)0.0015764283
Kurtosis-0.59536225
Mean35.816103
Median Absolute Deviation (MAD)0.034784
Skewness-0.50830359
Sum1396.828
Variance0.0031879031
MonotonicityNot monotonic
2024-03-23T06:40:04.151683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
35.851053 1
 
2.6%
35.7719 1
 
2.6%
35.877502 1
 
2.6%
35.731963 1
 
2.6%
35.889668 1
 
2.6%
35.881582 1
 
2.6%
35.818945 1
 
2.6%
35.818939 1
 
2.6%
35.851381 1
 
2.6%
35.811372 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
35.686847 1
2.6%
35.716639 1
2.6%
35.718562 1
2.6%
35.730735 1
2.6%
35.731963 1
2.6%
35.733515 1
2.6%
35.753189 1
2.6%
35.759312 1
2.6%
35.7719 1
2.6%
35.77555 1
2.6%
ValueCountFrequency (%)
35.904029 1
2.6%
35.89216 1
2.6%
35.890424 1
2.6%
35.889668 1
2.6%
35.882062 1
2.6%
35.881582 1
2.6%
35.877502 1
2.6%
35.863143 1
2.6%
35.859201 1
2.6%
35.854785 1
2.6%

경도
Real number (ℝ)

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.88477
Minimum126.70198
Maximum127.04167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-03-23T06:40:04.654307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.70198
5-th percentile126.73967
Q1126.8149
median126.87895
Q3126.96003
95-th percentile127.01557
Maximum127.04167
Range0.33969
Interquartile range (IQR)0.1451325

Descriptive statistics

Standard deviation0.090049255
Coefficient of variation (CV)0.00070969319
Kurtosis-0.85210685
Mean126.88477
Median Absolute Deviation (MAD)0.072924
Skewness-0.1312805
Sum4948.5059
Variance0.0081088682
MonotonicityNot monotonic
2024-03-23T06:40:05.374440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
126.814978 1
 
2.6%
126.814821 1
 
2.6%
126.951869 1
 
2.6%
126.866879 1
 
2.6%
126.878945 1
 
2.6%
126.8477 1
 
2.6%
126.79856 1
 
2.6%
126.829646 1
 
2.6%
126.701977 1
 
2.6%
126.97953 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
126.701977 1
2.6%
126.718053 1
2.6%
126.742073 1
2.6%
126.766095 1
2.6%
126.766629 1
2.6%
126.790319 1
2.6%
126.79856 1
2.6%
126.801033 1
2.6%
126.812341 1
2.6%
126.814821 1
2.6%
ValueCountFrequency (%)
127.041667 1
2.6%
127.03865 1
2.6%
127.013006 1
2.6%
127.003853 1
2.6%
126.995876 1
2.6%
126.97953 1
2.6%
126.971735 1
2.6%
126.970645 1
2.6%
126.967628 1
2.6%
126.962443 1
2.6%

전화번호
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-03-23T06:40:06.071090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters468
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

Unique39 ?
Unique (%)100.0%

Sample

1st row063-540-4024
2nd row063-540-4025
3rd row063-540-4030
4th row063-540-4035
5th row063-540-4042
ValueCountFrequency (%)
063-540-4024 1
 
2.6%
063-542-3444 1
 
2.6%
063-542-7742 1
 
2.6%
063-546-9372 1
 
2.6%
063-542-9803 1
 
2.6%
063-543-1392 1
 
2.6%
063-543-7774 1
 
2.6%
063-543-7786 1
 
2.6%
063-543-6517 1
 
2.6%
063-542-9246 1
 
2.6%
Other values (29) 29
74.4%
2024-03-23T06:40:07.148108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 78
16.7%
4 75
16.0%
0 73
15.6%
6 59
12.6%
3 58
12.4%
5 53
11.3%
2 24
 
5.1%
7 17
 
3.6%
8 15
 
3.2%
9 11
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 390
83.3%
Dash Punctuation 78
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 75
19.2%
0 73
18.7%
6 59
15.1%
3 58
14.9%
5 53
13.6%
2 24
 
6.2%
7 17
 
4.4%
8 15
 
3.8%
9 11
 
2.8%
1 5
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 78
16.7%
4 75
16.0%
0 73
15.6%
6 59
12.6%
3 58
12.4%
5 53
11.3%
2 24
 
5.1%
7 17
 
3.6%
8 15
 
3.2%
9 11
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 78
16.7%
4 75
16.0%
0 73
15.6%
6 59
12.6%
3 58
12.4%
5 53
11.3%
2 24
 
5.1%
7 17
 
3.6%
8 15
 
3.2%
9 11
 
2.4%

Interactions

2024-03-23T06:39:54.982851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:39:54.293560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:39:55.410346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:39:54.560740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:40:07.565914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
명칭구분주소위도경도전화번호
명칭1.0000.0001.0000.9581.0001.000
구분0.0001.0001.0000.0000.0001.000
주소1.0001.0001.0001.0001.0001.000
위도0.9580.0001.0001.0000.0001.000
경도1.0000.0001.0000.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000
2024-03-23T06:40:07.931818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도구분
위도1.000-0.2940.000
경도-0.2941.0000.000
구분0.0000.0001.000

Missing values

2024-03-23T06:39:56.301881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:39:57.395327image/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

명칭구분주소위도경도전화번호
0만경보건지소전북특별자치도 김제시 만경읍 만경3길 20 (만경리, 만경보건지소)35.851053126.814978063-540-4024
1죽산보건지소전북특별자치도 김제시 죽산면 해학로 35 (죽산리, 죽산보건지소)35.7719126.814821063-540-4025
2용지보건지소전북특별자치도 김제시 용지면 용지로 475 (구암리, 용지보건지소)35.854785126.957621063-540-4030
3백구보건지소전북특별자치도 김제시 백구면 황토로 1267-21 (반월리, 백구보건지소)35.890424126.967628063-540-4035
4부량보건지소전북특별자치도 김제시 부량면 벽골제로 208 (대평리, 부량보건지소)35.733515126.844211063-540-4042
5공덕보건지소전북특별자치도 김제시 공덕면 공덕로 222-13 (마현리, 공덕보건지소)35.89216126.913675063-540-4048
6청하보건지소전북특별자치도 김제시 청하면 만경로 1442 (동지산리, 청하보건지소)35.904029126.840767063-540-4050
7성덕보건지소전북특별자치도 김제시 성덕면 지평선로 99-14 (석동리, 성덕보건지소)35.810167126.790319063-540-4055
8진봉보건지소전북특별자치도 김제시 진봉면 진봉1길 15-3 (고사리, 진봉보건지소)35.859201126.766095063-540-4060
9금구보건지소전북특별자치도 김제시 금구면 양시7길 56 (금구리, 금구보건지소)35.77555127.013006063-540-4065
명칭구분주소위도경도전화번호
29불로보건진료소전북특별자치도 김제시 금구면 청운4길 68-16 (청운리, 불로보건진료소)35.811372126.97953063-546-4947
30회성보건진료소전북특별자치도 김제시 봉남면 봉황로 538 (회성리, 회성보건진료소)35.730735126.940156063-543-8583
31남산보건진료소전북특별자치도 김제시 황산면 봉진1길 8 (진흥리, 남산보건진료소)35.782962126.970645063-546-4762
32용화보건진료소전북특별자치도 김제시 금산면 모악13길 10 (금산리, 용화보건진료소)35.718562127.03865063-548-4243
33평지보건진료소전북특별자치도 김제시 금산면 수류6길 9 (금성리, 평지보건진료소)35.686847127.041667063-544-5236
34창제보건진료소전북특별자치도 김제시 광활면 창제1길 29 (창제리, 창제보건진료소)35.838477126.718053063-543-6324
35동부보건진료소전북특별자치도 김제시 광활면 광활6길 259 (옥포리, 동부보건진료소)35.832669126.766629063-543-6723
36양전보건진료소전북특별자치도 김제시 수월길 15 (월성동, 양전보건진료소)35.759312126.910183063-548-8824
37백학보건진료소전북특별자치도 김제시 백학3길 71 (순동, 백학보건진료소)35.807715126.935324063-547-4427
38복죽보건진료소전북특별자치도 김제시 복죽5길 34-4 (복죽동, 복죽보건진료소)35.804379126.849167063-542-1625