Overview

Dataset statistics

Number of variables16
Number of observations600
Missing cells0
Missing cells (%)0.0%
Duplicate rows22
Duplicate rows (%)3.7%
Total size in memory81.0 KiB
Average record size in memory138.2 B

Variable types

Numeric10
Text3
Categorical3

Alerts

Dataset has 22 (3.7%) duplicate rowsDuplicates
BASE_DE is highly overall correlated with WKDAY_NMHigh correlation
GID_LO is highly overall correlated with CTPRVN_NMHigh correlation
GID_LA is highly overall correlated with CTPRVN_NMHigh correlation
CTPRVN_CD is highly overall correlated with SIGNGU_CD and 2 other fieldsHigh correlation
SIGNGU_CD is highly overall correlated with CTPRVN_CD and 2 other fieldsHigh correlation
ADSTRD_CD is highly overall correlated with CTPRVN_CD and 2 other fieldsHigh correlation
CTPRVN_NM is highly overall correlated with GID_LO and 4 other fieldsHigh correlation
WKDAY_NM is highly overall correlated with BASE_DEHigh correlation
MVMN_TIME has 17 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-10 10:05:00.853688
Analysis finished2023-12-10 10:05:23.606681
Duration22.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BASE_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20201112
Minimum20201101
Maximum20201128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:05:23.711996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20201101
5-th percentile20201101
Q120201105
median20201111
Q320201116
95-th percentile20201126
Maximum20201128
Range27
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.3463559
Coefficient of variation (CV)3.6366098 × 10-7
Kurtosis-0.8400004
Mean20201112
Median Absolute Deviation (MAD)6
Skewness0.4642503
Sum1.2120667 × 1010
Variance53.968945
MonotonicityNot monotonic
2023-12-10T19:05:23.937589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
20201105 101
16.8%
20201112 64
10.7%
20201103 56
9.3%
20201109 55
9.2%
20201121 46
7.7%
20201126 38
 
6.3%
20201116 36
 
6.0%
20201101 34
 
5.7%
20201106 31
 
5.2%
20201120 28
 
4.7%
Other values (8) 111
18.5%
ValueCountFrequency (%)
20201101 34
 
5.7%
20201103 56
9.3%
20201105 101
16.8%
20201106 31
 
5.2%
20201108 17
 
2.8%
20201109 55
9.2%
20201111 9
 
1.5%
20201112 64
10.7%
20201114 27
 
4.5%
20201115 26
 
4.3%
ValueCountFrequency (%)
20201128 7
 
1.2%
20201126 38
6.3%
20201123 4
 
0.7%
20201121 46
7.7%
20201120 28
4.7%
20201119 4
 
0.7%
20201118 17
 
2.8%
20201116 36
6.0%
20201115 26
4.3%
20201114 27
4.5%

GID_ID
Text

Distinct179
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-10T19:05:24.349343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.12
Min length10

Characters and Unicode

Total characters6072
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)15.3%

Sample

1st row83,558,707
2nd row83,912,334
3rd row83,912,334
4th row83,559,754
5th row70,904,337
ValueCountFrequency (%)
73,704,352 37
 
6.2%
83,447,113 29
 
4.8%
83,559,754 25
 
4.2%
80,679,980 15
 
2.5%
79,111,932 15
 
2.5%
80,375,959 13
 
2.2%
83,558,707 12
 
2.0%
74,744,056 12
 
2.0%
79,591,919 12
 
2.0%
83,719,967 11
 
1.8%
Other values (169) 419
69.8%
2023-12-10T19:05:25.004191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 1200
19.8%
7 657
10.8%
8 583
9.6%
3 559
9.2%
5 523
8.6%
9 516
8.5%
4 491
8.1%
1 479
 
7.9%
0 422
 
6.9%
2 322
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4872
80.2%
Other Punctuation 1200
 
19.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 657
13.5%
8 583
12.0%
3 559
11.5%
5 523
10.7%
9 516
10.6%
4 491
10.1%
1 479
9.8%
0 422
8.7%
2 322
6.6%
6 320
6.6%
Other Punctuation
ValueCountFrequency (%)
, 1200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6072
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 1200
19.8%
7 657
10.8%
8 583
9.6%
3 559
9.2%
5 523
8.6%
9 516
8.5%
4 491
8.1%
1 479
 
7.9%
0 422
 
6.9%
2 322
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 1200
19.8%
7 657
10.8%
8 583
9.6%
3 559
9.2%
5 523
8.6%
9 516
8.5%
4 491
8.1%
1 479
 
7.9%
0 422
 
6.9%
2 322
 
5.3%

GID_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct179
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.15593
Minimum126.37806
Maximum129.35867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:05:25.282583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.37806
5-th percentile126.67984
Q1126.85957
median127.05022
Q3127.10501
95-th percentile128.87955
Maximum129.35867
Range2.9806137
Interquartile range (IQR)0.24543567

Descriptive statistics

Standard deviation0.59744696
Coefficient of variation (CV)0.0046985379
Kurtosis4.432617
Mean127.15593
Median Absolute Deviation (MAD)0.1191483
Skewness2.3129538
Sum76293.558
Variance0.35694287
MonotonicityNot monotonic
2023-12-10T19:05:25.680282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.711441 37
 
6.2%
127.057724 29
 
4.8%
127.059967 25
 
4.2%
126.9579468 15
 
2.5%
126.9029922 15
 
2.5%
126.9473267 13
 
2.2%
127.062706 12
 
2.0%
126.7492676 12
 
2.0%
126.9199066 12
 
2.0%
127.0650406 11
 
1.8%
Other values (169) 419
69.8%
ValueCountFrequency (%)
126.3780594 1
 
0.2%
126.4612122 1
 
0.2%
126.5546036 4
0.7%
126.5746994 1
 
0.2%
126.5834808 2
 
0.3%
126.6134415 9
1.5%
126.6209183 3
 
0.5%
126.6439285 1
 
0.2%
126.6545258 2
 
0.3%
126.6669159 1
 
0.2%
ValueCountFrequency (%)
129.3586731 1
 
0.2%
129.304306 1
 
0.2%
129.1733551 1
 
0.2%
129.150528 1
 
0.2%
129.127655 2
0.3%
129.1129456 3
0.5%
129.1056824 2
0.3%
129.0840302 2
0.3%
129.0785522 1
 
0.2%
129.0593567 4
0.7%

GID_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct179
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.2416
Minimum33.508072
Maximum37.870987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:05:26.239423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.508072
5-th percentile35.148392
Q137.284519
median37.522022
Q337.558235
95-th percentile37.755322
Maximum37.870987
Range4.362915
Interquartile range (IQR)0.27371597

Descriptive statistics

Standard deviation0.75809976
Coefficient of variation (CV)0.020356262
Kurtosis5.8087053
Mean37.2416
Median Absolute Deviation (MAD)0.081846235
Skewness-2.5487153
Sum22344.96
Variance0.57471525
MonotonicityNot monotonic
2023-12-10T19:05:26.723889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.52800369 37
 
6.2%
37.2458725 29
 
4.8%
37.53792572 25
 
4.2%
37.55823517 15
 
2.5%
37.49216843 15
 
2.5%
37.54015732 13
 
2.2%
37.06605911 12
 
2.0%
37.42414093 12
 
2.0%
37.49991608 12
 
2.0%
37.63844681 11
 
1.8%
Other values (169) 419
69.8%
ValueCountFrequency (%)
33.5080719 2
0.3%
33.52378845 1
0.2%
34.7346344 2
0.3%
34.74183655 1
0.2%
34.74321747 1
0.2%
34.75477219 1
0.2%
34.76402283 1
0.2%
34.78437042 1
0.2%
34.80362701 1
0.2%
34.83761597 1
0.2%
ValueCountFrequency (%)
37.87098694 1
 
0.2%
37.85033417 4
 
0.7%
37.83763886 9
1.5%
37.83538818 2
 
0.3%
37.80926895 6
1.0%
37.79885483 1
 
0.2%
37.79774475 4
 
0.7%
37.77287292 1
 
0.2%
37.7553215 10
1.7%
37.75510788 3
 
0.5%

CTPRVN_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.895
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:05:26.962424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median28
Q341
95-th percentile44.05
Maximum50
Range39
Interquartile range (IQR)30

Descriptive statistics

Standard deviation13.875332
Coefficient of variation (CV)0.51590751
Kurtosis-1.7027045
Mean26.895
Median Absolute Deviation (MAD)13.5
Skewness-0.052742422
Sum16137
Variance192.52485
MonotonicityNot monotonic
2023-12-10T19:05:27.195239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11 236
39.3%
41 178
29.7%
28 83
 
13.8%
42 29
 
4.8%
46 18
 
3.0%
26 17
 
2.8%
27 11
 
1.8%
48 6
 
1.0%
30 4
 
0.7%
44 3
 
0.5%
Other values (7) 15
 
2.5%
ValueCountFrequency (%)
11 236
39.3%
26 17
 
2.8%
27 11
 
1.8%
28 83
 
13.8%
29 3
 
0.5%
30 4
 
0.7%
31 2
 
0.3%
36 2
 
0.3%
41 178
29.7%
42 29
 
4.8%
ValueCountFrequency (%)
50 3
 
0.5%
48 6
 
1.0%
47 1
 
0.2%
46 18
 
3.0%
45 2
 
0.3%
44 3
 
0.5%
43 2
 
0.3%
42 29
 
4.8%
41 178
29.7%
36 2
 
0.3%

CTPRVN_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
서울특별시
236 
경기도
178 
인천광역시
83 
강원도
29 
전라남도
 
18
Other values (12)
56 

Length

Max length7
Median length5
Mean length4.2733333
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row경기도
2nd row경기도
3rd row경기도
4th row서울특별시
5th row인천광역시

Common Values

ValueCountFrequency (%)
서울특별시 236
39.3%
경기도 178
29.7%
인천광역시 83
 
13.8%
강원도 29
 
4.8%
전라남도 18
 
3.0%
부산광역시 17
 
2.8%
대구광역시 11
 
1.8%
경상남도 6
 
1.0%
대전광역시 4
 
0.7%
충청남도 3
 
0.5%
Other values (7) 15
 
2.5%

Length

2023-12-10T19:05:27.477314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 236
39.3%
경기도 178
29.7%
인천광역시 83
 
13.8%
강원도 29
 
4.8%
전라남도 18
 
3.0%
부산광역시 17
 
2.8%
대구광역시 11
 
1.8%
경상남도 6
 
1.0%
대전광역시 4
 
0.7%
충청남도 3
 
0.5%
Other values (7) 15
 
2.5%

SIGNGU_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2723.095
Minimum1114
Maximum5011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:05:27.730527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1114
5-th percentile1123
Q11156
median2823.5
Q34122
95-th percentile4481.55
Maximum5011
Range3897
Interquartile range (IQR)2966

Descriptive statistics

Standard deviation1379.5972
Coefficient of variation (CV)0.50662837
Kurtosis-1.7021426
Mean2723.095
Median Absolute Deviation (MAD)1363.5
Skewness-0.047355593
Sum1633857
Variance1903288.4
MonotonicityNot monotonic
2023-12-10T19:05:28.002436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1156 44
 
7.3%
4111 44
 
7.3%
2824 39
 
6.5%
1120 26
 
4.3%
1144 24
 
4.0%
4122 19
 
3.2%
1171 19
 
3.2%
4115 18
 
3.0%
4163 17
 
2.8%
1141 16
 
2.7%
Other values (79) 334
55.7%
ValueCountFrequency (%)
1114 3
 
0.5%
1120 26
4.3%
1123 14
2.3%
1126 9
 
1.5%
1129 9
 
1.5%
1132 9
 
1.5%
1135 13
2.2%
1138 4
 
0.7%
1141 16
2.7%
1144 24
4.0%
ValueCountFrequency (%)
5011 3
0.5%
4833 1
 
0.2%
4831 3
0.5%
4825 1
 
0.2%
4812 1
 
0.2%
4719 1
 
0.2%
4683 1
 
0.2%
4679 1
 
0.2%
4617 7
1.2%
4615 1
 
0.2%
Distinct80
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-10T19:05:28.462020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.14
Min length2

Characters and Unicode

Total characters1884
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)3.7%

Sample

1st row평택시
2nd row양주시
3rd row양주시
4th row성동구
5th row중구
ValueCountFrequency (%)
수원시 44
 
7.3%
영등포구 44
 
7.3%
계양구 39
 
6.5%
성동구 26
 
4.3%
마포구 24
 
4.0%
평택시 19
 
3.2%
송파구 19
 
3.2%
의정부시 18
 
3.0%
중구 18
 
3.0%
양주시 17
 
2.8%
Other values (70) 332
55.3%
2023-12-10T19:05:29.217224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
359
19.1%
237
 
12.6%
87
 
4.6%
74
 
3.9%
71
 
3.8%
61
 
3.2%
59
 
3.1%
58
 
3.1%
48
 
2.5%
47
 
2.5%
Other values (74) 783
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1884
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
359
19.1%
237
 
12.6%
87
 
4.6%
74
 
3.9%
71
 
3.8%
61
 
3.2%
59
 
3.1%
58
 
3.1%
48
 
2.5%
47
 
2.5%
Other values (74) 783
41.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1884
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
359
19.1%
237
 
12.6%
87
 
4.6%
74
 
3.9%
71
 
3.8%
61
 
3.2%
59
 
3.1%
58
 
3.1%
48
 
2.5%
47
 
2.5%
Other values (74) 783
41.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1884
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
359
19.1%
237
 
12.6%
87
 
4.6%
74
 
3.9%
71
 
3.8%
61
 
3.2%
59
 
3.1%
58
 
3.1%
48
 
2.5%
47
 
2.5%
Other values (74) 783
41.6%

ADSTRD_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct167
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27232676
Minimum11140550
Maximum50110610
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:05:29.453755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140550
5-th percentile11230660
Q111560540
median28241586
Q341220520
95-th percentile44815920
Maximum50110610
Range38970060
Interquartile range (IQR)29659980

Descriptive statistics

Standard deviation13796696
Coefficient of variation (CV)0.50662284
Kurtosis-1.7021907
Mean27232676
Median Absolute Deviation (MAD)13628884
Skewness-0.047435152
Sum1.6339606 × 1010
Variance1.9034882 × 1014
MonotonicityNot monotonic
2023-12-10T19:05:29.796670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28245601 37
 
6.2%
41117580 29
 
4.8%
11200670 25
 
4.2%
11560540 17
 
2.8%
11410555 15
 
2.5%
11560700 15
 
2.5%
41220520 13
 
2.2%
11440585 13
 
2.2%
28200655 12
 
2.0%
11560680 12
 
2.0%
Other values (157) 412
68.7%
ValueCountFrequency (%)
11140550 1
 
0.2%
11140680 2
 
0.3%
11200670 25
4.2%
11200790 1
 
0.2%
11230660 6
 
1.0%
11230705 3
 
0.5%
11230720 5
 
0.8%
11260540 7
 
1.2%
11260655 1
 
0.2%
11260680 1
 
0.2%
ValueCountFrequency (%)
50110610 2
0.3%
50110600 1
0.2%
48330560 1
0.2%
48310570 1
0.2%
48310560 1
0.2%
48310540 1
0.2%
48250620 1
0.2%
48125630 1
0.2%
47190621 1
0.2%
46830253 1
0.2%
Distinct166
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-10T19:05:30.309309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.7566667
Min length2

Characters and Unicode

Total characters2254
Distinct characters144
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

Unique80 ?
Unique (%)13.3%

Sample

1st row서정동
2nd row회천3동
3rd row회천3동
4th row성수2가1동
5th row연안동
ValueCountFrequency (%)
효성1동 37
 
6.2%
영통2동 29
 
4.8%
성수2가1동 25
 
4.2%
여의동 17
 
2.8%
북아현동 15
 
2.5%
대림1동 15
 
2.5%
서정동 13
 
2.2%
도화동 13
 
2.2%
서창2동 12
 
2.0%
신길6동 12
 
2.0%
Other values (156) 412
68.7%
2023-12-10T19:05:31.441042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
579
25.7%
1 167
 
7.4%
2 124
 
5.5%
86
 
3.8%
56
 
2.5%
3 56
 
2.5%
42
 
1.9%
41
 
1.8%
37
 
1.6%
36
 
1.6%
Other values (134) 1030
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1844
81.8%
Decimal Number 396
 
17.6%
Other Punctuation 14
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
579
31.4%
86
 
4.7%
56
 
3.0%
42
 
2.3%
41
 
2.2%
37
 
2.0%
36
 
2.0%
29
 
1.6%
27
 
1.5%
27
 
1.5%
Other values (126) 884
47.9%
Decimal Number
ValueCountFrequency (%)
1 167
42.2%
2 124
31.3%
3 56
 
14.1%
4 27
 
6.8%
6 13
 
3.3%
7 8
 
2.0%
5 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1844
81.8%
Common 410
 
18.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
579
31.4%
86
 
4.7%
56
 
3.0%
42
 
2.3%
41
 
2.2%
37
 
2.0%
36
 
2.0%
29
 
1.6%
27
 
1.5%
27
 
1.5%
Other values (126) 884
47.9%
Common
ValueCountFrequency (%)
1 167
40.7%
2 124
30.2%
3 56
 
13.7%
4 27
 
6.6%
. 14
 
3.4%
6 13
 
3.2%
7 8
 
2.0%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1844
81.8%
ASCII 410
 
18.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
579
31.4%
86
 
4.7%
56
 
3.0%
42
 
2.3%
41
 
2.2%
37
 
2.0%
36
 
2.0%
29
 
1.6%
27
 
1.5%
27
 
1.5%
Other values (126) 884
47.9%
ASCII
ValueCountFrequency (%)
1 167
40.7%
2 124
30.2%
3 56
 
13.7%
4 27
 
6.6%
. 14
 
3.4%
6 13
 
3.2%
7 8
 
2.0%
5 1
 
0.2%

WKDAY_NM
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
207 
95 
80 
77 
59 
Other values (2)
82 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
207
34.5%
95
15.8%
80
 
13.3%
77
 
12.8%
59
 
9.8%
56
 
9.3%
26
 
4.3%

Length

2023-12-10T19:05:31.716551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:31.985928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
207
34.5%
95
15.8%
80
 
13.3%
77
 
12.8%
59
 
9.8%
56
 
9.3%
26
 
4.3%

MVMN_TIME
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.71
Minimum0
Maximum23
Zeros17
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:05:32.212163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median14
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.8537461
Coefficient of variation (CV)0.42696908
Kurtosis-0.46632652
Mean13.71
Median Absolute Deviation (MAD)4
Skewness-0.43091277
Sum8226
Variance34.266344
MonotonicityNot monotonic
2023-12-10T19:05:32.427541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 48
 
8.0%
16 45
 
7.5%
21 37
 
6.2%
13 36
 
6.0%
11 33
 
5.5%
17 33
 
5.5%
19 32
 
5.3%
22 31
 
5.2%
14 31
 
5.2%
12 30
 
5.0%
Other values (14) 244
40.7%
ValueCountFrequency (%)
0 17
2.8%
1 7
 
1.2%
2 8
 
1.3%
3 8
 
1.3%
4 6
 
1.0%
5 9
 
1.5%
6 15
2.5%
7 20
3.3%
8 27
4.5%
9 27
4.5%
ValueCountFrequency (%)
23 22
3.7%
22 31
5.2%
21 37
6.2%
20 21
3.5%
19 32
5.3%
18 29
4.8%
17 33
5.5%
16 45
7.5%
15 48
8.0%
14 31
5.2%

SEXDSTN_FLAG_CD
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
M
319 
F
281 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 319
53.2%
F 281
46.8%

Length

2023-12-10T19:05:32.637599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:32.817637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 319
53.2%
f 281
46.8%

AGRDE_CO
Real number (ℝ)

Distinct8
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7983333
Minimum0
Maximum7
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:05:32.968829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.840283
Coefficient of variation (CV)0.48449751
Kurtosis-0.99019917
Mean3.7983333
Median Absolute Deviation (MAD)1
Skewness-0.056111522
Sum2279
Variance3.3866416
MonotonicityNot monotonic
2023-12-10T19:05:33.156919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 123
20.5%
5 115
19.2%
1 97
16.2%
3 83
13.8%
6 69
11.5%
2 65
10.8%
7 46
 
7.7%
0 2
 
0.3%
ValueCountFrequency (%)
0 2
 
0.3%
1 97
16.2%
2 65
10.8%
3 83
13.8%
4 123
20.5%
5 115
19.2%
6 69
11.5%
7 46
 
7.7%
ValueCountFrequency (%)
7 46
 
7.7%
6 69
11.5%
5 115
19.2%
4 123
20.5%
3 83
13.8%
2 65
10.8%
1 97
16.2%
0 2
 
0.3%

PDSTRN_POPLTN_CO
Real number (ℝ)

Distinct49
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.168333
Minimum3
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:05:33.405737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q16
median10
Q317
95-th percentile34
Maximum69
Range66
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.202949
Coefficient of variation (CV)0.77480943
Kurtosis4.2341091
Mean13.168333
Median Absolute Deviation (MAD)5
Skewness1.8336362
Sum7901
Variance104.10016
MonotonicityNot monotonic
2023-12-10T19:05:33.663497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
4 61
 
10.2%
3 41
 
6.8%
7 40
 
6.7%
6 39
 
6.5%
8 38
 
6.3%
10 33
 
5.5%
5 32
 
5.3%
11 29
 
4.8%
13 26
 
4.3%
12 25
 
4.2%
Other values (39) 236
39.3%
ValueCountFrequency (%)
3 41
6.8%
4 61
10.2%
5 32
5.3%
6 39
6.5%
7 40
6.7%
8 38
6.3%
9 21
 
3.5%
10 33
5.5%
11 29
4.8%
12 25
4.2%
ValueCountFrequency (%)
69 1
 
0.2%
60 1
 
0.2%
59 1
 
0.2%
52 1
 
0.2%
51 2
0.3%
49 2
0.3%
47 3
0.5%
46 1
 
0.2%
45 1
 
0.2%
44 2
0.3%

PDSTRN_ELSE_POPLTN_CO
Real number (ℝ)

Distinct30
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.355
Minimum3
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-10T19:05:33.983279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median5
Q38
95-th percentile20
Maximum57
Range54
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.7845839
Coefficient of variation (CV)0.78648319
Kurtosis12.675396
Mean7.355
Median Absolute Deviation (MAD)2
Skewness2.8820959
Sum4413
Variance33.461411
MonotonicityNot monotonic
2023-12-10T19:05:34.212959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4 124
20.7%
3 100
16.7%
5 87
14.5%
6 68
11.3%
7 41
 
6.8%
9 32
 
5.3%
8 31
 
5.2%
10 19
 
3.2%
14 11
 
1.8%
13 11
 
1.8%
Other values (20) 76
12.7%
ValueCountFrequency (%)
3 100
16.7%
4 124
20.7%
5 87
14.5%
6 68
11.3%
7 41
 
6.8%
8 31
 
5.2%
9 32
 
5.3%
10 19
 
3.2%
11 10
 
1.7%
12 9
 
1.5%
ValueCountFrequency (%)
57 1
 
0.2%
34 2
 
0.3%
33 1
 
0.2%
32 1
 
0.2%
29 1
 
0.2%
28 3
0.5%
26 3
0.5%
25 2
 
0.3%
24 3
0.5%
23 7
1.2%

Interactions

2023-12-10T19:05:21.304252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:02.461657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:04.197756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:06.040741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:08.379861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:10.457125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:12.413463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:14.615829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:16.568189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:19.170346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:21.454707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:02.635085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:04.369227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:06.220204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:08.628585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:10.629626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:12.592884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:14.812861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:16.747813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:19.444522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:21.680460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:02.823212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:04.545980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:06.726708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:08.881909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:10.835557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:12.780818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:15.026285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:16.958203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:19.651400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:21.852382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:02.988006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:04.705407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:06.866337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:09.097140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:10.982893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:12.951696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:15.189106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:17.117858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:19.825126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.021275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:03.149097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:04.874589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:07.056025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:09.303618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:11.170302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:13.150362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:15.349551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:17.311383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.023989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.178672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:03.350736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:05.084386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:07.229771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:09.489150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:11.341614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:13.406631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:15.576619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:17.497710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.236146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.335696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:03.558055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:05.290250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:07.416845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:09.755002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:11.560729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:13.618999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:15.852595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:17.751520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.467856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.480495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:03.709285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:05.503492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:07.576530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:09.929687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:11.776181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:13.783974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:16.064556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:18.045841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.755386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.643260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:03.878013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:05.694055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:07.816267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:10.112396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:12.051937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:14.084898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:16.221819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:18.672644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.952617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.798958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:04.052915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:05.879947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:08.105585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:10.281048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:12.254348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:14.359220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:16.406776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:18.902280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:21.141295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:05:34.447988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BASE_DEGID_LOGID_LACTPRVN_CDCTPRVN_NMSIGNGU_CDSIGNGU_NMADSTRD_CDWKDAY_NMMVMN_TIMESEXDSTN_FLAG_CDAGRDE_COPDSTRN_POPLTN_COPDSTRN_ELSE_POPLTN_CO
BASE_DE1.0000.6620.3100.3830.5680.3840.9280.3830.9010.2050.0990.0000.0620.000
GID_LO0.6621.0000.7810.7180.9260.7370.9990.7370.4110.1000.0000.1890.2160.000
GID_LA0.3100.7811.0000.8480.9730.8320.9930.8320.2000.1200.0000.2120.0220.000
CTPRVN_CD0.3830.7180.8481.0001.0001.0000.9951.0000.2870.0000.1190.2070.2040.000
CTPRVN_NM0.5680.9260.9731.0001.0000.9930.9960.9930.3990.0730.1410.2270.0000.000
SIGNGU_CD0.3840.7370.8321.0000.9931.0000.9961.0000.2810.0000.0000.2170.2030.000
SIGNGU_NM0.9280.9990.9930.9950.9960.9961.0000.9950.8900.0330.2650.2460.4080.000
ADSTRD_CD0.3830.7370.8321.0000.9931.0000.9951.0000.2810.0000.0000.2100.1980.000
WKDAY_NM0.9010.4110.2000.2870.3990.2810.8900.2811.0000.0000.0220.0000.0580.220
MVMN_TIME0.2050.1000.1200.0000.0730.0000.0330.0000.0001.0000.1130.1530.1770.114
SEXDSTN_FLAG_CD0.0990.0000.0000.1190.1410.0000.2650.0000.0220.1131.0000.0990.1230.000
AGRDE_CO0.0000.1890.2120.2070.2270.2170.2460.2100.0000.1530.0991.0000.0280.000
PDSTRN_POPLTN_CO0.0620.2160.0220.2040.0000.2030.4080.1980.0580.1770.1230.0281.0000.320
PDSTRN_ELSE_POPLTN_CO0.0000.0000.0000.0000.0000.0000.0000.0000.2200.1140.0000.0000.3201.000
2023-12-10T19:05:34.708226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WKDAY_NMSEXDSTN_FLAG_CDCTPRVN_NM
WKDAY_NM1.0000.0240.190
SEXDSTN_FLAG_CD0.0241.0000.124
CTPRVN_NM0.1900.1241.000
2023-12-10T19:05:34.898911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BASE_DEGID_LOGID_LACTPRVN_CDSIGNGU_CDADSTRD_CDMVMN_TIMEAGRDE_COPDSTRN_POPLTN_COPDSTRN_ELSE_POPLTN_COCTPRVN_NMWKDAY_NMSEXDSTN_FLAG_CD
BASE_DE1.0000.0010.088-0.261-0.194-0.1910.025-0.0090.002-0.0730.2620.7100.074
GID_LO0.0011.000-0.0790.1550.1520.152-0.078-0.007-0.223-0.1070.7070.2210.000
GID_LA0.088-0.0791.000-0.303-0.300-0.301-0.0650.1070.1920.0640.8650.1060.000
CTPRVN_CD-0.2610.155-0.3031.0000.9550.9540.004-0.063-0.329-0.0860.9920.1570.089
SIGNGU_CD-0.1940.152-0.3000.9551.0001.0000.039-0.050-0.332-0.0930.9610.1540.000
ADSTRD_CD-0.1910.152-0.3010.9541.0001.0000.038-0.050-0.328-0.0910.9610.1540.000
MVMN_TIME0.025-0.078-0.0650.0040.0390.0381.000-0.0980.0770.1330.0270.0000.086
AGRDE_CO-0.009-0.0070.107-0.063-0.050-0.050-0.0981.000-0.043-0.0020.0960.0000.074
PDSTRN_POPLTN_CO0.002-0.2230.192-0.329-0.332-0.3280.077-0.0431.0000.2600.0000.0290.094
PDSTRN_ELSE_POPLTN_CO-0.073-0.1070.064-0.086-0.093-0.0910.133-0.0020.2601.0000.0000.0780.000
CTPRVN_NM0.2620.7070.8650.9920.9610.9610.0270.0960.0000.0001.0000.1900.124
WKDAY_NM0.7100.2210.1060.1570.1540.1540.0000.0000.0290.0780.1901.0000.024
SEXDSTN_FLAG_CD0.0740.0000.0000.0890.0000.0000.0860.0740.0940.0000.1240.0241.000

Missing values

2023-12-10T19:05:23.040591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:05:23.450951image/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

BASE_DEGID_IDGID_LOGID_LACTPRVN_CDCTPRVN_NMSIGNGU_CDSIGNGU_NMADSTRD_CDADSTRD_NMWKDAY_NMMVMN_TIMESEXDSTN_FLAG_CDAGRDE_COPDSTRN_POPLTN_COPDSTRN_ELSE_POPLTN_CO
02020110583,558,707127.06270637.06605941경기도4122평택시41220520서정동22M6104
12020110583,912,334127.07085437.80926941경기도4163양주시41630550회천3동18M4410
22020110583,912,334127.07085437.80926941경기도4163양주시41630550회천3동16M534
32020110583,559,754127.05996737.53792611서울특별시1120성동구11200670성수2가1동16M44026
42020110570,904,337126.61344137.44168128인천광역시2811중구28110520연안동17M694
52020110579,911,943126.93106837.51979811서울특별시1156영등포구11560540여의동17M3295
62020110573,704,352126.71144137.52800428인천광역시2824계양구28245601효성1동13M6196
72020110584,055,102127.07907937.25811841경기도4111수원시41117570영통1동18F2104
82020110573,704,352126.71144137.52800428인천광역시2824계양구28245601효성1동19M3116
92020110583,863,801127.07054937.56771111서울특별시1123동대문구11230660장안2동20F6235
BASE_DEGID_IDGID_LOGID_LACTPRVN_CDCTPRVN_NMSIGNGU_CDSIGNGU_NMADSTRD_CDADSTRD_NMWKDAY_NMMVMN_TIMESEXDSTN_FLAG_CDAGRDE_COPDSTRN_POPLTN_COPDSTRN_ELSE_POPLTN_CO
5902020110675,208,582126.76319137.67435141경기도4128고양시41287560주엽2동15F6215
5912020110675,208,582126.76319137.67435141경기도4128고양시41287560주엽2동16F3156
5922020110676,583,771126.81494937.34792341경기도4127안산시41273600선부3동22F673
5932020110184,791,556127.10383637.48355111서울특별시1168강남구11680700세곡동22M4515
5942020110171,144,511126.62091837.52691328인천광역시2826서구28260539청라3동22F1204
5952020110176,215,741126.80218537.32396341경기도4127안산시41273555백운동10F7317
59620201101135,845,082128.91442937.79885542강원도4215강릉시42150580초당동17F285
59720201101143,363,831129.17335537.44352342강원도4223삼척시42230530교동12F6206
5982020110184,136,186127.07915537.74890941경기도4115의정부시41150568신곡2동15F387
5992020110194,244,397127.43900336.32672930대전광역시3011동구30110585대동15F5106

Duplicate rows

Most frequently occurring

BASE_DEGID_IDGID_LOGID_LACTPRVN_CDCTPRVN_NMSIGNGU_CDSIGNGU_NMADSTRD_CDADSTRD_NMWKDAY_NMMVMN_TIMESEXDSTN_FLAG_CDAGRDE_COPDSTRN_POPLTN_COPDSTRN_ELSE_POPLTN_CO# duplicates
02020110568,751,746126.58348133.50807250제주특별자치도5011제주시50110610삼양동0M1552
12020110570,904,337126.61344137.44168128인천광역시2811중구28110520연안동9M38132
22020110570,904,337126.61344137.44168128인천광역시2811중구28110520연안동14M5892
32020110573,704,352126.71144137.52800428인천광역시2824계양구28245601효성1동7M41232
42020110573,704,352126.71144137.52800428인천광역시2824계양구28245601효성1동8F31032
52020110573,704,352126.71144137.52800428인천광역시2824계양구28245601효성1동9F523112
62020110573,704,352126.71144137.52800428인천광역시2824계양구28245601효성1동15F511132
72020110573,704,352126.71144137.52800428인천광역시2824계양구28245601효성1동16M21042
82020110573,704,352126.71144137.52800428인천광역시2824계양구28245601효성1동22M21142
92020110576,056,105126.79504437.48346341경기도4119부천시41190603심곡동6F7732