Overview

Dataset statistics

Number of variables20
Number of observations8679
Missing cells5385
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory167.0 B

Variable types

Text5
Categorical6
Numeric6
Boolean2
DateTime1

Alerts

시도명 has constant value ""Constant
시군구명 is highly overall correlated with 시군구코드 and 5 other fieldsHigh correlation
관할소방서명 is highly overall correlated with 시군구코드 and 5 other fieldsHigh correlation
관할소방서전화번호 is highly overall correlated with 시군구코드 and 6 other fieldsHigh correlation
시군구코드 is highly overall correlated with 시군구명 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 시군구명 and 2 other fieldsHigh correlation
배관깊이 is highly overall correlated with 관할소방서전화번호High correlation
배관지름 is highly overall correlated with 시군구명 and 2 other fieldsHigh correlation
출수압력 is highly overall correlated with 시군구명 and 2 other fieldsHigh correlation
시설유형코드 is highly imbalanced (51.9%)Imbalance
사용가능여부 is highly imbalanced (91.4%)Imbalance
소재지도로명주소 has 179 (2.1%) missing valuesMissing
소재지지번주소 has 4631 (53.4%) missing valuesMissing
위도 has 123 (1.4%) missing valuesMissing
경도 has 128 (1.5%) missing valuesMissing
안전센터명 has 96 (1.1%) missing valuesMissing
보호틀유무 has 228 (2.6%) missing valuesMissing
시설번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 01:22:00.793877
Analysis finished2024-03-14 01:22:07.338874
Duration6.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설번호
Text

UNIQUE 

Distinct8679
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
2024-03-14T10:22:07.475314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.551331
Min length7

Characters and Unicode

Total characters100254
Distinct characters101
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8679 ?
Unique (%)100.0%

Sample

1st row완산-서부-급수-001
2nd row완산-서부-급수-002
3rd row완산-서부-급수-003
4th row완산-서부-급수-004
5th row완산-서부-저수조-001
ValueCountFrequency (%)
완산-서부-급수-001 1
 
< 0.1%
남원-식정-지상-026 1
 
< 0.1%
남원-식정-지상-040 1
 
< 0.1%
남원-식정-지상-039 1
 
< 0.1%
남원-식정-지상-038 1
 
< 0.1%
남원-식정-지상-037 1
 
< 0.1%
남원-식정-지상-036 1
 
< 0.1%
남원-식정-지상-035 1
 
< 0.1%
남원-식정-지상-034 1
 
< 0.1%
남원-식정-지상-033 1
 
< 0.1%
Other values (8669) 8669
99.9%
2024-03-14T10:22:07.770935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 24977
24.9%
지 8709
 
8.7%
0 7069
 
7.1%
상 5978
 
6.0%
1 4335
 
4.3%
산 3446
 
3.4%
하 2779
 
2.8%
2 2430
 
2.4%
3 1976
 
2.0%
4 1737
 
1.7%
Other values (91) 36818
36.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50118
50.0%
Decimal Number 25159
25.1%
Dash Punctuation 24977
24.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 8709
17.4%
상 5978
 
11.9%
산 3446
 
6.9%
하 2779
 
5.5%
완 1551
 
3.1%
창 1416
 
2.8%
진 1363
 
2.7%
덕 1321
 
2.6%
익 1182
 
2.4%
안 1126
 
2.2%
Other values (80) 21247
42.4%
Decimal Number
ValueCountFrequency (%)
0 7069
28.1%
1 4335
17.2%
2 2430
 
9.7%
3 1976
 
7.9%
4 1737
 
6.9%
5 1680
 
6.7%
6 1565
 
6.2%
7 1515
 
6.0%
8 1455
 
5.8%
9 1397
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 24977
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50136
50.0%
Hangul 50118
50.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 8709
17.4%
상 5978
 
11.9%
산 3446
 
6.9%
하 2779
 
5.5%
완 1551
 
3.1%
창 1416
 
2.8%
진 1363
 
2.7%
덕 1321
 
2.6%
익 1182
 
2.4%
안 1126
 
2.2%
Other values (80) 21247
42.4%
Common
ValueCountFrequency (%)
- 24977
49.8%
0 7069
 
14.1%
1 4335
 
8.6%
2 2430
 
4.8%
3 1976
 
3.9%
4 1737
 
3.5%
5 1680
 
3.4%
6 1565
 
3.1%
7 1515
 
3.0%
8 1455
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50136
50.0%
Hangul 50118
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 24977
49.8%
0 7069
 
14.1%
1 4335
 
8.6%
2 2430
 
4.8%
3 1976
 
3.9%
4 1737
 
3.5%
5 1680
 
3.4%
6 1565
 
3.1%
7 1515
 
3.0%
8 1455
 
2.9%
Hangul
ValueCountFrequency (%)
지 8709
17.4%
상 5978
 
11.9%
산 3446
 
6.9%
하 2779
 
5.5%
완 1551
 
3.1%
창 1416
 
2.8%
진 1363
 
2.7%
덕 1321
 
2.6%
익 1182
 
2.4%
안 1126
 
2.2%
Other values (80) 21247
42.4%

시설유형코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
1
5989 
2
2602 
3
 
82
4
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
1 5989
69.0%
2 2602
30.0%
3 82
 
0.9%
4 6
 
0.1%

Length

2024-03-14T10:22:07.875249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:22:07.954136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5989
69.0%
2 2602
30.0%
3 82
 
0.9%
4 6
 
0.1%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
전라북도
8679 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 8679
100.0%

Length

2024-03-14T10:22:08.065239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:22:08.170844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 8679
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
전주시 덕진구
1199 
익산시
1182 
군산시
1016 
완산구
786 
남원시
620 
Other values (10)
3876 

Length

Max length7
Median length3
Mean length3.5525982
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 덕진구 1199
13.8%
익산시 1182
13.6%
군산시 1016
11.7%
완산구 786
9.1%
남원시 620
7.1%
부안군 568
6.5%
완주군 562
6.5%
고창군 534
6.2%
정읍시 482
 
5.6%
김제시 453
 
5.2%
Other values (5) 1277
14.7%

Length

2024-03-14T10:22:08.270054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 1199
12.1%
덕진구 1199
12.1%
익산시 1182
12.0%
군산시 1016
10.3%
완산구 786
8.0%
남원시 620
 
6.3%
부안군 568
 
5.8%
완주군 562
 
5.7%
고창군 534
 
5.4%
정읍시 482
 
4.9%
Other values (6) 1730
17.5%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44486.986
Minimum25021
Maximum47900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2024-03-14T10:22:08.381417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25021
5-th percentile34021
Q145113
median45140
Q345720
95-th percentile47500
Maximum47900
Range22879
Interquartile range (IQR)607

Descriptive statistics

Standard deviation4184.6184
Coefficient of variation (CV)0.094063878
Kurtosis13.463487
Mean44486.986
Median Absolute Deviation (MAD)40
Skewness-3.7394695
Sum3.8610255 × 108
Variance17511031
MonotonicityNot monotonic
2024-03-14T10:22:08.486578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45113 1199
13.8%
45140 1182
13.6%
45130 1016
11.7%
45111 786
9.1%
45190 620
 
7.1%
45790 534
 
6.2%
45180 482
 
5.6%
45210 453
 
5.2%
47900 394
 
4.5%
45770 347
 
4.0%
Other values (81) 1666
19.2%
ValueCountFrequency (%)
25021 47
0.5%
25022 18
 
0.2%
25023 2
 
< 0.1%
25024 3
 
< 0.1%
25025 6
 
0.1%
25026 8
 
0.1%
25027 2
 
< 0.1%
25029 6
 
0.1%
25030 2
 
< 0.1%
25321 5
 
0.1%
ValueCountFrequency (%)
47900 394
4.5%
47500 223
 
2.6%
47400 244
 
2.8%
45800 174
 
2.0%
45790 534
6.2%
45770 347
4.0%
45750 203
 
2.3%
45720 260
3.0%
45210 453
5.2%
45190 620
7.1%

소재지도로명주소
Text

MISSING 

Distinct8074
Distinct (%)95.0%
Missing179
Missing (%)2.1%
Memory size67.9 KiB
2024-03-14T10:22:09.094440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length42
Mean length19.700706
Min length8

Characters and Unicode

Total characters167456
Distinct characters525
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7776 ?
Unique (%)91.5%

Sample

1st row전라북도 전주시 완산구 문학대6길 31-4
2nd row전라북도 전주시 완산구 서곡로95
3rd row전라북도 전주시 완산구 효자로 225
4th row전라북도 전주시 완산구 온고을로 211
5th row전라북도 전주시 완산구 유연로 220
ValueCountFrequency (%)
전라북도 8061
 
20.0%
전주시 1923
 
4.8%
덕진구 1140
 
2.8%
익산시 1127
 
2.8%
군산시 1016
 
2.5%
완산구 782
 
1.9%
남원시 599
 
1.5%
완주군 562
 
1.4%
고창군 534
 
1.3%
부안군 525
 
1.3%
Other values (7091) 24094
59.7%
2024-03-14T10:22:09.559796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31863
19.0%
전 10226
 
6.1%
도 8325
 
5.0%
북 8312
 
5.0%
라 8109
 
4.8%
시 5673
 
3.4%
1 5625
 
3.4%
길 5045
 
3.0%
산 4622
 
2.8%
군 3899
 
2.3%
Other values (515) 75757
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108976
65.1%
Space Separator 31863
 
19.0%
Decimal Number 24186
 
14.4%
Dash Punctuation 1912
 
1.1%
Close Punctuation 235
 
0.1%
Open Punctuation 235
 
0.1%
Uppercase Letter 23
 
< 0.1%
Other Punctuation 19
 
< 0.1%
Lowercase Letter 6
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 10226
 
9.4%
도 8325
 
7.6%
북 8312
 
7.6%
라 8109
 
7.4%
시 5673
 
5.2%
길 5045
 
4.6%
산 4622
 
4.2%
군 3899
 
3.6%
로 3815
 
3.5%
면 3428
 
3.1%
Other values (486) 47522
43.6%
Uppercase Letter
ValueCountFrequency (%)
N 6
26.1%
Y 5
21.7%
O 3
13.0%
M 2
 
8.7%
U 1
 
4.3%
C 1
 
4.3%
F 1
 
4.3%
T 1
 
4.3%
K 1
 
4.3%
G 1
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 5625
23.3%
2 3725
15.4%
3 2905
12.0%
4 2370
9.8%
5 2010
 
8.3%
6 1726
 
7.1%
7 1674
 
6.9%
8 1426
 
5.9%
9 1377
 
5.7%
0 1348
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 15
78.9%
, 4
 
21.1%
Space Separator
ValueCountFrequency (%)
31863
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1912
100.0%
Close Punctuation
ValueCountFrequency (%)
) 235
100.0%
Open Punctuation
ValueCountFrequency (%)
( 235
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 108977
65.1%
Common 58450
34.9%
Latin 29
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 10226
 
9.4%
도 8325
 
7.6%
북 8312
 
7.6%
라 8109
 
7.4%
시 5673
 
5.2%
길 5045
 
4.6%
산 4622
 
4.2%
군 3899
 
3.6%
로 3815
 
3.5%
면 3428
 
3.1%
Other values (487) 47523
43.6%
Common
ValueCountFrequency (%)
31863
54.5%
1 5625
 
9.6%
2 3725
 
6.4%
3 2905
 
5.0%
4 2370
 
4.1%
5 2010
 
3.4%
- 1912
 
3.3%
6 1726
 
3.0%
7 1674
 
2.9%
8 1426
 
2.4%
Other values (6) 3214
 
5.5%
Latin
ValueCountFrequency (%)
m 6
20.7%
N 6
20.7%
Y 5
17.2%
O 3
10.3%
M 2
 
6.9%
U 1
 
3.4%
C 1
 
3.4%
F 1
 
3.4%
T 1
 
3.4%
K 1
 
3.4%
Other values (2) 2
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 108976
65.1%
ASCII 58479
34.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31863
54.5%
1 5625
 
9.6%
2 3725
 
6.4%
3 2905
 
5.0%
4 2370
 
4.1%
5 2010
 
3.4%
- 1912
 
3.3%
6 1726
 
3.0%
7 1674
 
2.9%
8 1426
 
2.4%
Other values (18) 3243
 
5.5%
Hangul
ValueCountFrequency (%)
전 10226
 
9.4%
도 8325
 
7.6%
북 8312
 
7.6%
라 8109
 
7.4%
시 5673
 
5.2%
길 5045
 
4.6%
산 4622
 
4.2%
군 3899
 
3.6%
로 3815
 
3.5%
면 3428
 
3.1%
Other values (486) 47522
43.6%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지지번주소
Text

MISSING 

Distinct3850
Distinct (%)95.1%
Missing4631
Missing (%)53.4%
Memory size67.9 KiB
2024-03-14T10:22:09.870648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length40
Mean length18.688735
Min length3

Characters and Unicode

Total characters75652
Distinct characters361
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3715 ?
Unique (%)91.8%

Sample

1st row금암동 470-19
2nd row금암동 1547-8
3rd row인후동2가 236-6
4th row인후동2가 236-54
5th row인후동2가 202-47
ValueCountFrequency (%)
전라북도 2694
 
16.0%
군산시 1016
 
6.1%
부안군 566
 
3.4%
고창군 534
 
3.2%
덕진구 511
 
3.0%
전주시 449
 
2.7%
김제시 285
 
1.7%
진안군 164
 
1.0%
부안읍 111
 
0.7%
백산면 100
 
0.6%
Other values (4357) 10357
61.7%
2024-03-14T10:22:10.278441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12739
 
16.8%
1 3576
 
4.7%
전 3211
 
4.2%
- 3176
 
4.2%
도 2980
 
3.9%
북 2869
 
3.8%
라 2702
 
3.6%
리 2385
 
3.2%
군 2340
 
3.1%
2 2063
 
2.7%
Other values (351) 37611
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42635
56.4%
Decimal Number 16721
 
22.1%
Space Separator 12739
 
16.8%
Dash Punctuation 3176
 
4.2%
Close Punctuation 178
 
0.2%
Open Punctuation 177
 
0.2%
Uppercase Letter 13
 
< 0.1%
Lowercase Letter 9
 
< 0.1%
Other Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 3211
 
7.5%
도 2980
 
7.0%
북 2869
 
6.7%
라 2702
 
6.3%
리 2385
 
5.6%
군 2340
 
5.5%
동 1892
 
4.4%
시 1882
 
4.4%
산 1855
 
4.4%
면 1612
 
3.8%
Other values (326) 18907
44.3%
Decimal Number
ValueCountFrequency (%)
1 3576
21.4%
2 2063
12.3%
3 1864
11.1%
4 1589
9.5%
5 1561
9.3%
6 1431
8.6%
7 1286
 
7.7%
8 1253
 
7.5%
0 1090
 
6.5%
9 1008
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
O 3
23.1%
N 3
23.1%
M 2
15.4%
W 1
 
7.7%
C 1
 
7.7%
S 1
 
7.7%
G 1
 
7.7%
U 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
, 1
 
25.0%
Space Separator
ValueCountFrequency (%)
12739
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3176
100.0%
Close Punctuation
ValueCountFrequency (%)
) 178
100.0%
Open Punctuation
ValueCountFrequency (%)
( 177
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42635
56.4%
Common 32995
43.6%
Latin 22
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 3211
 
7.5%
도 2980
 
7.0%
북 2869
 
6.7%
라 2702
 
6.3%
리 2385
 
5.6%
군 2340
 
5.5%
동 1892
 
4.4%
시 1882
 
4.4%
산 1855
 
4.4%
면 1612
 
3.8%
Other values (326) 18907
44.3%
Common
ValueCountFrequency (%)
12739
38.6%
1 3576
 
10.8%
- 3176
 
9.6%
2 2063
 
6.3%
3 1864
 
5.6%
4 1589
 
4.8%
5 1561
 
4.7%
6 1431
 
4.3%
7 1286
 
3.9%
8 1253
 
3.8%
Other values (6) 2457
 
7.4%
Latin
ValueCountFrequency (%)
m 9
40.9%
O 3
 
13.6%
N 3
 
13.6%
M 2
 
9.1%
W 1
 
4.5%
C 1
 
4.5%
S 1
 
4.5%
G 1
 
4.5%
U 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42635
56.4%
ASCII 33017
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12739
38.6%
1 3576
 
10.8%
- 3176
 
9.6%
2 2063
 
6.2%
3 1864
 
5.6%
4 1589
 
4.8%
5 1561
 
4.7%
6 1431
 
4.3%
7 1286
 
3.9%
8 1253
 
3.8%
Other values (15) 2479
 
7.5%
Hangul
ValueCountFrequency (%)
전 3211
 
7.5%
도 2980
 
7.0%
북 2869
 
6.7%
라 2702
 
6.3%
리 2385
 
5.6%
군 2340
 
5.5%
동 1892
 
4.4%
시 1882
 
4.4%
산 1855
 
4.4%
면 1612
 
3.8%
Other values (326) 18907
44.3%

위도
Real number (ℝ)

MISSING 

Distinct8157
Distinct (%)95.3%
Missing123
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean35.771672
Minimum35.214332
Maximum40.407323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2024-03-14T10:22:10.426036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.214332
5-th percentile35.393494
Q135.613142
median35.830411
Q335.94209
95-th percentile36.005033
Maximum40.407323
Range5.192991
Interquartile range (IQR)0.32894857

Descriptive statistics

Standard deviation0.21121639
Coefficient of variation (CV)0.0059045713
Kurtosis32.953152
Mean35.771672
Median Absolute Deviation (MAD)0.12581483
Skewness1.0992728
Sum306062.43
Variance0.044612364
MonotonicityNot monotonic
2024-03-14T10:22:10.535572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.72711546 8
 
0.1%
35.86555298 7
 
0.1%
35.48908965 6
 
0.1%
35.84818484 6
 
0.1%
36.03169467 6
 
0.1%
35.61445105 6
 
0.1%
35.84434841 5
 
0.1%
36.03718784 5
 
0.1%
35.96099019 5
 
0.1%
35.94943573 5
 
0.1%
Other values (8147) 8497
97.9%
(Missing) 123
 
1.4%
ValueCountFrequency (%)
35.214332 1
< 0.1%
35.25162012 1
< 0.1%
35.30701902 2
< 0.1%
35.30937443 1
< 0.1%
35.3113665 1
< 0.1%
35.31143257 1
< 0.1%
35.31161832 1
< 0.1%
35.312117 2
< 0.1%
35.31217122 1
< 0.1%
35.312663 1
< 0.1%
ValueCountFrequency (%)
40.407323 1
< 0.1%
39.047356 1
< 0.1%
36.955568 1
< 0.1%
36.461288 1
< 0.1%
36.14592104 1
< 0.1%
36.145229 1
< 0.1%
36.14461765 1
< 0.1%
36.14360767 1
< 0.1%
36.13965202 1
< 0.1%
36.13861931 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8139
Distinct (%)95.2%
Missing128
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean127.01905
Minimum125.98053
Maximum127.8777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2024-03-14T10:22:10.641238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.98053
5-th percentile126.58479
Q1126.81469
median127.05795
Q3127.14777
95-th percentile127.52407
Maximum127.8777
Range1.8971664
Interquartile range (IQR)0.33308765

Descriptive statistics

Standard deviation0.27805682
Coefficient of variation (CV)0.0021890955
Kurtosis0.11280727
Mean127.01905
Median Absolute Deviation (MAD)0.1664757
Skewness0.2207977
Sum1086139.9
Variance0.077315594
MonotonicityNot monotonic
2024-03-14T10:22:10.749278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.2827109 7
 
0.1%
127.088396 7
 
0.1%
126.7329623 7
 
0.1%
126.9016572 6
 
0.1%
127.0797107 6
 
0.1%
127.0004195 6
 
0.1%
126.5946548 5
 
0.1%
126.5754752 5
 
0.1%
127.0583038 5
 
0.1%
126.9993128 5
 
0.1%
Other values (8129) 8492
97.8%
(Missing) 128
 
1.5%
ValueCountFrequency (%)
125.9805313 1
< 0.1%
125.9815741 1
< 0.1%
125.9818817 1
< 0.1%
125.9822705 1
< 0.1%
126.2548015 1
< 0.1%
126.2591543 1
< 0.1%
126.2624118 1
< 0.1%
126.262487 1
< 0.1%
126.2658145 2
< 0.1%
126.270303 1
< 0.1%
ValueCountFrequency (%)
127.8776977 1
< 0.1%
127.858138 1
< 0.1%
127.857283 1
< 0.1%
127.856692 1
< 0.1%
127.853167 1
< 0.1%
127.852412 1
< 0.1%
127.851651 1
< 0.1%
127.851579 1
< 0.1%
127.851244 1
< 0.1%
127.851231 1
< 0.1%

상세위치
Text

Distinct8220
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
2024-03-14T10:22:11.016249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length52
Mean length12.673234
Min length1

Characters and Unicode

Total characters109991
Distinct characters882
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8076 ?
Unique (%)93.1%

Sample

1st row여울꽃집 서부신시가지 현대아이파크101동 사이 대로변
2nd row전주세무소 정문 좌측 50m부근
3rd row전북도청 옆 화단, 도청 화장실 맞은편
4th row그랜드 힐스턴 호텔 앞
5th row대한방직 내
ValueCountFrequency (%)
앞 3621
 
13.7%
옆 1058
 
4.0%
입구 381
 
1.4%
맞은편 334
 
1.3%
주택 313
 
1.2%
인도 307
 
1.2%
덕진구 293
 
1.1%
전주시 255
 
1.0%
무주군 236
 
0.9%
정문 200
 
0.8%
Other values (10735) 19523
73.6%
2024-03-14T10:22:11.517479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17887
 
16.3%
앞 4271
 
3.9%
마 2053
 
1.9%
1 1937
 
1.8%
을 1775
 
1.6%
동 1733
 
1.6%
로 1596
 
1.5%
주 1541
 
1.4%
리 1404
 
1.3%
도 1339
 
1.2%
Other values (872) 74455
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79173
72.0%
Space Separator 17887
 
16.3%
Decimal Number 8119
 
7.4%
Close Punctuation 1218
 
1.1%
Open Punctuation 1213
 
1.1%
Dash Punctuation 843
 
0.8%
Uppercase Letter 740
 
0.7%
Lowercase Letter 431
 
0.4%
Other Punctuation 312
 
0.3%
Other Symbol 30
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 4271
 
5.4%
마 2053
 
2.6%
을 1775
 
2.2%
동 1733
 
2.2%
로 1596
 
2.0%
주 1541
 
1.9%
리 1404
 
1.8%
도 1339
 
1.7%
정 1295
 
1.6%
회 1274
 
1.6%
Other values (792) 60892
76.9%
Uppercase Letter
ValueCountFrequency (%)
C 90
12.2%
S 77
10.4%
G 73
 
9.9%
M 70
 
9.5%
K 63
 
8.5%
L 49
 
6.6%
B 37
 
5.0%
T 34
 
4.6%
A 28
 
3.8%
P 28
 
3.8%
Other values (16) 191
25.8%
Lowercase Letter
ValueCountFrequency (%)
m 279
64.7%
c 29
 
6.7%
s 19
 
4.4%
e 16
 
3.7%
g 12
 
2.8%
k 10
 
2.3%
i 8
 
1.9%
a 8
 
1.9%
b 7
 
1.6%
p 6
 
1.4%
Other values (13) 37
 
8.6%
Decimal Number
ValueCountFrequency (%)
1 1937
23.9%
2 1284
15.8%
0 939
11.6%
3 874
10.8%
5 705
 
8.7%
4 668
 
8.2%
7 481
 
5.9%
6 449
 
5.5%
9 411
 
5.1%
8 371
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 147
47.1%
/ 105
33.7%
@ 22
 
7.1%
. 19
 
6.1%
? 7
 
2.2%
& 6
 
1.9%
: 4
 
1.3%
＆ 1
 
0.3%
· 1
 
0.3%
Math Symbol
ValueCountFrequency (%)
~ 19
76.0%
→ 2
 
8.0%
> 2
 
8.0%
< 1
 
4.0%
↔ 1
 
4.0%
Close Punctuation
ValueCountFrequency (%)
) 1213
99.6%
] 5
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 1208
99.6%
[ 5
 
0.4%
Space Separator
ValueCountFrequency (%)
17887
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 843
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79201
72.0%
Common 29617
 
26.9%
Latin 1171
 
1.1%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 4271
 
5.4%
마 2053
 
2.6%
을 1775
 
2.2%
동 1733
 
2.2%
로 1596
 
2.0%
주 1541
 
1.9%
리 1404
 
1.8%
도 1339
 
1.7%
정 1295
 
1.6%
회 1274
 
1.6%
Other values (791) 60920
76.9%
Latin
ValueCountFrequency (%)
m 279
23.8%
C 90
 
7.7%
S 77
 
6.6%
G 73
 
6.2%
M 70
 
6.0%
K 63
 
5.4%
L 49
 
4.2%
B 37
 
3.2%
T 34
 
2.9%
c 29
 
2.5%
Other values (39) 370
31.6%
Common
ValueCountFrequency (%)
17887
60.4%
1 1937
 
6.5%
2 1284
 
4.3%
) 1213
 
4.1%
( 1208
 
4.1%
0 939
 
3.2%
3 874
 
3.0%
- 843
 
2.8%
5 705
 
2.4%
4 668
 
2.3%
Other values (20) 2059
 
7.0%
Han
ValueCountFrequency (%)
晁 1
50.0%
貧 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79170
72.0%
ASCII 30783
 
28.0%
None 32
 
< 0.1%
Arrows 3
 
< 0.1%
CJK 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17887
58.1%
1 1937
 
6.3%
2 1284
 
4.2%
) 1213
 
3.9%
( 1208
 
3.9%
0 939
 
3.1%
3 874
 
2.8%
- 843
 
2.7%
5 705
 
2.3%
4 668
 
2.2%
Other values (65) 3225
 
10.5%
Hangul
ValueCountFrequency (%)
앞 4271
 
5.4%
마 2053
 
2.6%
을 1775
 
2.2%
동 1733
 
2.2%
로 1596
 
2.0%
주 1541
 
1.9%
리 1404
 
1.8%
도 1339
 
1.7%
정 1295
 
1.6%
회 1274
 
1.6%
Other values (789) 60889
76.9%
None
ValueCountFrequency (%)
㈜ 30
93.8%
＆ 1
 
3.1%
· 1
 
3.1%
Arrows
ValueCountFrequency (%)
→ 2
66.7%
↔ 1
33.3%
CJK
ValueCountFrequency (%)
晁 1
50.0%
貧 1
50.0%
Compat Jamo
ValueCountFrequency (%)
ㅠ 1
100.0%

안전센터명
Text

MISSING 

Distinct56
Distinct (%)0.7%
Missing96
Missing (%)1.1%
Memory size67.9 KiB
2024-03-14T10:22:11.719286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.3117791
Min length2

Characters and Unicode

Total characters54174
Distinct characters82
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row서부안전센터
2nd row서부안전센터
3rd row서부안전센터
4th row서부안전센터
5th row서부안전센터
ValueCountFrequency (%)
팔복안전센터 449
 
5.2%
식정안전센터 402
 
4.7%
부안안전센터 394
 
4.6%
순창안전센터 347
 
4.0%
금암안전센터 336
 
3.9%
효자안전센터 311
 
3.6%
전미안전센터 288
 
3.4%
교동119안전센터 272
 
3.2%
금마센터 240
 
2.8%
봉동119안전센터 238
 
2.8%
Other values (46) 5306
61.8%
2024-03-14T10:22:12.028234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
센 8116
15.0%
터 8116
15.0%
안 7755
14.3%
전 7485
13.8%
1 4014
 
7.4%
9 2006
 
3.7%
금 962
 
1.8%
동 778
 
1.4%
정 626
 
1.2%
팔 595
 
1.1%
Other values (72) 13721
25.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48150
88.9%
Decimal Number 6024
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 8116
16.9%
터 8116
16.9%
안 7755
16.1%
전 7485
15.5%
금 962
 
2.0%
동 778
 
1.6%
정 626
 
1.3%
팔 595
 
1.2%
창 535
 
1.1%
부 524
 
1.1%
Other values (68) 12658
26.3%
Decimal Number
ValueCountFrequency (%)
1 4014
66.6%
9 2006
33.3%
2 2
 
< 0.1%
0 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48150
88.9%
Common 6024
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 8116
16.9%
터 8116
16.9%
안 7755
16.1%
전 7485
15.5%
금 962
 
2.0%
동 778
 
1.6%
정 626
 
1.3%
팔 595
 
1.2%
창 535
 
1.1%
부 524
 
1.1%
Other values (68) 12658
26.3%
Common
ValueCountFrequency (%)
1 4014
66.6%
9 2006
33.3%
2 2
 
< 0.1%
0 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48150
88.9%
ASCII 6024
 
11.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
센 8116
16.9%
터 8116
16.9%
안 7755
16.1%
전 7485
15.5%
금 962
 
2.0%
동 778
 
1.6%
정 626
 
1.3%
팔 595
 
1.2%
창 535
 
1.1%
부 524
 
1.1%
Other values (68) 12658
26.3%
ASCII
ValueCountFrequency (%)
1 4014
66.6%
9 2006
33.3%
2 2
 
< 0.1%
0 2
 
< 0.1%

보호틀유무
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing228
Missing (%)2.6%
Memory size17.1 KiB
False
5805 
True
2646 
(Missing)
 
228
ValueCountFrequency (%)
False 5805
66.9%
True 2646
30.5%
(Missing) 228
 
2.6%
2024-03-14T10:22:12.127283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

사용가능여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
True
8585 
False
 
94
ValueCountFrequency (%)
True 8585
98.9%
False 94
 
1.1%
2024-03-14T10:22:12.195173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

설치연도
Real number (ℝ)

Distinct52
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.7683
Minimum1905
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2024-03-14T10:22:12.283762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1905
5-th percentile1987
Q11997
median2006
Q32015
95-th percentile2020
Maximum2022
Range117
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.03012
Coefficient of variation (CV)0.0060007533
Kurtosis6.9709403
Mean2004.7683
Median Absolute Deviation (MAD)9
Skewness-1.3440244
Sum17399384
Variance144.72379
MonotonicityNot monotonic
2024-03-14T10:22:12.392443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020 913
 
10.5%
1990 737
 
8.5%
2000 702
 
8.1%
2012 424
 
4.9%
2007 396
 
4.6%
2010 363
 
4.2%
2016 352
 
4.1%
2019 336
 
3.9%
2006 314
 
3.6%
2001 270
 
3.1%
Other values (42) 3872
44.6%
ValueCountFrequency (%)
1905 13
 
0.1%
1910 2
 
< 0.1%
1970 7
 
0.1%
1974 1
 
< 0.1%
1975 1
 
< 0.1%
1976 231
2.7%
1977 2
 
< 0.1%
1978 2
 
< 0.1%
1979 4
 
< 0.1%
1980 27
 
0.3%
ValueCountFrequency (%)
2022 1
 
< 0.1%
2021 77
 
0.9%
2020 913
10.5%
2019 336
 
3.9%
2018 177
 
2.0%
2017 159
 
1.8%
2016 352
 
4.1%
2015 160
 
1.8%
2014 154
 
1.8%
2013 164
 
1.9%

배관깊이
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5679998
Minimum0.1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2024-03-14T10:22:12.540821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q10.6
median1
Q31
95-th percentile50
Maximum100
Range99.9
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation16.140355
Coefficient of variation (CV)2.8987708
Kurtosis12.163123
Mean5.5679998
Median Absolute Deviation (MAD)0.4
Skewness3.5216585
Sum48324.67
Variance260.51107
MonotonicityNot monotonic
2024-03-14T10:22:12.653683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1.0 3162
36.4%
0.5 1468
16.9%
0.6 1328
15.3%
1.5 1297
14.9%
50.0 508
 
5.9%
0.8 334
 
3.8%
60.0 86
 
1.0%
0.7 67
 
0.8%
0.3 61
 
0.7%
0.4 60
 
0.7%
Other values (19) 308
 
3.5%
ValueCountFrequency (%)
0.1 17
 
0.2%
0.15 1
 
< 0.1%
0.2 24
 
0.3%
0.25 3
 
< 0.1%
0.3 61
 
0.7%
0.35 3
 
< 0.1%
0.4 60
 
0.7%
0.45 3
 
< 0.1%
0.47 1
 
< 0.1%
0.5 1468
16.9%
ValueCountFrequency (%)
100.0 58
 
0.7%
90.0 6
 
0.1%
80.0 20
 
0.2%
70.0 38
 
0.4%
60.0 86
 
1.0%
50.0 508
 
5.9%
1.8 2
 
< 0.1%
1.5 1297
14.9%
1.4 40
 
0.5%
1.3 6
 
0.1%

출수압력
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
3.5
1532 
2
1242 
3
1217 
4.5
1039 
3.2
852 
Other values (44)
2797 

Length

Max length4
Median length3
Mean length2.3787303
Min length1

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3.5 1532
17.7%
2 1242
14.3%
3 1217
14.0%
4.5 1039
12.0%
3.2 852
9.8%
2.5 542
 
6.2%
3.3 389
 
4.5%
5 219
 
2.5%
3.1 167
 
1.9%
2.9 161
 
1.9%
Other values (39) 1319
15.2%

Length

2024-03-14T10:22:12.758244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.5 1532
17.7%
2 1242
14.3%
3 1217
14.0%
4.5 1039
12.0%
3.2 852
9.8%
2.5 542
 
6.2%
3.3 389
 
4.5%
5 219
 
2.5%
3.1 167
 
1.9%
2.9 161
 
1.9%
Other values (38) 1319
15.2%

배관지름
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.59523
Minimum50
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.4 KiB
2024-03-14T10:22:12.845009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile65
Q170
median100
Q3100
95-th percentile100
Maximum125
Range75
Interquartile range (IQR)30

Descriptive statistics

Standard deviation15.203199
Coefficient of variation (CV)0.16781456
Kurtosis-0.9504293
Mean90.59523
Median Absolute Deviation (MAD)0
Skewness-1.0059742
Sum786276
Variance231.13725
MonotonicityNot monotonic
2024-03-14T10:22:12.932222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
100 6255
72.1%
65 1831
 
21.1%
70 563
 
6.5%
80 12
 
0.1%
60 6
 
0.1%
75 5
 
0.1%
125 2
 
< 0.1%
50 2
 
< 0.1%
101 1
 
< 0.1%
102 1
 
< 0.1%
ValueCountFrequency (%)
50 2
 
< 0.1%
60 6
 
0.1%
65 1831
 
21.1%
70 563
 
6.5%
75 5
 
0.1%
80 12
 
0.1%
100 6255
72.1%
101 1
 
< 0.1%
102 1
 
< 0.1%
103 1
 
< 0.1%
ValueCountFrequency (%)
125 2
 
< 0.1%
103 1
 
< 0.1%
102 1
 
< 0.1%
101 1
 
< 0.1%
100 6255
72.1%
80 12
 
0.1%
75 5
 
0.1%
70 563
 
6.5%
65 1831
 
21.1%
60 6
 
0.1%

관할소방서명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
전주덕진소방서
1199 
익산소방서
1182 
군산소방서
1016 
전주완산소방서
989 
남원소방서
620 
Other values (8)
3673 

Length

Max length7
Median length5
Mean length5.5042056
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주덕진소방서 1199
13.8%
익산소방서 1182
13.6%
군산소방서 1016
11.7%
전주완산소방서 989
11.4%
남원소방서 620
7.1%
부안소방서 568
6.5%
완주소방서 562
6.5%
고창소방서 534
6.2%
정읍소방서 482
5.6%
장수소방서 467
 
5.4%
Other values (3) 1060
12.2%

Length

2024-03-14T10:22:13.068457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주덕진소방서 1199
13.8%
익산소방서 1182
13.6%
군산소방서 1016
11.7%
전주완산소방서 989
11.4%
남원소방서 620
7.1%
부안소방서 568
6.5%
완주소방서 562
6.5%
고창소방서 534
6.2%
정읍소방서 482
5.6%
장수소방서 467
 
5.4%
Other values (3) 1060
12.2%

관할소방서전화번호
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
063-250-4254
1199 
063-839-3253
1182 
063-450-0254
1016 
063-220-4253
989 
063-633-2119
620 
Other values (15)
3673 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row063-220-4253
2nd row063-220-4253
3rd row063-220-4253
4th row063-220-4253
5th row063-220-4253

Common Values

ValueCountFrequency (%)
063-250-4254 1199
13.8%
063-839-3253 1182
13.6%
063-450-0254 1016
11.7%
063-220-4253 989
11.4%
063-633-2119 620
7.1%
063-290-0254 562
6.5%
063-560-1252 534
6.2%
063-350-6255 467
 
5.4%
063-570-1253 411
 
4.7%
063-580-1272 394
 
4.5%
Other values (10) 1305
15.0%

Length

2024-03-14T10:22:13.166108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
063-250-4254 1199
13.8%
063-839-3253 1182
13.6%
063-450-0254 1016
11.7%
063-220-4253 989
11.4%
063-633-2119 620
7.1%
063-290-0254 562
6.5%
063-560-1252 534
6.2%
063-350-6255 467
 
5.4%
063-570-1253 411
 
4.7%
063-580-1272 394
 
4.5%
Other values (10) 1305
15.0%

데이터기준일자
Date

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.9 KiB
Minimum2021-03-31 00:00:00
Maximum2021-07-13 00:00:00
2024-03-14T10:22:13.258795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:13.359487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

Interactions

2024-03-14T10:22:06.301722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:03.324664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:04.008500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:04.739077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.293309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.763864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:06.381979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:03.402803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:04.093195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:04.829390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.363611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.866176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:06.459498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:03.468573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:04.178284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:04.907352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.436162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.994706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:06.539490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:03.784726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:04.279639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:04.996044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.515360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:06.071858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:06.615567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:03.857042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:04.394697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.100926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.593752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:06.146706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:06.723194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:03.927748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:04.607303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.203000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:05.663043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T10:22:06.213603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T10:22:13.500624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설유형코드시군구명시군구코드위도경도안전센터명보호틀유무사용가능여부설치연도배관깊이출수압력배관지름관할소방서명관할소방서전화번호데이터기준일자
시설유형코드1.0000.5740.3600.3510.2920.6870.6190.1020.4190.1630.5300.1920.5480.6400.534
시군구명0.5741.0000.8350.7020.9000.9990.5350.1570.5290.7650.9350.7991.0000.9941.000
시군구코드0.3600.8351.0000.3840.4440.9570.2100.0650.2610.4450.7270.5010.8180.8390.777
위도0.3510.7020.3841.0000.3820.7440.0130.0820.2830.2650.6010.1540.6480.6760.610
경도0.2920.9000.4440.3821.0000.9660.3060.0630.3250.4410.7720.5690.8600.9410.825
안전센터명0.6870.9990.9570.7440.9661.0000.6620.2160.7080.8820.9560.9410.9990.9960.999
보호틀유무0.6190.5350.2100.0130.3060.6621.0000.0880.4520.1490.3530.0720.5030.5940.457
사용가능여부0.1020.1570.0650.0820.0630.2160.0881.0000.0000.0210.1750.1010.1540.1800.150
설치연도0.4190.5290.2610.2830.3250.7080.4520.0001.0000.1210.4650.1770.4830.5260.469
배관깊이0.1630.7650.4450.2650.4410.8820.1490.0210.1211.0000.4700.5080.7560.8390.735
출수압력0.5300.9350.7270.6010.7720.9560.3530.1750.4650.4701.0000.6930.9290.9320.925
배관지름0.1920.7990.5010.1540.5690.9410.0720.1010.1770.5080.6931.0000.7910.8320.682
관할소방서명0.5481.0000.8180.6480.8600.9990.5030.1540.4830.7560.9290.7911.0001.0001.000
관할소방서전화번호0.6400.9940.8390.6760.9410.9960.5940.1800.5260.8390.9320.8321.0001.0001.000
데이터기준일자0.5341.0000.7770.6100.8250.9990.4570.1500.4690.7350.9250.6821.0001.0001.000
2024-03-14T10:22:13.647586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설유형코드출수압력시군구명보호틀유무사용가능여부관할소방서명관할소방서전화번호
시설유형코드1.0000.2920.3640.4300.0670.3520.353
출수압력0.2921.0000.5970.2930.1450.6080.534
시군구명0.3640.5971.0000.4910.1431.0000.925
보호틀유무0.4300.2930.4911.0000.0560.4700.473
사용가능여부0.0670.1450.1430.0561.0000.1430.142
관할소방서명0.3520.6081.0000.4700.1431.0001.000
관할소방서전화번호0.3530.5340.9250.4730.1421.0001.000
2024-03-14T10:22:13.758793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드위도경도설치연도배관깊이배관지름시설유형코드시군구명보호틀유무사용가능여부출수압력관할소방서명관할소방서전화번호
시군구코드1.000-0.481-0.1460.1920.344-0.2020.2580.5760.2250.0700.3740.5620.562
위도-0.4811.000-0.149-0.139-0.3750.2410.2320.4190.0090.0590.3040.3910.394
경도-0.146-0.1491.0000.1560.128-0.0770.1780.6140.2340.0480.3860.5880.624
설치연도0.192-0.1390.1561.0000.191-0.0020.2820.2860.3260.0000.2200.2710.286
배관깊이0.344-0.3750.1280.1911.000-0.2430.1120.4800.1600.0230.1960.4810.562
배관지름-0.2020.241-0.077-0.002-0.2431.0000.1330.5240.0770.1080.3400.5240.551
시설유형코드0.2580.2320.1780.2820.1120.1331.0000.3640.4300.0670.2920.3520.353
시군구명0.5760.4190.6140.2860.4800.5240.3641.0000.4910.1430.5971.0000.925
보호틀유무0.2250.0090.2340.3260.1600.0770.4300.4911.0000.0560.2930.4700.473
사용가능여부0.0700.0590.0480.0000.0230.1080.0670.1430.0561.0000.1450.1430.142
출수압력0.3740.3040.3860.2200.1960.3400.2920.5970.2930.1451.0000.6080.534
관할소방서명0.5620.3910.5880.2710.4810.5240.3521.0000.4700.1430.6081.0001.000
관할소방서전화번호0.5620.3940.6240.2860.5620.5510.3530.9250.4730.1420.5341.0001.000

Missing values

2024-03-14T10:22:06.909957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:22:07.140974image/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.
2024-03-14T10:22:07.270498image/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

시설번호시설유형코드시도명시군구명시군구코드소재지도로명주소소재지지번주소위도경도상세위치안전센터명보호틀유무사용가능여부설치연도배관깊이출수압력배관지름관할소방서명관할소방서전화번호데이터기준일자
0완산-서부-급수-0013전라북도완산구45111전라북도 전주시 완산구 문학대6길 31-4<NA>35.828435127.105614여울꽃집 서부신시가지 현대아이파크101동 사이 대로변서부안전센터NY19991.02100전주완산소방서063-220-42532021-07-13
1완산-서부-급수-0023전라북도완산구45111전라북도 전주시 완산구 서곡로95<NA>35.837646127.102121전주세무소 정문 좌측 50m부근서부안전센터NY19961.02100전주완산소방서063-220-42532021-07-13
2완산-서부-급수-0033전라북도완산구45111전라북도 전주시 완산구 효자로 225<NA>35.819447127.106374전북도청 옆 화단, 도청 화장실 맞은편서부안전센터NY20071.02100전주완산소방서063-220-42532021-07-13
3완산-서부-급수-0043전라북도완산구45111전라북도 전주시 완산구 온고을로 211<NA>35.838495127.100948그랜드 힐스턴 호텔 앞서부안전센터NY20011.02100전주완산소방서063-220-42532021-07-13
4완산-서부-저수조-0014전라북도완산구45111전라북도 전주시 완산구 유연로 220<NA>35.823761127.109685대한방직 내서부안전센터NY19961.02100전주완산소방서063-220-42532021-07-13
5완산-서부-지상-0011전라북도완산구45111전라북도 전주시 완산구 용호로 34<NA>35.807428127.100252휴먼시아 3단지 입구 크린플러스 맞은편 인도서부안전센터NY20071.02100전주완산소방서063-220-42532021-07-13
6완산-서부-지상-0021전라북도완산구45111전라북도 전주시 완산구 천잠로 303<NA>35.816164127.088884전주대 정문 직진 도로 끝 우회전 빨간건물학생회관 뒤서부안전센터NY20011.02100전주완산소방서063-220-42532021-07-13
7완산-서부-지상-0031전라북도완산구45111전라북도 전주시 완산구 백마산길 9<NA>35.817769127.092218전주대후문 방향 롯데마트 앞서부안전센터NY19851.02100전주완산소방서063-220-42532021-07-13
8완산-서부-지상-0041전라북도완산구45111전라북도 전주시 완산구 서곡로 11<NA>35.832287127.097499서곡대림아파트 101동 뒤 간선도로변 모서리쪽서부안전센터NY19911.02100전주완산소방서063-220-42532021-07-13
9완산-서부-지상-0051전라북도완산구45111전라북도 전주시 완산구 서곡로 8<NA>35.831779127.096613서곡주공 아파트 109동 뒤서부안전센터NY19981.02100전주완산소방서063-220-42532021-07-13
시설번호시설유형코드시도명시군구명시군구코드소재지도로명주소소재지지번주소위도경도상세위치안전센터명보호틀유무사용가능여부설치연도배관깊이출수압력배관지름관할소방서명관할소방서전화번호데이터기준일자
8669마령-지상-871전라북도진안군45720전라북도 진안군 부귀면 삼봉길 114<NA>35.840096127.417891삼봉길 114<NA>NY19981.53.570진안소방서063-432-31192021-03-31
8670마령-지상-881전라북도진안군45720전라북도 진안군 부귀면 원세동길 3-2<NA>35.802914127.327916원세동길 3-2<NA>NY20151.53.570진안소방서063-432-31192021-03-31
8671마령-지상-891전라북도진안군45720전라북도 진안군 부귀면 대곡안길 2<NA>35.83231127.390901대곡안길 2<NA>NY20201.53.570진안소방서063-432-31192021-03-31
8672마령-지상-901전라북도진안군45720전라북도 진안군 부귀면 중수항길 6<NA>35.853423127.394869중수항길 6<NA>NY19981.53.570진안소방서063-432-31192021-03-31
8673마령-지상-911전라북도진안군45720전라북도 진안군 부귀면 전진로 2611-26<NA>35.785892127.353272신정리 263-13<NA>NY20201.53.570진안소방서063-432-31192021-03-31
8674마령-지상-921전라북도진안군45720전라북도 진안군 부귀면 신거석길 15<NA>35.837173127.359899신거석길 15<NA>NY20201.53.570진안소방서063-432-31192021-03-31
8675마령-지상-931전라북도진안군45720전라북도 진안군 부귀면 오복2길 3<NA>35.817775127.349657오복2길 3<NA>NY20201.53.570진안소방서063-432-31192021-03-31
8676마령-지상-941전라북도진안군45720전라북도 진안군 부귀면 오산길 55<NA>35.832193127.344523오산안길 55<NA>NY20201.53.570진안소방서063-432-31192021-03-31
8677마령-지상-951전라북도진안군45720전라북도 진안군 부귀면 부귀로 83<NA>35.843756127.342717부귀로 83<NA>NY20201.53.570진안소방서063-432-31192021-03-31
8678마령-지상-961전라북도진안군45720전라북도 진안군 부귀면 운장로 112<NA>35.858247127.338977운장로 112<NA>NY20201.53.570진안소방서063-432-31192021-03-31