Overview

Dataset statistics

Number of variables8
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory71.8 B

Variable types

Text2
Numeric5
DateTime1

Alerts

city_do_cd is highly overall correlated with city_gn_gu_cdHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cdHigh correlation

Reproduction

Analysis started2023-12-10 09:41:47.074018
Analysis finished2023-12-10 09:41:53.048329
Duration5.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

Distinct29
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-10T18:41:53.273016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length5.625
Min length3

Characters and Unicode

Total characters270
Distinct characters96
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)25.0%

Sample

1st row정동진
2nd row강릉 경포해변
3rd row주문진
4th row강원랜드
5th row북한산국립공원
ValueCountFrequency (%)
해수욕장 5
 
8.5%
해운대 3
 
5.1%
롯데월드 3
 
5.1%
강원랜드 2
 
3.4%
청매실농원 2
 
3.4%
정동진 2
 
3.4%
성산일출봉 2
 
3.4%
경포해변 2
 
3.4%
강릉 2
 
3.4%
순천만국가정원·습지 2
 
3.4%
Other values (24) 34
57.6%
2023-12-10T18:41:53.908530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
해 12
 
4.4%
원 12
 
4.4%
11
 
4.1%
국 10
 
3.7%
립 8
 
3.0%
장 8
 
3.0%
산 7
 
2.6%
드 7
 
2.6%
공 6
 
2.2%
대 6
 
2.2%
Other values (86) 183
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 240
88.9%
Uppercase Letter 13
 
4.8%
Space Separator 11
 
4.1%
Decimal Number 4
 
1.5%
Other Punctuation 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 12
 
5.0%
원 12
 
5.0%
국 10
 
4.2%
립 8
 
3.3%
장 8
 
3.3%
산 7
 
2.9%
드 7
 
2.9%
공 6
 
2.5%
대 6
 
2.5%
진 6
 
2.5%
Other values (75) 158
65.8%
Uppercase Letter
ValueCountFrequency (%)
N 3
23.1%
X 2
15.4%
I 2
15.4%
T 2
15.4%
E 2
15.4%
K 2
15.4%
Decimal Number
ValueCountFrequency (%)
7 2
50.0%
0 1
25.0%
1 1
25.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 240
88.9%
Common 17
 
6.3%
Latin 13
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 12
 
5.0%
원 12
 
5.0%
국 10
 
4.2%
립 8
 
3.3%
장 8
 
3.3%
산 7
 
2.9%
드 7
 
2.9%
공 6
 
2.5%
대 6
 
2.5%
진 6
 
2.5%
Other values (75) 158
65.8%
Latin
ValueCountFrequency (%)
N 3
23.1%
X 2
15.4%
I 2
15.4%
T 2
15.4%
E 2
15.4%
K 2
15.4%
Common
ValueCountFrequency (%)
11
64.7%
7 2
 
11.8%
· 2
 
11.8%
0 1
 
5.9%
1 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 240
88.9%
ASCII 28
 
10.4%
None 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
해 12
 
5.0%
원 12
 
5.0%
국 10
 
4.2%
립 8
 
3.3%
장 8
 
3.3%
산 7
 
2.9%
드 7
 
2.9%
공 6
 
2.5%
대 6
 
2.5%
진 6
 
2.5%
Other values (75) 158
65.8%
ASCII
ValueCountFrequency (%)
11
39.3%
N 3
 
10.7%
7 2
 
7.1%
X 2
 
7.1%
I 2
 
7.1%
T 2
 
7.1%
E 2
 
7.1%
K 2
 
7.1%
0 1
 
3.6%
1 1
 
3.6%
None
ValueCountFrequency (%)
· 2
100.0%

load_addr
Text

Distinct30
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-10T18:41:54.452966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length19.270833
Min length12

Characters and Unicode

Total characters925
Distinct characters122
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)27.1%

Sample

1st row강원도 강릉시 강동면 정동진리
2nd row강원도 강릉시 강문동 산1-1
3rd row강원도 강릉시 주문진읍
4th row강원도 정선군 사북읍 하이원길 265
5th row경기도 고양시 덕양구 대서문길 375
ValueCountFrequency (%)
서울특별시 14
 
6.6%
강원도 8
 
3.8%
경기도 8
 
3.8%
부산광역시 6
 
2.8%
강릉시 6
 
2.8%
47 5
 
2.4%
고양시 4
 
1.9%
해운대구 4
 
1.9%
종로구 4
 
1.9%
전라남도 4
 
1.9%
Other values (86) 148
70.1%
2023-12-10T18:41:55.205772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
163
 
17.6%
시 47
 
5.1%
도 30
 
3.2%
1 28
 
3.0%
로 28
 
3.0%
구 26
 
2.8%
서 23
 
2.5%
산 23
 
2.5%
2 19
 
2.1%
강 19
 
2.1%
Other values (112) 519
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 626
67.7%
Space Separator 163
 
17.6%
Decimal Number 128
 
13.8%
Dash Punctuation 8
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 47
 
7.5%
도 30
 
4.8%
로 28
 
4.5%
구 26
 
4.2%
서 23
 
3.7%
산 23
 
3.7%
강 19
 
3.0%
동 16
 
2.6%
별 16
 
2.6%
특 16
 
2.6%
Other values (100) 382
61.0%
Decimal Number
ValueCountFrequency (%)
1 28
21.9%
2 19
14.8%
4 15
11.7%
7 14
10.9%
5 11
 
8.6%
0 11
 
8.6%
9 10
 
7.8%
6 10
 
7.8%
3 8
 
6.2%
8 2
 
1.6%
Space Separator
ValueCountFrequency (%)
163
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 626
67.7%
Common 299
32.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 47
 
7.5%
도 30
 
4.8%
로 28
 
4.5%
구 26
 
4.2%
서 23
 
3.7%
산 23
 
3.7%
강 19
 
3.0%
동 16
 
2.6%
별 16
 
2.6%
특 16
 
2.6%
Other values (100) 382
61.0%
Common
ValueCountFrequency (%)
163
54.5%
1 28
 
9.4%
2 19
 
6.4%
4 15
 
5.0%
7 14
 
4.7%
5 11
 
3.7%
0 11
 
3.7%
9 10
 
3.3%
6 10
 
3.3%
- 8
 
2.7%
Other values (2) 10
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 626
67.7%
ASCII 299
32.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163
54.5%
1 28
 
9.4%
2 19
 
6.4%
4 15
 
5.0%
7 14
 
4.7%
5 11
 
3.7%
0 11
 
3.7%
9 10
 
3.3%
6 10
 
3.3%
- 8
 
2.7%
Other values (2) 10
 
3.3%
Hangul
ValueCountFrequency (%)
시 47
 
7.5%
도 30
 
4.8%
로 28
 
4.5%
구 26
 
4.2%
서 23
 
3.7%
산 23
 
3.7%
강 19
 
3.0%
동 16
 
2.6%
별 16
 
2.6%
특 16
 
2.6%
Other values (100) 382
61.0%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.916667
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-10T18:41:55.438951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median41
Q342.5
95-th percentile48
Maximum50
Range39
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation14.880851
Coefficient of variation (CV)0.46624075
Kurtosis-1.5101356
Mean31.916667
Median Absolute Deviation (MAD)6
Skewness-0.51093606
Sum1532
Variance221.43972
MonotonicityNot monotonic
2023-12-10T18:41:55.708145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11 14
29.2%
42 8
16.7%
41 8
16.7%
26 6
12.5%
46 4
 
8.3%
48 2
 
4.2%
45 2
 
4.2%
50 2
 
4.2%
44 2
 
4.2%
ValueCountFrequency (%)
11 14
29.2%
26 6
12.5%
41 8
16.7%
42 8
16.7%
44 2
 
4.2%
45 2
 
4.2%
46 4
 
8.3%
48 2
 
4.2%
50 2
 
4.2%
ValueCountFrequency (%)
50 2
 
4.2%
48 2
 
4.2%
46 4
 
8.3%
45 2
 
4.2%
44 2
 
4.2%
42 8
16.7%
41 8
16.7%
26 6
12.5%
11 14
29.2%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32209.292
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-10T18:41:55.945154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11110
Q111710
median41373.5
Q343122.5
95-th percentile48240
Maximum50130
Range39020
Interquartile range (IQR)31412.5

Descriptive statistics

Standard deviation14852.531
Coefficient of variation (CV)0.46112568
Kurtosis-1.5102663
Mean32209.292
Median Absolute Deviation (MAD)5861.5
Skewness-0.51505099
Sum1546046
Variance2.2059769 × 108
MonotonicityNot monotonic
2023-12-10T18:41:56.196056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
42150 6
 
12.5%
11110 4
 
8.3%
26350 4
 
8.3%
11710 3
 
6.2%
11170 3
 
6.2%
42770 2
 
4.2%
11140 2
 
4.2%
44180 2
 
4.2%
50130 2
 
4.2%
45180 2
 
4.2%
Other values (11) 18
37.5%
ValueCountFrequency (%)
11110 4
8.3%
11140 2
4.2%
11170 3
6.2%
11440 1
 
2.1%
11680 1
 
2.1%
11710 3
6.2%
26110 1
 
2.1%
26350 4
8.3%
26380 1
 
2.1%
41281 2
4.2%
ValueCountFrequency (%)
50130 2
 
4.2%
48240 2
 
4.2%
46230 2
 
4.2%
46150 2
 
4.2%
45180 2
 
4.2%
44180 2
 
4.2%
42770 2
 
4.2%
42150 6
12.5%
41480 2
 
4.2%
41460 2
 
4.2%

xpos_lo
Real number (ℝ)

Distinct31
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.63929
Minimum126.53724
Maximum129.1725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-10T18:41:56.450732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.53724
5-th percentile126.74176
Q1126.97256
median127.09537
Q3128.82401
95-th percentile129.16386
Maximum129.1725
Range2.6352596
Interquartile range (IQR)1.8514501

Descriptive statistics

Standard deviation0.93256115
Coefficient of variation (CV)0.0073062229
Kurtosis-1.3087041
Mean127.63929
Median Absolute Deviation (MAD)0.3515871
Skewness0.67190742
Sum6126.6862
Variance0.86967029
MonotonicityNot monotonic
2023-12-10T18:41:56.698364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
129.03477 2
 
4.2%
129.1724636 2
 
4.2%
128.9109537 2
 
4.2%
126.9425485 2
 
4.2%
126.9074776 2
 
4.2%
127.4994861 2
 
4.2%
127.7162278 2
 
4.2%
126.9825451 2
 
4.2%
127.0953685 2
 
4.2%
126.5372444 2
 
4.2%
Other values (21) 28
58.3%
ValueCountFrequency (%)
126.5372444 2
4.2%
126.74176 2
4.2%
126.7458028 2
4.2%
126.9074776 2
4.2%
126.918204 1
2.1%
126.9425485 2
4.2%
126.968658 1
2.1%
126.9738653 2
4.2%
126.975168 1
2.1%
126.976965 1
2.1%
ValueCountFrequency (%)
129.172504 1
2.1%
129.1724636 2
4.2%
129.147885 1
2.1%
129.03477 2
4.2%
129.030604 1
2.1%
129.0106 1
2.1%
128.9109537 2
4.2%
128.831489 2
4.2%
128.8215218 2
4.2%
128.0612632 2
4.2%

ypos_la
Real number (ℝ)

Distinct31
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.649491
Minimum33.458398
Maximum37.889713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-10T18:41:56.909213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.458398
5-th percentile34.927154
Q135.157968
median37.511109
Q337.59649
95-th percentile37.882732
Maximum37.889713
Range4.431315
Interquartile range (IQR)2.4385225

Descriptive statistics

Standard deviation1.3121198
Coefficient of variation (CV)0.035801854
Kurtosis-0.62751335
Mean36.649491
Median Absolute Deviation (MAD)0.29458472
Skewness-0.86277299
Sum1759.1756
Variance1.7216582
MonotonicityNot monotonic
2023-12-10T18:41:57.477741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
37.690479 2
 
4.2%
35.15792518 2
 
4.2%
37.80246042 2
 
4.2%
33.45839817 2
 
4.2%
35.48791064 2
 
4.2%
34.92715401 2
 
4.2%
35.075331 2
 
4.2%
37.52465048 2
 
4.2%
37.51096772 2
 
4.2%
36.31645374 2
 
4.2%
Other values (21) 28
58.3%
ValueCountFrequency (%)
33.45839817 2
4.2%
34.92715401 2
4.2%
35.00798542 2
4.2%
35.075331 2
4.2%
35.096598 1
2.1%
35.097497 1
2.1%
35.15792518 2
4.2%
35.157982 1
2.1%
35.174371 1
2.1%
35.48791064 2
4.2%
ValueCountFrequency (%)
37.88971312 2
4.2%
37.882732 2
4.2%
37.80246042 2
4.2%
37.690479 2
4.2%
37.66893308 2
4.2%
37.64578429 2
4.2%
37.580059 1
2.1%
37.577427 1
2.1%
37.574356 1
2.1%
37.570714 1
2.1%

tursm_stsfdg_rt
Real number (ℝ)

Distinct20
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4166667
Minimum1.4
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-10T18:41:57.709909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2.3
Q12.5
median3.5
Q34.025
95-th percentile4.7
Maximum5
Range3.6
Interquartile range (IQR)1.525

Descriptive statistics

Standard deviation0.90655611
Coefficient of variation (CV)0.26533349
Kurtosis-0.98690939
Mean3.4166667
Median Absolute Deviation (MAD)0.7
Skewness-0.046949958
Sum164
Variance0.82184397
MonotonicityNot monotonic
2023-12-10T18:41:57.971926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2.5 6
12.5%
2.3 5
 
10.4%
4.6 4
 
8.3%
4.0 4
 
8.3%
2.9 4
 
8.3%
3.6 3
 
6.2%
3.9 3
 
6.2%
3.4 2
 
4.2%
3.0 2
 
4.2%
3.8 2
 
4.2%
Other values (10) 13
27.1%
ValueCountFrequency (%)
1.4 1
 
2.1%
2.0 1
 
2.1%
2.3 5
10.4%
2.5 6
12.5%
2.6 1
 
2.1%
2.9 4
8.3%
3.0 2
 
4.2%
3.1 1
 
2.1%
3.2 1
 
2.1%
3.4 2
 
4.2%
ValueCountFrequency (%)
5.0 2
4.2%
4.7 2
4.2%
4.6 4
8.3%
4.4 1
 
2.1%
4.3 1
 
2.1%
4.1 2
4.2%
4.0 4
8.3%
3.9 3
6.2%
3.8 2
4.2%
3.6 3
6.2%

base_ymd
Date

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
Minimum2019-12-09 00:00:00
Maximum2020-12-31 00:00:00
2023-12-10T18:41:58.164743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:58.331499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2023-12-10T18:41:51.595995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:47.622376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:48.623570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:49.834709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:50.789181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:51.773454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:47.884540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:48.960150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:49.999941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:50.949231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:51.974560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:48.052591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:49.210417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:50.146591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:51.129961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:52.149960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:48.224725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:49.407417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:50.397822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:51.261531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:52.388754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:48.394298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:49.621245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:50.606504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:51.398055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:41:58.491539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_latursm_stsfdg_rtbase_ymd
entrp_nm1.0001.0001.0001.0001.0001.0000.9940.000
load_addr1.0001.0001.0001.0001.0001.0001.0000.000
city_do_cd1.0001.0001.0000.9940.7880.7630.7110.185
city_gn_gu_cd1.0001.0000.9941.0000.7540.7270.6150.059
xpos_lo1.0001.0000.7880.7541.0000.6330.6360.000
ypos_la1.0001.0000.7630.7270.6331.0000.5190.000
tursm_stsfdg_rt0.9941.0000.7110.6150.6360.5191.0000.000
base_ymd0.0000.0000.1850.0590.0000.0000.0001.000
2023-12-10T18:41:58.741753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_latursm_stsfdg_rt
city_do_cd1.0000.9840.047-0.3860.081
city_gn_gu_cd0.9841.0000.055-0.4150.068
xpos_lo0.0470.0551.000-0.194-0.329
ypos_la-0.386-0.415-0.1941.000-0.038
tursm_stsfdg_rt0.0810.068-0.329-0.0381.000

Missing values

2023-12-10T18:41:52.684067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:41:52.958633image/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

entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_latursm_stsfdg_rtbase_ymd
0정동진강원도 강릉시 강동면 정동진리4242150129.0347737.6904792.52020-12-31
1강릉 경포해변강원도 강릉시 강문동 산1-14242150128.91095437.802463.42020-12-31
2주문진강원도 강릉시 주문진읍4242150128.83148937.8827322.32020-12-31
3강원랜드강원도 정선군 사북읍 하이원길 2654242770128.82152237.2132912.52020-12-31
4북한산국립공원경기도 고양시 덕양구 대서문길 3754141281126.97386537.6457844.12020-12-31
5KINTEX경기도 고양시 일산서구 킨텍스로 217-604141287126.74580337.6689334.62020-12-31
6에버랜드경기도 용인시 처인구 포곡읍 에버랜드로 1994141460127.21283637.2897584.02020-12-31
7임진각관광지경기도 파주시 문산읍 임진각로 1644141480126.7417637.8897134.72020-12-31
8한려해상국립공원경상남도 사천시 용현면 대밭담로 5-94848240128.06126335.0079852.92020-12-31
9해운대 해수욕장부산광역시 해운대구 달맞이길62번길 472626350129.17246435.1579252.52020-12-31
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_latursm_stsfdg_rtbase_ymd
38임진각관광지경기도 파주시 문산읍 임진각로 1644141480126.7417637.8897134.72019-12-09
39한려해상국립공원경상남도 사천시 용현면 대밭담로 5-94848240128.06126335.0079852.92019-12-09
40해운대 해수욕장부산광역시 해운대구 달맞이길62번길 472626350129.17246435.1579252.52019-12-09
41롯데월드서울특별시 송파구 올림픽로 2401111710127.09536937.5109683.62019-12-09
42국립중앙박물관서울특별시 용산구 서빙고로 1371111170126.98254537.524652.92019-12-09
43청매실농원전라남도 광양시 다압면 도사리 4144646230127.71622835.0753313.92019-12-09
44순천만국가정원·습지전라남도 순천시 국가정원1호길 474646150127.49948634.9271545.02019-12-09
45내장산 국립공원전라북도 정읍시 내장산로 12074545180126.90747835.4879112.32019-12-09
46성산일출봉제주특별자치도 서귀포시 성산읍 성산리 15050130126.94254933.4583984.02019-12-09
47대천 해수욕장충청남도 보령시 신흑동 1029-34444180126.53724436.3164543.82019-12-09