Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells11654
Missing cells (%)12.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory849.6 KiB
Average record size in memory87.0 B

Variable types

Text1
DateTime1
Numeric7

Dataset

Description파일 다운로드
Author강서구
URLhttps://data.seoul.go.kr/dataList/OA-21813/F/1/datasetView.do

Alerts

강서구청 is highly overall correlated with 가양펌프장 and 4 other fieldsHigh correlation
가양펌프장 is highly overall correlated with 강서구청 and 4 other fieldsHigh correlation
마곡2펌프장 is highly overall correlated with 강서구청 and 4 other fieldsHigh correlation
공항펌프장 is highly overall correlated with 강서구청 and 4 other fieldsHigh correlation
방화펌프장 is highly overall correlated with 강서구청 and 4 other fieldsHigh correlation
화곡동 is highly overall correlated with 강서구청 and 4 other fieldsHigh correlation
강서구청 has 1663 (16.6%) missing valuesMissing
가양펌프장 has 1663 (16.6%) missing valuesMissing
마곡2펌프장 has 1663 (16.6%) missing valuesMissing
공항펌프장 has 1663 (16.6%) missing valuesMissing
방화펌프장 has 1663 (16.6%) missing valuesMissing
염창1펌프장 has 1676 (16.8%) missing valuesMissing
화곡동 has 1663 (16.6%) missing valuesMissing
가양펌프장 is highly skewed (γ1 = -49.8792796)Skewed
마곡2펌프장 is highly skewed (γ1 = -52.62960771)Skewed
방화펌프장 is highly skewed (γ1 = -52.33668259)Skewed
염창1펌프장 is highly skewed (γ1 = -31.85245724)Skewed
화곡동 is highly skewed (γ1 = -60.66829403)Skewed
강서구청 has 8006 (80.1%) zerosZeros
가양펌프장 has 8012 (80.1%) zerosZeros
마곡2펌프장 has 7981 (79.8%) zerosZeros
공항펌프장 has 7985 (79.8%) zerosZeros
방화펌프장 has 7999 (80.0%) zerosZeros
염창1펌프장 has 7925 (79.2%) zerosZeros
화곡동 has 8077 (80.8%) zerosZeros

Reproduction

Analysis started2023-12-11 06:36:40.764579
Analysis finished2023-12-11 06:36:51.751428
Duration10.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Text

Distinct2035
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T15:36:51.963215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters130000
Distinct characters14
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

Unique68 ?
Unique (%)0.7%

Sample

1st row2020년 01월 06일
2nd row2019년 10월 14일
3rd row2019년 01월 22일
4th row2015년 07월 03일
5th row2018년 09월 24일
ValueCountFrequency (%)
2018년 1834
 
6.1%
2017년 1800
 
6.0%
2016년 1759
 
5.9%
2019년 1729
 
5.8%
2020년 1641
 
5.5%
2015년 1237
 
4.1%
07월 947
 
3.2%
08월 926
 
3.1%
05월 912
 
3.0%
11월 895
 
3.0%
Other values (39) 16320
54.4%
2023-12-11T15:36:52.655444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23921
18.4%
20000
15.4%
2 17213
13.2%
1 17039
13.1%
년 10000
7.7%
월 10000
7.7%
일 10000
7.7%
7 3774
 
2.9%
8 3705
 
2.9%
6 3614
 
2.8%
Other values (4) 10734
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
61.5%
Other Letter 30000
 
23.1%
Space Separator 20000
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23921
29.9%
2 17213
21.5%
1 17039
21.3%
7 3774
 
4.7%
8 3705
 
4.6%
6 3614
 
4.5%
9 3560
 
4.5%
5 3138
 
3.9%
3 2291
 
2.9%
4 1745
 
2.2%
Other Letter
ValueCountFrequency (%)
년 10000
33.3%
월 10000
33.3%
일 10000
33.3%
Space Separator
ValueCountFrequency (%)
20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100000
76.9%
Hangul 30000
 
23.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23921
23.9%
20000
20.0%
2 17213
17.2%
1 17039
17.0%
7 3774
 
3.8%
8 3705
 
3.7%
6 3614
 
3.6%
9 3560
 
3.6%
5 3138
 
3.1%
3 2291
 
2.3%
Hangul
ValueCountFrequency (%)
년 10000
33.3%
월 10000
33.3%
일 10000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
76.9%
Hangul 30000
 
23.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23921
23.9%
20000
20.0%
2 17213
17.2%
1 17039
17.0%
7 3774
 
3.8%
8 3705
 
3.7%
6 3614
 
3.6%
9 3560
 
3.6%
5 3138
 
3.1%
3 2291
 
2.3%
Hangul
ValueCountFrequency (%)
년 10000
33.3%
월 10000
33.3%
일 10000
33.3%

시간
Date

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-12-11 00:00:00
Maximum2023-12-11 23:00:00
2023-12-11T15:36:52.799297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:52.931262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

강서구청
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)0.4%
Missing1663
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean-0.025968574
Minimum-846
Maximum1033
Zeros8006
Zeros (%)80.1%
Negative3
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-11T15:36:53.057330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-846
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1033
Range1879
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.127969
Coefficient of variation (CV)-736.58144
Kurtosis2184.4705
Mean-0.025968574
Median Absolute Deviation (MAD)0
Skewness-8.375329
Sum-216.5
Variance365.87921
MonotonicityNot monotonic
2023-12-11T15:36:53.214967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0 8006
80.1%
1.0 141
 
1.4%
2.0 62
 
0.6%
3.0 35
 
0.4%
5.0 16
 
0.2%
7.0 11
 
0.1%
4.0 10
 
0.1%
0.5 8
 
0.1%
6.0 6
 
0.1%
10.0 4
 
< 0.1%
Other values (23) 38
 
0.4%
(Missing) 1663
 
16.6%
ValueCountFrequency (%)
-846.0 1
 
< 0.1%
-842.0 1
 
< 0.1%
-731.0 1
 
< 0.1%
0.0 8006
80.1%
0.5 8
 
0.1%
1.0 141
 
1.4%
1.5 2
 
< 0.1%
2.0 62
 
0.6%
2.5 2
 
< 0.1%
3.0 35
 
0.4%
ValueCountFrequency (%)
1033.0 1
 
< 0.1%
124.0 1
 
< 0.1%
24.0 1
 
< 0.1%
22.0 1
 
< 0.1%
21.0 2
< 0.1%
20.0 3
< 0.1%
17.0 1
 
< 0.1%
16.0 1
 
< 0.1%
15.0 2
< 0.1%
14.0 1
 
< 0.1%

가양펌프장
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct29
Distinct (%)0.3%
Missing1663
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean-0.16708648
Minimum-874
Maximum193
Zeros8012
Zeros (%)80.1%
Negative4
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-11T15:36:53.337592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-874
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum193
Range1067
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.223222
Coefficient of variation (CV)-97.09476
Kurtosis2591.9789
Mean-0.16708648
Median Absolute Deviation (MAD)0
Skewness-49.87928
Sum-1393
Variance263.19293
MonotonicityNot monotonic
2023-12-11T15:36:53.456059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 8012
80.1%
1 151
 
1.5%
2 48
 
0.5%
3 38
 
0.4%
4 17
 
0.2%
5 12
 
0.1%
6 11
 
0.1%
9 8
 
0.1%
7 5
 
0.1%
8 5
 
0.1%
Other values (19) 30
 
0.3%
(Missing) 1663
 
16.6%
ValueCountFrequency (%)
-874 1
 
< 0.1%
-836 1
 
< 0.1%
-807 1
 
< 0.1%
-181 1
 
< 0.1%
0 8012
80.1%
1 151
 
1.5%
2 48
 
0.5%
3 38
 
0.4%
4 17
 
0.2%
5 12
 
0.1%
ValueCountFrequency (%)
193 1
 
< 0.1%
29 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
20 2
< 0.1%
19 1
 
< 0.1%
18 3
< 0.1%
17 1
 
< 0.1%
16 2
< 0.1%

마곡2펌프장
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct23
Distinct (%)0.3%
Missing1663
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean-0.1856783
Minimum-926
Maximum34
Zeros7981
Zeros (%)79.8%
Negative4
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-11T15:36:53.593213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-926
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum34
Range960
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.468149
Coefficient of variation (CV)-88.691832
Kurtosis2792.601
Mean-0.1856783
Median Absolute Deviation (MAD)0
Skewness-52.629608
Sum-1548
Variance271.19992
MonotonicityNot monotonic
2023-12-11T15:36:53.745704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 7981
79.8%
1 189
 
1.9%
2 55
 
0.5%
3 30
 
0.3%
4 23
 
0.2%
5 11
 
0.1%
6 7
 
0.1%
7 6
 
0.1%
9 6
 
0.1%
22 5
 
0.1%
Other values (13) 24
 
0.2%
(Missing) 1663
 
16.6%
ValueCountFrequency (%)
-926 1
 
< 0.1%
-874 1
 
< 0.1%
-793 1
 
< 0.1%
-31 1
 
< 0.1%
0 7981
79.8%
1 189
 
1.9%
2 55
 
0.5%
3 30
 
0.3%
4 23
 
0.2%
5 11
 
0.1%
ValueCountFrequency (%)
34 1
 
< 0.1%
29 1
 
< 0.1%
22 5
0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 1
 
< 0.1%
14 3
< 0.1%
13 4
< 0.1%
11 2
 
< 0.1%
9 6
0.1%

공항펌프장
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)0.4%
Missing1663
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean-0.048338731
Minimum-900
Maximum1116
Zeros7985
Zeros (%)79.8%
Negative5
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-11T15:36:53.891782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-900
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1116
Range2016
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.744416
Coefficient of variation (CV)-429.14689
Kurtosis2180.8347
Mean-0.048338731
Median Absolute Deviation (MAD)0
Skewness-8.9723247
Sum-403
Variance430.3308
MonotonicityNot monotonic
2023-12-11T15:36:54.042996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 7985
79.8%
1 159
 
1.6%
2 76
 
0.8%
4 25
 
0.2%
3 22
 
0.2%
5 15
 
0.1%
8 9
 
0.1%
10 6
 
0.1%
7 5
 
0.1%
12 4
 
< 0.1%
Other values (20) 31
 
0.3%
(Missing) 1663
 
16.6%
ValueCountFrequency (%)
-900 1
 
< 0.1%
-896 1
 
< 0.1%
-848 1
 
< 0.1%
-5 1
 
< 0.1%
-1 1
 
< 0.1%
0 7985
79.8%
1 159
 
1.6%
2 76
 
0.8%
3 22
 
0.2%
4 25
 
0.2%
ValueCountFrequency (%)
1116 1
< 0.1%
32 1
< 0.1%
29 1
< 0.1%
26 1
< 0.1%
20 1
< 0.1%
19 1
< 0.1%
18 2
< 0.1%
17 1
< 0.1%
16 1
< 0.1%
15 2
< 0.1%

방화펌프장
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct26
Distinct (%)0.3%
Missing1663
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean-0.17836152
Minimum-861
Maximum33
Zeros7999
Zeros (%)80.0%
Negative5
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-11T15:36:54.193347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-861
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum33
Range894
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.640783
Coefficient of variation (CV)-87.691464
Kurtosis2756.9279
Mean-0.17836152
Median Absolute Deviation (MAD)0
Skewness-52.336683
Sum-1487
Variance244.63409
MonotonicityNot monotonic
2023-12-11T15:36:54.324854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 7999
80.0%
1 163
 
1.6%
2 70
 
0.7%
3 28
 
0.3%
4 17
 
0.2%
6 9
 
0.1%
11 6
 
0.1%
7 6
 
0.1%
5 6
 
0.1%
8 5
 
0.1%
Other values (16) 28
 
0.3%
(Missing) 1663
 
16.6%
ValueCountFrequency (%)
-861 1
 
< 0.1%
-805 1
 
< 0.1%
-799 1
 
< 0.1%
-52 1
 
< 0.1%
-5 1
 
< 0.1%
0 7999
80.0%
1 163
 
1.6%
2 70
 
0.7%
3 28
 
0.3%
4 17
 
0.2%
ValueCountFrequency (%)
33 1
 
< 0.1%
26 2
 
< 0.1%
22 2
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
13 1
 
< 0.1%
12 3
< 0.1%
11 6
0.1%

염창1펌프장
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct49
Distinct (%)0.6%
Missing1676
Missing (%)16.8%
Infinite0
Infinite (%)0.0%
Mean-0.033637674
Minimum-1463
Maximum1033
Zeros7925
Zeros (%)79.2%
Negative189
Negative (%)1.9%
Memory size166.0 KiB
2023-12-11T15:36:54.454995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1463
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1033
Range2496
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.701651
Coefficient of variation (CV)-585.70195
Kurtosis4561.6202
Mean-0.033637674
Median Absolute Deviation (MAD)0
Skewness-31.852457
Sum-280
Variance388.15506
MonotonicityNot monotonic
2023-12-11T15:36:54.631186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0 7925
79.2%
1.0 104
 
1.0%
-1.0 97
 
1.0%
2.0 35
 
0.4%
-2.0 30
 
0.3%
3.0 18
 
0.2%
4.0 12
 
0.1%
-3.0 12
 
0.1%
-4.0 8
 
0.1%
-6.0 7
 
0.1%
Other values (39) 76
 
0.8%
(Missing) 1676
 
16.8%
ValueCountFrequency (%)
-1463.0 1
 
< 0.1%
-22.0 1
 
< 0.1%
-20.0 1
 
< 0.1%
-19.0 1
 
< 0.1%
-16.0 1
 
< 0.1%
-15.0 1
 
< 0.1%
-14.0 1
 
< 0.1%
-13.0 1
 
< 0.1%
-12.0 3
< 0.1%
-11.0 1
 
< 0.1%
ValueCountFrequency (%)
1033.0 1
< 0.1%
123.0 1
< 0.1%
24.0 1
< 0.1%
22.0 1
< 0.1%
17.0 1
< 0.1%
15.0 1
< 0.1%
13.0 2
< 0.1%
12.0 2
< 0.1%
10.0 2
< 0.1%
9.0 1
< 0.1%

화곡동
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct25
Distinct (%)0.3%
Missing1663
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean-0.067890128
Minimum-901
Maximum222
Zeros8077
Zeros (%)80.8%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-11T15:36:54.799566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-901
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum222
Range1123
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.133089
Coefficient of variation (CV)-208.1759
Kurtosis3882.418
Mean-0.067890128
Median Absolute Deviation (MAD)0
Skewness-60.668294
Sum-566
Variance199.74419
MonotonicityNot monotonic
2023-12-11T15:36:54.929441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 8077
80.8%
1 116
 
1.2%
2 33
 
0.3%
3 29
 
0.3%
4 18
 
0.2%
5 11
 
0.1%
6 7
 
0.1%
8 7
 
0.1%
19 5
 
0.1%
9 5
 
0.1%
Other values (15) 29
 
0.3%
(Missing) 1663
 
16.6%
ValueCountFrequency (%)
-901 1
 
< 0.1%
-891 1
 
< 0.1%
0 8077
80.8%
1 116
 
1.2%
2 33
 
0.3%
3 29
 
0.3%
4 18
 
0.2%
5 11
 
0.1%
6 7
 
0.1%
7 5
 
0.1%
ValueCountFrequency (%)
222 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
22 2
 
< 0.1%
19 5
0.1%
16 3
< 0.1%
15 4
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%

Interactions

2023-12-11T15:36:49.833022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:42.868566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:44.178862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:45.325328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:46.433191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:47.629072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:48.615519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:50.020751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:43.068584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:44.357843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:45.475419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:46.620028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:47.776405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:48.794163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:50.173617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:43.262292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:44.525012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:45.645241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:46.773298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:47.905656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:48.965011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:50.323456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:43.443321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:44.690719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:45.788027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:46.926405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:48.049604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:49.129219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:50.513163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:43.632740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:44.875360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:45.954415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:47.134422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:48.202577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:49.293884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:50.726426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:43.821971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:45.007208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:46.103338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:47.317291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:48.328608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:49.480231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:50.936238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:43.991030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:45.164817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:46.267692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:47.475366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:48.470576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:36:49.684864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:36:55.028903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간강서구청가양펌프장마곡2펌프장공항펌프장방화펌프장염창1펌프장화곡동
시간1.0000.0000.0160.0170.0000.0000.0070.000
강서구청0.0001.0000.8950.6761.0001.0001.0000.000
가양펌프장0.0160.8951.0000.6760.6761.0000.0000.000
마곡2펌프장0.0170.6760.6761.0001.0000.707NaN0.000
공항펌프장0.0001.0000.6761.0001.0001.0001.0000.827
방화펌프장0.0001.0001.0000.7071.0001.000NaN1.000
염창1펌프장0.0071.0000.000NaN1.000NaN1.0000.000
화곡동0.0000.0000.0000.0000.8271.0000.0001.000
2023-12-11T15:36:55.177930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강서구청가양펌프장마곡2펌프장공항펌프장방화펌프장염창1펌프장화곡동
강서구청1.0000.7480.7160.7620.7100.4090.700
가양펌프장0.7481.0000.7230.7370.6960.1940.666
마곡2펌프장0.7160.7231.0000.7720.7320.2080.624
공항펌프장0.7620.7370.7721.0000.7510.2090.656
방화펌프장0.7100.6960.7320.7511.0000.1680.611
염창1펌프장0.4090.1940.2080.2090.1681.0000.179
화곡동0.7000.6660.6240.6560.6110.1791.000

Missing values

2023-12-11T15:36:51.145160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:36:51.371511image/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.
2023-12-11T15:36:51.605823image/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

일자시간강서구청가양펌프장마곡2펌프장공항펌프장방화펌프장염창1펌프장화곡동
410672020년 01월 06일01:000.000000.00
390502019년 10월 14일00:000.000000.00
327092019년 01월 22일19:000.000000.00
15122015년 07월 03일00:000.000000.00
298122018년 09월 24일02:000.000000.00
18152015년 07월 15일15:000.000000.00
89812016년 05월 09일03:000.000000.00
342202019년 03월 26일18:000.000000.00
45672015년 11월 07일06:000.000010.00
336782019년 03월 04일04:000.000000.00
일자시간강서구청가양펌프장마곡2펌프장공항펌프장방화펌프장염창1펌프장화곡동
341172019년 03월 22일11:000.000000.00
16992015년 07월 10일19:000.000000.00
39382015년 10월 12일02:000.000000.00
263262018년 05월 01일20:000.000000.00
255632018년 03월 31일01:000.000000.00
148462017년 01월 08일12:00<NA><NA><NA><NA><NA><NA><NA>
164342017년 03월 15일16:000.000000.00
2432015년 05월 11일03:000.000000.00
283252018년 07월 24일03:000.000000.00
460222020년 07월 30일12:000.000000.00