Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells11594
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
Categorical1
Numeric7

Dataset

Description서울틀별시 강서구 관측점별 강우량 정보입니다. 측정 장비와 서버간에 통신연결이 원활하지 않아 데이터가 저장되지 않은 경우 공란 및 기호 등으로 표시되었습니다.
Author서울특별시 강서구
URLhttps://www.data.go.kr/data/15087101/fileData.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 1654 (16.5%) missing valuesMissing
가양펌프장 has 1654 (16.5%) missing valuesMissing
마곡2펌프장 has 1654 (16.5%) missing valuesMissing
공항펌프장 has 1654 (16.5%) missing valuesMissing
방화펌프장 has 1654 (16.5%) missing valuesMissing
염창1펌프장 has 1670 (16.7%) missing valuesMissing
화곡동 has 1654 (16.5%) missing valuesMissing
강서구청 is highly skewed (γ1 = -52.38224015)Skewed
가양펌프장 is highly skewed (γ1 = -61.72467378)Skewed
마곡2펌프장 is highly skewed (γ1 = -61.07811776)Skewed
공항펌프장 is highly skewed (γ1 = -63.6349977)Skewed
방화펌프장 is highly skewed (γ1 = -63.30961502)Skewed
화곡동 is highly skewed (γ1 = 91.28557635)Skewed
강서구청 has 7989 (79.9%) zerosZeros
가양펌프장 has 7998 (80.0%) zerosZeros
마곡2펌프장 has 7981 (79.8%) zerosZeros
공항펌프장 has 7960 (79.6%) zerosZeros
방화펌프장 has 7977 (79.8%) zerosZeros
염창1펌프장 has 7896 (79.0%) zerosZeros
화곡동 has 8061 (80.6%) zerosZeros

Reproduction

Analysis started2023-12-11 23:54:46.123151
Analysis finished2023-12-11 23:54:54.018854
Duration7.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Text

Distinct2032
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:54:54.161210image/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

Unique54 ?
Unique (%)0.5%

Sample

1st row2020년 04월 28일
2nd row2016년 02월 17일
3rd row2017년 09월 17일
4th row2016년 04월 09일
5th row2019년 09월 05일
ValueCountFrequency (%)
2018년 1830
 
6.1%
2017년 1803
 
6.0%
2019년 1780
 
5.9%
2016년 1762
 
5.9%
2020년 1629
 
5.4%
2015년 1196
 
4.0%
08월 923
 
3.1%
07월 914
 
3.0%
06월 905
 
3.0%
05월 901
 
3.0%
Other values (39) 16357
54.5%
2023-12-12T08:54:54.556804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23929
18.4%
20000
15.4%
2 17298
13.3%
1 16948
13.0%
년 10000
7.7%
월 10000
7.7%
일 10000
7.7%
8 3718
 
2.9%
6 3666
 
2.8%
7 3665
 
2.8%
Other values (4) 10776
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 23929
29.9%
2 17298
21.6%
1 16948
21.2%
8 3718
 
4.6%
6 3666
 
4.6%
7 3665
 
4.6%
9 3621
 
4.5%
5 3085
 
3.9%
3 2292
 
2.9%
4 1778
 
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 23929
23.9%
20000
20.0%
2 17298
17.3%
1 16948
16.9%
8 3718
 
3.7%
6 3666
 
3.7%
7 3665
 
3.7%
9 3621
 
3.6%
5 3085
 
3.1%
3 2292
 
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 23929
23.9%
20000
20.0%
2 17298
17.3%
1 16948
16.9%
8 3718
 
3.7%
6 3666
 
3.7%
7 3665
 
3.7%
9 3621
 
3.6%
5 3085
 
3.1%
3 2292
 
2.3%
Hangul
ValueCountFrequency (%)
년 10000
33.3%
월 10000
33.3%
일 10000
33.3%

시간
Categorical

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
01:00
 
443
16:00
 
442
09:00
 
440
21:00
 
435
02:00
 
431
Other values (19)
7809 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row08:00
2nd row20:00
3rd row05:00
4th row18:00
5th row01:00

Common Values

ValueCountFrequency (%)
01:00 443
 
4.4%
16:00 442
 
4.4%
09:00 440
 
4.4%
21:00 435
 
4.3%
02:00 431
 
4.3%
04:00 429
 
4.3%
14:00 427
 
4.3%
06:00 427
 
4.3%
18:00 426
 
4.3%
03:00 422
 
4.2%
Other values (14) 5678
56.8%

Length

2023-12-12T08:54:54.734041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01:00 443
 
4.4%
16:00 442
 
4.4%
09:00 440
 
4.4%
21:00 435
 
4.3%
02:00 431
 
4.3%
04:00 429
 
4.3%
14:00 427
 
4.3%
06:00 427
 
4.3%
18:00 426
 
4.3%
03:00 422
 
4.2%
Other values (14) 5678
56.8%

강서구청
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct33
Distinct (%)0.4%
Missing1654
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean-0.1534867
Minimum-846
Maximum52
Zeros7989
Zeros (%)79.9%
Negative5
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T08:54:54.867723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-846
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum52
Range898
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.534715
Coefficient of variation (CV)-101.21212
Kurtosis2768.0189
Mean-0.1534867
Median Absolute Deviation (MAD)0
Skewness-52.38224
Sum-1281
Variance241.32737
MonotonicityNot monotonic
2023-12-12T08:54:55.015237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0 7989
79.9%
1.0 151
 
1.5%
2.0 71
 
0.7%
3.0 29
 
0.3%
4.0 23
 
0.2%
5.0 20
 
0.2%
0.5 8
 
0.1%
10.0 6
 
0.1%
8.0 6
 
0.1%
7.0 6
 
0.1%
Other values (23) 37
 
0.4%
(Missing) 1654
 
16.5%
ValueCountFrequency (%)
-846.0 1
 
< 0.1%
-842.0 1
 
< 0.1%
-759.0 1
 
< 0.1%
-2.0 1
 
< 0.1%
-1.0 1
 
< 0.1%
0.0 7989
79.9%
0.5 8
 
0.1%
1.0 151
 
1.5%
1.5 2
 
< 0.1%
2.0 71
 
0.7%
ValueCountFrequency (%)
52.0 1
< 0.1%
48.0 1
< 0.1%
34.0 1
< 0.1%
24.0 1
< 0.1%
21.0 1
< 0.1%
19.0 1
< 0.1%
18.0 1
< 0.1%
17.0 1
< 0.1%
15.0 2
< 0.1%
14.0 2
< 0.1%

가양펌프장
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct27
Distinct (%)0.3%
Missing1654
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean-0.077402348
Minimum-874
Maximum68
Zeros7998
Zeros (%)80.0%
Negative5
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T08:54:55.139156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-874
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum68
Range942
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.463761
Coefficient of variation (CV)-173.94512
Kurtosis3911.6324
Mean-0.077402348
Median Absolute Deviation (MAD)0
Skewness-61.724674
Sum-646
Variance181.27286
MonotonicityNot monotonic
2023-12-12T08:54:55.258219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 7998
80.0%
1 152
 
1.5%
2 70
 
0.7%
3 28
 
0.3%
4 23
 
0.2%
6 15
 
0.1%
5 13
 
0.1%
7 7
 
0.1%
9 6
 
0.1%
10 6
 
0.1%
Other values (17) 28
 
0.3%
(Missing) 1654
 
16.5%
ValueCountFrequency (%)
-874 1
 
< 0.1%
-836 1
 
< 0.1%
-181 1
 
< 0.1%
-2 1
 
< 0.1%
-1 1
 
< 0.1%
0 7998
80.0%
1 152
 
1.5%
2 70
 
0.7%
3 28
 
0.3%
4 23
 
0.2%
ValueCountFrequency (%)
68 1
 
< 0.1%
58 1
 
< 0.1%
26 2
< 0.1%
25 1
 
< 0.1%
19 1
 
< 0.1%
18 3
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 4
< 0.1%
14 4
< 0.1%

마곡2펌프장
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct28
Distinct (%)0.3%
Missing1654
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean-0.063862928
Minimum-926
Maximum210
Zeros7981
Zeros (%)79.8%
Negative4
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T08:54:55.377601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-926
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum210
Range1136
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.176534
Coefficient of variation (CV)-221.98377
Kurtosis3917.0954
Mean-0.063862928
Median Absolute Deviation (MAD)0
Skewness-61.078118
Sum-533
Variance200.97411
MonotonicityNot monotonic
2023-12-12T08:54:55.507847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 7981
79.8%
1 188
 
1.9%
2 60
 
0.6%
3 29
 
0.3%
4 24
 
0.2%
5 14
 
0.1%
6 10
 
0.1%
7 8
 
0.1%
9 5
 
0.1%
13 4
 
< 0.1%
Other values (18) 23
 
0.2%
(Missing) 1654
 
16.5%
ValueCountFrequency (%)
-926 1
 
< 0.1%
-874 1
 
< 0.1%
-31 1
 
< 0.1%
-2 1
 
< 0.1%
0 7981
79.8%
1 188
 
1.9%
2 60
 
0.6%
3 29
 
0.3%
4 24
 
0.2%
5 14
 
0.1%
ValueCountFrequency (%)
210 1
 
< 0.1%
52 1
 
< 0.1%
28 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
15 3
< 0.1%

공항펌프장
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct27
Distinct (%)0.3%
Missing1654
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean-0.063982746
Minimum-900
Maximum67
Zeros7960
Zeros (%)79.6%
Negative3
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T08:54:55.627294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-900
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum67
Range967
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.969949
Coefficient of variation (CV)-218.33932
Kurtosis4090.9356
Mean-0.063982746
Median Absolute Deviation (MAD)0
Skewness-63.634998
Sum-534
Variance195.15948
MonotonicityNot monotonic
2023-12-12T08:54:55.817343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 7960
79.6%
1 178
 
1.8%
2 78
 
0.8%
3 38
 
0.4%
4 21
 
0.2%
5 19
 
0.2%
7 8
 
0.1%
6 6
 
0.1%
12 6
 
0.1%
9 5
 
0.1%
Other values (17) 27
 
0.3%
(Missing) 1654
 
16.5%
ValueCountFrequency (%)
-900 1
 
< 0.1%
-896 1
 
< 0.1%
-2 1
 
< 0.1%
0 7960
79.6%
1 178
 
1.8%
2 78
 
0.8%
3 38
 
0.4%
4 21
 
0.2%
5 19
 
0.2%
6 6
 
0.1%
ValueCountFrequency (%)
67 1
< 0.1%
52 1
< 0.1%
29 1
< 0.1%
26 1
< 0.1%
23 1
< 0.1%
21 1
< 0.1%
19 1
< 0.1%
16 1
< 0.1%
15 2
< 0.1%
14 1
< 0.1%

방화펌프장
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct27
Distinct (%)0.3%
Missing1654
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean-0.05248023
Minimum-805
Maximum93
Zeros7977
Zeros (%)79.8%
Negative4
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T08:54:55.966811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-805
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum93
Range898
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.495938
Coefficient of variation (CV)-238.10754
Kurtosis4066.1257
Mean-0.05248023
Median Absolute Deviation (MAD)0
Skewness-63.309615
Sum-438
Variance156.14847
MonotonicityNot monotonic
2023-12-12T08:54:56.110477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 7977
79.8%
1 174
 
1.7%
2 76
 
0.8%
3 27
 
0.3%
4 25
 
0.2%
6 14
 
0.1%
5 13
 
0.1%
7 7
 
0.1%
8 4
 
< 0.1%
12 3
 
< 0.1%
Other values (17) 26
 
0.3%
(Missing) 1654
 
16.5%
ValueCountFrequency (%)
-805 1
 
< 0.1%
-799 1
 
< 0.1%
-3 1
 
< 0.1%
-1 1
 
< 0.1%
0 7977
79.8%
1 174
 
1.7%
2 76
 
0.8%
3 27
 
0.3%
4 25
 
0.2%
5 13
 
0.1%
ValueCountFrequency (%)
93 1
 
< 0.1%
32 1
 
< 0.1%
22 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
15 2
< 0.1%
14 2
< 0.1%
13 3
< 0.1%

염창1펌프장
Real number (ℝ)

MISSING  ZEROS 

Distinct40
Distinct (%)0.5%
Missing1670
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean-0.0022208884
Minimum-1033
Maximum759
Zeros7896
Zeros (%)79.0%
Negative215
Negative (%)2.1%
Memory size166.0 KiB
2023-12-12T08:54:56.259048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1033
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum759
Range1792
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.26365
Coefficient of variation (CV)-8223.5784
Kurtosis2253.6292
Mean-0.0022208884
Median Absolute Deviation (MAD)0
Skewness-13.96019
Sum-18.5
Variance333.5609
MonotonicityNot monotonic
2023-12-12T08:54:56.391844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 7896
79.0%
-1.0 121
 
1.2%
1.0 112
 
1.1%
2.0 40
 
0.4%
-2.0 35
 
0.4%
-3.0 18
 
0.2%
3.0 15
 
0.1%
-4.0 13
 
0.1%
4.0 9
 
0.1%
5.0 7
 
0.1%
Other values (30) 64
 
0.6%
(Missing) 1670
 
16.7%
ValueCountFrequency (%)
-1033.0 1
 
< 0.1%
-759.0 1
 
< 0.1%
-14.0 1
 
< 0.1%
-13.0 2
 
< 0.1%
-10.0 1
 
< 0.1%
-9.0 2
 
< 0.1%
-8.0 4
< 0.1%
-7.0 4
< 0.1%
-6.0 3
< 0.1%
-5.0 5
0.1%
ValueCountFrequency (%)
759.0 1
 
< 0.1%
732.0 1
 
< 0.1%
120.0 1
 
< 0.1%
46.0 1
 
< 0.1%
34.0 1
 
< 0.1%
19.0 1
 
< 0.1%
16.0 1
 
< 0.1%
14.0 1
 
< 0.1%
13.0 3
< 0.1%
12.0 1
 
< 0.1%

화곡동
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct34
Distinct (%)0.4%
Missing1654
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean7.2535346
Minimum-901
Maximum61161
Zeros8061
Zeros (%)80.6%
Negative5
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T08:54:56.547746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-901
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum61161
Range62062
Interquartile range (IQR)0

Descriptive statistics

Standard deviation669.64911
Coefficient of variation (CV)92.320385
Kurtosis8337.4405
Mean7.2535346
Median Absolute Deviation (MAD)0
Skewness91.285576
Sum60538
Variance448429.93
MonotonicityNot monotonic
2023-12-12T08:54:56.700098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 8061
80.6%
1 126
 
1.3%
2 43
 
0.4%
3 28
 
0.3%
4 14
 
0.1%
5 13
 
0.1%
6 10
 
0.1%
7 9
 
0.1%
9 4
 
< 0.1%
14 4
 
< 0.1%
Other values (24) 34
 
0.3%
(Missing) 1654
 
16.5%
ValueCountFrequency (%)
-901 1
 
< 0.1%
-891 1
 
< 0.1%
-422 1
 
< 0.1%
-18 1
 
< 0.1%
-1 1
 
< 0.1%
0 8061
80.6%
1 126
 
1.3%
2 43
 
0.4%
3 28
 
0.3%
4 14
 
0.1%
ValueCountFrequency (%)
61161 1
< 0.1%
307 1
< 0.1%
140 1
< 0.1%
88 1
< 0.1%
50 1
< 0.1%
33 1
< 0.1%
32 2
< 0.1%
25 1
< 0.1%
22 1
< 0.1%
20 1
< 0.1%

Interactions

2023-12-12T08:54:52.663955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:47.707628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:48.570317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:49.343848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:50.195853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:51.034502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:51.856212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:52.766635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:47.823711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:48.674814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:49.471392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:50.314509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:51.132777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:51.976341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:52.866251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:47.935556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:48.767588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:49.569298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:50.428246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:51.251763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:52.095461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:52.968848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:48.037062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:48.884665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:49.671020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:50.527894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:51.364047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:52.212034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:53.064709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:48.148885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:48.993734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:49.787675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:50.651771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:51.483657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:52.342633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:53.419185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:48.285881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:49.106278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:49.898623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:50.776992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:51.616277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:52.444092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:53.517779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:48.436325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:49.227111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:50.061556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:50.932037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:51.742721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:52.558514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:54:56.809751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간강서구청가양펌프장마곡2펌프장공항펌프장방화펌프장염창1펌프장화곡동
시간1.0000.0640.0000.0000.0080.0260.0000.000
강서구청0.0641.0000.4570.895NaN0.0001.0000.000
가양펌프장0.0000.4571.0000.676NaN0.0000.0000.000
마곡2펌프장0.0000.8950.6761.000NaN0.0000.0000.000
공항펌프장0.008NaNNaNNaN1.0001.000NaN0.000
방화펌프장0.0260.0000.0000.0001.0001.0000.2240.000
염창1펌프장0.0001.0000.0000.000NaN0.2241.0000.000
화곡동0.0000.0000.0000.0000.0000.0000.0001.000
2023-12-12T08:54:56.958412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강서구청가양펌프장마곡2펌프장공항펌프장방화펌프장염창1펌프장화곡동시간
강서구청1.0000.7500.7280.7660.6990.3970.6840.040
가양펌프장0.7501.0000.7200.7350.7020.1870.6780.000
마곡2펌프장0.7280.7201.0000.7570.7080.1930.6350.000
공항펌프장0.7660.7350.7571.0000.7530.1770.6580.000
방화펌프장0.6990.7020.7080.7531.0000.1490.6020.007
염창1펌프장0.3970.1870.1930.1770.1491.0000.1730.000
화곡동0.6840.6780.6350.6580.6020.1731.0000.000
시간0.0400.0000.0000.0000.0070.0000.0001.000

Missing values

2023-12-12T08:54:53.646834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:54:53.773654image/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.
2023-12-12T08:54:53.922438image/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
437862020년 04월 28일08:000.000000.00
70302016년 02월 17일20:000.000000.00
208872017년 09월 17일05:00<NA><NA><NA><NA><NA><NA><NA>
82762016년 04월 09일18:000.000000.00
381152019년 09월 05일01:000.000000.00
251762018년 03월 14일22:00<NA><NA><NA><NA><NA><NA><NA>
367902019년 07월 11일20:000.000000.00
343642019년 04월 01일18:000.000000.00
466222020년 08월 24일12:000.000000.00
65352016년 01월 28일05:000.000000.00
일자시간강서구청가양펌프장마곡2펌프장공항펌프장방화펌프장염창1펌프장화곡동
242072018년 02월 02일13:00<NA><NA><NA><NA><NA><NA><NA>
34562015년 09월 22일00:000.000000.00
199612017년 08월 09일15:000.000000.00
426152020년 03월 10일13:000.000000.00
48262015년 11월 18일01:000.000000.00
246282018년 02월 20일02:00<NA><NA><NA><NA><NA><NA><NA>
192802017년 07월 12일06:000.000000.00
409802020년 01월 02일10:000.000000.00
396522019년 11월 08일02:000.000000.00
409932020년 01월 02일23:000.000000.00