Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory159.0 B

Variable types

Numeric14
Categorical2
Text1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-22266/F/1/datasetView.do

Alerts

행정동코드 is highly overall correlated with 자치구High correlation
연령대 is highly overall correlated with 외출이 매우 많은 집단High correlation
총인구 is highly overall correlated with 1인가구수 and 3 other fieldsHigh correlation
1인가구수 is highly overall correlated with 총인구 and 7 other fieldsHigh correlation
커뮤니케이션이 적은 집단 is highly overall correlated with 1인가구수 and 6 other fieldsHigh correlation
평일 외출이 적은 집단 is highly overall correlated with 1인가구수 and 7 other fieldsHigh correlation
휴일 외출이 적은 집단 is highly overall correlated with 1인가구수 and 6 other fieldsHigh correlation
출근소요시간 및 근무시간이 많은 집단 is highly overall correlated with 1인가구수 and 8 other fieldsHigh correlation
외출이 매우 적은 집단(전체) is highly overall correlated with 총인구 and 3 other fieldsHigh correlation
외출이 매우 많은 집단 is highly overall correlated with 연령대 and 1 other fieldsHigh correlation
동영상서비스 이용이 많은 집단 is highly overall correlated with 1인가구수 and 8 other fieldsHigh correlation
생활서비스 이용이 많은 집단 is highly overall correlated with 1인가구수 and 6 other fieldsHigh correlation
재정상태에 대한 관심집단 is highly overall correlated with 1인가구수 and 6 other fieldsHigh correlation
외출-커뮤니케이션이 모두 적은 집단(전체) is highly overall correlated with 총인구 and 4 other fieldsHigh correlation
자치구 is highly overall correlated with 행정동코드High correlation
휴일 외출이 적은 집단 has 117 (1.2%) zerosZeros
외출이 매우 적은 집단(전체) has 368 (3.7%) zerosZeros
외출-커뮤니케이션이 모두 적은 집단(전체) has 456 (4.6%) zerosZeros

Reproduction

Analysis started2024-05-04 06:13:17.006586
Analysis finished2024-05-04 06:14:50.962290
Duration1 minute and 33.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct424
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1113653.4
Minimum1101053
Maximum1125074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:14:51.342710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1102058
Q11107069.8
median1114068
Q31120073
95-th percentile1124079
Maximum1125074
Range24021
Interquartile range (IQR)13003.25

Descriptive statistics

Standard deviation7403.0462
Coefficient of variation (CV)0.0066475314
Kurtosis-1.2519907
Mean1113653.4
Median Absolute Deviation (MAD)6990
Skewness-0.081926632
Sum1.1136534 × 1010
Variance54805092
MonotonicityNot monotonic
2024-05-04T06:14:51.830246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1122051 24
 
0.2%
1112056 24
 
0.2%
1102065 24
 
0.2%
1113072 24
 
0.2%
1102060 24
 
0.2%
1113073 24
 
0.2%
1110053 24
 
0.2%
1117065 24
 
0.2%
1125065 24
 
0.2%
1103059 24
 
0.2%
Other values (414) 9760
97.6%
ValueCountFrequency (%)
1101053 24
0.2%
1101054 23
0.2%
1101055 23
0.2%
1101056 23
0.2%
1101057 23
0.2%
1101058 24
0.2%
1101060 23
0.2%
1101061 23
0.2%
1101063 23
0.2%
1101064 24
0.2%
ValueCountFrequency (%)
1125074 24
0.2%
1125073 24
0.2%
1125072 23
0.2%
1125071 23
0.2%
1125070 24
0.2%
1125067 24
0.2%
1125066 23
0.2%
1125065 24
0.2%
1125063 24
0.2%
1125061 24
0.2%

자치구
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
송파구
 
638
강남구
 
518
관악구
 
494
성북구
 
476
강서구
 
470
Other values (20)
7404 

Length

Max length4
Median length3
Mean length3.0714
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서초구
2nd row양천구
3rd row동대문구
4th row영등포구
5th row영등포구

Common Values

ValueCountFrequency (%)
송파구 638
 
6.4%
강남구 518
 
5.2%
관악구 494
 
4.9%
성북구 476
 
4.8%
강서구 470
 
4.7%
노원구 447
 
4.5%
양천구 425
 
4.2%
강동구 424
 
4.2%
서초구 423
 
4.2%
영등포구 412
 
4.1%
Other values (15) 5273
52.7%

Length

2024-05-04T06:14:52.341861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 638
 
6.4%
강남구 518
 
5.2%
관악구 494
 
4.9%
성북구 476
 
4.8%
강서구 470
 
4.7%
노원구 447
 
4.5%
양천구 425
 
4.2%
강동구 424
 
4.2%
서초구 423
 
4.2%
영등포구 412
 
4.1%
Other values (15) 5273
52.7%
Distinct423
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T06:14:53.079316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.7905
Min length2

Characters and Unicode

Total characters37905
Distinct characters188
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
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 row서초1동
2nd row신월7동
3rd row장안2동
4th row양평1동
5th row당산1동
ValueCountFrequency (%)
신사동 47
 
0.5%
신월1동 24
 
0.2%
돈암1동 24
 
0.2%
가락2동 24
 
0.2%
잠실본동 24
 
0.2%
방학2동 24
 
0.2%
가락본동 24
 
0.2%
성현동 24
 
0.2%
원효로2동 24
 
0.2%
증산동 24
 
0.2%
Other values (413) 9737
97.4%
2024-05-04T06:14:54.586785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10048
26.5%
1 2297
 
6.1%
2 2285
 
6.0%
3 1007
 
2.7%
887
 
2.3%
4 611
 
1.6%
542
 
1.4%
424
 
1.1%
400
 
1.1%
399
 
1.1%
Other values (178) 19005
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30823
81.3%
Decimal Number 6874
 
18.1%
Other Punctuation 208
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10048
32.6%
887
 
2.9%
542
 
1.8%
424
 
1.4%
400
 
1.3%
399
 
1.3%
380
 
1.2%
380
 
1.2%
375
 
1.2%
371
 
1.2%
Other values (167) 16617
53.9%
Decimal Number
ValueCountFrequency (%)
1 2297
33.4%
2 2285
33.2%
3 1007
14.6%
4 611
 
8.9%
5 256
 
3.7%
6 162
 
2.4%
7 138
 
2.0%
8 71
 
1.0%
9 24
 
0.3%
0 23
 
0.3%
Other Punctuation
ValueCountFrequency (%)
· 208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30823
81.3%
Common 7082
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10048
32.6%
887
 
2.9%
542
 
1.8%
424
 
1.4%
400
 
1.3%
399
 
1.3%
380
 
1.2%
380
 
1.2%
375
 
1.2%
371
 
1.2%
Other values (167) 16617
53.9%
Common
ValueCountFrequency (%)
1 2297
32.4%
2 2285
32.3%
3 1007
14.2%
4 611
 
8.6%
5 256
 
3.6%
· 208
 
2.9%
6 162
 
2.3%
7 138
 
1.9%
8 71
 
1.0%
9 24
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30823
81.3%
ASCII 6874
 
18.1%
None 208
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
10048
32.6%
887
 
2.9%
542
 
1.8%
424
 
1.4%
400
 
1.3%
399
 
1.3%
380
 
1.2%
380
 
1.2%
375
 
1.2%
371
 
1.2%
Other values (167) 16617
53.9%
ASCII
ValueCountFrequency (%)
1 2297
33.4%
2 2285
33.2%
3 1007
14.6%
4 611
 
8.9%
5 256
 
3.7%
6 162
 
2.4%
7 138
 
2.0%
8 71
 
1.0%
9 24
 
0.3%
0 23
 
0.3%
None
ValueCountFrequency (%)
· 208
100.0%

성별
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5001 
1
4999 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 5001
50.0%
1 4999
50.0%

Length

2024-05-04T06:14:55.229586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:14:55.681561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5001
50.0%
1 4999
50.0%

연령대
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.522
Minimum20
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:14:56.077248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q135
median50
Q365
95-th percentile75
Maximum75
Range55
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.256283
Coefficient of variation (CV)0.36312198
Kurtosis-1.2165749
Mean47.522
Median Absolute Deviation (MAD)15
Skewness-0.00096631443
Sum475220
Variance297.77929
MonotonicityNot monotonic
2024-05-04T06:14:56.486699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
45 839
8.4%
65 836
8.4%
35 835
8.3%
70 835
8.3%
60 835
8.3%
50 835
8.3%
75 834
8.3%
25 833
8.3%
30 832
8.3%
40 830
8.3%
Other values (2) 1656
16.6%
ValueCountFrequency (%)
20 829
8.3%
25 833
8.3%
30 832
8.3%
35 835
8.3%
40 830
8.3%
45 839
8.4%
50 835
8.3%
55 827
8.3%
60 835
8.3%
65 836
8.4%
ValueCountFrequency (%)
75 834
8.3%
70 835
8.3%
65 836
8.4%
60 835
8.3%
55 827
8.3%
50 835
8.3%
45 839
8.4%
40 830
8.3%
35 835
8.3%
30 832
8.3%

총인구
Real number (ℝ)

HIGH CORRELATION 

Distinct2814
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean849.64278
Minimum22
Maximum4764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:14:56.936268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile282.6
Q1561.6
median795.8
Q31078
95-th percentile1589
Maximum4764
Range4742
Interquartile range (IQR)516.4

Descriptive statistics

Standard deviation410.86356
Coefficient of variation (CV)0.48357212
Kurtosis2.5347883
Mean849.64278
Median Absolute Deviation (MAD)252.2
Skewness1.0270699
Sum8496427.8
Variance168808.87
MonotonicityNot monotonic
2024-05-04T06:14:57.531561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
738.0 21
 
0.2%
711.0 20
 
0.2%
603.0 19
 
0.2%
801.0 19
 
0.2%
927.0 19
 
0.2%
900.0 17
 
0.2%
891.0 17
 
0.2%
748.0 16
 
0.2%
774.0 16
 
0.2%
936.0 16
 
0.2%
Other values (2804) 9820
98.2%
ValueCountFrequency (%)
22.0 1
 
< 0.1%
27.0 1
 
< 0.1%
32.4 1
 
< 0.1%
41.4 1
 
< 0.1%
46.8 1
 
< 0.1%
48.6 2
< 0.1%
50.4 1
 
< 0.1%
54.0 3
< 0.1%
55.8 1
 
< 0.1%
57.6 1
 
< 0.1%
ValueCountFrequency (%)
4764.0 1
< 0.1%
3437.0 1
< 0.1%
3112.0 1
< 0.1%
3091.0 1
< 0.1%
3076.0 1
< 0.1%
3063.0 1
< 0.1%
3061.0 1
< 0.1%
2998.0 1
< 0.1%
2965.0 1
< 0.1%
2916.0 1
< 0.1%

1인가구수
Real number (ℝ)

HIGH CORRELATION 

Distinct8163
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.04952
Minimum8.7
Maximum2867.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:14:57.985554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.7
5-th percentile35.188
Q172.795
median117.12
Q3188.5425
95-th percentile430.8285
Maximum2867.9
Range2859.2
Interquartile range (IQR)115.7475

Descriptive statistics

Standard deviation173.15391
Coefficient of variation (CV)1.0619713
Kurtosis40.44881
Mean163.04952
Median Absolute Deviation (MAD)52.545
Skewness4.9494124
Sum1630495.2
Variance29982.275
MonotonicityNot monotonic
2024-05-04T06:14:58.419137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.25 6
 
0.1%
108.01 5
 
0.1%
71.56 5
 
0.1%
60.69 5
 
0.1%
163.8 5
 
0.1%
49.98 4
 
< 0.1%
104.19 4
 
< 0.1%
107.27 4
 
< 0.1%
37.04 4
 
< 0.1%
84.87 4
 
< 0.1%
Other values (8153) 9954
99.5%
ValueCountFrequency (%)
8.7 1
< 0.1%
9.32 1
< 0.1%
10.01 1
< 0.1%
10.05 1
< 0.1%
10.24 1
< 0.1%
10.55 1
< 0.1%
10.75 1
< 0.1%
10.77 1
< 0.1%
10.89 1
< 0.1%
11.04 1
< 0.1%
ValueCountFrequency (%)
2867.9 1
< 0.1%
2708.36 1
< 0.1%
2367.82 1
< 0.1%
2255.55 1
< 0.1%
2219.2 1
< 0.1%
2189.98 1
< 0.1%
2125.27 1
< 0.1%
2108.5 1
< 0.1%
1932.44 1
< 0.1%
1917.06 1
< 0.1%

커뮤니케이션이 적은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3575
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.296893
Minimum0
Maximum275.29
Zeros71
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:14:58.895991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.67
Q16.55
median11.45
Q319.7125
95-th percentile45.3615
Maximum275.29
Range275.29
Interquartile range (IQR)13.1625

Descriptive statistics

Standard deviation17.397003
Coefficient of variation (CV)1.0675043
Kurtosis31.275098
Mean16.296893
Median Absolute Deviation (MAD)5.81
Skewness4.2212609
Sum162968.93
Variance302.65573
MonotonicityNot monotonic
2024-05-04T06:14:59.336062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 71
 
0.7%
5.99 14
 
0.1%
7.83 13
 
0.1%
7.42 13
 
0.1%
4.42 13
 
0.1%
6.09 13
 
0.1%
3.4 12
 
0.1%
3.88 12
 
0.1%
3.77 12
 
0.1%
10.8 12
 
0.1%
Other values (3565) 9815
98.2%
ValueCountFrequency (%)
0.0 71
0.7%
0.28 1
 
< 0.1%
0.3 1
 
< 0.1%
0.31 1
 
< 0.1%
0.41 1
 
< 0.1%
0.44 1
 
< 0.1%
0.49 2
 
< 0.1%
0.53 1
 
< 0.1%
0.6 2
 
< 0.1%
0.65 1
 
< 0.1%
ValueCountFrequency (%)
275.29 1
< 0.1%
251.28 1
< 0.1%
249.82 1
< 0.1%
217.8 1
< 0.1%
216.52 1
< 0.1%
199.25 1
< 0.1%
175.64 1
< 0.1%
174.65 1
< 0.1%
174.58 1
< 0.1%
173.57 1
< 0.1%

평일 외출이 적은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3634
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.30302
Minimum0
Maximum306.87
Zeros92
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:14:59.748452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.78
Q15.53
median10.675
Q319.785
95-th percentile47.4705
Maximum306.87
Range306.87
Interquartile range (IQR)14.255

Descriptive statistics

Standard deviation20.109836
Coefficient of variation (CV)1.2335037
Kurtosis40.213963
Mean16.30302
Median Absolute Deviation (MAD)6.155
Skewness4.9067774
Sum163030.2
Variance404.40549
MonotonicityNot monotonic
2024-05-04T06:15:00.300674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 92
 
0.9%
5.91 15
 
0.1%
4.72 14
 
0.1%
7.38 13
 
0.1%
7.08 12
 
0.1%
4.59 12
 
0.1%
4.68 12
 
0.1%
6.55 12
 
0.1%
5.06 11
 
0.1%
3.05 11
 
0.1%
Other values (3624) 9796
98.0%
ValueCountFrequency (%)
0.0 92
0.9%
0.16 1
 
< 0.1%
0.2 1
 
< 0.1%
0.21 2
 
< 0.1%
0.22 1
 
< 0.1%
0.23 2
 
< 0.1%
0.24 1
 
< 0.1%
0.25 2
 
< 0.1%
0.28 3
 
< 0.1%
0.29 2
 
< 0.1%
ValueCountFrequency (%)
306.87 1
< 0.1%
281.52 1
< 0.1%
278.03 1
< 0.1%
274.55 1
< 0.1%
269.25 1
< 0.1%
263.93 1
< 0.1%
261.31 1
< 0.1%
251.29 1
< 0.1%
249.16 1
< 0.1%
226.62 1
< 0.1%

휴일 외출이 적은 집단
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3664
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.315504
Minimum0
Maximum501.21
Zeros117
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:15:00.721827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.02
Q15.07
median10.82
Q319.7925
95-th percentile46.1615
Maximum501.21
Range501.21
Interquartile range (IQR)14.7225

Descriptive statistics

Standard deviation21.953984
Coefficient of variation (CV)1.3455903
Kurtosis64.838848
Mean16.315504
Median Absolute Deviation (MAD)6.73
Skewness5.9437287
Sum163155.04
Variance481.97744
MonotonicityNot monotonic
2024-05-04T06:15:01.171418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 117
 
1.2%
6.48 16
 
0.2%
5.98 14
 
0.1%
4.53 13
 
0.1%
11.88 12
 
0.1%
4.1 12
 
0.1%
5.54 12
 
0.1%
2.57 12
 
0.1%
2.4 12
 
0.1%
6.24 12
 
0.1%
Other values (3654) 9768
97.7%
ValueCountFrequency (%)
0.0 117
1.2%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
0.1 1
 
< 0.1%
0.11 2
 
< 0.1%
0.13 3
 
< 0.1%
0.14 3
 
< 0.1%
0.15 2
 
< 0.1%
0.16 2
 
< 0.1%
ValueCountFrequency (%)
501.21 1
< 0.1%
417.06 1
< 0.1%
264.2 1
< 0.1%
256.81 1
< 0.1%
256.52 1
< 0.1%
254.74 1
< 0.1%
252.92 1
< 0.1%
252.05 1
< 0.1%
250.32 1
< 0.1%
248.87 1
< 0.1%

출근소요시간 및 근무시간이 많은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3617
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.292899
Minimum0
Maximum353.22
Zeros85
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:15:01.591369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.04
Q15.9
median11.085
Q319.53
95-th percentile46.3205
Maximum353.22
Range353.22
Interquartile range (IQR)13.63

Descriptive statistics

Standard deviation19.813457
Coefficient of variation (CV)1.2160793
Kurtosis49.812568
Mean16.292899
Median Absolute Deviation (MAD)6.145
Skewness5.4023049
Sum162928.99
Variance392.57307
MonotonicityNot monotonic
2024-05-04T06:15:02.047615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 85
 
0.9%
3.05 14
 
0.1%
7.89 14
 
0.1%
6.27 13
 
0.1%
3.69 13
 
0.1%
4.43 13
 
0.1%
4.17 12
 
0.1%
11.24 12
 
0.1%
8.43 11
 
0.1%
7.17 11
 
0.1%
Other values (3607) 9802
98.0%
ValueCountFrequency (%)
0.0 85
0.9%
0.18 1
 
< 0.1%
0.21 1
 
< 0.1%
0.24 1
 
< 0.1%
0.29 2
 
< 0.1%
0.32 1
 
< 0.1%
0.33 1
 
< 0.1%
0.34 1
 
< 0.1%
0.36 1
 
< 0.1%
0.37 1
 
< 0.1%
ValueCountFrequency (%)
353.22 1
< 0.1%
306.25 1
< 0.1%
297.65 1
< 0.1%
276.45 1
< 0.1%
265.87 1
< 0.1%
263.55 1
< 0.1%
257.43 1
< 0.1%
247.51 1
< 0.1%
238.75 1
< 0.1%
238.53 1
< 0.1%

외출이 매우 적은 집단(전체)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3109
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.992057
Minimum0
Maximum156.36
Zeros368
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:15:02.478400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.86
Q17.63
median14.28
Q323.2
95-th percentile41.081
Maximum156.36
Range156.36
Interquartile range (IQR)15.57

Descriptive statistics

Standard deviation12.983842
Coefficient of variation (CV)0.76411242
Kurtosis7.0032727
Mean16.992057
Median Absolute Deviation (MAD)7.14
Skewness1.76984
Sum169920.57
Variance168.58015
MonotonicityNot monotonic
2024-05-04T06:15:02.996257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 368
 
3.7%
5.36 251
 
2.5%
8.93 228
 
2.3%
12.5 221
 
2.2%
7.14 220
 
2.2%
10.71 213
 
2.1%
3.57 197
 
2.0%
14.28 177
 
1.8%
16.07 164
 
1.6%
17.85 123
 
1.2%
Other values (3099) 7838
78.4%
ValueCountFrequency (%)
0.0 368
3.7%
1.79 121
 
1.2%
1.8 2
 
< 0.1%
1.81 1
 
< 0.1%
1.82 2
 
< 0.1%
1.83 3
 
< 0.1%
1.85 1
 
< 0.1%
1.86 5
 
0.1%
1.87 1
 
< 0.1%
1.88 2
 
< 0.1%
ValueCountFrequency (%)
156.36 1
< 0.1%
151.36 1
< 0.1%
139.61 1
< 0.1%
133.57 1
< 0.1%
105.59 1
< 0.1%
101.6 1
< 0.1%
100.67 1
< 0.1%
98.96 1
< 0.1%
98.34 1
< 0.1%
97.72 1
< 0.1%

외출이 매우 많은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3824
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.933937
Minimum0.05
Maximum182.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:15:03.471395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.76
Q13
median10.785
Q324.9925
95-th percentile60.091
Maximum182.86
Range182.81
Interquartile range (IQR)21.9925

Descriptive statistics

Standard deviation20.391688
Coefficient of variation (CV)1.1370447
Kurtosis5.5992966
Mean17.933937
Median Absolute Deviation (MAD)9.015
Skewness2.0280609
Sum179339.37
Variance415.82094
MonotonicityNot monotonic
2024-05-04T06:15:04.151186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.01 28
 
0.3%
0.89 26
 
0.3%
0.99 24
 
0.2%
1.14 24
 
0.2%
1.03 23
 
0.2%
1.23 23
 
0.2%
0.76 22
 
0.2%
1.1 22
 
0.2%
0.61 21
 
0.2%
0.92 21
 
0.2%
Other values (3814) 9766
97.7%
ValueCountFrequency (%)
0.05 1
 
< 0.1%
0.07 1
 
< 0.1%
0.09 2
< 0.1%
0.12 2
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.15 4
< 0.1%
0.18 2
< 0.1%
0.19 3
< 0.1%
0.2 2
< 0.1%
ValueCountFrequency (%)
182.86 1
< 0.1%
182.65 1
< 0.1%
171.06 1
< 0.1%
167.6 1
< 0.1%
167.29 1
< 0.1%
156.03 1
< 0.1%
150.79 1
< 0.1%
146.12 1
< 0.1%
133.49 1
< 0.1%
130.02 1
< 0.1%

동영상서비스 이용이 많은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3565
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.295985
Minimum0
Maximum307.54
Zeros73
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:15:04.665420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.41
Q16.33
median11.24
Q319.52
95-th percentile44.99
Maximum307.54
Range307.54
Interquartile range (IQR)13.19

Descriptive statistics

Standard deviation18.644254
Coefficient of variation (CV)1.1441011
Kurtosis46.507707
Mean16.295985
Median Absolute Deviation (MAD)5.89
Skewness5.173229
Sum162959.85
Variance347.60821
MonotonicityNot monotonic
2024-05-04T06:15:05.153992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 73
 
0.7%
6.68 12
 
0.1%
10.19 11
 
0.1%
5.23 11
 
0.1%
5.06 11
 
0.1%
4.31 11
 
0.1%
4.04 11
 
0.1%
7.39 11
 
0.1%
5.28 11
 
0.1%
10.27 11
 
0.1%
Other values (3555) 9827
98.3%
ValueCountFrequency (%)
0.0 73
0.7%
0.17 1
 
< 0.1%
0.24 2
 
< 0.1%
0.25 2
 
< 0.1%
0.3 1
 
< 0.1%
0.33 1
 
< 0.1%
0.43 1
 
< 0.1%
0.49 2
 
< 0.1%
0.51 1
 
< 0.1%
0.52 1
 
< 0.1%
ValueCountFrequency (%)
307.54 1
< 0.1%
291.22 1
< 0.1%
274.03 1
< 0.1%
269.88 1
< 0.1%
256.66 1
< 0.1%
256.5 1
< 0.1%
246.56 1
< 0.1%
234.37 1
< 0.1%
226.17 1
< 0.1%
219.77 1
< 0.1%

생활서비스 이용이 많은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3621
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.313191
Minimum0
Maximum371.29
Zeros83
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:15:05.725115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6495
Q15.38
median10.515
Q319.1725
95-th percentile49.421
Maximum371.29
Range371.29
Interquartile range (IQR)13.7925

Descriptive statistics

Standard deviation21.681492
Coefficient of variation (CV)1.3290773
Kurtosis53.25088
Mean16.313191
Median Absolute Deviation (MAD)6.105
Skewness5.6401206
Sum163131.91
Variance470.08709
MonotonicityNot monotonic
2024-05-04T06:15:06.689495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 83
 
0.8%
7.65 13
 
0.1%
5.92 13
 
0.1%
3.53 12
 
0.1%
6.98 12
 
0.1%
7.94 12
 
0.1%
5.51 12
 
0.1%
11.31 12
 
0.1%
5.59 12
 
0.1%
2.53 11
 
0.1%
Other values (3611) 9808
98.1%
ValueCountFrequency (%)
0.0 83
0.8%
0.06 2
 
< 0.1%
0.11 1
 
< 0.1%
0.13 1
 
< 0.1%
0.16 2
 
< 0.1%
0.22 2
 
< 0.1%
0.23 3
 
< 0.1%
0.24 1
 
< 0.1%
0.25 1
 
< 0.1%
0.26 1
 
< 0.1%
ValueCountFrequency (%)
371.29 1
< 0.1%
359.34 1
< 0.1%
331.07 1
< 0.1%
319.46 1
< 0.1%
315.67 1
< 0.1%
304.18 1
< 0.1%
289.14 1
< 0.1%
278.77 1
< 0.1%
253.66 1
< 0.1%
250.43 1
< 0.1%

재정상태에 대한 관심집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3614
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.317194
Minimum0
Maximum398.64
Zeros81
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:15:07.151187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.74
Q15.42
median10.73
Q319.69
95-th percentile46.012
Maximum398.64
Range398.64
Interquartile range (IQR)14.27

Descriptive statistics

Standard deviation21.462717
Coefficient of variation (CV)1.3153436
Kurtosis58.23758
Mean16.317194
Median Absolute Deviation (MAD)6.3
Skewness5.8954571
Sum163171.94
Variance460.6482
MonotonicityNot monotonic
2024-05-04T06:15:07.699953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 81
 
0.8%
6.66 13
 
0.1%
3.57 12
 
0.1%
8.23 12
 
0.1%
6.76 12
 
0.1%
6.5 12
 
0.1%
10.44 11
 
0.1%
4.42 11
 
0.1%
2.62 11
 
0.1%
6.48 11
 
0.1%
Other values (3604) 9814
98.1%
ValueCountFrequency (%)
0.0 81
0.8%
0.11 2
 
< 0.1%
0.13 1
 
< 0.1%
0.17 1
 
< 0.1%
0.24 4
 
< 0.1%
0.25 1
 
< 0.1%
0.29 2
 
< 0.1%
0.31 3
 
< 0.1%
0.33 1
 
< 0.1%
0.35 3
 
< 0.1%
ValueCountFrequency (%)
398.64 1
< 0.1%
377.38 1
< 0.1%
331.05 1
< 0.1%
321.67 1
< 0.1%
302.06 1
< 0.1%
293.58 1
< 0.1%
263.91 1
< 0.1%
260.3 1
< 0.1%
256.42 1
< 0.1%
248.77 1
< 0.1%

외출-커뮤니케이션이 모두 적은 집단(전체)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3127
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.983518
Minimum0
Maximum130.65
Zeros456
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:15:08.184581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.78
Q17.1
median14.19
Q323.36
95-th percentile42.123
Maximum130.65
Range130.65
Interquartile range (IQR)16.26

Descriptive statistics

Standard deviation13.344465
Coefficient of variation (CV)0.78573031
Kurtosis4.4241421
Mean16.983518
Median Absolute Deviation (MAD)7.59
Skewness1.5591223
Sum169835.18
Variance178.07474
MonotonicityNot monotonic
2024-05-04T06:15:08.775678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 456
 
4.6%
7.1 235
 
2.4%
3.55 234
 
2.3%
5.33 219
 
2.2%
8.88 211
 
2.1%
1.78 194
 
1.9%
10.65 188
 
1.9%
12.42 187
 
1.9%
14.19 152
 
1.5%
15.97 150
 
1.5%
Other values (3117) 7774
77.7%
ValueCountFrequency (%)
0.0 456
4.6%
1.78 194
1.9%
1.79 5
 
0.1%
1.8 3
 
< 0.1%
1.81 1
 
< 0.1%
1.85 7
 
0.1%
1.86 2
 
< 0.1%
1.87 4
 
< 0.1%
1.88 3
 
< 0.1%
1.9 5
 
0.1%
ValueCountFrequency (%)
130.65 1
< 0.1%
125.91 1
< 0.1%
125.4 1
< 0.1%
121.69 1
< 0.1%
101.51 1
< 0.1%
96.91 1
< 0.1%
95.07 1
< 0.1%
94.86 1
< 0.1%
94.28 1
< 0.1%
93.0 1
< 0.1%

Interactions

2024-05-04T06:14:43.007476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:36.333806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:42.677806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:46.613042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:50.974108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:55.741273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:01.111643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:06.091355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:11.759372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:18.058090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:22.316864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:26.618842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:32.764569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:37.833708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:43.389519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:36.674701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:42.990890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:46.899613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:51.329134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:56.120095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:01.404567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:06.433850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:12.246652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:18.410053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:22.602500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:26.890517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:33.164799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:38.149014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:43.766971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:36.965841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:43.299675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:47.174408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:51.598243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:56.583189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:01.699619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:06.793776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:12.806450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:18.722676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:23.130674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:27.297619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:33.527567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:38.554614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:44.036237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:37.252282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:43.570064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:47.446759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:51.853937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:57.175065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:02.028347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:07.135584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:13.347723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:19.019575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:23.413594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:27.753417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:33.849247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:38.903111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:44.370903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:37.687253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:43.841661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:47.964088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:52.126122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:57.555143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:02.373623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:07.536176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:13.736452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:19.381143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:23.692856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:28.187232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:34.166819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:39.338143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:44.719016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:38.291887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:44.114107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:48.339713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:52.542750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:57.846509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:02.748919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:07.877876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:14.233594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:19.661348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:23.968223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:28.532815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:34.494904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:39.779770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:45.104306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:38.800443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:44.386459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:48.619740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:52.818466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:58.148458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:03.063805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:08.271690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:14.803916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:19.939956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:24.234991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:29.343838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:34.846340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:40.144092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:45.500731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:39.376306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:44.654802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:48.888661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:53.096498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:58.538470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:03.492854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:08.938244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:15.326822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:20.215435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:24.505924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:29.755600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:35.190608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:40.437464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:45.916077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:39.956830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:44.939563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:49.251952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:53.409087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:58.936859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:03.828375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:09.424599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:15.790898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:20.503355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:24.791396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:30.217070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:35.598041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:40.804139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:46.379561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:40.500511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:45.231689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:49.547497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:53.695651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:59.362300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:04.214995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:09.824363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:16.245625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:20.819013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:25.197647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:30.659438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:35.991725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:41.134695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:46.781734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:40.927342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:45.512858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:49.826896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:53.976500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:59.741275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:04.589357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:10.213380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:16.598464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:21.109188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:25.484012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:31.101180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:36.325749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:41.511249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:47.206048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:41.455233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:45.789839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:50.093443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:54.346295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:00.079149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:04.934696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:10.591900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:16.891712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:21.396813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:25.757624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:31.543673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:36.681604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:41.877586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:47.812705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:41.906737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:46.081248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:50.383025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:54.946049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:00.465023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:05.313047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:10.942302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:17.266773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:21.703084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:26.040600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:31.961704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:37.087143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:42.250589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:48.298018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:42.364939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:46.370084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:50.675960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:13:55.357798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:00.817892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:05.727403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:11.320410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:17.716439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:22.040451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:26.337674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:32.410832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:37.517250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:14:42.631297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:15:09.181144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드자치구성별연령대총인구1인가구수커뮤니케이션이 적은 집단평일 외출이 적은 집단휴일 외출이 적은 집단출근소요시간 및 근무시간이 많은 집단외출이 매우 적은 집단(전체)외출이 매우 많은 집단동영상서비스 이용이 많은 집단생활서비스 이용이 많은 집단재정상태에 대한 관심집단외출-커뮤니케이션이 모두 적은 집단(전체)
행정동코드1.0001.0000.0000.0000.3790.1540.1100.1780.1000.1320.1810.2010.1300.1240.1390.184
자치구1.0001.0000.0000.0000.4640.2130.1960.2690.1900.1950.2610.1890.2060.1880.1880.272
성별0.0000.0001.0000.0000.0500.0420.0880.0550.0190.0640.2590.0440.0520.0320.0000.132
연령대0.0000.0000.0001.0000.2980.3270.1890.2290.1880.2280.1950.7100.2530.2270.2700.198
총인구0.3790.4640.0500.2981.0000.7540.7480.5200.8310.4810.5710.6020.5060.4820.5610.550
1인가구수0.1540.2130.0420.3270.7541.0000.7770.8490.8560.8400.3520.6860.8440.8740.9160.276
커뮤니케이션이 적은 집단0.1100.1960.0880.1890.7480.7771.0000.7880.6820.7650.6440.5090.8230.6860.6860.581
평일 외출이 적은 집단0.1780.2690.0550.2290.5200.8490.7881.0000.6640.9330.6560.5400.9260.8150.8450.542
휴일 외출이 적은 집단0.1000.1900.0190.1880.8310.8560.6820.6641.0000.6370.2470.5530.6510.6760.7700.205
출근소요시간 및 근무시간이 많은 집단0.1320.1950.0640.2280.4810.8400.7650.9330.6371.0000.6630.5030.9480.8390.8400.520
외출이 매우 적은 집단(전체)0.1810.2610.2590.1950.5710.3520.6440.6560.2470.6631.0000.2560.5870.4060.3650.878
외출이 매우 많은 집단0.2010.1890.0440.7100.6020.6860.5090.5400.5530.5030.2561.0000.5280.6200.6950.229
동영상서비스 이용이 많은 집단0.1300.2060.0520.2530.5060.8440.8230.9260.6510.9480.5870.5281.0000.8340.8250.508
생활서비스 이용이 많은 집단0.1240.1880.0320.2270.4820.8740.6860.8150.6760.8390.4060.6200.8341.0000.9170.317
재정상태에 대한 관심집단0.1390.1880.0000.2700.5610.9160.6860.8450.7700.8400.3650.6950.8250.9171.0000.274
외출-커뮤니케이션이 모두 적은 집단(전체)0.1840.2720.1320.1980.5500.2760.5810.5420.2050.5200.8780.2290.5080.3170.2741.000
2024-05-04T06:15:09.775499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별자치구
성별1.0000.000
자치구0.0001.000
2024-05-04T06:15:10.256008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드연령대총인구1인가구수커뮤니케이션이 적은 집단평일 외출이 적은 집단휴일 외출이 적은 집단출근소요시간 및 근무시간이 많은 집단외출이 매우 적은 집단(전체)외출이 매우 많은 집단동영상서비스 이용이 많은 집단생활서비스 이용이 많은 집단재정상태에 대한 관심집단외출-커뮤니케이션이 모두 적은 집단(전체)자치구성별
행정동코드1.000-0.0010.2740.0800.017-0.0630.000-0.0090.1090.141-0.0100.0050.0230.0490.9950.000
연령대-0.0011.000-0.260-0.083-0.093-0.092-0.110-0.080-0.134-0.837-0.071-0.087-0.093-0.1450.0000.000
총인구0.274-0.2601.0000.5340.4160.3800.4370.4430.5730.6040.4290.4580.4900.5540.1960.050
1인가구수0.080-0.0830.5341.0000.8770.8310.8390.8650.3890.2980.8850.8820.8730.3910.0760.032
커뮤니케이션이 적은 집단0.017-0.0930.4160.8771.0000.8170.7680.8300.4900.2460.9010.8310.7720.4660.0760.088
평일 외출이 적은 집단-0.063-0.0920.3800.8310.8171.0000.6700.9200.4930.1840.9190.8210.7220.5490.0980.042
휴일 외출이 적은 집단0.000-0.1100.4370.8390.7680.6701.0000.6920.3270.3090.7350.7630.8210.3040.0760.015
출근소요시간 및 근무시간이 많은 집단-0.009-0.0800.4430.8650.8300.9200.6921.0000.5170.2120.9510.8430.7500.5590.0700.049
외출이 매우 적은 집단(전체)0.109-0.1340.5730.3890.4900.4930.3270.5171.0000.3290.5320.4130.3960.8490.1020.259
외출이 매우 많은 집단0.141-0.8370.6040.2980.2460.1840.3090.2120.3291.0000.2140.2720.3070.3170.0670.033
동영상서비스 이용이 많은 집단-0.010-0.0710.4290.8850.9010.9190.7350.9510.5320.2141.0000.8510.7800.5560.0740.040
생활서비스 이용이 많은 집단0.005-0.0870.4580.8820.8310.8210.7630.8430.4130.2720.8511.0000.8350.4480.0670.024
재정상태에 대한 관심집단0.023-0.0930.4900.8730.7720.7220.8210.7500.3960.3070.7800.8351.0000.3820.0670.000
외출-커뮤니케이션이 모두 적은 집단(전체)0.049-0.1450.5540.3910.4660.5490.3040.5590.8490.3170.5560.4480.3821.0000.1070.131
자치구0.9950.0000.1960.0760.0760.0980.0760.0700.1020.0670.0740.0670.0670.1071.0000.000
성별0.0000.0000.0500.0320.0880.0420.0150.0490.2590.0330.0400.0240.0000.1310.0001.000

Missing values

2024-05-04T06:14:48.860166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:14:50.132820image/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

행정동코드자치구행정동명성별연령대총인구1인가구수커뮤니케이션이 적은 집단평일 외출이 적은 집단휴일 외출이 적은 집단출근소요시간 및 근무시간이 많은 집단외출이 매우 적은 집단(전체)외출이 매우 많은 집단동영상서비스 이용이 많은 집단생활서비스 이용이 많은 집단재정상태에 대한 관심집단외출-커뮤니케이션이 모두 적은 집단(전체)
81361122051서초구서초1동120780.0157.317.668.747.119.9513.918.9111.858.87.313.82
55711115063양천구신월7동135903.084.2210.957.668.329.5724.2145.4511.299.566.8924.07
21741106088동대문구장안2동2301206.0176.5410.5810.6117.8717.8612.1156.8514.8819.0214.8912.04
69261119061영등포구양평1동230991.0313.4119.820.7820.3918.5421.7963.120.1718.7428.4319.25
68751119055영등포구당산1동175518.4204.414.77.2516.0715.695.361.1216.496.6318.571.78
4231102052중구소공동235111.654.5113.888.167.476.767.144.6911.922.462.561.78
83711122060서초구반포4동255764.048.882.674.452.095.6613.336.195.353.443.4511.36
52681114076마포구서강동2201444.0494.7369.3452.3543.6246.213.3934.5546.5523.3114.347.99
65061117069구로구수궁동1301048.0109.110.3111.2513.418.847.9759.839.3112.19.7211.88
63791117061구로구고척1동2551164.695.336.547.1111.968.2116.079.038.277.989.1412.42
행정동코드자치구행정동명성별연령대총인구1인가구수커뮤니케이션이 적은 집단평일 외출이 적은 집단휴일 외출이 적은 집단출근소요시간 및 근무시간이 많은 집단외출이 매우 적은 집단(전체)외출이 매우 많은 집단동영상서비스 이용이 많은 집단생활서비스 이용이 많은 집단재정상태에 대한 관심집단외출-커뮤니케이션이 모두 적은 집단(전체)
33351109071강북구송중동2751234.8492.4563.0457.328.4557.2533.911.8353.5160.3333.3919.52
42671112052은평구불광1동2551728.0184.9716.6529.8710.0221.9131.8411.421.4718.3816.6528.78
64511117065구로구개봉3동2551011.0102.076.424.3515.485.57.6810.18.828.028.837.63
1961101063종로구종로5·6가동140212.474.3110.5614.618.9113.318.935.7212.83.536.115.97
61441116071강서구방화2동120730.0159.1220.3418.378.5616.647.4915.2120.7510.0113.5811.19
15641105053광진구화양동140695.0259.0537.7643.7231.2518.3917.6214.728.541.2335.6617.52
31331109060강북구번1동225850.0331.6718.1825.6543.4722.135.8533.9821.1849.4646.885.81
39801111064노원구중계4동260761.0100.45.086.4811.813.075.544.487.487.564.992.75
94811124067송파구가락2동1251189.0116.7714.5413.339.0415.2240.9237.616.7613.0712.1444.38
16041105054광진구군자동260649.0134.7610.5121.0616.1716.466.633.115.6910.814.946.59