Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory86.3 B

Variable types

Text3
Numeric5
Categorical1
DateTime1

Alerts

base_ymd has constant value ""Constant
city_do_cd is highly overall correlated with city_gn_gu_cd and 1 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with city_do_cd and 1 other fieldsHigh correlation
xpos_lo is highly overall correlated with ypos_la and 1 other fieldsHigh correlation
ypos_la is highly overall correlated with xpos_lo and 1 other fieldsHigh correlation
area_nm is highly overall correlated with city_do_cd and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 09:51:01.344322
Analysis finished2023-12-10 09:51:11.102477
Duration9.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:11.530089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9.5
Mean length4.74
Min length2

Characters and Unicode

Total characters474
Distinct characters167
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row동승춘
2nd row삼미족발
3rd row함흥냉면옥
4th row진미통닭
5th row시골집
ValueCountFrequency (%)
평남식당 2
 
1.9%
본점 2
 
1.9%
선동 2
 
1.9%
보리밥 2
 
1.9%
해태식당 1
 
0.9%
창성옥 1
 
0.9%
막국수 1
 
0.9%
성천 1
 
0.9%
대호정 1
 
0.9%
동신면가 1
 
0.9%
Other values (93) 93
86.9%
2023-12-10T18:51:12.511819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 20
 
4.2%
당 19
 
4.0%
집 15
 
3.2%
동 14
 
3.0%
미 11
 
2.3%
옥 10
 
2.1%
장 9
 
1.9%
대 9
 
1.9%
국 9
 
1.9%
점 8
 
1.7%
Other values (157) 350
73.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 461
97.3%
Space Separator 7
 
1.5%
Decimal Number 4
 
0.8%
Uppercase Letter 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 20
 
4.3%
당 19
 
4.1%
집 15
 
3.3%
동 14
 
3.0%
미 11
 
2.4%
옥 10
 
2.2%
장 9
 
2.0%
대 9
 
2.0%
국 9
 
2.0%
점 8
 
1.7%
Other values (152) 337
73.1%
Decimal Number
ValueCountFrequency (%)
8 2
50.0%
7 2
50.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
O 1
50.0%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 461
97.3%
Common 11
 
2.3%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 20
 
4.3%
당 19
 
4.1%
집 15
 
3.3%
동 14
 
3.0%
미 11
 
2.4%
옥 10
 
2.2%
장 9
 
2.0%
대 9
 
2.0%
국 9
 
2.0%
점 8
 
1.7%
Other values (152) 337
73.1%
Common
ValueCountFrequency (%)
7
63.6%
8 2
 
18.2%
7 2
 
18.2%
Latin
ValueCountFrequency (%)
B 1
50.0%
O 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 461
97.3%
ASCII 13
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 20
 
4.3%
당 19
 
4.1%
집 15
 
3.3%
동 14
 
3.0%
미 11
 
2.4%
옥 10
 
2.2%
장 9
 
2.0%
대 9
 
2.0%
국 9
 
2.0%
점 8
 
1.7%
Other values (152) 337
73.1%
ASCII
ValueCountFrequency (%)
7
53.8%
8 2
 
15.4%
7 2
 
15.4%
B 1
 
7.7%
O 1
 
7.7%

load_addr
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:13.172439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length18.76
Min length12

Characters and Unicode

Total characters1876
Distinct characters174
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

Unique98 ?
Unique (%)98.0%

Sample

1st row강원도 삼척시 대학로 12
2nd row충청북도 청주시 청원구 향군로 35
3rd row강원도 속초시 청초호반로 299
4th row강원도 원주시 우산로 66
5th row강원도 원주시 중평길 10-1
ValueCountFrequency (%)
서울특별시 15
 
3.5%
경기도 13
 
3.1%
경상북도 10
 
2.3%
강원도 10
 
2.3%
인천광역시 10
 
2.3%
중구 10
 
2.3%
경상남도 9
 
2.1%
부산광역시 7
 
1.6%
서울 6
 
1.4%
대구광역시 5
 
1.2%
Other values (254) 331
77.7%
2023-12-10T18:51:14.064852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
326
 
17.4%
시 92
 
4.9%
1 77
 
4.1%
로 76
 
4.1%
구 69
 
3.7%
길 67
 
3.6%
도 50
 
2.7%
2 49
 
2.6%
3 37
 
2.0%
경 35
 
1.9%
Other values (164) 998
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1189
63.4%
Decimal Number 335
 
17.9%
Space Separator 326
 
17.4%
Dash Punctuation 26
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 92
 
7.7%
로 76
 
6.4%
구 69
 
5.8%
길 67
 
5.6%
도 50
 
4.2%
경 35
 
2.9%
광 28
 
2.4%
번 28
 
2.4%
서 26
 
2.2%
동 25
 
2.1%
Other values (152) 693
58.3%
Decimal Number
ValueCountFrequency (%)
1 77
23.0%
2 49
14.6%
3 37
11.0%
5 34
10.1%
4 30
 
9.0%
6 24
 
7.2%
0 22
 
6.6%
9 21
 
6.3%
8 21
 
6.3%
7 20
 
6.0%
Space Separator
ValueCountFrequency (%)
326
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1189
63.4%
Common 687
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 92
 
7.7%
로 76
 
6.4%
구 69
 
5.8%
길 67
 
5.6%
도 50
 
4.2%
경 35
 
2.9%
광 28
 
2.4%
번 28
 
2.4%
서 26
 
2.2%
동 25
 
2.1%
Other values (152) 693
58.3%
Common
ValueCountFrequency (%)
326
47.5%
1 77
 
11.2%
2 49
 
7.1%
3 37
 
5.4%
5 34
 
4.9%
4 30
 
4.4%
- 26
 
3.8%
6 24
 
3.5%
0 22
 
3.2%
9 21
 
3.1%
Other values (2) 41
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1189
63.4%
ASCII 687
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
326
47.5%
1 77
 
11.2%
2 49
 
7.1%
3 37
 
5.4%
5 34
 
4.9%
4 30
 
4.4%
- 26
 
3.8%
6 24
 
3.5%
0 22
 
3.2%
9 21
 
3.1%
Other values (2) 41
 
6.0%
Hangul
ValueCountFrequency (%)
시 92
 
7.7%
로 76
 
6.4%
구 69
 
5.8%
길 67
 
5.6%
도 50
 
4.2%
경 35
 
2.9%
광 28
 
2.4%
번 28
 
2.4%
서 26
 
2.2%
동 25
 
2.1%
Other values (152) 693
58.3%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.04
Minimum11
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:14.331360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q126
median30.5
Q342.25
95-th percentile48
Maximum48
Range37
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation13.302935
Coefficient of variation (CV)0.41519772
Kurtosis-1.1878369
Mean32.04
Median Absolute Deviation (MAD)11.5
Skewness-0.428707
Sum3204
Variance176.96808
MonotonicityNot monotonic
2023-12-10T18:51:14.566399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
11 21
21.0%
41 13
13.0%
28 11
11.0%
42 10
10.0%
47 10
10.0%
26 10
10.0%
48 9
9.0%
27 6
 
6.0%
43 3
 
3.0%
46 3
 
3.0%
Other values (3) 4
 
4.0%
ValueCountFrequency (%)
11 21
21.0%
26 10
10.0%
27 6
 
6.0%
28 11
11.0%
29 1
 
1.0%
30 1
 
1.0%
31 2
 
2.0%
41 13
13.0%
42 10
10.0%
43 3
 
3.0%
ValueCountFrequency (%)
48 9
9.0%
47 10
10.0%
46 3
 
3.0%
43 3
 
3.0%
42 10
10.0%
41 13
13.0%
31 2
 
2.0%
30 1
 
1.0%
29 1
 
1.0%
28 11
11.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32331.11
Minimum11110
Maximum48740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:14.908486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q126395
median30655
Q342860
95-th percentile48172.5
Maximum48740
Range37630
Interquartile range (IQR)16465

Descriptive statistics

Standard deviation13289.298
Coefficient of variation (CV)0.41103749
Kurtosis-1.1876347
Mean32331.11
Median Absolute Deviation (MAD)11505
Skewness-0.42861727
Sum3233111
Variance1.7660545 × 108
MonotonicityNot monotonic
2023-12-10T18:51:15.187336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11110 4
 
4.0%
11140 4
 
4.0%
26410 3
 
3.0%
42130 3
 
3.0%
42110 3
 
3.0%
28110 3
 
3.0%
47130 3
 
3.0%
41110 3
 
3.0%
48125 2
 
2.0%
48170 2
 
2.0%
Other values (53) 70
70.0%
ValueCountFrequency (%)
11110 4
4.0%
11140 4
4.0%
11170 1
 
1.0%
11230 2
2.0%
11290 2
2.0%
11350 1
 
1.0%
11380 2
2.0%
11545 1
 
1.0%
11560 1
 
1.0%
11710 1
 
1.0%
ValueCountFrequency (%)
48740 1
 
1.0%
48720 1
 
1.0%
48250 1
 
1.0%
48220 2
2.0%
48170 2
2.0%
48125 2
2.0%
47210 2
2.0%
47150 2
2.0%
47130 3
3.0%
47113 2
2.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.79164
Minimum126.49289
Maximum129.39887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:15.469548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.49289
5-th percentile126.64654
Q1126.98631
median127.536
Q3128.62423
95-th percentile129.21391
Maximum129.39887
Range2.9059836
Interquartile range (IQR)1.6379138

Descriptive statistics

Standard deviation0.93293719
Coefficient of variation (CV)0.0073004559
Kurtosis-1.4613805
Mean127.79164
Median Absolute Deviation (MAD)0.73284195
Skewness0.31850296
Sum12779.164
Variance0.87037181
MonotonicityNot monotonic
2023-12-10T18:51:15.740809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.3654507 2
 
2.0%
126.9863118 2
 
2.0%
126.995139 2
 
2.0%
127.0578139 2
 
2.0%
129.163506 1
 
1.0%
129.0301169 1
 
1.0%
127.1092361 1
 
1.0%
126.9017543 1
 
1.0%
127.1281369 1
 
1.0%
126.9786312 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
126.4928883 1
1.0%
126.513101 1
1.0%
126.6262619 1
1.0%
126.6285746 1
1.0%
126.6316652 1
1.0%
126.6473205 1
1.0%
126.6581121 1
1.0%
126.6785581 1
1.0%
126.7090121 1
1.0%
126.7239514 1
1.0%
ValueCountFrequency (%)
129.3988719 1
1.0%
129.3654507 2
2.0%
129.3213142 1
1.0%
129.2151396 1
1.0%
129.2138444 1
1.0%
129.1955855 1
1.0%
129.163506 1
1.0%
129.1548166 1
1.0%
129.1217837 1
1.0%
129.1088122 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.650364
Minimum34.63925
Maximum38.201993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:16.024047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.63925
5-th percentile35.094309
Q135.775168
median37.210356
Q337.527711
95-th percentile37.747903
Maximum38.201993
Range3.5627426
Interquartile range (IQR)1.7525427

Descriptive statistics

Standard deviation1.0375874
Coefficient of variation (CV)0.028310425
Kurtosis-1.3593056
Mean36.650364
Median Absolute Deviation (MAD)0.42548399
Skewness-0.49183229
Sum3665.0364
Variance1.0765875
MonotonicityNot monotonic
2023-12-10T18:51:16.295194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.03584143 2
 
2.0%
37.57311986 2
 
2.0%
37.59468334 2
 
2.0%
37.56990105 2
 
2.0%
37.44220264 1
 
1.0%
35.10052926 1
 
1.0%
37.50115127 1
 
1.0%
37.45359918 1
 
1.0%
37.5528781 1
 
1.0%
37.56562883 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
34.63925042 1
1.0%
34.6400889 1
1.0%
34.84042976 1
1.0%
34.8416119 1
1.0%
34.97612047 1
1.0%
35.10052926 1
1.0%
35.11450263 1
1.0%
35.1499914 1
1.0%
35.15072688 1
1.0%
35.15719355 1
1.0%
ValueCountFrequency (%)
38.20199299 1
1.0%
37.93285994 1
1.0%
37.89224079 1
1.0%
37.87468438 1
1.0%
37.84407578 1
1.0%
37.74284168 1
1.0%
37.73996173 1
1.0%
37.72313005 1
1.0%
37.65584825 1
1.0%
37.61583115 1
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
21 
경기
13 
인천
11 
강원
10 
경북
10 
Other values (8)
35 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 21
21.0%
경기 13
13.0%
인천 11
11.0%
강원 10
10.0%
경북 10
10.0%
부산 10
10.0%
경남 9
9.0%
대구 6
 
6.0%
충북 3
 
3.0%
전남 3
 
3.0%
Other values (3) 4
 
4.0%

Length

2023-12-10T18:51:16.743349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 21
21.0%
경기 13
13.0%
인천 11
11.0%
강원 10
10.0%
경북 10
10.0%
부산 10
10.0%
경남 9
9.0%
대구 6
 
6.0%
충북 3
 
3.0%
전남 3
 
3.0%
Other values (3) 4
 
4.0%

reprsnt_menu_nm
Text

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:17.240435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16.5
Mean length6.08
Min length2

Characters and Unicode

Total characters608
Distinct characters182
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)81.0%

Sample

1st row짜장면
2nd row미니족발
3rd row함흥냉면
4th row후라이드치킨
5th row양구이
ValueCountFrequency (%)
짜장면 6
 
4.2%
정식 3
 
2.1%
한정식 3
 
2.1%
막국수 2
 
1.4%
물 2
 
1.4%
1인분 2
 
1.4%
비빔 2
 
1.4%
2
 
1.4%
복국 2
 
1.4%
불고기 2
 
1.4%
Other values (112) 117
81.8%
2023-12-10T18:51:18.116061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45
 
7.4%
( 20
 
3.3%
) 19
 
3.1%
면 17
 
2.8%
국 15
 
2.5%
1 15
 
2.5%
장 13
 
2.1%
탕 12
 
2.0%
인 11
 
1.8%
한 11
 
1.8%
Other values (172) 430
70.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 478
78.6%
Space Separator 45
 
7.4%
Decimal Number 34
 
5.6%
Open Punctuation 20
 
3.3%
Close Punctuation 19
 
3.1%
Lowercase Letter 9
 
1.5%
Other Punctuation 2
 
0.3%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 17
 
3.6%
국 15
 
3.1%
장 13
 
2.7%
탕 12
 
2.5%
인 11
 
2.3%
한 11
 
2.3%
식 10
 
2.1%
리 9
 
1.9%
정 9
 
1.9%
냉 9
 
1.9%
Other values (158) 362
75.7%
Decimal Number
ValueCountFrequency (%)
1 15
44.1%
0 11
32.4%
2 4
 
11.8%
7 1
 
2.9%
3 1
 
2.9%
5 1
 
2.9%
8 1
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
g 8
88.9%
k 1
 
11.1%
Space Separator
ValueCountFrequency (%)
45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 477
78.5%
Common 120
 
19.7%
Latin 10
 
1.6%
Han 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 17
 
3.6%
국 15
 
3.1%
장 13
 
2.7%
탕 12
 
2.5%
인 11
 
2.3%
한 11
 
2.3%
식 10
 
2.1%
리 9
 
1.9%
정 9
 
1.9%
냉 9
 
1.9%
Other values (157) 361
75.7%
Common
ValueCountFrequency (%)
45
37.5%
( 20
16.7%
) 19
15.8%
1 15
 
12.5%
0 11
 
9.2%
2 4
 
3.3%
/ 2
 
1.7%
7 1
 
0.8%
3 1
 
0.8%
5 1
 
0.8%
Latin
ValueCountFrequency (%)
g 8
80.0%
k 1
 
10.0%
B 1
 
10.0%
Han
ValueCountFrequency (%)
小 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 477
78.5%
ASCII 130
 
21.4%
CJK 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45
34.6%
( 20
15.4%
) 19
14.6%
1 15
 
11.5%
0 11
 
8.5%
g 8
 
6.2%
2 4
 
3.1%
/ 2
 
1.5%
k 1
 
0.8%
7 1
 
0.8%
Other values (4) 4
 
3.1%
Hangul
ValueCountFrequency (%)
면 17
 
3.6%
국 15
 
3.1%
장 13
 
2.7%
탕 12
 
2.5%
인 11
 
2.3%
한 11
 
2.3%
식 10
 
2.1%
리 9
 
1.9%
정 9
 
1.9%
냉 9
 
1.9%
Other values (157) 361
75.7%
CJK
ValueCountFrequency (%)
小 1
100.0%

menu_pc
Real number (ℝ)

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16842
Minimum1000
Maximum90000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:18.360387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile5000
Q17000
median11000
Q325000
95-th percentile39050
Maximum90000
Range89000
Interquartile range (IQR)18000

Descriptive statistics

Standard deviation14906.681
Coefficient of variation (CV)0.88508969
Kurtosis7.5824706
Mean16842
Median Absolute Deviation (MAD)4500
Skewness2.3543094
Sum1684200
Variance2.2220913 × 108
MonotonicityNot monotonic
2023-12-10T18:51:18.555387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7000 10
 
10.0%
10000 8
 
8.0%
9000 7
 
7.0%
30000 6
 
6.0%
35000 6
 
6.0%
11000 5
 
5.0%
12000 5
 
5.0%
5000 5
 
5.0%
8000 4
 
4.0%
25000 4
 
4.0%
Other values (24) 40
40.0%
ValueCountFrequency (%)
1000 1
 
1.0%
4000 2
 
2.0%
4500 1
 
1.0%
5000 5
5.0%
5500 2
 
2.0%
6000 3
 
3.0%
6500 4
 
4.0%
6800 1
 
1.0%
7000 10
10.0%
8000 4
 
4.0%
ValueCountFrequency (%)
90000 1
 
1.0%
80000 1
 
1.0%
50000 1
 
1.0%
49000 1
 
1.0%
40000 1
 
1.0%
39000 2
 
2.0%
35000 6
6.0%
32000 2
 
2.0%
30000 6
6.0%
27000 1
 
1.0%

base_ymd
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2019-12-09 00:00:00
Maximum2019-12-09 00:00:00
2023-12-10T18:51:18.665319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:18.782675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T18:51:09.423640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:04.164385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:05.493074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:06.716985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:08.278344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:09.760994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:04.535239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:05.826421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:06.928283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:08.495385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:09.968103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:04.838666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:06.063118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:07.689731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:08.734100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:10.132032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:05.053393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:06.275414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:07.905455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:08.917887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:10.358009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:05.314707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:06.496585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:08.118372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:09.168978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:51:18.911202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmreprsnt_menu_nmmenu_pc
entrp_nm1.0001.0001.0001.0001.0001.0001.0000.9980.985
load_addr1.0001.0001.0001.0001.0001.0001.0000.9960.968
city_do_cd1.0001.0001.0001.0000.8870.9411.0000.9160.179
city_gn_gu_cd1.0001.0001.0001.0000.7180.7740.9490.9610.146
xpos_lo1.0001.0000.8870.7181.0000.8960.8870.7990.144
ypos_la1.0001.0000.9410.7740.8961.0000.9140.8450.146
area_nm1.0001.0001.0000.9490.8870.9141.0000.9670.000
reprsnt_menu_nm0.9980.9960.9160.9610.7990.8450.9671.0000.998
menu_pc0.9850.9680.1790.1460.1440.1460.0000.9981.000
2023-12-10T18:51:19.115516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdcity_gn_gu_cdxpos_loypos_lamenu_pcarea_nm
city_do_cd1.0000.9920.366-0.4410.1000.957
city_gn_gu_cd0.9921.0000.369-0.4370.0870.804
xpos_lo0.3660.3691.000-0.5850.0400.632
ypos_la-0.441-0.437-0.5851.000-0.1130.692
menu_pc0.1000.0870.040-0.1131.0000.000
area_nm0.9570.8040.6320.6920.0001.000

Missing values

2023-12-10T18:51:10.664814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:51:10.979272image/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_nmreprsnt_menu_nmmenu_pcbase_ymd
0동승춘강원도 삼척시 대학로 124242230129.16350637.442203강원짜장면45002019-12-09
1삼미족발충청북도 청주시 청원구 향군로 354343710127.48634336.648861충북미니족발250002019-12-09
2함흥냉면옥강원도 속초시 청초호반로 2994242210128.58986938.201993강원함흥냉면80002019-12-09
3진미통닭강원도 원주시 우산로 664242130127.9369837.369474강원후라이드치킨160002019-12-09
4시골집강원도 원주시 중평길 10-14242130127.95024637.349177강원양구이320002019-12-09
5원주복추어탕강원도 원주시 치악로 17484242130127.95641237.338561강원한우 추어탕(통)120002019-12-09
6미락정갈비강원도 정선군 정선읍 녹송1길 184242770128.67197837.381981강원미락정 정식100002019-12-09
7복서울해장국충청북도 충주시 관아1길 164343130127.93510836.970265충북선지해장국70002019-12-09
8명가춘천막국수강원도 춘천시 당간지주길 764242110127.72387237.892241강원순메밀막국수70002019-12-09
9도지골등나무집강원도 춘천시 상천3길 24242110127.79276637.93286강원잡어매운탕400002019-12-09
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmreprsnt_menu_nmmenu_pcbase_ymd
90송원산오리인천광역시 미추홀구 미추홀대로719번길 282828170126.67855837.461925인천오리코스요리900002019-12-09
91복화루인천광역시 부평구 부평대로32번길 162828237126.72395137.494305인천짜장면55002019-12-09
92함흥냉면인천광역시 부평구 시장로20번길 212828237126.72631537.49257인천함흥냉면70002019-12-09
93전동집인천광역시 연수구 앵고개로103번길 252828185126.65811237.413873인천제육밥상90002019-12-09
94돈비어천가인천광역시 중구 개항로 53-12828110126.62857537.472564인천왕갈비 300g180002019-12-09
95신동양인천광역시 중구 서해대로449번길 522828110126.63166537.46715인천짜장면40002019-12-09
96신포순대인천광역시 중구 제물량로166번길 332828110126.62626237.471532인천순대 (1인분)70002019-12-09
97우성식당전라남도 강진군 강진읍 남문길 24646810126.7692234.640089전남목살1인분(200g100002019-12-09
98해태식당전라남도 강진군 서성안길 64646810126.76718934.63925전남해태정식(1인)300002019-12-09
99대한식당전라남도 광양시 매일시장길 12-154646230127.5856534.97612전남광양 불고기 국내산(180g)230002019-12-09