Overview

Dataset statistics

Number of variables4
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory39.3 B

Variable types

Text1
Numeric3

Alerts

음식물류폐기물발생량(톤) is highly overall correlated with 인구수(명)High correlation
인구수(명) is highly overall correlated with 음식물류폐기물발생량(톤)High correlation
시군명 has unique valuesUnique
음식물류폐기물발생량(톤) has unique valuesUnique
인구수(명) has unique valuesUnique

Reproduction

Analysis started2023-12-16 05:44:15.348268
Analysis finished2023-12-16 05:44:22.218466
Duration6.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-16T05:44:23.553859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Characters and Unicode

Total characters96
Distinct characters38
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

Unique31 ?
Unique (%)100.0%

Sample

1st row수원시
2nd row고양시
3rd row성남시
4th row용인시
5th row부천시
ValueCountFrequency (%)
수원시 1
 
3.2%
군포시 1
 
3.2%
가평군 1
 
3.2%
과천시 1
 
3.2%
동두천시 1
 
3.2%
양평군 1
 
3.2%
여주시 1
 
3.2%
하남시 1
 
3.2%
의왕시 1
 
3.2%
포천시 1
 
3.2%
Other values (21) 21
67.7%
2023-12-16T05:44:25.867867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
주 5
 
5.2%
양 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
안 3
 
3.1%
포 3
 
3.1%
남 3
 
3.1%
평 3
 
3.1%
Other values (28) 32
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
주 5
 
5.2%
양 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
안 3
 
3.1%
포 3
 
3.1%
남 3
 
3.1%
평 3
 
3.1%
Other values (28) 32
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
주 5
 
5.2%
양 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
안 3
 
3.1%
포 3
 
3.1%
남 3
 
3.1%
평 3
 
3.1%
Other values (28) 32
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
주 5
 
5.2%
양 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
안 3
 
3.1%
포 3
 
3.1%
남 3
 
3.1%
평 3
 
3.1%
Other values (28) 32
33.3%

음식물류폐기물발생량(톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36504.058
Minimum2774.3
Maximum105111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-16T05:44:26.789701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2774.3
5-th percentile5851.2
Q114855.6
median30125.2
Q349994.25
95-th percentile100613.9
Maximum105111
Range102336.7
Interquartile range (IQR)35138.65

Descriptive statistics

Standard deviation28983.697
Coefficient of variation (CV)0.79398561
Kurtosis0.53130707
Mean36504.058
Median Absolute Deviation (MAD)15895.3
Skewness1.1420245
Sum1131625.8
Variance8.4005467 × 108
MonotonicityNot monotonic
2023-12-16T05:44:27.359383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
101098.3 1
 
3.2%
105111.0 1
 
3.2%
2774.3 1
 
3.2%
7107.9 1
 
3.2%
4613.6 1
 
3.2%
10171.6 1
 
3.2%
7088.8 1
 
3.2%
13924.1 1
 
3.2%
25386.9 1
 
3.2%
15481.3 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
2774.3 1
3.2%
4613.6 1
3.2%
7088.8 1
3.2%
7107.9 1
3.2%
10171.6 1
3.2%
11818.2 1
3.2%
13924.1 1
3.2%
14229.9 1
3.2%
15481.3 1
3.2%
17602.4 1
3.2%
ValueCountFrequency (%)
105111.0 1
3.2%
101098.3 1
3.2%
100129.5 1
3.2%
80981.6 1
3.2%
63907.7 1
3.2%
59539.6 1
3.2%
58439.8 1
3.2%
55922.7 1
3.2%
44065.8 1
3.2%
41619.6 1
3.2%

인구수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449221.35
Minimum43553
Maximum1216965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-16T05:44:27.824598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43553
5-th percentile68398
Q1178857
median322271
Q3644092
95-th percentile1092002
Maximum1216965
Range1173412
Interquartile range (IQR)465235

Descriptive statistics

Standard deviation342502.81
Coefficient of variation (CV)0.76243662
Kurtosis-0.4546538
Mean449221.35
Median Absolute Deviation (MAD)206954
Skewness0.79786106
Sum13925862
Variance1.1730818 × 1011
MonotonicityNot monotonic
2023-12-16T05:44:28.987685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1216965 1
 
3.2%
1090339 1
 
3.2%
43553 1
 
3.2%
63268 1
 
3.2%
73528 1
 
3.2%
96860 1
 
3.2%
122539 1
 
3.2%
115317 1
 
3.2%
322271 1
 
3.2%
164363 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
43553 1
3.2%
63268 1
3.2%
73528 1
3.2%
96860 1
3.2%
115317 1
3.2%
122539 1
3.2%
160209 1
3.2%
164363 1
3.2%
193351 1
3.2%
200408 1
3.2%
ValueCountFrequency (%)
1216965 1
3.2%
1093665 1
3.2%
1090339 1
3.2%
945037 1
3.2%
922092 1
3.2%
829846 1
3.2%
740856 1
3.2%
700138 1
3.2%
588046 1
3.2%
553249 1
3.2%

1일1인발생량(kg)
Real number (ℝ)

Distinct18
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23
Minimum0.11
Maximum0.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-16T05:44:29.843466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile0.135
Q10.185
median0.23
Q30.26
95-th percentile0.325
Maximum0.37
Range0.26
Interquartile range (IQR)0.075

Descriptive statistics

Standard deviation0.060937126
Coefficient of variation (CV)0.26494403
Kurtosis0.011802454
Mean0.23
Median Absolute Deviation (MAD)0.04
Skewness0.082199096
Sum7.13
Variance0.0037133333
MonotonicityNot monotonic
2023-12-16T05:44:30.377184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.25 4
12.9%
0.17 3
 
9.7%
0.22 3
 
9.7%
0.24 2
 
6.5%
0.26 2
 
6.5%
0.16 2
 
6.5%
0.23 2
 
6.5%
0.28 2
 
6.5%
0.11 2
 
6.5%
0.18 1
 
3.2%
Other values (8) 8
25.8%
ValueCountFrequency (%)
0.11 2
6.5%
0.16 2
6.5%
0.17 3
9.7%
0.18 1
 
3.2%
0.19 1
 
3.2%
0.2 1
 
3.2%
0.21 1
 
3.2%
0.22 3
9.7%
0.23 2
6.5%
0.24 2
6.5%
ValueCountFrequency (%)
0.37 1
 
3.2%
0.33 1
 
3.2%
0.32 1
 
3.2%
0.31 1
 
3.2%
0.29 1
 
3.2%
0.28 2
6.5%
0.26 2
6.5%
0.25 4
12.9%
0.24 2
6.5%
0.23 2
6.5%

Interactions

2023-12-16T05:44:19.406209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T05:44:15.824866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T05:44:17.789636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T05:44:19.827988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T05:44:16.642859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T05:44:18.204875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T05:44:20.326056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T05:44:17.246831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T05:44:18.881781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T05:44:30.809074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명음식물류폐기물발생량(톤)인구수(명)1일1인발생량(kg)
시군명1.0001.0001.0001.000
음식물류폐기물발생량(톤)1.0001.0000.8600.209
인구수(명)1.0000.8601.0000.000
1일1인발생량(kg)1.0000.2090.0001.000
2023-12-16T05:44:31.313742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
음식물류폐기물발생량(톤)인구수(명)1일1인발생량(kg)
음식물류폐기물발생량(톤)1.0000.9490.152
인구수(명)0.9491.000-0.113
1일1인발생량(kg)0.152-0.1131.000

Missing values

2023-12-16T05:44:21.213557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T05:44:21.877915image/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

시군명음식물류폐기물발생량(톤)인구수(명)1일1인발생량(kg)
0수원시101098.312169650.23
1고양시105111.010903390.26
2성남시80981.69450370.23
3용인시100129.510936650.25
4부천시33505.98298460.11
5안산시63907.77001380.25
6남양주시58439.87408560.22
7안양시44065.85532490.22
8화성시38697.29220920.11
9평택시59539.65880460.28
시군명음식물류폐기물발생량(톤)인구수(명)1일1인발생량(kg)
21구리시17602.41933510.25
22포천시11818.21602090.2
23의왕시15481.31643630.26
24하남시25386.93222710.22
25여주시13924.11153170.33
26양평군7088.81225390.16
27동두천시10171.6968600.29
28과천시4613.6735280.17
29가평군7107.9632680.31
30연천군2774.3435530.17