Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory88.3 B

Variable types

Categorical2
Text2
Numeric6

Alerts

base_ym has constant value ""Constant
adstrd_cd is highly overall correlated with area_nmHigh correlation
gid_la is highly overall correlated with area_nmHigh correlation
gid_lo is highly overall correlated with area_nmHigh correlation
adult_entrn_nmpr_co is highly overall correlated with child_entrn_nmpr_co and 1 other fieldsHigh correlation
child_entrn_nmpr_co is highly overall correlated with adult_entrn_nmpr_co and 1 other fieldsHigh correlation
tot_entrn_nmpr_co is highly overall correlated with adult_entrn_nmpr_co and 1 other fieldsHigh correlation
area_nm is highly overall correlated with adstrd_cd and 2 other fieldsHigh correlation
adult_entrn_nmpr_co has 17 (17.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:50:26.207311
Analysis finished2023-12-10 09:50:34.965461
Duration8.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

base_ym
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
202003
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202003
2nd row202003
3rd row202003
4th row202003
5th row202003

Common Values

ValueCountFrequency (%)
202003 100
100.0%

Length

2023-12-10T18:50:35.077296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:35.300538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202003 100
100.0%

str_id
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:35.784385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.48
Min length4

Characters and Unicode

Total characters748
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st rowlil02
2nd rowinori01
3rd rowipangpnag01
4th rowdori01
5th rowtayo01
ValueCountFrequency (%)
kidsmong01 2
 
2.0%
bonbon09 1
 
1.0%
momo01 1
 
1.0%
lil16 1
 
1.0%
lil15 1
 
1.0%
kingkong05 1
 
1.0%
bonbon05 1
 
1.0%
bonbon03 1
 
1.0%
lil12 1
 
1.0%
bonbon04 1
 
1.0%
Other values (89) 89
89.0%
2023-12-10T18:50:36.557868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 78
 
10.4%
1 68
 
9.1%
i 67
 
9.0%
n 64
 
8.6%
o 59
 
7.9%
k 50
 
6.7%
l 46
 
6.1%
b 36
 
4.8%
g 35
 
4.7%
e 26
 
3.5%
Other values (26) 219
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 542
72.5%
Decimal Number 204
 
27.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 67
12.4%
n 64
11.8%
o 59
10.9%
k 50
9.2%
l 46
 
8.5%
b 36
 
6.6%
g 35
 
6.5%
e 26
 
4.8%
t 22
 
4.1%
s 19
 
3.5%
Other values (14) 118
21.8%
Decimal Number
ValueCountFrequency (%)
0 78
38.2%
1 68
33.3%
2 18
 
8.8%
3 14
 
6.9%
5 7
 
3.4%
4 5
 
2.5%
9 4
 
2.0%
8 4
 
2.0%
6 4
 
2.0%
7 2
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
50.0%
P 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 544
72.7%
Common 204
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 67
12.3%
n 64
11.8%
o 59
10.8%
k 50
9.2%
l 46
 
8.5%
b 36
 
6.6%
g 35
 
6.4%
e 26
 
4.8%
t 22
 
4.0%
s 19
 
3.5%
Other values (16) 120
22.1%
Common
ValueCountFrequency (%)
0 78
38.2%
1 68
33.3%
2 18
 
8.8%
3 14
 
6.9%
5 7
 
3.4%
4 5
 
2.5%
9 4
 
2.0%
8 4
 
2.0%
6 4
 
2.0%
7 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 78
 
10.4%
1 68
 
9.1%
i 67
 
9.0%
n 64
 
8.6%
o 59
 
7.9%
k 50
 
6.7%
l 46
 
6.1%
b 36
 
4.8%
g 35
 
4.7%
e 26
 
3.5%
Other values (26) 219
29.3%

area_nm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
41 
서울특별시
18 
전라남도
7 
충청남도
6 
경상남도
 
4
Other values (11)
24 

Length

Max length7
Median length5
Mean length3.97
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 41
41.0%
서울특별시 18
18.0%
전라남도 7
 
7.0%
충청남도 6
 
6.0%
경상남도 4
 
4.0%
강원도 3
 
3.0%
제주특별자치도 3
 
3.0%
인천광역시 3
 
3.0%
경상북도 3
 
3.0%
충청북도 2
 
2.0%
Other values (6) 10
 
10.0%

Length

2023-12-10T18:50:36.914772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 41
41.0%
서울특별시 18
18.0%
전라남도 7
 
7.0%
충청남도 6
 
6.0%
경상남도 4
 
4.0%
강원도 3
 
3.0%
제주특별자치도 3
 
3.0%
인천광역시 3
 
3.0%
경상북도 3
 
3.0%
충청북도 2
 
2.0%
Other values (6) 10
 
10.0%

adstrd_nm
Text

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:37.455386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.55
Min length3

Characters and Unicode

Total characters355
Distinct characters118
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

Unique84 ?
Unique (%)84.0%

Sample

1st row사당제4동
2nd row신곡2동
3rd row금곡동
4th row전곡읍
5th row율량.사천동
ValueCountFrequency (%)
불당동 3
 
3.0%
평내동 3
 
3.0%
갈매동 2
 
2.0%
영덕1동 2
 
2.0%
원신동 2
 
2.0%
송산3동 2
 
2.0%
오포읍 2
 
2.0%
동백3동 1
 
1.0%
상계1동 1
 
1.0%
역삼2동 1
 
1.0%
Other values (81) 81
81.0%
2023-12-10T18:50:38.404257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 94
26.5%
1 16
 
4.5%
2 12
 
3.4%
곡 11
 
3.1%
산 8
 
2.3%
읍 7
 
2.0%
신 7
 
2.0%
제 7
 
2.0%
3 6
 
1.7%
당 6
 
1.7%
Other values (108) 181
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 316
89.0%
Decimal Number 38
 
10.7%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 94
29.7%
곡 11
 
3.5%
산 8
 
2.5%
읍 7
 
2.2%
신 7
 
2.2%
제 7
 
2.2%
당 6
 
1.9%
정 6
 
1.9%
삼 5
 
1.6%
원 5
 
1.6%
Other values (102) 160
50.6%
Decimal Number
ValueCountFrequency (%)
1 16
42.1%
2 12
31.6%
3 6
 
15.8%
4 3
 
7.9%
5 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 316
89.0%
Common 39
 
11.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 94
29.7%
곡 11
 
3.5%
산 8
 
2.5%
읍 7
 
2.2%
신 7
 
2.2%
제 7
 
2.2%
당 6
 
1.9%
정 6
 
1.9%
삼 5
 
1.6%
원 5
 
1.6%
Other values (102) 160
50.6%
Common
ValueCountFrequency (%)
1 16
41.0%
2 12
30.8%
3 6
 
15.4%
4 3
 
7.7%
5 1
 
2.6%
. 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 316
89.0%
ASCII 39
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 94
29.7%
곡 11
 
3.5%
산 8
 
2.5%
읍 7
 
2.2%
신 7
 
2.2%
제 7
 
2.2%
당 6
 
1.9%
정 6
 
1.9%
삼 5
 
1.6%
원 5
 
1.6%
Other values (102) 160
50.6%
ASCII
ValueCountFrequency (%)
1 16
41.0%
2 12
30.8%
3 6
 
15.4%
4 3
 
7.7%
5 1
 
2.6%
. 1
 
2.6%

adstrd_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9677978 × 109
Minimum1.120055 × 109
Maximum2.1474836 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:38.673630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.120055 × 109
5-th percentile1.1319768 × 109
Q12.1474836 × 109
median2.1474836 × 109
Q32.1474836 × 109
95-th percentile2.1474836 × 109
Maximum2.1474836 × 109
Range1.0274286 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.8553363 × 108
Coefficient of variation (CV)0.19592136
Kurtosis0.88693992
Mean1.9677978 × 109
Median Absolute Deviation (MAD)0
Skewness-1.6930765
Sum1.9677978 × 1011
Variance1.4863618 × 1017
MonotonicityNot monotonic
2023-12-10T18:50:38.935468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2147483647 82
82.0%
1159065000 1
 
1.0%
1132051500 1
 
1.0%
1171056200 1
 
1.0%
1129068500 1
 
1.0%
1168065600 1
 
1.0%
1120055000 1
 
1.0%
1168070000 1
 
1.0%
1168065000 1
 
1.0%
1121576000 1
 
1.0%
Other values (9) 9
 
9.0%
ValueCountFrequency (%)
1120055000 1
1.0%
1121576000 1
1.0%
1126052000 1
1.0%
1129068500 1
1.0%
1130557500 1
1.0%
1132051500 1
1.0%
1135061900 1
1.0%
1135063000 1
1.0%
1159060500 1
1.0%
1159065000 1
1.0%
ValueCountFrequency (%)
2147483647 82
82.0%
1171056200 1
 
1.0%
1168074000 1
 
1.0%
1168070000 1
 
1.0%
1168065600 1
 
1.0%
1168065000 1
 
1.0%
1168056500 1
 
1.0%
1165065100 1
 
1.0%
1162061500 1
 
1.0%
1159065000 1
 
1.0%

gid_la
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.84373
Minimum33.264612
Maximum38.091344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:39.246909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.264612
5-th percentile34.80731
Q136.355621
median37.378777
Q337.59712
95-th percentile37.74474
Maximum38.091344
Range4.826732
Interquartile range (IQR)1.2414995

Descriptive statistics

Standard deviation1.1213533
Coefficient of variation (CV)0.030435389
Kurtosis1.3403333
Mean36.84373
Median Absolute Deviation (MAD)0.280932
Skewness-1.4690622
Sum3684.373
Variance1.2574331
MonotonicityNot monotonic
2023-12-10T18:50:39.542517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.645783 2
 
2.0%
37.48328 1
 
1.0%
37.772183 1
 
1.0%
37.292092 1
 
1.0%
37.474123 1
 
1.0%
37.392607 1
 
1.0%
37.728248 1
 
1.0%
37.644579 1
 
1.0%
37.497979 1
 
1.0%
37.538563 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
33.264612 1
1.0%
33.26682 1
1.0%
33.485149 1
1.0%
34.746909 1
1.0%
34.804184 1
1.0%
34.807474 1
1.0%
34.810042 1
1.0%
34.867268 1
1.0%
34.934491 1
1.0%
34.937073 1
1.0%
ValueCountFrequency (%)
38.091344 1
1.0%
38.025645 1
1.0%
37.772183 1
1.0%
37.751444 1
1.0%
37.746324 1
1.0%
37.744657 1
1.0%
37.740052 1
1.0%
37.729648 1
1.0%
37.728248 1
1.0%
37.705119 1
1.0%

gid_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.29379
Minimum126.42619
Maximum129.33226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:39.839405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.42619
5-th percentile126.57319
Q1126.9589
median127.09263
Q3127.2592
95-th percentile128.86372
Maximum129.33226
Range2.906077
Interquartile range (IQR)0.3002925

Descriptive statistics

Standard deviation0.6586188
Coefficient of variation (CV)0.0051740059
Kurtosis1.8150954
Mean127.29379
Median Absolute Deviation (MAD)0.1504445
Skewness1.5912182
Sum12729.379
Variance0.43377873
MonotonicityNot monotonic
2023-12-10T18:50:40.155272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.235793 2
 
2.0%
126.973578 1
 
1.0%
128.877142 1
 
1.0%
127.04906 1
 
1.0%
127.114452 1
 
1.0%
127.232887 1
 
1.0%
126.733382 1
 
1.0%
126.886411 1
 
1.0%
127.04446 1
 
1.0%
126.737749 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
126.426185 1
1.0%
126.426255 1
1.0%
126.462368 1
1.0%
126.536208 1
1.0%
126.557575 1
1.0%
126.574013 1
1.0%
126.696413 1
1.0%
126.706831 1
1.0%
126.724305 1
1.0%
126.726892 1
1.0%
ValueCountFrequency (%)
129.332262 1
1.0%
129.308698 1
1.0%
129.117997 1
1.0%
128.924729 1
1.0%
128.877142 1
1.0%
128.863014 1
1.0%
128.748539 1
1.0%
128.635417 1
1.0%
128.571126 1
1.0%
128.503098 1
1.0%

adult_entrn_nmpr_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.87
Minimum0
Maximum697
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:40.578389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.5
median72
Q3135.75
95-th percentile266
Maximum697
Range697
Interquartile range (IQR)127.25

Descriptive statistics

Standard deviation113.44466
Coefficient of variation (CV)1.2085294
Kurtosis9.5801643
Mean93.87
Median Absolute Deviation (MAD)64
Skewness2.5300781
Sum9387
Variance12869.69
MonotonicityNot monotonic
2023-12-10T18:50:40.883218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
17.0%
1 3
 
3.0%
74 2
 
2.0%
138 2
 
2.0%
2 2
 
2.0%
18 2
 
2.0%
82 2
 
2.0%
156 2
 
2.0%
90 2
 
2.0%
104 2
 
2.0%
Other values (63) 64
64.0%
ValueCountFrequency (%)
0 17
17.0%
1 3
 
3.0%
2 2
 
2.0%
3 1
 
1.0%
5 1
 
1.0%
7 1
 
1.0%
9 1
 
1.0%
11 1
 
1.0%
12 1
 
1.0%
13 1
 
1.0%
ValueCountFrequency (%)
697 1
1.0%
566 1
1.0%
339 1
1.0%
310 1
1.0%
304 1
1.0%
264 1
1.0%
253 1
1.0%
238 1
1.0%
225 1
1.0%
223 1
1.0%

child_entrn_nmpr_co
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.04
Minimum0
Maximum831
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:41.163627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.85
Q136.75
median115
Q3190.25
95-th percentile374.3
Maximum831
Range831
Interquartile range (IQR)153.5

Descriptive statistics

Standard deviation143.24817
Coefficient of variation (CV)0.98764597
Kurtosis6.047092
Mean145.04
Median Absolute Deviation (MAD)77.5
Skewness2.019399
Sum14504
Variance20520.039
MonotonicityNot monotonic
2023-12-10T18:50:41.436589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2
 
2.0%
113 2
 
2.0%
147 2
 
2.0%
10 2
 
2.0%
121 2
 
2.0%
354 2
 
2.0%
163 2
 
2.0%
1 2
 
2.0%
49 2
 
2.0%
153 2
 
2.0%
Other values (74) 80
80.0%
ValueCountFrequency (%)
0 1
1.0%
1 2
2.0%
2 2
2.0%
5 1
1.0%
6 2
2.0%
8 1
1.0%
10 2
2.0%
11 1
1.0%
12 1
1.0%
14 1
1.0%
ValueCountFrequency (%)
831 1
1.0%
687 1
1.0%
521 1
1.0%
403 1
1.0%
399 1
1.0%
373 1
1.0%
360 1
1.0%
354 2
2.0%
346 1
1.0%
308 1
1.0%

tot_entrn_nmpr_co
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.91
Minimum1
Maximum1528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:41.710393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.8
Q163.25
median193.5
Q3321
95-th percentile654.35
Maximum1528
Range1527
Interquartile range (IQR)257.75

Descriptive statistics

Standard deviation251.32281
Coefficient of variation (CV)1.051956
Kurtosis8.2396437
Mean238.91
Median Absolute Deviation (MAD)130
Skewness2.3524196
Sum23891
Variance63163.153
MonotonicityNot monotonic
2023-12-10T18:50:42.038845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
153 3
 
3.0%
2 3
 
3.0%
116 2
 
2.0%
313 2
 
2.0%
6 2
 
2.0%
1 2
 
2.0%
195 2
 
2.0%
205 2
 
2.0%
61 2
 
2.0%
42 2
 
2.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
1 2
2.0%
2 3
3.0%
6 2
2.0%
8 1
 
1.0%
10 1
 
1.0%
11 1
 
1.0%
12 1
 
1.0%
16 1
 
1.0%
17 1
 
1.0%
23 1
 
1.0%
ValueCountFrequency (%)
1528 1
1.0%
1253 1
1.0%
831 1
1.0%
707 1
1.0%
699 1
1.0%
652 1
1.0%
637 1
1.0%
592 1
1.0%
560 1
1.0%
526 1
1.0%

Interactions

2023-12-10T18:50:32.935234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:27.779847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.837166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:29.989169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:31.070702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:32.059866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:33.070812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:27.927843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.998364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:30.152096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:31.239846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:32.203218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:33.237111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.109357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:29.149636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:30.425232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:31.389909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:32.337908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:33.819055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.308409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:29.414015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:30.584954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:31.568894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:32.476366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:34.043188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.501259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:29.586590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:30.747987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:31.749418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:32.638288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:34.215514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:28.666937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:29.739545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:30.892372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:31.888734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:32.776438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:50:42.320030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
str_idarea_nmadstrd_nmadstrd_cdgid_lagid_loadult_entrn_nmpr_cochild_entrn_nmpr_cotot_entrn_nmpr_co
str_id1.0000.9550.9991.0000.0000.8641.0000.9880.988
area_nm0.9551.0001.0001.0000.9890.9170.0000.0000.000
adstrd_nm0.9991.0001.0001.0001.0001.0000.0000.0000.000
adstrd_cd1.0001.0001.0001.0000.3640.2370.0310.0000.000
gid_la0.0000.9891.0000.3641.0000.7600.0000.0000.000
gid_lo0.8640.9171.0000.2370.7601.0000.2210.1590.000
adult_entrn_nmpr_co1.0000.0000.0000.0310.0000.2211.0000.8960.929
child_entrn_nmpr_co0.9880.0000.0000.0000.0000.1590.8961.0000.992
tot_entrn_nmpr_co0.9880.0000.0000.0000.0000.0000.9290.9921.000
2023-12-10T18:50:42.551977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
adstrd_cdgid_lagid_loadult_entrn_nmpr_cochild_entrn_nmpr_cotot_entrn_nmpr_coarea_nm
adstrd_cd1.000-0.3320.202-0.0340.002-0.0190.926
gid_la-0.3321.000-0.2280.0260.0500.0350.796
gid_lo0.202-0.2281.000-0.127-0.085-0.1000.663
adult_entrn_nmpr_co-0.0340.026-0.1271.0000.7970.8870.000
child_entrn_nmpr_co0.0020.050-0.0850.7971.0000.9820.000
tot_entrn_nmpr_co-0.0190.035-0.1000.8870.9821.0000.000
area_nm0.9260.7960.6630.0000.0000.0001.000

Missing values

2023-12-10T18:50:34.592059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:50:34.854273image/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_ymstr_idarea_nmadstrd_nmadstrd_cdgid_lagid_loadult_entrn_nmpr_cochild_entrn_nmpr_cotot_entrn_nmpr_co
0202003lil02서울특별시사당제4동115906500037.48328126.9735786275137
1202003inori01경기도신곡2동214748364737.740052127.061644404989
2202003ipangpnag01경기도금곡동214748364737.273506126.95378192301493
3202003dori01경기도전곡읍214748364738.025645127.069487132538
4202003tayo01충청북도율량.사천동214748364736.667769127.483937111829
5202003lil03서울특별시흑석동115906050037.504725126.965568138221359
6202003ikiki01경상남도충무공동214748364735.175916128.14439822224
7202003yj38317hj충청남도불당동214748364736.822395127.10690251116
8202003kingkong09서울특별시양재1동116506510037.470702127.025053223153
9202003bonbon11강원도반곡관설동214748364737.329342127.9881090116116
base_ymstr_idarea_nmadstrd_nmadstrd_cdgid_lagid_loadult_entrn_nmpr_cochild_entrn_nmpr_cotot_entrn_nmpr_co
90202003kka01제주특별자치도아라동214748364733.485149126.536208157121278
91202003lovely01충청남도청룡동214748364736.783268127.152776413576
92202003with01서울특별시방이2동117105620037.510719127.11654138186324
93202003ggom01전라남도풍덕동214748364734.937073127.495453218295513
94202003like0626서울특별시창제5동113205150037.655794127.04227896153249
95202003hbb05경기도배곧1동214748364737.377821126.726892134186320
96202003kingkong21전라남도신흥동214748364734.804184126.426185022
97202003kidsmong01경기도평내동214748364737.645783127.235793122133
98202003kingkong23경기도평내동214748364737.645783127.23579370122192
99202003eoul01경기도송산3동214748364737.746324127.0947975666871253