Overview

Dataset statistics

Number of variables13
Number of observations71
Missing cells364
Missing cells (%)39.4%
Duplicate rows1
Duplicate rows (%)1.4%
Total size in memory7.5 KiB
Average record size in memory108.9 B

Variable types

Unsupported5
Text3
Categorical4
Numeric1

Dataset

Description충청북도 보은군의 임도현황에 대한 데이터로 보은군에 있는 임도현황(노선명, 거리, 조성년도 등) 대한 정보를 제공합니다.
Author충청북도 보은군
URLhttps://www.data.go.kr/data/15054744/fileData.do

Alerts

Dataset has 1 (1.4%) duplicate rowsDuplicates
Unnamed: 4 is highly overall correlated with Unnamed: 10High correlation
Unnamed: 10 is highly overall correlated with Unnamed: 4High correlation
Unnamed: 9 is highly imbalanced (51.1%)Imbalance
Unnamed: 12 is highly imbalanced (89.3%)Imbalance
보은군 임도시설 현황 has 34 (47.9%) missing valuesMissing
Unnamed: 1 has 34 (47.9%) missing valuesMissing
Unnamed: 2 has 21 (29.6%) missing valuesMissing
Unnamed: 3 has 8 (11.3%) missing valuesMissing
Unnamed: 5 has 30 (42.3%) missing valuesMissing
Unnamed: 6 has 53 (74.6%) missing valuesMissing
Unnamed: 7 has 52 (73.2%) missing valuesMissing
Unnamed: 8 has 61 (85.9%) missing valuesMissing
Unnamed: 11 has 71 (100.0%) missing valuesMissing
보은군 임도시설 현황 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 10:12:35.056468
Analysis finished2023-12-12 10:12:36.375357
Duration1.32 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

보은군 임도시설 현황
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing34
Missing (%)47.9%
Memory size700.0 B

Unnamed: 1
Text

MISSING 

Distinct37
Distinct (%)100.0%
Missing34
Missing (%)47.9%
Memory size700.0 B
2023-12-12T19:12:36.523079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.1351351
Min length1

Characters and Unicode

Total characters264
Distinct characters86
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

Unique37 ?
Unique (%)100.0%

Sample

1st row노 선 명
2nd row계
3rd row내속 중판-중판
4th row보은 노티-내북 하궁
5th row산외 아시-백석
ValueCountFrequency (%)
산외 7
 
9.0%
내북 5
 
6.4%
회남 4
 
5.1%
보은 4
 
5.1%
수한 4
 
5.1%
탄부 3
 
3.8%
2
 
2.6%
탄부대양 2
 
2.6%
판장-수한 1
 
1.3%
노성 1
 
1.3%
Other values (45) 45
57.7%
2023-12-12T19:12:36.873658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41
 
15.5%
- 17
 
6.4%
산 10
 
3.8%
외 8
 
3.0%
내 8
 
3.0%
북 7
 
2.7%
대 6
 
2.3%
수 6
 
2.3%
부 6
 
2.3%
판 5
 
1.9%
Other values (76) 150
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 202
76.5%
Space Separator 41
 
15.5%
Dash Punctuation 17
 
6.4%
Math Symbol 4
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 10
 
5.0%
외 8
 
4.0%
내 8
 
4.0%
북 7
 
3.5%
대 6
 
3.0%
수 6
 
3.0%
부 6
 
3.0%
판 5
 
2.5%
한 5
 
2.5%
은 5
 
2.5%
Other values (73) 136
67.3%
Space Separator
ValueCountFrequency (%)
41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 202
76.5%
Common 62
 
23.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 10
 
5.0%
외 8
 
4.0%
내 8
 
4.0%
북 7
 
3.5%
대 6
 
3.0%
수 6
 
3.0%
부 6
 
3.0%
판 5
 
2.5%
한 5
 
2.5%
은 5
 
2.5%
Other values (73) 136
67.3%
Common
ValueCountFrequency (%)
41
66.1%
- 17
27.4%
~ 4
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 202
76.5%
ASCII 62
 
23.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41
66.1%
- 17
27.4%
~ 4
 
6.5%
Hangul
ValueCountFrequency (%)
산 10
 
5.0%
외 8
 
4.0%
내 8
 
4.0%
북 7
 
3.5%
대 6
 
3.0%
수 6
 
3.0%
부 6
 
3.0%
판 5
 
2.5%
한 5
 
2.5%
은 5
 
2.5%
Other values (73) 136
67.3%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)29.6%
Memory size700.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)11.3%
Memory size700.0 B

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Memory size700.0 B
<NA>
30 
간선
30 
작업
7 
비 고
 
1
국유림 소관
 
1
Other values (2)
 
2

Length

Max length33
Median length2
Mean length3.3661972
Min length2

Unique

Unique4 ?
Unique (%)5.6%

Sample

1st row<NA>
2nd row<NA>
3rd row비 고
4th row<NA>
5th row간선

Common Values

ValueCountFrequency (%)
<NA> 30
42.3%
간선 30
42.3%
작업 7
 
9.9%
비 고 1
 
1.4%
국유림 소관 1
 
1.4%
간선임도 : 7.58 작업임도 : 2.19 합계 : 9.77 1
 
1.4%
간선 1
 
1.4%

Length

2023-12-12T19:12:37.026614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:12:37.160266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
간선 31
38.3%
na 30
37.0%
작업 7
 
8.6%
3
 
3.7%
비 1
 
1.2%
고 1
 
1.2%
국유림 1
 
1.2%
소관 1
 
1.2%
간선임도 1
 
1.2%
7.58 1
 
1.2%
Other values (4) 4
 
4.9%

Unnamed: 5
Text

MISSING 

Distinct41
Distinct (%)100.0%
Missing30
Missing (%)42.3%
Memory size700.0 B
2023-12-12T19:12:37.405811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.9512195
Min length4

Characters and Unicode

Total characters367
Distinct characters86
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st row진입위치
2nd row중판리 281-2
3rd row보은읍 노티리 131-1
4th row산외면 아시리 123-2
5th row산외면 백석리 산39-4
ValueCountFrequency (%)
산외면 6
 
6.6%
대양리 4
 
4.4%
중판리 3
 
3.3%
백석리 2
 
2.2%
상장리 2
 
2.2%
탄부면 1
 
1.1%
335-2 1
 
1.1%
정상 1
 
1.1%
말티재 1
 
1.1%
245 1
 
1.1%
Other values (69) 69
75.8%
2023-12-12T19:12:38.154648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50
 
13.6%
리 38
 
10.4%
2 26
 
7.1%
1 22
 
6.0%
산 21
 
5.7%
- 20
 
5.4%
3 17
 
4.6%
4 12
 
3.3%
면 9
 
2.5%
8 8
 
2.2%
Other values (76) 144
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 173
47.1%
Decimal Number 121
33.0%
Space Separator 50
 
13.6%
Dash Punctuation 20
 
5.4%
Lowercase Letter 2
 
0.5%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 38
22.0%
산 21
 
12.1%
면 9
 
5.2%
외 6
 
3.5%
대 6
 
3.5%
판 4
 
2.3%
상 4
 
2.3%
정 4
 
2.3%
양 4
 
2.3%
장 4
 
2.3%
Other values (61) 73
42.2%
Decimal Number
ValueCountFrequency (%)
2 26
21.5%
1 22
18.2%
3 17
14.0%
4 12
9.9%
8 8
 
6.6%
6 8
 
6.6%
5 8
 
6.6%
9 7
 
5.8%
7 7
 
5.8%
0 6
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%
Space Separator
ValueCountFrequency (%)
50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 192
52.3%
Hangul 173
47.1%
Latin 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 38
22.0%
산 21
 
12.1%
면 9
 
5.2%
외 6
 
3.5%
대 6
 
3.5%
판 4
 
2.3%
상 4
 
2.3%
정 4
 
2.3%
양 4
 
2.3%
장 4
 
2.3%
Other values (61) 73
42.2%
Common
ValueCountFrequency (%)
50
26.0%
2 26
13.5%
1 22
11.5%
- 20
 
10.4%
3 17
 
8.9%
4 12
 
6.2%
8 8
 
4.2%
6 8
 
4.2%
5 8
 
4.2%
9 7
 
3.6%
Other values (3) 14
 
7.3%
Latin
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194
52.9%
Hangul 173
47.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50
25.8%
2 26
13.4%
1 22
11.3%
- 20
 
10.3%
3 17
 
8.8%
4 12
 
6.2%
8 8
 
4.1%
6 8
 
4.1%
5 8
 
4.1%
9 7
 
3.6%
Other values (5) 16
 
8.2%
Hangul
ValueCountFrequency (%)
리 38
22.0%
산 21
 
12.1%
면 9
 
5.2%
외 6
 
3.5%
대 6
 
3.5%
판 4
 
2.3%
상 4
 
2.3%
정 4
 
2.3%
양 4
 
2.3%
장 4
 
2.3%
Other values (61) 73
42.2%

Unnamed: 6
Text

MISSING 

Distinct18
Distinct (%)100.0%
Missing53
Missing (%)74.6%
Memory size700.0 B
2023-12-12T19:12:38.426296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length9.6666667
Min length6

Characters and Unicode

Total characters174
Distinct characters59
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

Unique18 ?
Unique (%)100.0%

Sample

1st row내북면 하궁리 47
2nd row산외면 백석리 산11-3
3rd row탄부면 벽지리 344
4th row산외면 원평리 232-2
5th row동정리 441, 노성리 120
ValueCountFrequency (%)
산외면 2
 
4.7%
대양리 2
 
4.7%
내북면 1
 
2.3%
상궁리 1
 
2.3%
성암리 1
 
2.3%
66 1
 
2.3%
서지리 1
 
2.3%
229-1 1
 
2.3%
안내면 1
 
2.3%
용촌리 1
 
2.3%
Other values (31) 31
72.1%
2023-12-12T19:12:38.845397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25
 
14.4%
리 18
 
10.3%
1 13
 
7.5%
2 11
 
6.3%
4 9
 
5.2%
- 9
 
5.2%
산 6
 
3.4%
3 6
 
3.4%
면 5
 
2.9%
7 5
 
2.9%
Other values (49) 67
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82
47.1%
Decimal Number 55
31.6%
Space Separator 25
 
14.4%
Dash Punctuation 9
 
5.2%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%
Other Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 18
22.0%
산 6
 
7.3%
면 5
 
6.1%
대 3
 
3.7%
성 2
 
2.4%
내 2
 
2.4%
용 2
 
2.4%
화 2
 
2.4%
원 2
 
2.4%
지 2
 
2.4%
Other values (35) 38
46.3%
Decimal Number
ValueCountFrequency (%)
1 13
23.6%
2 11
20.0%
4 9
16.4%
3 6
10.9%
7 5
 
9.1%
9 4
 
7.3%
6 3
 
5.5%
0 3
 
5.5%
8 1
 
1.8%
Space Separator
ValueCountFrequency (%)
25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 92
52.9%
Hangul 82
47.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 18
22.0%
산 6
 
7.3%
면 5
 
6.1%
대 3
 
3.7%
성 2
 
2.4%
내 2
 
2.4%
용 2
 
2.4%
화 2
 
2.4%
원 2
 
2.4%
지 2
 
2.4%
Other values (35) 38
46.3%
Common
ValueCountFrequency (%)
25
27.2%
1 13
14.1%
2 11
12.0%
4 9
 
9.8%
- 9
 
9.8%
3 6
 
6.5%
7 5
 
5.4%
9 4
 
4.3%
6 3
 
3.3%
0 3
 
3.3%
Other values (4) 4
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92
52.9%
Hangul 82
47.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25
27.2%
1 13
14.1%
2 11
12.0%
4 9
 
9.8%
- 9
 
9.8%
3 6
 
6.5%
7 5
 
5.4%
9 4
 
4.3%
6 3
 
3.3%
0 3
 
3.3%
Other values (4) 4
 
4.3%
Hangul
ValueCountFrequency (%)
리 18
22.0%
산 6
 
7.3%
면 5
 
6.1%
대 3
 
3.7%
성 2
 
2.4%
내 2
 
2.4%
용 2
 
2.4%
화 2
 
2.4%
원 2
 
2.4%
지 2
 
2.4%
Other values (35) 38
46.3%

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)73.2%
Memory size700.0 B

Unnamed: 8
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)100.0%
Missing61
Missing (%)85.9%
Infinite0
Infinite (%)0.0%
Mean44579224
Minimum5222810
Maximum1.2174046 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size771.0 B
2023-12-12T19:12:38.984727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5222810
5-th percentile7539315.5
Q113597915
median35189055
Q347972242
95-th percentile1.1696581 × 108
Maximum1.2174046 × 108
Range1.1651765 × 108
Interquartile range (IQR)34374328

Descriptive statistics

Standard deviation40823938
Coefficient of variation (CV)0.91576152
Kurtosis0.4081993
Mean44579224
Median Absolute Deviation (MAD)19409275
Skewness1.2256493
Sum4.4579224 × 108
Variance1.6665939 × 1015
MonotonicityNot monotonic
2023-12-12T19:12:39.148777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
33770540 1
 
1.4%
47141370 1
 
1.4%
20143510 1
 
1.4%
48249200 1
 
1.4%
111130130 1
 
1.4%
121740460 1
 
1.4%
11416050 1
 
1.4%
5222810 1
 
1.4%
10370600 1
 
1.4%
36607570 1
 
1.4%
(Missing) 61
85.9%
ValueCountFrequency (%)
5222810 1
1.4%
10370600 1
1.4%
11416050 1
1.4%
20143510 1
1.4%
33770540 1
1.4%
36607570 1
1.4%
47141370 1
1.4%
48249200 1
1.4%
111130130 1
1.4%
121740460 1
1.4%
ValueCountFrequency (%)
121740460 1
1.4%
111130130 1
1.4%
48249200 1
1.4%
47141370 1
1.4%
36607570 1
1.4%
33770540 1
1.4%
20143510 1
1.4%
11416050 1
1.4%
10370600 1
1.4%
5222810 1
1.4%

Unnamed: 9
Categorical

IMBALANCE 

Distinct8
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size700.0 B
<NA>
49 
보은군산림조합
14 
단양군산림조합
 
2
㈜영진산림
 
2
시공사
 
1
Other values (3)
 
3

Length

Max length7
Median length4
Mean length4.7605634
Min length3

Unique

Unique4 ?
Unique (%)5.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 49
69.0%
보은군산림조합 14
 
19.7%
단양군산림조합 2
 
2.8%
㈜영진산림 2
 
2.8%
시공사 1
 
1.4%
옥천군산림조합 1
 
1.4%
㈜우원건설 1
 
1.4%
㈜남부산림 1
 
1.4%

Length

2023-12-12T19:12:39.327438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:12:39.536044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 49
69.0%
보은군산림조합 14
 
19.7%
단양군산림조합 2
 
2.8%
㈜영진산림 2
 
2.8%
시공사 1
 
1.4%
옥천군산림조합 1
 
1.4%
㈜우원건설 1
 
1.4%
㈜남부산림 1
 
1.4%

Unnamed: 10
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size700.0 B
<NA>
37 
부
21 
여
11 
안전입간판 설치여부
 
1
여(교환필요)
 
1

Length

Max length10
Median length4
Mean length2.7746479
Min length1

Unique

Unique2 ?
Unique (%)2.8%

Sample

1st row<NA>
2nd row<NA>
3rd row안전입간판 설치여부
4th row<NA>
5th row부

Common Values

ValueCountFrequency (%)
<NA> 37
52.1%
부 21
29.6%
여 11
 
15.5%
안전입간판 설치여부 1
 
1.4%
여(교환필요) 1
 
1.4%

Length

2023-12-12T19:12:39.701020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:12:39.862320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 37
51.4%
부 21
29.2%
여 11
 
15.3%
안전입간판 1
 
1.4%
설치여부 1
 
1.4%
여(교환필요 1
 
1.4%

Unnamed: 11
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing71
Missing (%)100.0%
Memory size771.0 B

Unnamed: 12
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
<NA>
70 
2014
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 70
98.6%
2014 1
 
1.4%

Length

2023-12-12T19:12:40.023085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:12:40.176086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 70
98.6%
2014 1
 
1.4%

Interactions

2023-12-12T19:12:35.543786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:12:40.274348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 8Unnamed: 9Unnamed: 10
Unnamed: 11.0001.0001.0001.0001.0001.0001.000
Unnamed: 41.0001.0001.0001.0000.4230.6540.631
Unnamed: 51.0001.0001.0001.0001.0001.0001.000
Unnamed: 61.0001.0001.0001.000NaNNaN1.000
Unnamed: 81.0000.4231.000NaN1.0000.8050.000
Unnamed: 91.0000.6541.000NaN0.8051.0000.458
Unnamed: 101.0000.6311.0001.0000.0000.4581.000
2023-12-12T19:12:40.424957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 10Unnamed: 9Unnamed: 4Unnamed: 12
Unnamed: 101.0000.1330.547NaN
Unnamed: 90.1331.0000.428NaN
Unnamed: 40.5470.4281.000NaN
Unnamed: 12NaNNaNNaN1.000
2023-12-12T19:12:40.561113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 8Unnamed: 4Unnamed: 9Unnamed: 10Unnamed: 12
Unnamed: 81.0000.2740.3980.0000.000
Unnamed: 40.2741.0000.4280.5470.000
Unnamed: 90.3980.4281.0000.133NaN
Unnamed: 100.0000.5470.1331.0000.000
Unnamed: 120.0000.000NaN0.0001.000

Missing values

2023-12-12T19:12:35.711089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:12:35.952541image/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.
2023-12-12T19:12:36.205390image/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

보은군 임도시설 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12
0NaN<NA>NaNNaN<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
1NaN<NA>NaNNaN<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
2연 번노 선 명거 리 (㎞)조 성 년 도비 고진입위치<NA>사업비<NA>시공사안전입간판 설치여부<NA><NA>
3NaN계100.115NaN<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
41내속 중판-중판3.361987간선중판리 281-2<NA>NaN<NA><NA>부<NA><NA>
5NaN<NA>NaN1995<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
62보은 노티-내북 하궁3.481988간선보은읍 노티리 131-1내북면 하궁리 47NaN<NA><NA>여<NA><NA>
73산외 아시-백석6.71989간선산외면 아시리 123-2<NA>NaN<NA><NA>여(교환필요)<NA><NA>
8NaN<NA>NaN2009<NA>산외면 백석리 산39-4<NA>NaN<NA><NA><NA><NA><NA>
94산외 백석-백석3.121990간선산외면 백석리 491-2산외면 백석리 산11-3NaN<NA><NA>여<NA><NA>
보은군 임도시설 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12
6135금곡임도22022간선금곡리 산58<NA>NaN<NA><NA><NA><NA><NA>
62NaN<NA>NaNNaN<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
63NaN<NA>NaNNaN<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
64NaN<NA>2.712017<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
65NaN<NA>2.32018<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
66NaN<NA>2.32019<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
67NaN<NA>1.932020<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
68NaN<NA>NaNNaN<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
69NaN<NA>9.24NaN<NA><NA><NA>NaN<NA><NA><NA><NA><NA>
70NaN<NA>2.31NaN<NA><NA><NA>NaN<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 12# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA>22