Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells128
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.8 KiB
Average record size in memory71.3 B

Variable types

Numeric7
Categorical1

Dataset

Description샘플 데이터
Author신한은행
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=317

Alerts

인프라지수(INFRASTRUCTURE) has 68 (13.6%) missing valuesMissing
금융지수(DEPOSIT) has 57 (11.4%) missing valuesMissing
상권번호(TRDAR_NO) has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:57:43.473973
Analysis finished2023-12-10 14:57:55.443722
Duration11.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자(DATE)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201862.23
Minimum201801
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:55.574630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201803
Q1201807
median201902
Q3201907
95-th percentile201911
Maximum201912
Range111
Interquartile range (IQR)100

Descriptive statistics

Standard deviation49.57344
Coefficient of variation (CV)0.00024558057
Kurtosis-1.9394521
Mean201862.23
Median Absolute Deviation (MAD)9
Skewness-0.22339674
Sum1.0093111 × 108
Variance2457.526
MonotonicityNot monotonic
2023-12-10T23:57:55.840367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201905 35
 
7.0%
201806 30
 
6.0%
201903 29
 
5.8%
201911 25
 
5.0%
201901 25
 
5.0%
201908 24
 
4.8%
201810 24
 
4.8%
201909 24
 
4.8%
201812 22
 
4.4%
201802 21
 
4.2%
Other values (14) 241
48.2%
ValueCountFrequency (%)
201801 1
 
0.2%
201802 21
4.2%
201803 20
4.0%
201804 18
3.6%
201805 17
3.4%
201806 30
6.0%
201807 19
3.8%
201808 18
3.6%
201809 17
3.4%
201810 24
4.8%
ValueCountFrequency (%)
201912 21
4.2%
201911 25
5.0%
201910 21
4.2%
201909 24
4.8%
201908 24
4.8%
201907 16
3.2%
201906 19
3.8%
201905 35
7.0%
201904 19
3.8%
201903 29
5.8%

상권구분코드(TRDAR_SE_CD)
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A
349 
R
77 
D
74 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
A 349
69.8%
R 77
 
15.4%
D 74
 
14.8%

Length

2023-12-10T23:57:56.089729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:56.288469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 349
69.8%
r 77
 
15.4%
d 74
 
14.8%

상권번호(TRDAR_NO)
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1000756.9
Minimum1000003
Maximum1001490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:56.533983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000003
5-th percentile1000083.8
Q11000386.8
median1000774.5
Q31001121.8
95-th percentile1001410.1
Maximum1001490
Range1487
Interquartile range (IQR)735

Descriptive statistics

Standard deviation423.20419
Coefficient of variation (CV)0.00042288412
Kurtosis-1.1898758
Mean1000756.9
Median Absolute Deviation (MAD)365
Skewness-0.053242799
Sum5.0037844 × 108
Variance179101.79
MonotonicityNot monotonic
2023-12-10T23:57:56.830613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000433 1
 
0.2%
1000592 1
 
0.2%
1000757 1
 
0.2%
1000474 1
 
0.2%
1001179 1
 
0.2%
1000792 1
 
0.2%
1001035 1
 
0.2%
1000606 1
 
0.2%
1000107 1
 
0.2%
1000293 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
1000003 1
0.2%
1000004 1
0.2%
1000012 1
0.2%
1000013 1
0.2%
1000015 1
0.2%
1000022 1
0.2%
1000024 1
0.2%
1000033 1
0.2%
1000034 1
0.2%
1000035 1
0.2%
ValueCountFrequency (%)
1001490 1
0.2%
1001482 1
0.2%
1001475 1
0.2%
1001474 1
0.2%
1001473 1
0.2%
1001472 1
0.2%
1001471 1
0.2%
1001468 1
0.2%
1001464 1
0.2%
1001463 1
0.2%

매출지수(SALES)
Real number (ℝ)

Distinct457
Distinct (%)91.6%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean20.005571
Minimum0.99
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:57.108148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile5.154
Q19.76
median14.8
Q324.82
95-th percentile52.543
Maximum100
Range99.01
Interquartile range (IQR)15.06

Descriptive statistics

Standard deviation15.459995
Coefficient of variation (CV)0.77278449
Kurtosis3.6893603
Mean20.005571
Median Absolute Deviation (MAD)6.4
Skewness1.8114121
Sum9982.78
Variance239.01145
MonotonicityNot monotonic
2023-12-10T23:57:57.417223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.84 3
 
0.6%
13.23 3
 
0.6%
8.87 3
 
0.6%
16.28 3
 
0.6%
10.69 2
 
0.4%
8.21 2
 
0.4%
16.85 2
 
0.4%
11.45 2
 
0.4%
6.31 2
 
0.4%
13.73 2
 
0.4%
Other values (447) 475
95.0%
ValueCountFrequency (%)
0.99 1
0.2%
3.0 1
0.2%
3.04 1
0.2%
3.08 1
0.2%
3.32 1
0.2%
3.44 1
0.2%
3.52 1
0.2%
3.57 1
0.2%
3.81 1
0.2%
3.97 1
0.2%
ValueCountFrequency (%)
100.0 1
0.2%
83.99 1
0.2%
82.88 1
0.2%
76.7 1
0.2%
75.12 1
0.2%
73.46 1
0.2%
72.5 1
0.2%
71.06 1
0.2%
69.54 1
0.2%
69.06 1
0.2%

인프라지수(INFRASTRUCTURE)
Real number (ℝ)

MISSING 

Distinct411
Distinct (%)95.1%
Missing68
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean26.238981
Minimum1.59
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:58.100727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.59
5-th percentile6.627
Q113.2675
median20.08
Q332.01
95-th percentile74.4275
Maximum100
Range98.41
Interquartile range (IQR)18.7425

Descriptive statistics

Standard deviation19.21336
Coefficient of variation (CV)0.7322449
Kurtosis2.2201614
Mean26.238981
Median Absolute Deviation (MAD)8.125
Skewness1.5994352
Sum11335.24
Variance369.15322
MonotonicityNot monotonic
2023-12-10T23:57:58.387717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.2 3
 
0.6%
22.67 2
 
0.4%
11.75 2
 
0.4%
12.87 2
 
0.4%
11.93 2
 
0.4%
15.27 2
 
0.4%
9.5 2
 
0.4%
14.29 2
 
0.4%
17.38 2
 
0.4%
13.96 2
 
0.4%
Other values (401) 411
82.2%
(Missing) 68
 
13.6%
ValueCountFrequency (%)
1.59 1
0.2%
3.37 1
0.2%
3.45 1
0.2%
4.16 1
0.2%
4.42 1
0.2%
4.52 1
0.2%
4.86 1
0.2%
4.98 1
0.2%
5.08 1
0.2%
5.58 1
0.2%
ValueCountFrequency (%)
100.0 1
0.2%
95.63 1
0.2%
94.69 1
0.2%
88.51 1
0.2%
87.76 1
0.2%
86.23 1
0.2%
85.37 1
0.2%
81.6 1
0.2%
81.18 1
0.2%
79.68 1
0.2%

가맹점지수(STORE)
Real number (ℝ)

Distinct478
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.09808
Minimum4.81
Maximum96.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:58.665196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.81
5-th percentile11.9595
Q120.4575
median30.305
Q343.195
95-th percentile72.3795
Maximum96.37
Range91.56
Interquartile range (IQR)22.7375

Descriptive statistics

Standard deviation18.248445
Coefficient of variation (CV)0.53517514
Kurtosis0.81933062
Mean34.09808
Median Absolute Deviation (MAD)11.22
Skewness1.0336442
Sum17049.04
Variance333.00574
MonotonicityNot monotonic
2023-12-10T23:57:59.048551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.27 2
 
0.4%
32.45 2
 
0.4%
47.33 2
 
0.4%
15.14 2
 
0.4%
32.98 2
 
0.4%
41.62 2
 
0.4%
22.78 2
 
0.4%
17.66 2
 
0.4%
31.05 2
 
0.4%
26.92 2
 
0.4%
Other values (468) 480
96.0%
ValueCountFrequency (%)
4.81 1
0.2%
5.28 1
0.2%
5.31 1
0.2%
5.75 1
0.2%
5.81 1
0.2%
6.81 1
0.2%
6.82 1
0.2%
7.9 1
0.2%
8.4 1
0.2%
8.46 1
0.2%
ValueCountFrequency (%)
96.37 1
0.2%
95.77 1
0.2%
94.45 1
0.2%
93.05 1
0.2%
89.86 1
0.2%
88.64 1
0.2%
87.1 1
0.2%
84.13 1
0.2%
83.96 1
0.2%
82.58 1
0.2%

인구지수(POPULATION)
Real number (ℝ)

Distinct453
Distinct (%)91.0%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean27.728072
Minimum4.25
Maximum82.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:59.440760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.25
5-th percentile13.6265
Q118.8125
median23.605
Q332.45
95-th percentile56.27
Maximum82.01
Range77.76
Interquartile range (IQR)13.6375

Descriptive statistics

Standard deviation13.408627
Coefficient of variation (CV)0.48357588
Kurtosis1.5503272
Mean27.728072
Median Absolute Deviation (MAD)5.97
Skewness1.355176
Sum13808.58
Variance179.79128
MonotonicityNot monotonic
2023-12-10T23:57:59.824654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.52 3
 
0.6%
24.01 3
 
0.6%
19.51 3
 
0.6%
19.14 2
 
0.4%
29.81 2
 
0.4%
20.92 2
 
0.4%
25.82 2
 
0.4%
21.8 2
 
0.4%
17.32 2
 
0.4%
21.71 2
 
0.4%
Other values (443) 475
95.0%
ValueCountFrequency (%)
4.25 1
0.2%
4.6 1
0.2%
8.23 1
0.2%
8.75 1
0.2%
8.78 1
0.2%
9.75 1
0.2%
10.72 1
0.2%
10.98 1
0.2%
11.1 1
0.2%
11.12 1
0.2%
ValueCountFrequency (%)
82.01 1
0.2%
76.09 1
0.2%
74.38 1
0.2%
73.94 1
0.2%
73.4 1
0.2%
72.84 1
0.2%
66.17 1
0.2%
64.88 1
0.2%
63.9 1
0.2%
63.63 1
0.2%

금융지수(DEPOSIT)
Real number (ℝ)

MISSING 

Distinct403
Distinct (%)91.0%
Missing57
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean49.919029
Minimum9.41
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:00.222708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.41
5-th percentile35.93
Q142.94
median48.09
Q355.385
95-th percentile72.808
Maximum90
Range80.59
Interquartile range (IQR)12.445

Descriptive statistics

Standard deviation11.101241
Coefficient of variation (CV)0.22238496
Kurtosis1.7347058
Mean49.919029
Median Absolute Deviation (MAD)6.07
Skewness0.79642764
Sum22114.13
Variance123.23755
MonotonicityNot monotonic
2023-12-10T23:58:00.549594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.02 3
 
0.6%
45.52 3
 
0.6%
53.63 3
 
0.6%
42.64 2
 
0.4%
52.7 2
 
0.4%
37.47 2
 
0.4%
62.45 2
 
0.4%
45.18 2
 
0.4%
46.6 2
 
0.4%
47.21 2
 
0.4%
Other values (393) 420
84.0%
(Missing) 57
 
11.4%
ValueCountFrequency (%)
9.41 1
0.2%
12.53 1
0.2%
28.06 1
0.2%
29.4 1
0.2%
30.11 1
0.2%
30.3 1
0.2%
30.96 1
0.2%
31.14 1
0.2%
31.83 1
0.2%
32.22 1
0.2%
ValueCountFrequency (%)
90.0 1
0.2%
89.87 1
0.2%
89.12 1
0.2%
89.03 1
0.2%
84.79 1
0.2%
81.3 1
0.2%
80.78 1
0.2%
79.28 1
0.2%
78.95 1
0.2%
78.39 1
0.2%

Interactions

2023-12-10T23:57:52.260400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.007253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:45.104732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.016961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.267339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.651677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.937259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.441642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.174850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:45.279042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.194196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.448313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.830456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.115690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.645334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.331383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:45.482451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.392936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.644893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.045255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.324775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.837839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.473015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:45.765000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.571390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.820753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.227074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.504742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:53.312307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.618768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.012395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.738587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.033715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.396623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.699343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:53.717482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.766430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.191618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:47.903538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.212398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.542867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:51.865262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:54.074890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:44.936486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:46.803218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:48.072366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:49.430009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:50.752047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:52.066467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:58:00.779019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자(DATE)상권구분코드(TRDAR_SE_CD)상권번호(TRDAR_NO)매출지수(SALES)인프라지수(INFRASTRUCTURE)가맹점지수(STORE)인구지수(POPULATION)금융지수(DEPOSIT)
일자(DATE)1.0000.0470.0330.1010.0000.0000.0540.000
상권구분코드(TRDAR_SE_CD)0.0471.0000.0000.1560.1610.0000.2300.153
상권번호(TRDAR_NO)0.0330.0001.0000.0000.0000.1330.1130.000
매출지수(SALES)0.1010.1560.0001.0000.2400.0000.0000.086
인프라지수(INFRASTRUCTURE)0.0000.1610.0000.2401.0000.2270.0000.130
가맹점지수(STORE)0.0000.0000.1330.0000.2271.0000.0000.129
인구지수(POPULATION)0.0540.2300.1130.0000.0000.0001.0000.177
금융지수(DEPOSIT)0.0000.1530.0000.0860.1300.1290.1771.000
2023-12-10T23:58:01.015192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자(DATE)상권번호(TRDAR_NO)매출지수(SALES)인프라지수(INFRASTRUCTURE)가맹점지수(STORE)인구지수(POPULATION)금융지수(DEPOSIT)상권구분코드(TRDAR_SE_CD)
일자(DATE)1.000-0.0170.0520.0600.0850.0260.0440.076
상권번호(TRDAR_NO)-0.0171.000-0.0090.007-0.009-0.0040.0310.000
매출지수(SALES)0.052-0.0091.000-0.069-0.009-0.016-0.0430.093
인프라지수(INFRASTRUCTURE)0.0600.007-0.0691.000-0.045-0.0070.0160.095
가맹점지수(STORE)0.085-0.009-0.009-0.0451.0000.0120.0830.000
인구지수(POPULATION)0.026-0.004-0.016-0.0070.0121.0000.0230.140
금융지수(DEPOSIT)0.0440.031-0.0430.0160.0830.0231.0000.067
상권구분코드(TRDAR_SE_CD)0.0760.0000.0930.0950.0000.1400.0671.000

Missing values

2023-12-10T23:57:54.710505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:57:55.081137image/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.
2023-12-10T23:57:55.318256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

일자(DATE)상권구분코드(TRDAR_SE_CD)상권번호(TRDAR_NO)매출지수(SALES)인프라지수(INFRASTRUCTURE)가맹점지수(STORE)인구지수(POPULATION)금융지수(DEPOSIT)
0201903A100043314.1930.0741.6936.0544.34
1201811A10006897.713.3325.5129.2540.9
2201804A100117413.617.3220.2534.2754.34
3201906R100058313.2352.7928.1841.1552.19
4201806R100091648.8113.6121.2672.8448.56
5201908A100114116.2119.3722.2827.239.69
6201912A100107620.1329.1516.7217.5455.03
7201812R100085713.1517.514.3619.9742.98
8201802A10004959.11<NA>18.018.7845.24
9201905R100111210.3117.1418.9735.5146.44
일자(DATE)상권구분코드(TRDAR_SE_CD)상권번호(TRDAR_NO)매출지수(SALES)인프라지수(INFRASTRUCTURE)가맹점지수(STORE)인구지수(POPULATION)금융지수(DEPOSIT)
490201910D10003876.3110.9296.3750.0543.69
491201911A100050212.69<NA>22.3260.7689.87
492201807A10011925.8826.2747.3315.3558.53
493201907A100085575.1229.0812.3929.41<NA>
494201808A10006604.0117.3820.3125.3937.68
495201802A100116239.4728.674.8122.1944.94
496201906A10006298.8710.4553.7129.259.92
497201905A100083311.3112.1540.4132.2140.87
498201804A100107330.248.229.4715.5834.64
499201904A100096625.9435.2232.6519.38<NA>