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 07:50:37.087131
Analysis finished2024-04-16 07:50:38.059674
Duration0.97 seconds
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%
Mean7745.8764
Minimum3
Maximum15468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:38.114463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile729.95
Q13889.75
median7773.5
Q311639.25
95-th percentile14682.1
Maximum15468
Range15465
Interquartile range (IQR)7749.5

Descriptive statistics

Standard deviation4474.8603
Coefficient of variation (CV)0.57770871
Kurtosis-1.1947379
Mean7745.8764
Median Absolute Deviation (MAD)3876
Skewness-0.0075805911
Sum77458764
Variance20024375
MonotonicityNot monotonic
2024-04-16T16:50:38.230606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12802 1
 
< 0.1%
6453 1
 
< 0.1%
14795 1
 
< 0.1%
12412 1
 
< 0.1%
4731 1
 
< 0.1%
5984 1
 
< 0.1%
14452 1
 
< 0.1%
1003 1
 
< 0.1%
13195 1
 
< 0.1%
5955 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
15468 1
< 0.1%
15466 1
< 0.1%
15464 1
< 0.1%
15463 1
< 0.1%
15462 1
< 0.1%
15461 1
< 0.1%
15460 1
< 0.1%
15459 1
< 0.1%
15456 1
< 0.1%
15455 1
< 0.1%

d_year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
2024 
2016
2016 
2019
2010 
2015
1994 
2018
1956 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2016
4th row2017
5th row2019

Common Values

ValueCountFrequency (%)
2017 2024
20.2%
2016 2016
20.2%
2019 2010
20.1%
2015 1994
19.9%
2018 1956
19.6%

Length

2024-04-16T16:50:38.370270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:50:38.460273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 2024
20.2%
2016 2016
20.2%
2019 2010
20.1%
2015 1994
19.9%
2018 1956
19.6%

d_month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4888
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:38.543016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.465065
Coefficient of variation (CV)0.53400705
Kurtosis-1.2188161
Mean6.4888
Median Absolute Deviation (MAD)3
Skewness0.0082898173
Sum64888
Variance12.006675
MonotonicityNot monotonic
2024-04-16T16:50:38.627347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 861
8.6%
1 851
8.5%
7 840
8.4%
5 838
8.4%
2 837
8.4%
10 834
8.3%
3 833
8.3%
6 831
8.3%
4 829
8.3%
8 828
8.3%
Other values (2) 1618
16.2%
ValueCountFrequency (%)
1 851
8.5%
2 837
8.4%
3 833
8.3%
4 829
8.3%
5 838
8.4%
6 831
8.3%
7 840
8.4%
8 828
8.3%
9 801
8.0%
10 834
8.3%
ValueCountFrequency (%)
12 861
8.6%
11 817
8.2%
10 834
8.3%
9 801
8.0%
8 828
8.3%
7 840
8.4%
6 831
8.3%
5 838
8.4%
4 829
8.3%
3 833
8.3%

sigungu
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 기장군
2699 
부산광역시 중구
1634 
부산광역시 서구
942 
부산광역시 강서구
861 
부산광역시 영도구
785 
Other values (11)
3079 

Length

Max length10
Median length9
Mean length8.7541
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 중구
2nd row부산광역시 서구
3rd row부산광역시 중구
4th row부산광역시 영도구
5th row부산광역시 기장군

Common Values

ValueCountFrequency (%)
부산광역시 기장군 2699
27.0%
부산광역시 중구 1634
16.3%
부산광역시 서구 942
 
9.4%
부산광역시 강서구 861
 
8.6%
부산광역시 영도구 785
 
7.8%
부산광역시 금정구 508
 
5.1%
부산광역시 부산진구 412
 
4.1%
부산광역시 동래구 358
 
3.6%
부산광역시 사하구 329
 
3.3%
부산광역시 사상구 310
 
3.1%
Other values (6) 1162
11.6%

Length

2024-04-16T16:50:38.733800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
기장군 2699
 
13.5%
중구 1634
 
8.2%
서구 942
 
4.7%
강서구 861
 
4.3%
영도구 785
 
3.9%
금정구 508
 
2.5%
부산진구 412
 
2.1%
동래구 358
 
1.8%
사하구 329
 
1.6%
Other values (7) 1472
 
7.4%

area
Text

Distinct261
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:38.969510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.6562
Min length2

Characters and Unicode

Total characters46562
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 row광복동2가
2nd row부민동3가
3rd row중앙동1가
4th row대평동1가
5th row일광면 용천리
ValueCountFrequency (%)
기장읍 610
 
4.8%
장안읍 521
 
4.1%
일광면 467
 
3.7%
철마면 413
 
3.3%
정관읍 393
 
3.1%
정관면 295
 
2.3%
송정동 86
 
0.7%
방곡리 83
 
0.7%
달산리 80
 
0.6%
용수리 77
 
0.6%
Other values (244) 9570
76.0%
2024-04-16T16:50:39.301360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7952
 
17.1%
가 3168
 
6.8%
리 2633
 
5.7%
2595
 
5.6%
읍 1565
 
3.4%
장 1405
 
3.0%
면 1175
 
2.5%
2 943
 
2.0%
대 940
 
2.0%
정 931
 
2.0%
Other values (134) 23255
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40755
87.5%
Decimal Number 3212
 
6.9%
Space Separator 2595
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7952
19.5%
가 3168
 
7.8%
리 2633
 
6.5%
읍 1565
 
3.8%
장 1405
 
3.4%
면 1175
 
2.9%
대 940
 
2.3%
정 931
 
2.3%
광 848
 
2.1%
안 701
 
1.7%
Other values (126) 19437
47.7%
Decimal Number
ValueCountFrequency (%)
2 943
29.4%
1 929
28.9%
3 652
20.3%
4 374
 
11.6%
5 190
 
5.9%
6 82
 
2.6%
7 42
 
1.3%
Space Separator
ValueCountFrequency (%)
2595
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40755
87.5%
Common 5807
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7952
19.5%
가 3168
 
7.8%
리 2633
 
6.5%
읍 1565
 
3.8%
장 1405
 
3.4%
면 1175
 
2.9%
대 940
 
2.3%
정 931
 
2.3%
광 848
 
2.1%
안 701
 
1.7%
Other values (126) 19437
47.7%
Common
ValueCountFrequency (%)
2595
44.7%
2 943
 
16.2%
1 929
 
16.0%
3 652
 
11.2%
4 374
 
6.4%
5 190
 
3.3%
6 82
 
1.4%
7 42
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40755
87.5%
ASCII 5807
 
12.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7952
19.5%
가 3168
 
7.8%
리 2633
 
6.5%
읍 1565
 
3.8%
장 1405
 
3.4%
면 1175
 
2.9%
대 940
 
2.3%
정 931
 
2.3%
광 848
 
2.1%
안 701
 
1.7%
Other values (126) 19437
47.7%
ASCII
ValueCountFrequency (%)
2595
44.7%
2 943
 
16.2%
1 929
 
16.0%
3 652
 
11.2%
4 374
 
6.4%
5 190
 
3.3%
6 82
 
1.4%
7 42
 
0.7%

elect
Text

Distinct4848
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:39.603505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6178
Min length1

Characters and Unicode

Total characters36178
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

Unique3693 ?
Unique (%)36.9%

Sample

1st row249
2nd row324
3rd row154
4th row255
5th row191
ValueCountFrequency (%)
0 161
 
1.6%
2 56
 
0.6%
197 28
 
0.3%
1 26
 
0.3%
98 25
 
0.2%
85 23
 
0.2%
91 22
 
0.2%
63 22
 
0.2%
67 21
 
0.2%
94 21
 
0.2%
Other values (4838) 9595
96.0%
2024-04-16T16:50:40.013364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5135
14.2%
2 4186
11.6%
3 3540
9.8%
6 3271
9.0%
5 3177
8.8%
4 3101
8.6%
7 3017
8.3%
8 2823
7.8%
0 2731
7.5%
9 2613
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33594
92.9%
Other Punctuation 2584
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5135
15.3%
2 4186
12.5%
3 3540
10.5%
6 3271
9.7%
5 3177
9.5%
4 3101
9.2%
7 3017
9.0%
8 2823
8.4%
0 2731
8.1%
9 2613
7.8%
Other Punctuation
ValueCountFrequency (%)
, 2584
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5135
14.2%
2 4186
11.6%
3 3540
9.8%
6 3271
9.0%
5 3177
8.8%
4 3101
8.6%
7 3017
8.3%
8 2823
7.8%
0 2731
7.5%
9 2613
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5135
14.2%
2 4186
11.6%
3 3540
9.8%
6 3271
9.0%
5 3177
8.8%
4 3101
8.6%
7 3017
8.3%
8 2823
7.8%
0 2731
7.5%
9 2613
7.2%

gas
Text

Distinct2908
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:40.308364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.5935
Min length1

Characters and Unicode

Total characters25935
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

Unique1991 ?
Unique (%)19.9%

Sample

1st row0
2nd row44
3rd row9
4th row47
5th row0
ValueCountFrequency (%)
0 2216
 
22.2%
2 110
 
1.1%
4 72
 
0.7%
3 70
 
0.7%
1 69
 
0.7%
7 67
 
0.7%
5 61
 
0.6%
6 58
 
0.6%
9 56
 
0.6%
11 51
 
0.5%
Other values (2898) 7170
71.7%
2024-04-16T16:50:40.746985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3935
15.2%
0 3830
14.8%
2 3010
11.6%
3 2436
9.4%
4 2130
8.2%
5 2031
7.8%
6 1860
7.2%
8 1823
7.0%
7 1807
7.0%
9 1721
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24583
94.8%
Other Punctuation 1352
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3935
16.0%
0 3830
15.6%
2 3010
12.2%
3 2436
9.9%
4 2130
8.7%
5 2031
8.3%
6 1860
7.6%
8 1823
7.4%
7 1807
7.4%
9 1721
7.0%
Other Punctuation
ValueCountFrequency (%)
, 1352
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25935
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3935
15.2%
0 3830
14.8%
2 3010
11.6%
3 2436
9.4%
4 2130
8.2%
5 2031
7.8%
6 1860
7.2%
8 1823
7.0%
7 1807
7.0%
9 1721
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3935
15.2%
0 3830
14.8%
2 3010
11.6%
3 2436
9.4%
4 2130
8.2%
5 2031
7.8%
6 1860
7.2%
8 1823
7.0%
7 1807
7.0%
9 1721
6.6%

heating
Text

Distinct285
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:41.040981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.0722
Min length1

Characters and Unicode

Total characters10722
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

Unique237 ?
Unique (%)2.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 9651
96.5%
82 4
 
< 0.1%
124 4
 
< 0.1%
344 4
 
< 0.1%
182 3
 
< 0.1%
102 3
 
< 0.1%
393 3
 
< 0.1%
65 3
 
< 0.1%
151 3
 
< 0.1%
1 3
 
< 0.1%
Other values (275) 319
 
3.2%
2024-04-16T16:50:41.422834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9729
90.7%
1 198
 
1.8%
2 127
 
1.2%
3 112
 
1.0%
4 102
 
1.0%
7 96
 
0.9%
5 92
 
0.9%
6 80
 
0.7%
8 75
 
0.7%
9 69
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10680
99.6%
Other Punctuation 42
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9729
91.1%
1 198
 
1.9%
2 127
 
1.2%
3 112
 
1.0%
4 102
 
1.0%
7 96
 
0.9%
5 92
 
0.9%
6 80
 
0.7%
8 75
 
0.7%
9 69
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10722
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9729
90.7%
1 198
 
1.8%
2 127
 
1.2%
3 112
 
1.0%
4 102
 
1.0%
7 96
 
0.9%
5 92
 
0.9%
6 80
 
0.7%
8 75
 
0.7%
9 69
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10722
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9729
90.7%
1 198
 
1.8%
2 127
 
1.2%
3 112
 
1.0%
4 102
 
1.0%
7 96
 
0.9%
5 92
 
0.9%
6 80
 
0.7%
8 75
 
0.7%
9 69
 
0.6%

total
Text

Distinct5163
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:41.732645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.7437
Min length1

Characters and Unicode

Total characters37437
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

Unique4002 ?
Unique (%)40.0%

Sample

1st row249
2nd row368
3rd row163
4th row302
5th row191
ValueCountFrequency (%)
0 65
 
0.7%
2 45
 
0.4%
63 23
 
0.2%
102 22
 
0.2%
79 21
 
0.2%
107 20
 
0.2%
88 20
 
0.2%
94 20
 
0.2%
66 20
 
0.2%
61 20
 
0.2%
Other values (5153) 9724
97.2%
2024-04-16T16:50:42.379401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5382
14.4%
2 4287
11.5%
3 3667
9.8%
4 3345
8.9%
5 3200
8.5%
6 3175
8.5%
7 3046
8.1%
9 2899
7.7%
8 2880
7.7%
0 2810
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34691
92.7%
Other Punctuation 2746
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5382
15.5%
2 4287
12.4%
3 3667
10.6%
4 3345
9.6%
5 3200
9.2%
6 3175
9.2%
7 3046
8.8%
9 2899
8.4%
8 2880
8.3%
0 2810
8.1%
Other Punctuation
ValueCountFrequency (%)
, 2746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37437
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5382
14.4%
2 4287
11.5%
3 3667
9.8%
4 3345
8.9%
5 3200
8.5%
6 3175
8.5%
7 3046
8.1%
9 2899
7.7%
8 2880
7.7%
0 2810
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5382
14.4%
2 4287
11.5%
3 3667
9.8%
4 3345
8.9%
5 3200
8.5%
6 3175
8.5%
7 3046
8.1%
9 2899
7.7%
8 2880
7.7%
0 2810
7.5%

last_load_dttm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-02-01 05:37:03
6475 
2021-02-01 05:37:04
3525 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-02-01 05:37:04
2nd row2021-02-01 05:37:04
3rd row2021-02-01 05:37:03
4th row2021-02-01 05:37:03
5th row2021-02-01 05:37:04

Common Values

ValueCountFrequency (%)
2021-02-01 05:37:03 6475
64.8%
2021-02-01 05:37:04 3525
35.2%

Length

2024-04-16T16:50:42.515705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:50:42.595103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-02-01 10000
50.0%
05:37:03 6475
32.4%
05:37:04 3525
 
17.6%

Interactions

2024-04-16T16:50:37.704261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:37.552621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:37.781917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:37.631768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T16:50:42.647294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_yeard_monthsigungulast_load_dttm
skey1.0001.0000.0000.6510.996
d_year1.0001.0000.0000.0000.791
d_month0.0000.0001.0000.0000.000
sigungu0.6510.0000.0001.0000.205
last_load_dttm0.9960.7910.0000.2051.000
2024-04-16T16:50:42.725765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
last_load_dttmd_yearsigungu
last_load_dttm1.0000.9220.161
d_year0.9221.0000.000
sigungu0.1610.0001.000
2024-04-16T16:50:42.797824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_monthd_yearsigungulast_load_dttm
skey1.0000.0330.9880.3220.944
d_month0.0331.0000.0000.0000.000
d_year0.9880.0001.0000.0000.922
sigungu0.3220.0000.0001.0000.161
last_load_dttm0.9440.0000.9220.1611.000

Missing values

2024-04-16T16:50:37.884355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T16:50:38.007096image/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
1280412802201910부산광역시 중구광복동2가249002492021-02-01 05:37:04
130701307020198부산광역시 서구부민동3가3244403682021-02-01 05:37:04
3330332920166부산광역시 중구중앙동1가154901632021-02-01 05:37:03
72837281201711부산광역시 영도구대평동1가2554703022021-02-01 05:37:03
1545315454201912부산광역시 기장군일광면 용천리191001912021-02-01 05:37:04
7378737620176부산광역시 부산진구양정동5234904061382021-02-01 05:37:03
8185818320177부산광역시 강서구녹산동192182020032021-02-01 05:37:03
38813879201612부산광역시 서구동대신동3가1505729022342021-02-01 05:37:03
7469746620172부산광역시 동래구복천동728711014392021-02-01 05:37:03
129714220154부산광역시 중구대청동4가59238609782021-02-01 05:37:03
skeyd_yeard_monthsigunguareaelectgasheatingtotallast_load_dttm
3624362320161부산광역시 서구부민동3가30525805632021-02-01 05:37:03
57149020155부산광역시 해운대구중동7,5422,1332719,9462021-02-01 05:37:03
133131331320195부산광역시 영도구대교동2가44211305552021-02-01 05:37:04
3302330120165부산광역시 중구대청동2가1471401612021-02-01 05:37:03
8530852720171부산광역시 기장군일광면 동백리252002522021-02-01 05:37:03
148571485820194부산광역시 기장군기장읍 청강리1,12344101,5642021-02-01 05:37:04
1228012278201811부산광역시 기장군장안읍 효암리6900692021-02-01 05:37:04
1139111390201812부산광역시 강서구천성동731007312021-02-01 05:37:04
5099509720169부산광역시 강서구대저1동380785038922021-02-01 05:37:03
1073452201512부산광역시 중구영주동1,30272002,0222021-02-01 05:37:03