Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.2 KiB
Average record size in memory80.3 B

Variable types

Numeric7
Categorical2

Alerts

주중유동인구수(WD_SMS_CSCNT_AVG) has 15 (3.0%) zerosZeros
주말유동인구수(SW_SMS_CSCNT_AVG) has 30 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:54:51.019706
Analysis finished2023-12-10 14:54:59.309881
Duration8.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월(STD_YM)
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201603.23
Minimum201601
Maximum201606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:59.387552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201601
5-th percentile201601
Q1201602
median201603
Q3201605
95-th percentile201606
Maximum201606
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5620897
Coefficient of variation (CV)7.7483365 × 10-6
Kurtosis-1.1388799
Mean201603.23
Median Absolute Deviation (MAD)1
Skewness0.091754887
Sum1.0080162 × 108
Variance2.4401242
MonotonicityNot monotonic
2023-12-10T23:54:59.545954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
201602 96
19.2%
201603 94
18.8%
201605 93
18.6%
201604 92
18.4%
201601 88
17.6%
201606 37
 
7.4%
ValueCountFrequency (%)
201601 88
17.6%
201602 96
19.2%
201603 94
18.8%
201604 92
18.4%
201605 93
18.6%
201606 37
 
7.4%
ValueCountFrequency (%)
201606 37
 
7.4%
201605 93
18.6%
201604 92
18.4%
201603 94
18.8%
201602 96
19.2%
201601 88
17.6%

X좌표(X_COORD)
Real number (ℝ)

Distinct240
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean961426.08
Minimum958030
Maximum962740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:59.764824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum958030
5-th percentile960248.95
Q1960788
median961414
Q3962105.25
95-th percentile962547
Maximum962740
Range4710
Interquartile range (IQR)1317.25

Descriptive statistics

Standard deviation758.21044
Coefficient of variation (CV)0.00078863103
Kurtosis-0.53646388
Mean961426.08
Median Absolute Deviation (MAD)675
Skewness-0.23849218
Sum4.8071304 × 108
Variance574883.07
MonotonicityNot monotonic
2023-12-10T23:54:59.959138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
962008 7
 
1.4%
961612 6
 
1.2%
962595 6
 
1.2%
960152 6
 
1.2%
961021 5
 
1.0%
961122 5
 
1.0%
962156 5
 
1.0%
960641 5
 
1.0%
961118 5
 
1.0%
961023 5
 
1.0%
Other values (230) 445
89.0%
ValueCountFrequency (%)
958030 1
 
0.2%
960051 1
 
0.2%
960103 2
 
0.4%
960149 1
 
0.2%
960150 1
 
0.2%
960151 2
 
0.4%
960152 6
1.2%
960153 1
 
0.2%
960199 1
 
0.2%
960200 3
0.6%
ValueCountFrequency (%)
962740 1
 
0.2%
962691 1
 
0.2%
962644 1
 
0.2%
962643 4
0.8%
962642 2
 
0.4%
962597 1
 
0.2%
962595 6
1.2%
962594 4
0.8%
962548 3
0.6%
962547 3
0.6%

Y좌표(Y_COORD)
Real number (ℝ)

Distinct236
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1953285.3
Minimum1951521
Maximum1955484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:55:00.175333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1951521
5-th percentile1951885.9
Q11952548.8
median1952913
Q31954558.5
95-th percentile1955124
Maximum1955484
Range3963
Interquartile range (IQR)2009.75

Descriptive statistics

Standard deviation1056.5381
Coefficient of variation (CV)0.0005409031
Kurtosis-0.95449974
Mean1953285.3
Median Absolute Deviation (MAD)559.5
Skewness0.60433161
Sum9.7664265 × 108
Variance1116272.7
MonotonicityNot monotonic
2023-12-10T23:55:00.367765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1955074 9
 
1.8%
1953165 8
 
1.6%
1954971 7
 
1.4%
1952552 7
 
1.4%
1951885 6
 
1.2%
1953266 6
 
1.2%
1953012 6
 
1.2%
1954662 6
 
1.2%
1952295 6
 
1.2%
1953060 5
 
1.0%
Other values (226) 434
86.8%
ValueCountFrequency (%)
1951521 1
0.2%
1951524 1
0.2%
1951578 1
0.2%
1951627 1
0.2%
1951675 1
0.2%
1951678 1
0.2%
1951680 1
0.2%
1951730 1
0.2%
1951732 1
0.2%
1951782 1
0.2%
ValueCountFrequency (%)
1955484 1
 
0.2%
1955433 1
 
0.2%
1955381 3
0.6%
1955331 1
 
0.2%
1955330 1
 
0.2%
1955280 2
0.4%
1955279 4
0.8%
1955278 1
 
0.2%
1955229 1
 
0.2%
1955228 1
 
0.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
261 
2
239 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 261
52.2%
2 239
47.8%

Length

2023-12-10T23:55:00.573326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:55:00.713078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 261
52.2%
2 239
47.8%
Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4439.92
Minimum4
Maximum9599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:55:01.221413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile1519
Q13034
median4549
Q36064
95-th percentile7579
Maximum9599
Range9595
Interquartile range (IQR)3030

Descriptive statistics

Standard deviation1910.8749
Coefficient of variation (CV)0.43038498
Kurtosis-0.73317358
Mean4439.92
Median Absolute Deviation (MAD)1515
Skewness0.088343158
Sum2219960
Variance3651442.8
MonotonicityNot monotonic
2023-12-10T23:55:01.405687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4549 56
11.2%
3539 42
8.4%
3034 42
8.4%
4044 42
8.4%
5559 40
 
8.0%
2024 38
 
7.6%
5054 38
 
7.6%
6569 37
 
7.4%
2529 32
 
6.4%
6064 32
 
6.4%
Other values (10) 101
20.2%
ValueCountFrequency (%)
4 1
 
0.2%
509 4
 
0.8%
1014 14
 
2.8%
1519 21
 
4.2%
2024 38
7.6%
2529 32
6.4%
3034 42
8.4%
3539 42
8.4%
4044 42
8.4%
4549 56
11.2%
ValueCountFrequency (%)
9599 1
 
0.2%
9094 2
 
0.4%
8589 3
 
0.6%
8084 7
 
1.4%
7579 24
4.8%
7074 24
4.8%
6569 37
7.4%
6064 32
6.4%
5559 40
8.0%
5054 38
7.6%
Distinct281
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1408435 × 109
Minimum1.1110515 × 109
Maximum1.17407 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:55:01.586614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110515 × 109
5-th percentile1.114055 × 109
Q11.126054 × 109
median1.1365615 × 109
Q31.156817 × 109
95-th percentile1.1710642 × 109
Maximum1.17407 × 109
Range63018500
Interquartile range (IQR)30763000

Descriptive statistics

Standard deviation18890906
Coefficient of variation (CV)0.016558718
Kurtosis-1.2008545
Mean1.1408435 × 109
Median Absolute Deviation (MAD)14984500
Skewness0.23512487
Sum5.7042176 × 1011
Variance3.5686632 × 1014
MonotonicityNot monotonic
2023-12-10T23:55:01.793857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1174070000 5
 
1.0%
1123065000 5
 
1.0%
1156071000 5
 
1.0%
1162076500 5
 
1.0%
1130554500 4
 
0.8%
1135057000 4
 
0.8%
1129078000 4
 
0.8%
1126057500 4
 
0.8%
1135061100 4
 
0.8%
1135066500 4
 
0.8%
Other values (271) 456
91.2%
ValueCountFrequency (%)
1111051500 3
0.6%
1111054000 2
0.4%
1111055000 1
 
0.2%
1111056000 2
0.4%
1111057000 1
 
0.2%
1111058000 1
 
0.2%
1111060000 1
 
0.2%
1111061500 2
0.4%
1111063000 4
0.8%
1111064000 1
 
0.2%
ValueCountFrequency (%)
1174070000 5
1.0%
1174069000 1
 
0.2%
1174066000 1
 
0.2%
1174065000 2
 
0.4%
1174062000 2
 
0.4%
1174060000 1
 
0.2%
1174059000 1
 
0.2%
1174058000 1
 
0.2%
1174057000 2
 
0.4%
1174055000 1
 
0.2%
Distinct137
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0085488
Minimum0
Maximum0.3919
Zeros15
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:55:02.007900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0004
Q10.0008
median0.0019
Q30.004425
95-th percentile0.02839
Maximum0.3919
Range0.3919
Interquartile range (IQR)0.003625

Descriptive statistics

Standard deviation0.032647174
Coefficient of variation (CV)3.8189189
Kurtosis81.301541
Mean0.0085488
Median Absolute Deviation (MAD)0.0013
Skewness8.4972406
Sum4.2744
Variance0.001065838
MonotonicityNot monotonic
2023-12-10T23:55:02.232659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0006 27
 
5.4%
0.0004 24
 
4.8%
0.0005 23
 
4.6%
0.0009 22
 
4.4%
0.0007 17
 
3.4%
0.0012 16
 
3.2%
0.0 15
 
3.0%
0.0008 14
 
2.8%
0.001 13
 
2.6%
0.0011 13
 
2.6%
Other values (127) 316
63.2%
ValueCountFrequency (%)
0.0 15
3.0%
0.0002 1
 
0.2%
0.0003 8
 
1.6%
0.0004 24
4.8%
0.0005 23
4.6%
0.0006 27
5.4%
0.0007 17
3.4%
0.0008 14
2.8%
0.0009 22
4.4%
0.001 13
2.6%
ValueCountFrequency (%)
0.3919 1
0.2%
0.3441 1
0.2%
0.295 1
0.2%
0.2674 1
0.2%
0.1851 1
0.2%
0.1542 1
0.2%
0.1291 1
0.2%
0.0847 1
0.2%
0.066 2
0.4%
0.0643 1
0.2%
Distinct131
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0073626
Minimum0
Maximum0.2258
Zeros30
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:55:02.439351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0007
median0.0018
Q30.004125
95-th percentile0.030755
Maximum0.2258
Range0.2258
Interquartile range (IQR)0.003425

Descriptive statistics

Standard deviation0.021879354
Coefficient of variation (CV)2.9716885
Kurtosis47.311
Mean0.0073626
Median Absolute Deviation (MAD)0.0013
Skewness6.2308927
Sum3.6813
Variance0.00047870611
MonotonicityNot monotonic
2023-12-10T23:55:02.616491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
 
6.0%
0.0005 30
 
6.0%
0.0006 22
 
4.4%
0.0007 22
 
4.4%
0.0008 21
 
4.2%
0.001 16
 
3.2%
0.0004 15
 
3.0%
0.0013 14
 
2.8%
0.0009 13
 
2.6%
0.002 12
 
2.4%
Other values (121) 305
61.0%
ValueCountFrequency (%)
0.0 30
6.0%
0.0002 2
 
0.4%
0.0003 12
 
2.4%
0.0004 15
3.0%
0.0005 30
6.0%
0.0006 22
4.4%
0.0007 22
4.4%
0.0008 21
4.2%
0.0009 13
2.6%
0.001 16
3.2%
ValueCountFrequency (%)
0.2258 1
0.2%
0.2213 1
0.2%
0.148 1
0.2%
0.1414 1
0.2%
0.1161 1
0.2%
0.1078 1
0.2%
0.1029 1
0.2%
0.1004 1
0.2%
0.0839 1
0.2%
0.0832 1
0.2%
Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
종로구
 
29
구로구
 
28
노원구
 
25
중구
 
23
동작구
 
23
Other values (20)
372 

Length

Max length4
Median length3
Mean length3.07
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강동구
2nd row영등포구
3rd row서대문구
4th row중구
5th row금천구

Common Values

ValueCountFrequency (%)
종로구 29
 
5.8%
구로구 28
 
5.6%
노원구 25
 
5.0%
중구 23
 
4.6%
동작구 23
 
4.6%
강동구 22
 
4.4%
서대문구 22
 
4.4%
강남구 22
 
4.4%
은평구 21
 
4.2%
금천구 21
 
4.2%
Other values (15) 264
52.8%

Length

2023-12-10T23:55:02.805010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 29
 
5.8%
구로구 28
 
5.6%
노원구 25
 
5.0%
중구 23
 
4.6%
동작구 23
 
4.6%
강동구 22
 
4.4%
서대문구 22
 
4.4%
강남구 22
 
4.4%
은평구 21
 
4.2%
금천구 21
 
4.2%
Other values (15) 264
52.8%

Interactions

2023-12-10T23:54:58.052301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:51.561132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:52.609245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:53.996359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:55.094610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:55.987488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:56.983140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:58.174758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:51.726662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:52.752022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:54.133437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:55.206902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:56.121119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:57.130746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:58.294545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:51.854025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:52.881237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:54.290789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:55.315017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:56.269320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:57.271775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:58.444621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:52.011013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:53.405426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:54.478588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:55.494975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:56.447281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:57.449252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:58.552307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:52.143952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:53.536146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:54.666326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:55.609671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:56.590849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:57.594811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:58.656880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:52.311473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:53.696658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:54.831348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:55.756335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:56.707417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:57.787244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:58.794827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:52.472117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:53.851624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:54.977302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:55.885993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:56.858092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:57.928618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:55:02.942245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)X좌표(X_COORD)Y좌표(Y_COORD)성별코드(GNDR_CD)연령대구분코드(AGE_GR_SCTN_CD)유입지코드(INFLOW_CD)주중유동인구수(WD_SMS_CSCNT_AVG)주말유동인구수(SW_SMS_CSCNT_AVG)자치구(SIGUNGU)
기준년월(STD_YM)1.0000.1420.0000.0530.1060.0000.0000.0000.000
X좌표(X_COORD)0.1421.0000.1110.0000.0000.0000.0000.0600.000
Y좌표(Y_COORD)0.0000.1111.0000.0000.1470.1420.0000.0000.000
성별코드(GNDR_CD)0.0530.0000.0001.0000.1290.0000.0210.0660.000
연령대구분코드(AGE_GR_SCTN_CD)0.1060.0000.1470.1291.0000.1090.0000.2100.000
유입지코드(INFLOW_CD)0.0000.0000.1420.0000.1091.0000.1380.1410.127
주중유동인구수(WD_SMS_CSCNT_AVG)0.0000.0000.0000.0210.0000.1381.0000.1090.000
주말유동인구수(SW_SMS_CSCNT_AVG)0.0000.0600.0000.0660.2100.1410.1091.0000.262
자치구(SIGUNGU)0.0000.0000.0000.0000.0000.1270.0000.2621.000
2023-12-10T23:55:03.141376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드(GNDR_CD)자치구(SIGUNGU)
성별코드(GNDR_CD)1.0000.000
자치구(SIGUNGU)0.0001.000
2023-12-10T23:55:03.274426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)X좌표(X_COORD)Y좌표(Y_COORD)연령대구분코드(AGE_GR_SCTN_CD)유입지코드(INFLOW_CD)주중유동인구수(WD_SMS_CSCNT_AVG)주말유동인구수(SW_SMS_CSCNT_AVG)성별코드(GNDR_CD)자치구(SIGUNGU)
기준년월(STD_YM)1.000-0.039-0.018-0.029-0.0340.017-0.1000.0380.000
X좌표(X_COORD)-0.0391.000-0.0420.0150.0890.056-0.0220.0000.000
Y좌표(Y_COORD)-0.018-0.0421.0000.044-0.035-0.027-0.0490.0000.000
연령대구분코드(AGE_GR_SCTN_CD)-0.0290.0150.0441.0000.0000.040-0.0260.0980.000
유입지코드(INFLOW_CD)-0.0340.089-0.0350.0001.0000.0050.0110.0000.032
주중유동인구수(WD_SMS_CSCNT_AVG)0.0170.056-0.0270.0400.0051.000-0.0530.0200.000
주말유동인구수(SW_SMS_CSCNT_AVG)-0.100-0.022-0.049-0.0260.011-0.0531.0000.0490.105
성별코드(GNDR_CD)0.0380.0000.0000.0980.0000.0200.0491.0000.000
자치구(SIGUNGU)0.0000.0000.0000.0000.0320.0000.1050.0001.000

Missing values

2023-12-10T23:54:58.954164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:54:59.196882image/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

기준년월(STD_YM)X좌표(X_COORD)Y좌표(Y_COORD)성별코드(GNDR_CD)연령대구분코드(AGE_GR_SCTN_CD)유입지코드(INFLOW_CD)주중유동인구수(WD_SMS_CSCNT_AVG)주말유동인구수(SW_SMS_CSCNT_AVG)자치구(SIGUNGU)
020160496102119529102757911710561000.00030.1004강동구
120160296229819532682656911680700000.00260.0009영등포구
220160296141419518851303411305630000.00530.0028서대문구
320160396185919534742202411260660000.00120.0007중구
420160296069119532162252911305590000.00050.0005금천구
520160496054619530142555911260690000.0080.0016서대문구
620160496131819545602505411320660000.0030.0도봉구
720160596259719548172555911650651000.00110.0015노원구
820160696244719515242404411230570000.00090.001양천구
920160296034719552791454911110690000.02430.0019종로구
기준년월(STD_YM)X좌표(X_COORD)Y좌표(Y_COORD)성별코드(GNDR_CD)연령대구분코드(AGE_GR_SCTN_CD)유입지코드(INFLOW_CD)주중유동인구수(WD_SMS_CSCNT_AVG)주말유동인구수(SW_SMS_CSCNT_AVG)자치구(SIGUNGU)
49020160596014919545612707411170510000.00230.0031동대문구
49120160296015019534211252911110515000.00040.0구로구
49220160396049419526542404411560720000.0030.0054강서구
49320160696195719549191353911260655000.0040.0종로구
49420160396131619550231454911410615000.0180.0097강남구
49520160496259419530622454911230536000.01990.0048성북구
49620160296190719550221555911140570000.01170.002중구
49720160296161219523472353911620695000.00060.0004관악구
49820160296215019523421606411680580000.00050.0008서대문구
49920160196049619550211707411260540000.00270.0006동대문구