Overview

Dataset statistics

Number of variables7
Number of observations103
Missing cells7
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory62.2 B

Variable types

Numeric4
Categorical2
Text1

Dataset

Description전북특별자치도 사방사업 추진현황 데이터입니다. 위치, 사업명, 사업량, 사업비 총액, 사업비 계약액, 사업비 관급액이이 보여집니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055678/fileData.do

Alerts

구 분 is highly overall correlated with 사업명High correlation
사 업 비 총액 is highly overall correlated with 사 업 비 계약액 and 2 other fieldsHigh correlation
사 업 비 계약액 is highly overall correlated with 사 업 비 총액 and 2 other fieldsHigh correlation
사 업 비 관급액 is highly overall correlated with 사 업 비 총액 and 2 other fieldsHigh correlation
사업명 is highly overall correlated with 구 분 and 3 other fieldsHigh correlation
사업량 is highly imbalanced (61.6%)Imbalance
사 업 비 총액 has 2 (1.9%) missing valuesMissing
사 업 비 계약액 has 2 (1.9%) missing valuesMissing
사 업 비 관급액 has 3 (2.9%) missing valuesMissing
구 분 has unique valuesUnique

Reproduction

Analysis started2024-03-14 18:53:31.286652
Analysis finished2024-03-14 18:53:35.707971
Duration4.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52
Minimum1
Maximum103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T03:53:36.040838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.1
Q126.5
median52
Q377.5
95-th percentile97.9
Maximum103
Range102
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.877528
Coefficient of variation (CV)0.57456784
Kurtosis-1.2
Mean52
Median Absolute Deviation (MAD)26
Skewness0
Sum5356
Variance892.66667
MonotonicityStrictly increasing
2024-03-15T03:53:36.311973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
2 1
 
1.0%
77 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
Other values (93) 93
90.3%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
103 1
1.0%
102 1
1.0%
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

사업명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size952.0 B
사방댐
60 
계류
29 
산지
10 
산사태복구
 
1
해안침식
 
1
Other values (2)
 
2

Length

Max length5
Median length3
Mean length2.6699029
Min length2

Unique

Unique4 ?
Unique (%)3.9%

Sample

1st row산지
2nd row산지
3rd row산지
4th row산지
5th row산지

Common Values

ValueCountFrequency (%)
사방댐 60
58.3%
계류 29
28.2%
산지 10
 
9.7%
산사태복구 1
 
1.0%
해안침식 1
 
1.0%
유역관리 1
 
1.0%
안전조치 1
 
1.0%

Length

2024-03-15T03:53:36.580157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:53:36.786485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사방댐 60
58.3%
계류 29
28.2%
산지 10
 
9.7%
산사태복구 1
 
1.0%
해안침식 1
 
1.0%
유역관리 1
 
1.0%
안전조치 1
 
1.0%

위 치
Text

Distinct102
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size952.0 B
2024-03-15T03:53:38.045562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length16.281553
Min length11

Characters and Unicode

Total characters1677
Distinct characters133
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

Unique101 ?
Unique (%)98.1%

Sample

1st row완주군 상관면 용암리 544
2nd row진안군 진안읍 군상리 산10-1
3rd row진안군 부귀면 세동리 산292-2
4th row장수군 장수읍 선창리 산15-1
5th row장수군 산서면 사계리 산16
ValueCountFrequency (%)
남원시 18
 
4.3%
장수군 17
 
4.1%
완주군 13
 
3.1%
임실군 12
 
2.9%
산동면 11
 
2.6%
정읍시 9
 
2.2%
진안군 8
 
1.9%
부절리 7
 
1.7%
산 7
 
1.7%
부안군 6
 
1.4%
Other values (236) 308
74.0%
2024-03-15T03:53:40.074914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
316
18.8%
산 125
 
7.5%
리 96
 
5.7%
면 84
 
5.0%
1 84
 
5.0%
군 72
 
4.3%
- 49
 
2.9%
2 36
 
2.1%
4 35
 
2.1%
시 34
 
2.0%
Other values (123) 746
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1004
59.9%
Space Separator 316
 
18.8%
Decimal Number 308
 
18.4%
Dash Punctuation 49
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 125
 
12.5%
리 96
 
9.6%
면 84
 
8.4%
군 72
 
7.2%
시 34
 
3.4%
장 30
 
3.0%
동 27
 
2.7%
수 26
 
2.6%
주 23
 
2.3%
남 21
 
2.1%
Other values (111) 466
46.4%
Decimal Number
ValueCountFrequency (%)
1 84
27.3%
2 36
11.7%
4 35
11.4%
3 29
 
9.4%
6 25
 
8.1%
5 24
 
7.8%
9 21
 
6.8%
7 20
 
6.5%
8 17
 
5.5%
0 17
 
5.5%
Space Separator
ValueCountFrequency (%)
316
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1004
59.9%
Common 673
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 125
 
12.5%
리 96
 
9.6%
면 84
 
8.4%
군 72
 
7.2%
시 34
 
3.4%
장 30
 
3.0%
동 27
 
2.7%
수 26
 
2.6%
주 23
 
2.3%
남 21
 
2.1%
Other values (111) 466
46.4%
Common
ValueCountFrequency (%)
316
47.0%
1 84
 
12.5%
- 49
 
7.3%
2 36
 
5.3%
4 35
 
5.2%
3 29
 
4.3%
6 25
 
3.7%
5 24
 
3.6%
9 21
 
3.1%
7 20
 
3.0%
Other values (2) 34
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1004
59.9%
ASCII 673
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
316
47.0%
1 84
 
12.5%
- 49
 
7.3%
2 36
 
5.3%
4 35
 
5.2%
3 29
 
4.3%
6 25
 
3.7%
5 24
 
3.6%
9 21
 
3.1%
7 20
 
3.0%
Other values (2) 34
 
5.1%
Hangul
ValueCountFrequency (%)
산 125
 
12.5%
리 96
 
9.6%
면 84
 
8.4%
군 72
 
7.2%
시 34
 
3.4%
장 30
 
3.0%
동 27
 
2.7%
수 26
 
2.6%
주 23
 
2.3%
남 21
 
2.1%
Other values (111) 466
46.4%

사업량
Categorical

IMBALANCE 

Distinct4
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size952.0 B
1.0
88 
2.0
10 
1.5
 
4
0.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 88
85.4%
2.0 10
 
9.7%
1.5 4
 
3.9%
0.5 1
 
1.0%

Length

2024-03-15T03:53:40.450006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:53:40.774171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 88
85.4%
2.0 10
 
9.7%
1.5 4
 
3.9%
0.5 1
 
1.0%

사 업 비 총액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct101
Distinct (%)100.0%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean216751.19
Minimum35577
Maximum968516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T03:53:41.153332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35577
5-th percentile71940
Q1147249
median215162
Q3262194
95-th percentile386946
Maximum968516
Range932939
Interquartile range (IQR)114945

Descriptive statistics

Standard deviation115509.12
Coefficient of variation (CV)0.53291112
Kurtosis16.821962
Mean216751.19
Median Absolute Deviation (MAD)60897
Skewness2.7812092
Sum21891870
Variance1.3342357 × 1010
MonotonicityNot monotonic
2024-03-15T03:53:41.461164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
251147 1
 
1.0%
151365 1
 
1.0%
256769 1
 
1.0%
243695 1
 
1.0%
228471 1
 
1.0%
224696 1
 
1.0%
196504 1
 
1.0%
216260 1
 
1.0%
200457 1
 
1.0%
215162 1
 
1.0%
Other values (91) 91
88.3%
(Missing) 2
 
1.9%
ValueCountFrequency (%)
35577 1
1.0%
56515 1
1.0%
64015 1
1.0%
67519 1
1.0%
70458 1
1.0%
71940 1
1.0%
76700 1
1.0%
79516 1
1.0%
80085 1
1.0%
80430 1
1.0%
ValueCountFrequency (%)
968516 1
1.0%
414046 1
1.0%
410897 1
1.0%
390959 1
1.0%
388835 1
1.0%
386946 1
1.0%
357016 1
1.0%
354555 1
1.0%
336721 1
1.0%
325656 1
1.0%

사 업 비 계약액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct101
Distinct (%)100.0%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean162512
Minimum32444
Maximum751240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T03:53:41.774487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32444
5-th percentile55545
Q1105758
median159396
Q3197529
95-th percentile280732
Maximum751240
Range718796
Interquartile range (IQR)91771

Descriptive statistics

Standard deviation87664.27
Coefficient of variation (CV)0.5394326
Kurtosis19.335934
Mean162512
Median Absolute Deviation (MAD)45873
Skewness3.0781366
Sum16413712
Variance7.6850242 × 109
MonotonicityNot monotonic
2024-03-15T03:53:42.162340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187649 1
 
1.0%
105758 1
 
1.0%
205172 1
 
1.0%
183223 1
 
1.0%
179565 1
 
1.0%
178016 1
 
1.0%
152702 1
 
1.0%
165445 1
 
1.0%
158884 1
 
1.0%
164224 1
 
1.0%
Other values (91) 91
88.3%
(Missing) 2
 
1.9%
ValueCountFrequency (%)
32444 1
1.0%
42794 1
1.0%
44353 1
1.0%
50456 1
1.0%
54987 1
1.0%
55545 1
1.0%
55763 1
1.0%
57199 1
1.0%
62753 1
1.0%
64481 1
1.0%
ValueCountFrequency (%)
751240 1
1.0%
325048 1
1.0%
312530 1
1.0%
295997 1
1.0%
281230 1
1.0%
280732 1
1.0%
273420 1
1.0%
262349 1
1.0%
260882 1
1.0%
257867 1
1.0%

사 업 비 관급액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)100.0%
Missing3
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean54781.58
Minimum3133
Maximum217276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-15T03:53:42.586374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3133
5-th percentile13222.65
Q135080.5
median51032.5
Q368883.5
95-th percentile100310.7
Maximum217276
Range214143
Interquartile range (IQR)33803

Descriptive statistics

Standard deviation31858.852
Coefficient of variation (CV)0.5815614
Kurtosis6.1313956
Mean54781.58
Median Absolute Deviation (MAD)17257.5
Skewness1.6460631
Sum5478158
Variance1.0149865 × 109
MonotonicityNot monotonic
2024-03-15T03:53:43.013233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43924 1
 
1.0%
35556 1
 
1.0%
45607 1
 
1.0%
51597 1
 
1.0%
60472 1
 
1.0%
48906 1
 
1.0%
46680 1
 
1.0%
43802 1
 
1.0%
50815 1
 
1.0%
41573 1
 
1.0%
Other values (90) 90
87.4%
(Missing) 3
 
2.9%
ValueCountFrequency (%)
3133 1
1.0%
8377 1
1.0%
8470 1
1.0%
9803 1
1.0%
12532 1
1.0%
13259 1
1.0%
13721 1
1.0%
15174 1
1.0%
15604 1
1.0%
16616 1
1.0%
ValueCountFrequency (%)
217276 1
1.0%
151697 1
1.0%
112812 1
1.0%
107605 1
1.0%
106214 1
1.0%
100000 1
1.0%
98367 1
1.0%
96759 1
1.0%
94962 1
1.0%
93673 1
1.0%

Interactions

2024-03-15T03:53:34.494812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:31.621266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:32.582069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:33.535064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:34.654876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:31.860496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:32.783392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:33.849958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:34.863290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:32.114731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:33.031065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:34.108497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:35.020308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:32.446138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:33.292139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:53:34.327087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:53:43.180789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분사업명사업량사 업 비 총액사 업 비 계약액사 업 비 관급액
구 분1.0000.7840.1490.5150.5250.369
사업명0.7841.0000.0000.8830.8810.715
사업량0.1490.0001.0000.1810.2780.000
사 업 비 총액0.5150.8830.1811.0000.9770.867
사 업 비 계약액0.5250.8810.2780.9771.0000.818
사 업 비 관급액0.3690.7150.0000.8670.8181.000
2024-03-15T03:53:43.353592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업명사업량
사업명1.0000.000
사업량0.0001.000
2024-03-15T03:53:43.593819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분사 업 비 총액사 업 비 계약액사 업 비 관급액사업명사업량
구 분1.0000.4080.4050.3670.5420.082
사 업 비 총액0.4081.0000.9840.9340.5250.115
사 업 비 계약액0.4050.9841.0000.8680.5220.180
사 업 비 관급액0.3670.9340.8681.0000.5030.000
사업명0.5420.5250.5220.5031.0000.000
사업량0.0820.1150.1800.0000.0001.000

Missing values

2024-03-15T03:53:35.206384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:53:35.439687image/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.
2024-03-15T03:53:35.612883image/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 분사업명위 치사업량사 업 비 총액사 업 비 계약액사 업 비 관급액
01산지완주군 상관면 용암리 5441.035577324443133
12산지진안군 진안읍 군상리 산10-11.0704585719913259
23산지진안군 부귀면 세동리 산292-21.0767005045626244
34산지장수군 장수읍 선창리 산15-11.0719404435327587
45산지장수군 산서면 사계리 산161.094582862058377
56산지장수군 계북면 어전리 산542.0800856448115604
67산지장수군 계북면 월현리 산60-11.0795166275316763
78산지고창군 심원면 월산리 산42-11.0565154279413721
89산지임실군 임실읍 두곡리 산91-31.01087517438634365
910산지부안군 보안면 남포리 산12-81.0675195498712532
구 분사업명위 치사업량사 업 비 총액사 업 비 계약액사 업 비 관급액
9394사방댐순창군 동계면 구미리 11021.016859413677731817
9495사방댐순창군 동계면 신흥리 산801.0<NA><NA><NA>
9596사방댐순창군 구림면 운북리 산142-11.025002818203667992
9697사방댐부안군 하서면 백련리 산1791.0414046262349151697
9798사방댐부안군 하서면 석상리 산59-11.026459320399360600
9899사방댐부안군 변산면 마포리 산64-61.023477217895955813
99100사방댐부안군 진서면 운호리 산89-11.024105418762153433
100101해안침식고창군 상하면 장호리 산28-11.01310391212369803
101102유역관리고창군 성송면 계당리 산1111.0968516751240217276
102103안전조치정읍시 산내면 종성리 4081.01318529973032122