Overview

Dataset statistics

Number of variables10
Number of observations42
Missing cells109
Missing cells (%)26.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory85.1 B

Variable types

Categorical4
Text4
Numeric2

Dataset

Description경기도 고양시 가로등에 대한 데이터로 도로조명에 대한 항목을 제공합니다. *고양시의 경우 가로등보안등 연간단가 공사 연말에 준공 시 연도별 수량 현황자료를 일괄 현행화함
URLhttps://www.data.go.kr/data/15106465/fileData.do

Alerts

소계_가로등_전기료_납입시_가로등_적용(CDM) is highly overall correlated with 소계_가로등_전기료_납입시_가로등_적용(LED) and 1 other fieldsHigh correlation
소계_가로등_전기료_납입시_가로등_적용(LED) is highly overall correlated with 소계_가로등_전기료_납입시_가로등_적용(CDM) and 2 other fieldsHigh correlation
구별 is highly overall correlated with 총계_램프종류별_등수(NH) and 1 other fieldsHigh correlation
총계_램프종류별_등수(NH) is highly overall correlated with 소계_가로등_전기료_납입시_가로등_적용(LED) and 2 other fieldsHigh correlation
총계_램프종류별_등수(CDM) is highly overall correlated with 소계_가로등_전기료_납입시_가로등_적용(CDM) and 3 other fieldsHigh correlation
총계_램프종류별_등수 has 6 (14.3%) missing valuesMissing
총계_램프종류별_등수(LED) has 7 (16.7%) missing valuesMissing
소계_가로등_전기료_납입시_가로등_적용 has 6 (14.3%) missing valuesMissing
소계_가로등_전기료_납입시_가로등_적용(NH) has 34 (81.0%) missing valuesMissing
소계_가로등_전기료_납입시_가로등_적용(CDM) has 31 (73.8%) missing valuesMissing
소계_가로등_전기료_납입시_가로등_적용(LED) has 25 (59.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 07:11:32.414210
Analysis finished2023-12-12 07:11:33.884327
Duration1.47 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size468.0 B
덕양구
14 
일산동구
14 
일산서구
14 

Length

Max length4
Median length4
Mean length3.6666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row덕양구
2nd row덕양구
3rd row덕양구
4th row덕양구
5th row덕양구

Common Values

ValueCountFrequency (%)
덕양구 14
33.3%
일산동구 14
33.3%
일산서구 14
33.3%

Length

2023-12-12T16:11:33.953189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:11:34.093032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
덕양구 14
33.3%
일산동구 14
33.3%
일산서구 14
33.3%

구분
Categorical

Distinct14
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size468.0 B
소계
3 
40W
3 
50W
3 
70W
3 
75W
3 
Other values (9)
27 

Length

Max length5
Median length4
Mean length3.6428571
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소계
2nd row40W
3rd row50W
4th row70W
5th row75W

Common Values

ValueCountFrequency (%)
소계 3
 
7.1%
40W 3
 
7.1%
50W 3
 
7.1%
70W 3
 
7.1%
75W 3
 
7.1%
100W 3
 
7.1%
125W 3
 
7.1%
150W 3
 
7.1%
175W 3
 
7.1%
200W 3
 
7.1%
Other values (4) 12
28.6%

Length

2023-12-12T16:11:34.227594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소계 3
 
7.1%
40w 3
 
7.1%
50w 3
 
7.1%
70w 3
 
7.1%
75w 3
 
7.1%
100w 3
 
7.1%
125w 3
 
7.1%
150w 3
 
7.1%
175w 3
 
7.1%
200w 3
 
7.1%
Other values (4) 12
28.6%

총계_램프종류별_등수
Text

MISSING 

Distinct25
Distinct (%)69.4%
Missing6
Missing (%)14.3%
Memory size468.0 B
2023-12-12T16:11:34.463238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.2222222
Min length2

Characters and Unicode

Total characters116
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

Unique24 ?
Unique (%)66.7%

Sample

1st row26937
2nd row737
3rd row4955
4th row5305
5th row504
ValueCountFrequency (%)
4955 1
 
4.2%
5305 1
 
4.2%
26937 1
 
4.2%
288 1
 
4.2%
3730 1
 
4.2%
1196 1
 
4.2%
911 1
 
4.2%
87 1
 
4.2%
604 1
 
4.2%
1771 1
 
4.2%
Other values (14) 14
58.3%
2023-12-12T16:11:34.899786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24
20.7%
1 22
19.0%
5 10
8.6%
3 10
8.6%
9 9
 
7.8%
0 9
 
7.8%
2 7
 
6.0%
6 7
 
6.0%
7 7
 
6.0%
8 6
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 92
79.3%
Space Separator 24
 
20.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
23.9%
5 10
10.9%
3 10
10.9%
9 9
9.8%
0 9
9.8%
2 7
 
7.6%
6 7
 
7.6%
7 7
 
7.6%
8 6
 
6.5%
4 5
 
5.4%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
24
20.7%
1 22
19.0%
5 10
8.6%
3 10
8.6%
9 9
 
7.8%
0 9
 
7.8%
2 7
 
6.0%
6 7
 
6.0%
7 7
 
6.0%
8 6
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24
20.7%
1 22
19.0%
5 10
8.6%
3 10
8.6%
9 9
 
7.8%
0 9
 
7.8%
2 7
 
6.0%
6 7
 
6.0%
7 7
 
6.0%
8 6
 
5.2%

총계_램프종류별_등수(NH)
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
25 
<NA>
11 
3880
 
1
457
 
1
3423
 
1
Other values (3)
3 

Length

Max length4
Median length2
Mean length2.7142857
Min length2

Unique

Unique6 ?
Unique (%)14.3%

Sample

1st row3880
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
25
59.5%
<NA> 11
26.2%
3880 1
 
2.4%
457 1
 
2.4%
3423 1
 
2.4%
748 1
 
2.4%
350 1
 
2.4%
398 1
 
2.4%

Length

2023-12-12T16:11:35.091769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:11:35.266412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 11
64.7%
3880 1
 
5.9%
457 1
 
5.9%
3423 1
 
5.9%
748 1
 
5.9%
350 1
 
5.9%
398 1
 
5.9%

총계_램프종류별_등수(CDM)
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Memory size468.0 B
19 
<NA>
10 
13967
 
1
5195
 
1
676
 
1
Other values (10)
10 

Length

Max length5
Median length4
Mean length3.0238095
Min length2

Unique

Unique13 ?
Unique (%)31.0%

Sample

1st row13967
2nd row<NA>
3rd row<NA>
4th row5195
5th row<NA>

Common Values

ValueCountFrequency (%)
19
45.2%
<NA> 10
23.8%
13967 1
 
2.4%
5195 1
 
2.4%
676 1
 
2.4%
8096 1
 
2.4%
7054 1
 
2.4%
1658 1
 
2.4%
484 1
 
2.4%
4912 1
 
2.4%
Other values (5) 5
 
11.9%

Length

2023-12-12T16:11:35.447005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 10
43.5%
13967 1
 
4.3%
5195 1
 
4.3%
676 1
 
4.3%
8096 1
 
4.3%
7054 1
 
4.3%
1658 1
 
4.3%
484 1
 
4.3%
4912 1
 
4.3%
7315 1
 
4.3%
Other values (4) 4
 
17.4%

총계_램프종류별_등수(LED)
Text

MISSING 

Distinct19
Distinct (%)54.3%
Missing7
Missing (%)16.7%
Memory size468.0 B
2023-12-12T16:11:35.660233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8285714
Min length2

Characters and Unicode

Total characters99
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

Unique17 ?
Unique (%)48.6%

Sample

1st row9090
2nd row737
3rd row4955
4th row110
5th row504
ValueCountFrequency (%)
176 2
 
10.5%
3264 1
 
5.3%
9090 1
 
5.3%
198 1
 
5.3%
1196 1
 
5.3%
87 1
 
5.3%
1771 1
 
5.3%
4603 1
 
5.3%
1233 1
 
5.3%
288 1
 
5.3%
Other values (8) 8
42.1%
2023-12-12T16:11:36.053471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32
32.3%
1 14
14.1%
7 8
 
8.1%
0 8
 
8.1%
3 7
 
7.1%
9 6
 
6.1%
5 6
 
6.1%
6 5
 
5.1%
2 5
 
5.1%
4 4
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
67.7%
Space Separator 32
32.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
20.9%
7 8
11.9%
0 8
11.9%
3 7
10.4%
9 6
9.0%
5 6
9.0%
6 5
 
7.5%
2 5
 
7.5%
4 4
 
6.0%
8 4
 
6.0%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
32
32.3%
1 14
14.1%
7 8
 
8.1%
0 8
 
8.1%
3 7
 
7.1%
9 6
 
6.1%
5 6
 
6.1%
6 5
 
5.1%
2 5
 
5.1%
4 4
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32
32.3%
1 14
14.1%
7 8
 
8.1%
0 8
 
8.1%
3 7
 
7.1%
9 6
 
6.1%
5 6
 
6.1%
6 5
 
5.1%
2 5
 
5.1%
4 4
 
4.0%

소계_가로등_전기료_납입시_가로등_적용
Text

MISSING 

Distinct23
Distinct (%)63.9%
Missing6
Missing (%)14.3%
Memory size468.0 B
2023-12-12T16:11:36.327670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.9444444
Min length2

Characters and Unicode

Total characters106
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

Unique22 ?
Unique (%)61.1%

Sample

1st row18591
2nd row482
3rd row120
4th row2179
5th row367
ValueCountFrequency (%)
120 1
 
4.5%
2179 1
 
4.5%
18591 1
 
4.5%
198 1
 
4.5%
3730 1
 
4.5%
1176 1
 
4.5%
60 1
 
4.5%
66 1
 
4.5%
8651 1
 
4.5%
5310 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T16:11:36.652711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
26.4%
1 18
17.0%
0 9
 
8.5%
6 9
 
8.5%
2 8
 
7.5%
8 8
 
7.5%
3 7
 
6.6%
9 6
 
5.7%
5 6
 
5.7%
7 5
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78
73.6%
Space Separator 28
 
26.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18
23.1%
0 9
11.5%
6 9
11.5%
2 8
10.3%
8 8
10.3%
3 7
 
9.0%
9 6
 
7.7%
5 6
 
7.7%
7 5
 
6.4%
4 2
 
2.6%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 106
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
28
26.4%
1 18
17.0%
0 9
 
8.5%
6 9
 
8.5%
2 8
 
7.5%
8 8
 
7.5%
3 7
 
6.6%
9 6
 
5.7%
5 6
 
5.7%
7 5
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
26.4%
1 18
17.0%
0 9
 
8.5%
6 9
 
8.5%
2 8
 
7.5%
8 8
 
7.5%
3 7
 
6.6%
9 6
 
5.7%
5 6
 
5.7%
7 5
 
4.7%

소계_가로등_전기료_납입시_가로등_적용(NH)
Text

MISSING 

Distinct7
Distinct (%)87.5%
Missing34
Missing (%)81.0%
Memory size468.0 B
2023-12-12T16:11:36.793090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3.5
Mean length3
Min length2

Characters and Unicode

Total characters24
Distinct characters10
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

Unique6 ?
Unique (%)75.0%

Sample

1st row3632
2nd row209
3rd row3423
4th row518
5th row120
ValueCountFrequency (%)
3632 1
16.7%
209 1
16.7%
3423 1
16.7%
518 1
16.7%
120 1
16.7%
398 1
16.7%
2023-12-12T16:11:37.068900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 5
20.8%
4
16.7%
2 4
16.7%
0 2
 
8.3%
9 2
 
8.3%
1 2
 
8.3%
8 2
 
8.3%
6 1
 
4.2%
4 1
 
4.2%
5 1
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
83.3%
Space Separator 4
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 5
25.0%
2 4
20.0%
0 2
 
10.0%
9 2
 
10.0%
1 2
 
10.0%
8 2
 
10.0%
6 1
 
5.0%
4 1
 
5.0%
5 1
 
5.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 5
20.8%
4
16.7%
2 4
16.7%
0 2
 
8.3%
9 2
 
8.3%
1 2
 
8.3%
8 2
 
8.3%
6 1
 
4.2%
4 1
 
4.2%
5 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 5
20.8%
4
16.7%
2 4
16.7%
0 2
 
8.3%
9 2
 
8.3%
1 2
 
8.3%
8 2
 
8.3%
6 1
 
4.2%
4 1
 
4.2%
5 1
 
4.2%

소계_가로등_전기료_납입시_가로등_적용(CDM)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)100.0%
Missing31
Missing (%)73.8%
Infinite0
Infinite (%)0.0%
Mean4079.8182
Minimum116
Maximum10951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T16:11:37.178849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile300
Q11427.5
median3619
Q35744
95-th percentile9523.5
Maximum10951
Range10835
Interquartile range (IQR)4316.5

Descriptive statistics

Standard deviation3416.1871
Coefficient of variation (CV)0.83733806
Kurtosis-0.024670636
Mean4079.8182
Median Absolute Deviation (MAD)2357
Skewness0.73341001
Sum44878
Variance11670334
MonotonicityNot monotonic
2023-12-12T16:11:37.278362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10951 1
 
2.4%
2179 1
 
2.4%
676 1
 
2.4%
8096 1
 
2.4%
5512 1
 
2.4%
116 1
 
2.4%
484 1
 
2.4%
4912 1
 
2.4%
5976 1
 
2.4%
2357 1
 
2.4%
(Missing) 31
73.8%
ValueCountFrequency (%)
116 1
2.4%
484 1
2.4%
676 1
2.4%
2179 1
2.4%
2357 1
2.4%
3619 1
2.4%
4912 1
2.4%
5512 1
2.4%
5976 1
2.4%
8096 1
2.4%
ValueCountFrequency (%)
10951 1
2.4%
8096 1
2.4%
5976 1
2.4%
5512 1
2.4%
4912 1
2.4%
3619 1
2.4%
2357 1
2.4%
2179 1
2.4%
676 1
2.4%
484 1
2.4%

소계_가로등_전기료_납입시_가로등_적용(LED)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing25
Missing (%)59.5%
Infinite0
Infinite (%)0.0%
Mean1063.4118
Minimum60
Maximum4008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T16:11:37.398091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile64.8
Q1198
median482
Q31373
95-th percentile2941.6
Maximum4008
Range3948
Interquartile range (IQR)1175

Descriptive statistics

Standard deviation1149.4641
Coefficient of variation (CV)1.080921
Kurtosis1.1741323
Mean1063.4118
Median Absolute Deviation (MAD)422
Skewness1.3101405
Sum18078
Variance1321267.8
MonotonicityNot monotonic
2023-12-12T16:11:37.525803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
482 1
 
2.4%
1373 1
 
2.4%
1176 1
 
2.4%
60 1
 
2.4%
66 1
 
2.4%
2675 1
 
2.4%
176 1
 
2.4%
1233 1
 
2.4%
4008 1
 
2.4%
198 1
 
2.4%
Other values (7) 7
 
16.7%
(Missing) 25
59.5%
ValueCountFrequency (%)
60 1
2.4%
66 1
2.4%
120 1
2.4%
176 1
2.4%
198 1
2.4%
220 1
2.4%
288 1
2.4%
367 1
2.4%
482 1
2.4%
1021 1
2.4%
ValueCountFrequency (%)
4008 1
2.4%
2675 1
2.4%
2500 1
2.4%
2115 1
2.4%
1373 1
2.4%
1233 1
2.4%
1176 1
2.4%
1021 1
2.4%
482 1
2.4%
367 1
2.4%

Interactions

2023-12-12T16:11:33.142283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:32.964209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:33.241791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:33.052850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:11:37.623915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구별구분총계_램프종류별_등수총계_램프종류별_등수(NH)총계_램프종류별_등수(CDM)총계_램프종류별_등수(LED)소계_가로등_전기료_납입시_가로등_적용소계_가로등_전기료_납입시_가로등_적용(NH)소계_가로등_전기료_납입시_가로등_적용(CDM)소계_가로등_전기료_납입시_가로등_적용(LED)
구별1.0000.0000.7950.7450.8060.7700.8000.0000.8080.000
구분0.0001.0000.0000.0000.0000.0000.0000.4600.7640.822
총계_램프종류별_등수0.7950.0001.0001.0001.0001.0001.0001.0001.0001.000
총계_램프종류별_등수(NH)0.7450.0001.0001.0001.0000.8801.0001.0000.0000.703
총계_램프종류별_등수(CDM)0.8060.0001.0001.0001.0000.8420.9701.0001.0000.930
총계_램프종류별_등수(LED)0.7700.0001.0000.8800.8421.0000.9970.9560.7801.000
소계_가로등_전기료_납입시_가로등_적용0.8000.0001.0001.0000.9700.9971.0001.0001.0001.000
소계_가로등_전기료_납입시_가로등_적용(NH)0.0000.4601.0001.0001.0000.9561.0001.0001.0001.000
소계_가로등_전기료_납입시_가로등_적용(CDM)0.8080.7641.0000.0001.0000.7801.0001.0001.0001.000
소계_가로등_전기료_납입시_가로등_적용(LED)0.0000.8221.0000.7030.9301.0001.0001.0001.0001.000
2023-12-12T16:11:37.805487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총계_램프종류별_등수(CDM)총계_램프종류별_등수(NH)구별구분
총계_램프종류별_등수(CDM)1.0000.8660.5090.000
총계_램프종류별_등수(NH)0.8661.0000.6210.000
구별0.5090.6211.0000.000
구분0.0000.0000.0001.000
2023-12-12T16:11:37.921936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소계_가로등_전기료_납입시_가로등_적용(CDM)소계_가로등_전기료_납입시_가로등_적용(LED)구별구분총계_램프종류별_등수(NH)총계_램프종류별_등수(CDM)
소계_가로등_전기료_납입시_가로등_적용(CDM)1.0001.0000.4080.0730.0001.000
소계_가로등_전기료_납입시_가로등_적용(LED)1.0001.0000.0000.3850.5000.782
구별0.4080.0001.0000.0000.6210.509
구분0.0730.3850.0001.0000.0000.000
총계_램프종류별_등수(NH)0.0000.5000.6210.0001.0000.866
총계_램프종류별_등수(CDM)1.0000.7820.5090.0000.8661.000

Missing values

2023-12-12T16:11:33.397423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:11:33.594625image/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.
2023-12-12T16:11:33.777560image/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

구별구분총계_램프종류별_등수총계_램프종류별_등수(NH)총계_램프종류별_등수(CDM)총계_램프종류별_등수(LED)소계_가로등_전기료_납입시_가로등_적용소계_가로등_전기료_납입시_가로등_적용(NH)소계_가로등_전기료_납입시_가로등_적용(CDM)소계_가로등_전기료_납입시_가로등_적용(LED)
0덕양구소계269373880139679090185913632109514008
1덕양구40W737<NA><NA>737482<NA><NA>482
2덕양구50W4955<NA><NA>4955120<NA><NA>120
3덕양구70W5305<NA>51951102179<NA>2179<NA>
4덕양구75W<NA><NA><NA><NA><NA><NA><NA><NA>
5덕양구100W504<NA><NA>504367<NA><NA>367
6덕양구125W2500<NA><NA>25002500<NA><NA>2500
7덕양구150W2154457676102119062096761021
8덕양구175W<NA><NA><NA><NA><NA><NA><NA><NA>
9덕양구200W<NA><NA><NA><NA><NA><NA><NA><NA>
구별구분총계_램프종류별_등수총계_램프종류별_등수(NH)총계_램프종류별_등수(CDM)총계_램프종류별_등수(LED)소계_가로등_전기료_납입시_가로등_적용소계_가로등_전기료_납입시_가로등_적용(NH)소계_가로등_전기료_납입시_가로등_적용(CDM)소계_가로등_전기료_납입시_가로등_적용(LED)
32일산서구75W878760<NA><NA>60
33일산서구100W911911<NA><NA><NA>
34일산서구125W119611961176<NA><NA>1176
35일산서구150W3730235713733730<NA>23571373
36일산서구175W<NA><NA><NA>
37일산서구200W<NA><NA><NA>
38일산서구250W361936193619<NA>3619<NA>
39일산서구350W<NA><NA><NA>
40일산서구400W<NA><NA><NA>
41일산서구기타_전력<NA><NA><NA>