Overview

Dataset statistics

Number of variables7
Number of observations275
Missing cells243
Missing cells (%)12.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 KiB
Average record size in memory62.5 B

Variable types

Categorical1
Text1
Numeric5

Alerts

세출총계액(원) is highly overall correlated with 일반회계액(원) and 3 other fieldsHigh correlation
일반회계액(원) is highly overall correlated with 세출총계액(원) and 3 other fieldsHigh correlation
공기업특별회계액(원) is highly overall correlated with 세출총계액(원) and 2 other fieldsHigh correlation
기타특별회계액(원) is highly overall correlated with 세출총계액(원) and 1 other fieldsHigh correlation
기금액(원) is highly overall correlated with 세출총계액(원) and 2 other fieldsHigh correlation
시군명 has 243 (88.4%) missing valuesMissing
세출총계액(원) has unique valuesUnique
기타특별회계액(원) has unique valuesUnique
기금액(원) has unique valuesUnique
공기업특별회계액(원) has 118 (42.9%) zerosZeros

Reproduction

Analysis started2023-12-10 21:44:55.585531
Analysis finished2023-12-10 21:44:58.517583
Duration2.93 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

2023-12-11T06:44:58.574135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:44:58.687512image/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
2023-12-11T06:44:59.036781image/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%
2023-12-11T06:44:59.363731image/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%

세출총계액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9500405 × 1012
Minimum2.3793174 × 1011
Maximum4.7721111 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:44:59.533330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3793174 × 1011
5-th percentile4.103728 × 1011
Q15.9999346 × 1011
median8.4156416 × 1011
Q31.4311241 × 1012
95-th percentile7.1301976 × 1012
Maximum4.7721111 × 1013
Range4.7483179 × 1013
Interquartile range (IQR)8.3113064 × 1011

Descriptive statistics

Standard deviation4.7495473 × 1012
Coefficient of variation (CV)2.4356146
Kurtosis57.541928
Mean1.9500405 × 1012
Median Absolute Deviation (MAD)3.0970433 × 1011
Skewness7.1030014
Sum5.3626115 × 1014
Variance2.2558199 × 1025
MonotonicityNot monotonic
2023-12-11T06:44:59.715796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
658434801000 1
 
0.4%
884931251000 1
 
0.4%
631859416000 1
 
0.4%
1068234258000 1
 
0.4%
635061671000 1
 
0.4%
1029003397000 1
 
0.4%
1036851046000 1
 
0.4%
724980807000 1
 
0.4%
416910107000 1
 
0.4%
559197646000 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
237931741000 1
0.4%
254502310000 1
0.4%
271833450000 1
0.4%
311750984000 1
0.4%
322047457000 1
0.4%
332639862000 1
0.4%
352156147000 1
0.4%
358314021000 1
0.4%
369429983000 1
0.4%
383076176000 1
0.4%
ValueCountFrequency (%)
47721110506000 1
0.4%
39723621157000 1
0.4%
38229645876000 1
0.4%
15529614860000 1
0.4%
14397052515000 1
0.4%
12513454899000 1
0.4%
11844410855000 1
0.4%
11275009220000 1
0.4%
11110717302000 1
0.4%
9380086299000 1
0.4%

일반회계액(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
2023-12-11T06:44:59.858206image/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
2023-12-11T06:44:59.994839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720000000000 2
 
0.7%
462298820000 1
 
0.4%
686211839000 1
 
0.4%
613806172000 1
 
0.4%
1014806400000 1
 
0.4%
599974742000 1
 
0.4%
996101059000 1
 
0.4%
989720118000 1
 
0.4%
860418355000 1
 
0.4%
407000000000 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%

공기업특별회계액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0602089 × 1010
Minimum0
Maximum1.811485 × 1012
Zeros118
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:45:00.148041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.2273364 × 1010
Q31.0122043 × 1011
95-th percentile2.7555658 × 1011
Maximum1.811485 × 1012
Range1.811485 × 1012
Interquartile range (IQR)1.0122043 × 1011

Descriptive statistics

Standard deviation1.7612999 × 1011
Coefficient of variation (CV)2.1851789
Kurtosis58.822531
Mean8.0602089 × 1010
Median Absolute Deviation (MAD)2.2273364 × 1010
Skewness6.7122305
Sum2.2165574 × 1013
Variance3.1021772 × 1022
MonotonicityNot monotonic
2023-12-11T06:45:00.283111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
42.9%
33387566000 1
 
0.4%
275155249000 1
 
0.4%
269946304000 1
 
0.4%
261344766000 1
 
0.4%
153708353000 1
 
0.4%
252459618000 1
 
0.4%
215682582000 1
 
0.4%
139536302000 1
 
0.4%
310024318000 1
 
0.4%
Other values (148) 148
53.8%
ValueCountFrequency (%)
0 118
42.9%
3262067000 1
 
0.4%
9695600000 1
 
0.4%
11423216000 1
 
0.4%
12096925000 1
 
0.4%
12203867000 1
 
0.4%
12627486000 1
 
0.4%
13940246000 1
 
0.4%
14214000000 1
 
0.4%
14977677000 1
 
0.4%
ValueCountFrequency (%)
1811484968000 1
0.4%
1683407992000 1
0.4%
793258219000 1
0.4%
560900000000 1
0.4%
453033156000 1
0.4%
358943409000 1
0.4%
354320478000 1
0.4%
352000000000 1
0.4%
333280805000 1
0.4%
310024318000 1
0.4%

기타특별회계액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6021387 × 1011
Minimum2.315 × 108
Maximum1.1193181 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:45:00.444228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.315 × 108
5-th percentile3.4309812 × 109
Q11.2476902 × 1010
median2.2966696 × 1010
Q35.3314016 × 1010
95-th percentile6.6153273 × 1011
Maximum1.1193181 × 1013
Range1.1192949 × 1013
Interquartile range (IQR)4.0837114 × 1010

Descriptive statistics

Standard deviation7.8315092 × 1011
Coefficient of variation (CV)4.8881594
Kurtosis147.43369
Mean1.6021387 × 1011
Median Absolute Deviation (MAD)1.4867007 × 1010
Skewness11.145124
Sum4.4058814 × 1013
Variance6.1332537 × 1023
MonotonicityNot monotonic
2023-12-11T06:45:00.608623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000883000 1
 
0.4%
15646934000 1
 
0.4%
868024000 1
 
0.4%
24664202000 1
 
0.4%
17555361000 1
 
0.4%
13086911000 1
 
0.4%
18426080000 1
 
0.4%
16507656000 1
 
0.4%
2338197000 1
 
0.4%
6450000000 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
231500000 1
0.4%
440000000 1
0.4%
868024000 1
0.4%
1100000000 1
0.4%
1502000000 1
0.4%
1604000000 1
0.4%
1853805000 1
0.4%
2205454000 1
0.4%
2338197000 1
0.4%
2399888000 1
0.4%
ValueCountFrequency (%)
11193180963000 1
0.4%
3789968249000 1
0.4%
3579887998000 1
0.4%
2347599439000 1
0.4%
2006290248000 1
0.4%
1555900000000 1
0.4%
1495317000000 1
0.4%
1221959820000 1
0.4%
1054856575000 1
0.4%
995399765000 1
0.4%

기금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9969525 × 1011
Minimum3.647391 × 109
Maximum6.1200333 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:45:00.744181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.647391 × 109
5-th percentile7.4600801 × 109
Q12.6996072 × 1010
median6.5589884 × 1010
Q31.5146371 × 1011
95-th percentile6.7948948 × 1011
Maximum6.1200333 × 1012
Range6.1163859 × 1012
Interquartile range (IQR)1.2446764 × 1011

Descriptive statistics

Standard deviation5.3858036 × 1011
Coefficient of variation (CV)2.6970113
Kurtosis71.168239
Mean1.9969525 × 1011
Median Absolute Deviation (MAD)4.5905853 × 1010
Skewness7.7016384
Sum5.4916195 × 1013
Variance2.9006881 × 1023
MonotonicityNot monotonic
2023-12-11T06:45:00.878515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118528562000 1
 
0.4%
8865962000 1
 
0.4%
17185220000 1
 
0.4%
28763656000 1
 
0.4%
17531568000 1
 
0.4%
19815427000 1
 
0.4%
28704848000 1
 
0.4%
22261312000 1
 
0.4%
134136187000 1
 
0.4%
74347646000 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
3647391000 1
0.4%
3660148000 1
0.4%
4395182000 1
0.4%
4469076000 1
0.4%
4547307000 1
0.4%
4570000000 1
0.4%
4860529000 1
0.4%
5482837000 1
0.4%
6592673000 1
0.4%
6664285000 1
0.4%
ValueCountFrequency (%)
6120033307000 1
0.4%
4419190879000 1
0.4%
3502061704000 1
0.4%
1366172617000 1
0.4%
1260737899000 1
0.4%
1260590351000 1
0.4%
1252880865000 1
0.4%
1232167917000 1
0.4%
1227967037000 1
0.4%
966317302000 1
0.4%

Interactions

2023-12-11T06:44:57.847098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.090335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.554338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.984466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:57.417088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:57.938466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.186363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.641084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:57.062425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:57.492631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:58.026363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.271933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.722856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:57.146417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:57.567244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:58.127589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.368546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.816143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:57.232660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:57.665788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:58.230923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.454734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:56.895170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:57.330611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:44:57.755454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:45:00.979610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명세출총계액(원)일반회계액(원)공기업특별회계액(원)기타특별회계액(원)기금액(원)
회계연도1.000NaN0.0000.0000.3000.0000.164
시군명NaN1.0001.0001.0001.0001.0001.000
세출총계액(원)0.0001.0001.0000.8960.9280.9700.975
일반회계액(원)0.0001.0000.8961.0000.6230.9470.783
공기업특별회계액(원)0.3001.0000.9280.6231.0000.7920.819
기타특별회계액(원)0.0001.0000.9700.9470.7921.0000.850
기금액(원)0.1641.0000.9750.7830.8190.8501.000
2023-12-11T06:45:01.092263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세출총계액(원)일반회계액(원)공기업특별회계액(원)기타특별회계액(원)기금액(원)회계연도
세출총계액(원)1.0000.9760.6660.6440.7110.000
일반회계액(원)0.9761.0000.5940.6160.5930.000
공기업특별회계액(원)0.6660.5941.0000.4350.5210.214
기타특별회계액(원)0.6440.6160.4351.0000.4720.000
기금액(원)0.7110.5930.5210.4721.0000.118
회계연도0.0000.0000.2140.0000.1181.000

Missing values

2023-12-11T06:44:58.351336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:44:58.455956image/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

회계연도시군명세출총계액(원)일반회계액(원)공기업특별회계액(원)기타특별회계액(원)기금액(원)
02023가평군6584348010004622988200005260653600025000883000118528562000
12023경기도38229645876000299770179790004346876900037899682490004419190879000
22023고양시36722067600002567500410000205318390000223515512000675872448000
32023과천시739666246000377702837000748601390005526508000281576762000
42023광명시1147202749000886744415000961634760007062864300093666215000
52023광주시1610253606000109000233600019332276900047568848000279359653000
62023구리시9112994050005968015500007337122800024351604000216775023000
72023군포시100881443300072332097300010240688500031882152000151204423000
82023김포시1738232574000140626593500013447333900069587869000127905431000
92023남양주시23740120030001907535437000149490426000114420126000202566014000
회계연도시군명세출총계액(원)일반회계액(원)공기업특별회계액(원)기타특별회계액(원)기금액(원)
2652022<NA>609447474000501645164000359321710002127423300050595906000
2662022<NA>681468888000567372198000203497230004392355700049823410000
2672022<NA>27183345000024302427800011423216000652701700010858939000
2682022<NA>640350633000534599983000640404210002003055600021679673000
2692022<NA>56363115700051028678700003537885800017965512000
2702022<NA>784189913000671092200000731861360001892221500020989362000
2712022<NA>4086172650003730439610009695600000210171750004860529000
2722022<NA>938008629900078200000000000918724299000641362000000
2732022<NA>25332000000001830000000000352000000000158000000000193200000000
2742022<NA>9899000000007705000000006430000000020700000000134400000000