Overview

Dataset statistics

Number of variables6
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory53.3 B

Variable types

Categorical2
Text1
Numeric3

Alerts

년도 has constant value ""Constant
개발가능량(천㎥/년) is highly overall correlated with 이용량(천㎥/년) and 1 other fieldsHigh correlation
이용량(천㎥/년) is highly overall correlated with 개발가능량(천㎥/년) and 1 other fieldsHigh correlation
행정구역시도(시도) is highly overall correlated with 개발가능량(천㎥/년) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 10:42:11.789245
Analysis finished2023-12-10 10:42:14.086661
Duration2.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2018
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 100
100.0%

Length

2023-12-10T19:42:14.197113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:42:14.380790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 100
100.0%

행정구역시도(시도)
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
26 
부산광역시
17 
인천광역시
11 
경기도
9 
대구광역시
9 
Other values (12)
28 

Length

Max length7
Median length5
Mean length4.8
Min length3

Unique

Unique8 ?
Unique (%)8.0%

Sample

1st row부산광역시
2nd row대구광역시
3rd row인천광역시
4th row광주광역시
5th row대전광역시

Common Values

ValueCountFrequency (%)
서울특별시 26
26.0%
부산광역시 17
17.0%
인천광역시 11
11.0%
경기도 9
 
9.0%
대구광역시 9
 
9.0%
광주광역시 6
 
6.0%
대전광역시 6
 
6.0%
울산광역시 6
 
6.0%
세종특별자치시 2
 
2.0%
충청북도 1
 
1.0%
Other values (7) 7
 
7.0%

Length

2023-12-10T19:42:14.696184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 26
26.0%
부산광역시 17
17.0%
인천광역시 11
11.0%
경기도 9
 
9.0%
대구광역시 9
 
9.0%
광주광역시 6
 
6.0%
대전광역시 6
 
6.0%
울산광역시 6
 
6.0%
세종특별자치시 2
 
2.0%
충청북도 1
 
1.0%
Other values (7) 7
 
7.0%

행정구역시군구(시군구)
Text

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:42:15.094079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.64
Min length2

Characters and Unicode

Total characters264
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)56.0%

Sample

1st row전체
2nd row전체
3rd row전체
4th row전체
5th row전체
ValueCountFrequency (%)
전체 17
 
17.0%
중구 6
 
6.0%
동구 6
 
6.0%
서구 5
 
5.0%
북구 4
 
4.0%
남구 4
 
4.0%
강서구 2
 
2.0%
해운대구 1
 
1.0%
수성구 1
 
1.0%
달성군 1
 
1.0%
Other values (53) 53
53.0%
2023-12-10T19:42:15.853603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 71
26.9%
전 17
 
6.4%
체 17
 
6.4%
동 12
 
4.5%
서 10
 
3.8%
시 8
 
3.0%
군 7
 
2.7%
중 7
 
2.7%
북 6
 
2.3%
남 6
 
2.3%
Other values (61) 103
39.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 71
26.9%
전 17
 
6.4%
체 17
 
6.4%
동 12
 
4.5%
서 10
 
3.8%
시 8
 
3.0%
군 7
 
2.7%
중 7
 
2.7%
북 6
 
2.3%
남 6
 
2.3%
Other values (61) 103
39.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 264
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 71
26.9%
전 17
 
6.4%
체 17
 
6.4%
동 12
 
4.5%
서 10
 
3.8%
시 8
 
3.0%
군 7
 
2.7%
중 7
 
2.7%
북 6
 
2.3%
남 6
 
2.3%
Other values (61) 103
39.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 264
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 71
26.9%
전 17
 
6.4%
체 17
 
6.4%
동 12
 
4.5%
서 10
 
3.8%
시 8
 
3.0%
군 7
 
2.7%
중 7
 
2.7%
북 6
 
2.3%
남 6
 
2.3%
Other values (61) 103
39.0%

개발가능량(천㎥/년)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138611.61
Minimum457.7
Maximum2263659.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:42:16.116828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum457.7
5-th percentile1272.43
Q12169
median5490.25
Q328697.15
95-th percentile1024001.2
Maximum2263659.1
Range2263201.4
Interquartile range (IQR)26528.15

Descriptive statistics

Standard deviation417819.78
Coefficient of variation (CV)3.0143203
Kurtosis13.687288
Mean138611.61
Median Absolute Deviation (MAD)3910.75
Skewness3.6858711
Sum13861161
Variance1.7457337 × 1011
MonotonicityNot monotonic
2023-12-10T19:42:16.449168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56315.0 2
 
2.0%
97553.0 1
 
1.0%
7328.0 1
 
1.0%
21454.8 1
 
1.0%
5341.8 1
 
1.0%
14353.5 1
 
1.0%
4212.5 1
 
1.0%
3105.1 1
 
1.0%
898.4 1
 
1.0%
7109.7 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
457.7 1
1.0%
674.1 1
1.0%
885.5 1
1.0%
898.4 1
1.0%
1141.9 1
1.0%
1279.3 1
1.0%
1298.0 1
1.0%
1362.1 1
1.0%
1395.6 1
1.0%
1450.6 1
1.0%
ValueCountFrequency (%)
2263659.1 1
1.0%
2196456.6 1
1.0%
1523179.6 1
1.0%
1342939.0 1
1.0%
1325656.5 1
1.0%
1008124.6 1
1.0%
1006129.8 1
1.0%
893669.8 1
1.0%
659873.3 1
1.0%
149419.7 1
1.0%

이용량(천㎥/년)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31803.126
Minimum224.3
Maximum406171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:42:16.713386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum224.3
5-th percentile317.97
Q1824.875
median1917.5
Q39034.925
95-th percentile260092.72
Maximum406171
Range405946.7
Interquartile range (IQR)8210.05

Descriptive statistics

Standard deviation88349.099
Coefficient of variation (CV)2.7780005
Kurtosis8.9971963
Mean31803.126
Median Absolute Deviation (MAD)1506.15
Skewness3.180583
Sum3180312.6
Variance7.8055633 × 109
MonotonicityNot monotonic
2023-12-10T19:42:16.982651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21531.5 2
 
2.0%
28558.8 1
 
1.0%
1567.6 1
 
1.0%
4340.8 1
 
1.0%
224.3 1
 
1.0%
1652.3 1
 
1.0%
1204.7 1
 
1.0%
434.6 1
 
1.0%
1231.0 1
 
1.0%
2229.6 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
224.3 1
1.0%
255.0 1
1.0%
267.5 1
1.0%
274.9 1
1.0%
287.0 1
1.0%
319.6 1
1.0%
324.6 1
1.0%
346.1 1
1.0%
386.9 1
1.0%
390.0 1
1.0%
ValueCountFrequency (%)
406171.0 1
1.0%
376400.2 1
1.0%
363662.9 1
1.0%
359677.0 1
1.0%
288426.0 1
1.0%
258601.5 1
1.0%
241577.2 1
1.0%
233634.5 1
1.0%
186143.9 1
1.0%
40607.6 1
1.0%

이용량/개발가능량(%)
Real number (ℝ)

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.066
Minimum4.2
Maximum215.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:42:17.268407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.2
5-th percentile11.44
Q121.2
median31
Q342.225
95-th percentile94.895
Maximum215.1
Range210.9
Interquartile range (IQR)21.025

Descriptive statistics

Standard deviation32.077942
Coefficient of variation (CV)0.82112175
Kurtosis9.7676864
Mean39.066
Median Absolute Deviation (MAD)10.85
Skewness2.6819262
Sum3906.6
Variance1028.9944
MonotonicityNot monotonic
2023-12-10T19:42:17.574592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 3
 
3.0%
22.4 2
 
2.0%
31.5 2
 
2.0%
30.0 2
 
2.0%
36.1 2
 
2.0%
31.4 2
 
2.0%
38.2 2
 
2.0%
30.6 2
 
2.0%
23.2 2
 
2.0%
29.3 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
4.2 1
1.0%
5.0 1
1.0%
8.2 1
1.0%
9.1 1
1.0%
10.3 1
1.0%
11.5 1
1.0%
12.1 1
1.0%
13.5 1
1.0%
13.7 1
1.0%
14.0 1
1.0%
ValueCountFrequency (%)
215.1 1
1.0%
137.0 1
1.0%
133.0 1
1.0%
132.5 1
1.0%
102.4 1
1.0%
94.5 1
1.0%
90.3 1
1.0%
83.7 1
1.0%
81.0 1
1.0%
79.2 1
1.0%

Interactions

2023-12-10T19:42:13.253489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:12.152279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:12.703388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:13.404680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:12.381719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:12.918347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:13.586838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:12.567309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:13.099810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:42:17.747719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역시도(시도)행정구역시군구(시군구)개발가능량(천㎥/년)이용량(천㎥/년)이용량/개발가능량(%)
행정구역시도(시도)1.0000.0000.9870.9870.000
행정구역시군구(시군구)0.0001.0000.0000.0000.697
개발가능량(천㎥/년)0.9870.0001.0000.9840.000
이용량(천㎥/년)0.9870.0000.9841.0000.000
이용량/개발가능량(%)0.0000.6970.0000.0001.000
2023-12-10T19:42:18.006888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개발가능량(천㎥/년)이용량(천㎥/년)이용량/개발가능량(%)행정구역시도(시도)
개발가능량(천㎥/년)1.0000.877-0.2990.901
이용량(천㎥/년)0.8771.0000.1310.901
이용량/개발가능량(%)-0.2990.1311.0000.000
행정구역시도(시도)0.9010.9010.0001.000

Missing values

2023-12-10T19:42:13.787922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:42:13.999737image/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

년도행정구역시도(시도)행정구역시군구(시군구)개발가능량(천㎥/년)이용량(천㎥/년)이용량/개발가능량(%)
02018부산광역시전체97553.028558.829.3
12018대구광역시전체84742.922144.626.1
22018인천광역시전체129036.840607.631.5
32018광주광역시전체57818.617360.830.0
42018대전광역시전체69842.025223.236.1
52018울산광역시전체149419.723804.615.9
62018세종특별자치시전체56315.021531.538.2
72018경기도전체1325656.5406171.030.6
82018강원도전체2263659.1186143.98.2
92018충청북도전체893669.8258601.528.9
년도행정구역시도(시도)행정구역시군구(시군구)개발가능량(천㎥/년)이용량(천㎥/년)이용량/개발가능량(%)
902018세종특별자치시세종시56315.021531.538.2
912018경기도가평군106260.39623.59.1
922018경기도고양시30628.123911.478.1
932018경기도과천시4064.71343.133.0
942018경기도광명시4526.63667.381.0
952018경기도광주시61843.511864.919.2
962018경기도구리시3792.51288.734.0
972018경기도군포시4308.31318.730.6
982018경기도김포시28402.014526.651.1
992018서울특별시전체54095.320005.937.0