Overview

Dataset statistics

Number of variables9
Number of observations21
Missing cells75
Missing cells (%)39.7%
Duplicate rows1
Duplicate rows (%)4.8%
Total size in memory1.7 KiB
Average record size in memory83.3 B

Variable types

Text4
Numeric2
Unsupported3

Dataset

Description시군별지적재조사사업대상현황
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202242

Alerts

Dataset has 1 (4.8%) duplicate rowsDuplicates
필지(%) is highly overall correlated with 면적(%)High correlation
면적(%) is highly overall correlated with 필지(%)High correlation
구 분 has 2 (9.5%) missing valuesMissing
지구수 has 2 (9.5%) missing valuesMissing
필지수(필지) has 2 (9.5%) missing valuesMissing
대장면적(천㎢) has 2 (9.5%) missing valuesMissing
필지(%) has 2 (9.5%) missing valuesMissing
면적(%) has 2 (9.5%) missing valuesMissing
Unnamed: 6 has 21 (100.0%) missing valuesMissing
Unnamed: 7 has 21 (100.0%) missing valuesMissing
Unnamed: 8 has 21 (100.0%) missing valuesMissing
Unnamed: 6 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: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 02:00:41.042107
Analysis finished2024-03-14 02:00:41.954736
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing2
Missing (%)9.5%
Memory size300.0 B
2024-03-14T11:00:42.066190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8947368
Min length2

Characters and Unicode

Total characters55
Distinct characters33
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

Unique19 ?
Unique (%)100.0%

Sample

1st row합 계
2nd row전주시
3rd row완산구
4th row덕진구
5th row군산시
ValueCountFrequency (%)
전주시 1
 
5.0%
완산구 1
 
5.0%
부안군 1
 
5.0%
고창군 1
 
5.0%
순창군 1
 
5.0%
임실군 1
 
5.0%
장수군 1
 
5.0%
무주군 1
 
5.0%
진안군 1
 
5.0%
완주군 1
 
5.0%
Other values (10) 10
50.0%
2024-03-14T11:00:42.336282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 9
 
16.4%
시 6
 
10.9%
산 3
 
5.5%
주 3
 
5.5%
안 2
 
3.6%
완 2
 
3.6%
구 2
 
3.6%
진 2
 
3.6%
창 2
 
3.6%
임 1
 
1.8%
Other values (23) 23
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
98.2%
Space Separator 1
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 9
16.7%
시 6
 
11.1%
산 3
 
5.6%
주 3
 
5.6%
안 2
 
3.7%
완 2
 
3.7%
구 2
 
3.7%
진 2
 
3.7%
창 2
 
3.7%
임 1
 
1.9%
Other values (22) 22
40.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
98.2%
Common 1
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 9
16.7%
시 6
 
11.1%
산 3
 
5.6%
주 3
 
5.6%
안 2
 
3.7%
완 2
 
3.7%
구 2
 
3.7%
진 2
 
3.7%
창 2
 
3.7%
임 1
 
1.9%
Other values (22) 22
40.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
98.2%
ASCII 1
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 9
16.7%
시 6
 
11.1%
산 3
 
5.6%
주 3
 
5.6%
안 2
 
3.7%
완 2
 
3.7%
구 2
 
3.7%
진 2
 
3.7%
창 2
 
3.7%
임 1
 
1.9%
Other values (22) 22
40.7%
ASCII
ValueCountFrequency (%)
1
100.0%

지구수
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing2
Missing (%)9.5%
Memory size300.0 B
2024-03-14T11:00:42.486391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1052632
Min length2

Characters and Unicode

Total characters59
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row7,255
2nd row924
3rd row406
4th row518
5th row537
ValueCountFrequency (%)
924 1
 
5.3%
310 1
 
5.3%
252 1
 
5.3%
646 1
 
5.3%
192 1
 
5.3%
21 1
 
5.3%
529 1
 
5.3%
960 1
 
5.3%
871 1
 
5.3%
7,255 1
 
5.3%
Other values (9) 9
47.4%
2024-03-14T11:00:42.735990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10
16.9%
5 8
13.6%
1 8
13.6%
0 7
11.9%
6 6
10.2%
4 5
8.5%
9 4
 
6.8%
7 4
 
6.8%
8 3
 
5.1%
3 2
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57
96.6%
Other Punctuation 2
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10
17.5%
5 8
14.0%
1 8
14.0%
0 7
12.3%
6 6
10.5%
4 5
8.8%
9 4
 
7.0%
7 4
 
7.0%
8 3
 
5.3%
3 2
 
3.5%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10
16.9%
5 8
13.6%
1 8
13.6%
0 7
11.9%
6 6
10.2%
4 5
8.5%
9 4
 
6.8%
7 4
 
6.8%
8 3
 
5.1%
3 2
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10
16.9%
5 8
13.6%
1 8
13.6%
0 7
11.9%
6 6
10.2%
4 5
8.5%
9 4
 
6.8%
7 4
 
6.8%
8 3
 
5.1%
3 2
 
3.4%

필지수(필지)
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing2
Missing (%)9.5%
Memory size300.0 B
2024-03-14T11:00:42.878953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0526316
Min length5

Characters and Unicode

Total characters115
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row558,818
2nd row66,077
3rd row26,342
4th row39,735
5th row35,499
ValueCountFrequency (%)
66,077 1
 
5.3%
29,001 1
 
5.3%
18,759 1
 
5.3%
41,876 1
 
5.3%
18,371 1
 
5.3%
1,327 1
 
5.3%
15,146 1
 
5.3%
41,768 1
 
5.3%
32,519 1
 
5.3%
558,818 1
 
5.3%
Other values (9) 9
47.4%
2024-03-14T11:00:43.162843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 19
16.5%
1 15
13.0%
5 13
11.3%
7 11
9.6%
6 10
8.7%
3 10
8.7%
4 10
8.7%
9 8
7.0%
8 7
 
6.1%
0 6
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
83.5%
Other Punctuation 19
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
15.6%
5 13
13.5%
7 11
11.5%
6 10
10.4%
3 10
10.4%
4 10
10.4%
9 8
8.3%
8 7
7.3%
0 6
 
6.2%
2 6
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 19
16.5%
1 15
13.0%
5 13
11.3%
7 11
9.6%
6 10
8.7%
3 10
8.7%
4 10
8.7%
9 8
7.0%
8 7
 
6.1%
0 6
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 19
16.5%
1 15
13.0%
5 13
11.3%
7 11
9.6%
6 10
8.7%
3 10
8.7%
4 10
8.7%
9 8
7.0%
8 7
 
6.1%
0 6
 
5.2%

대장면적(천㎢)
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing2
Missing (%)9.5%
Memory size300.0 B
2024-03-14T11:00:43.311134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.8421053
Min length3

Characters and Unicode

Total characters111
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row426,819
2nd row20,426
3rd row5,207
4th row15,219
5th row12,449
ValueCountFrequency (%)
20,426 1
 
5.3%
72,730 1
 
5.3%
10,628 1
 
5.3%
50,668 1
 
5.3%
12,651 1
 
5.3%
491 1
 
5.3%
9,108 1
 
5.3%
24,903 1
 
5.3%
21,873 1
 
5.3%
426,819 1
 
5.3%
Other values (9) 9
47.4%
2024-03-14T11:00:43.598047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 18
16.2%
1 15
13.5%
2 14
12.6%
6 13
11.7%
0 8
7.2%
5 8
7.2%
7 8
7.2%
9 8
7.2%
4 7
 
6.3%
8 7
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93
83.8%
Other Punctuation 18
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
16.1%
2 14
15.1%
6 13
14.0%
0 8
8.6%
5 8
8.6%
7 8
8.6%
9 8
8.6%
4 7
7.5%
8 7
7.5%
3 5
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 111
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 18
16.2%
1 15
13.5%
2 14
12.6%
6 13
11.7%
0 8
7.2%
5 8
7.2%
7 8
7.2%
9 8
7.2%
4 7
 
6.3%
8 7
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 18
16.2%
1 15
13.5%
2 14
12.6%
6 13
11.7%
0 8
7.2%
5 8
7.2%
7 8
7.2%
9 8
7.2%
4 7
 
6.3%
8 7
 
6.3%

필지(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)89.5%
Missing2
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean19.563158
Minimum0.6
Maximum38.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-14T11:00:43.743853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile7.08
Q19.9
median14.4
Q332.75
95-th percentile38.6
Maximum38.6
Range38
Interquartile range (IQR)22.85

Descriptive statistics

Standard deviation12.442142
Coefficient of variation (CV)0.63599866
Kurtosis-1.3968846
Mean19.563158
Median Absolute Deviation (MAD)5.3
Skewness0.44252632
Sum371.7
Variance154.8069
MonotonicityNot monotonic
2024-03-14T11:00:43.865039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
38.6 2
 
9.5%
9.1 2
 
9.5%
11.2 1
 
4.8%
7.8 1
 
4.8%
13.2 1
 
4.8%
0.6 1
 
4.8%
28.3 1
 
4.8%
13.0 1
 
4.8%
15.1 1
 
4.8%
33.9 1
 
4.8%
Other values (7) 7
33.3%
(Missing) 2
 
9.5%
ValueCountFrequency (%)
0.6 1
4.8%
7.8 1
4.8%
9.1 2
9.5%
9.6 1
4.8%
10.2 1
4.8%
11.2 1
4.8%
13.0 1
4.8%
13.2 1
4.8%
14.4 1
4.8%
15.1 1
4.8%
ValueCountFrequency (%)
38.6 2
9.5%
37.3 1
4.8%
33.9 1
4.8%
33.8 1
4.8%
31.7 1
4.8%
28.3 1
4.8%
16.2 1
4.8%
15.1 1
4.8%
14.4 1
4.8%
13.2 1
4.8%

면적(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)94.7%
Missing2
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean7.8263158
Minimum0.1
Maximum25.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-14T11:00:43.974875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.54
Q12.6
median5.2
Q39.4
95-th percentile23.41
Maximum25.3
Range25.2
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation7.7105874
Coefficient of variation (CV)0.98521291
Kurtosis0.77955999
Mean7.8263158
Median Absolute Deviation (MAD)3.1
Skewness1.3900879
Sum148.7
Variance59.453158
MonotonicityNot monotonic
2024-03-14T11:00:44.064997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2.1 2
 
9.5%
5.4 1
 
4.8%
8.9 1
 
4.8%
8.3 1
 
4.8%
2.5 1
 
4.8%
0.1 1
 
4.8%
1.7 1
 
4.8%
3.9 1
 
4.8%
2.7 1
 
4.8%
3.4 1
 
4.8%
Other values (8) 8
38.1%
(Missing) 2
 
9.5%
ValueCountFrequency (%)
0.1 1
4.8%
1.7 1
4.8%
2.1 2
9.5%
2.5 1
4.8%
2.7 1
4.8%
3.2 1
4.8%
3.4 1
4.8%
3.9 1
4.8%
5.2 1
4.8%
5.3 1
4.8%
ValueCountFrequency (%)
25.3 1
4.8%
23.2 1
4.8%
21.8 1
4.8%
13.7 1
4.8%
9.9 1
4.8%
8.9 1
4.8%
8.3 1
4.8%
5.4 1
4.8%
5.3 1
4.8%
5.2 1
4.8%

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)100.0%
Memory size321.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)100.0%
Memory size321.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)100.0%
Memory size321.0 B

Interactions

2024-03-14T11:00:41.490548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:00:41.273782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:00:41.574302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:00:41.382567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:00:44.136370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분지구수필지수(필지)대장면적(천㎢)필지(%)면적(%)
구 분1.0001.0001.0001.0001.0001.000
지구수1.0001.0001.0001.0001.0001.000
필지수(필지)1.0001.0001.0001.0001.0001.000
대장면적(천㎢)1.0001.0001.0001.0001.0001.000
필지(%)1.0001.0001.0001.0001.0000.000
면적(%)1.0001.0001.0001.0000.0001.000
2024-03-14T11:00:44.246334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
필지(%)면적(%)
필지(%)1.0000.819
면적(%)0.8191.000

Missing values

2024-03-14T11:00:41.674718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:00:41.790267image/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.
2024-03-14T11:00:41.886290image/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

구 분지구수필지수(필지)대장면적(천㎢)필지(%)면적(%)Unnamed: 6Unnamed: 7Unnamed: 8
0합 계7,255558,818426,81915.15.3<NA><NA><NA>
1전주시92466,07720,42633.99.9<NA><NA><NA>
2완산구40626,3425,20738.65.4<NA><NA><NA>
3덕진구51839,73515,21938.613.7<NA><NA><NA>
4군산시53735,49912,44914.43.2<NA><NA><NA>
5익산시1,020131,695117,92633.823.2<NA><NA><NA>
6본청60877,14466,27031.721.8<NA><NA><NA>
7함열41254,55151,65637.325.3<NA><NA><NA>
8정읍시5739,45437,83410.25.2<NA><NA><NA>
9남원시51030,26615,8769.62.1<NA><NA><NA>
구 분지구수필지수(필지)대장면적(천㎢)필지(%)면적(%)Unnamed: 6Unnamed: 7Unnamed: 8
11완주군31029,00172,73011.28.9<NA><NA><NA>
12진안군87132,51921,87313.02.7<NA><NA><NA>
13무주군96041,76824,90328.33.9<NA><NA><NA>
14장수군52915,1469,1089.11.7<NA><NA><NA>
15임실군211,3274910.60.1<NA><NA><NA>
16순창군19218,37112,6519.12.5<NA><NA><NA>
17고창군64641,87650,66813.28.3<NA><NA><NA>
18부안군25218,75910,6287.82.1<NA><NA><NA>
19<NA><NA><NA><NA><NA><NA><NA><NA><NA>
20<NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

구 분지구수필지수(필지)대장면적(천㎢)필지(%)면적(%)# duplicates
0<NA><NA><NA><NA><NA><NA>2