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

Dataset

Description재정자주도[최종] 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=HNH67SU4VRSVCP19Y4IE22741928&infSeq=1

Alerts

자체수입금액(원) is highly overall correlated with 자치단체예산규모액(원)High correlation
자주재원금액(원) is highly overall correlated with 자치단체예산규모액(원)High correlation
자치단체예산규모액(원) is highly overall correlated with 자체수입금액(원) and 1 other fieldsHigh correlation
시군명 has 243 (88.4%) missing valuesMissing
자체수입금액(원) has unique valuesUnique
자주재원금액(원) has unique valuesUnique
자치단체예산규모액(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:45:04.297783
Analysis finished2023-12-10 21:45:06.068609
Duration1.77 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

2023-12-11T06:45:06.136911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:45:06.236719image/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
2023-12-11T06:45:06.421360image/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%
2023-12-11T06:45:06.711647image/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
2023-12-11T06:45:07.010083image/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%
2023-12-11T06:45:07.438760image/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%

자체수입금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0475118 × 1011
Minimum4.0346027 × 1010
Maximum2.2727251 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:45:07.575960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.0346027 × 1010
5-th percentile7.3221332 × 1010
Q11.1603305 × 1011
median2.0363209 × 1011
Q33.870565 × 1011
95-th percentile1.9088236 × 1012
Maximum2.2727251 × 1013
Range2.2686905 × 1013
Interquartile range (IQR)2.7102345 × 1011

Descriptive statistics

Standard deviation1.9825775 × 1012
Coefficient of variation (CV)3.2783359
Kurtosis84.629389
Mean6.0475118 × 1011
Median Absolute Deviation (MAD)9.86438 × 1010
Skewness8.7373485
Sum1.6630657 × 1014
Variance3.9306136 × 1024
MonotonicityNot monotonic
2023-12-11T06:45:07.955462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165590549000 1
 
0.4%
232801869000 1
 
0.4%
461556253000 1
 
0.4%
353428838000 1
 
0.4%
1693406118000 1
 
0.4%
110527929000 1
 
0.4%
152580791000 1
 
0.4%
167438405000 1
 
0.4%
115829037000 1
 
0.4%
261796747000 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
40346027000 1
0.4%
51699332000 1
0.4%
53820748000 1
0.4%
54030005000 1
0.4%
59069767000 1
0.4%
60243369000 1
0.4%
61022667000 1
0.4%
64721529000 1
0.4%
65497298000 1
0.4%
67222609000 1
0.4%
ValueCountFrequency (%)
22727250620000 1
0.4%
18360088389000 1
0.4%
12970552400000 1
0.4%
4847399165000 1
0.4%
4672063910000 1
0.4%
3426372818000 1
0.4%
3214850475000 1
0.4%
2450480287000 1
0.4%
2210662725000 1
0.4%
2204892790000 1
0.4%

자주재원금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6764832 × 1011
Minimum1.7799018 × 1010
Maximum1.4338716 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:45:08.100664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.7799018 × 1010
5-th percentile6.8187848 × 1010
Q11.5430949 × 1011
median2.2473063 × 1011
Q33.2386319 × 1011
95-th percentile6.5416407 × 1011
Maximum1.4338716 × 1012
Range1.4160725 × 1012
Interquartile range (IQR)1.695537 × 1011

Descriptive statistics

Standard deviation2.0483372 × 1011
Coefficient of variation (CV)0.76530919
Kurtosis8.4244055
Mean2.6764832 × 1011
Median Absolute Deviation (MAD)8.0695664 × 1010
Skewness2.5219057
Sum7.3603289 × 1013
Variance4.1956854 × 1022
MonotonicityNot monotonic
2023-12-11T06:45:08.259636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300685232000 1
 
0.4%
77079450000 1
 
0.4%
419693587000 1
 
0.4%
454263815000 1
 
0.4%
1053942800000 1
 
0.4%
264019219000 1
 
0.4%
217332000000 1
 
0.4%
293929000000 1
 
0.4%
178768777000 1
 
0.4%
256836597000 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
17799018000 1
0.4%
27828565000 1
0.4%
30161995000 1
0.4%
33009449000 1
0.4%
34945699000 1
0.4%
42200000000 1
0.4%
50981578000 1
0.4%
58019990000 1
0.4%
62101520000 1
0.4%
62822545000 1
0.4%
ValueCountFrequency (%)
1433871551000 1
0.4%
1192926509000 1
0.4%
1116267000000 1
0.4%
1053942800000 1
0.4%
1049179114000 1
0.4%
1038722029000 1
0.4%
1027350599000 1
0.4%
959979000000 1
0.4%
955107342000 1
0.4%
784013105000 1
0.4%

자치단체예산규모액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6104034 × 1012
Minimum2.0507508 × 1011
Maximum3.3988274 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:45:08.455852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0507508 × 1011
5-th percentile3.9543663 × 1011
Q15.5194993 × 1011
median8.29 × 1011
Q31.2756868 × 1012
95-th percentile5.3487502 × 1012
Maximum3.3988274 × 1013
Range3.3783199 × 1013
Interquartile range (IQR)7.2373689 × 1011

Descriptive statistics

Standard deviation3.515424 × 1012
Coefficient of variation (CV)2.1829462
Kurtosis55.790683
Mean1.6104034 × 1012
Median Absolute Deviation (MAD)2.896859 × 1011
Skewness7.0015305
Sum4.4286095 × 1014
Variance1.2358206 × 1025
MonotonicityNot monotonic
2023-12-11T06:45:08.592157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
635733341000 1
 
0.4%
391571753000 1
 
0.4%
1549354535000 1
 
0.4%
1376602507000 1
 
0.4%
6636927489000 1
 
0.4%
579076945000 1
 
0.4%
566323380000 1
 
0.4%
733105032000 1
 
0.4%
493432496000 1
 
0.4%
938226984000 1
 
0.4%
Other values (265) 265
96.4%
ValueCountFrequency (%)
205075078000 1
0.4%
228378127000 1
0.4%
253538534000 1
0.4%
257300000000 1
0.4%
300588454000 1
0.4%
329000000000 1
0.4%
333726786000 1
0.4%
355732793000 1
0.4%
361500000000 1
0.4%
363835000000 1
0.4%
ValueCountFrequency (%)
33988273898000 1
0.4%
31147949987000 1
0.4%
28440162409000 1
0.4%
11239005369000 1
0.4%
10177658472000 1
0.4%
9965900000000 1
0.4%
8745718280000 1
0.4%
8547201076000 1
0.4%
8036955797000 1
0.4%
7790559582000 1
0.4%

재정자주비율(%)
Real number (ℝ)

Distinct262
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.890909
Minimum26.23
Maximum85.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-11T06:45:08.740165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.23
5-th percentile32.195
Q147.415
median58.18
Q362.8
95-th percentile69.099
Maximum85.56
Range59.33
Interquartile range (IQR)15.385

Descriptive statistics

Standard deviation11.364129
Coefficient of variation (CV)0.20703117
Kurtosis-0.18494005
Mean54.890909
Median Absolute Deviation (MAD)5.75
Skewness-0.7012006
Sum15095
Variance129.14343
MonotonicityNot monotonic
2023-12-11T06:45:08.877967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.69 2
 
0.7%
52.49 2
 
0.7%
60.44 2
 
0.7%
61.41 2
 
0.7%
59.25 2
 
0.7%
61.9 2
 
0.7%
29.98 2
 
0.7%
57.72 2
 
0.7%
39.26 2
 
0.7%
65.43 2
 
0.7%
Other values (252) 255
92.7%
ValueCountFrequency (%)
26.23 1
0.4%
27.23 1
0.4%
27.25 1
0.4%
28.06 1
0.4%
28.89 1
0.4%
29.24 1
0.4%
29.8 1
0.4%
29.98 2
0.7%
30.14 1
0.4%
30.91 1
0.4%
ValueCountFrequency (%)
85.56 1
0.4%
79.14 1
0.4%
73.54 1
0.4%
73.34 1
0.4%
72.83 1
0.4%
72.1 1
0.4%
72.02 1
0.4%
70.29 1
0.4%
70.21 1
0.4%
69.44 1
0.4%

Interactions

2023-12-11T06:45:05.575032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:04.549397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:04.821120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:05.204195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:05.651235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:04.611975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:04.892660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:05.299270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:05.733439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:04.679720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:04.979395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:05.409431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:05.816114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:04.751353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:05.070950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:05.491538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:45:08.989427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계연도시군명자체수입금액(원)자주재원금액(원)자치단체예산규모액(원)재정자주비율(%)
회계연도1.000NaN0.1580.3740.0000.414
시군명NaN1.0001.0001.0001.0001.000
자체수입금액(원)0.1581.0001.0000.5310.9760.127
자주재원금액(원)0.3741.0000.5311.0000.7460.591
자치단체예산규모액(원)0.0001.0000.9760.7461.0000.198
재정자주비율(%)0.4141.0000.1270.5910.1981.000
2023-12-11T06:45:09.101135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자체수입금액(원)자주재원금액(원)자치단체예산규모액(원)재정자주비율(%)회계연도
자체수입금액(원)1.0000.4760.917-0.0570.113
자주재원금액(원)0.4761.0000.6010.3350.283
자치단체예산규모액(원)0.9170.6011.000-0.1840.000
재정자주비율(%)-0.0570.335-0.1841.0000.313
회계연도0.1130.2830.0000.3131.000

Missing values

2023-12-11T06:45:05.915639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:45:06.016879image/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

회계연도시군명자치단체명자체수입금액(원)자주재원금액(원)자치단체예산규모액(원)재정자주비율(%)
02021가평군경기가평군16559054900030068523200063573334100073.34
12021경기도경기본청183600883890002024799770003398827389800054.61
22021고양시경기고양시1116146288000597001451000285006298600060.11
32021과천시경기과천시3346437230009336401000050022875000085.56
42021광명시경기광명시39713447700022490936000095456470700065.17
52021광주시경기광주시485539489000369231301000135361162700063.15
62021구리시경기구리시29981516300020103700000076048505100065.86
72021군포시경기군포시28264931300023745469300082963249700062.69
82021김포시경기김포시632317780000342277322000165129886800059.02
92021남양주시경기남양주시710793866000595744723000217253031400060.14
회계연도시군명자치단체명자체수입금액(원)자주재원금액(원)자치단체예산규모액(원)재정자주비율(%)
2652020<NA>전남함평군7132491800020368733100047266554500058.18
2662020<NA>전남영광군11588843100022481342400057100926800059.67
2672020<NA>전남장성군11151251800020369400000050491747300062.43
2682020<NA>전남완도군9990837800024073260000060683266100056.13
2692020<NA>전남진도군14959639800019256100000052992702700064.57
2702020<NA>전남신안군19680485500029448600000081819291400060.05
2712020<NA>경북본청24504802870001192926509000996590000000036.56
2722020<NA>경북포항시571532258000590872700000217688000000053.4
2732020<NA>경북경주시425599991000452573200000150000000000058.54
2742020<NA>경북김천시260659678000419770579000104000000000065.43