Overview

Dataset statistics

Number of variables5
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 KiB
Average record size in memory49.1 B

Variable types

Text2
Numeric3

Dataset

Description전북특별자치도 2017년 지적재조사 사업지구 현황 데이터입니다. 시군, 사업지구명, 면적, 필수 등의 데이터를 포함하고 있습니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15045403/fileData.do

Alerts

필 수 is highly overall correlated with 면적 and 1 other fieldsHigh correlation
면적 is highly overall correlated with 필 수 and 1 other fieldsHigh correlation
사업비(천원) is highly overall correlated with 필 수 and 1 other fieldsHigh correlation
사업지구명 has unique valuesUnique
필 수 has unique valuesUnique

Reproduction

Analysis started2024-03-14 18:27:25.810841
Analysis finished2024-03-14 18:27:28.452718
Duration2.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size296.0 B
2024-03-15T03:27:28.919240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.3809524
Min length3

Characters and Unicode

Total characters71
Distinct characters27
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

Unique11 ?
Unique (%)52.4%

Sample

1st row전주시 완산구
2nd row전주시 덕진구
3rd row군산시
4th row익산시
5th row익산시
ValueCountFrequency (%)
익산시 3
13.0%
진안군 3
13.0%
정읍시 2
 
8.7%
남원시 2
 
8.7%
전주시 2
 
8.7%
완산구 1
 
4.3%
덕진구 1
 
4.3%
군산시 1
 
4.3%
김제시 1
 
4.3%
완주군 1
 
4.3%
Other values (6) 6
26.1%
2024-03-15T03:27:30.039303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 11
15.5%
군 11
15.5%
산 5
 
7.0%
주 4
 
5.6%
진 4
 
5.6%
안 4
 
5.6%
익 3
 
4.2%
창 2
 
2.8%
구 2
 
2.8%
2
 
2.8%
Other values (17) 23
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
97.2%
Space Separator 2
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 11
15.9%
군 11
15.9%
산 5
 
7.2%
주 4
 
5.8%
진 4
 
5.8%
안 4
 
5.8%
익 3
 
4.3%
창 2
 
2.9%
구 2
 
2.9%
완 2
 
2.9%
Other values (16) 21
30.4%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
97.2%
Common 2
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 11
15.9%
군 11
15.9%
산 5
 
7.2%
주 4
 
5.8%
진 4
 
5.8%
안 4
 
5.8%
익 3
 
4.3%
창 2
 
2.9%
구 2
 
2.9%
완 2
 
2.9%
Other values (16) 21
30.4%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
97.2%
ASCII 2
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 11
15.9%
군 11
15.9%
산 5
 
7.2%
주 4
 
5.8%
진 4
 
5.8%
안 4
 
5.8%
익 3
 
4.3%
창 2
 
2.9%
구 2
 
2.9%
완 2
 
2.9%
Other values (16) 21
30.4%
ASCII
ValueCountFrequency (%)
2
100.0%

사업지구명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size296.0 B
2024-03-15T03:27:30.806637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.5714286
Min length4

Characters and Unicode

Total characters96
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row모과,석산지구
2nd row용정1지구
3rd row내초지구
4th row발산지구
5th row신목지구
ValueCountFrequency (%)
모과,석산지구 1
 
4.8%
중촌지구 1
 
4.8%
고수지구 1
 
4.8%
순화남계지구 1
 
4.8%
양지지구 1
 
4.8%
무농지구 1
 
4.8%
율평지구 1
 
4.8%
원평지구 1
 
4.8%
솔안평산지구 1
 
4.8%
대량지구 1
 
4.8%
Other values (11) 11
52.4%
2024-03-15T03:27:32.086016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 23
24.0%
구 21
21.9%
산 3
 
3.1%
1 3
 
3.1%
평 3
 
3.1%
신 2
 
2.1%
농 1
 
1.0%
촌 1
 
1.0%
대 1
 
1.0%
량 1
 
1.0%
Other values (37) 37
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
94.8%
Decimal Number 4
 
4.2%
Other Punctuation 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 23
25.3%
구 21
23.1%
산 3
 
3.3%
평 3
 
3.3%
신 2
 
2.2%
농 1
 
1.1%
촌 1
 
1.1%
대 1
 
1.1%
량 1
 
1.1%
솔 1
 
1.1%
Other values (34) 34
37.4%
Decimal Number
ValueCountFrequency (%)
1 3
75.0%
2 1
 
25.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
94.8%
Common 5
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 23
25.3%
구 21
23.1%
산 3
 
3.3%
평 3
 
3.3%
신 2
 
2.2%
농 1
 
1.1%
촌 1
 
1.1%
대 1
 
1.1%
량 1
 
1.1%
솔 1
 
1.1%
Other values (34) 34
37.4%
Common
ValueCountFrequency (%)
1 3
60.0%
, 1
 
20.0%
2 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
94.8%
ASCII 5
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 23
25.3%
구 21
23.1%
산 3
 
3.3%
평 3
 
3.3%
신 2
 
2.2%
농 1
 
1.1%
촌 1
 
1.1%
대 1
 
1.1%
량 1
 
1.1%
솔 1
 
1.1%
Other values (34) 34
37.4%
ASCII
ValueCountFrequency (%)
1 3
60.0%
, 1
 
20.0%
2 1
 
20.0%

필 수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean494
Minimum37
Maximum1681
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-03-15T03:27:32.450971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile180
Q1348
median430
Q3543
95-th percentile723
Maximum1681
Range1644
Interquartile range (IQR)195

Descriptive statistics

Standard deviation322.39184
Coefficient of variation (CV)0.65261507
Kurtosis9.3523993
Mean494
Median Absolute Deviation (MAD)113
Skewness2.5095491
Sum10374
Variance103936.5
MonotonicityNot monotonic
2024-03-15T03:27:32.830564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
422 1
 
4.8%
424 1
 
4.8%
507 1
 
4.8%
180 1
 
4.8%
1681 1
 
4.8%
430 1
 
4.8%
406 1
 
4.8%
183 1
 
4.8%
494 1
 
4.8%
652 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
37 1
4.8%
180 1
4.8%
183 1
4.8%
295 1
4.8%
300 1
4.8%
348 1
4.8%
400 1
4.8%
406 1
4.8%
422 1
4.8%
424 1
4.8%
ValueCountFrequency (%)
1681 1
4.8%
723 1
4.8%
705 1
4.8%
652 1
4.8%
590 1
4.8%
543 1
4.8%
541 1
4.8%
513 1
4.8%
507 1
4.8%
494 1
4.8%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.47619
Minimum25
Maximum640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-03-15T03:27:33.196332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile54
Q1153
median229
Q3331
95-th percentile459
Maximum640
Range615
Interquartile range (IQR)178

Descriptive statistics

Standard deviation144.26144
Coefficient of variation (CV)0.58293057
Kurtosis1.4402073
Mean247.47619
Median Absolute Deviation (MAD)83
Skewness0.98135438
Sum5197
Variance20811.362
MonotonicityNot monotonic
2024-03-15T03:27:33.577545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
331 2
 
9.5%
245 1
 
4.8%
640 1
 
4.8%
237 1
 
4.8%
54 1
 
4.8%
431 1
 
4.8%
131 1
 
4.8%
182 1
 
4.8%
309 1
 
4.8%
459 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
25 1
4.8%
54 1
4.8%
111 1
4.8%
131 1
4.8%
146 1
4.8%
153 1
4.8%
172 1
4.8%
182 1
4.8%
192 1
4.8%
194 1
4.8%
ValueCountFrequency (%)
640 1
4.8%
459 1
4.8%
431 1
4.8%
350 1
4.8%
331 2
9.5%
309 1
4.8%
275 1
4.8%
245 1
4.8%
237 1
4.8%
229 1
4.8%

사업비(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85857.619
Minimum5555
Maximum277800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-03-15T03:27:33.947902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5555
5-th percentile30000
Q163400
median84500
Q398220
95-th percentile125600
Maximum277800
Range272245
Interquartile range (IQR)34820

Descriptive statistics

Standard deviation53297.974
Coefficient of variation (CV)0.62077163
Kurtosis8.4257047
Mean85857.619
Median Absolute Deviation (MAD)20500
Skewness2.2618557
Sum1803010
Variance2.840674 × 109
MonotonicityNot monotonic
2024-03-15T03:27:34.336296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
91200 2
 
9.5%
30000 2
 
9.5%
80000 1
 
4.8%
84500 1
 
4.8%
277800 1
 
4.8%
72300 1
 
4.8%
63400 1
 
4.8%
82240 1
 
4.8%
108540 1
 
4.8%
98220 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
5555 1
4.8%
30000 2
9.5%
48615 1
4.8%
51150 1
4.8%
63400 1
4.8%
65550 1
4.8%
72300 1
4.8%
80000 1
4.8%
82240 1
4.8%
84500 1
4.8%
ValueCountFrequency (%)
277800 1
4.8%
125600 1
4.8%
114500 1
4.8%
108540 1
4.8%
105000 1
4.8%
98220 1
4.8%
92900 1
4.8%
91200 2
9.5%
84740 1
4.8%
84500 1
4.8%

Interactions

2024-03-15T03:27:27.376876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:27:26.007532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:27:26.770969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:27:27.629681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:27:26.264887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:27:26.957081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:27:27.811489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:27:26.515673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:27:27.159476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:27:34.583185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군사업지구명필 수면적사업비(천원)
시군1.0001.0000.9170.0000.881
사업지구명1.0001.0001.0001.0001.000
필 수0.9171.0001.0000.7940.993
면적0.0001.0000.7941.0000.697
사업비(천원)0.8811.0000.9930.6971.000
2024-03-15T03:27:34.844440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
필 수면적사업비(천원)
필 수1.0000.6880.958
면적0.6881.0000.648
사업비(천원)0.9580.6481.000

Missing values

2024-03-15T03:27:27.994840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:27:28.350690image/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

시군사업지구명필 수면적사업비(천원)
0전주시 완산구모과,석산지구42224591200
1전주시 덕진구용정1지구42419491200
2군산시내초지구40017280000
3익산시발산지구51333184740
4익산시신목지구29515348615
5익산시영만지구37255555
6정읍시연지2지구543146105000
7정읍시풍월1지구54122992900
8남원시요천지구30011151150
9남원시신파지구34819265550
시군사업지구명필 수면적사업비(천원)
11완주군중촌지구723350125600
12진안군대량지구59064098220
13진안군솔안평산지구652275108540
14진안군원평지구49445982240
15무주군율평지구18330930000
16장수군무농지구40618263400
17임실군양지지구43013172300
18순창군순화남계지구1681431277800
19고창군고수지구1805430000
20부안군장동1지구50723784500