Overview

Dataset statistics

Number of variables10
Number of observations783
Missing cells789
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.1 KiB
Average record size in memory81.2 B

Variable types

Text6
Categorical3
Unsupported1

Dataset

Description민방위비상대피소현황1510월
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202471

Alerts

Unnamed: 4 is highly overall correlated with Unnamed: 1 and 1 other fieldsHigh correlation
Unnamed: 2 is highly overall correlated with Unnamed: 1 and 1 other fieldsHigh correlation
Unnamed: 1 is highly overall correlated with Unnamed: 2 and 1 other fieldsHigh correlation
Unnamed: 1 is highly imbalanced (98.2%)Imbalance
Unnamed: 4 is highly imbalanced (94.6%)Imbalance
Unnamed: 9 has 783 (100.0%) missing valuesMissing
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 00:14:11.575501
Analysis finished2024-03-14 00:14:12.619067
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Text

Distinct782
Distinct (%)100.0%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
2024-03-14T09:14:12.895068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8606138
Min length1

Characters and Unicode

Total characters2237
Distinct characters12
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

Unique782 ?
Unique (%)100.0%

Sample

1st row연번
2nd row1
3rd row2
4th row3
5th row4
ValueCountFrequency (%)
연번 1
 
0.1%
489 1
 
0.1%
524 1
 
0.1%
515 1
 
0.1%
516 1
 
0.1%
517 1
 
0.1%
518 1
 
0.1%
519 1
 
0.1%
520 1
 
0.1%
521 1
 
0.1%
Other values (772) 772
98.7%
2024-03-14T09:14:13.418256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 259
11.6%
4 258
11.5%
2 258
11.5%
3 258
11.5%
5 258
11.5%
6 258
11.5%
7 240
10.7%
8 150
6.7%
0 148
6.6%
9 148
6.6%
Other values (2) 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2235
99.9%
Other Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 259
11.6%
4 258
11.5%
2 258
11.5%
3 258
11.5%
5 258
11.5%
6 258
11.5%
7 240
10.7%
8 150
6.7%
0 148
6.6%
9 148
6.6%
Other Letter
ValueCountFrequency (%)
연 1
50.0%
번 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2235
99.9%
Hangul 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 259
11.6%
4 258
11.5%
2 258
11.5%
3 258
11.5%
5 258
11.5%
6 258
11.5%
7 240
10.7%
8 150
6.7%
0 148
6.6%
9 148
6.6%
Hangul
ValueCountFrequency (%)
연 1
50.0%
번 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2235
99.9%
Hangul 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 259
11.6%
4 258
11.5%
2 258
11.5%
3 258
11.5%
5 258
11.5%
6 258
11.5%
7 240
10.7%
8 150
6.7%
0 148
6.6%
9 148
6.6%
Hangul
ValueCountFrequency (%)
연 1
50.0%
번 1
50.0%

Unnamed: 1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
전라북도
781 
시도명
 
1
<NA>
 
1

Length

Max length4
Median length4
Mean length3.9987229
Min length3

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 781
99.7%
시도명 1
 
0.1%
<NA> 1
 
0.1%

Length

2024-03-14T09:14:13.530076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:14:13.605518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 781
99.7%
시도명 1
 
0.1%
na 1
 
0.1%

Unnamed: 2
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
전주시
331 
군산시
129 
익산시
86 
정읍시
83 
남원시
 
33
Other values (11)
121 

Length

Max length4
Median length3
Mean length3.0012771
Min length3

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 331
42.3%
군산시 129
 
16.5%
익산시 86
 
11.0%
정읍시 83
 
10.6%
남원시 33
 
4.2%
고창군 24
 
3.1%
김제시 20
 
2.6%
완주군 20
 
2.6%
부안군 17
 
2.2%
순창군 14
 
1.8%
Other values (6) 26
 
3.3%

Length

2024-03-14T09:14:13.687783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 331
42.3%
군산시 129
 
16.5%
익산시 86
 
11.0%
정읍시 83
 
10.6%
남원시 33
 
4.2%
고창군 24
 
3.1%
김제시 20
 
2.6%
완주군 20
 
2.6%
부안군 17
 
2.2%
순창군 14
 
1.8%
Other values (6) 26
 
3.3%

Unnamed: 3
Text

Distinct99
Distinct (%)12.7%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
2024-03-14T09:14:13.882321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2647059
Min length2

Characters and Unicode

Total characters2553
Distinct characters100
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

Unique15 ?
Unique (%)1.9%

Sample

1st row읍면동
2nd row노송동
3rd row중앙동
4th row풍남동
5th row완산동
ValueCountFrequency (%)
평화2 34
 
4.3%
내장상동 28
 
3.6%
효자4동 28
 
3.6%
조촌동 27
 
3.5%
고창읍 24
 
3.1%
중앙동 22
 
2.8%
수성동 22
 
2.8%
시기동 20
 
2.6%
수송동 18
 
2.3%
부안읍 15
 
1.9%
Other values (89) 544
69.6%
2024-03-14T09:14:14.202561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 572
22.4%
2 105
 
4.1%
1 97
 
3.8%
읍 93
 
3.6%
화 76
 
3.0%
효 61
 
2.4%
자 61
 
2.4%
중 56
 
2.2%
산 54
 
2.1%
삼 50
 
2.0%
Other values (90) 1328
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2276
89.2%
Decimal Number 277
 
10.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 572
25.1%
읍 93
 
4.1%
화 76
 
3.3%
효 61
 
2.7%
자 61
 
2.7%
중 56
 
2.5%
산 54
 
2.4%
삼 50
 
2.2%
평 49
 
2.2%
천 47
 
2.1%
Other values (86) 1157
50.8%
Decimal Number
ValueCountFrequency (%)
2 105
37.9%
1 97
35.0%
3 47
17.0%
4 28
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2276
89.2%
Common 277
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 572
25.1%
읍 93
 
4.1%
화 76
 
3.3%
효 61
 
2.7%
자 61
 
2.7%
중 56
 
2.5%
산 54
 
2.4%
삼 50
 
2.2%
평 49
 
2.2%
천 47
 
2.1%
Other values (86) 1157
50.8%
Common
ValueCountFrequency (%)
2 105
37.9%
1 97
35.0%
3 47
17.0%
4 28
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2276
89.2%
ASCII 277
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 572
25.1%
읍 93
 
4.1%
화 76
 
3.3%
효 61
 
2.7%
자 61
 
2.7%
중 56
 
2.5%
산 54
 
2.4%
삼 50
 
2.2%
평 49
 
2.2%
천 47
 
2.1%
Other values (86) 1157
50.8%
ASCII
ValueCountFrequency (%)
2 105
37.9%
1 97
35.0%
3 47
17.0%
4 28
 
10.1%

Unnamed: 4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
공공
772 
정부지원
 
8
대피소 구분
 
1
<NA>
 
1
정부
 
1

Length

Max length6
Median length2
Mean length2.0280971
Min length2

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st row대피소 구분
2nd row<NA>
3rd row정부지원
4th row정부지원
5th row정부지원

Common Values

ValueCountFrequency (%)
공공 772
98.6%
정부지원 8
 
1.0%
대피소 구분 1
 
0.1%
<NA> 1
 
0.1%
정부 1
 
0.1%

Length

2024-03-14T09:14:14.327553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:14:14.440851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공 772
98.5%
정부지원 8
 
1.0%
대피소 1
 
0.1%
구분 1
 
0.1%
na 1
 
0.1%
정부 1
 
0.1%

Unnamed: 5
Text

Distinct307
Distinct (%)39.3%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
2024-03-14T09:14:14.701029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length8.8222506
Min length4

Characters and Unicode

Total characters6899
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)23.1%

Sample

1st row설치년도
2nd row03.05.26
3rd row80.09.02
4th row88.10.20
5th row89.10.14
ValueCountFrequency (%)
11.1.20 71
 
8.8%
2005-03-15 27
 
3.3%
2005-03-14 26
 
3.2%
1998-04 17
 
2.1%
1998 14
 
1.7%
2003-01-02 12
 
1.5%
2011-01-06 11
 
1.4%
2003-01-01 11
 
1.4%
2003-05-12 9
 
1.1%
2010.12.08 9
 
1.1%
Other values (302) 599
74.3%
2024-03-14T09:14:15.097775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1702
24.7%
1 1275
18.5%
2 931
13.5%
- 850
12.3%
. 635
 
9.2%
9 502
 
7.3%
3 348
 
5.0%
5 182
 
2.6%
8 150
 
2.2%
4 140
 
2.0%
Other values (7) 184
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5386
78.1%
Dash Punctuation 850
 
12.3%
Other Punctuation 635
 
9.2%
Space Separator 24
 
0.3%
Other Letter 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1702
31.6%
1 1275
23.7%
2 931
17.3%
9 502
 
9.3%
3 348
 
6.5%
5 182
 
3.4%
8 150
 
2.8%
4 140
 
2.6%
6 83
 
1.5%
7 73
 
1.4%
Other Letter
ValueCountFrequency (%)
설 1
25.0%
치 1
25.0%
년 1
25.0%
도 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 850
100.0%
Other Punctuation
ValueCountFrequency (%)
. 635
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6895
99.9%
Hangul 4
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1702
24.7%
1 1275
18.5%
2 931
13.5%
- 850
12.3%
. 635
 
9.2%
9 502
 
7.3%
3 348
 
5.0%
5 182
 
2.6%
8 150
 
2.2%
4 140
 
2.0%
Other values (3) 180
 
2.6%
Hangul
ValueCountFrequency (%)
설 1
25.0%
치 1
25.0%
년 1
25.0%
도 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6895
99.9%
Hangul 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1702
24.7%
1 1275
18.5%
2 931
13.5%
- 850
12.3%
. 635
 
9.2%
9 502
 
7.3%
3 348
 
5.0%
5 182
 
2.6%
8 150
 
2.2%
4 140
 
2.0%
Other values (3) 180
 
2.6%
Hangul
ValueCountFrequency (%)
설 1
25.0%
치 1
25.0%
년 1
25.0%
도 1
25.0%

Unnamed: 6
Text

Distinct768
Distinct (%)98.2%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
2024-03-14T09:14:15.412467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length18.375959
Min length4

Characters and Unicode

Total characters14370
Distinct characters397
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique758 ?
Unique (%)96.9%

Sample

1st row대피소위치
2nd row전주시청
3rd row다가대피소
4th row풍남초등학교대피소
5th row시립도서관(곤지산 4길12)
ValueCountFrequency (%)
남원시 33
 
1.6%
고창읍 24
 
1.1%
고창군 24
 
1.1%
전라북도 21
 
1.0%
완주군 20
 
0.9%
아파트 18
 
0.8%
부안군 16
 
0.8%
부안읍 14
 
0.7%
순창읍 14
 
0.7%
조촌 12
 
0.6%
Other values (1346) 1926
90.8%
2024-03-14T09:14:15.769900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1393
 
9.7%
( 816
 
5.7%
) 795
 
5.5%
1 707
 
4.9%
로 525
 
3.7%
2 430
 
3.0%
아 387
 
2.7%
동 356
 
2.5%
트 345
 
2.4%
파 341
 
2.4%
Other values (387) 8275
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8405
58.5%
Decimal Number 2718
 
18.9%
Space Separator 1393
 
9.7%
Open Punctuation 816
 
5.7%
Close Punctuation 795
 
5.5%
Dash Punctuation 107
 
0.7%
Control 58
 
0.4%
Other Punctuation 50
 
0.3%
Lowercase Letter 15
 
0.1%
Uppercase Letter 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 525
 
6.2%
아 387
 
4.6%
동 356
 
4.2%
트 345
 
4.1%
파 341
 
4.1%
길 280
 
3.3%
대 169
 
2.0%
산 139
 
1.7%
차 134
 
1.6%
지 124
 
1.5%
Other values (357) 5605
66.7%
Decimal Number
ValueCountFrequency (%)
1 707
26.0%
2 430
15.8%
3 302
11.1%
0 273
 
10.0%
5 208
 
7.7%
4 188
 
6.9%
6 179
 
6.6%
9 162
 
6.0%
7 148
 
5.4%
8 121
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
k 5
33.3%
e 2
 
13.3%
s 2
 
13.3%
b 2
 
13.3%
w 1
 
6.7%
i 1
 
6.7%
v 1
 
6.7%
t 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 36
72.0%
@ 13
 
26.0%
. 1
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
A 6
50.0%
T 3
25.0%
P 3
25.0%
Space Separator
ValueCountFrequency (%)
1393
100.0%
Open Punctuation
ValueCountFrequency (%)
( 816
100.0%
Close Punctuation
ValueCountFrequency (%)
) 795
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 107
100.0%
Control
ValueCountFrequency (%)
58
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8406
58.5%
Common 5937
41.3%
Latin 27
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 525
 
6.2%
아 387
 
4.6%
동 356
 
4.2%
트 345
 
4.1%
파 341
 
4.1%
길 280
 
3.3%
대 169
 
2.0%
산 139
 
1.7%
차 134
 
1.6%
지 124
 
1.5%
Other values (358) 5606
66.7%
Common
ValueCountFrequency (%)
1393
23.5%
( 816
13.7%
) 795
13.4%
1 707
11.9%
2 430
 
7.2%
3 302
 
5.1%
0 273
 
4.6%
5 208
 
3.5%
4 188
 
3.2%
6 179
 
3.0%
Other values (8) 646
10.9%
Latin
ValueCountFrequency (%)
A 6
22.2%
k 5
18.5%
T 3
11.1%
P 3
11.1%
e 2
 
7.4%
s 2
 
7.4%
b 2
 
7.4%
w 1
 
3.7%
i 1
 
3.7%
v 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8405
58.5%
ASCII 5964
41.5%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1393
23.4%
( 816
13.7%
) 795
13.3%
1 707
11.9%
2 430
 
7.2%
3 302
 
5.1%
0 273
 
4.6%
5 208
 
3.5%
4 188
 
3.2%
6 179
 
3.0%
Other values (19) 673
11.3%
Hangul
ValueCountFrequency (%)
로 525
 
6.2%
아 387
 
4.6%
동 356
 
4.2%
트 345
 
4.1%
파 341
 
4.1%
길 280
 
3.3%
대 169
 
2.0%
산 139
 
1.7%
차 134
 
1.6%
지 124
 
1.5%
Other values (357) 5605
66.7%
None
ValueCountFrequency (%)
㈜ 1
100.0%

Unnamed: 7
Text

Distinct681
Distinct (%)87.1%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
2024-03-14T09:14:16.087554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.9501279
Min length2

Characters and Unicode

Total characters3871
Distinct characters15
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

Unique603 ?
Unique (%)77.1%

Sample

1st row면적
2nd row1,372
3rd row1,296
4th row840
5th row621
ValueCountFrequency (%)
2,981 5
 
0.6%
1,518 4
 
0.5%
330 4
 
0.5%
1887 4
 
0.5%
825 4
 
0.5%
2,805 4
 
0.5%
2,640 3
 
0.4%
856 3
 
0.4%
234 3
 
0.4%
485 3
 
0.4%
Other values (650) 745
95.3%
2024-03-14T09:14:16.537674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
534
13.8%
, 433
11.2%
1 416
10.7%
2 360
9.3%
0 308
8.0%
3 306
7.9%
4 295
7.6%
8 264
6.8%
5 257
6.6%
6 253
6.5%
Other values (5) 445
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2895
74.8%
Space Separator 534
 
13.8%
Other Punctuation 440
 
11.4%
Other Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 416
14.4%
2 360
12.4%
0 308
10.6%
3 306
10.6%
4 295
10.2%
8 264
9.1%
5 257
8.9%
6 253
8.7%
7 232
8.0%
9 204
7.0%
Other Punctuation
ValueCountFrequency (%)
, 433
98.4%
. 7
 
1.6%
Other Letter
ValueCountFrequency (%)
면 1
50.0%
적 1
50.0%
Space Separator
ValueCountFrequency (%)
534
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3869
99.9%
Hangul 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
534
13.8%
, 433
11.2%
1 416
10.8%
2 360
9.3%
0 308
8.0%
3 306
7.9%
4 295
7.6%
8 264
6.8%
5 257
6.6%
6 253
6.5%
Other values (3) 443
11.4%
Hangul
ValueCountFrequency (%)
면 1
50.0%
적 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3869
99.9%
Hangul 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
534
13.8%
, 433
11.2%
1 416
10.8%
2 360
9.3%
0 308
8.0%
3 306
7.9%
4 295
7.6%
8 264
6.8%
5 257
6.6%
6 253
6.5%
Other values (3) 443
11.4%
Hangul
ValueCountFrequency (%)
면 1
50.0%
적 1
50.0%

Unnamed: 8
Text

Distinct657
Distinct (%)84.0%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
2024-03-14T09:14:16.903934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length4.741688
Min length2

Characters and Unicode

Total characters3708
Distinct characters21
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique580 ?
Unique (%)74.2%

Sample

1st row수용 인원(명)
2nd row1,660
3rd row1,568
4th row1,016
5th row751
ValueCountFrequency (%)
800 7
 
0.9%
3,300 6
 
0.8%
1,000 6
 
0.8%
3,560 5
 
0.6%
400 5
 
0.6%
760 5
 
0.6%
1,300 5
 
0.6%
1,600 4
 
0.5%
1,780 4
 
0.5%
2287 4
 
0.5%
Other values (624) 732
93.5%
2024-03-14T09:14:17.391180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 539
14.5%
, 410
11.1%
2 377
10.2%
1 364
9.8%
334
9.0%
4 279
7.5%
8 279
7.5%
3 272
7.3%
6 250
6.7%
5 240
6.5%
Other values (11) 364
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2950
79.6%
Other Punctuation 416
 
11.2%
Space Separator 334
 
9.0%
Other Letter 5
 
0.1%
Control 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 539
18.3%
2 377
12.8%
1 364
12.3%
4 279
9.5%
8 279
9.5%
3 272
9.2%
6 250
8.5%
5 240
8.1%
7 200
 
6.8%
9 150
 
5.1%
Other Letter
ValueCountFrequency (%)
수 1
20.0%
용 1
20.0%
인 1
20.0%
원 1
20.0%
명 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 410
98.6%
. 6
 
1.4%
Space Separator
ValueCountFrequency (%)
334
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3703
99.9%
Hangul 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 539
14.6%
, 410
11.1%
2 377
10.2%
1 364
9.8%
334
9.0%
4 279
7.5%
8 279
7.5%
3 272
7.3%
6 250
6.8%
5 240
6.5%
Other values (6) 359
9.7%
Hangul
ValueCountFrequency (%)
수 1
20.0%
용 1
20.0%
인 1
20.0%
원 1
20.0%
명 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3703
99.9%
Hangul 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 539
14.6%
, 410
11.1%
2 377
10.2%
1 364
9.8%
334
9.0%
4 279
7.5%
8 279
7.5%
3 272
7.3%
6 250
6.8%
5 240
6.5%
Other values (6) 359
9.7%
Hangul
ValueCountFrequency (%)
수 1
20.0%
용 1
20.0%
인 1
20.0%
원 1
20.0%
명 1
20.0%

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing783
Missing (%)100.0%
Memory size7.0 KiB

Correlations

2024-03-14T09:14:17.516984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
Unnamed: 11.0001.0001.0001.000
Unnamed: 21.0001.0001.0000.787
Unnamed: 31.0001.0001.0000.882
Unnamed: 41.0000.7870.8821.000
2024-03-14T09:14:17.605137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 2Unnamed: 1
Unnamed: 41.0000.5790.999
Unnamed: 20.5791.0000.992
Unnamed: 10.9990.9921.000
2024-03-14T09:14:17.687549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 4
Unnamed: 11.0000.9920.999
Unnamed: 20.9921.0000.579
Unnamed: 40.9990.5791.000

Missing values

2024-03-14T09:14:12.053147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:14:12.159176image/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.
2024-03-14T09:14:12.507128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
0연번시도명시군구읍면동대피소 구분설치년도대피소위치면적수용 인원(명)<NA>
1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21전라북도전주시노송동정부지원03.05.26전주시청1,3721,660<NA>
32전라북도전주시중앙동정부지원80.09.02다가대피소1,2961,568<NA>
43전라북도전주시풍남동정부지원88.10.20풍남초등학교대피소8401,016<NA>
54전라북도전주시완산동공공89.10.14시립도서관(곤지산 4길12)621751<NA>
65전라북도전주시완산동공공11.1.20완산교회(전주천서로 181)626758<NA>
76전라북도전주시완산동공공11.1.20오페라하우스(안행로 150)1419717179<NA>
87전라북도전주시완산동공공91.1.30완산동주민센터(강당2길7)112136<NA>
98전라북도전주시완산동공공02.1.30대풍한마을(따박골7길22)396480<NA>
Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
773772전라북도부안군부안읍공공1998-11-20전라북도 부안군 부안읍 낭주길 5(대림아파트)9921200<NA>
774773전라북도부안군부안읍공공1998-0101전라북도 부안군 부안읍 학동길 11-6(동원아파트)22482720<NA>
775774전라북도부안군부안읍공공1997-08-29전라북도 부안군 부안읍 봉신길 5(동영아파트)21362572<NA>
776775전라북도부안군부안읍공공1991-02-28전라북도 부안군 부안읍 상원길 19(상원아파트)774936<NA>
777776전라북도부안군부안읍공공1990-12-15전아북도 부안군 부안읍 오리정로 172(하이안아파트)36204380<NA>
778777전라북도부안군부안읍공공1993-11-19전라북도 부안군 부안읍 당산로 91(부안군청)883410707<NA>
779778전라북도부안군부안읍공공2005-10-21전라북도 부안군 변산면 지서로 80(송림아파트)26213176<NA>
780779전라북도부안군변산면공공2010-11-03전라북도 부안군변산면 변산해변로 51(대명리조트)1483317978<NA>
781780전라북도부안군변산면공공2000-11-29전라북도 부안군 변산면 격포윗길 20(양우아파트)14001696<NA>
782781전라북도부안군부안읍공공2008-07-11전라북도 부안군 부안읍 봉신길 19(현대아파트)19242328<NA>