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

사회복지분야결산액(원) is highly overall correlated with 세출결산액(원) and 1 other fieldsHigh correlation
세출결산액(원) is highly overall correlated with 사회복지분야결산액(원)High correlation
사회복지비비율(%) is highly overall correlated with 사회복지분야결산액(원)High correlation
시군명 has 243 (88.4%) missing valuesMissing
사회복지분야결산액(원) has unique valuesUnique
세출결산액(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:11:47.000293
Analysis finished2023-12-10 23:11:48.699165
Duration1.7 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

2023-12-11T08:11:48.762723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:11:48.864129image/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
2023-12-11T08:11:49.042805image/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%
2023-12-11T08:11:49.385492image/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
2023-12-11T08:11:49.765124image/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%
2023-12-11T08:11:50.296322image/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%

사회복지분야결산액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2582028 × 1011
Minimum1.7129475 × 1010
Maximum1.4222307 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T08:11:50.482216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.7129475 × 1010
5-th percentile7.3428411 × 1010
Q11.3295279 × 1011
median2.9907101 × 1011
Q35.2741141 × 1011
95-th percentile1.9735745 × 1012
Maximum1.4222307 × 1013
Range1.4205177 × 1013
Interquartile range (IQR)3.9445863 × 1011

Descriptive statistics

Standard deviation1.5474565 × 1012
Coefficient of variation (CV)2.4726851
Kurtosis56.79721
Mean6.2582028 × 1011
Median Absolute Deviation (MAD)1.8096941 × 1011
Skewness7.1217021
Sum1.7210058 × 1014
Variance2.3946216 × 1024
MonotonicityNot monotonic
2023-12-11T08:11:50.635449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149459487229 1
 
0.4%
3044538254140 1
 
0.4%
236621586356 1
 
0.4%
207878106847 1
 
0.4%
283529526744 1
 
0.4%
616597774927 1
 
0.4%
532385102346 1
 
0.4%
845789116099 1
 
0.4%
122169017235 1
 
0.4%
64088130887 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
17129475014 1
0.4%
52657848680 1
0.4%
53834201125 1
0.4%
57693058326 1
0.4%
60049183160 1
0.4%
64088130887 1
0.4%
65582458639 1
0.4%
68125878205 1
0.4%
70445577130 1
0.4%
70619993728 1
0.4%
ValueCountFrequency (%)
14222306917980 1
0.4%
13797788366700 1
0.4%
13385073377666 1
0.4%
5477078801420 1
0.4%
4446700439760 1
0.4%
4182321808139 1
0.4%
3579996388732 1
0.4%
3454211291154 1
0.4%
3044538254140 1
0.4%
3031464767667 1
0.4%

세출결산액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.589749 × 1012
Minimum1.9776779 × 1011
Maximum3.5414408 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T08:11:50.807465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9776779 × 1011
5-th percentile3.5484197 × 1011
Q15.2326295 × 1011
median7.738949 × 1011
Q31.214491 × 1012
95-th percentile5.2740029 × 1012
Maximum3.5414408 × 1013
Range3.521664 × 1013
Interquartile range (IQR)6.9122808 × 1011

Descriptive statistics

Standard deviation3.6604453 × 1012
Coefficient of variation (CV)2.3025304
Kurtosis56.592463
Mean1.589749 × 1012
Median Absolute Deviation (MAD)2.7304387 × 1011
Skewness7.0752606
Sum4.3718097 × 1014
Variance1.339886 × 1025
MonotonicityNot monotonic
2023-12-11T08:11:50.958055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
627701847937 1
 
0.4%
7633943887854 1
 
0.4%
935561010558 1
 
0.4%
884357039415 1
 
0.4%
966214985452 1
 
0.4%
1408664384649 1
 
0.4%
1391014917553 1
 
0.4%
2085056099749 1
 
0.4%
593011667627 1
 
0.4%
220834492812 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
197767788320 1
0.4%
206093632978 1
0.4%
220834492812 1
0.4%
241683889120 1
0.4%
271540996093 1
0.4%
314267279691 1
0.4%
328353120047 1
0.4%
336872668680 1
0.4%
341956394407 1
0.4%
343712572400 1
0.4%
ValueCountFrequency (%)
35414408138443 1
0.4%
31952141756841 1
0.4%
30265897238043 1
0.4%
11495800974570 1
0.4%
10551719887982 1
0.4%
10360701614267 1
0.4%
8919629759110 1
0.4%
8894496711282 1
0.4%
8251316785631 1
0.4%
7990852530568 1
0.4%

사회복지비비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct269
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.132909
Minimum8.31
Maximum70.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T08:11:51.134576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.31
5-th percentile17.039
Q122.94
median35.65
Q346.34
95-th percentile63.089
Maximum70.73
Range62.42
Interquartile range (IQR)23.4

Descriptive statistics

Standard deviation14.447796
Coefficient of variation (CV)0.39985146
Kurtosis-0.66580473
Mean36.132909
Median Absolute Deviation (MAD)11.7
Skewness0.42021868
Sum9936.55
Variance208.73882
MonotonicityNot monotonic
2023-12-11T08:11:51.284445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.78 2
 
0.7%
38.27 2
 
0.7%
20.6 2
 
0.7%
18.4 2
 
0.7%
34.87 2
 
0.7%
21.55 2
 
0.7%
40.56 1
 
0.4%
39.88 1
 
0.4%
22.83 1
 
0.4%
29.74 1
 
0.4%
Other values (259) 259
94.2%
ValueCountFrequency (%)
8.31 1
0.4%
12.77 1
0.4%
14.81 1
0.4%
14.86 1
0.4%
15.13 1
0.4%
15.19 1
0.4%
15.28 1
0.4%
15.88 1
0.4%
16.09 1
0.4%
16.3 1
0.4%
ValueCountFrequency (%)
70.73 1
0.4%
70.56 1
0.4%
70.2 1
0.4%
69.32 1
0.4%
68.47 1
0.4%
68.39 1
0.4%
67.86 1
0.4%
67.05 1
0.4%
66.95 1
0.4%
65.52 1
0.4%

Interactions

2023-12-11T08:11:47.815823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:47.231431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:47.501355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:47.959318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:47.321390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:47.605575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:48.068842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:47.417946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:47.714120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:11:51.381091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명사회복지분야결산액(원)세출결산액(원)사회복지비비율(%)
회계연도1.000NaN0.0000.0000.249
시군명NaN1.0001.0001.0001.000
사회복지분야결산액(원)0.0001.0001.0000.9080.199
세출결산액(원)0.0001.0000.9081.0000.118
사회복지비비율(%)0.2491.0000.1990.1181.000
2023-12-11T08:11:51.526560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사회복지분야결산액(원)세출결산액(원)사회복지비비율(%)회계연도
사회복지분야결산액(원)1.0000.8910.7230.000
세출결산액(원)0.8911.0000.3720.000
사회복지비비율(%)0.7230.3721.0000.188
회계연도0.0000.0000.1881.000

Missing values

2023-12-11T08:11:48.534455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:11:48.659203image/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

회계연도시군명자치단체명사회복지분야결산액(원)세출결산액(원)사회복지비비율(%)
02021가평군경기가평군14945948722962770184793723.81
12021경기도경기본청137977883667003541440813844338.96
22021고양시경기고양시1326285958480269196781720049.27
32021과천시경기과천시9170102026044468175071320.62
42021광명시경기광명시42136285321086363799505148.79
52021광주시경기광주시519344422556132236676712939.27
62021구리시경기구리시25366102031066376301352038.22
72021군포시경기군포시36914013957479483649090346.44
82021김포시경기김포시634848576880149609660393042.43
92021남양주시경기남양주시973606423677200465582140548.57
회계연도시군명자치단체명사회복지분야결산액(원)세출결산액(원)사회복지비비율(%)
2652020<NA>서울종로구18137050655052149752139034.78
2662020<NA>서울중구19664339410955178535278535.64
2672020<NA>서울용산구23068756870559028984199039.08
2682020<NA>서울성동구29912171940072534825192041.24
2692020<NA>서울광진구33443520560976987867781043.44
2702020<NA>서울동대문구40935637392483945910156048.76
2712020<NA>서울중랑구489163738761100040781807248.9
2722020<NA>서울성북구45853019038698722399711646.45
2732020<NA>서울강북구42570060730483924104107850.72
2742020<NA>서울도봉구38875506334382433360249147.16