Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells3
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory85.3 B

Variable types

Text3
Numeric4
Categorical2
DateTime1

Alerts

base_ymd has constant value ""Constant
area_nm is highly overall correlated with city_do_cd and 4 other fieldsHigh correlation
parking_at is highly overall correlated with city_do_cd and 4 other fieldsHigh correlation
city_do_cd is highly overall correlated with city_gn_gu_cd and 3 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cd and 3 other fieldsHigh correlation
xpos_lo is highly overall correlated with city_do_cd and 3 other fieldsHigh correlation
ypos_la is highly overall correlated with area_nm and 1 other fieldsHigh correlation
city_gn_gu_cd has 3 (3.0%) missing valuesMissing
entrp_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:08:43.342278
Analysis finished2023-12-10 10:08:48.627761
Duration5.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:48.863566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.93
Min length3

Characters and Unicode

Total characters793
Distinct characters206
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row강릉병산옹심이골목
2nd row하이디라오
3rd row학사평콩꽃마을순두부촌
4th row대포항원조튀김골목
5th row새우튀김 골목
ValueCountFrequency (%)
강릉병산옹심이골목 1
 
0.9%
하이디라오 1
 
0.9%
도예촌쌀밥거리 1
 
0.9%
의정부부대찌개거리 1
 
0.9%
백운산토속음식마을 1
 
0.9%
음식마을 1
 
0.9%
백운호수 1
 
0.9%
수지외식타운 1
 
0.9%
신봉동외식타운 1
 
0.9%
기흥맛깔촌 1
 
0.9%
Other values (98) 98
90.7%
2023-12-10T19:08:49.408717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 49
 
6.2%
거 42
 
5.3%
식 22
 
2.8%
촌 21
 
2.6%
골 21
 
2.6%
목 18
 
2.3%
장 18
 
2.3%
화 18
 
2.3%
마 17
 
2.1%
음 17
 
2.1%
Other values (196) 550
69.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 785
99.0%
Space Separator 8
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 49
 
6.2%
거 42
 
5.4%
식 22
 
2.8%
촌 21
 
2.7%
골 21
 
2.7%
목 18
 
2.3%
장 18
 
2.3%
화 18
 
2.3%
마 17
 
2.2%
음 17
 
2.2%
Other values (195) 542
69.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 785
99.0%
Common 8
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 49
 
6.2%
거 42
 
5.4%
식 22
 
2.8%
촌 21
 
2.7%
골 21
 
2.7%
목 18
 
2.3%
장 18
 
2.3%
화 18
 
2.3%
마 17
 
2.2%
음 17
 
2.2%
Other values (195) 542
69.0%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 785
99.0%
ASCII 8
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 49
 
6.2%
거 42
 
5.4%
식 22
 
2.8%
촌 21
 
2.7%
골 21
 
2.7%
목 18
 
2.3%
장 18
 
2.3%
화 18
 
2.3%
마 17
 
2.2%
음 17
 
2.2%
Other values (195) 542
69.0%
ASCII
ValueCountFrequency (%)
8
100.0%

load_addr
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:49.962976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length15.24
Min length10

Characters and Unicode

Total characters1524
Distinct characters166
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

Unique92 ?
Unique (%)92.0%

Sample

1st row강원도 강릉시 병산동
2nd row서울특별시 중구명동1가59-5
3rd row강원도 속초시 노학동
4th row강원도 속초시 대포동
5th row강원도 속초시 대포동 965
ValueCountFrequency (%)
경기도 53
 
13.9%
강원도 22
 
5.8%
경상남도 13
 
3.4%
경상북도 9
 
2.4%
수원시 7
 
1.8%
고양시 5
 
1.3%
팔달구 5
 
1.3%
성남시 5
 
1.3%
속초시 5
 
1.3%
원주시 4
 
1.0%
Other values (208) 254
66.5%
2023-12-10T19:08:50.831887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
282
 
18.5%
도 99
 
6.5%
시 87
 
5.7%
경 79
 
5.2%
동 69
 
4.5%
기 55
 
3.6%
원 40
 
2.6%
구 32
 
2.1%
리 29
 
1.9%
상 28
 
1.8%
Other values (156) 724
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1147
75.3%
Space Separator 282
 
18.5%
Decimal Number 85
 
5.6%
Dash Punctuation 10
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 99
 
8.6%
시 87
 
7.6%
경 79
 
6.9%
동 69
 
6.0%
기 55
 
4.8%
원 40
 
3.5%
구 32
 
2.8%
리 29
 
2.5%
상 28
 
2.4%
주 25
 
2.2%
Other values (144) 604
52.7%
Decimal Number
ValueCountFrequency (%)
1 21
24.7%
7 12
14.1%
3 11
12.9%
5 8
 
9.4%
2 8
 
9.4%
4 6
 
7.1%
9 6
 
7.1%
0 5
 
5.9%
6 4
 
4.7%
8 4
 
4.7%
Space Separator
ValueCountFrequency (%)
282
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1147
75.3%
Common 377
 
24.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 99
 
8.6%
시 87
 
7.6%
경 79
 
6.9%
동 69
 
6.0%
기 55
 
4.8%
원 40
 
3.5%
구 32
 
2.8%
리 29
 
2.5%
상 28
 
2.4%
주 25
 
2.2%
Other values (144) 604
52.7%
Common
ValueCountFrequency (%)
282
74.8%
1 21
 
5.6%
7 12
 
3.2%
3 11
 
2.9%
- 10
 
2.7%
5 8
 
2.1%
2 8
 
2.1%
4 6
 
1.6%
9 6
 
1.6%
0 5
 
1.3%
Other values (2) 8
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1147
75.3%
ASCII 377
 
24.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
282
74.8%
1 21
 
5.6%
7 12
 
3.2%
3 11
 
2.9%
- 10
 
2.7%
5 8
 
2.1%
2 8
 
2.1%
4 6
 
1.6%
9 6
 
1.6%
0 5
 
1.3%
Other values (2) 8
 
2.1%
Hangul
ValueCountFrequency (%)
도 99
 
8.6%
시 87
 
7.6%
경 79
 
6.9%
동 69
 
6.0%
기 55
 
4.8%
원 40
 
3.5%
구 32
 
2.8%
리 29
 
2.5%
상 28
 
2.4%
주 25
 
2.2%
Other values (144) 604
52.7%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.55
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:51.114834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile41
Q141
median41
Q342
95-th percentile48
Maximum50
Range39
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.2696368
Coefficient of variation (CV)0.10034399
Kurtosis29.598118
Mean42.55
Median Absolute Deviation (MAD)0
Skewness-3.69217
Sum4255
Variance18.229798
MonotonicityNot monotonic
2023-12-10T19:08:51.316193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
41 53
53.0%
42 22
22.0%
48 13
 
13.0%
47 9
 
9.0%
50 2
 
2.0%
11 1
 
1.0%
ValueCountFrequency (%)
11 1
 
1.0%
41 53
53.0%
42 22
22.0%
47 9
 
9.0%
48 13
 
13.0%
50 2
 
2.0%
ValueCountFrequency (%)
50 2
 
2.0%
48 13
 
13.0%
47 9
 
9.0%
42 22
22.0%
41 53
53.0%
11 1
 
1.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)59.8%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean43090.588
Minimum41113
Maximum48890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:51.621378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41113
5-th percentile41115
Q141273
median41630
Q342790
95-th percentile48250
Maximum48890
Range7777
Interquartile range (IQR)1517

Descriptive statistics

Standard deviation2714.9876
Coefficient of variation (CV)0.063006512
Kurtosis-0.21566762
Mean43090.588
Median Absolute Deviation (MAD)500
Skewness1.2546086
Sum4179787
Variance7371157.8
MonotonicityNot monotonic
2023-12-10T19:08:51.884546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42210 5
 
5.0%
41115 5
 
5.0%
42130 4
 
4.0%
41135 4
 
4.0%
47130 3
 
3.0%
41281 3
 
3.0%
48250 3
 
3.0%
41465 2
 
2.0%
41370 2
 
2.0%
41730 2
 
2.0%
Other values (48) 64
64.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
41113 1
 
1.0%
41115 5
5.0%
41117 1
 
1.0%
41131 1
 
1.0%
41135 4
4.0%
41150 1
 
1.0%
41171 2
 
2.0%
41173 1
 
1.0%
41190 2
 
2.0%
41210 1
 
1.0%
ValueCountFrequency (%)
48890 1
 
1.0%
48880 1
 
1.0%
48850 1
 
1.0%
48730 1
 
1.0%
48250 3
3.0%
48240 2
2.0%
48220 1
 
1.0%
48125 2
2.0%
48123 1
 
1.0%
47920 2
2.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.6149
Minimum126.48162
Maximum129.29902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:52.170382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.48162
5-th percentile126.62019
Q1126.99165
median127.18568
Q3128.38321
95-th percentile128.92306
Maximum129.29902
Range2.817398
Interquartile range (IQR)1.391561

Descriptive statistics

Standard deviation0.79304013
Coefficient of variation (CV)0.0062143225
Kurtosis-1.1833365
Mean127.6149
Median Absolute Deviation (MAD)0.518874
Skewness0.45082686
Sum12761.49
Variance0.62891265
MonotonicityNot monotonic
2023-12-10T19:08:52.464247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.950558 4
 
4.0%
128.923056 2
 
2.0%
126.916118 2
 
2.0%
128.944649 1
 
1.0%
127.110878 1
 
1.0%
126.678047 1
 
1.0%
126.79403 1
 
1.0%
127.417893 1
 
1.0%
127.049645 1
 
1.0%
126.983785 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
126.481621 1
1.0%
126.534336 1
1.0%
126.545028 1
1.0%
126.55303 1
1.0%
126.57029 1
1.0%
126.622812 1
1.0%
126.678047 1
1.0%
126.687279 1
1.0%
126.741261 1
1.0%
126.751881 1
1.0%
ValueCountFrequency (%)
129.299019 1
1.0%
129.258833 1
1.0%
129.216043 1
1.0%
128.944649 1
1.0%
128.923056 2
2.0%
128.88001 1
1.0%
128.827745 1
1.0%
128.815704 1
1.0%
128.754907 1
1.0%
128.717676 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.02586
Minimum33.489032
Maximum38.214106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:52.741066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.489032
5-th percentile35.052567
Q137.013514
median37.354477
Q337.63939
95-th percentile38.174198
Maximum38.214106
Range4.725074
Interquartile range (IQR)0.6258765

Descriptive statistics

Standard deviation1.0223571
Coefficient of variation (CV)0.027611975
Kurtosis1.837899
Mean37.02586
Median Absolute Deviation (MAD)0.2999895
Skewness-1.5380675
Sum3702.586
Variance1.0452141
MonotonicityNot monotonic
2023-12-10T19:08:53.069217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.349783 4
 
4.0%
35.222002 2
 
2.0%
37.388556 2
 
2.0%
37.759655 1
 
1.0%
37.265517 1
 
1.0%
37.781713 1
 
1.0%
37.885724 1
 
1.0%
37.294917 1
 
1.0%
37.744724 1
 
1.0%
37.35917 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
33.489032 1
1.0%
33.490974 1
1.0%
34.841649 1
1.0%
34.924665 1
1.0%
34.939607 1
1.0%
35.058512 1
1.0%
35.199841 1
1.0%
35.205508 1
1.0%
35.222002 2
2.0%
35.234401 1
1.0%
ValueCountFrequency (%)
38.214106 1
1.0%
38.194125 1
1.0%
38.190603 1
1.0%
38.188672 1
1.0%
38.174254 1
1.0%
38.174195 1
1.0%
38.083497 1
1.0%
37.948066 1
1.0%
37.922646 1
1.0%
37.903863 1
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기
53 
강원
22 
경남
13 
경북
9 
제주
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row강원
2nd row서울
3rd row강원
4th row강원
5th row강원

Common Values

ValueCountFrequency (%)
경기 53
53.0%
강원 22
22.0%
경남 13
 
13.0%
경북 9
 
9.0%
제주 2
 
2.0%
서울 1
 
1.0%

Length

2023-12-10T19:08:53.314087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:53.500324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 53
53.0%
강원 22
22.0%
경남 13
 
13.0%
경북 9
 
9.0%
제주 2
 
2.0%
서울 1
 
1.0%

near_pbtrnsp_nm
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:53.996997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length5.46
Min length2

Characters and Unicode

Total characters546
Distinct characters189
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)92.0%

Sample

1st row자루뫼
2nd row훠궈, 샤브샤브, 중국식샤브샤브
3rd row원암로입구
4th row대포항입구
5th row대포항입구
ValueCountFrequency (%)
대포항입구 2
 
1.9%
수원역 2
 
1.9%
평원로중앙시장 2
 
1.9%
남한산성(종점 2
 
1.9%
강변장어타운 1
 
0.9%
대천동성당 1
 
0.9%
자루뫼 1
 
0.9%
상갈역 1
 
0.9%
프로방스마을입구 1
 
0.9%
임진리 1
 
0.9%
Other values (94) 94
87.0%
2023-12-10T19:08:55.121813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 20
 
3.7%
원 17
 
3.1%
구 16
 
2.9%
장 16
 
2.9%
동 15
 
2.7%
리 12
 
2.2%
시 12
 
2.2%
산 11
 
2.0%
입 10
 
1.8%
대 9
 
1.6%
Other values (179) 408
74.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 522
95.6%
Space Separator 8
 
1.5%
Other Punctuation 8
 
1.5%
Decimal Number 4
 
0.7%
Open Punctuation 2
 
0.4%
Close Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 20
 
3.8%
원 17
 
3.3%
구 16
 
3.1%
장 16
 
3.1%
동 15
 
2.9%
리 12
 
2.3%
시 12
 
2.3%
산 11
 
2.1%
입 10
 
1.9%
대 9
 
1.7%
Other values (172) 384
73.6%
Other Punctuation
ValueCountFrequency (%)
. 6
75.0%
, 2
 
25.0%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
1 1
 
25.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 522
95.6%
Common 24
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 20
 
3.8%
원 17
 
3.3%
구 16
 
3.1%
장 16
 
3.1%
동 15
 
2.9%
리 12
 
2.3%
시 12
 
2.3%
산 11
 
2.1%
입 10
 
1.9%
대 9
 
1.7%
Other values (172) 384
73.6%
Common
ValueCountFrequency (%)
8
33.3%
. 6
25.0%
2 3
 
12.5%
( 2
 
8.3%
, 2
 
8.3%
) 2
 
8.3%
1 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 522
95.6%
ASCII 24
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 20
 
3.8%
원 17
 
3.3%
구 16
 
3.1%
장 16
 
3.1%
동 15
 
2.9%
리 12
 
2.3%
시 12
 
2.3%
산 11
 
2.1%
입 10
 
1.9%
대 9
 
1.7%
Other values (172) 384
73.6%
ASCII
ValueCountFrequency (%)
8
33.3%
. 6
25.0%
2 3
 
12.5%
( 2
 
8.3%
, 2
 
8.3%
) 2
 
8.3%
1 1
 
4.2%

parking_at
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
유
83 
<NA>
17 

Length

Max length4
Median length1
Mean length1.51
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유
2nd row<NA>
3rd row유
4th row유
5th row유

Common Values

ValueCountFrequency (%)
유 83
83.0%
<NA> 17
 
17.0%

Length

2023-12-10T19:08:55.416821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:55.601618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유 83
83.0%
na 17
 
17.0%

base_ymd
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-31 00:00:00
Maximum2020-12-31 00:00:00
2023-12-10T19:08:55.774822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:55.957927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T19:08:47.535340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:45.064148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:45.758821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:46.798710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:47.732280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:45.262398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:45.914433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:46.959833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:47.909334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:45.447091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:46.077754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:47.114916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:48.080453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:45.620625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:46.236860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:47.286558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:08:56.107433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmnear_pbtrnsp_nm
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0000.994
city_do_cd1.0001.0001.0001.0000.5650.9021.0001.000
city_gn_gu_cd1.0001.0001.0001.0000.7720.8031.0001.000
xpos_lo1.0001.0000.5650.7721.0000.6030.7461.000
ypos_la1.0001.0000.9020.8030.6031.0000.9021.000
area_nm1.0001.0001.0001.0000.7460.9021.0001.000
near_pbtrnsp_nm1.0000.9941.0001.0001.0001.0001.0001.000
2023-12-10T19:08:56.313490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
area_nmparking_at
area_nm1.0001.000
parking_at1.0001.000
2023-12-10T19:08:56.464134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmparking_at
city_do_cd1.0000.9070.720-0.4930.9841.000
city_gn_gu_cd0.9071.0000.777-0.3380.9891.000
xpos_lo0.7200.7771.000-0.2260.5041.000
ypos_la-0.493-0.338-0.2261.0000.7001.000
area_nm0.9840.9890.5040.7001.0001.000
parking_at1.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T19:08:48.264380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:08:48.526261image/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_laarea_nmnear_pbtrnsp_nmparking_atbase_ymd
0강릉병산옹심이골목강원도 강릉시 병산동4242150128.94464937.759655강원자루뫼유2020-12-31
1하이디라오서울특별시 중구명동1가59-511<NA>126.9974737.563763서울훠궈, 샤브샤브, 중국식샤브샤브<NA>2020-12-31
2학사평콩꽃마을순두부촌강원도 속초시 노학동4242210128.52988738.190603강원원암로입구유2020-12-31
3대포항원조튀김골목강원도 속초시 대포동4242210128.59966238.174195강원대포항입구유2020-12-31
4새우튀김 골목강원도 속초시 대포동 9654242210128.60737838.174254강원대포항입구유2020-12-31
5속초먹거리단지강원도 속초시 먹거리7길 14242210128.57427638.194125강원청초교유2020-12-31
6장사항횟집타운강원도 속초시 장사동4242210128.55790938.214106강원우림연립유2020-12-31
7해오름 식당제주특별자치도 제주시 노형동 1297-350<NA>126.48162133.489032제주흑돼지 구이<NA>2020-12-31
8오색약수산채음식촌강원도 양양군 서면 오색리4242830128.46099238.083497강원오색유2020-12-31
9고씨굴칡국수촌강원도 영월군 김삿갓면 진별리4242750128.54676637.145383강원고씨굴유2020-12-31
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmnear_pbtrnsp_nmparking_atbase_ymd
90합천한우거리경상남도 합천군 삼가면 일부3길 154848890128.12478135.411383경남삼가유2020-12-31
91오렌지타운거리경상북도 경산시 조영동4747290128.75490735.841634경북영남대역 대구2호선유2020-12-31
92순두부골목경상북도 경주시 북군동4747130129.25883335.859637경북북군동펜션마을 동궁원유2020-12-31
93화산한우숯불단지경상북도 경주시 천북면 화산리4747130129.29901935.920459경북화산유2020-12-31
94팔우정해장국거리경상북도 경주시 황오동 317-44747130129.21604335.840899경북팔우정유2020-12-31
95진평음식특화거리경상북도 구미시 진평동 10784747190128.42959436.093486경북진평중학교입구<NA>2020-12-31
96지례흑돼지골목경상북도 김천시 지례면 교리4747150128.03481335.996071경북삼거리유2020-12-31
97봉성돼지숯불단지경상북도 봉화군 봉성면 봉명로 558 봉성우체국4747920128.81570436.884032경북봉성유2020-12-31
98다덕약수탕토속음식단지경상북도 봉화군 봉성면 우곡리4747920128.82774536.94146경북다덕약수관광단지<NA>2020-12-31
99낙동강한우먹거리촌경상북도 상주시 낙동면 낙동리4747250128.29932136.364949경북낙동정류장유2020-12-31