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

자치단체예산규모총계액(원) is highly overall correlated with 사회복지분야예산액(원)High correlation
사회복지분야예산액(원) is highly overall correlated with 자치단체예산규모총계액(원) and 1 other fieldsHigh correlation
사회복지비중비율(%) is highly overall correlated with 사회복지분야예산액(원)High correlation
시군명 has 243 (88.4%) missing valuesMissing
사회복지분야예산액(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:55:39.202408
Analysis finished2023-12-10 22:55:40.413191
Duration1.21 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

2023-12-11T07:55:40.477129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:55:40.556686image/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
2023-12-11T07:55:40.721411image/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%
2023-12-11T07:55:41.047886image/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
2023-12-11T07:55:41.339771image/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%
2023-12-11T07:55:42.005742image/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%

자치단체예산규모총계액(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
2023-12-11T07:55:42.137219image/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
2023-12-11T07:55:42.275423image/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%

사회복지분야예산액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8461331 × 1011
Minimum1.7505731 × 1010
Maximum1.2919848 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:55:42.406101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.7505731 × 1010
5-th percentile7.5531353 × 1010
Q11.3980056 × 1011
median2.8313766 × 1011
Q34.9993734 × 1011
95-th percentile2.0121642 × 1012
Maximum1.2919848 × 1013
Range1.2902343 × 1013
Interquartile range (IQR)3.6013678 × 1011

Descriptive statistics

Standard deviation1.3586625 × 1012
Coefficient of variation (CV)2.3240362
Kurtosis55.177254
Mean5.8461331 × 1011
Median Absolute Deviation (MAD)1.6377286 × 1011
Skewness6.9725766
Sum1.6076866 × 1014
Variance1.8459638 × 1024
MonotonicityNot monotonic
2023-12-11T07:55:42.545712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149604375000 1
 
0.4%
1169457701000 1
 
0.4%
624418618000 1
 
0.4%
897587800000 1
 
0.4%
839523220000 1
 
0.4%
759573816000 1
 
0.4%
889054355000 1
 
0.4%
999733686000 1
 
0.4%
1086558072000 1
 
0.4%
1276979279000 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
17505731000 1
0.4%
54963260000 1
0.4%
58265360000 1
0.4%
60533354000 1
0.4%
60659148000 1
0.4%
61253490000 1
0.4%
63668490000 1
0.4%
66152759000 1
0.4%
67712426000 1
0.4%
68559176000 1
0.4%
ValueCountFrequency (%)
12919848368000 1
0.4%
11630777199000 1
0.4%
11536486863000 1
0.4%
4858905122000 1
0.4%
3766900950000 1
0.4%
3753077307000 1
0.4%
3474288282000 1
0.4%
3198253955000 1
0.4%
2729564148000 1
0.4%
2684778363000 1
0.4%

사회복지비중비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct266
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.605855
Minimum8.12
Maximum70.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T07:55:42.665071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.12
5-th percentile18.445
Q124.85
median36.81
Q347.97
95-th percentile62.777
Maximum70.46
Range62.34
Interquartile range (IQR)23.12

Descriptive statistics

Standard deviation14.515202
Coefficient of variation (CV)0.38598251
Kurtosis-0.92204408
Mean37.605855
Median Absolute Deviation (MAD)11.58
Skewness0.35889636
Sum10341.61
Variance210.69109
MonotonicityNot monotonic
2023-12-11T07:55:42.793858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.72 2
 
0.7%
23.07 2
 
0.7%
23.71 2
 
0.7%
36.81 2
 
0.7%
59.54 2
 
0.7%
18.29 2
 
0.7%
29.65 2
 
0.7%
18.82 2
 
0.7%
25.55 2
 
0.7%
31.37 1
 
0.4%
Other values (256) 256
93.1%
ValueCountFrequency (%)
8.12 1
0.4%
15.09 1
0.4%
16.52 1
0.4%
16.65 1
0.4%
16.87 1
0.4%
16.9 1
0.4%
17.53 1
0.4%
17.67 1
0.4%
17.68 1
0.4%
17.81 1
0.4%
ValueCountFrequency (%)
70.46 1
0.4%
69.43 1
0.4%
69.0 1
0.4%
67.22 1
0.4%
67.06 1
0.4%
66.05 1
0.4%
65.89 1
0.4%
65.37 1
0.4%
65.02 1
0.4%
64.24 1
0.4%

Interactions

2023-12-11T07:55:39.977584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:39.432923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:39.666018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:40.055988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:39.510701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:39.745474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:40.139023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:39.586255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:55:39.899490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:55:42.876667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명자치단체예산규모총계액(원)사회복지분야예산액(원)사회복지비중비율(%)
회계연도1.000NaN0.0000.0000.468
시군명NaN1.0001.0001.0001.000
자치단체예산규모총계액(원)0.0001.0001.0000.9030.311
사회복지분야예산액(원)0.0001.0000.9031.0000.243
사회복지비중비율(%)0.4681.0000.3110.2431.000
2023-12-11T07:55:42.983624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치단체예산규모총계액(원)사회복지분야예산액(원)사회복지비중비율(%)회계연도
자치단체예산규모총계액(원)1.0000.8790.2770.000
사회복지분야예산액(원)0.8791.0000.6680.000
사회복지비중비율(%)0.2770.6681.0000.354
회계연도0.0000.0000.3541.000

Missing values

2023-12-11T07:55:40.245610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:55:40.364905image/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

회계연도시군명자치단체명자치단체예산규모총계액(원)사회복지분야예산액(원)사회복지비중비율(%)
02023가평군경기가평군46229882000014960437500032.36
12023경기도경기본청299770179790001291984836800043.1
22023고양시경기고양시2567500410000125122898600048.73
32023과천시경기과천시37770283700010722512100028.39
42023광명시경기광명시88674441500038457074700043.37
52023광주시경기광주시109000233600048366223100044.37
62023구리시경기구리시59680155000026997073400045.24
72023군포시경기군포시72332097300035803366700049.5
82023김포시경기김포시140626593500059169642200042.08
92023남양주시경기남양주시190753543700091822275600048.14
회계연도시군명자치단체명자치단체예산규모총계액(원)사회복지분야예산액(원)사회복지비중비율(%)
2652022<NA>전북진안군4345546620008569206100019.72
2662022<NA>전북무주군4156429190007349505200017.68
2672022<NA>전북임실군4842519440008858371600018.29
2682022<NA>전북순창군4460130000009045847600020.28
2692022<NA>전북고창군70447275000013841020800019.65
2702022<NA>전북부안군67550736300011936479400017.67
2712022<NA>전남본청9058346252000268477836300029.64
2722022<NA>전남목포시80647449800040190219200049.83
2732022<NA>전남여수시122414008000044937019200036.71
2742022<NA>전남순천시117953539900039633302100033.6