Overview

Dataset statistics

Number of variables12
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory108.5 B

Variable types

Categorical7
Numeric5

Dataset

Description상기 데이터는 연도별 지방세의 유형별 미환급금 현황 및 연간 누적률을 제공하여 자치단체의 환급금 해소노력을 확인 가능하게 함
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=349&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080037

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
미환급유형 has constant value ""Constant
당해미환급건수 is highly overall correlated with 당해미환급금액 and 2 other fieldsHigh correlation
당해미환급금액 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급건수 is highly overall correlated with 당해미환급건수 and 3 other fieldsHigh correlation
누적미환급금액 is highly overall correlated with 당해미환급건수 and 3 other fieldsHigh correlation
누적미환급금액증감 is highly overall correlated with 누적미환급건수 and 1 other fieldsHigh correlation
당해미환급금액 has unique valuesUnique
누적미환급금액 has unique valuesUnique
누적미환급금액증감 has 4 (16.7%) zerosZeros

Reproduction

Analysis started2024-01-09 22:17:32.380249
Analysis finished2024-01-09 22:17:34.631579
Duration2.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
충청남도
24 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 24
100.0%

Length

2024-01-10T07:17:34.696415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:17:34.783698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 24
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
부여군
24 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부여군 24
100.0%

Length

2024-01-10T07:17:34.877225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:17:34.969407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부여군 24
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
44760
24 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44760
2nd row44760
3rd row44760
4th row44760
5th row44760

Common Values

ValueCountFrequency (%)
44760 24
100.0%

Length

2024-01-10T07:17:35.061483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:17:35.148154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44760 24
100.0%

세목명
Categorical

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
자동차세
6 
지방소득세
6 
재산세
4 
주민세
4 
등록면허세
3 

Length

Max length5
Median length4
Mean length4
Min length3

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row등록면허세
2nd row자동차세
3rd row자동차세
4th row재산세
5th row주민세

Common Values

ValueCountFrequency (%)
자동차세 6
25.0%
지방소득세 6
25.0%
재산세 4
16.7%
주민세 4
16.7%
등록면허세 3
12.5%
취득세 1
 
4.2%

Length

2024-01-10T07:17:35.241517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:17:35.327629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 6
25.0%
지방소득세 6
25.0%
재산세 4
16.7%
주민세 4
16.7%
등록면허세 3
12.5%
취득세 1
 
4.2%

과세년도
Categorical

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
2019
9 
2018
8 
2017
7 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2019 9
37.5%
2018 8
33.3%
2017 7
29.2%

Length

2024-01-10T07:17:35.421662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:17:35.496651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 9
37.5%
2018 8
33.3%
2017 7
29.2%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
신규
24 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신규 24
100.0%

Length

2024-01-10T07:17:35.574072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:17:35.641901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 24
100.0%

납세자유형
Categorical

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
개인
16 
법인
8 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row개인
3rd row법인
4th row개인
5th row개인

Common Values

ValueCountFrequency (%)
개인 16
66.7%
법인 8
33.3%

Length

2024-01-10T07:17:35.710636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:17:35.781315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 16
66.7%
법인 8
33.3%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.125
Minimum1
Maximum209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T07:17:35.848697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5.5
Q317
95-th percentile128.7
Maximum209
Range208
Interquartile range (IQR)16

Descriptive statistics

Standard deviation49.969828
Coefficient of variation (CV)1.9888489
Kurtosis8.6087111
Mean25.125
Median Absolute Deviation (MAD)4.5
Skewness2.9161411
Sum603
Variance2496.9837
MonotonicityNot monotonic
2024-01-10T07:17:35.929791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 9
37.5%
5 2
 
8.3%
8 2
 
8.3%
59 1
 
4.2%
16 1
 
4.2%
141 1
 
4.2%
6 1
 
4.2%
20 1
 
4.2%
3 1
 
4.2%
31 1
 
4.2%
Other values (4) 4
16.7%
ValueCountFrequency (%)
1 9
37.5%
3 1
 
4.2%
5 2
 
8.3%
6 1
 
4.2%
8 2
 
8.3%
10 1
 
4.2%
15 1
 
4.2%
16 1
 
4.2%
20 1
 
4.2%
31 1
 
4.2%
ValueCountFrequency (%)
209 1
4.2%
141 1
4.2%
59 1
4.2%
58 1
4.2%
31 1
4.2%
20 1
4.2%
16 1
4.2%
15 1
4.2%
10 1
4.2%
8 2
8.3%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251227.08
Minimum190
Maximum2115800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T07:17:36.025102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum190
5-th percentile1072
Q19602.5
median47965
Q3144582.5
95-th percentile1306238.5
Maximum2115800
Range2115610
Interquartile range (IQR)134980

Descriptive statistics

Standard deviation509285.21
Coefficient of variation (CV)2.0271907
Kurtosis8.3230918
Mean251227.08
Median Absolute Deviation (MAD)45885
Skewness2.8625772
Sum6029450
Variance2.5937143 × 1011
MonotonicityNot monotonic
2024-01-10T07:17:36.115609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
970 1
 
4.2%
416410 1
 
4.2%
14700 1
 
4.2%
5410 1
 
4.2%
688620 1
 
4.2%
1650 1
 
4.2%
54850 1
 
4.2%
110880 1
 
4.2%
61590 1
 
4.2%
2115800 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
190 1
4.2%
970 1
4.2%
1650 1
4.2%
2510 1
4.2%
4310 1
4.2%
5410 1
4.2%
11000 1
4.2%
14700 1
4.2%
17350 1
4.2%
23500 1
4.2%
ValueCountFrequency (%)
2115800 1
4.2%
1415230 1
4.2%
688620 1
4.2%
507520 1
4.2%
416410 1
4.2%
191390 1
4.2%
128980 1
4.2%
127480 1
4.2%
110880 1
4.2%
61590 1
4.2%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.625
Minimum1
Maximum433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T07:17:36.209861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median9.5
Q332.5
95-th percentile208.25
Maximum433
Range432
Interquartile range (IQR)29.75

Descriptive statistics

Standard deviation96.59004
Coefficient of variation (CV)2.0716363
Kurtosis11.713482
Mean46.625
Median Absolute Deviation (MAD)8
Skewness3.2939393
Sum1119
Variance9329.6359
MonotonicityNot monotonic
2024-01-10T07:17:36.293402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 4
16.7%
17 2
 
8.3%
2 2
 
8.3%
9 2
 
8.3%
61 1
 
4.2%
119 1
 
4.2%
40 1
 
4.2%
433 1
 
4.2%
10 1
 
4.2%
16 1
 
4.2%
Other values (8) 8
33.3%
ValueCountFrequency (%)
1 4
16.7%
2 2
8.3%
3 1
 
4.2%
4 1
 
4.2%
5 1
 
4.2%
6 1
 
4.2%
9 2
8.3%
10 1
 
4.2%
16 1
 
4.2%
17 2
8.3%
ValueCountFrequency (%)
433 1
4.2%
224 1
4.2%
119 1
4.2%
83 1
4.2%
61 1
4.2%
40 1
4.2%
30 1
4.2%
25 1
4.2%
17 2
8.3%
16 1
4.2%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501360.83
Minimum970
Maximum4311020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T07:17:36.380081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum970
5-th percentile1822.5
Q118300
median61495
Q3382340
95-th percentile2125353
Maximum4311020
Range4310050
Interquartile range (IQR)364040

Descriptive statistics

Standard deviation993019.58
Coefficient of variation (CV)1.9806485
Kurtosis9.4539676
Mean501360.83
Median Absolute Deviation (MAD)59270
Skewness2.9369582
Sum12032660
Variance9.8608789 × 1011
MonotonicityNot monotonic
2024-01-10T07:17:36.470756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
970 1
 
4.2%
1040820 1
 
4.2%
14700 1
 
4.2%
49720 1
 
4.2%
1729440 1
 
4.2%
1650 1
 
4.2%
99030 1
 
4.2%
301650 1
 
4.2%
231790 1
 
4.2%
4311020 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
970 1
4.2%
1650 1
4.2%
2800 1
4.2%
5280 1
4.2%
11000 1
4.2%
14700 1
4.2%
19500 1
4.2%
41220 1
4.2%
44180 1
4.2%
44310 1
4.2%
ValueCountFrequency (%)
4311020 1
4.2%
2195220 1
4.2%
1729440 1
4.2%
1040820 1
4.2%
779990 1
4.2%
624410 1
4.2%
301650 1
4.2%
231790 1
4.2%
190770 1
4.2%
170200 1
4.2%

누적미환급금액증감
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean530.13375
Minimum0
Maximum10163.16
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T07:17:36.558339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.0875
median54.4
Q3155.6925
95-th percentile737.635
Maximum10163.16
Range10163.16
Interquartile range (IQR)144.605

Descriptive statistics

Standard deviation2058.8543
Coefficient of variation (CV)3.8836507
Kurtosis23.628443
Mean530.13375
Median Absolute Deviation (MAD)54.4
Skewness4.8463444
Sum12723.21
Variance4238881.1
MonotonicityNot monotonic
2024-01-10T07:17:36.646315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 4
 
16.7%
53.69 1
 
4.2%
819.04 1
 
4.2%
151.15 1
 
4.2%
80.55 1
 
4.2%
172.05 1
 
4.2%
276.34 1
 
4.2%
103.75 1
 
4.2%
9.7 1
 
4.2%
6.75 1
 
4.2%
Other values (11) 11
45.8%
ValueCountFrequency (%)
0.0 4
16.7%
6.75 1
 
4.2%
9.7 1
 
4.2%
11.55 1
 
4.2%
22.51 1
 
4.2%
31.96 1
 
4.2%
33.15 1
 
4.2%
49.65 1
 
4.2%
53.69 1
 
4.2%
55.11 1
 
4.2%
ValueCountFrequency (%)
10163.16 1
4.2%
819.04 1
4.2%
276.34 1
4.2%
226.25 1
4.2%
172.05 1
4.2%
169.32 1
4.2%
151.15 1
4.2%
149.95 1
4.2%
137.58 1
4.2%
103.75 1
4.2%

Interactions

2024-01-10T07:17:34.087522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:32.668803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:32.974451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.279779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.658421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:34.149608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:32.726095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.036583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.339723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.735265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:34.212671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:32.787459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.098924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.425600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.822039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:34.271254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:32.846627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.156562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.499885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.922209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:34.332058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:32.912065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.219355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:33.578731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:17:34.017060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:17:36.939425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
세목명1.0000.0000.0000.0000.0000.0000.0000.149
과세년도0.0001.0000.0000.0000.0990.0000.5480.038
납세자유형0.0000.0001.0000.0000.0000.0000.0000.000
당해미환급건수0.0000.0000.0001.0001.0000.9930.9600.000
당해미환급금액0.0000.0990.0001.0001.0001.0000.998NaN
누적미환급건수0.0000.0000.0000.9931.0001.0000.9600.000
누적미환급금액0.0000.5480.0000.9600.9980.9601.0000.000
누적미환급금액증감0.1490.0380.0000.000NaN0.0000.0001.000
2024-01-10T07:17:37.030999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명납세자유형
과세년도1.0000.0000.000
세목명0.0001.0000.000
납세자유형0.0000.0001.000
2024-01-10T07:17:37.105353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명과세년도납세자유형
당해미환급건수1.0000.9110.9190.8710.2350.0000.0000.000
당해미환급금액0.9111.0000.8800.9510.3080.0000.0000.000
누적미환급건수0.9190.8801.0000.9320.5120.0000.0000.000
누적미환급금액0.8710.9510.9321.0000.5560.0000.2010.000
누적미환급금액증감0.2350.3080.5120.5561.0000.0000.0000.000
세목명0.0000.0000.0000.0000.0001.0000.0000.000
과세년도0.0000.0000.0000.2010.0000.0001.0000.000
납세자유형0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-01-10T07:17:34.422294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:17:34.566018image/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

시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
0충청남도부여군44760등록면허세2017신규개인197019700.0
1충청남도부여군44760자동차세2017신규개인595075208377999053.69
2충청남도부여군44760자동차세2017신규법인117350341220137.58
3충청남도부여군44760재산세2017신규개인123500563290169.32
4충청남도부여군44760주민세2017신규개인1110001110000.0
5충청남도부여군44760지방소득세2017신규개인1619139030624410226.25
6충청남도부여군44760지방소득세2017신규법인525106280011.55
7충청남도부여군44760등록면허세2018신규개인143102528022.51
8충청남도부여군44760자동차세2018신규개인1411415230224219522055.11
9충청남도부여군44760자동차세2018신규법인6128980917020031.96
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
14충청남도부여군44760지방소득세2018신규법인104151016443106.75
15충청남도부여군44760등록면허세2019신규개인85442010597009.7
16충청남도부여군44760자동차세2019신규개인20921158004334311020103.75
17충청남도부여군44760자동차세2019신규법인86159017231790276.34
18충청남도부여군44760재산세2019신규개인1511088040301650172.05
19충청남도부여군44760주민세2019신규개인55485099903080.55
20충청남도부여군44760주민세2019신규법인11650116500.0
21충청남도부여군44760지방소득세2019신규개인586886201191729440151.15
22충청남도부여군44760지방소득세2019신규법인154101749720819.04
23충청남도부여군44760취득세2019신규개인1147001147000.0