Overview

Dataset statistics

Number of variables8
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory75.1 B

Variable types

Categorical2
Text1
Numeric5

Dataset

Description2014년부산광역시강서구사회조사결과(절약가능한주된에너지)
Author부산광역시 강서구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3045858

Alerts

수도 is highly overall correlated with 쓰레기 배출량(일반쓰레기, 음식물 쓰레기)High correlation
난방용 가스 및 유류 is highly overall correlated with 쓰레기 배출량(일반쓰레기, 음식물 쓰레기)High correlation
쓰레기 배출량(일반쓰레기, 음식물 쓰레기) is highly overall correlated with 수도 and 1 other fieldsHigh correlation
항목 has unique valuesUnique
쓰레기 배출량(일반쓰레기, 음식물 쓰레기) has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:11:29.930907
Analysis finished2023-12-10 17:11:33.875332
Duration3.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct6
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size340.0 B
월가구소득
7 
연령
6 
직업
5 
교육수준
4 
성별
2 

Length

Max length5
Median length2
Mean length3.1153846
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성별
2nd row성별
3rd row연령
4th row연령
5th row연령

Common Values

ValueCountFrequency (%)
월가구소득 7
26.9%
연령 6
23.1%
직업 5
19.2%
교육수준 4
15.4%
성별 2
 
7.7%
구역 2
 
7.7%

Length

2023-12-11T02:11:33.973477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:11:34.213287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
월가구소득 7
26.9%
연령 6
23.1%
직업 5
19.2%
교육수준 4
15.4%
성별 2
 
7.7%
구역 2
 
7.7%

항목
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T02:11:34.458361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length7
Mean length6.1923077
Min length1

Characters and Unicode

Total characters161
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row남
2nd row여
3rd row15-19세
4th row20-29세
5th row30-39세
ValueCountFrequency (%)
미만 6
 
14.0%
5
 
11.6%
남 1
 
2.3%
300 1
 
2.3%
100 1
 
2.3%
200만원 1
 
2.3%
200 1
 
2.3%
300만원 1
 
2.3%
400만원 1
 
2.3%
여 1
 
2.3%
Other values (24) 24
55.8%
2023-12-11T02:11:34.918419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29
18.0%
17
 
10.6%
만 13
 
8.1%
원 7
 
4.3%
세 6
 
3.7%
미 6
 
3.7%
5 5
 
3.1%
9 5
 
3.1%
- 5
 
3.1%
~ 5
 
3.1%
Other values (38) 63
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76
47.2%
Decimal Number 58
36.0%
Space Separator 17
 
10.6%
Dash Punctuation 5
 
3.1%
Math Symbol 5
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
만 13
17.1%
원 7
 
9.2%
세 6
 
7.9%
미 6
 
7.9%
졸 4
 
5.3%
이 4
 
5.3%
상 3
 
3.9%
역 2
 
2.6%
구 2
 
2.6%
무 2
 
2.6%
Other values (27) 27
35.5%
Decimal Number
ValueCountFrequency (%)
0 29
50.0%
5 5
 
8.6%
9 5
 
8.6%
2 4
 
6.9%
1 4
 
6.9%
3 4
 
6.9%
4 4
 
6.9%
6 3
 
5.2%
Space Separator
ValueCountFrequency (%)
17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 85
52.8%
Hangul 76
47.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
만 13
17.1%
원 7
 
9.2%
세 6
 
7.9%
미 6
 
7.9%
졸 4
 
5.3%
이 4
 
5.3%
상 3
 
3.9%
역 2
 
2.6%
구 2
 
2.6%
무 2
 
2.6%
Other values (27) 27
35.5%
Common
ValueCountFrequency (%)
0 29
34.1%
17
20.0%
5 5
 
5.9%
9 5
 
5.9%
- 5
 
5.9%
~ 5
 
5.9%
2 4
 
4.7%
1 4
 
4.7%
3 4
 
4.7%
4 4
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85
52.8%
Hangul 76
47.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29
34.1%
17
20.0%
5 5
 
5.9%
9 5
 
5.9%
- 5
 
5.9%
~ 5
 
5.9%
2 4
 
4.7%
1 4
 
4.7%
3 4
 
4.7%
4 4
 
4.7%
Hangul
ValueCountFrequency (%)
만 13
17.1%
원 7
 
9.2%
세 6
 
7.9%
미 6
 
7.9%
졸 4
 
5.3%
이 4
 
5.3%
상 3
 
3.9%
역 2
 
2.6%
구 2
 
2.6%
무 2
 
2.6%
Other values (27) 27
35.5%

전기
Real number (ℝ)

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.446154
Minimum42
Maximum67.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T02:11:35.122876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile43.625
Q147.2
median50.8
Q352.575
95-th percentile57.35
Maximum67.4
Range25.4
Interquartile range (IQR)5.375

Descriptive statistics

Standard deviation5.1293064
Coefficient of variation (CV)0.10167884
Kurtosis3.778504
Mean50.446154
Median Absolute Deviation (MAD)2.45
Skewness1.2645965
Sum1311.6
Variance26.309785
MonotonicityNot monotonic
2023-12-11T02:11:35.371574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
50.8 2
 
7.7%
50.2 1
 
3.8%
58.1 1
 
3.8%
55.1 1
 
3.8%
43.4 1
 
3.8%
67.4 1
 
3.8%
51.9 1
 
3.8%
44.3 1
 
3.8%
51.1 1
 
3.8%
50.1 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
42.0 1
3.8%
43.4 1
3.8%
44.3 1
3.8%
45.1 1
3.8%
45.7 1
3.8%
46.3 1
3.8%
47.0 1
3.8%
47.8 1
3.8%
48.3 1
3.8%
49.2 1
3.8%
ValueCountFrequency (%)
67.4 1
3.8%
58.1 1
3.8%
55.1 1
3.8%
54.0 1
3.8%
53.2 1
3.8%
53.1 1
3.8%
52.8 1
3.8%
51.9 1
3.8%
51.4 1
3.8%
51.3 1
3.8%

수도
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2615385
Minimum2.9
Maximum11.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T02:11:35.547861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile3.025
Q15.225
median6.3
Q37.375
95-th percentile8.675
Maximum11.3
Range8.4
Interquartile range (IQR)2.15

Descriptive statistics

Standard deviation1.9530647
Coefficient of variation (CV)0.3119145
Kurtosis0.50756444
Mean6.2615385
Median Absolute Deviation (MAD)1.1
Skewness0.26064794
Sum162.8
Variance3.8144615
MonotonicityNot monotonic
2023-12-11T02:11:35.727866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5.7 3
 
11.5%
8.0 2
 
7.7%
7.3 2
 
7.7%
6.2 1
 
3.8%
4.4 1
 
3.8%
3.1 1
 
3.8%
7.9 1
 
3.8%
8.3 1
 
3.8%
6.4 1
 
3.8%
2.9 1
 
3.8%
Other values (12) 12
46.2%
ValueCountFrequency (%)
2.9 1
 
3.8%
3.0 1
 
3.8%
3.1 1
 
3.8%
4.1 1
 
3.8%
4.4 1
 
3.8%
4.7 1
 
3.8%
5.2 1
 
3.8%
5.3 1
 
3.8%
5.6 1
 
3.8%
5.7 3
11.5%
ValueCountFrequency (%)
11.3 1
3.8%
8.8 1
3.8%
8.3 1
3.8%
8.0 2
7.7%
7.9 1
3.8%
7.4 1
3.8%
7.3 2
7.7%
7.2 1
3.8%
6.8 1
3.8%
6.5 1
3.8%

난방용 가스 및 유류
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.780769
Minimum5.5
Maximum36.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T02:11:35.878157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.5
5-th percentile13.8
Q117.95
median22.35
Q327.325
95-th percentile34.1
Maximum36.4
Range30.9
Interquartile range (IQR)9.375

Descriptive statistics

Standard deviation7.2443092
Coefficient of variation (CV)0.31800108
Kurtosis0.006271092
Mean22.780769
Median Absolute Deviation (MAD)5.2
Skewness-0.16541814
Sum592.3
Variance52.480015
MonotonicityNot monotonic
2023-12-11T02:11:36.057971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20.3 3
 
11.5%
15.2 2
 
7.7%
30.4 2
 
7.7%
25.7 2
 
7.7%
22.2 1
 
3.8%
22.5 1
 
3.8%
17.4 1
 
3.8%
5.5 1
 
3.8%
33.2 1
 
3.8%
19.6 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
5.5 1
 
3.8%
13.5 1
 
3.8%
14.7 1
 
3.8%
15.2 2
7.7%
15.6 1
 
3.8%
17.4 1
 
3.8%
19.6 1
 
3.8%
20.3 3
11.5%
22.1 1
 
3.8%
22.2 1
 
3.8%
ValueCountFrequency (%)
36.4 1
3.8%
34.4 1
3.8%
33.2 1
3.8%
30.4 2
7.7%
28.4 1
3.8%
27.8 1
3.8%
25.9 1
3.8%
25.7 2
7.7%
25.1 1
3.8%
24.5 1
3.8%

자동차 유류 및 가스
Real number (ℝ)

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.2
Minimum3.8
Maximum14.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T02:11:36.234946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.125
Q18.825
median10.35
Q311.8
95-th percentile14.175
Maximum14.5
Range10.7
Interquartile range (IQR)2.975

Descriptive statistics

Standard deviation2.7000741
Coefficient of variation (CV)0.26471314
Kurtosis0.33171725
Mean10.2
Median Absolute Deviation (MAD)1.5
Skewness-0.61825323
Sum265.2
Variance7.2904
MonotonicityNot monotonic
2023-12-11T02:11:36.410822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
11.8 3
 
11.5%
10.2 2
 
7.7%
13.2 1
 
3.8%
11.5 1
 
3.8%
8.9 1
 
3.8%
4.5 1
 
3.8%
11.9 1
 
3.8%
14.3 1
 
3.8%
7.2 1
 
3.8%
7.8 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
3.8 1
3.8%
4.5 1
3.8%
7.0 1
3.8%
7.2 1
3.8%
7.8 1
3.8%
8.4 1
3.8%
8.8 1
3.8%
8.9 1
3.8%
9.3 1
3.8%
9.4 1
3.8%
ValueCountFrequency (%)
14.5 1
 
3.8%
14.3 1
 
3.8%
13.8 1
 
3.8%
13.2 1
 
3.8%
12.3 1
 
3.8%
11.9 1
 
3.8%
11.8 3
11.5%
11.5 1
 
3.8%
11.2 1
 
3.8%
10.9 1
 
3.8%

쓰레기 배출량(일반쓰레기, 음식물 쓰레기)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.269231
Minimum5
Maximum15.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T02:11:36.580520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5.3
Q17.925
median10.4
Q312.55
95-th percentile15.1
Maximum15.7
Range10.7
Interquartile range (IQR)4.625

Descriptive statistics

Standard deviation3.0861976
Coefficient of variation (CV)0.3005286
Kurtosis-0.86273922
Mean10.269231
Median Absolute Deviation (MAD)2.45
Skewness-0.020643494
Sum267
Variance9.5246154
MonotonicityNot monotonic
2023-12-11T02:11:36.810075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8.7 1
 
3.8%
10.2 1
 
3.8%
12.9 1
 
3.8%
7.2 1
 
3.8%
12.4 1
 
3.8%
7.4 1
 
3.8%
15.7 1
 
3.8%
10.5 1
 
3.8%
8.0 1
 
3.8%
11.0 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
5.0 1
3.8%
5.2 1
3.8%
5.6 1
3.8%
6.7 1
3.8%
7.2 1
3.8%
7.4 1
3.8%
7.9 1
3.8%
8.0 1
3.8%
8.7 1
3.8%
9.3 1
3.8%
ValueCountFrequency (%)
15.7 1
3.8%
15.3 1
3.8%
14.5 1
3.8%
14.0 1
3.8%
13.4 1
3.8%
12.9 1
3.8%
12.6 1
3.8%
12.4 1
3.8%
12.0 1
3.8%
11.1 1
3.8%

기타
Categorical

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
0.0
21 
0.1
3 
0.2
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.1
3rd row0.0
4th row0.0
5th row0.2

Common Values

ValueCountFrequency (%)
0.0 21
80.8%
0.1 3
 
11.5%
0.2 2
 
7.7%

Length

2023-12-11T02:11:36.984645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:11:37.153200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 21
80.8%
0.1 3
 
11.5%
0.2 2
 
7.7%

Interactions

2023-12-11T02:11:33.019172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:30.713456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:31.299917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:31.816060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:32.425520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:33.118644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:30.822496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:31.421878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:31.947573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:32.535121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:33.207830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:30.936660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:31.518084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:32.046757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:32.638297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:33.324526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:31.045902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:31.621897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:32.149300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:32.769602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:33.467869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:31.169101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:31.722005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:32.267822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:11:32.894030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:11:37.266162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분항목전기수도난방용 가스 및 유류자동차 유류 및 가스쓰레기 배출량(일반쓰레기, 음식물 쓰레기)기타
구분1.0001.0000.0000.0000.0000.6550.2840.277
항목1.0001.0001.0001.0001.0001.0001.0001.000
전기0.0001.0001.0000.0000.6230.1570.0000.276
수도0.0001.0000.0001.0000.6650.2850.3930.000
난방용 가스 및 유류0.0001.0000.6230.6651.0000.0000.7600.000
자동차 유류 및 가스0.6551.0000.1570.2850.0001.0000.5830.000
쓰레기 배출량(일반쓰레기, 음식물 쓰레기)0.2841.0000.0000.3930.7600.5831.0000.525
기타0.2771.0000.2760.0000.0000.0000.5251.000
2023-12-11T02:11:37.478167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기타
구분1.0000.064
기타0.0641.000
2023-12-11T02:11:37.627597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전기수도난방용 가스 및 유류자동차 유류 및 가스쓰레기 배출량(일반쓰레기, 음식물 쓰레기)구분기타
전기1.000-0.321-0.500-0.2410.1390.0000.120
수도-0.3211.000-0.4620.3990.5730.0000.000
난방용 가스 및 유류-0.500-0.4621.000-0.438-0.7870.0000.000
자동차 유류 및 가스-0.2410.399-0.4381.0000.2670.3460.000
쓰레기 배출량(일반쓰레기, 음식물 쓰레기)0.1390.573-0.7870.2671.0000.0430.287
구분0.0000.0000.0000.3460.0431.0000.064
기타0.1200.0000.0000.0000.2870.0641.000

Missing values

2023-12-11T02:11:33.632341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:11:33.792488image/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

구분항목전기수도난방용 가스 및 유류자동차 유류 및 가스쓰레기 배출량(일반쓰레기, 음식물 쓰레기)기타
0성별남50.25.722.213.28.70.0
1성별여49.27.224.57.012.00.1
2연령15-19세54.08.014.79.314.00.0
3연령20-29세42.011.322.111.213.40.0
4연령30-39세53.15.715.210.315.30.2
5연령40-49세50.86.520.311.810.60.0
6연령50-59세48.35.627.810.47.90.0
7연령60세 이상50.84.730.48.45.60.0
8교육수준초졸이하51.25.334.43.85.20.0
9교육수준중졸51.34.125.99.49.30.0
구분항목전기수도난방용 가스 및 유류자동차 유류 및 가스쓰레기 배출량(일반쓰레기, 음식물 쓰레기)기타
16직업기능노무45.15.225.714.59.50.0
17월가구소득100만원 미만46.32.936.47.86.70.0
18월가구소득100 ~ 200만원 미만47.06.428.47.211.00.0
19월가구소득200 ~ 300만원 미만50.17.320.314.38.00.0
20월가구소득300 ~ 400만원 미만51.18.319.610.210.50.2
21월가구소득400 ~ 500만원 미만44.37.920.311.915.70.0
22월가구소득500 ~ 600만원 미만51.93.133.24.57.40.0
23월가구소득600만원이상67.44.45.510.212.40.0
24구역일반구역43.47.330.411.87.20.0
25구역개발구역55.15.717.48.912.90.1