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

자체사업예산액(원) is highly overall correlated with 일반회계예산액(원) and 1 other fieldsHigh correlation
일반회계예산액(원) is highly overall correlated with 자체사업예산액(원)High correlation
자체사업비중비율(%) is highly overall correlated with 자체사업예산액(원)High correlation
시군명 has 243 (88.4%) missing valuesMissing
자체사업예산액(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:03:00.338103
Analysis finished2023-12-10 22:03:01.739426
Duration1.4 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
2022
243 
2023
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2022 243
88.4%
2023 32
 
11.6%

Length

2023-12-11T07:03:01.808588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:03:01.895018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 243
88.4%
2023 32
 
11.6%

시군명
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing243
Missing (%)88.4%
Memory size2.3 KiB
2023-12-11T07:03:02.077096image/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%
2023-12-11T07:03:02.430375image/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
2023-12-11T07:03:02.760256image/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%
2023-12-11T07:03:03.187846image/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%

자체사업예산액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0951617 × 1011
Minimum3.4272961 × 1010
Maximum1.5603525 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:03:03.310812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.4272961 × 1010
5-th percentile5.602932 × 1010
Q11.2618472 × 1011
median1.852253 × 1011
Q33.1915107 × 1011
95-th percentile1.9055598 × 1012
Maximum1.5603525 × 1013
Range1.5569252 × 1013
Interquartile range (IQR)1.9296635 × 1011

Descriptive statistics

Standard deviation1.5736263 × 1012
Coefficient of variation (CV)3.0884718
Kurtosis67.785384
Mean5.0951617 × 1011
Median Absolute Deviation (MAD)8.1663663 × 1010
Skewness7.9019539
Sum1.4011695 × 1014
Variance2.4762997 × 1024
MonotonicityNot monotonic
2023-12-11T07:03:03.428899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149215406000 1
 
0.4%
709289045000 1
 
0.4%
584130786000 1
 
0.4%
1014008605000 1
 
0.4%
537581049000 1
 
0.4%
479836150000 1
 
0.4%
504913187000 1
 
0.4%
903624430000 1
 
0.4%
1121718258000 1
 
0.4%
1453488029000 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
34272961000 1
0.4%
38021409000 1
0.4%
38104051000 1
0.4%
39388012000 1
0.4%
39608368000 1
0.4%
45110071000 1
0.4%
50412931000 1
0.4%
50482371000 1
0.4%
51998843000 1
0.4%
52815248000 1
0.4%
ValueCountFrequency (%)
15603524529000 1
0.4%
14188826636000 1
0.4%
13564500357000 1
0.4%
3956384450000 1
0.4%
3771828041000 1
0.4%
3172860835000 1
0.4%
2919907052000 1
0.4%
2786910658000 1
0.4%
2700084196000 1
0.4%
2366151617000 1
0.4%

일반회계예산액(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct274
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5095293 × 1012
Minimum1.6694544 × 1011
Maximum3.134246 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:03:03.551591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6694544 × 1011
5-th percentile3.5384416 × 1011
Q15.0052025 × 1011
median7.2 × 1011
Q31.0712129 × 1012
95-th percentile5.4103729 × 1012
Maximum3.134246 × 1013
Range3.1175514 × 1013
Interquartile range (IQR)5.7069266 × 1011

Descriptive statistics

Standard deviation3.4509361 × 1012
Coefficient of variation (CV)2.2861008
Kurtosis53.190142
Mean1.5095293 × 1012
Median Absolute Deviation (MAD)2.521893 × 1011
Skewness6.8566682
Sum4.1512056 × 1014
Variance1.190896 × 1025
MonotonicityNot monotonic
2023-12-11T07:03:03.677114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720000000000 2
 
0.7%
462298820000 1
 
0.4%
2515721349000 1
 
0.4%
1693115718000 1
 
0.4%
2575005687000 1
 
0.4%
1813406305000 1
 
0.4%
1717423441000 1
 
0.4%
1813531925000 1
 
0.4%
2597624002000 1
 
0.4%
2593956137000 1
 
0.4%
Other values (264) 264
96.0%
ValueCountFrequency (%)
166945436000 1
0.4%
196476971000 1
0.4%
215500000000 1
0.4%
243024278000 1
0.4%
280435723000 1
0.4%
287000000000 1
0.4%
301251647000 1
0.4%
308000000000 1
0.4%
314807864000 1
0.4%
324724497000 1
0.4%
ValueCountFrequency (%)
31342459847000 1
0.4%
29977017979000 1
0.4%
29975489088000 1
0.4%
11128166851000 1
0.4%
10108293256000 1
0.4%
9757400000000 1
0.4%
9326396434000 1
0.4%
9058346252000 1
0.4%
8027600000000 1
0.4%
7820000000000 1
0.4%

자체사업비중비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.526182
Minimum8.1
Maximum56.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:03:03.798486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.1
5-th percentile11.9
Q122.7
median28.3
Q332.65
95-th percentile39.63
Maximum56.4
Range48.3
Interquartile range (IQR)9.95

Descriptive statistics

Standard deviation8.4299456
Coefficient of variation (CV)0.30625191
Kurtosis0.18880823
Mean27.526182
Median Absolute Deviation (MAD)5.2
Skewness-0.10403568
Sum7569.7
Variance71.063984
MonotonicityNot monotonic
2023-12-11T07:03:03.913563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.0 5
 
1.8%
28.9 5
 
1.8%
32.3 4
 
1.5%
32.1 4
 
1.5%
27.7 4
 
1.5%
34.7 4
 
1.5%
22.3 3
 
1.1%
27.9 3
 
1.1%
20.4 3
 
1.1%
24.8 3
 
1.1%
Other values (172) 237
86.2%
ValueCountFrequency (%)
8.1 1
0.4%
9.1 1
0.4%
9.3 1
0.4%
9.4 1
0.4%
9.8 1
0.4%
9.9 1
0.4%
10.0 1
0.4%
10.1 1
0.4%
10.3 2
0.7%
10.4 1
0.4%
ValueCountFrequency (%)
56.4 1
0.4%
49.8 1
0.4%
49.7 1
0.4%
47.3 1
0.4%
45.3 1
0.4%
45.2 1
0.4%
43.2 1
0.4%
42.8 1
0.4%
42.2 1
0.4%
41.9 1
0.4%

Interactions

2023-12-11T07:03:01.213600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:00.629341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:00.925373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:01.305492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:00.745677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:01.029669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:01.421869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:00.838847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:01.129245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:03:03.998075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명자체사업예산액(원)일반회계예산액(원)자체사업비중비율(%)
회계연도1.000NaN0.1160.0000.381
시군명NaN1.0001.0001.0001.000
자체사업예산액(원)0.1161.0001.0000.9570.810
일반회계예산액(원)0.0001.0000.9571.0000.705
자체사업비중비율(%)0.3811.0000.8100.7051.000
2023-12-11T07:03:04.103493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자체사업예산액(원)일반회계예산액(원)자체사업비중비율(%)회계연도
자체사업예산액(원)1.0000.8590.6300.141
일반회계예산액(원)0.8591.0000.2250.000
자체사업비중비율(%)0.6300.2251.0000.288
회계연도0.1410.0000.2881.000

Missing values

2023-12-11T07:03:01.548392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:03:01.679609image/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

회계연도시군명자치단체명자체사업예산액(원)일반회계예산액(원)자체사업비중비율(%)
02023가평군경기가평군14921540600046229882000032.3
12023경기도경기본청135645003570002997701797900045.2
22023고양시경기고양시693213249000256750041000027.0
32023과천시경기과천시18786048800037770283700049.7
42023광명시경기광명시31652128500088674441500035.7
52023광주시경기광주시361011904000109000233600033.1
62023구리시경기구리시20642808700059680155000034.6
72023군포시경기군포시21246128300072332097300029.4
82023김포시경기김포시488084166000140626593500034.7
92023남양주시경기남양주시598003113000190753543700031.3
회계연도시군명자치단체명자체사업예산액(원)일반회계예산액(원)자체사업비중비율(%)
2652022<NA>전북진안군10292821200043455466200023.7
2662022<NA>전북무주군12617872200041564291900030.4
2672022<NA>전북장수군11135683800040060526500027.8
2682022<NA>전북임실군11708780500048425194400024.2
2692022<NA>전북고창군17090802400070447275000024.3
2702022<NA>전북부안군15334329800067550736300022.7
2712022<NA>전남본청2786910658000905834625200030.8
2722022<NA>전남목포시11627825700080647449800014.4
2732022<NA>전남여수시363282780000122414008000029.7
2742022<NA>전남순천시315585327000117953539900026.8