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

Numeric3
Categorical1
Text5
DateTime1

Alerts

skey is highly overall correlated with d_yearHigh correlation
d_year is highly overall correlated with skeyHigh correlation
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 07:50:15.207147
Analysis finished2024-04-16 07:50:16.681199
Duration1.47 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%
Mean16976.726
Minimum11
Maximum34025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:16.741441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile1680.9
Q18520.25
median16916.5
Q325511.75
95-th percentile32293.2
Maximum34025
Range34014
Interquartile range (IQR)16991.5

Descriptive statistics

Standard deviation9822.2371
Coefficient of variation (CV)0.57857074
Kurtosis-1.195732
Mean16976.726
Median Absolute Deviation (MAD)8500
Skewness0.0034603794
Sum1.6976726 × 108
Variance96476341
MonotonicityNot monotonic
2024-04-16T16:50:16.866230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26962 1
 
< 0.1%
23188 1
 
< 0.1%
11619 1
 
< 0.1%
22481 1
 
< 0.1%
7629 1
 
< 0.1%
15739 1
 
< 0.1%
26275 1
 
< 0.1%
20582 1
 
< 0.1%
24481 1
 
< 0.1%
3894 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
21 1
< 0.1%
24 1
< 0.1%
26 1
< 0.1%
ValueCountFrequency (%)
34025 1
< 0.1%
34022 1
< 0.1%
34021 1
< 0.1%
34016 1
< 0.1%
34007 1
< 0.1%
34002 1
< 0.1%
34000 1
< 0.1%
33998 1
< 0.1%
33995 1
< 0.1%
33994 1
< 0.1%

d_year
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.2591
Minimum2015
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:16.962754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2017
Q32019
95-th percentile2020
Maximum2020
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6075527
Coefficient of variation (CV)0.00079689946
Kurtosis-1.1768774
Mean2017.2591
Median Absolute Deviation (MAD)1
Skewness0.08788474
Sum20172591
Variance2.5842256
MonotonicityNot monotonic
2024-04-16T16:50:17.048397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2015 1879
18.8%
2019 1812
18.1%
2018 1806
18.1%
2017 1802
18.0%
2016 1796
18.0%
2020 905
9.0%
ValueCountFrequency (%)
2015 1879
18.8%
2016 1796
18.0%
2017 1802
18.0%
2018 1806
18.1%
2019 1812
18.1%
2020 905
9.0%
ValueCountFrequency (%)
2020 905
9.0%
2019 1812
18.1%
2018 1806
18.1%
2017 1802
18.0%
2016 1796
18.0%
2015 1879
18.8%

d_month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5061
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-16T16:50:17.148397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation3.465222
Coefficient of variation (CV)0.53261124
Kurtosis-1.2216089
Mean6.5061
Median Absolute Deviation (MAD)3.5
Skewness0.0012381799
Sum65061
Variance12.007764
MonotonicityNot monotonic
2024-04-16T16:50:17.253862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 865
8.6%
1 857
8.6%
3 855
8.6%
10 847
8.5%
9 841
8.4%
5 840
8.4%
4 839
8.4%
7 833
8.3%
6 816
8.2%
11 813
8.1%
Other values (2) 1594
15.9%
ValueCountFrequency (%)
1 857
8.6%
2 793
7.9%
3 855
8.6%
4 839
8.4%
5 840
8.4%
6 816
8.2%
7 833
8.3%
8 801
8.0%
9 841
8.4%
10 847
8.5%
ValueCountFrequency (%)
12 865
8.6%
11 813
8.1%
10 847
8.5%
9 841
8.4%
8 801
8.0%
7 833
8.3%
6 816
8.2%
5 840
8.4%
4 839
8.4%
3 855
8.6%

sigungu
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시 기장군
2823 
부산광역시 중구
1587 
부산광역시 서구
916 
부산광역시 강서구
841 
부산광역시 영도구
795 
Other values (11)
3038 

Length

Max length10
Median length9
Mean length8.7674
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 기장군 2823
28.2%
부산광역시 중구 1587
15.9%
부산광역시 서구 916
 
9.2%
부산광역시 강서구 841
 
8.4%
부산광역시 영도구 795
 
8.0%
부산광역시 금정구 508
 
5.1%
부산광역시 부산진구 420
 
4.2%
부산광역시 동래구 332
 
3.3%
부산광역시 해운대구 326
 
3.3%
부산광역시 사상구 325
 
3.2%
Other values (6) 1127
 
11.3%

Length

2024-04-16T16:50:17.375307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 10000
50.0%
기장군 2823
 
14.1%
중구 1587
 
7.9%
서구 916
 
4.6%
강서구 841
 
4.2%
영도구 795
 
4.0%
금정구 508
 
2.5%
부산진구 420
 
2.1%
동래구 332
 
1.7%
해운대구 326
 
1.6%
Other values (7) 1452
 
7.3%

area
Text

Distinct261
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:17.635667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.6941
Min length2

Characters and Unicode

Total characters46941
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

Unique0 ?
Unique (%)0.0%

Sample

1st row학장동
2nd row칠산동
3rd row부민동1가
4th row보수동1가
5th row기장읍 죽성리
ValueCountFrequency (%)
기장읍 637
 
5.0%
장안읍 534
 
4.2%
일광면 510
 
4.0%
철마면 427
 
3.4%
정관읍 415
 
3.3%
정관면 300
 
2.4%
모전리 81
 
0.6%
매학리 80
 
0.6%
송정동 80
 
0.6%
임곡리 79
 
0.6%
Other values (244) 9575
75.3%
2024-04-16T16:50:17.990893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7849
 
16.7%
가 3114
 
6.6%
리 2755
 
5.9%
2718
 
5.8%
읍 1620
 
3.5%
장 1420
 
3.0%
면 1237
 
2.6%
정 956
 
2.0%
대 944
 
2.0%
2 934
 
2.0%
Other values (134) 23394
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41079
87.5%
Decimal Number 3144
 
6.7%
Space Separator 2718
 
5.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7849
19.1%
가 3114
 
7.6%
리 2755
 
6.7%
읍 1620
 
3.9%
장 1420
 
3.5%
면 1237
 
3.0%
정 956
 
2.3%
대 944
 
2.3%
광 898
 
2.2%
안 734
 
1.8%
Other values (126) 19552
47.6%
Decimal Number
ValueCountFrequency (%)
2 934
29.7%
1 924
29.4%
3 644
20.5%
4 337
 
10.7%
5 182
 
5.8%
6 76
 
2.4%
7 47
 
1.5%
Space Separator
ValueCountFrequency (%)
2718
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41079
87.5%
Common 5862
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7849
19.1%
가 3114
 
7.6%
리 2755
 
6.7%
읍 1620
 
3.9%
장 1420
 
3.5%
면 1237
 
3.0%
정 956
 
2.3%
대 944
 
2.3%
광 898
 
2.2%
안 734
 
1.8%
Other values (126) 19552
47.6%
Common
ValueCountFrequency (%)
2718
46.4%
2 934
 
15.9%
1 924
 
15.8%
3 644
 
11.0%
4 337
 
5.7%
5 182
 
3.1%
6 76
 
1.3%
7 47
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41079
87.5%
ASCII 5862
 
12.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7849
19.1%
가 3114
 
7.6%
리 2755
 
6.7%
읍 1620
 
3.9%
장 1420
 
3.5%
면 1237
 
3.0%
정 956
 
2.3%
대 944
 
2.3%
광 898
 
2.2%
안 734
 
1.8%
Other values (126) 19552
47.6%
ASCII
ValueCountFrequency (%)
2718
46.4%
2 934
 
15.9%
1 924
 
15.8%
3 644
 
11.0%
4 337
 
5.7%
5 182
 
3.1%
6 76
 
1.3%
7 47
 
0.8%

elect
Text

Distinct4264
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:18.295447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6005
Min length1

Characters and Unicode

Total characters36005
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

Unique2669 ?
Unique (%)26.7%

Sample

1st row17,720
2nd row295
3rd row321
4th row493
5th row1,167
ValueCountFrequency (%)
0 164
 
1.6%
2 50
 
0.5%
197 31
 
0.3%
67 28
 
0.3%
1 27
 
0.3%
162 24
 
0.2%
225 23
 
0.2%
91 23
 
0.2%
100 23
 
0.2%
56 22
 
0.2%
Other values (4254) 9585
95.9%
2024-04-16T16:50:18.696534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5287
14.7%
2 4156
11.5%
3 3433
9.5%
5 3253
9.0%
6 3240
9.0%
4 3117
8.7%
7 3064
8.5%
0 2736
7.6%
8 2718
7.5%
9 2578
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33582
93.3%
Other Punctuation 2423
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5287
15.7%
2 4156
12.4%
3 3433
10.2%
5 3253
9.7%
6 3240
9.6%
4 3117
9.3%
7 3064
9.1%
0 2736
8.1%
8 2718
8.1%
9 2578
7.7%
Other Punctuation
ValueCountFrequency (%)
, 2423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5287
14.7%
2 4156
11.5%
3 3433
9.5%
5 3253
9.0%
6 3240
9.0%
4 3117
8.7%
7 3064
8.5%
0 2736
7.6%
8 2718
7.5%
9 2578
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5287
14.7%
2 4156
11.5%
3 3433
9.5%
5 3253
9.0%
6 3240
9.0%
4 3117
8.7%
7 3064
8.5%
0 2736
7.6%
8 2718
7.5%
9 2578
7.2%

gas
Text

Distinct2626
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:18.992471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.5852
Min length1

Characters and Unicode

Total characters25852
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

Unique1504 ?
Unique (%)15.0%

Sample

1st row7,906
2nd row24
3rd row101
4th row90
5th row250
ValueCountFrequency (%)
0 2301
 
23.0%
2 101
 
1.0%
1 80
 
0.8%
4 71
 
0.7%
3 70
 
0.7%
7 67
 
0.7%
11 66
 
0.7%
8 59
 
0.6%
5 58
 
0.6%
15 50
 
0.5%
Other values (2616) 7077
70.8%
2024-04-16T16:50:19.397586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4002
15.5%
1 3937
15.2%
2 3002
11.6%
3 2358
9.1%
4 2110
8.2%
5 2008
7.8%
6 1843
7.1%
8 1814
7.0%
7 1786
6.9%
9 1708
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24568
95.0%
Other Punctuation 1284
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4002
16.3%
1 3937
16.0%
2 3002
12.2%
3 2358
9.6%
4 2110
8.6%
5 2008
8.2%
6 1843
7.5%
8 1814
7.4%
7 1786
7.3%
9 1708
7.0%
Other Punctuation
ValueCountFrequency (%)
, 1284
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25852
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4002
15.5%
1 3937
15.2%
2 3002
11.6%
3 2358
9.1%
4 2110
8.2%
5 2008
7.8%
6 1843
7.1%
8 1814
7.0%
7 1786
6.9%
9 1708
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4002
15.5%
1 3937
15.2%
2 3002
11.6%
3 2358
9.1%
4 2110
8.2%
5 2008
7.8%
6 1843
7.1%
8 1814
7.0%
7 1786
6.9%
9 1708
6.6%

heating
Text

Distinct264
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:19.696655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.0756
Min length1

Characters and Unicode

Total characters10756
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

Unique182 ?
Unique (%)1.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 9634
96.3%
161 4
 
< 0.1%
195 4
 
< 0.1%
17 4
 
< 0.1%
108 3
 
< 0.1%
102 3
 
< 0.1%
123 3
 
< 0.1%
168 3
 
< 0.1%
131 3
 
< 0.1%
152 3
 
< 0.1%
Other values (254) 336
 
3.4%
2024-04-16T16:50:20.080180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9712
90.3%
1 207
 
1.9%
2 143
 
1.3%
5 107
 
1.0%
3 106
 
1.0%
7 102
 
0.9%
6 101
 
0.9%
8 86
 
0.8%
9 82
 
0.8%
4 81
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10727
99.7%
Other Punctuation 29
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9712
90.5%
1 207
 
1.9%
2 143
 
1.3%
5 107
 
1.0%
3 106
 
1.0%
7 102
 
1.0%
6 101
 
0.9%
8 86
 
0.8%
9 82
 
0.8%
4 81
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10756
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9712
90.3%
1 207
 
1.9%
2 143
 
1.3%
5 107
 
1.0%
3 106
 
1.0%
7 102
 
0.9%
6 101
 
0.9%
8 86
 
0.8%
9 82
 
0.8%
4 81
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9712
90.3%
1 207
 
1.9%
2 143
 
1.3%
5 107
 
1.0%
3 106
 
1.0%
7 102
 
0.9%
6 101
 
0.9%
8 86
 
0.8%
9 82
 
0.8%
4 81
 
0.8%

total
Text

Distinct4558
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-16T16:50:20.404916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.7176
Min length1

Characters and Unicode

Total characters37176
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

Unique2923 ?
Unique (%)29.2%

Sample

1st row25,626
2nd row319
3rd row422
4th row583
5th row1,417
ValueCountFrequency (%)
0 80
 
0.8%
2 41
 
0.4%
107 27
 
0.3%
100 24
 
0.2%
79 23
 
0.2%
63 23
 
0.2%
56 22
 
0.2%
91 22
 
0.2%
234 21
 
0.2%
260 21
 
0.2%
Other values (4548) 9696
97.0%
2024-04-16T16:50:20.814698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5374
14.5%
2 4462
12.0%
3 3520
9.5%
4 3405
9.2%
5 3223
8.7%
6 3143
8.5%
7 3021
8.1%
8 2881
7.7%
9 2811
7.6%
0 2792
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34632
93.2%
Other Punctuation 2544
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5374
15.5%
2 4462
12.9%
3 3520
10.2%
4 3405
9.8%
5 3223
9.3%
6 3143
9.1%
7 3021
8.7%
8 2881
8.3%
9 2811
8.1%
0 2792
8.1%
Other Punctuation
ValueCountFrequency (%)
, 2544
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5374
14.5%
2 4462
12.0%
3 3520
9.5%
4 3405
9.2%
5 3223
8.7%
6 3143
8.5%
7 3021
8.1%
8 2881
7.7%
9 2811
7.6%
0 2792
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5374
14.5%
2 4462
12.0%
3 3520
9.5%
4 3405
9.2%
5 3223
8.7%
6 3143
8.5%
7 3021
8.1%
8 2881
7.7%
9 2811
7.6%
0 2792
7.5%

last_load_dttm
Date

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-05-01 05:37:03
Maximum2021-05-01 05:37:05
2024-04-16T16:50:20.914215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:20.998319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

Interactions

2024-04-16T16:50:16.247628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:15.783350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:16.023402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:16.334285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:15.870315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:16.099224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:16.406154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:15.944720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-16T16:50:16.171673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-16T16:50:21.069232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_yeard_monthsigungulast_load_dttm
skey1.0000.9800.0550.1710.963
d_year0.9801.0000.0000.0000.589
d_month0.0550.0001.0000.0000.000
sigungu0.1710.0000.0001.0000.092
last_load_dttm0.9630.5890.0000.0921.000
2024-04-16T16:50:21.145166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeyd_yeard_monthsigungu
skey1.0000.6160.0180.068
d_year0.6161.0000.0050.000
d_month0.0180.0051.0000.000
sigungu0.0680.0000.0001.000

Missing values

2024-04-16T16:50:16.505873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T16:50:16.623245image/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
269622696220182부산광역시 사상구학장동17,7207,906025,6262021-05-01 05:37:05
351126920157부산광역시 동래구칠산동2952403192021-05-01 05:37:03
1935819358201612부산광역시 서구부민동1가32110104222021-05-01 05:37:04
6468646520176부산광역시 중구보수동1가4939005832021-05-01 05:37:03
148541485520194부산광역시 기장군기장읍 죽성리1,16725001,4172021-05-01 05:37:04
160491605020158부산광역시 영도구봉래동5가1,9388602,0242021-05-01 05:37:04
19981999201510부산광역시 강서구대저1동3,1862803,2142021-05-01 05:37:03
162471624720158부산광역시 서구토성동5가2353202672021-05-01 05:37:04
327553275620208부산광역시 중구대청동4가61213607482021-05-01 05:37:05
2855285320159부산광역시 기장군장안읍 월내리543505482021-05-01 05:37:03
skeyd_yeard_monthsigunguareaelectgasheatingtotallast_load_dttm
192401924020167부산광역시 서구부민동3가2875303402021-05-01 05:37:04
281052810620196부산광역시 중구신창동3가6240662021-05-01 05:37:05
2162216220152부산광역시 사상구주례동7,9095,003012,9122021-05-01 05:37:03
2586525865201812부산광역시 영도구남항동2가3715204232021-05-01 05:37:05
76847682201710부산광역시 북구구포동73442151094952021-05-01 05:37:03
257642576420187부산광역시 영도구영선동3가2022302252021-05-01 05:37:05
2705270420157부산광역시 기장군장안읍 용소리3400342021-05-01 05:37:03
1851185220153부산광역시 강서구봉림동625006252021-05-01 05:37:03
8325832220179부산광역시 연제구연산동2323226490258812021-05-01 05:37:03
135401354020196부산광역시 부산진구연지동1,65039102,0412021-05-01 05:37:04