Overview

Dataset statistics

Number of variables12
Number of observations362
Missing cells0
Missing cells (%)0.0%
Duplicate rows162
Duplicate rows (%)44.8%
Total size in memory37.2 KiB
Average record size in memory105.4 B

Variable types

Categorical7
Text1
Numeric4

Dataset

Description대전광역시 2021년_공원관리사업소 공원등 설치 현황정보입니다. 2022년 공공데이터 기업매칭지원사업으로 수행되었습니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15111060/fileData.do

Alerts

Dataset has 162 (44.8%) duplicate rowsDuplicates
소비전력 91-100(w) is highly overall correlated with 소비전력 51-60(w)High correlation
소비전력 51-60(w) is highly overall correlated with 소비전력 91-100(w)High correlation
소비전력 51-60(w) is highly imbalanced (91.5%)Imbalance
소비전력 61-70(w) is highly imbalanced (92.6%)Imbalance
소비전력 151-175(w) is highly imbalanced (97.3%)Imbalance
소비전력 201-250(w) is highly imbalanced (89.0%)Imbalance
소비전력 1000(w)초과 is highly imbalanced (95.1%)Imbalance
소비전력 15-50(w) has 352 (97.2%) zerosZeros
소비전력 71-80(w) has 346 (95.6%) zerosZeros
소비전력 91-100(w) has 312 (86.2%) zerosZeros
소비전력 125-150(w) has 352 (97.2%) zerosZeros

Reproduction

Analysis started2023-12-12 07:36:12.166857
Analysis finished2023-12-12 07:36:15.043351
Duration2.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
보문산 공원
288 
갸앙, 세천, 용전, 장동 공원
74 

Length

Max length17
Median length6
Mean length8.2486188
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보문산 공원
2nd row보문산 공원
3rd row보문산 공원
4th row보문산 공원
5th row보문산 공원

Common Values

ValueCountFrequency (%)
보문산 공원 288
79.6%
갸앙, 세천, 용전, 장동 공원 74
 
20.4%

Length

2023-12-12T16:36:15.117154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:15.217185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공원 362
38.3%
보문산 288
30.4%
갸앙 74
 
7.8%
세천 74
 
7.8%
용전 74
 
7.8%
장동 74
 
7.8%

구역
Text

Distinct62
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-12T16:36:15.437633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length18
Mean length12.093923
Min length3

Characters and Unicode

Total characters4378
Distinct characters149
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)2.2%

Sample

1st row경익운수 ~ 느티나무구간
2nd row경익운수 ~ 느티나무구간
3rd row경익운수 ~ 느티나무구간
4th row경익운수 ~ 느티나무구간
5th row보훈공원삼거리 ~ 보훈공원입구
ValueCountFrequency (%)
212
 
22.4%
청년광장 40
 
4.2%
가양공원 30
 
3.2%
주차장 28
 
3.0%
숲속공연장 24
 
2.5%
송학사 16
 
1.7%
문화주차장 16
 
1.7%
보석천약수터 16
 
1.7%
문화배수지 16
 
1.7%
배드민턴장 16
 
1.7%
Other values (81) 534
56.3%
2023-12-12T16:36:15.925932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
594
 
13.6%
장 236
 
5.4%
~ 220
 
5.0%
사 130
 
3.0%
공 126
 
2.9%
주 84
 
1.9%
원 82
 
1.9%
수 80
 
1.8%
실 74
 
1.7%
무 74
 
1.7%
Other values (139) 2678
61.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3408
77.8%
Space Separator 594
 
13.6%
Math Symbol 220
 
5.0%
Decimal Number 60
 
1.4%
Open Punctuation 36
 
0.8%
Close Punctuation 36
 
0.8%
Other Punctuation 16
 
0.4%
Dash Punctuation 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 236
 
6.9%
사 130
 
3.8%
공 126
 
3.7%
주 84
 
2.5%
원 82
 
2.4%
수 80
 
2.3%
실 74
 
2.2%
무 74
 
2.2%
화 72
 
2.1%
구 64
 
1.9%
Other values (126) 2386
70.0%
Decimal Number
ValueCountFrequency (%)
1 16
26.7%
3 15
25.0%
7 7
11.7%
5 6
 
10.0%
2 6
 
10.0%
4 6
 
10.0%
6 4
 
6.7%
Space Separator
ValueCountFrequency (%)
594
100.0%
Math Symbol
ValueCountFrequency (%)
~ 220
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3408
77.8%
Common 970
 
22.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 236
 
6.9%
사 130
 
3.8%
공 126
 
3.7%
주 84
 
2.5%
원 82
 
2.4%
수 80
 
2.3%
실 74
 
2.2%
무 74
 
2.2%
화 72
 
2.1%
구 64
 
1.9%
Other values (126) 2386
70.0%
Common
ValueCountFrequency (%)
594
61.2%
~ 220
 
22.7%
( 36
 
3.7%
) 36
 
3.7%
1 16
 
1.6%
, 16
 
1.6%
3 15
 
1.5%
- 8
 
0.8%
7 7
 
0.7%
5 6
 
0.6%
Other values (3) 16
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3408
77.8%
ASCII 970
 
22.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
594
61.2%
~ 220
 
22.7%
( 36
 
3.7%
) 36
 
3.7%
1 16
 
1.6%
, 16
 
1.6%
3 15
 
1.5%
- 8
 
0.8%
7 7
 
0.7%
5 6
 
0.6%
Other values (3) 16
 
1.6%
Hangul
ValueCountFrequency (%)
장 236
 
6.9%
사 130
 
3.8%
공 126
 
3.7%
주 84
 
2.5%
원 82
 
2.4%
수 80
 
2.3%
실 74
 
2.2%
무 74
 
2.2%
화 72
 
2.1%
구 64
 
1.9%
Other values (126) 2386
70.0%

램프
Categorical

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
나트륨
91 
메탈
91 
LED
90 
기타
90 

Length

Max length3
Median length3
Mean length2.7486188
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row나트륨
2nd row메탈
3rd rowLED
4th row기타
5th row나트륨

Common Values

ValueCountFrequency (%)
나트륨 91
25.1%
메탈 91
25.1%
LED 90
24.9%
기타 90
24.9%

Length

2023-12-12T16:36:16.092891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:16.206525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
나트륨 91
25.1%
메탈 91
25.1%
led 90
24.9%
기타 90
24.9%

소비전력 15-50(w)
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2651934
Minimum0
Maximum330
Zeros352
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T16:36:16.350652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum330
Range330
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.08019
Coefficient of variation (CV)8.2935945
Kurtosis120.51845
Mean3.2651934
Median Absolute Deviation (MAD)0
Skewness10.60646
Sum1182
Variance733.33668
MonotonicityNot monotonic
2023-12-12T16:36:16.489208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 352
97.2%
42 2
 
0.6%
330 2
 
0.6%
44 2
 
0.6%
144 2
 
0.6%
31 2
 
0.6%
ValueCountFrequency (%)
0 352
97.2%
31 2
 
0.6%
42 2
 
0.6%
44 2
 
0.6%
144 2
 
0.6%
330 2
 
0.6%
ValueCountFrequency (%)
330 2
 
0.6%
144 2
 
0.6%
44 2
 
0.6%
42 2
 
0.6%
31 2
 
0.6%
0 352
97.2%

소비전력 51-60(w)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
354 
16
 
2
22
 
2
2
 
2
5
 
2

Length

Max length2
Median length1
Mean length1.0110497
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 354
97.8%
16 2
 
0.6%
22 2
 
0.6%
2 2
 
0.6%
5 2
 
0.6%

Length

2023-12-12T16:36:16.650048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:16.797682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 354
97.8%
16 2
 
0.6%
22 2
 
0.6%
2 2
 
0.6%
5 2
 
0.6%

소비전력 61-70(w)
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
356 
13
 
2
48
 
2
6
 
2

Length

Max length2
Median length1
Mean length1.0110497
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 356
98.3%
13 2
 
0.6%
48 2
 
0.6%
6 2
 
0.6%

Length

2023-12-12T16:36:16.962138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:17.114038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 356
98.3%
13 2
 
0.6%
48 2
 
0.6%
6 2
 
0.6%

소비전력 71-80(w)
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70165746
Minimum0
Maximum97
Zeros346
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T16:36:17.242769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.7898993
Coefficient of variation (CV)8.2517462
Kurtosis218.42053
Mean0.70165746
Median Absolute Deviation (MAD)0
Skewness13.83906
Sum254
Variance33.522934
MonotonicityNot monotonic
2023-12-12T16:36:17.394927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 346
95.6%
8 2
 
0.6%
2 2
 
0.6%
6 2
 
0.6%
3 2
 
0.6%
16 2
 
0.6%
9 2
 
0.6%
15 2
 
0.6%
39 1
 
0.3%
97 1
 
0.3%
ValueCountFrequency (%)
0 346
95.6%
2 2
 
0.6%
3 2
 
0.6%
6 2
 
0.6%
8 2
 
0.6%
9 2
 
0.6%
15 2
 
0.6%
16 2
 
0.6%
39 1
 
0.3%
97 1
 
0.3%
ValueCountFrequency (%)
97 1
 
0.3%
39 1
 
0.3%
16 2
 
0.6%
15 2
 
0.6%
9 2
 
0.6%
8 2
 
0.6%
6 2
 
0.6%
3 2
 
0.6%
2 2
 
0.6%
0 346
95.6%

소비전력 91-100(w)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4723757
Minimum0
Maximum27
Zeros312
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T16:36:17.536561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13.9
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.4981448
Coefficient of variation (CV)3.0550252
Kurtosis11.33177
Mean1.4723757
Median Absolute Deviation (MAD)0
Skewness3.3920565
Sum533
Variance20.233307
MonotonicityNot monotonic
2023-12-12T16:36:17.702917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 312
86.2%
2 6
 
1.7%
9 6
 
1.7%
10 5
 
1.4%
20 4
 
1.1%
4 4
 
1.1%
15 3
 
0.8%
3 2
 
0.6%
12 2
 
0.6%
6 2
 
0.6%
Other values (8) 16
 
4.4%
ValueCountFrequency (%)
0 312
86.2%
1 2
 
0.6%
2 6
 
1.7%
3 2
 
0.6%
4 4
 
1.1%
5 2
 
0.6%
6 2
 
0.6%
9 6
 
1.7%
10 5
 
1.4%
12 2
 
0.6%
ValueCountFrequency (%)
27 2
 
0.6%
20 4
1.1%
19 2
 
0.6%
18 2
 
0.6%
17 2
 
0.6%
16 2
 
0.6%
15 3
0.8%
14 2
 
0.6%
12 2
 
0.6%
10 5
1.4%

소비전력 125-150(w)
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63535912
Minimum0
Maximum48
Zeros352
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T16:36:17.828349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.4534737
Coefficient of variation (CV)7.0093803
Kurtosis77.90563
Mean0.63535912
Median Absolute Deviation (MAD)0
Skewness8.4212215
Sum230
Variance19.833428
MonotonicityNot monotonic
2023-12-12T16:36:17.961954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 352
97.2%
14 2
 
0.6%
26 2
 
0.6%
48 2
 
0.6%
6 2
 
0.6%
21 2
 
0.6%
ValueCountFrequency (%)
0 352
97.2%
6 2
 
0.6%
14 2
 
0.6%
21 2
 
0.6%
26 2
 
0.6%
48 2
 
0.6%
ValueCountFrequency (%)
48 2
 
0.6%
26 2
 
0.6%
21 2
 
0.6%
14 2
 
0.6%
6 2
 
0.6%
0 352
97.2%

소비전력 151-175(w)
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
361 
44
 
1

Length

Max length2
Median length1
Mean length1.0027624
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 361
99.7%
44 1
 
0.3%

Length

2023-12-12T16:36:18.119448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:18.225776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 361
99.7%
44 1
 
0.3%

소비전력 201-250(w)
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
351 
8
 
4
18
 
3
6
 
2
13
 
2

Length

Max length2
Median length1
Mean length1.0138122
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row8

Common Values

ValueCountFrequency (%)
0 351
97.0%
8 4
 
1.1%
18 3
 
0.8%
6 2
 
0.6%
13 2
 
0.6%

Length

2023-12-12T16:36:18.341592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:18.499195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 351
97.0%
8 4
 
1.1%
18 3
 
0.8%
6 2
 
0.6%
13 2
 
0.6%

소비전력 1000(w)초과
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
0
360 
44
 
2

Length

Max length2
Median length1
Mean length1.0055249
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 360
99.4%
44 2
 
0.6%

Length

2023-12-12T16:36:18.638179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:18.773807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 360
99.4%
44 2
 
0.6%

Interactions

2023-12-12T16:36:14.364870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:13.216104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:13.665712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:14.013347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:14.467811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:13.319185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:13.755564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:14.097670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:14.558303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:13.440279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:13.849959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:14.185595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:14.638155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:13.541771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:13.929636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:36:14.268491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:36:18.868850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분구역램프소비전력 15-50(w)소비전력 51-60(w)소비전력 61-70(w)소비전력 71-80(w)소비전력 91-100(w)소비전력 125-150(w)소비전력 151-175(w)소비전력 201-250(w)소비전력 1000(w)초과
구분1.0001.0000.0000.0000.0000.0000.1920.0000.0000.0000.0000.000
구역1.0001.0000.0000.5500.5770.5520.5520.8130.6040.3870.5730.382
램프0.0000.0001.0000.2970.1330.2310.2310.4340.1850.0000.1350.138
소비전력 15-50(w)0.0000.5500.2971.0000.4650.0000.0000.2090.7340.0000.0000.000
소비전력 51-60(w)0.0000.5770.1330.4651.0000.0000.0000.7240.0000.0000.0000.000
소비전력 61-70(w)0.0000.5520.2310.0000.0001.0000.0000.3000.0000.0000.0000.000
소비전력 71-80(w)0.1920.5520.2310.0000.0000.0001.0000.0000.0000.0000.0000.000
소비전력 91-100(w)0.0000.8130.4340.2090.7240.3000.0001.0000.0000.0000.0000.000
소비전력 125-150(w)0.0000.6040.1850.7340.0000.0000.0000.0001.0000.0000.4620.000
소비전력 151-175(w)0.0000.3870.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
소비전력 201-250(w)0.0000.5730.1350.0000.0000.0000.0000.0000.4620.0001.0000.000
소비전력 1000(w)초과0.0000.3820.1380.0000.0000.0000.0000.0000.0000.0000.0001.000
2023-12-12T16:36:19.040047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소비전력 151-175(w)소비전력 61-70(w)램프소비전력 51-60(w)소비전력 1000(w)초과구분소비전력 201-250(w)
소비전력 151-175(w)1.0000.0000.0000.0000.0000.0000.000
소비전력 61-70(w)0.0001.0000.0930.0000.0000.0000.000
램프0.0000.0931.0000.1090.0910.0000.110
소비전력 51-60(w)0.0000.0000.1091.0000.0000.0000.000
소비전력 1000(w)초과0.0000.0000.0910.0001.0000.0000.000
구분0.0000.0000.0000.0000.0001.0000.000
소비전력 201-250(w)0.0000.0000.1100.0000.0000.0001.000
2023-12-12T16:36:19.202665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소비전력 15-50(w)소비전력 71-80(w)소비전력 91-100(w)소비전력 125-150(w)구분램프소비전력 51-60(w)소비전력 61-70(w)소비전력 151-175(w)소비전력 201-250(w)소비전력 1000(w)초과
소비전력 15-50(w)1.0000.1250.0230.1820.0000.1200.3950.0000.0000.0000.000
소비전력 71-80(w)0.1251.000-0.086-0.0360.1270.0930.0000.0000.0000.0000.000
소비전력 91-100(w)0.023-0.0861.000-0.0670.0000.2900.5250.1940.0000.0000.000
소비전력 125-150(w)0.182-0.036-0.0671.0000.0000.1200.0000.0000.0000.3340.000
구분0.0000.1270.0000.0001.0000.0000.0000.0000.0000.0000.000
램프0.1200.0930.2900.1200.0001.0000.1090.0930.0000.1100.091
소비전력 51-60(w)0.3950.0000.5250.0000.0000.1091.0000.0000.0000.0000.000
소비전력 61-70(w)0.0000.0000.1940.0000.0000.0930.0001.0000.0000.0000.000
소비전력 151-175(w)0.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
소비전력 201-250(w)0.0000.0000.0000.3340.0000.1100.0000.0000.0001.0000.000
소비전력 1000(w)초과0.0000.0000.0000.0000.0000.0910.0000.0000.0000.0001.000

Missing values

2023-12-12T16:36:14.754938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:36:14.975236image/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

구분구역램프소비전력 15-50(w)소비전력 51-60(w)소비전력 61-70(w)소비전력 71-80(w)소비전력 91-100(w)소비전력 125-150(w)소비전력 151-175(w)소비전력 201-250(w)소비전력 1000(w)초과
0보문산 공원경익운수 ~ 느티나무구간나트륨000000000
1보문산 공원경익운수 ~ 느티나무구간메탈000000000
2보문산 공원경익운수 ~ 느티나무구간LED000000000
3보문산 공원경익운수 ~ 느티나무구간기타0000014000
4보문산 공원보훈공원삼거리 ~ 보훈공원입구나트륨000000080
5보문산 공원보훈공원삼거리 ~ 보훈공원입구메탈000000000
6보문산 공원보훈공원삼거리 ~ 보훈공원입구LED000000000
7보문산 공원보훈공원삼거리 ~ 보훈공원입구기타000000000
8보문산 공원문화농장 ~ 까치약수터 ~ 까치탑나트륨000000000
9보문산 공원문화농장 ~ 까치약수터 ~ 까치탑메탈000000060
구분구역램프소비전력 15-50(w)소비전력 51-60(w)소비전력 61-70(w)소비전력 71-80(w)소비전력 91-100(w)소비전력 125-150(w)소비전력 151-175(w)소비전력 201-250(w)소비전력 1000(w)초과
352갸앙, 세천, 용전, 장동 공원장동산림욕장 ~ 산책로LED0000150000
353갸앙, 세천, 용전, 장동 공원장동산림욕장 ~ 산책로기타000000000
354갸앙, 세천, 용전, 장동 공원장태산휴양림나트륨000000000
355갸앙, 세천, 용전, 장동 공원장태산휴양림메탈0000004400
356갸앙, 세천, 용전, 장동 공원장태산휴양림LED0003900000
357갸앙, 세천, 용전, 장동 공원장태산휴양림기타000000000
358갸앙, 세천, 용전, 장동 공원만인산학습원나트륨000000000
359갸앙, 세천, 용전, 장동 공원만인산학습원메탈000000000
360갸앙, 세천, 용전, 장동 공원만인산학습원LED0009700000
361갸앙, 세천, 용전, 장동 공원만인산학습원기타000000000

Duplicate rows

Most frequently occurring

구분구역램프소비전력 15-50(w)소비전력 51-60(w)소비전력 61-70(w)소비전력 71-80(w)소비전력 91-100(w)소비전력 125-150(w)소비전력 151-175(w)소비전력 201-250(w)소비전력 1000(w)초과# duplicates
0갸앙, 세천, 용전, 장동 공원가양공원 광장LED0000000002
1갸앙, 세천, 용전, 장동 공원가양공원 광장기타0000000002
2갸앙, 세천, 용전, 장동 공원가양공원 광장나트륨0000000002
3갸앙, 세천, 용전, 장동 공원가양공원 광장메탈0000000002
4갸앙, 세천, 용전, 장동 공원가양공원 사무실위나트륨0000000002
5갸앙, 세천, 용전, 장동 공원가양공원 사무실위메탈0000000002
6갸앙, 세천, 용전, 장동 공원가양공원 약수터입구 ~ 대전터널LED0000000002
7갸앙, 세천, 용전, 장동 공원가양공원 약수터입구 ~ 대전터널기타0000000002
8갸앙, 세천, 용전, 장동 공원가양공원 약수터입구 ~ 대전터널나트륨0000000002
9갸앙, 세천, 용전, 장동 공원가양공원 약수터입구 ~ 대전터널메탈0000000002