Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1005.9 KiB
Average record size in memory103.0 B

Variable types

Numeric7
Text2
Categorical1
Boolean1

Dataset

Description순번,소화용수ID,소화용수번호,서소코드,소화용수구분코드,공사설구분,사용구분,최종변경일시,새주소,위도,경도
Author서울종합방재센터 전산통신과
URLhttps://data.seoul.go.kr/dataList/OA-12811/S/1/datasetView.do

Alerts

소화용수ID is highly overall correlated with 서소코드High correlation
서소코드 is highly overall correlated with 소화용수IDHigh correlation
사용구분 is highly overall correlated with 소화용수구분코드High correlation
최종변경일시 is highly overall correlated with 공사설구분High correlation
소화용수구분코드 is highly overall correlated with 사용구분High correlation
공사설구분 is highly overall correlated with 최종변경일시High correlation
소화용수구분코드 is highly imbalanced (62.6%)Imbalance
순번 has unique valuesUnique
소화용수ID has unique valuesUnique

Reproduction

Analysis started2023-12-11 06:12:53.045117
Analysis finished2023-12-11 06:13:01.852088
Duration8.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34129.38
Minimum1
Maximum68047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:13:01.946406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3198.8
Q116889.5
median34239.5
Q351257.5
95-th percentile64787.55
Maximum68047
Range68046
Interquartile range (IQR)34368

Descriptive statistics

Standard deviation19747.697
Coefficient of variation (CV)0.57861282
Kurtosis-1.2030798
Mean34129.38
Median Absolute Deviation (MAD)17197
Skewness-0.0086511202
Sum3.412938 × 108
Variance3.8997154 × 108
MonotonicityNot monotonic
2023-12-11T15:13:02.135769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13165 1
 
< 0.1%
63049 1
 
< 0.1%
48075 1
 
< 0.1%
22450 1
 
< 0.1%
19668 1
 
< 0.1%
16795 1
 
< 0.1%
19422 1
 
< 0.1%
37812 1
 
< 0.1%
65657 1
 
< 0.1%
56367 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
14 1
< 0.1%
16 1
< 0.1%
22 1
< 0.1%
29 1
< 0.1%
33 1
< 0.1%
36 1
< 0.1%
57 1
< 0.1%
58 1
< 0.1%
59 1
< 0.1%
ValueCountFrequency (%)
68047 1
< 0.1%
68046 1
< 0.1%
68042 1
< 0.1%
68025 1
< 0.1%
68024 1
< 0.1%
68016 1
< 0.1%
68009 1
< 0.1%
68007 1
< 0.1%
68005 1
< 0.1%
68000 1
< 0.1%

소화용수ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1220997 × 108
Minimum7.10004 × 108
Maximum9.3991257 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:13:02.344525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.10004 × 108
5-th percentile7.1991233 × 108
Q17.6870802 × 108
median8.0960909 × 108
Q38.599101 × 108
95-th percentile8.994031 × 108
Maximum9.3991257 × 108
Range2.2990857 × 108
Interquartile range (IQR)91202083

Descriptive statistics

Standard deviation56590638
Coefficient of variation (CV)0.069674887
Kurtosis-1.125391
Mean8.1220997 × 108
Median Absolute Deviation (MAD)49904089
Skewness0.004343263
Sum8.1220997 × 1012
Variance3.2025003 × 1015
MonotonicityNot monotonic
2023-12-11T15:13:02.564878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
859601414 1
 
< 0.1%
819610047 1
 
< 0.1%
869110001 1
 
< 0.1%
719210129 1
 
< 0.1%
860511004 1
 
< 0.1%
818805012 1
 
< 0.1%
779807070 1
 
< 0.1%
880006248 1
 
< 0.1%
710501005 1
 
< 0.1%
929912169 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
710004001 1
< 0.1%
710008001 1
< 0.1%
710011001 1
< 0.1%
710011002 1
< 0.1%
710011007 1
< 0.1%
710105002 1
< 0.1%
710105004 1
< 0.1%
710105008 1
< 0.1%
710107006 1
< 0.1%
710107008 1
< 0.1%
ValueCountFrequency (%)
939912568 1
< 0.1%
939912553 1
< 0.1%
939912551 1
< 0.1%
929912493 1
< 0.1%
929912492 1
< 0.1%
929912471 1
< 0.1%
929912461 1
< 0.1%
929912460 1
< 0.1%
929912446 1
< 0.1%
929912410 1
< 0.1%

소화용수번호
Text

Distinct7999
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T15:13:03.032310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length6
Mean length5.5027
Min length1

Characters and Unicode

Total characters55027
Distinct characters30
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

Unique6464 ?
Unique (%)64.6%

Sample

1st row001816
2nd row3432
3rd row002866
4th row002542
5th row004524
ValueCountFrequency (%)
000119 7
 
0.1%
000040 6
 
0.1%
000405 5
 
< 0.1%
000115 5
 
< 0.1%
000253 5
 
< 0.1%
000179 5
 
< 0.1%
000124 5
 
< 0.1%
000031 5
 
< 0.1%
000251 5
 
< 0.1%
000172 5
 
< 0.1%
Other values (7992) 9953
99.5%
2023-12-11T15:13:03.999728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18585
33.8%
1 6350
 
11.5%
2 5714
 
10.4%
3 4823
 
8.8%
4 4087
 
7.4%
5 3355
 
6.1%
6 3227
 
5.9%
7 2991
 
5.4%
8 2308
 
4.2%
9 2282
 
4.1%
Other values (20) 1305
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53722
97.6%
Dash Punctuation 663
 
1.2%
Other Letter 505
 
0.9%
Open Punctuation 63
 
0.1%
Close Punctuation 63
 
0.1%
Space Separator 6
 
< 0.1%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
숭 110
21.8%
연 88
17.4%
영 74
14.7%
종 69
13.7%
신 61
12.1%
세 52
10.3%
사 15
 
3.0%
저 8
 
1.6%
급 8
 
1.6%
소 6
 
1.2%
Other values (5) 14
 
2.8%
Decimal Number
ValueCountFrequency (%)
0 18585
34.6%
1 6350
 
11.8%
2 5714
 
10.6%
3 4823
 
9.0%
4 4087
 
7.6%
5 3355
 
6.2%
6 3227
 
6.0%
7 2991
 
5.6%
8 2308
 
4.3%
9 2282
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 663
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 63
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 54522
99.1%
Hangul 505
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18585
34.1%
1 6350
 
11.6%
2 5714
 
10.5%
3 4823
 
8.8%
4 4087
 
7.5%
5 3355
 
6.2%
6 3227
 
5.9%
7 2991
 
5.5%
8 2308
 
4.2%
9 2282
 
4.2%
Other values (5) 800
 
1.5%
Hangul
ValueCountFrequency (%)
숭 110
21.8%
연 88
17.4%
영 74
14.7%
종 69
13.7%
신 61
12.1%
세 52
10.3%
사 15
 
3.0%
저 8
 
1.6%
급 8
 
1.6%
소 6
 
1.2%
Other values (5) 14
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54522
99.1%
Hangul 505
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18585
34.1%
1 6350
 
11.6%
2 5714
 
10.5%
3 4823
 
8.8%
4 4087
 
7.5%
5 3355
 
6.2%
6 3227
 
5.9%
7 2991
 
5.5%
8 2308
 
4.2%
9 2282
 
4.2%
Other values (5) 800
 
1.5%
Hangul
ValueCountFrequency (%)
숭 110
21.8%
연 88
17.4%
영 74
14.7%
종 69
13.7%
신 61
12.1%
세 52
10.3%
사 15
 
3.0%
저 8
 
1.6%
급 8
 
1.6%
소 6
 
1.2%
Other values (5) 14
 
2.8%

서소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81688.637
Minimum71250
Maximum93253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:13:04.172931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71250
5-th percentile72250
Q176252
median81251
Q387251
95-th percentile92251
Maximum93253
Range22003
Interquartile range (IQR)10999

Descriptive statistics

Standard deviation6382.5023
Coefficient of variation (CV)0.07813207
Kurtosis-1.1300413
Mean81688.637
Median Absolute Deviation (MAD)5002
Skewness0.091413025
Sum8.1688637 × 108
Variance40736336
MonotonicityNot monotonic
2023-12-11T15:13:04.368119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79250 222
 
2.2%
76250 177
 
1.8%
87250 171
 
1.7%
79252 170
 
1.7%
79251 167
 
1.7%
84250 158
 
1.6%
80253 134
 
1.3%
77251 129
 
1.3%
74251 128
 
1.3%
78250 128
 
1.3%
Other values (105) 8416
84.2%
ValueCountFrequency (%)
71250 58
0.6%
71252 61
0.6%
71253 69
0.7%
71254 88
0.9%
71255 77
0.8%
71257 114
1.1%
72250 100
1.0%
72251 82
0.8%
72252 36
 
0.4%
72254 84
0.8%
ValueCountFrequency (%)
93253 58
0.6%
93252 78
0.8%
93251 72
0.7%
93250 79
0.8%
92253 78
0.8%
92252 94
0.9%
92251 73
0.7%
92250 88
0.9%
91253 120
1.2%
91252 84
0.8%

소화용수구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
502012
5966 
502011
3657 
502012N
 
120
502011N
 
120
50202
 
55
Other values (5)
 
82

Length

Max length7
Median length6
Mean length6.016
Min length5

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row502012
2nd row502011
3rd row502012
4th row502012
5th row502012

Common Values

ValueCountFrequency (%)
502012 5966
59.7%
502011 3657
36.6%
502012N 120
 
1.2%
502011N 120
 
1.2%
50202 55
 
0.5%
50203 26
 
0.3%
50203N 25
 
0.2%
502013 25
 
0.2%
50202N 5
 
0.1%
502013N 1
 
< 0.1%

Length

2023-12-11T15:13:04.523821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:13:04.656640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
502012 5966
59.7%
502011 3657
36.6%
502012n 120
 
1.2%
502011n 120
 
1.2%
50202 55
 
0.5%
50203 26
 
0.3%
50203n 25
 
0.2%
502013 25
 
0.2%
50202n 5
 
< 0.1%
502013n 1
 
< 0.1%

공사설구분
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
True
8678 
False
1322 
ValueCountFrequency (%)
True 8678
86.8%
False 1322
 
13.2%
2023-12-11T15:13:04.775030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

사용구분
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1087
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:13:04.860992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61637838
Coefficient of variation (CV)0.55594694
Kurtosis44.815588
Mean1.1087
Median Absolute Deviation (MAD)0
Skewness6.5702618
Sum11087
Variance0.3799223
MonotonicityNot monotonic
2023-12-11T15:13:04.979012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 9586
95.9%
2 143
 
1.4%
3 116
 
1.2%
6 90
 
0.9%
5 64
 
0.6%
7 1
 
< 0.1%
ValueCountFrequency (%)
1 9586
95.9%
2 143
 
1.4%
3 116
 
1.2%
5 64
 
0.6%
6 90
 
0.9%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 90
 
0.9%
5 64
 
0.6%
3 116
 
1.2%
2 143
 
1.4%
1 9586
95.9%

최종변경일시
Real number (ℝ)

HIGH CORRELATION 

Distinct9966
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0129758 × 1013
Minimum2.0010101 × 1013
Maximum2.0140109 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:13:05.156162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0010101 × 1013
5-th percentile2.0120118 × 1013
Q12.0131205 × 1013
median2.0131217 × 1013
Q32.0131224 × 1013
95-th percentile2.0131229 × 1013
Maximum2.0140109 × 1013
Range1.3000814 × 1011
Interquartile range (IQR)19095250

Descriptive statistics

Standard deviation5.0246184 × 109
Coefficient of variation (CV)0.00024961147
Kurtosis54.934541
Mean2.0129758 × 1013
Median Absolute Deviation (MAD)8059600
Skewness-5.2214435
Sum2.0129758 × 1017
Variance2.524679 × 1019
MonotonicityNot monotonic
2023-12-11T15:13:05.331345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20131227160707 2
 
< 0.1%
20131219162602 2
 
< 0.1%
20131229092229 2
 
< 0.1%
20131223212320 2
 
< 0.1%
20131203151713 2
 
< 0.1%
20060519134505 2
 
< 0.1%
20131229110637 2
 
< 0.1%
20131227094232 2
 
< 0.1%
20131217170134 2
 
< 0.1%
20131229233532 2
 
< 0.1%
Other values (9956) 9980
99.8%
ValueCountFrequency (%)
20010101000000 1
< 0.1%
20060519134505 2
< 0.1%
20070826192221 1
< 0.1%
20070829194421 1
< 0.1%
20070829194817 1
< 0.1%
20080216185151 1
< 0.1%
20080605210432 1
< 0.1%
20081128125209 1
< 0.1%
20090518144957 1
< 0.1%
20090521110201 1
< 0.1%
ValueCountFrequency (%)
20140109144301 1
< 0.1%
20140109085728 1
< 0.1%
20140108192912 1
< 0.1%
20140108111358 1
< 0.1%
20140108101352 1
< 0.1%
20140107210058 1
< 0.1%
20140107104821 1
< 0.1%
20140105215030 1
< 0.1%
20140103200210 1
< 0.1%
20140103154241 1
< 0.1%

새주소
Text

Distinct9510
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T15:13:05.737427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length8.9843
Min length1

Characters and Unicode

Total characters89843
Distinct characters309
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

Unique9206 ?
Unique (%)92.1%

Sample

1st row시흥대로88가길 17
2nd row가좌로 108
3rd row선릉로155길 29
4th row경인로 273
5th row사당로14다길 28
ValueCountFrequency (%)
16 141
 
0.7%
7 133
 
0.7%
13 133
 
0.7%
15 132
 
0.7%
9 130
 
0.7%
17 129
 
0.6%
14 127
 
0.6%
10 126
 
0.6%
22 125
 
0.6%
20 125
 
0.6%
Other values (7065) 18651
93.5%
2023-12-11T15:13:06.361212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9976
 
11.1%
로 9397
 
10.5%
1 7805
 
8.7%
길 6488
 
7.2%
2 5480
 
6.1%
3 4251
 
4.7%
4 3560
 
4.0%
5 3173
 
3.5%
6 2775
 
3.1%
7 2505
 
2.8%
Other values (299) 34433
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41755
46.5%
Decimal Number 36290
40.4%
Space Separator 9976
 
11.1%
Dash Punctuation 1803
 
2.0%
Uppercase Letter 18
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 9397
22.5%
길 6488
 
15.5%
대 1141
 
2.7%
동 855
 
2.0%
가 733
 
1.8%
산 670
 
1.6%
천 510
 
1.2%
봉 434
 
1.0%
도 431
 
1.0%
서 405
 
1.0%
Other values (285) 20691
49.6%
Decimal Number
ValueCountFrequency (%)
1 7805
21.5%
2 5480
15.1%
3 4251
11.7%
4 3560
9.8%
5 3173
8.7%
6 2775
 
7.6%
7 2505
 
6.9%
8 2367
 
6.5%
0 2215
 
6.1%
9 2159
 
5.9%
Space Separator
ValueCountFrequency (%)
9976
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1803
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 18
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48070
53.5%
Hangul 41755
46.5%
Latin 18
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 9397
22.5%
길 6488
 
15.5%
대 1141
 
2.7%
동 855
 
2.0%
가 733
 
1.8%
산 670
 
1.6%
천 510
 
1.2%
봉 434
 
1.0%
도 431
 
1.0%
서 405
 
1.0%
Other values (285) 20691
49.6%
Common
ValueCountFrequency (%)
9976
20.8%
1 7805
16.2%
2 5480
11.4%
3 4251
8.8%
4 3560
 
7.4%
5 3173
 
6.6%
6 2775
 
5.8%
7 2505
 
5.2%
8 2367
 
4.9%
0 2215
 
4.6%
Other values (3) 3963
 
8.2%
Latin
ValueCountFrequency (%)
A 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48088
53.5%
Hangul 41755
46.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9976
20.7%
1 7805
16.2%
2 5480
11.4%
3 4251
8.8%
4 3560
 
7.4%
5 3173
 
6.6%
6 2775
 
5.8%
7 2505
 
5.2%
8 2367
 
4.9%
0 2215
 
4.6%
Other values (4) 3981
 
8.3%
Hangul
ValueCountFrequency (%)
로 9397
22.5%
길 6488
 
15.5%
대 1141
 
2.7%
동 855
 
2.0%
가 733
 
1.8%
산 670
 
1.6%
천 510
 
1.2%
봉 434
 
1.0%
도 431
 
1.0%
서 405
 
1.0%
Other values (285) 20691
49.6%

위도
Real number (ℝ)

Distinct9883
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.548747
Minimum37.434085
Maximum37.693936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:13:06.514645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.434085
5-th percentile37.475798
Q137.504908
median37.546248
Q337.582763
95-th percentile37.646691
Maximum37.693936
Range0.2598513
Interquartile range (IQR)0.07785565

Descriptive statistics

Standard deviation0.052069503
Coefficient of variation (CV)0.0013867174
Kurtosis-0.53514022
Mean37.548747
Median Absolute Deviation (MAD)0.0394323
Skewness0.4140325
Sum375487.47
Variance0.0027112331
MonotonicityNot monotonic
2023-12-11T15:13:06.657845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5563436 61
 
0.6%
37.5524085 23
 
0.2%
37.5007551 8
 
0.1%
37.5294436 2
 
< 0.1%
37.5535737 2
 
< 0.1%
37.5146502 2
 
< 0.1%
37.5949677 2
 
< 0.1%
37.497695 2
 
< 0.1%
37.5135076 2
 
< 0.1%
37.4916523 2
 
< 0.1%
Other values (9873) 9894
98.9%
ValueCountFrequency (%)
37.434085 1
< 0.1%
37.4348341 1
< 0.1%
37.4350649 1
< 0.1%
37.4355805 1
< 0.1%
37.4392621 1
< 0.1%
37.440397 1
< 0.1%
37.4415077 1
< 0.1%
37.4427971 1
< 0.1%
37.4429982 1
< 0.1%
37.4431409 1
< 0.1%
ValueCountFrequency (%)
37.6939363 1
< 0.1%
37.6911082 1
< 0.1%
37.6885686 1
< 0.1%
37.6882697 1
< 0.1%
37.6875017 1
< 0.1%
37.6872813 1
< 0.1%
37.6871679 1
< 0.1%
37.6868375 1
< 0.1%
37.6863979 1
< 0.1%
37.6863227 1
< 0.1%

경도
Real number (ℝ)

Distinct9884
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.99293
Minimum126.79429
Maximum127.17951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T15:13:06.792154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.79429
5-th percentile126.84869
Q1126.92534
median127.00493
Q3127.05318
95-th percentile127.12706
Maximum127.17951
Range0.3852208
Interquartile range (IQR)0.12784027

Descriptive statistics

Standard deviation0.08234253
Coefficient of variation (CV)0.00064840247
Kurtosis-0.74630284
Mean126.99293
Median Absolute Deviation (MAD)0.06161215
Skewness-0.17672908
Sum1269929.3
Variance0.0067802922
MonotonicityNot monotonic
2023-12-11T15:13:06.955832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9961279 61
 
0.6%
127.0004924 23
 
0.2%
127.0369077 8
 
0.1%
126.9102916 2
 
< 0.1%
127.024104 2
 
< 0.1%
127.0857685 2
 
< 0.1%
126.9242825 2
 
< 0.1%
127.0173992 2
 
< 0.1%
127.0293185 2
 
< 0.1%
126.8965728 2
 
< 0.1%
Other values (9874) 9894
98.9%
ValueCountFrequency (%)
126.7942872 1
< 0.1%
126.7987817 1
< 0.1%
126.7992355 1
< 0.1%
126.799258 1
< 0.1%
126.8003358 1
< 0.1%
126.8010134 1
< 0.1%
126.8014471 1
< 0.1%
126.8017106 1
< 0.1%
126.8017272 1
< 0.1%
126.8017358 1
< 0.1%
ValueCountFrequency (%)
127.179508 1
< 0.1%
127.179033 1
< 0.1%
127.17857 1
< 0.1%
127.1766863 1
< 0.1%
127.1764249 1
< 0.1%
127.1731482 1
< 0.1%
127.1731247 1
< 0.1%
127.1729171 1
< 0.1%
127.1727545 1
< 0.1%
127.1726363 1
< 0.1%

Interactions

2023-12-11T15:13:00.531998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:55.225232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:56.371694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:57.191240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:57.853486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:58.643138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:59.563594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:13:00.670819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:55.358625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:56.487459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:57.309423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:57.945491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:58.750136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:59.697844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:13:00.849177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:55.473130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:56.601081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:57.398639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:58.037975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:58.885627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:59.812701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:13:00.971807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:55.588166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:56.701475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:57.475239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:58.137614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:58.992248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:59.938984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:13:01.093005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:55.725120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:56.832722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:57.565870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:58.227749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:59.126320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:13:00.088073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:13:01.224625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:56.131522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:56.959801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:57.658182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:58.333160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:59.250406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:13:00.246967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:13:01.334211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:56.253356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:57.080157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:57.759339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:58.443195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:12:59.427061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:13:00.376281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:13:07.089160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번소화용수ID서소코드소화용수구분코드공사설구분사용구분최종변경일시위도경도
순번1.0000.5920.6000.1690.0750.1460.0790.3600.296
소화용수ID0.5921.0000.9730.0920.1270.0670.1480.8160.807
서소코드0.6000.9731.0000.1030.1370.0800.1600.8030.825
소화용수구분코드0.1690.0920.1031.0000.5940.7350.3310.1200.109
공사설구분0.0750.1270.1370.5941.0000.1270.7030.1490.120
사용구분0.1460.0670.0800.7350.1271.0000.0800.0660.050
최종변경일시0.0790.1480.1600.3310.7030.0801.0000.1350.101
위도0.3600.8160.8030.1200.1490.0660.1351.0000.581
경도0.2960.8070.8250.1090.1200.0500.1010.5811.000
2023-12-11T15:13:07.200403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소화용수구분코드공사설구분
소화용수구분코드1.0000.459
공사설구분0.4591.000
2023-12-11T15:13:07.289271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번소화용수ID서소코드사용구분최종변경일시위도경도소화용수구분코드공사설구분
순번1.0000.0650.0880.082-0.0100.002-0.0360.0530.057
소화용수ID0.0651.0000.855-0.020-0.036-0.259-0.0480.0280.098
서소코드0.0880.8551.000-0.003-0.009-0.112-0.0460.0320.104
사용구분0.082-0.020-0.0031.000-0.024-0.018-0.0330.5030.091
최종변경일시-0.010-0.036-0.009-0.0241.0000.0420.0850.1780.760
위도0.002-0.259-0.112-0.0180.0421.0000.2030.0370.114
경도-0.036-0.048-0.046-0.0330.0850.2031.0000.0340.092
소화용수구분코드0.0530.0280.0320.5030.1780.0370.0341.0000.459
공사설구분0.0570.0980.1040.0910.7600.1140.0920.4591.000

Missing values

2023-12-11T15:13:01.525349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:13:01.755228image/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

순번소화용수ID소화용수번호서소코드소화용수구분코드공사설구분사용구분최종변경일시새주소위도경도
197291316585960141400181685254502012Y120131223211302시흥대로88가길 1737.461713126.900272
733744709929912117343292252502011Y120131227210936가좌로 10837.582296126.926297
103165915979031002400286679251502012Y120131227014814선릉로155길 2937.525826127.037123
377631474585980905900254285250502012Y120131216065818경인로 27337.498427126.851097
431764020791011209700452491253502012Y120131212095451사당로14다길 2837.480242126.968642
5031252385848303004125484250502012Y120131205144624도봉로125길 8837.656337127.037396
385861292777909002438477253502012Y120131229111030장위로38길 19-237.612513127.050915
32909566869912220528986254502012Y120131229154727동일로243길 4237.679021127.053149
87144254987981247500430291253502012Y120131227132553사당로 17437.486236126.968322
256925898288980701160014388255502012Y120131221155108오금로34길 5437.499503127.122073
순번소화용수ID소화용수번호서소코드소화용수구분코드공사설구분사용구분최종변경일시새주소위도경도
38525802782950907200363882250502012Y120131215162255천호대로186길 5237.531452127.142775
283965945283011100800153283250502011Y120131220130711신촌로 10437.55483126.937044
5124457152799912587356679253502011Y120131204195235헌릉로590길 8837.463445127.100914
5896275282991255700502682253502012Y120131228150911올림픽로 80037.552338127.128412
52325279280970803900713580253502012Y120131228200011사임당로23길 1237.490966127.020767
3048515670839912056537583254502011Y120131219103649백범로36가길 1437.544292126.948752
3240740818909910213124090250502012Y120131218170310숙선옹주로3길 2237.615299127.079612
282313265181900500601002081250502012Y120131220133059공항대로45길 2437.556235126.854915
454612600174981107700205974251502012Y120131210162952독서당로20길 1-1237.533713127.009954
605374753175711000400011575251502011N120130427101055경희대로 2337.594179127.051106