Overview

Dataset statistics

Number of variables8
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory75.3 B

Variable types

Categorical1
Text1
Numeric6

Alerts

집계년도 has constant value ""Constant
총인구수 is highly overall correlated with 급수인구수 and 2 other fieldsHigh correlation
급수인구수 is highly overall correlated with 총인구수 and 2 other fieldsHigh correlation
보급률(%) is highly overall correlated with 총인구수 and 2 other fieldsHigh correlation
광역시설용량(㎥/일) is highly overall correlated with 총인구수 and 3 other fieldsHigh correlation
지방시설용량(㎥/일) is highly overall correlated with 광역시설용량(㎥/일)High correlation
시군명 has unique valuesUnique
총인구수 has unique valuesUnique
급수인구수 has unique valuesUnique
광역시설용량(㎥/일) has 7 (22.6%) zerosZeros
지방시설용량(㎥/일) has 13 (41.9%) zerosZeros
타시도시설용량(㎥/일) has 26 (83.9%) zerosZeros

Reproduction

Analysis started2023-12-10 22:30:26.643518
Analysis finished2023-12-10 22:30:30.427850
Duration3.78 seconds
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

2023-12-11T07:30:30.499827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:30:30.602419image/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
2023-12-11T07:30:30.783521image/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%
2023-12-11T07:30:31.147015image/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)

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
2023-12-11T07:30:31.285024image/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
2023-12-11T07:30:31.410611image/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%

급수인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443048.52
Minimum34222
Maximum1216459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:30:31.565256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34222
5-th percentile57763
Q1175485.5
median321001
Q3643559
95-th percentile1088006
Maximum1216459
Range1182237
Interquartile range (IQR)468073.5

Descriptive statistics

Standard deviation344920.67
Coefficient of variation (CV)0.77851671
Kurtosis-0.45624677
Mean443048.52
Median Absolute Deviation (MAD)221416
Skewness0.79391321
Sum13734504
Variance1.1897027 × 1011
MonotonicityNot monotonic
2023-12-11T07:30:31.709541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
34222 1
 
3.2%
1088144 1
 
3.2%
922033 1
 
3.2%
321001 1
 
3.2%
145678 1
 
3.2%
588046 1
 
3.2%
489066 1
 
3.2%
218852 1
 
3.2%
465416 1
 
3.2%
164363 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
34222 1
3.2%
42643 1
3.2%
72883 1
3.2%
94600 1
3.2%
99585 1
3.2%
105520 1
3.2%
145678 1
3.2%
164363 1
3.2%
186608 1
3.2%
193304 1
3.2%
ValueCountFrequency (%)
1216459 1
3.2%
1088144 1
3.2%
1087868 1
3.2%
944792 1
3.2%
922033 1
3.2%
829846 1
3.2%
723816 1
3.2%
699072 1
3.2%
588046 1
3.2%
553249 1
3.2%

보급률(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.232258
Minimum54.1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:30:31.850658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54.1
5-th percentile86.1
Q197.1
median99.5
Q3100
95-th percentile100
Maximum100
Range45.9
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation8.801719
Coefficient of variation (CV)0.091463291
Kurtosis18.425068
Mean96.232258
Median Absolute Deviation (MAD)0.5
Skewness-4.0523714
Sum2983.2
Variance77.470258
MonotonicityNot monotonic
2023-12-11T07:30:31.994336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
100.0 10
32.3%
99.1 2
 
6.5%
99.9 2
 
6.5%
97.7 2
 
6.5%
99.8 2
 
6.5%
54.1 1
 
3.2%
97.9 1
 
3.2%
99.6 1
 
3.2%
90.9 1
 
3.2%
95.2 1
 
3.2%
Other values (8) 8
25.8%
ValueCountFrequency (%)
54.1 1
3.2%
81.3 1
3.2%
90.9 1
3.2%
91.5 1
3.2%
93.0 1
3.2%
93.1 1
3.2%
95.2 1
3.2%
96.5 1
3.2%
97.7 2
6.5%
97.9 1
3.2%
ValueCountFrequency (%)
100.0 10
32.3%
99.9 2
 
6.5%
99.8 2
 
6.5%
99.6 1
 
3.2%
99.5 1
 
3.2%
99.4 1
 
3.2%
99.1 2
 
6.5%
98.2 1
 
3.2%
97.9 1
 
3.2%
97.7 2
 
6.5%

광역시설용량(㎥/일)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189693.55
Minimum0
Maximum515000
Zeros7
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:30:32.128797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142000
median148000
Q3336750
95-th percentile483500
Maximum515000
Range515000
Interquartile range (IQR)294750

Descriptive statistics

Standard deviation175701.42
Coefficient of variation (CV)0.92623822
Kurtosis-1.0076317
Mean189693.55
Median Absolute Deviation (MAD)148000
Skewness0.61638738
Sum5880500
Variance3.0870987 × 1010
MonotonicityNot monotonic
2023-12-11T07:30:32.265649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 7
22.6%
475000 2
 
6.5%
471200 1
 
3.2%
115000 1
 
3.2%
392000 1
 
3.2%
355500 1
 
3.2%
174000 1
 
3.2%
78000 1
 
3.2%
248000 1
 
3.2%
133000 1
 
3.2%
Other values (14) 14
45.2%
ValueCountFrequency (%)
0 7
22.6%
34000 1
 
3.2%
50000 1
 
3.2%
53000 1
 
3.2%
78000 1
 
3.2%
86000 1
 
3.2%
111000 1
 
3.2%
115000 1
 
3.2%
133000 1
 
3.2%
148000 1
 
3.2%
ValueCountFrequency (%)
515000 1
3.2%
492000 1
3.2%
475000 2
6.5%
471200 1
3.2%
392800 1
3.2%
392000 1
3.2%
355500 1
3.2%
318000 1
3.2%
248000 1
3.2%
220000 1
3.2%

지방시설용량(㎥/일)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35858.065
Minimum0
Maximum280000
Zeros13
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:30:32.372370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8000
Q355000
95-th percentile112000
Maximum280000
Range280000
Interquartile range (IQR)55000

Descriptive statistics

Standard deviation57527.233
Coefficient of variation (CV)1.6043039
Kurtosis10.325647
Mean35858.065
Median Absolute Deviation (MAD)8000
Skewness2.8328027
Sum1111600
Variance3.3093825 × 109
MonotonicityNot monotonic
2023-12-11T07:30:32.487333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 13
41.9%
50000 2
 
6.5%
30000 2
 
6.5%
60000 2
 
6.5%
31000 1
 
3.2%
70000 1
 
3.2%
3200 1
 
3.2%
96000 1
 
3.2%
8000 1
 
3.2%
100000 1
 
3.2%
Other values (6) 6
19.4%
ValueCountFrequency (%)
0 13
41.9%
3200 1
 
3.2%
5500 1
 
3.2%
8000 1
 
3.2%
10900 1
 
3.2%
30000 2
 
6.5%
31000 1
 
3.2%
32000 1
 
3.2%
50000 2
 
6.5%
60000 2
 
6.5%
ValueCountFrequency (%)
280000 1
3.2%
124000 1
3.2%
100000 1
3.2%
96000 1
3.2%
71000 1
3.2%
70000 1
3.2%
60000 2
6.5%
50000 2
6.5%
32000 1
3.2%
31000 1
3.2%

타시도시설용량(㎥/일)
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6956.129
Minimum0
Maximum110000
Zeros26
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:30:32.610414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile51550
Maximum110000
Range110000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23698.238
Coefficient of variation (CV)3.406814
Kurtosis13.74733
Mean6956.129
Median Absolute Deviation (MAD)0
Skewness3.7123161
Sum215640
Variance5.6160646 × 108
MonotonicityNot monotonic
2023-12-11T07:30:32.729713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 26
83.9%
440 1
 
3.2%
30000 1
 
3.2%
110000 1
 
3.2%
2100 1
 
3.2%
73100 1
 
3.2%
ValueCountFrequency (%)
0 26
83.9%
440 1
 
3.2%
2100 1
 
3.2%
30000 1
 
3.2%
73100 1
 
3.2%
110000 1
 
3.2%
ValueCountFrequency (%)
110000 1
 
3.2%
73100 1
 
3.2%
30000 1
 
3.2%
2100 1
 
3.2%
440 1
 
3.2%
0 26
83.9%

Interactions

2023-12-11T07:30:29.702581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:26.833833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:27.543248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.027242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.543784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:29.106929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:29.805002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:26.910937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:27.626105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.120744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.635394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:29.189986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:29.880352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:27.004109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:27.707683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.224731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.720754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:29.302790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:29.954352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:27.091746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:27.777196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.292482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.795075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:29.407308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:30.048690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:27.398177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:27.876150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.397058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.882510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:29.520967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:30.135213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:27.470426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:27.948390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:28.469465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:29.017255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:30:29.613937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:30:32.810086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명총인구수급수인구수보급률(%)광역시설용량(㎥/일)지방시설용량(㎥/일)타시도시설용량(㎥/일)
시군명1.0001.0001.0001.0001.0001.0001.000
총인구수1.0001.0000.9990.0000.5720.0000.454
급수인구수1.0000.9991.0000.0000.6450.0000.000
보급률(%)1.0000.0000.0001.0000.0000.1090.000
광역시설용량(㎥/일)1.0000.5720.6450.0001.0000.4630.000
지방시설용량(㎥/일)1.0000.0000.0000.1090.4631.0000.327
타시도시설용량(㎥/일)1.0000.4540.0000.0000.0000.3271.000
2023-12-11T07:30:32.932359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총인구수급수인구수보급률(%)광역시설용량(㎥/일)지방시설용량(㎥/일)타시도시설용량(㎥/일)
총인구수1.0000.9980.5260.813-0.1210.150
급수인구수0.9981.0000.5420.816-0.1190.151
보급률(%)0.5260.5421.0000.619-0.4610.081
광역시설용량(㎥/일)0.8130.8160.6191.000-0.507-0.139
지방시설용량(㎥/일)-0.121-0.119-0.461-0.5071.0000.303
타시도시설용량(㎥/일)0.1500.1510.081-0.1390.3031.000

Missing values

2023-12-11T07:30:30.237176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:30:30.370374image/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

집계년도시군명총인구수급수인구수보급률(%)광역시설용량(㎥/일)지방시설용량(㎥/일)타시도시설용량(㎥/일)
02021가평군632683422254.10310000
12021고양시1090339108814499.849200000
22021과천시735287288399.15000000
32021광명시29647129627499.92200000440
42021광주시39822537030693.001240000
52021구리시193351193304100.0340003000030000
62021군포시274100274083100.015600000
72021김포시50426748661896.517500000
82021남양주시74085672381697.711100071000110000
92021동두천시968609460097.753000600000
집계년도시군명총인구수급수인구수보급률(%)광역시설용량(㎥/일)지방시설용량(㎥/일)타시도시설용량(㎥/일)
212021오산시238579238579100.013300000
222021용인시1093665108786899.52480001000002100
232021의왕시164363164363100.07800000
242021의정부시46833946541699.417400080000
252021이천시22985421885295.20600000
262021파주시49350348906699.1355500960000
272021평택시588046588046100.0392000300000
282021포천시16020914567890.911500032000
292021하남시32227132100199.607000073100
302021화성시922092922033100.047500000