Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.1 KiB
Average record size in memory88.3 B

Variable types

Categorical3
Numeric6
Text1

Dataset

Description샘플 데이터
Author서울시
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=32

Alerts

기준_년월_코드(STDR_YM_CD) has constant value ""Constant
도로링크_ID(RD_LINK_ID) has unique valuesUnique
연령대코드(AGRDE_CD) has 79 (15.8%) zerosZeros
시간대코드(TMZON_CD) has 77 (15.4%) zerosZeros
유동인구_수(FLPOP_CO) has 173 (34.6%) zerosZeros

Reproduction

Analysis started2023-12-10 14:59:50.443716
Analysis finished2023-12-10 15:00:05.018975
Duration14.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준_년월_코드(STDR_YM_CD)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
201612
500 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201612
2nd row201612
3rd row201612
4th row201612
5th row201612

Common Values

ValueCountFrequency (%)
201612 500
100.0%

Length

2023-12-11T00:00:05.168145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:00:05.451371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201612 500
100.0%

도로링크_ID(RD_LINK_ID)
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88047.046
Minimum442
Maximum179949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:05.772794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum442
5-th percentile7550.6
Q140755.25
median84913
Q3132197.5
95-th percentile173534.85
Maximum179949
Range179507
Interquartile range (IQR)91442.25

Descriptive statistics

Standard deviation52687.005
Coefficient of variation (CV)0.59839606
Kurtosis-1.1816429
Mean88047.046
Median Absolute Deviation (MAD)45286
Skewness0.08306038
Sum44023523
Variance2.7759205 × 109
MonotonicityNot monotonic
2023-12-11T00:00:06.138632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132349 1
 
0.2%
70157 1
 
0.2%
37946 1
 
0.2%
94948 1
 
0.2%
24625 1
 
0.2%
162301 1
 
0.2%
141297 1
 
0.2%
169930 1
 
0.2%
74875 1
 
0.2%
126224 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
442 1
0.2%
588 1
0.2%
704 1
0.2%
726 1
0.2%
818 1
0.2%
1390 1
0.2%
1621 1
0.2%
1759 1
0.2%
1792 1
0.2%
2352 1
0.2%
ValueCountFrequency (%)
179949 1
0.2%
179942 1
0.2%
179282 1
0.2%
179111 1
0.2%
178987 1
0.2%
178931 1
0.2%
178636 1
0.2%
178122 1
0.2%
177696 1
0.2%
177081 1
0.2%

시군구코드(SIGNGU_CD)
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11395.33
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:06.408417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q111230
median11350
Q311560
95-th percentile11680
Maximum11740
Range630
Interquartile range (IQR)330

Descriptive statistics

Standard deviation180.59615
Coefficient of variation (CV)0.01584826
Kurtosis-1.1848765
Mean11395.33
Median Absolute Deviation (MAD)150
Skewness0.24163109
Sum5697665
Variance32614.971
MonotonicityNot monotonic
2023-12-11T00:00:06.670910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11230 37
 
7.4%
11560 34
 
6.8%
11620 30
 
6.0%
11290 28
 
5.6%
11305 28
 
5.6%
11170 28
 
5.6%
11260 28
 
5.6%
11440 25
 
5.0%
11410 23
 
4.6%
11680 23
 
4.6%
Other values (15) 216
43.2%
ValueCountFrequency (%)
11110 22
4.4%
11140 12
 
2.4%
11170 28
5.6%
11200 21
4.2%
11215 10
 
2.0%
11230 37
7.4%
11260 28
5.6%
11290 28
5.6%
11305 28
5.6%
11320 19
3.8%
ValueCountFrequency (%)
11740 9
 
1.8%
11710 14
2.8%
11680 23
4.6%
11650 15
3.0%
11620 30
6.0%
11590 10
 
2.0%
11560 34
6.8%
11545 13
 
2.6%
11530 18
3.6%
11500 11
 
2.2%

시군구명(SIGNGU_NM)
Categorical

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
성북구
35 
강북구
 
32
서대문구
 
31
종로구
 
27
광진구
 
26
Other values (20)
349 

Length

Max length4
Median length3
Mean length3.128
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row강북구
3rd row구로구
4th row금천구
5th row중랑구

Common Values

ValueCountFrequency (%)
성북구 35
 
7.0%
강북구 32
 
6.4%
서대문구 31
 
6.2%
종로구 27
 
5.4%
광진구 26
 
5.2%
마포구 26
 
5.2%
관악구 25
 
5.0%
동대문구 25
 
5.0%
송파구 22
 
4.4%
구로구 22
 
4.4%
Other values (15) 229
45.8%

Length

2023-12-11T00:00:06.988070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성북구 35
 
7.0%
강북구 32
 
6.4%
서대문구 31
 
6.2%
종로구 27
 
5.4%
광진구 26
 
5.2%
마포구 26
 
5.2%
관악구 25
 
5.0%
동대문구 25
 
5.0%
송파구 22
 
4.4%
구로구 22
 
4.4%
Other values (15) 229
45.8%

행정동코드(ADSTRD_CD)
Real number (ℝ)

Distinct257
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11398559
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:07.299983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11110650
Q111230716
median11380566
Q311560535
95-th percentile11710590
Maximum11740700
Range630185
Interquartile range (IQR)329818.75

Descriptive statistics

Standard deviation186563.3
Coefficient of variation (CV)0.016367271
Kurtosis-1.1681962
Mean11398559
Median Absolute Deviation (MAD)150199
Skewness0.21520425
Sum5.6992793 × 109
Variance3.4805865 × 1010
MonotonicityNot monotonic
2023-12-11T00:00:07.658484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11230536 7
 
1.4%
11110515 7
 
1.4%
11110650 6
 
1.2%
11560585 6
 
1.2%
11170625 6
 
1.2%
11290555 6
 
1.2%
11260565 5
 
1.0%
11305534 5
 
1.0%
11410620 5
 
1.0%
11290770 5
 
1.0%
Other values (247) 442
88.4%
ValueCountFrequency (%)
11110515 7
1.4%
11110530 1
 
0.2%
11110540 1
 
0.2%
11110550 2
 
0.4%
11110580 3
0.6%
11110600 1
 
0.2%
11110615 3
0.6%
11110630 2
 
0.4%
11110640 1
 
0.2%
11110650 6
1.2%
ValueCountFrequency (%)
11740700 1
 
0.2%
11740685 1
 
0.2%
11740660 1
 
0.2%
11740620 1
 
0.2%
11740610 5
1.0%
11740600 1
 
0.2%
11740590 1
 
0.2%
11740570 4
0.8%
11740550 1
 
0.2%
11710650 1
 
0.2%

행정동명(ADSTRD_NM)
Text

Distinct254
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:00:08.412933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.772
Min length2

Characters and Unicode

Total characters1886
Distinct characters169
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)23.0%

Sample

1st row등촌2동
2nd row화곡6동
3rd row성수1가1동
4th row공릉1동
5th row오류2동
ValueCountFrequency (%)
혜화동 6
 
1.2%
연희동 6
 
1.2%
논현2동 6
 
1.2%
청파동 6
 
1.2%
삼선동 5
 
1.0%
흑석동 5
 
1.0%
을지로동 5
 
1.0%
우이동 5
 
1.0%
종로1.2.3.4가동 5
 
1.0%
성북동 4
 
0.8%
Other values (244) 447
89.4%
2023-12-11T00:00:09.366211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 501
26.6%
1 114
 
6.0%
2 111
 
5.9%
3 40
 
2.1%
신 37
 
2.0%
가 30
 
1.6%
화 28
 
1.5%
성 26
 
1.4%
수 26
 
1.4%
로 23
 
1.2%
Other values (159) 950
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1557
82.6%
Decimal Number 311
 
16.5%
Other Punctuation 18
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 501
32.2%
신 37
 
2.4%
가 30
 
1.9%
화 28
 
1.8%
성 26
 
1.7%
수 26
 
1.7%
로 23
 
1.5%
양 18
 
1.2%
이 17
 
1.1%
대 17
 
1.1%
Other values (150) 834
53.6%
Decimal Number
ValueCountFrequency (%)
1 114
36.7%
2 111
35.7%
3 40
 
12.9%
4 23
 
7.4%
5 13
 
4.2%
6 7
 
2.3%
7 2
 
0.6%
8 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1557
82.6%
Common 329
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 501
32.2%
신 37
 
2.4%
가 30
 
1.9%
화 28
 
1.8%
성 26
 
1.7%
수 26
 
1.7%
로 23
 
1.5%
양 18
 
1.2%
이 17
 
1.1%
대 17
 
1.1%
Other values (150) 834
53.6%
Common
ValueCountFrequency (%)
1 114
34.7%
2 111
33.7%
3 40
 
12.2%
4 23
 
7.0%
. 18
 
5.5%
5 13
 
4.0%
6 7
 
2.1%
7 2
 
0.6%
8 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1557
82.6%
ASCII 329
 
17.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 501
32.2%
신 37
 
2.4%
가 30
 
1.9%
화 28
 
1.8%
성 26
 
1.7%
수 26
 
1.7%
로 23
 
1.5%
양 18
 
1.2%
이 17
 
1.1%
대 17
 
1.1%
Other values (150) 834
53.6%
ASCII
ValueCountFrequency (%)
1 114
34.7%
2 111
33.7%
3 40
 
12.2%
4 23
 
7.0%
. 18
 
5.5%
5 13
 
4.0%
6 7
 
2.1%
7 2
 
0.6%
8 1
 
0.3%

요일코드(DAYWEEK_CD)
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
271 
2
229 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 271
54.2%
2 229
45.8%

Length

2023-12-11T00:00:09.712144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:00:10.003500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 271
54.2%
2 229
45.8%

연령대코드(AGRDE_CD)
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.16
Minimum0
Maximum60
Zeros79
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:10.248354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median30
Q350
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)40

Descriptive statistics

Standard deviation20.455103
Coefficient of variation (CV)0.67821958
Kurtosis-1.2772919
Mean30.16
Median Absolute Deviation (MAD)20
Skewness-0.018858939
Sum15080
Variance418.41122
MonotonicityNot monotonic
2023-12-11T00:00:10.489509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 79
15.8%
60 78
15.6%
30 76
15.2%
50 71
14.2%
20 66
13.2%
40 65
13.0%
10 65
13.0%
ValueCountFrequency (%)
0 79
15.8%
10 65
13.0%
20 66
13.2%
30 76
15.2%
40 65
13.0%
50 71
14.2%
60 78
15.6%
ValueCountFrequency (%)
60 78
15.6%
50 71
14.2%
40 65
13.0%
30 76
15.2%
20 66
13.2%
10 65
13.0%
0 79
15.8%

시간대코드(TMZON_CD)
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.076
Minimum0
Maximum6
Zeros77
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:10.780833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0392501
Coefficient of variation (CV)0.66295518
Kurtosis-1.2755539
Mean3.076
Median Absolute Deviation (MAD)2
Skewness-0.09332592
Sum1538
Variance4.1585411
MonotonicityNot monotonic
2023-12-11T00:00:11.007884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 77
15.4%
0 77
15.4%
6 76
15.2%
3 75
15.0%
4 74
14.8%
1 66
13.2%
2 55
11.0%
ValueCountFrequency (%)
0 77
15.4%
1 66
13.2%
2 55
11.0%
3 75
15.0%
4 74
14.8%
5 77
15.4%
6 76
15.2%
ValueCountFrequency (%)
6 76
15.2%
5 77
15.4%
4 74
14.8%
3 75
15.0%
2 55
11.0%
1 66
13.2%
0 77
15.4%

유동인구_수(FLPOP_CO)
Real number (ℝ)

ZEROS 

Distinct325
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.721357
Minimum0
Maximum543.16125
Zeros173
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:00:11.344210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.2535556
Q38.574375
95-th percentile68.293239
Maximum543.16125
Range543.16125
Interquartile range (IQR)8.574375

Descriptive statistics

Standard deviation43.002831
Coefficient of variation (CV)3.1340073
Kurtosis62.56489
Mean13.721357
Median Absolute Deviation (MAD)1.2535556
Skewness6.8435658
Sum6860.6783
Variance1849.2435
MonotonicityNot monotonic
2023-12-11T00:00:11.759527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 173
34.6%
0.2425 2
 
0.4%
0.42 2
 
0.4%
2.992 2
 
0.4%
2.878 1
 
0.2%
0.3775 1
 
0.2%
1.068 1
 
0.2%
1.46875 1
 
0.2%
0.36025 1
 
0.2%
22.04625 1
 
0.2%
Other values (315) 315
63.0%
ValueCountFrequency (%)
0.0 173
34.6%
0.0328 1
 
0.2%
0.037 1
 
0.2%
0.0453333333333333 1
 
0.2%
0.0525 1
 
0.2%
0.055 1
 
0.2%
0.0625 1
 
0.2%
0.0663636363636364 1
 
0.2%
0.0779166666666667 1
 
0.2%
0.07975 1
 
0.2%
ValueCountFrequency (%)
543.16125 1
0.2%
373.6425 1
0.2%
298.9195 1
0.2%
224.473571428571 1
0.2%
214.066666666667 1
0.2%
198.428 1
0.2%
191.8171 1
0.2%
162.22375 1
0.2%
161.52 1
0.2%
153.9259375 1
0.2%

Interactions

2023-12-11T00:00:03.281413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.035371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:57.397110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:59.164983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:01.877568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:03.500684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.279483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:57.639313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:59.402111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:02.186435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:03.752762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.525485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:58.316341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:59.676421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:02.410894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:03.982396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.743739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:58.549937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:00.078728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:02.616830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:04.195431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.961371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:58.757586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:00.862880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:02.836108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:00:11.957970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로링크_ID(RD_LINK_ID)시군구코드(SIGNGU_CD)시군구명(SIGNGU_NM)행정동코드(ADSTRD_CD)요일코드(DAYWEEK_CD)연령대코드(AGRDE_CD)시간대코드(TMZON_CD)유동인구_수(FLPOP_CO)
도로링크_ID(RD_LINK_ID)1.0000.2150.2430.0000.0980.0000.0000.138
시군구코드(SIGNGU_CD)0.2151.0000.0000.0000.0000.0510.0870.023
시군구명(SIGNGU_NM)0.2430.0001.0000.0530.1490.0000.1320.000
행정동코드(ADSTRD_CD)0.0000.0000.0531.0000.0000.0000.1310.090
요일코드(DAYWEEK_CD)0.0980.0000.1490.0001.0000.0000.0730.034
연령대코드(AGRDE_CD)0.0000.0510.0000.0000.0001.0000.0000.041
시간대코드(TMZON_CD)0.0000.0870.1320.1310.0730.0001.0000.000
유동인구_수(FLPOP_CO)0.1380.0230.0000.0900.0340.0410.0001.000
2023-12-11T00:00:12.260999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명(SIGNGU_NM)요일코드(DAYWEEK_CD)
시군구명(SIGNGU_NM)1.0000.126
요일코드(DAYWEEK_CD)0.1261.000
2023-12-11T00:00:13.063423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로링크_ID(RD_LINK_ID)시군구코드(SIGNGU_CD)행정동코드(ADSTRD_CD)연령대코드(AGRDE_CD)시간대코드(TMZON_CD)유동인구_수(FLPOP_CO)시군구명(SIGNGU_NM)요일코드(DAYWEEK_CD)
도로링크_ID(RD_LINK_ID)1.000-0.087-0.005-0.001-0.0620.0620.0880.074
시군구코드(SIGNGU_CD)-0.0871.0000.042-0.0330.0470.0040.0000.000
행정동코드(ADSTRD_CD)-0.0050.0421.000-0.0620.0040.0440.0320.000
연령대코드(AGRDE_CD)-0.001-0.033-0.0621.000-0.034-0.0540.0000.000
시간대코드(TMZON_CD)-0.0620.0470.004-0.0341.0000.0680.0550.078
유동인구_수(FLPOP_CO)0.0620.0040.044-0.0540.0681.0000.0000.025
시군구명(SIGNGU_NM)0.0880.0000.0320.0000.0550.0001.0000.126
요일코드(DAYWEEK_CD)0.0740.0000.0000.0000.0780.0250.1261.000

Missing values

2023-12-11T00:00:04.526149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:00:04.886384image/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

기준_년월_코드(STDR_YM_CD)도로링크_ID(RD_LINK_ID)시군구코드(SIGNGU_CD)시군구명(SIGNGU_NM)행정동코드(ADSTRD_CD)행정동명(ADSTRD_NM)요일코드(DAYWEEK_CD)연령대코드(AGRDE_CD)시간대코드(TMZON_CD)유동인구_수(FLPOP_CO)
020161213234911290종로구11560515등촌2동22058.5935
12016128275211545강북구11110600화곡6동25051.015
22016129736511305구로구11620765성수1가1동230320.125778
32016122104911560금천구11260580공릉1동10610.4875
42016123776811545중랑구11290555오류2동25023.784667
520161213847611140강북구11380625미아동1011.736875
620161216837611170동작구11350670이화동1400116.199286
72016128266411530중랑구11305534수색동1604191.8171
82016123367611530광진구11500590천호1동250576.647222
920161215196911620관악구11590510하계2동12040.0
기준_년월_코드(STDR_YM_CD)도로링크_ID(RD_LINK_ID)시군구코드(SIGNGU_CD)시군구명(SIGNGU_NM)행정동코드(ADSTRD_CD)행정동명(ADSTRD_NM)요일코드(DAYWEEK_CD)연령대코드(AGRDE_CD)시간대코드(TMZON_CD)유동인구_수(FLPOP_CO)
4902016127661211560동대문구11350630신원동110456.92125
49120161210930811620용산구11170685독산3동22057.8025
49220161212643611620영등포구11680510번2동23000.0
4932016124233311350서대문구11740590양재1동23060.0
49420161212486511350성북구11560535중화1동24000.0
4952016125867511230관악구11215750을지로동260140.2735
4962016121406911290성북구11620625이문1동13060.0
49720161215600111230관악구11410660혜화동23003.64
49820161211250511230광진구11305606수유3동150311.596
49920161214778711560중랑구11110700삼성동11001.275