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 7 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 4 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 125 (1.2%) zerosZeros
외출이 매우 적은 집단(전체) has 348 (3.5%) zerosZeros
외출-커뮤니케이션이 모두 적은 집단(전체) has 448 (4.5%) zerosZeros

Reproduction

Analysis started2024-05-04 06:06:56.046649
Analysis finished2024-05-04 06:08:27.682938
Duration1 minute and 31.64 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%
Mean1113665.1
Minimum1101053
Maximum1125074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:27.912634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1102058
Q11107069.8
median1114068.5
Q31121052
95-th percentile1124079.1
Maximum1125074
Range24021
Interquartile range (IQR)13982.25

Descriptive statistics

Standard deviation7406.4928
Coefficient of variation (CV)0.0066505564
Kurtosis-1.2529458
Mean1113665.1
Median Absolute Deviation (MAD)6992.5
Skewness-0.083484412
Sum1.1136651 × 1010
Variance54856135
MonotonicityNot monotonic
2024-05-04T06:08:28.365192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101057 24
 
0.2%
1104066 24
 
0.2%
1111051 24
 
0.2%
1101073 24
 
0.2%
1112073 24
 
0.2%
1119054 24
 
0.2%
1116059 24
 
0.2%
1116069 24
 
0.2%
1113052 24
 
0.2%
1125051 24
 
0.2%
Other values (414) 9760
97.6%
ValueCountFrequency (%)
1101053 23
0.2%
1101054 24
0.2%
1101055 24
0.2%
1101056 22
0.2%
1101057 24
0.2%
1101058 24
0.2%
1101060 24
0.2%
1101061 24
0.2%
1101063 22
0.2%
1101064 24
0.2%
ValueCountFrequency (%)
1125074 24
0.2%
1125073 24
0.2%
1125072 23
0.2%
1125071 24
0.2%
1125070 23
0.2%
1125067 24
0.2%
1125066 24
0.2%
1125065 23
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
 
636
강남구
 
524
관악구
 
496
강서구
 
474
성북구
 
472
Other values (20)
7398 

Length

Max length4
Median length3
Mean length3.0724
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송파구
2nd row금천구
3rd row성북구
4th row송파구
5th row서대문구

Common Values

ValueCountFrequency (%)
송파구 636
 
6.4%
강남구 524
 
5.2%
관악구 496
 
5.0%
강서구 474
 
4.7%
성북구 472
 
4.7%
노원구 447
 
4.5%
강동구 428
 
4.3%
양천구 427
 
4.3%
영등포구 422
 
4.2%
서초구 422
 
4.2%
Other values (15) 5252
52.5%

Length

2024-05-04T06:08:28.988459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 636
 
6.4%
강남구 524
 
5.2%
관악구 496
 
5.0%
강서구 474
 
4.7%
성북구 472
 
4.7%
노원구 447
 
4.5%
강동구 428
 
4.3%
양천구 427
 
4.3%
영등포구 422
 
4.2%
서초구 422
 
4.2%
Other values (15) 5252
52.5%
Distinct423
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T06:08:29.764068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.7923
Min length2

Characters and Unicode

Total characters37923
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 row방이2동
2nd row독산3동
3rd row종암동
4th row삼전동
5th row충현동
ValueCountFrequency (%)
신사동 46
 
0.5%
장위1동 24
 
0.2%
중곡1동 24
 
0.2%
대림1동 24
 
0.2%
월계1동 24
 
0.2%
암사3동 24
 
0.2%
가회동 24
 
0.2%
상도2동 24
 
0.2%
신정7동 24
 
0.2%
미성동 24
 
0.2%
Other values (413) 9738
97.4%
2024-05-04T06:08:31.193376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10046
26.5%
1 2290
 
6.0%
2 2282
 
6.0%
3 1015
 
2.7%
902
 
2.4%
4 614
 
1.6%
540
 
1.4%
425
 
1.1%
402
 
1.1%
399
 
1.1%
Other values (178) 19008
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30829
81.3%
Decimal Number 6882
 
18.1%
Other Punctuation 212
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10046
32.6%
902
 
2.9%
540
 
1.8%
425
 
1.4%
402
 
1.3%
399
 
1.3%
381
 
1.2%
377
 
1.2%
376
 
1.2%
376
 
1.2%
Other values (167) 16605
53.9%
Decimal Number
ValueCountFrequency (%)
1 2290
33.3%
2 2282
33.2%
3 1015
14.7%
4 614
 
8.9%
5 255
 
3.7%
6 166
 
2.4%
7 141
 
2.0%
8 71
 
1.0%
0 24
 
0.3%
9 24
 
0.3%
Other Punctuation
ValueCountFrequency (%)
· 212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30829
81.3%
Common 7094
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10046
32.6%
902
 
2.9%
540
 
1.8%
425
 
1.4%
402
 
1.3%
399
 
1.3%
381
 
1.2%
377
 
1.2%
376
 
1.2%
376
 
1.2%
Other values (167) 16605
53.9%
Common
ValueCountFrequency (%)
1 2290
32.3%
2 2282
32.2%
3 1015
14.3%
4 614
 
8.7%
5 255
 
3.6%
· 212
 
3.0%
6 166
 
2.3%
7 141
 
2.0%
8 71
 
1.0%
0 24
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30829
81.3%
ASCII 6882
 
18.1%
None 212
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
10046
32.6%
902
 
2.9%
540
 
1.8%
425
 
1.4%
402
 
1.3%
399
 
1.3%
381
 
1.2%
377
 
1.2%
376
 
1.2%
376
 
1.2%
Other values (167) 16605
53.9%
ASCII
ValueCountFrequency (%)
1 2290
33.3%
2 2282
33.2%
3 1015
14.7%
4 614
 
8.9%
5 255
 
3.7%
6 166
 
2.4%
7 141
 
2.0%
8 71
 
1.0%
0 24
 
0.3%
9 24
 
0.3%
None
ValueCountFrequency (%)
· 212
100.0%

성별
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5020 
1
4980 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 5020
50.2%
1 4980
49.8%

Length

2024-05-04T06:08:31.806990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:08:32.183706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5020
50.2%
1 4980
49.8%

연령대
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.4885
Minimum20
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:32.622351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q130
median47.5
Q365
95-th percentile75
Maximum75
Range55
Interquartile range (IQR)35

Descriptive statistics

Standard deviation17.262667
Coefficient of variation (CV)0.36351257
Kurtosis-1.2176875
Mean47.4885
Median Absolute Deviation (MAD)17.5
Skewness-0.00029010161
Sum474885
Variance297.99967
MonotonicityNot monotonic
2024-05-04T06:08:33.096318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
20 837
8.4%
70 837
8.4%
65 836
8.4%
50 835
8.3%
30 835
8.3%
35 834
8.3%
40 833
8.3%
55 832
8.3%
60 832
8.3%
25 831
8.3%
Other values (2) 1658
16.6%
ValueCountFrequency (%)
20 837
8.4%
25 831
8.3%
30 835
8.3%
35 834
8.3%
40 833
8.3%
45 830
8.3%
50 835
8.3%
55 832
8.3%
60 832
8.3%
65 836
8.4%
ValueCountFrequency (%)
75 828
8.3%
70 837
8.4%
65 836
8.4%
60 832
8.3%
55 832
8.3%
50 835
8.3%
45 830
8.3%
40 833
8.3%
35 834
8.3%
30 835
8.3%

총인구
Real number (ℝ)

HIGH CORRELATION 

Distinct2929
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean845.69639
Minimum22
Maximum4777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:33.482941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile279.942
Q1555.895
median790
Q31072
95-th percentile1592.045
Maximum4777
Range4755
Interquartile range (IQR)516.105

Descriptive statistics

Standard deviation412.30362
Coefficient of variation (CV)0.48753149
Kurtosis2.5691544
Mean845.69639
Median Absolute Deviation (MAD)253.69
Skewness1.0411417
Sum8456963.9
Variance169994.28
MonotonicityNot monotonic
2024-05-04T06:08:33.912277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
748.0 20
 
0.2%
758.0 16
 
0.2%
971.0 16
 
0.2%
759.0 15
 
0.1%
765.0 15
 
0.1%
965.0 15
 
0.1%
714.0 15
 
0.1%
561.0 14
 
0.1%
893.0 14
 
0.1%
751.0 14
 
0.1%
Other values (2919) 9846
98.5%
ValueCountFrequency (%)
22.0 1
< 0.1%
24.14 1
< 0.1%
30.6 1
< 0.1%
32.3 1
< 0.1%
47.6 1
< 0.1%
49.3 1
< 0.1%
51.0 2
< 0.1%
52.7 1
< 0.1%
53.86 1
< 0.1%
54.4 2
< 0.1%
ValueCountFrequency (%)
4777.0 1
< 0.1%
3446.0 1
< 0.1%
3121.0 1
< 0.1%
3095.0 1
< 0.1%
3079.0 1
< 0.1%
3075.0 1
< 0.1%
3047.0 1
< 0.1%
2985.0 1
< 0.1%
2950.0 1
< 0.1%
2923.0 1
< 0.1%

1인가구수
Real number (ℝ)

HIGH CORRELATION 

Distinct8125
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.2607
Minimum8.54
Maximum2865.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:34.316763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.54
5-th percentile34.82
Q171.9
median115.735
Q3186.2225
95-th percentile425.985
Maximum2865.27
Range2856.73
Interquartile range (IQR)114.3225

Descriptive statistics

Standard deviation172.74467
Coefficient of variation (CV)1.0712137
Kurtosis40.910559
Mean161.2607
Median Absolute Deviation (MAD)51.925
Skewness4.9936233
Sum1612607
Variance29840.721
MonotonicityNot monotonic
2024-05-04T06:08:34.817955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.99 6
 
0.1%
68.34 6
 
0.1%
87.42 5
 
0.1%
88.33 5
 
0.1%
88.87 5
 
0.1%
48.27 5
 
0.1%
38.37 5
 
0.1%
57.86 5
 
0.1%
93.25 5
 
0.1%
82.23 5
 
0.1%
Other values (8115) 9948
99.5%
ValueCountFrequency (%)
8.54 1
< 0.1%
9.01 1
< 0.1%
9.44 1
< 0.1%
9.92 1
< 0.1%
9.98 1
< 0.1%
10.13 1
< 0.1%
10.67 1
< 0.1%
10.91 1
< 0.1%
11.0 1
< 0.1%
11.11 2
< 0.1%
ValueCountFrequency (%)
2865.27 1
< 0.1%
2692.23 1
< 0.1%
2391.01 1
< 0.1%
2267.34 1
< 0.1%
2211.67 1
< 0.1%
2173.08 1
< 0.1%
2123.41 1
< 0.1%
2115.59 1
< 0.1%
1925.04 1
< 0.1%
1902.39 1
< 0.1%

커뮤니케이션이 적은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3558
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.111479
Minimum0
Maximum266.27
Zeros62
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:35.274525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.65
Q16.44
median11.16
Q319.58
95-th percentile45.934
Maximum266.27
Range266.27
Interquartile range (IQR)13.14

Descriptive statistics

Standard deviation17.465166
Coefficient of variation (CV)1.08402
Kurtosis31.999603
Mean16.111479
Median Absolute Deviation (MAD)5.725
Skewness4.3056752
Sum161114.79
Variance305.03202
MonotonicityNot monotonic
2024-05-04T06:08:35.749374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 62
 
0.6%
6.71 14
 
0.1%
7.32 13
 
0.1%
5.33 12
 
0.1%
5.84 12
 
0.1%
7.39 12
 
0.1%
7.66 12
 
0.1%
8.13 12
 
0.1%
4.18 11
 
0.1%
6.84 11
 
0.1%
Other values (3548) 9829
98.3%
ValueCountFrequency (%)
0.0 62
0.6%
0.32 2
 
< 0.1%
0.44 1
 
< 0.1%
0.47 1
 
< 0.1%
0.48 1
 
< 0.1%
0.49 1
 
< 0.1%
0.5 1
 
< 0.1%
0.52 2
 
< 0.1%
0.54 1
 
< 0.1%
0.55 1
 
< 0.1%
ValueCountFrequency (%)
266.27 1
< 0.1%
260.42 1
< 0.1%
251.66 1
< 0.1%
221.14 1
< 0.1%
200.97 1
< 0.1%
197.06 1
< 0.1%
195.2 1
< 0.1%
185.5 1
< 0.1%
185.46 1
< 0.1%
180.64 1
< 0.1%

평일 외출이 적은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3618
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.112425
Minimum0
Maximum322.59
Zeros86
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:36.325689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7095
Q15.35
median10.36
Q319.5325
95-th percentile47.177
Maximum322.59
Range322.59
Interquartile range (IQR)14.1825

Descriptive statistics

Standard deviation20.746219
Coefficient of variation (CV)1.2875913
Kurtosis46.064145
Mean16.112425
Median Absolute Deviation (MAD)6.07
Skewness5.3165406
Sum161124.25
Variance430.40559
MonotonicityNot monotonic
2024-05-04T06:08:37.159823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 86
 
0.9%
4.34 16
 
0.2%
6.4 13
 
0.1%
6.77 12
 
0.1%
8.78 12
 
0.1%
9.02 12
 
0.1%
5.26 12
 
0.1%
8.05 12
 
0.1%
5.04 11
 
0.1%
1.87 11
 
0.1%
Other values (3608) 9803
98.0%
ValueCountFrequency (%)
0.0 86
0.9%
0.15 1
 
< 0.1%
0.18 2
 
< 0.1%
0.2 3
 
< 0.1%
0.21 2
 
< 0.1%
0.22 2
 
< 0.1%
0.23 2
 
< 0.1%
0.24 2
 
< 0.1%
0.26 1
 
< 0.1%
0.27 3
 
< 0.1%
ValueCountFrequency (%)
322.59 1
< 0.1%
304.77 1
< 0.1%
295.56 1
< 0.1%
288.27 1
< 0.1%
272.23 1
< 0.1%
268.2 1
< 0.1%
267.18 1
< 0.1%
266.8 1
< 0.1%
246.88 1
< 0.1%
246.8 1
< 0.1%

휴일 외출이 적은 집단
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3665
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.122598
Minimum0
Maximum406.9
Zeros125
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:37.726331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.96
Q15.0875
median10.94
Q319.7325
95-th percentile45.4205
Maximum406.9
Range406.9
Interquartile range (IQR)14.645

Descriptive statistics

Standard deviation20.764212
Coefficient of variation (CV)1.2878949
Kurtosis52.040447
Mean16.122598
Median Absolute Deviation (MAD)6.78
Skewness5.4206538
Sum161225.98
Variance431.15251
MonotonicityNot monotonic
2024-05-04T06:08:38.318088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 125
 
1.2%
5.61 12
 
0.1%
1.79 12
 
0.1%
0.96 12
 
0.1%
1.6 11
 
0.1%
6.89 11
 
0.1%
6.92 11
 
0.1%
8.06 11
 
0.1%
3.12 10
 
0.1%
0.92 10
 
0.1%
Other values (3655) 9775
97.8%
ValueCountFrequency (%)
0.0 125
1.2%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
0.1 1
 
< 0.1%
0.12 2
 
< 0.1%
0.14 4
 
< 0.1%
0.15 1
 
< 0.1%
0.17 2
 
< 0.1%
0.19 1
 
< 0.1%
0.2 3
 
< 0.1%
ValueCountFrequency (%)
406.9 1
< 0.1%
364.08 1
< 0.1%
317.6 1
< 0.1%
271.01 1
< 0.1%
267.24 1
< 0.1%
243.87 1
< 0.1%
242.86 1
< 0.1%
237.45 1
< 0.1%
234.93 1
< 0.1%
233.67 1
< 0.1%

출근소요시간 및 근무시간이 많은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3557
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.118261
Minimum0
Maximum426.81
Zeros80
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:38.859894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.94
Q15.73
median10.57
Q319.03
95-th percentile45.0415
Maximum426.81
Range426.81
Interquartile range (IQR)13.3

Descriptive statistics

Standard deviation21.407146
Coefficient of variation (CV)1.32813
Kurtosis71.228258
Mean16.118261
Median Absolute Deviation (MAD)5.86
Skewness6.5266785
Sum161182.61
Variance458.26591
MonotonicityNot monotonic
2024-05-04T06:08:39.361080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 80
 
0.8%
4.09 14
 
0.1%
3.22 12
 
0.1%
5.5 12
 
0.1%
5.6 12
 
0.1%
2.99 12
 
0.1%
7.93 12
 
0.1%
4.06 11
 
0.1%
4.46 11
 
0.1%
2.37 11
 
0.1%
Other values (3547) 9813
98.1%
ValueCountFrequency (%)
0.0 80
0.8%
0.21 1
 
< 0.1%
0.24 2
 
< 0.1%
0.25 1
 
< 0.1%
0.26 1
 
< 0.1%
0.33 1
 
< 0.1%
0.34 2
 
< 0.1%
0.35 2
 
< 0.1%
0.36 2
 
< 0.1%
0.38 1
 
< 0.1%
ValueCountFrequency (%)
426.81 1
< 0.1%
388.1 1
< 0.1%
372.36 1
< 0.1%
324.53 1
< 0.1%
314.94 1
< 0.1%
303.33 1
< 0.1%
279.53 1
< 0.1%
269.4 1
< 0.1%
269.38 1
< 0.1%
251.98 1
< 0.1%

외출이 매우 적은 집단(전체)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3113
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.895503
Minimum0
Maximum173.16
Zeros348
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:39.923851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.85
Q17.9
median13.835
Q322.9525
95-th percentile41.08
Maximum173.16
Range173.16
Interquartile range (IQR)15.0525

Descriptive statistics

Standard deviation13.008792
Coefficient of variation (CV)0.76995586
Kurtosis6.7942409
Mean16.895503
Median Absolute Deviation (MAD)7.095
Skewness1.7835452
Sum168955.03
Variance169.22866
MonotonicityNot monotonic
2024-05-04T06:08:40.423900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 348
 
3.5%
8.43 223
 
2.2%
6.74 221
 
2.2%
5.05 216
 
2.2%
10.11 197
 
2.0%
11.8 192
 
1.9%
13.48 167
 
1.7%
3.37 166
 
1.7%
15.17 166
 
1.7%
16.86 133
 
1.3%
Other values (3103) 7971
79.7%
ValueCountFrequency (%)
0.0 348
3.5%
1.69 117
 
1.2%
1.7 5
 
0.1%
1.71 1
 
< 0.1%
1.72 1
 
< 0.1%
1.74 1
 
< 0.1%
1.75 2
 
< 0.1%
1.76 2
 
< 0.1%
1.77 3
 
< 0.1%
1.79 1
 
< 0.1%
ValueCountFrequency (%)
173.16 1
< 0.1%
133.32 1
< 0.1%
118.68 1
< 0.1%
113.37 1
< 0.1%
112.95 1
< 0.1%
109.81 1
< 0.1%
102.54 1
< 0.1%
95.61 1
< 0.1%
94.43 1
< 0.1%
94.38 1
< 0.1%

외출이 매우 많은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3713
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.435754
Minimum0.02
Maximum174.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:40.969827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.74
Q12.95
median10.645
Q324.29
95-th percentile57.873
Maximum174.87
Range174.85
Interquartile range (IQR)21.34

Descriptive statistics

Standard deviation19.579574
Coefficient of variation (CV)1.1229554
Kurtosis5.3261301
Mean17.435754
Median Absolute Deviation (MAD)8.855
Skewness1.9853228
Sum174357.54
Variance383.35973
MonotonicityNot monotonic
2024-05-04T06:08:41.513874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.81 25
 
0.2%
0.85 25
 
0.2%
1.07 24
 
0.2%
1.01 23
 
0.2%
0.73 23
 
0.2%
1.09 22
 
0.2%
0.93 22
 
0.2%
1.34 22
 
0.2%
1.22 22
 
0.2%
0.9 22
 
0.2%
Other values (3703) 9770
97.7%
ValueCountFrequency (%)
0.02 1
< 0.1%
0.08 1
< 0.1%
0.09 1
< 0.1%
0.11 2
< 0.1%
0.12 2
< 0.1%
0.13 2
< 0.1%
0.14 1
< 0.1%
0.15 2
< 0.1%
0.16 1
< 0.1%
0.18 1
< 0.1%
ValueCountFrequency (%)
174.87 1
< 0.1%
171.63 1
< 0.1%
164.18 1
< 0.1%
162.33 1
< 0.1%
149.31 1
< 0.1%
144.62 1
< 0.1%
142.1 1
< 0.1%
140.26 1
< 0.1%
127.07 1
< 0.1%
126.22 1
< 0.1%

동영상서비스 이용이 많은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3539
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.110027
Minimum0
Maximum324.11
Zeros69
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:42.115260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.37
Q16.12
median10.92
Q319.1125
95-th percentile44.853
Maximum324.11
Range324.11
Interquartile range (IQR)12.9925

Descriptive statistics

Standard deviation19.456959
Coefficient of variation (CV)1.2077546
Kurtosis52.11534
Mean16.110027
Median Absolute Deviation (MAD)5.78
Skewness5.6090114
Sum161100.27
Variance378.57325
MonotonicityNot monotonic
2024-05-04T06:08:42.624871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 69
 
0.7%
10.0 16
 
0.2%
8.15 14
 
0.1%
6.34 13
 
0.1%
3.8 13
 
0.1%
6.28 13
 
0.1%
4.15 12
 
0.1%
6.01 12
 
0.1%
8.75 11
 
0.1%
6.2 11
 
0.1%
Other values (3529) 9816
98.2%
ValueCountFrequency (%)
0.0 69
0.7%
0.23 1
 
< 0.1%
0.33 1
 
< 0.1%
0.36 1
 
< 0.1%
0.41 1
 
< 0.1%
0.43 1
 
< 0.1%
0.44 2
 
< 0.1%
0.48 1
 
< 0.1%
0.49 2
 
< 0.1%
0.5 1
 
< 0.1%
ValueCountFrequency (%)
324.11 1
< 0.1%
321.38 1
< 0.1%
268.21 1
< 0.1%
265.23 1
< 0.1%
265.2 1
< 0.1%
264.89 1
< 0.1%
261.38 1
< 0.1%
260.78 1
< 0.1%
257.02 1
< 0.1%
239.26 1
< 0.1%

생활서비스 이용이 많은 집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3579
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.12465
Minimum0
Maximum364
Zeros81
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:43.183484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6
Q15.27
median10.22
Q318.9625
95-th percentile48.1
Maximum364
Range364
Interquartile range (IQR)13.6925

Descriptive statistics

Standard deviation21.72719
Coefficient of variation (CV)1.3474519
Kurtosis55.506293
Mean16.12465
Median Absolute Deviation (MAD)5.94
Skewness5.7803157
Sum161246.5
Variance472.07079
MonotonicityNot monotonic
2024-05-04T06:08:43.684192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 81
 
0.8%
2.7 14
 
0.1%
4.38 14
 
0.1%
3.35 13
 
0.1%
4.86 13
 
0.1%
4.63 12
 
0.1%
6.61 12
 
0.1%
3.42 12
 
0.1%
6.18 11
 
0.1%
7.45 11
 
0.1%
Other values (3569) 9807
98.1%
ValueCountFrequency (%)
0.0 81
0.8%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.1 3
 
< 0.1%
0.11 1
 
< 0.1%
0.12 2
 
< 0.1%
0.13 1
 
< 0.1%
0.17 1
 
< 0.1%
0.2 1
 
< 0.1%
0.21 2
 
< 0.1%
ValueCountFrequency (%)
364.0 1
< 0.1%
358.67 1
< 0.1%
345.72 1
< 0.1%
341.38 1
< 0.1%
323.0 1
< 0.1%
305.74 1
< 0.1%
275.22 1
< 0.1%
265.66 1
< 0.1%
258.9 1
< 0.1%
255.87 1
< 0.1%

재정상태에 대한 관심집단
Real number (ℝ)

HIGH CORRELATION 

Distinct3593
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.128059
Minimum0
Maximum369.72
Zeros74
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:44.196211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.74
Q15.2675
median10.47
Q319.39
95-th percentile45.5805
Maximum369.72
Range369.72
Interquartile range (IQR)14.1225

Descriptive statistics

Standard deviation21.314156
Coefficient of variation (CV)1.3215574
Kurtosis53.849248
Mean16.128059
Median Absolute Deviation (MAD)6.2
Skewness5.7391119
Sum161280.59
Variance454.29323
MonotonicityNot monotonic
2024-05-04T06:08:44.648570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 74
 
0.7%
2.32 13
 
0.1%
6.79 13
 
0.1%
4.91 12
 
0.1%
3.97 12
 
0.1%
4.42 12
 
0.1%
3.96 12
 
0.1%
4.35 12
 
0.1%
6.03 12
 
0.1%
3.66 12
 
0.1%
Other values (3583) 9816
98.2%
ValueCountFrequency (%)
0.0 74
0.7%
0.11 1
 
< 0.1%
0.14 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 2
 
< 0.1%
0.21 1
 
< 0.1%
0.27 2
 
< 0.1%
0.29 1
 
< 0.1%
0.3 1
 
< 0.1%
0.35 1
 
< 0.1%
ValueCountFrequency (%)
369.72 1
< 0.1%
364.1 1
< 0.1%
314.3 1
< 0.1%
306.5 1
< 0.1%
298.32 1
< 0.1%
273.03 1
< 0.1%
267.66 1
< 0.1%
263.45 1
< 0.1%
257.72 1
< 0.1%
254.09 1
< 0.1%

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

HIGH CORRELATION  ZEROS 

Distinct3150
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.894162
Minimum0
Maximum133.47
Zeros448
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:08:45.291556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.68
Q17.13
median13.77
Q323.44
95-th percentile42.2105
Maximum133.47
Range133.47
Interquartile range (IQR)16.31

Descriptive statistics

Standard deviation13.359516
Coefficient of variation (CV)0.79077708
Kurtosis4.6529341
Mean16.894162
Median Absolute Deviation (MAD)7.35
Skewness1.6031736
Sum168941.62
Variance178.47667
MonotonicityNot monotonic
2024-05-04T06:08:45.934113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 448
 
4.5%
6.7 227
 
2.3%
5.02 213
 
2.1%
8.37 203
 
2.0%
3.35 194
 
1.9%
11.73 188
 
1.9%
10.05 175
 
1.8%
1.68 171
 
1.7%
13.39 151
 
1.5%
15.07 129
 
1.3%
Other values (3140) 7901
79.0%
ValueCountFrequency (%)
0.0 448
4.5%
1.68 171
 
1.7%
1.69 1
 
< 0.1%
1.7 2
 
< 0.1%
1.71 3
 
< 0.1%
1.73 6
 
0.1%
1.74 5
 
0.1%
1.75 3
 
< 0.1%
1.78 2
 
< 0.1%
1.79 4
 
< 0.1%
ValueCountFrequency (%)
133.47 1
< 0.1%
129.85 1
< 0.1%
127.51 1
< 0.1%
120.52 1
< 0.1%
107.52 1
< 0.1%
100.47 1
< 0.1%
98.01 1
< 0.1%
95.89 1
< 0.1%
93.82 1
< 0.1%
92.1 1
< 0.1%

Interactions

2024-05-04T06:08:22.192323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:11.673718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:16.848865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:23.055893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:29.766167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:35.465364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:40.897938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:45.447649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:49.733522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:53.991763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:00.460814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:05.458003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:10.388974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:16.111386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:22.470427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:12.045422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:17.169524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:23.551977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:30.171566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:35.902634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:41.190141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:45.738643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:50.007228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:54.711829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:00.814212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:05.841960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:10.767971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:16.631099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:22.753002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:12.405179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:17.446958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:24.206122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:30.524748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:36.280742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:41.461128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:46.023829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:50.284911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:55.310115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:01.154135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:06.197364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:11.156786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:17.174560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:23.083884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:12.774208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:17.723507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:24.596054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:30.838261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:36.719673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:41.815617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:46.305449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:50.548683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:55.719280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:01.465694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:06.458346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:11.559199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:17.687202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:23.353720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:13.123126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:18.014911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:25.105085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:31.218595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:37.129714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:42.099766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:46.594646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:50.842137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:56.163553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:01.777016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:06.824779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:11.856756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:18.107543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:23.627048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:13.474408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:18.384470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:25.509543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:31.579541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:37.777659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:42.379899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:46.888164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:51.126764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:56.597198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:02.104382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:07.135229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:12.225956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:18.649680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:23.911935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:14.025402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:18.803137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:25.892927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:31.914447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:38.165840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:42.686410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:47.169296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:51.383718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:57.002304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:02.443213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:07.451160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:12.650475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:19.389698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:24.204685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:14.405996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:19.290462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:26.347815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:32.351269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:38.581338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:43.043859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:47.504579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:51.680599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:57.401258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:02.842093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:07.772873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:13.083147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:19.731947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:24.507751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:14.749348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:19.716162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:26.887885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:32.732873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:38.976245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:43.338758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:47.786381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:51.957416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:58.218537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:03.166821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:08.170301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:13.478682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:20.081216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:24.883025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:15.126378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:20.076834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:27.345284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:33.119731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:39.368686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:43.668222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:48.100358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:52.243437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:58.618732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:03.479087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:08.560852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:13.828094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:20.507932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:25.331786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:15.488855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:20.543687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:27.838387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:33.604046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:39.696378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:44.073354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:48.401148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:52.544128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:58.961186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:03.791428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:08.859744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:14.282218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:20.914577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:25.644263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:15.846187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:21.120124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:28.278003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:34.241951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:39.990017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:44.550715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:48.691637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:52.931676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:59.284225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:04.160344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:09.244072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:14.752159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:21.247648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:25.952269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:16.181089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:21.676638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:28.668812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:34.690537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:40.265425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:44.852665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:48.987178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:53.210592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:59.678323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:04.638942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:09.625801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:15.239234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:21.643690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:26.235841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:16.535125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:22.315471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:29.298876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:35.071718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:40.560137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:45.157907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:49.296958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:07:53.490510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:00.085200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:05.058941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:09.988511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:15.610904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:08:21.936097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:08:46.266850image/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)
행정동코드1.0001.0000.0000.0000.3730.1600.1620.1550.1390.1090.1680.2110.1180.1370.1460.247
자치구1.0001.0000.0000.0000.4570.2150.2120.2490.1990.1680.2460.1960.1840.1960.1980.282
성별0.0000.0001.0000.0000.0510.0460.1180.0530.0350.0340.2430.0510.0540.0150.0000.162
연령대0.0000.0000.0001.0000.3080.3250.2640.2310.2670.2090.2100.7170.2500.2370.2850.257
총인구0.3730.4570.0510.3081.0000.7440.6120.5640.8200.5210.5860.6170.5660.4750.5600.437
1인가구수0.1600.2150.0460.3250.7441.0000.8930.8700.9600.8860.3380.6850.8900.8670.9150.479
커뮤니케이션이 적은 집단0.1620.2120.1180.2640.6120.8931.0000.8820.8480.8580.5760.6190.9200.8490.8600.562
평일 외출이 적은 집단0.1550.2490.0530.2310.5640.8700.8821.0000.8440.8990.5800.4780.9290.8260.8290.705
휴일 외출이 적은 집단0.1390.1990.0350.2670.8200.9600.8480.8441.0000.8460.2610.6580.8310.8880.9260.347
출근소요시간 및 근무시간이 많은 집단0.1090.1680.0340.2090.5210.8860.8580.8990.8461.0000.5360.4960.9380.8140.8100.734
외출이 매우 적은 집단(전체)0.1680.2460.2430.2100.5860.3380.5760.5800.2610.5361.0000.2980.6010.4150.3530.795
외출이 매우 많은 집단0.2110.1960.0510.7170.6170.6850.6190.4780.6580.4960.2981.0000.5240.6370.7040.325
동영상서비스 이용이 많은 집단0.1180.1840.0540.2500.5660.8900.9200.9290.8310.9380.6010.5241.0000.8060.8410.742
생활서비스 이용이 많은 집단0.1370.1960.0150.2370.4750.8670.8490.8260.8880.8140.4150.6370.8061.0000.9300.452
재정상태에 대한 관심집단0.1460.1980.0000.2850.5600.9150.8600.8290.9260.8100.3530.7040.8410.9301.0000.597
외출-커뮤니케이션이 모두 적은 집단(전체)0.2470.2820.1620.2570.4370.4790.5620.7050.3470.7340.7950.3250.7420.4520.5971.000
2024-05-04T06:08:46.759686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별자치구
성별1.0000.000
자치구0.0001.000
2024-05-04T06:08:47.122791image/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
행정동코드1.0000.0000.2740.0810.015-0.065-0.008-0.0250.1020.144-0.0180.0050.0270.0440.9950.000
연령대0.0001.000-0.276-0.095-0.101-0.099-0.132-0.080-0.148-0.832-0.074-0.091-0.106-0.1550.0000.000
총인구0.274-0.2761.0000.5340.4110.3770.4400.4370.5790.6220.4250.4550.4900.5630.1930.051
1인가구수0.081-0.0950.5341.0000.8730.8310.8350.8690.3990.3050.8860.8810.8750.4060.0770.035
커뮤니케이션이 적은 집단0.015-0.1010.4110.8731.0000.8110.7660.8300.5040.2480.9000.8200.7710.4820.0760.090
평일 외출이 적은 집단-0.065-0.0990.3770.8310.8111.0000.6630.9170.4930.1820.9170.8230.7260.5510.0900.040
휴일 외출이 적은 집단-0.008-0.1320.4400.8350.7660.6631.0000.6980.3430.3230.7350.7570.8200.3200.0710.026
출근소요시간 및 근무시간이 많은 집단-0.025-0.0800.4370.8690.8300.9170.6981.0000.5150.2120.9540.8550.7650.5640.0600.026
외출이 매우 적은 집단(전체)0.102-0.1480.5790.3990.5040.4930.3430.5151.0000.3450.5400.4200.4050.8560.0960.243
외출이 매우 많은 집단0.144-0.8320.6220.3050.2480.1820.3230.2120.3451.0000.2160.2700.3130.3290.0700.039
동영상서비스 이용이 많은 집단-0.018-0.0740.4250.8860.9000.9170.7350.9540.5400.2161.0000.8580.7890.5670.0660.042
생활서비스 이용이 많은 집단0.005-0.0910.4550.8810.8200.8230.7570.8550.4200.2700.8581.0000.8350.4610.0700.011
재정상태에 대한 관심집단0.027-0.1060.4900.8750.7710.7260.8200.7650.4050.3130.7890.8351.0000.4020.0710.000
외출-커뮤니케이션이 모두 적은 집단(전체)0.044-0.1550.5630.4060.4820.5510.3200.5640.8560.3290.5670.4610.4021.0000.1030.124
자치구0.9950.0000.1930.0770.0760.0900.0710.0600.0960.0700.0660.0700.0710.1031.0000.000
성별0.0000.0000.0510.0350.0900.0400.0260.0260.2430.0390.0420.0110.0000.1240.0001.000

Missing values

2024-05-04T06:08:26.695483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:08:27.388130image/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)
92761124058송파구방이2동220872.0357.926.8831.3131.2230.032.7525.5733.7644.3819.7613.64
66781118054금천구독산3동1501914.0393.149.5139.9256.7444.6783.9317.6850.055.6950.464.85
30831108084성북구종암동1751115.2397.9855.6262.4124.1446.3842.142.0158.1515.2329.953.58
94251124064송파구삼전동2451305.0333.833.0943.3916.4936.9636.1418.8531.5842.234.8433.51
48531113073서대문구충현동145780.0125.0215.948.5810.556.111.1118.0311.367.357.6411.04
22771107052중랑구면목2동265817.7219.9917.1325.239.4428.0623.61.6224.5537.0124.7526.79
80861121081관악구난곡동270678.3180.3622.2842.352.1429.8723.61.0932.526.4913.140.19
71031119069영등포구신길7동275401.2113.2411.354.416.827.75.050.796.4412.0511.133.35
89931123076강남구세곡동2451650.7167.357.8217.14.2514.3216.8620.6410.4114.757.3321.77
62711117052구로구구로1동155784.049.543.340.496.251.182.7911.381.714.816.032.77
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)
94881124067송파구가락2동160949.065.315.033.455.264.1110.828.444.034.334.2112.89
15441104073성동구옥수동160662.057.495.323.795.124.0512.696.384.263.42.7614.41
72431119075영등포구도림동2551045.0175.6914.4323.3516.7924.040.96.9122.914.3314.1540.63
15151104072성동구금호2·3가동1351038.0161.5719.9715.9516.9113.6630.7245.8713.8716.1512.1215.25
59081116058강서구화곡3동140794.089.5714.039.0113.498.3421.5618.6410.5911.4113.0421.42
39001111059노원구하계2동220856.027.73.011.981.930.847.7917.640.971.30.9811.6
88741123071강남구개포4동250960.099.595.123.535.655.265.519.485.99.129.13.65
100261125066강동구성내2동2501239.0271.5732.5930.9529.4831.8141.058.9136.0433.7535.0732.04
82681122056서초구반포본동220324.024.894.910.912.830.9412.8511.883.821.850.960.0
68211118061금천구시흥5동145942.0133.9717.4518.0215.4215.8136.715.3715.5913.359.2536.46