Overview

Dataset statistics

Number of variables13
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory119.9 B

Variable types

Categorical1
Text1
Numeric11

Dataset

Description2015년부산광역시강서구사회조사결과(통근,통학교통수단)
Author부산광역시 강서구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3045858

Alerts

통근·통학한다 is highly overall correlated with 자전거(통근·통학시교통수단) and 1 other fieldsHigh correlation
도보(통근·통학시교통수단) is highly overall correlated with 자전거(통근·통학시교통수단) and 2 other fieldsHigh correlation
자전거(통근·통학시교통수단) is highly overall correlated with 통근·통학한다 and 5 other fieldsHigh correlation
오토바이(통근·통학시교통수단) is highly overall correlated with 자전거(통근·통학시교통수단)High correlation
도시철도(지하철,통근·통학시교통수단) is highly overall correlated with 시내버스 마을버스(통근·통학시교통수단) and 1 other fieldsHigh correlation
시내버스 마을버스(통근·통학시교통수단) is highly overall correlated with 도시철도(지하철,통근·통학시교통수단) and 1 other fieldsHigh correlation
택시(통근·통학시교통수단) is highly overall correlated with 자전거(통근·통학시교통수단)High correlation
승용차(통근·통학시교통수단) is highly overall correlated with 도보(통근·통학시교통수단) and 2 other fieldsHigh correlation
통근·통학시간 is highly overall correlated with 도보(통근·통학시교통수단) and 1 other fieldsHigh correlation
통근·통학안한다 is highly overall correlated with 통근·통학한다 and 1 other fieldsHigh correlation
항목 has unique valuesUnique
도보(통근·통학시교통수단) has unique valuesUnique
승용차(통근·통학시교통수단) has unique valuesUnique
자전거(통근·통학시교통수단) has 1 (3.7%) zerosZeros
오토바이(통근·통학시교통수단) has 5 (18.5%) zerosZeros
도시철도(지하철,통근·통학시교통수단) has 1 (3.7%) zerosZeros
택시(통근·통학시교통수단) has 14 (51.9%) zerosZeros
승용차(통근·통학시교통수단) has 1 (3.7%) zerosZeros

Reproduction

Analysis started2023-12-10 16:54:16.429899
Analysis finished2023-12-10 16:54:37.980051
Duration21.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct6
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size348.0 B
월가구소득
연령별
직업별
교육수준
성별

Length

Max length5
Median length4
Mean length3.5925926
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
월가구소득 8
29.6%
연령별 6
22.2%
직업별 5
18.5%
교육수준 4
14.8%
성별 2
 
7.4%
구역 2
 
7.4%

Length

2023-12-11T01:54:38.121391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:54:38.332959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
월가구소득 8
29.6%
연령별 6
22.2%
직업별 5
18.5%
교육수준 4
14.8%
성별 2
 
7.4%
구역 2
 
7.4%

항목
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-11T01:54:38.727004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.3703704
Min length1

Characters and Unicode

Total characters145
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row
2nd row
3rd row15-19세
4th row20-29세
5th row30-39세
ValueCountFrequency (%)
1
 
3.4%
농어업 1
 
3.4%
일반구역 1
 
3.4%
이상 1
 
3.4%
700만원 1
 
3.4%
600-700만원 1
 
3.4%
500-600만원 1
 
3.4%
400-500만원 1
 
3.4%
300-400만원 1
 
3.4%
200-300만원 1
 
3.4%
Other values (19) 19
65.5%
2023-12-11T01:54:39.263911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
22.8%
- 11
 
7.6%
9
 
6.2%
8
 
5.5%
6
 
4.1%
5 5
 
3.4%
9 5
 
3.4%
3 4
 
2.8%
4 4
 
2.8%
4
 
2.8%
Other values (38) 56
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
46.9%
Decimal Number 64
44.1%
Dash Punctuation 11
 
7.6%
Space Separator 2
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9
 
13.2%
8
 
11.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
3
 
4.4%
2
 
2.9%
2
 
2.9%
2
 
2.9%
1
 
1.5%
Other values (27) 27
39.7%
Decimal Number
ValueCountFrequency (%)
0 33
51.6%
5 5
 
7.8%
9 5
 
7.8%
3 4
 
6.2%
4 4
 
6.2%
2 4
 
6.2%
1 4
 
6.2%
6 3
 
4.7%
7 2
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77
53.1%
Hangul 68
46.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9
 
13.2%
8
 
11.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
3
 
4.4%
2
 
2.9%
2
 
2.9%
2
 
2.9%
1
 
1.5%
Other values (27) 27
39.7%
Common
ValueCountFrequency (%)
0 33
42.9%
- 11
 
14.3%
5 5
 
6.5%
9 5
 
6.5%
3 4
 
5.2%
4 4
 
5.2%
2 4
 
5.2%
1 4
 
5.2%
6 3
 
3.9%
7 2
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77
53.1%
Hangul 68
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
42.9%
- 11
 
14.3%
5 5
 
6.5%
9 5
 
6.5%
3 4
 
5.2%
4 4
 
5.2%
2 4
 
5.2%
1 4
 
5.2%
6 3
 
3.9%
7 2
 
2.6%
Hangul
ValueCountFrequency (%)
9
 
13.2%
8
 
11.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
3
 
4.4%
2
 
2.9%
2
 
2.9%
2
 
2.9%
1
 
1.5%
Other values (27) 27
39.7%

통근·통학한다
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.837037
Minimum24.4
Maximum98.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:39.508152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24.4
5-th percentile37.9
Q164.15
median67.4
Q377.1
95-th percentile97.45
Maximum98.5
Range74.1
Interquartile range (IQR)12.95

Descriptive statistics

Standard deviation18.376472
Coefficient of variation (CV)0.26695618
Kurtosis0.35984335
Mean68.837037
Median Absolute Deviation (MAD)4.9
Skewness-0.36112278
Sum1858.6
Variance337.69473
MonotonicityNot monotonic
2023-12-11T01:54:39.697467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
68.1 2
 
7.4%
79.0 1
 
3.7%
89.2 1
 
3.7%
64.7 1
 
3.7%
58.8 1
 
3.7%
75.2 1
 
3.7%
71.3 1
 
3.7%
67.4 1
 
3.7%
64.4 1
 
3.7%
63.2 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
24.4 1
3.7%
36.7 1
3.7%
40.7 1
3.7%
44.1 1
3.7%
58.8 1
3.7%
63.2 1
3.7%
63.9 1
3.7%
64.4 1
3.7%
64.7 1
3.7%
64.8 1
3.7%
ValueCountFrequency (%)
98.5 1
3.7%
97.9 1
3.7%
96.4 1
3.7%
92.8 1
3.7%
91.0 1
3.7%
89.2 1
3.7%
79.0 1
3.7%
75.2 1
3.7%
72.3 1
3.7%
71.3 1
3.7%

도보(통근·통학시교통수단)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.022222
Minimum4.5
Maximum44.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:39.938573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile6.41
Q112.45
median16.6
Q325.5
95-th percentile34.06
Maximum44.9
Range40.4
Interquartile range (IQR)13.05

Descriptive statistics

Standard deviation9.7587962
Coefficient of variation (CV)0.51302083
Kurtosis0.39895911
Mean19.022222
Median Absolute Deviation (MAD)7
Skewness0.76359041
Sum513.6
Variance95.234103
MonotonicityNot monotonic
2023-12-11T01:54:40.188393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
13.0 1
 
3.7%
26.0 1
 
3.7%
19.1 1
 
3.7%
15.0 1
 
3.7%
6.9 1
 
3.7%
9.6 1
 
3.7%
14.2 1
 
3.7%
16.2 1
 
3.7%
15.9 1
 
3.7%
19.2 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
4.5 1
3.7%
6.2 1
3.7%
6.9 1
3.7%
9.1 1
3.7%
9.3 1
3.7%
9.6 1
3.7%
11.9 1
3.7%
13.0 1
3.7%
14.2 1
3.7%
15.0 1
3.7%
ValueCountFrequency (%)
44.9 1
3.7%
34.3 1
3.7%
33.5 1
3.7%
30.3 1
3.7%
28.8 1
3.7%
28.1 1
3.7%
26.0 1
3.7%
25.0 1
3.7%
21.4 1
3.7%
20.9 1
3.7%

자전거(통근·통학시교통수단)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.562963
Minimum0
Maximum25.7
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:40.351991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.82
Q12.15
median6.4
Q310.7
95-th percentile19.18
Maximum25.7
Range25.7
Interquartile range (IQR)8.55

Descriptive statistics

Standard deviation6.81069
Coefficient of variation (CV)0.90053198
Kurtosis0.41912085
Mean7.562963
Median Absolute Deviation (MAD)4.5
Skewness1.0288679
Sum204.2
Variance46.385499
MonotonicityNot monotonic
2023-12-11T01:54:40.483536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8.3 2
 
7.4%
6.4 1
 
3.7%
25.7 1
 
3.7%
1.7 1
 
3.7%
15.1 1
 
3.7%
2.8 1
 
3.7%
2.6 1
 
3.7%
0.7 1
 
3.7%
2.5 1
 
3.7%
7.1 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0.0 1
3.7%
0.7 1
3.7%
1.1 1
3.7%
1.3 1
3.7%
1.4 1
3.7%
1.7 1
3.7%
1.9 1
3.7%
2.4 1
3.7%
2.5 1
3.7%
2.6 1
3.7%
ValueCountFrequency (%)
25.7 1
3.7%
19.3 1
3.7%
18.9 1
3.7%
15.9 1
3.7%
15.1 1
3.7%
13.5 1
3.7%
11.0 1
3.7%
10.4 1
3.7%
10.0 1
3.7%
8.5 1
3.7%

오토바이(통근·통학시교통수단)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7148148
Minimum0
Maximum17.4
Zeros5
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:40.609857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.3
median2.8
Q34.8
95-th percentile10.34
Maximum17.4
Range17.4
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.8534404
Coefficient of variation (CV)1.037317
Kurtosis5.4124936
Mean3.7148148
Median Absolute Deviation (MAD)1.9
Skewness2.0436135
Sum100.3
Variance14.849003
MonotonicityNot monotonic
2023-12-11T01:54:40.756808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 5
18.5%
4.7 2
 
7.4%
1.1 2
 
7.4%
2.3 2
 
7.4%
2.2 1
 
3.7%
8.1 1
 
3.7%
3.0 1
 
3.7%
2.5 1
 
3.7%
4.9 1
 
3.7%
7.0 1
 
3.7%
Other values (10) 10
37.0%
ValueCountFrequency (%)
0.0 5
18.5%
1.1 2
 
7.4%
1.5 1
 
3.7%
1.8 1
 
3.7%
2.2 1
 
3.7%
2.3 2
 
7.4%
2.5 1
 
3.7%
2.8 1
 
3.7%
3.0 1
 
3.7%
3.3 1
 
3.7%
ValueCountFrequency (%)
17.4 1
3.7%
11.3 1
3.7%
8.1 1
3.7%
7.0 1
3.7%
5.9 1
3.7%
5.3 1
3.7%
4.9 1
3.7%
4.7 2
7.4%
3.6 1
3.7%
3.5 1
3.7%

도시철도(지하철,통근·통학시교통수단)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1148148
Minimum0
Maximum19.9
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:40.902807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99
Q11.75
median4
Q36.75
95-th percentile16.7
Maximum19.9
Range19.9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.8987992
Coefficient of variation (CV)0.95776668
Kurtosis4.6849673
Mean5.1148148
Median Absolute Deviation (MAD)2.7
Skewness2.0874446
Sum138.1
Variance23.998234
MonotonicityNot monotonic
2023-12-11T01:54:41.046352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1.3 3
 
11.1%
4.0 2
 
7.4%
3.4 2
 
7.4%
4.6 2
 
7.4%
2.1 1
 
3.7%
9.7 1
 
3.7%
6.9 1
 
3.7%
6.7 1
 
3.7%
2.9 1
 
3.7%
5.5 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
0.0 1
 
3.7%
0.9 1
 
3.7%
1.2 1
 
3.7%
1.3 3
11.1%
1.4 1
 
3.7%
2.1 1
 
3.7%
2.2 1
 
3.7%
2.9 1
 
3.7%
3.4 2
7.4%
3.5 1
 
3.7%
ValueCountFrequency (%)
19.9 1
3.7%
19.7 1
3.7%
9.7 1
3.7%
7.6 1
3.7%
7.5 1
3.7%
6.9 1
3.7%
6.8 1
3.7%
6.7 1
3.7%
5.7 1
3.7%
5.5 1
3.7%
Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.766667
Minimum0.8
Maximum43.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:41.211173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile4.26
Q110.2
median16.1
Q321.55
95-th percentile37.45
Maximum43.8
Range43
Interquartile range (IQR)11.35

Descriptive statistics

Standard deviation9.9825617
Coefficient of variation (CV)0.59538141
Kurtosis2.0471742
Mean16.766667
Median Absolute Deviation (MAD)6.2
Skewness1.2169575
Sum452.7
Variance99.651538
MonotonicityNot monotonic
2023-12-11T01:54:41.404404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
16.3 2
 
7.4%
16.9 2
 
7.4%
12.2 1
 
3.7%
16.1 1
 
3.7%
15.4 1
 
3.7%
3.3 1
 
3.7%
16.4 1
 
3.7%
24.2 1
 
3.7%
14.8 1
 
3.7%
13.0 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
0.8 1
3.7%
3.3 1
3.7%
6.5 1
3.7%
8.3 1
3.7%
9.0 1
3.7%
9.4 1
3.7%
9.6 1
3.7%
10.8 1
3.7%
11.5 1
3.7%
12.2 1
3.7%
ValueCountFrequency (%)
43.8 1
3.7%
42.4 1
3.7%
25.9 1
3.7%
25.5 1
3.7%
24.3 1
3.7%
24.2 1
3.7%
22.3 1
3.7%
20.8 1
3.7%
16.9 2
7.4%
16.4 1
3.7%

택시(통근·통학시교통수단)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38148148
Minimum0
Maximum1.8
Zeros14
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:41.583586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile1.47
Maximum1.8
Range1.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.54985105
Coefficient of variation (CV)1.4413571
Kurtosis0.97338655
Mean0.38148148
Median Absolute Deviation (MAD)0
Skewness1.4557162
Sum10.3
Variance0.30233618
MonotonicityNot monotonic
2023-12-11T01:54:41.775337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.0 14
51.9%
0.5 4
 
14.8%
0.3 2
 
7.4%
1.4 2
 
7.4%
0.2 1
 
3.7%
1.0 1
 
3.7%
0.4 1
 
3.7%
1.8 1
 
3.7%
1.5 1
 
3.7%
ValueCountFrequency (%)
0.0 14
51.9%
0.2 1
 
3.7%
0.3 2
 
7.4%
0.4 1
 
3.7%
0.5 4
 
14.8%
1.0 1
 
3.7%
1.4 2
 
7.4%
1.5 1
 
3.7%
1.8 1
 
3.7%
ValueCountFrequency (%)
1.8 1
 
3.7%
1.5 1
 
3.7%
1.4 2
 
7.4%
1.0 1
 
3.7%
0.5 4
 
14.8%
0.4 1
 
3.7%
0.3 2
 
7.4%
0.2 1
 
3.7%
0.0 14
51.9%

승용차(통근·통학시교통수단)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.877778
Minimum0
Maximum76.5
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:41.984374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.97
Q125.45
median45.6
Q355.95
95-th percentile73.34
Maximum76.5
Range76.5
Interquartile range (IQR)30.5

Descriptive statistics

Standard deviation21.291445
Coefficient of variation (CV)0.5084187
Kurtosis-0.92107877
Mean41.877778
Median Absolute Deviation (MAD)19
Skewness-0.18905378
Sum1130.7
Variance453.32564
MonotonicityNot monotonic
2023-12-11T01:54:42.143409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
53.2 1
 
3.7%
26.6 1
 
3.7%
53.1 1
 
3.7%
30.6 1
 
3.7%
76.5 1
 
3.7%
58.0 1
 
3.7%
48.6 1
 
3.7%
52.4 1
 
3.7%
45.6 1
 
3.7%
39.6 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.0 1
3.7%
10.4 1
3.7%
12.3 1
3.7%
13.1 1
3.7%
17.6 1
3.7%
23.1 1
3.7%
24.4 1
3.7%
26.5 1
3.7%
26.6 1
3.7%
30.6 1
3.7%
ValueCountFrequency (%)
76.5 1
3.7%
75.5 1
3.7%
68.3 1
3.7%
67.7 1
3.7%
66.1 1
3.7%
59.6 1
3.7%
58.0 1
3.7%
53.9 1
3.7%
53.2 1
3.7%
53.1 1
3.7%
Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6111111
Minimum1
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:42.268652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.85
Q14.85
median5.8
Q36.7
95-th percentile7.68
Maximum8.7
Range7.7
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation1.7290134
Coefficient of variation (CV)0.30814099
Kurtosis0.66327212
Mean5.6111111
Median Absolute Deviation (MAD)1
Skewness-0.77284899
Sum151.5
Variance2.9894872
MonotonicityNot monotonic
2023-12-11T01:54:42.398536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6.2 2
 
7.4%
3.5 2
 
7.4%
7.2 2
 
7.4%
6.6 2
 
7.4%
5.0 2
 
7.4%
5.8 2
 
7.4%
6.5 2
 
7.4%
4.6 1
 
3.7%
4.7 1
 
3.7%
7.4 1
 
3.7%
Other values (10) 10
37.0%
ValueCountFrequency (%)
1.0 1
3.7%
2.7 1
3.7%
3.2 1
3.7%
3.5 2
7.4%
4.6 1
3.7%
4.7 1
3.7%
5.0 2
7.4%
5.1 1
3.7%
5.2 1
3.7%
5.7 1
3.7%
ValueCountFrequency (%)
8.7 1
3.7%
7.8 1
3.7%
7.4 1
3.7%
7.2 2
7.4%
7.0 1
3.7%
6.8 1
3.7%
6.6 2
7.4%
6.5 2
7.4%
6.2 2
7.4%
5.8 2
7.4%

통근·통학시간
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.359259
Minimum19
Maximum46.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:42.543652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile23.27
Q127.85
median29.8
Q332.1
95-th percentile38.38
Maximum46.8
Range27.8
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation5.2990753
Coefficient of variation (CV)0.17454561
Kurtosis2.9612987
Mean30.359259
Median Absolute Deviation (MAD)2.2
Skewness0.91019293
Sum819.7
Variance28.080199
MonotonicityNot monotonic
2023-12-11T01:54:42.659719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
28.3 2
 
7.4%
29.8 1
 
3.7%
31.1 1
 
3.7%
33.8 1
 
3.7%
30.2 1
 
3.7%
37.4 1
 
3.7%
29.9 1
 
3.7%
29.7 1
 
3.7%
31.6 1
 
3.7%
26.9 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
19.0 1
3.7%
22.7 1
3.7%
24.6 1
3.7%
26.0 1
3.7%
26.9 1
3.7%
27.4 1
3.7%
27.6 1
3.7%
28.1 1
3.7%
28.3 2
7.4%
28.6 1
3.7%
ValueCountFrequency (%)
46.8 1
3.7%
38.8 1
3.7%
37.4 1
3.7%
34.8 1
3.7%
33.8 1
3.7%
33.7 1
3.7%
32.4 1
3.7%
31.8 1
3.7%
31.6 1
3.7%
31.2 1
3.7%

통근·통학안한다
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.162963
Minimum1.5
Maximum75.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:54:42.769265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.55
Q122.9
median32.6
Q335.85
95-th percentile62.1
Maximum75.6
Range74.1
Interquartile range (IQR)12.95

Descriptive statistics

Standard deviation18.376472
Coefficient of variation (CV)0.5896895
Kurtosis0.35984335
Mean31.162963
Median Absolute Deviation (MAD)4.9
Skewness0.36112278
Sum841.4
Variance337.69473
MonotonicityNot monotonic
2023-12-11T01:54:42.893997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
31.9 2
 
7.4%
21.0 1
 
3.7%
10.8 1
 
3.7%
35.3 1
 
3.7%
41.2 1
 
3.7%
24.8 1
 
3.7%
28.7 1
 
3.7%
32.6 1
 
3.7%
35.6 1
 
3.7%
36.8 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
1.5 1
3.7%
2.1 1
3.7%
3.6 1
3.7%
7.2 1
3.7%
9.0 1
3.7%
10.8 1
3.7%
21.0 1
3.7%
24.8 1
3.7%
27.7 1
3.7%
28.7 1
3.7%
ValueCountFrequency (%)
75.6 1
3.7%
63.3 1
3.7%
59.3 1
3.7%
55.9 1
3.7%
41.2 1
3.7%
36.8 1
3.7%
36.1 1
3.7%
35.6 1
3.7%
35.3 1
3.7%
35.2 1
3.7%

Interactions

2023-12-11T01:54:35.601237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:17.224784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:19.737802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:21.887185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:23.845587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:25.758477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:27.487195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:29.128732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:30.711388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:32.316656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:33.962256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:35.731173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:17.471773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:19.928599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:22.078878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:24.050084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:25.920922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:27.608597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:29.264007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:30.868857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:32.473165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:34.120411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:35.851223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:17.689044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:20.121095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:22.269645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:24.215306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:26.089639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:27.749316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:29.399360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:31.011201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:32.633798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:34.257160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:35.978769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:17.858348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:20.308641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:22.436719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:24.353033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:26.253252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:27.889822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:29.522090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:31.127060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:32.770606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:34.404064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:36.115825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:18.038200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:20.544901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:22.595241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:24.522016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:26.451797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:28.016594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:29.635509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:31.240047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:32.943840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:34.575117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:36.280199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:18.236247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:20.734007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:22.782430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:24.700852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:26.646121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:28.133867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:29.789399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:31.390253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:33.125667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:34.757204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:36.390755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:18.398537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:20.949505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:22.971226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:24.868295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:26.783437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:28.242123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:29.938473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:31.508617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:33.257052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:34.883262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:36.539362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:18.578766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:21.145826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:23.150937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:25.013387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:26.936392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:28.359794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:30.152097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:31.649412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:33.398200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:35.016591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:36.685102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:18.753913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:21.325835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:23.323022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:25.166955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:27.093428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:28.455410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:30.293705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:31.805603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:33.519445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:35.166249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:36.819771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:19.364588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:21.522297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:23.520006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:25.351993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:27.250889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:28.616081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:30.445656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:31.967622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:33.662684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:35.320481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:37.274868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:19.534922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:21.707782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:23.672253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:25.562698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:27.359340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:29.011161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:30.577673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:32.138306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:33.819200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:54:35.464881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:54:42.982600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분항목통근·통학한다도보(통근·통학시교통수단)자전거(통근·통학시교통수단)오토바이(통근·통학시교통수단)도시철도(지하철,통근·통학시교통수단)시내버스 마을버스(통근·통학시교통수단)택시(통근·통학시교통수단)승용차(통근·통학시교통수단)기타(통근·통학시교통수단)통근·통학시간통근·통학안한다
구분1.0001.0000.7950.0000.0000.0000.7390.2930.3300.0000.0000.0000.795
항목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
통근·통학한다0.7951.0001.0000.6160.8550.5500.6520.0000.0000.5280.5850.6981.000
도보(통근·통학시교통수단)0.0001.0000.6161.0000.8410.6780.0000.0000.4580.8160.9360.7410.616
자전거(통근·통학시교통수단)0.0001.0000.8550.8411.0000.8120.6390.0000.5740.3880.8230.8030.855
오토바이(통근·통학시교통수단)0.0001.0000.5500.6780.8121.0000.4850.3000.0000.0000.6900.6390.550
도시철도(지하철,통근·통학시교통수단)0.7391.0000.6520.0000.6390.4851.0000.6710.0000.5580.0000.6610.652
시내버스 마을버스(통근·통학시교통수단)0.2931.0000.0000.0000.0000.3000.6711.0000.3070.5920.0000.5880.000
택시(통근·통학시교통수단)0.3301.0000.0000.4580.5740.0000.0000.3071.0000.3970.3530.0000.000
승용차(통근·통학시교통수단)0.0001.0000.5280.8160.3880.0000.5580.5920.3971.0000.6790.6130.528
기타(통근·통학시교통수단)0.0001.0000.5850.9360.8230.6900.0000.0000.3530.6791.0000.6840.585
통근·통학시간0.0001.0000.6980.7410.8030.6390.6610.5880.0000.6130.6841.0000.698
통근·통학안한다0.7951.0001.0000.6160.8550.5500.6520.0000.0000.5280.5850.6981.000
2023-12-11T01:54:43.127531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통근·통학한다도보(통근·통학시교통수단)자전거(통근·통학시교통수단)오토바이(통근·통학시교통수단)도시철도(지하철,통근·통학시교통수단)시내버스 마을버스(통근·통학시교통수단)택시(통근·통학시교통수단)승용차(통근·통학시교통수단)기타(통근·통학시교통수단)통근·통학시간통근·통학안한다구분
통근·통학한다1.000-0.387-0.508-0.334-0.116-0.260-0.3740.4320.3500.224-1.0000.495
도보(통근·통학시교통수단)-0.3871.0000.6490.468-0.1930.2750.405-0.761-0.253-0.5210.3870.000
자전거(통근·통학시교통수단)-0.5080.6491.0000.711-0.146-0.0230.583-0.650-0.157-0.4810.5080.000
오토바이(통근·통학시교통수단)-0.3340.4680.7111.000-0.182-0.2040.356-0.426-0.237-0.4860.3340.000
도시철도(지하철,통근·통학시교통수단)-0.116-0.193-0.146-0.1821.0000.678-0.016-0.274-0.2430.7900.1160.339
시내버스 마을버스(통근·통학시교통수단)-0.2600.275-0.023-0.2040.6781.0000.215-0.573-0.4870.3950.2600.000
택시(통근·통학시교통수단)-0.3740.4050.5830.356-0.0160.2151.000-0.341-0.233-0.3070.3740.171
승용차(통근·통학시교통수단)0.432-0.761-0.650-0.426-0.274-0.573-0.3411.0000.3080.151-0.4320.000
기타(통근·통학시교통수단)0.350-0.253-0.157-0.237-0.243-0.487-0.2330.3081.0000.051-0.3500.000
통근·통학시간0.224-0.521-0.481-0.4860.7900.395-0.3070.1510.0511.000-0.2240.000
통근·통학안한다-1.0000.3870.5080.3340.1160.2600.374-0.432-0.350-0.2241.0000.495
구분0.4950.0000.0000.0000.3390.0000.1710.0000.0000.0000.4951.000

Missing values

2023-12-11T01:54:37.503576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:54:37.841159image/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

구분항목통근·통학한다도보(통근·통학시교통수단)자전거(통근·통학시교통수단)오토바이(통근·통학시교통수단)도시철도(지하철,통근·통학시교통수단)시내버스 마을버스(통근·통학시교통수단)택시(통근·통학시교통수단)승용차(통근·통학시교통수단)기타(통근·통학시교통수단)통근·통학시간통근·통학안한다
0성별79.013.06.44.74.012.20.553.26.229.821.0
1성별44.126.08.32.37.524.30.226.65.031.855.9
2연령별15-19세91.028.11.40.019.743.80.00.07.038.89.0
3연령별20-29세68.04.52.41.119.942.40.026.53.246.832.0
4연령별30-39세63.911.91.31.51.39.60.067.76.828.336.1
5연령별40-49세72.317.64.35.31.26.50.559.65.027.427.7
6연령별50-59세64.820.913.53.30.99.41.043.67.426.035.2
7연령별60세이상40.728.818.911.31.411.50.423.14.624.659.3
8교육수준초졸이하36.744.915.95.91.316.30.012.33.522.763.3
9교육수준중졸64.930.310.43.65.725.91.817.64.727.635.1
구분항목통근·통학한다도보(통근·통학시교통수단)자전거(통근·통학시교통수단)오토바이(통근·통학시교통수단)도시철도(지하철,통근·통학시교통수단)시내버스 마을버스(통근·통학시교통수단)택시(통근·통학시교통수단)승용차(통근·통학시교통수단)기타(통근·통학시교통수단)통근·통학시간통근·통학안한다
17월가구소득100만원 미만24.433.519.31.84.625.51.410.43.528.175.6
18월가구소득100-200만원63.225.010.03.56.822.31.424.46.633.736.8
19월가구소득200-300만원64.419.211.07.04.613.00.539.65.126.935.6
20월가구소득300-400만원68.115.97.14.95.514.80.045.66.231.631.9
21월가구소득400-500만원67.416.22.52.52.916.30.052.47.229.732.6
22월가구소득500-600만원71.314.20.73.06.724.20.048.62.729.928.7
23월가구소득600-700만원75.29.62.60.06.916.40.058.06.637.424.8
24월가구소득700만원 이상68.16.92.80.03.43.30.076.57.230.231.9
25구역일반구역58.815.015.18.19.715.40.530.65.733.841.2
26구역개발구역64.719.11.71.12.116.90.353.15.828.335.3