Overview

Dataset statistics

Number of variables6
Number of observations275
Missing cells243
Missing cells (%)14.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.1 KiB
Average record size in memory52.5 B

Variable types

Categorical1
Text2
Numeric3

Alerts

부채금액(원) is highly overall correlated with 자산금액(원) and 1 other fieldsHigh correlation
자산금액(원) is highly overall correlated with 부채금액(원)High correlation
지자체부채비율(%) is highly overall correlated with 부채금액(원)High correlation
시군명 has 243 (88.4%) missing valuesMissing
부채금액(원) has unique valuesUnique
자산금액(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:00:54.338862
Analysis finished2023-12-10 21:00:55.908011
Duration1.57 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계연도
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2020
243 
2021
32 

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 (%)
2020 243
88.4%
2021 32
 
11.6%

Length

2023-12-11T06:00:55.992141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:00:56.129277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 243
88.4%
2021 32
 
11.6%

시군명
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing243
Missing (%)88.4%
Memory size2.3 KiB
2023-12-11T06:00:56.412574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.09375
Min length3

Characters and Unicode

Total characters99
Distinct characters41
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

Unique32 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row경기도
3rd row고양시
4th row과천시
5th row광명시
ValueCountFrequency (%)
경기도 1
 
3.1%
고양시 1
 
3.1%
화성시 1
 
3.1%
하남시 1
 
3.1%
포천시 1
 
3.1%
평택시 1
 
3.1%
파주시 1
 
3.1%
이천시 1
 
3.1%
의정부시 1
 
3.1%
의왕시 1
 
3.1%
Other values (22) 22
68.8%
2023-12-11T06:00:56.877972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
29.3%
천 6
 
6.1%
주 5
 
5.1%
양 5
 
5.1%
군 4
 
4.0%
안 3
 
3.0%
평 3
 
3.0%
남 3
 
3.0%
포 3
 
3.0%
성 3
 
3.0%
Other values (31) 35
35.4%

자치단체명
Text

Distinct243
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-11T06:00:57.269480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8872727
Min length4

Characters and Unicode

Total characters1344
Distinct characters133
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

Unique211 ?
Unique (%)76.7%

Sample

1st row경기가평군
2nd row경기본청
3rd row경기고양시
4th row경기과천시
5th row경기광명시
ValueCountFrequency (%)
경기가평군 2
 
0.7%
경기평택시 2
 
0.7%
경기안성시 2
 
0.7%
경기여주시 2
 
0.7%
경기용인시 2
 
0.7%
경기연천군 2
 
0.7%
경기양평군 2
 
0.7%
경기의왕시 2
 
0.7%
경기하남시 2
 
0.7%
경기이천시 2
 
0.7%
Other values (233) 255
92.7%
2023-12-11T06:00:57.814256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1344
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1344
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1344
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 110
 
8.2%
시 105
 
7.8%
군 89
 
6.6%
구 84
 
6.2%
남 73
 
5.4%
기 65
 
4.8%
북 57
 
4.2%
전 45
 
3.3%
산 41
 
3.1%
천 39
 
2.9%
Other values (123) 636
47.3%

부채금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4033002 × 1011
Minimum7.7733878 × 109
Maximum1.4562811 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:00:57.980372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.7733878 × 109
5-th percentile1.179469 × 1010
Q12.2095486 × 1010
median3.7778824 × 1010
Q38.8856663 × 1010
95-th percentile1.0522459 × 1012
Maximum1.4562811 × 1013
Range1.4555037 × 1013
Interquartile range (IQR)6.6761178 × 1010

Descriptive statistics

Standard deviation1.019688 × 1012
Coefficient of variation (CV)4.2428655
Kurtosis144.71486
Mean2.4033002 × 1011
Median Absolute Deviation (MAD)2.2125836 × 1010
Skewness10.924608
Sum6.6090756 × 1013
Variance1.0397635 × 1024
MonotonicityNot monotonic
2023-12-11T06:00:58.126314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27697246945 1
 
0.4%
120590938515 1
 
0.4%
40928872208 1
 
0.4%
132331732354 1
 
0.4%
108756090029 1
 
0.4%
46039688821 1
 
0.4%
68528089782 1
 
0.4%
363195043908 1
 
0.4%
292668143018 1
 
0.4%
39403794480 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
7773387838 1
0.4%
8558946781 1
0.4%
8791606648 1
0.4%
9252055367 1
0.4%
9527939100 1
0.4%
9800769948 1
0.4%
10676900928 1
0.4%
10934620787 1
0.4%
11185284081 1
0.4%
11203877275 1
0.4%
ValueCountFrequency (%)
14562810729859 1
0.4%
4560385074690 1
0.4%
3775397804884 1
0.4%
3350635546641 1
0.4%
3184854093441 1
0.4%
2741067951332 1
0.4%
2078765576098 1
0.4%
1871812076323 1
0.4%
1402087721313 1
0.4%
1332956920853 1
0.4%

자산금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9096483 × 1012
Minimum3.7768489 × 1011
Maximum1.4125753 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:00:58.294559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.7768489 × 1011
5-th percentile8.8287669 × 1011
Q11.9803204 × 1012
median2.9262712 × 1012
Q35.4005826 × 1012
95-th percentile2.0249406 × 1013
Maximum1.4125753 × 1014
Range1.4087985 × 1014
Interquartile range (IQR)3.4202622 × 1012

Descriptive statistics

Standard deviation1.1018156 × 1013
Coefficient of variation (CV)1.8644352
Kurtosis85.823408
Mean5.9096483 × 1012
Median Absolute Deviation (MAD)1.2172487 × 1012
Skewness7.8649538
Sum1.6251533 × 1015
Variance1.2139977 × 1026
MonotonicityNot monotonic
2023-12-11T06:00:58.439608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2366944425649 1
 
0.4%
5532888684542 1
 
0.4%
2178028507586 1
 
0.4%
3539565902567 1
 
0.4%
3139429038363 1
 
0.4%
3300663457994 1
 
0.4%
3641672138240 1
 
0.4%
5398578640110 1
 
0.4%
10833825929288 1
 
0.4%
2359862521338 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
377684892354 1
0.4%
524410176802 1
0.4%
563340703829 1
0.4%
600887299025 1
0.4%
628718155930 1
0.4%
652135343732 1
0.4%
681027401103 1
0.4%
700404028005 1
0.4%
737520967115 1
0.4%
780314710263 1
0.4%
ValueCountFrequency (%)
141257533321371 1
0.4%
58927573376048 1
0.4%
46801850561560 1
0.4%
41057060680369 1
0.4%
39753749837739 1
0.4%
35621211513039 1
0.4%
28052651761418 1
0.4%
25407634783325 1
0.4%
24286520435638 1
0.4%
23821892369558 1
0.4%

지자체부채비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct194
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1745091
Minimum0.3
Maximum10.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:00:58.599956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.517
Q10.84
median1.6
Q32.585
95-th percentile6.688
Maximum10.97
Range10.67
Interquartile range (IQR)1.745

Descriptive statistics

Standard deviation1.9852285
Coefficient of variation (CV)0.91295479
Kurtosis4.8743068
Mean2.1745091
Median Absolute Deviation (MAD)0.83
Skewness2.1280607
Sum597.99
Variance3.9411321
MonotonicityNot monotonic
2023-12-11T06:00:58.732887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53 6
 
2.2%
1.17 5
 
1.8%
0.6 5
 
1.8%
0.73 5
 
1.8%
0.69 4
 
1.5%
1.32 3
 
1.1%
0.92 3
 
1.1%
0.62 3
 
1.1%
1.18 3
 
1.1%
1.88 3
 
1.1%
Other values (184) 235
85.5%
ValueCountFrequency (%)
0.3 1
0.4%
0.33 1
0.4%
0.35 1
0.4%
0.39 1
0.4%
0.44 2
0.7%
0.46 2
0.7%
0.47 1
0.4%
0.48 2
0.7%
0.5 2
0.7%
0.51 1
0.4%
ValueCountFrequency (%)
10.97 1
0.4%
10.31 1
0.4%
9.99 1
0.4%
9.74 1
0.4%
9.33 1
0.4%
9.2 1
0.4%
8.96 1
0.4%
8.01 1
0.4%
7.91 1
0.4%
7.71 1
0.4%

Interactions

2023-12-11T06:00:55.328501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:54.614054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:54.974230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:55.442456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:54.730888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:55.088742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:55.573430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:54.855443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:00:55.222543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:00:58.829750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명부채금액(원)자산금액(원)지자체부채비율(%)
회계연도1.000NaN0.0000.0230.000
시군명NaN1.0001.0001.0001.000
부채금액(원)0.0001.0001.0000.9070.851
자산금액(원)0.0231.0000.9071.0000.722
지자체부채비율(%)0.0001.0000.8510.7221.000
2023-12-11T06:00:58.953689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부채금액(원)자산금액(원)지자체부채비율(%)회계연도
부채금액(원)1.0000.7200.6900.000
자산금액(원)0.7201.0000.0930.019
지자체부채비율(%)0.6900.0931.0000.000
회계연도0.0000.0190.0001.000

Missing values

2023-12-11T06:00:55.714367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:00:55.849583image/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

회계연도시군명자치단체명부채금액(원)자산금액(원)지자체부채비율(%)
02021가평군경기가평군2769724694523669444256491.17
12021경기도경기본청3775397804884410570606803699.2
22021고양시경기고양시106519906263200125121464840.53
32021과천시경기과천시2923134500123043665567821.27
42021광명시경기광명시2926040838138832768911580.75
52021광주시경기광주시17760806018941252706887124.31
62021구리시경기구리시5479418164036682495142391.49
72021군포시경기군포시2592559683544570358602170.58
82021김포시경기김포시18693973980776517337658562.44
92021남양주시경기남양주시4729682244482729610682980.57
회계연도시군명자치단체명부채금액(원)자산금액(원)지자체부채비율(%)
2652020<NA>서울용산구6728441247326743057005182.52
2662020<NA>서울성동구4523556713620750775945612.18
2672020<NA>서울광진구3389645418122080787216751.54
2682020<NA>서울동대문구3954017932121381782891571.85
2692020<NA>서울중랑구2149465012718184860369591.18
2702020<NA>서울성북구2898430113025870308834951.12
2712020<NA>서울강북구2635471072017877090125151.47
2722020<NA>서울도봉구2767086003517220577534281.61
2732020<NA>서울노원구5965691398324569196473992.43
2742020<NA>서울은평구4291216205318281141770752.35