Overview

Dataset statistics

Number of variables7
Number of observations275
Missing cells243
Missing cells (%)12.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 KiB
Average record size in memory61.5 B

Variable types

Categorical1
Text2
Numeric4

Alerts

의회비비중비율(%) is highly overall correlated with 의회사무처경비(천원)High correlation
의회비(천원) is highly overall correlated with 일반회계예산액(천원)High correlation
의회사무처경비(천원) is highly overall correlated with 의회비비중비율(%)High correlation
일반회계예산액(천원) is highly overall correlated with 의회비(천원)High correlation
시군명 has 243 (88.4%) missing valuesMissing
의회사무처경비(천원) has 34 (12.4%) zerosZeros

Reproduction

Analysis started2023-12-10 21:05:28.845731
Analysis finished2023-12-10 21:05:31.058558
Duration2.21 seconds
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

2023-12-11T06:05:31.124793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:05:31.218765image/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
2023-12-11T06:05:31.379273image/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%
2023-12-11T06:05:31.680736image/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
2023-12-11T06:05:31.957594image/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%
2023-12-11T06:05:32.390645image/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%

의회비비중비율(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25574545
Minimum0.05
Maximum0.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:05:32.507909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.1
Q10.16
median0.2
Q30.32
95-th percentile0.563
Maximum0.93
Range0.88
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.14422613
Coefficient of variation (CV)0.56394405
Kurtosis2.107445
Mean0.25574545
Median Absolute Deviation (MAD)0.06
Skewness1.4401799
Sum70.33
Variance0.020801176
MonotonicityNot monotonic
2023-12-11T06:05:32.609520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17 26
 
9.5%
0.18 18
 
6.5%
0.2 15
 
5.5%
0.16 13
 
4.7%
0.21 11
 
4.0%
0.19 10
 
3.6%
0.13 9
 
3.3%
0.15 9
 
3.3%
0.22 9
 
3.3%
0.14 9
 
3.3%
Other values (47) 146
53.1%
ValueCountFrequency (%)
0.05 1
 
0.4%
0.07 2
 
0.7%
0.08 4
1.5%
0.09 5
1.8%
0.1 5
1.8%
0.11 7
2.5%
0.12 6
2.2%
0.13 9
3.3%
0.14 9
3.3%
0.15 9
3.3%
ValueCountFrequency (%)
0.93 1
 
0.4%
0.76 1
 
0.4%
0.7 1
 
0.4%
0.63 2
0.7%
0.62 1
 
0.4%
0.61 3
1.1%
0.6 1
 
0.4%
0.59 1
 
0.4%
0.57 3
1.1%
0.56 2
0.7%

의회비(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct274
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1175477.4
Minimum390067
Maximum15605084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:05:32.711301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum390067
5-th percentile437017.3
Q1511567.5
median761253
Q31236577
95-th percentile2775073
Maximum15605084
Range15215017
Interquartile range (IQR)725009.5

Descriptive statistics

Standard deviation1544256.3
Coefficient of variation (CV)1.3137269
Kurtosis49.038563
Mean1175477.4
Median Absolute Deviation (MAD)278442
Skewness6.2731105
Sum3.2325628 × 108
Variance2.3847276 × 1012
MonotonicityNot monotonic
2023-12-11T06:05:32.827859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
482811 2
 
0.7%
501020 1
 
0.4%
940438 1
 
0.4%
13865714 1
 
0.4%
1708517 1
 
0.4%
761253 1
 
0.4%
477204 1
 
0.4%
443086 1
 
0.4%
733531 1
 
0.4%
2687581 1
 
0.4%
Other values (264) 264
96.0%
ValueCountFrequency (%)
390067 1
0.4%
396050 1
0.4%
399238 1
0.4%
402305 1
0.4%
404467 1
0.4%
410423 1
0.4%
414019 1
0.4%
418783 1
0.4%
425242 1
0.4%
430989 1
0.4%
ValueCountFrequency (%)
15605084 1
0.4%
13865714 1
0.4%
10898597 1
0.4%
5380233 1
0.4%
4934183 1
0.4%
4919813 1
0.4%
4233473 1
0.4%
4219162 1
0.4%
3979504 1
0.4%
3906037 1
0.4%

의회사무처경비(천원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct242
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2204939.7
Minimum0
Maximum72316445
Zeros34
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:05:32.943459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359908.5
median789193
Q31854370
95-th percentile7542864.5
Maximum72316445
Range72316445
Interquartile range (IQR)1494461.5

Descriptive statistics

Standard deviation6433130.9
Coefficient of variation (CV)2.9175995
Kurtosis80.875288
Mean2204939.7
Median Absolute Deviation (MAD)593117
Skewness8.3345609
Sum6.0635841 × 108
Variance4.1385173 × 1013
MonotonicityNot monotonic
2023-12-11T06:05:33.055215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
 
12.4%
410962 1
 
0.4%
2277753 1
 
0.4%
7445906 1
 
0.4%
1841542 1
 
0.4%
862738 1
 
0.4%
1301620 1
 
0.4%
1446894 1
 
0.4%
4906683 1
 
0.4%
63050052 1
 
0.4%
Other values (232) 232
84.4%
ValueCountFrequency (%)
0 34
12.4%
15020 1
 
0.4%
17280 1
 
0.4%
66738 1
 
0.4%
99154 1
 
0.4%
141000 1
 
0.4%
172085 1
 
0.4%
185044 1
 
0.4%
196076 1
 
0.4%
206443 1
 
0.4%
ValueCountFrequency (%)
72316445 1
0.4%
63050052 1
0.4%
27274252 1
0.4%
18751103 1
0.4%
17662771 1
0.4%
17587058 1
0.4%
14988305 1
0.4%
12113802 1
0.4%
11566915 1
0.4%
11459870 1
0.4%

일반회계예산액(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct274
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5095293 × 109
Minimum1.6694544 × 108
Maximum3.134246 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:05:33.172599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6694544 × 108
5-th percentile3.5384416 × 108
Q15.0052025 × 108
median7.2 × 108
Q31.0712129 × 109
95-th percentile5.4103729 × 109
Maximum3.134246 × 1010
Range3.1175514 × 1010
Interquartile range (IQR)5.7069266 × 108

Descriptive statistics

Standard deviation3.4509361 × 109
Coefficient of variation (CV)2.2861008
Kurtosis53.190142
Mean1.5095293 × 109
Median Absolute Deviation (MAD)2.521893 × 108
Skewness6.8566682
Sum4.1512056 × 1011
Variance1.190896 × 1019
MonotonicityNot monotonic
2023-12-11T06:05:33.297629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720000000 2
 
0.7%
462298820 1
 
0.4%
314807864 1
 
0.4%
2593956137 1
 
0.4%
2571760968 1
 
0.4%
29975489088 1
 
0.4%
1580135370 1
 
0.4%
941543052 1
 
0.4%
438508794 1
 
0.4%
554071654 1
 
0.4%
Other values (264) 264
96.0%
ValueCountFrequency (%)
166945436 1
0.4%
196476971 1
0.4%
215500000 1
0.4%
243024278 1
0.4%
280435723 1
0.4%
287000000 1
0.4%
301251647 1
0.4%
308000000 1
0.4%
314807864 1
0.4%
324724497 1
0.4%
ValueCountFrequency (%)
31342459847 1
0.4%
29977017979 1
0.4%
29975489088 1
0.4%
11128166851 1
0.4%
10108293256 1
0.4%
9757400000 1
0.4%
9326396434 1
0.4%
9058346252 1
0.4%
8027600000 1
0.4%
7820000000 1
0.4%

Interactions

2023-12-11T06:05:30.501357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:29.113500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:29.721424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:30.096844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:30.579727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:29.437549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:29.806570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:30.175870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:30.670907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:29.526184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:29.910383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:30.313367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:30.761940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:29.612278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:30.007366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:30.412781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:05:33.392735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명의회비비중비율(%)의회비(천원)의회사무처경비(천원)일반회계예산액(천원)
회계연도1.000NaN0.0000.1720.1790.000
시군명NaN1.0001.0001.0001.0001.000
의회비비중비율(%)0.0001.0001.0000.0000.0000.000
의회비(천원)0.1721.0000.0001.0000.9300.815
의회사무처경비(천원)0.1791.0000.0000.9301.0000.806
일반회계예산액(천원)0.0001.0000.0000.8150.8061.000
2023-12-11T06:05:33.501590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의회비비중비율(%)의회비(천원)의회사무처경비(천원)일반회계예산액(천원)회계연도
의회비비중비율(%)1.000-0.0120.670-0.1900.000
의회비(천원)-0.0121.0000.4490.8610.183
의회사무처경비(천원)0.6700.4491.0000.4710.127
일반회계예산액(천원)-0.1900.8610.4711.0000.000
회계연도0.0000.1830.1270.0001.000

Missing values

2023-12-11T06:05:30.874892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:05:31.003988image/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

회계연도시군명자치단체명의회비비중비율(%)의회비(천원)의회사무처경비(천원)일반회계예산액(천원)
02023가평군경기가평군0.2501020410962462298820
12023경기도경기본청0.29156050847231644529977017979
22023고양시경기고양시0.17266792116028782567500410
32023과천시경기과천시0.29528494576752377702837
42023광명시경기광명시0.18841237768452886744415
52023광주시경기광주시0.1979278312660301090002336
62023구리시경기구리시0.25612319862922596801550
72023군포시경기군포시0.15690509410936723320973
82023김포시경기김포시0.1510504949965481406265935
92023남양주시경기남양주시0.17157897116296931907535437
회계연도시군명자치단체명의회비비중비율(%)의회비(천원)의회사무처경비(천원)일반회계예산액(천원)
2652022<NA>전북본청0.23601521115669157440808664
2662022<NA>전북전주시0.35248636647547952050363742
2672022<NA>전북군산시0.2144734611499541310716370
2682022<NA>전북익산시0.19159896110862201433482653
2692022<NA>전북정읍시0.3210656232078616972189300
2702022<NA>전북남원시0.19989400699678888954502
2712022<NA>전북김제시0.2866038857027862434940
2722022<NA>전북완주군0.247688071079030763043091
2732022<NA>전북진안군0.26484421656908434554662
2742022<NA>전북무주군0.1247765317280415642919