Overview

Dataset statistics

Number of variables6
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory57.3 B

Variable types

Categorical1
Text1
Numeric4

Alerts

집계년도 has constant value ""Constant
총면적(㎢) is highly overall correlated with 하수처리구역면적(㎢) and 1 other fieldsHigh correlation
하수처리구역면적(㎢) is highly overall correlated with 총면적(㎢)High correlation
보급률(%) is highly overall correlated with 총면적(㎢)High correlation
시군명 has unique valuesUnique
총인구수 has unique valuesUnique
총면적(㎢) has unique valuesUnique
하수처리구역면적(㎢) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:16:59.439801
Analysis finished2023-12-10 22:17:00.819438
Duration1.38 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2021
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 31
100.0%

Length

2023-12-11T07:17:00.867891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:17:00.938699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 31
100.0%

시군명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T07:17:01.078563image/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 row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시
ValueCountFrequency (%)
가평군 1
 
3.2%
안양시 1
 
3.2%
하남시 1
 
3.2%
포천시 1
 
3.2%
평택시 1
 
3.2%
파주시 1
 
3.2%
이천시 1
 
3.2%
의정부시 1
 
3.2%
의왕시 1
 
3.2%
용인시 1
 
3.2%
Other values (21) 21
67.7%
2023-12-11T07:17:01.336736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

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

총인구수
Real number (ℝ)

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
2023-12-11T07:17:01.440032image/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
2023-12-11T07:17:01.556375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
63268 1
 
3.2%
1090339 1
 
3.2%
922092 1
 
3.2%
322271 1
 
3.2%
160209 1
 
3.2%
588046 1
 
3.2%
493503 1
 
3.2%
229854 1
 
3.2%
468339 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%

총면적(㎢)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean328.61613
Minimum33.3
Maximum877.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:17:01.664162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.3
5-th percentile36.35
Q168.65
median269.8
Q3571.3
95-th percentile835.05
Maximum877.8
Range844.5
Interquartile range (IQR)502.65

Descriptive statistics

Standard deviation284.32583
Coefficient of variation (CV)0.86522177
Kurtosis-1.1240903
Mean328.61613
Median Absolute Deviation (MAD)215.4
Skewness0.56581999
Sum10187.1
Variance80841.176
MonotonicityNot monotonic
2023-12-11T07:17:01.765443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
843.6 1
 
3.2%
269.8 1
 
3.2%
694.2 1
 
3.2%
92.4 1
 
3.2%
826.5 1
 
3.2%
458.3 1
 
3.2%
682.0 1
 
3.2%
461.3 1
 
3.2%
78.8 1
 
3.2%
54.4 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
33.3 1
3.2%
35.8 1
3.2%
36.9 1
3.2%
38.5 1
3.2%
42.8 1
3.2%
53.1 1
3.2%
54.4 1
3.2%
58.5 1
3.2%
78.8 1
3.2%
92.4 1
3.2%
ValueCountFrequency (%)
877.8 1
3.2%
843.6 1
3.2%
826.5 1
3.2%
694.2 1
3.2%
682.0 1
3.2%
676.4 1
3.2%
608.5 1
3.2%
591.2 1
3.2%
551.4 1
3.2%
461.3 1
3.2%

하수처리구역면적(㎢)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.987097
Minimum5.2
Maximum140.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:17:01.868509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile12.2
Q123.75
median42.6
Q360.8
95-th percentile116.3
Maximum140.5
Range135.3
Interquartile range (IQR)37.05

Descriptive statistics

Standard deviation35.855513
Coefficient of variation (CV)0.7032272
Kurtosis0.11368643
Mean50.987097
Median Absolute Deviation (MAD)19.4
Skewness0.95761074
Sum1580.6
Variance1285.6178
MonotonicityNot monotonic
2023-12-11T07:17:01.996641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
49.9 1
 
3.2%
59.6 1
 
3.2%
140.5 1
 
3.2%
28.1 1
 
3.2%
85.4 1
 
3.2%
118.3 1
 
3.2%
50.6 1
 
3.2%
50.0 1
 
3.2%
20.7 1
 
3.2%
17.5 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
5.2 1
3.2%
11.9 1
3.2%
12.5 1
3.2%
13.3 1
3.2%
13.8 1
3.2%
17.5 1
3.2%
20.7 1
3.2%
21.5 1
3.2%
26.0 1
3.2%
27.2 1
3.2%
ValueCountFrequency (%)
140.5 1
3.2%
118.3 1
3.2%
114.3 1
3.2%
107.7 1
3.2%
106.0 1
3.2%
85.4 1
3.2%
82.5 1
3.2%
62.0 1
3.2%
59.6 1
3.2%
57.7 1
3.2%

보급률(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.570968
Minimum72.5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:17:02.109628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72.5
5-th percentile78.45
Q190.85
median96.4
Q399.45
95-th percentile99.95
Maximum100
Range27.5
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation7.6270656
Coefficient of variation (CV)0.081511026
Kurtosis0.8721445
Mean93.570968
Median Absolute Deviation (MAD)3.4
Skewness-1.3126626
Sum2900.7
Variance58.172129
MonotonicityNot monotonic
2023-12-11T07:17:02.206146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
100.0 2
 
6.5%
98.7 2
 
6.5%
99.6 2
 
6.5%
99.8 2
 
6.5%
99.0 2
 
6.5%
79.2 1
 
3.2%
87.7 1
 
3.2%
99.5 1
 
3.2%
85.6 1
 
3.2%
90.3 1
 
3.2%
Other values (16) 16
51.6%
ValueCountFrequency (%)
72.5 1
3.2%
77.7 1
3.2%
79.2 1
3.2%
82.8 1
3.2%
83.7 1
3.2%
85.6 1
3.2%
87.7 1
3.2%
90.3 1
3.2%
91.4 1
3.2%
92.4 1
3.2%
ValueCountFrequency (%)
100.0 2
6.5%
99.9 1
3.2%
99.8 2
6.5%
99.6 2
6.5%
99.5 1
3.2%
99.4 1
3.2%
99.0 2
6.5%
98.9 1
3.2%
98.7 2
6.5%
96.8 1
3.2%

Interactions

2023-12-11T07:17:00.425827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:16:59.579515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:16:59.889513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:17:00.158140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:17:00.492028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:16:59.668981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:16:59.956111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:17:00.228525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:17:00.548954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:16:59.746809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:17:00.019854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:17:00.290864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:17:00.615711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:16:59.826369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:17:00.097070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:17:00.361355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:17:02.284898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명총인구수총면적(㎢)하수처리구역면적(㎢)보급률(%)
시군명1.0001.0001.0001.0001.000
총인구수1.0001.0000.1160.2560.000
총면적(㎢)1.0000.1161.0000.8380.720
하수처리구역면적(㎢)1.0000.2560.8381.0000.712
보급률(%)1.0000.0000.7200.7121.000
2023-12-11T07:17:02.370262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총인구수총면적(㎢)하수처리구역면적(㎢)보급률(%)
총인구수1.000-0.0820.4640.311
총면적(㎢)-0.0821.0000.699-0.839
하수처리구역면적(㎢)0.4640.6991.000-0.416
보급률(%)0.311-0.839-0.4161.000

Missing values

2023-12-11T07:17:00.700482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:17:00.786505image/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

집계년도시군명총인구수총면적(㎢)하수처리구역면적(㎢)보급률(%)
02021가평군63268843.649.982.8
12021고양시1090339269.859.693.0
22021과천시7352835.85.298.7
32021광명시29647138.511.998.7
42021광주시398225429.082.596.8
52021구리시19335133.313.899.6
62021군포시27410036.913.399.8
72021김포시504267277.242.591.4
82021남양주시740856458.0106.096.0
92021동두천시9686095.727.298.9
집계년도시군명총인구수총면적(㎢)하수처리구역면적(㎢)보급률(%)
212021오산시23857942.812.599.0
222021용인시1093665591.2107.792.9
232021의왕시16436354.417.5100.0
242021의정부시46833978.820.799.0
252021이천시229854461.350.092.4
262021파주시493503682.050.694.2
272021평택시588046458.3118.390.3
282021포천시160209826.585.485.6
292021하남시32227192.428.199.5
302021화성시922092694.2140.587.7