Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory888.7 KiB
Average record size in memory91.0 B

Variable types

Numeric2
Categorical3
Text5

Alerts

last_load_dttm is highly overall correlated with skey and 1 other fieldsHigh correlation
d_year is highly overall correlated with skey and 1 other fieldsHigh correlation
skey is highly overall correlated with d_year and 1 other fieldsHigh correlation
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 07:50:22.623626
Analysis finished2024-04-16 07:50:23.843955
Duration1.22 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7705.5401
Minimum1
Maximum15469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:23.901563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile760.9
Q13838.75
median7711
Q311555.25
95-th percentile14717.05
Maximum15469
Range15468
Interquartile range (IQR)7716.5

Descriptive statistics

Standard deviation4456.2632
Coefficient of variation (CV)0.57831938
Kurtosis-1.1915311
Mean7705.5401
Median Absolute Deviation (MAD)3856.5
Skewness0.008650093
Sum77055401
Variance19858281
MonotonicityNot monotonic
2024-04-16T16:50:24.011983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8436 1
 
< 0.1%
13611 1
 
< 0.1%
1119 1
 
< 0.1%
6950 1
 
< 0.1%
10430 1
 
< 0.1%
8580 1
 
< 0.1%
5425 1
 
< 0.1%
14560 1
 
< 0.1%
10439 1
 
< 0.1%
3510 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
15469 1
< 0.1%
15468 1
< 0.1%
15466 1
< 0.1%
15465 1
< 0.1%
15464 1
< 0.1%
15463 1
< 0.1%
15462 1
< 0.1%
15461 1
< 0.1%
15459 1
< 0.1%
15458 1
< 0.1%

d_year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2015
2029 
2016
2027 
2018
2006 
2017
2000 
2019
1938 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2015
3rd row2019
4th row2017
5th row2019

Common Values

ValueCountFrequency (%)
2015 2029
20.3%
2016 2027
20.3%
2018 2006
20.1%
2017 2000
20.0%
2019 1938
19.4%

Length

2024-04-16T16:50:24.119605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:50:24.212326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 2029
20.3%
2016 2027
20.3%
2018 2006
20.1%
2017 2000
20.0%
2019 1938
19.4%

d_month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5025
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:24.315396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4488524
Coefficient of variation (CV)0.53038869
Kurtosis-1.2169811
Mean6.5025
Median Absolute Deviation (MAD)3
Skewness0.00077357365
Sum65025
Variance11.894583
MonotonicityNot monotonic
2024-04-16T16:50:24.421729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 866
8.7%
10 857
8.6%
7 847
8.5%
5 846
8.5%
11 832
8.3%
1 831
8.3%
12 828
8.3%
8 825
8.2%
3 824
8.2%
2 820
8.2%
Other values (2) 1624
16.2%
ValueCountFrequency (%)
1 831
8.3%
2 820
8.2%
3 824
8.2%
4 866
8.7%
5 846
8.5%
6 805
8.1%
7 847
8.5%
8 825
8.2%
9 819
8.2%
10 857
8.6%
ValueCountFrequency (%)
12 828
8.3%
11 832
8.3%
10 857
8.6%
9 819
8.2%
8 825
8.2%
7 847
8.5%
6 805
8.1%
5 846
8.5%
4 866
8.7%
3 824
8.2%

sigungu
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 기장군
2792 
부산광역시 중구
1593 
부산광역시 서구
957 
부산광역시 강서구
858 
부산광역시 영도구
806 
Other values (11)
2994 

Length

Max length10
Median length9
Mean length8.7608
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 사상구
2nd row부산광역시 사상구
3rd row부산광역시 기장군
4th row부산광역시 연제구
5th row부산광역시 영도구

Common Values

ValueCountFrequency (%)
부산광역시 기장군 2792
27.9%
부산광역시 중구 1593
15.9%
부산광역시 서구 957
 
9.6%
부산광역시 강서구 858
 
8.6%
부산광역시 영도구 806
 
8.1%
부산광역시 금정구 497
 
5.0%
부산광역시 부산진구 409
 
4.1%
부산광역시 동래구 364
 
3.6%
부산광역시 해운대구 311
 
3.1%
부산광역시 사상구 299
 
3.0%
Other values (6) 1114
 
11.1%

Length

2024-04-16T16:50:24.547452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
기장군 2792
 
14.0%
중구 1593
 
8.0%
서구 957
 
4.8%
강서구 858
 
4.3%
영도구 806
 
4.0%
금정구 497
 
2.5%
부산진구 409
 
2.0%
동래구 364
 
1.8%
해운대구 311
 
1.6%
Other values (7) 1413
 
7.1%

area
Text

Distinct261
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:24.806769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.692
Min length2

Characters and Unicode

Total characters46920
Distinct characters144
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row엄궁동
2nd row엄궁동
3rd row장안읍 고리
4th row연산동
5th row봉래동4가
ValueCountFrequency (%)
기장읍 630
 
5.0%
장안읍 535
 
4.2%
일광면 515
 
4.1%
철마면 422
 
3.3%
정관읍 393
 
3.1%
정관면 297
 
2.3%
달산리 82
 
0.6%
송정동 81
 
0.6%
용수리 80
 
0.6%
방곡리 79
 
0.6%
Other values (244) 9577
75.5%
2024-04-16T16:50:25.163514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7861
 
16.8%
가 3162
 
6.7%
리 2728
 
5.8%
2691
 
5.7%
읍 1601
 
3.4%
장 1425
 
3.0%
면 1234
 
2.6%
2 923
 
2.0%
정 919
 
2.0%
1 913
 
1.9%
Other values (134) 23463
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41031
87.4%
Decimal Number 3198
 
6.8%
Space Separator 2691
 
5.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7861
19.2%
가 3162
 
7.7%
리 2728
 
6.6%
읍 1601
 
3.9%
장 1425
 
3.5%
면 1234
 
3.0%
정 919
 
2.2%
광 898
 
2.2%
대 890
 
2.2%
안 738
 
1.8%
Other values (126) 19575
47.7%
Decimal Number
ValueCountFrequency (%)
2 923
28.9%
1 913
28.5%
3 676
21.1%
4 348
 
10.9%
5 207
 
6.5%
6 84
 
2.6%
7 47
 
1.5%
Space Separator
ValueCountFrequency (%)
2691
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41031
87.4%
Common 5889
 
12.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7861
19.2%
가 3162
 
7.7%
리 2728
 
6.6%
읍 1601
 
3.9%
장 1425
 
3.5%
면 1234
 
3.0%
정 919
 
2.2%
광 898
 
2.2%
대 890
 
2.2%
안 738
 
1.8%
Other values (126) 19575
47.7%
Common
ValueCountFrequency (%)
2691
45.7%
2 923
 
15.7%
1 913
 
15.5%
3 676
 
11.5%
4 348
 
5.9%
5 207
 
3.5%
6 84
 
1.4%
7 47
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41031
87.4%
ASCII 5889
 
12.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7861
19.2%
가 3162
 
7.7%
리 2728
 
6.6%
읍 1601
 
3.9%
장 1425
 
3.5%
면 1234
 
3.0%
정 919
 
2.2%
광 898
 
2.2%
대 890
 
2.2%
안 738
 
1.8%
Other values (126) 19575
47.7%
ASCII
ValueCountFrequency (%)
2691
45.7%
2 923
 
15.7%
1 913
 
15.5%
3 676
 
11.5%
4 348
 
5.9%
5 207
 
3.5%
6 84
 
1.4%
7 47
 
0.8%

elect
Text

Distinct4826
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:25.457969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6065
Min length1

Characters and Unicode

Total characters36065
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3690 ?
Unique (%)36.9%

Sample

1st row4350
2nd row4,448
3rd row2,965
4th row20043
5th row672
ValueCountFrequency (%)
0 169
 
1.7%
2 57
 
0.6%
85 27
 
0.3%
88 25
 
0.2%
98 25
 
0.2%
230 24
 
0.2%
119 24
 
0.2%
197 23
 
0.2%
224 21
 
0.2%
95 21
 
0.2%
Other values (4816) 9584
95.8%
2024-04-16T16:50:25.856167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5113
14.2%
2 4210
11.7%
3 3431
9.5%
6 3220
8.9%
5 3175
8.8%
4 3149
8.7%
7 2953
8.2%
0 2801
7.8%
8 2792
7.7%
9 2640
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33484
92.8%
Other Punctuation 2581
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5113
15.3%
2 4210
12.6%
3 3431
10.2%
6 3220
9.6%
5 3175
9.5%
4 3149
9.4%
7 2953
8.8%
0 2801
8.4%
8 2792
8.3%
9 2640
7.9%
Other Punctuation
ValueCountFrequency (%)
, 2581
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36065
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5113
14.2%
2 4210
11.7%
3 3431
9.5%
6 3220
8.9%
5 3175
8.8%
4 3149
8.7%
7 2953
8.2%
0 2801
7.8%
8 2792
7.7%
9 2640
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5113
14.2%
2 4210
11.7%
3 3431
9.5%
6 3220
8.9%
5 3175
8.8%
4 3149
8.7%
7 2953
8.2%
0 2801
7.8%
8 2792
7.7%
9 2640
7.3%

gas
Text

Distinct2884
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:26.174177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.5783
Min length1

Characters and Unicode

Total characters25783
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1977 ?
Unique (%)19.8%

Sample

1st row1019
2nd row1,119
3rd row0
4th row4913
5th row27
ValueCountFrequency (%)
0 2292
 
22.9%
2 104
 
1.0%
1 77
 
0.8%
4 66
 
0.7%
3 63
 
0.6%
6 62
 
0.6%
11 60
 
0.6%
5 59
 
0.6%
7 58
 
0.6%
9 56
 
0.6%
Other values (2874) 7103
71.0%
2024-04-16T16:50:26.563593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4032
15.6%
0 3908
15.2%
2 3018
11.7%
3 2417
9.4%
4 2065
8.0%
5 1959
7.6%
6 1833
7.1%
7 1770
6.9%
8 1757
6.8%
9 1681
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24440
94.8%
Other Punctuation 1343
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4032
16.5%
0 3908
16.0%
2 3018
12.3%
3 2417
9.9%
4 2065
8.4%
5 1959
8.0%
6 1833
7.5%
7 1770
7.2%
8 1757
7.2%
9 1681
6.9%
Other Punctuation
ValueCountFrequency (%)
, 1343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25783
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4032
15.6%
0 3908
15.2%
2 3018
11.7%
3 2417
9.4%
4 2065
8.0%
5 1959
7.6%
6 1833
7.1%
7 1770
6.9%
8 1757
6.8%
9 1681
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4032
15.6%
0 3908
15.2%
2 3018
11.7%
3 2417
9.4%
4 2065
8.0%
5 1959
7.6%
6 1833
7.1%
7 1770
6.9%
8 1757
6.8%
9 1681
6.5%

heating
Text

Distinct295
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:26.851647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.0742
Min length1

Characters and Unicode

Total characters10742
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique245 ?
Unique (%)2.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 9641
96.4%
124 4
 
< 0.1%
161 3
 
< 0.1%
55 3
 
< 0.1%
123 3
 
< 0.1%
130 3
 
< 0.1%
344 3
 
< 0.1%
73 3
 
< 0.1%
67 3
 
< 0.1%
144 3
 
< 0.1%
Other values (285) 331
 
3.3%
2024-04-16T16:50:27.245418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9718
90.5%
1 215
 
2.0%
2 125
 
1.2%
3 113
 
1.1%
7 97
 
0.9%
4 95
 
0.9%
5 88
 
0.8%
6 84
 
0.8%
8 83
 
0.8%
9 77
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10695
99.6%
Other Punctuation 47
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9718
90.9%
1 215
 
2.0%
2 125
 
1.2%
3 113
 
1.1%
7 97
 
0.9%
4 95
 
0.9%
5 88
 
0.8%
6 84
 
0.8%
8 83
 
0.8%
9 77
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10742
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9718
90.5%
1 215
 
2.0%
2 125
 
1.2%
3 113
 
1.1%
7 97
 
0.9%
4 95
 
0.9%
5 88
 
0.8%
6 84
 
0.8%
8 83
 
0.8%
9 77
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10742
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9718
90.5%
1 215
 
2.0%
2 125
 
1.2%
3 113
 
1.1%
7 97
 
0.9%
4 95
 
0.9%
5 88
 
0.8%
6 84
 
0.8%
8 83
 
0.8%
9 77
 
0.7%

total
Text

Distinct5095
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:27.568910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.728
Min length1

Characters and Unicode

Total characters37280
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3930 ?
Unique (%)39.3%

Sample

1st row5369
2nd row5,567
3rd row2,965
4th row24956
5th row699
ValueCountFrequency (%)
0 72
 
0.7%
2 42
 
0.4%
94 24
 
0.2%
107 22
 
0.2%
234 22
 
0.2%
79 21
 
0.2%
85 21
 
0.2%
100 20
 
0.2%
88 20
 
0.2%
98 20
 
0.2%
Other values (5085) 9716
97.2%
2024-04-16T16:50:27.989659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5280
14.2%
2 4346
11.7%
3 3594
9.6%
4 3441
9.2%
5 3143
8.4%
6 3105
8.3%
7 3038
8.1%
9 2896
7.8%
8 2864
7.7%
0 2839
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34546
92.7%
Other Punctuation 2734
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5280
15.3%
2 4346
12.6%
3 3594
10.4%
4 3441
10.0%
5 3143
9.1%
6 3105
9.0%
7 3038
8.8%
9 2896
8.4%
8 2864
8.3%
0 2839
8.2%
Other Punctuation
ValueCountFrequency (%)
, 2734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37280
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5280
14.2%
2 4346
11.7%
3 3594
9.6%
4 3441
9.2%
5 3143
8.4%
6 3105
8.3%
7 3038
8.1%
9 2896
7.8%
8 2864
7.7%
0 2839
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5280
14.2%
2 4346
11.7%
3 3594
9.6%
4 3441
9.2%
5 3143
8.4%
6 3105
8.3%
7 3038
8.1%
9 2896
7.8%
8 2864
7.7%
0 2839
7.6%

last_load_dttm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-04-01 05:37:03
6515 
2021-04-01 05:37:04
3485 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-01 05:37:03
2nd row2021-04-01 05:37:03
3rd row2021-04-01 05:37:04
4th row2021-04-01 05:37:03
5th row2021-04-01 05:37:04

Common Values

ValueCountFrequency (%)
2021-04-01 05:37:03 6515
65.1%
2021-04-01 05:37:04 3485
34.8%

Length

2024-04-16T16:50:28.123717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:50:28.197892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 10000
50.0%
05:37:03 6515
32.6%
05:37:04 3485
 
17.4%

Interactions

2024-04-16T16:50:23.259973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:23.104411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:23.547093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:23.186261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T16:50:28.252355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_yeard_monthsigungulast_load_dttm
skey1.0001.0000.0000.6500.996
d_year1.0001.0000.0000.0000.787
d_month0.0000.0001.0000.0000.000
sigungu0.6500.0000.0001.0000.199
last_load_dttm0.9960.7870.0000.1991.000
2024-04-16T16:50:28.333209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
last_load_dttmd_yearsigungu
last_load_dttm1.0000.9190.156
d_year0.9191.0000.000
sigungu0.1560.0001.000
2024-04-16T16:50:28.403548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_monthd_yearsigungulast_load_dttm
skey1.0000.0350.9870.3210.942
d_month0.0351.0000.0000.0000.000
d_year0.9870.0001.0000.0000.919
sigungu0.3210.0000.0001.0000.156
last_load_dttm0.9420.0000.9190.1561.000

Missing values

2024-04-16T16:50:23.648637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T16:50:23.783267image/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

skeyd_yeard_monthsigunguareaelectgasheatingtotallast_load_dttm
8439843620176부산광역시 사상구엄궁동43501019053692021-04-01 05:37:03
2196219620156부산광역시 사상구엄궁동4,4481,11905,5672021-04-01 05:37:03
148671486820194부산광역시 기장군장안읍 고리2,965002,9652021-04-01 05:37:04
8319831620176부산광역시 연제구연산동2004349130249562021-04-01 05:37:03
133711337120197부산광역시 영도구봉래동4가6722706992021-04-01 05:37:04
9542954020186부산광역시 중구중앙동5가171801792021-04-01 05:37:03
5729572620165부산광역시 기장군철마면 이곡리4900492021-04-01 05:37:03
150671506820197부산광역시 기장군기장읍 만화리141001412021-04-01 05:37:04
1359113591201911부산광역시 부산진구전포동5,8201,28507,1052021-04-01 05:37:04
7505750220176부산광역시 동래구복천동61017607862021-04-01 05:37:03
skeyd_yeard_monthsigunguareaelectgasheatingtotallast_load_dttm
1371314201512부산광역시 동래구칠산동3783404122021-04-01 05:37:03
147331473420192부산광역시 기장군장안읍 용소리5200522021-04-01 05:37:04
4107410520168부산광역시 영도구영선동3가2512102722021-04-01 05:37:03
105511054920182부산광역시 동래구명장동3,9662,29906,2652021-04-01 05:37:04
5026502420165부산광역시 강서구범방동3906804582021-04-01 05:37:03
92056120153부산광역시 서구충무동2가1376402012021-04-01 05:37:03
3305330420165부산광역시 중구보수동1가54614906952021-04-01 05:37:03
322124020154부산광역시 동래구명장동2,7831,42404,2072021-04-01 05:37:03
91255320153부산광역시 서구토성동2가917901702021-04-01 05:37:03
7131712920173부산광역시 영도구봉래동5가1085166012512021-04-01 05:37:03