Overview

Dataset statistics

Number of variables18
Number of observations38
Missing cells264
Missing cells (%)38.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory164.5 B

Variable types

Text1
Numeric17

Dataset

Description차세대 예산회계시스템 구축 추진을 위한 "차세대디브레인(dBrain)추진단"의 과거 회계정보를 제공합니다.
Author기획재정부
URLhttps://www.data.go.kr/data/15087660/fileData.do

Alerts

1990 is highly overall correlated with 1991 and 15 other fieldsHigh correlation
1991 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
1992 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
1993 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
1994 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
1995 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
1996 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
1997 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
1998 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
1999 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
2000 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
2001 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
2002 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
2003 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
2004 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
2005 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
2006 is highly overall correlated with 1990 and 15 other fieldsHigh correlation
1990 has 20 (52.6%) missing valuesMissing
1991 has 16 (42.1%) missing valuesMissing
1992 has 15 (39.5%) missing valuesMissing
1993 has 14 (36.8%) missing valuesMissing
1994 has 17 (44.7%) missing valuesMissing
1995 has 14 (36.8%) missing valuesMissing
1996 has 15 (39.5%) missing valuesMissing
1997 has 15 (39.5%) missing valuesMissing
1998 has 15 (39.5%) missing valuesMissing
1999 has 15 (39.5%) missing valuesMissing
2000 has 14 (36.8%) missing valuesMissing
2001 has 15 (39.5%) missing valuesMissing
2002 has 15 (39.5%) missing valuesMissing
2003 has 15 (39.5%) missing valuesMissing
2004 has 15 (39.5%) missing valuesMissing
2005 has 18 (47.4%) missing valuesMissing
2006 has 16 (42.1%) missing valuesMissing
회계명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:27:38.736687
Analysis finished2023-12-12 06:28:13.299847
Duration34.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계명
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T15:28:13.486423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.7631579
Min length4

Characters and Unicode

Total characters371
Distinct characters97
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

Unique38 ?
Unique (%)100.0%

Sample

1st row일반회계
2nd row재정투융자특별회계
3rd row재정융자특별회계
4th row국유재산관리특별회계
5th row농어촌구조개선특별회계
ValueCountFrequency (%)
일반회계 1
 
2.6%
교육환경개선특별회계 1
 
2.6%
정부청사시설특별회계 1
 
2.6%
행정중심복합도시건설특별회계 1
 
2.6%
양곡관리특별회계 1
 
2.6%
책임운영기관특별회계 1
 
2.6%
철도사업특별회계 1
 
2.6%
통신사업특별회계 1
 
2.6%
조달특별회계 1
 
2.6%
국유임야관리특별회계 1
 
2.6%
Other values (28) 28
73.7%
2023-12-12T15:28:13.913140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39
 
10.5%
38
 
10.2%
38
 
10.2%
38
 
10.2%
10
 
2.7%
9
 
2.4%
8
 
2.2%
7
 
1.9%
7
 
1.9%
7
 
1.9%
Other values (87) 170
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 371
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
39
 
10.5%
38
 
10.2%
38
 
10.2%
38
 
10.2%
10
 
2.7%
9
 
2.4%
8
 
2.2%
7
 
1.9%
7
 
1.9%
7
 
1.9%
Other values (87) 170
45.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 371
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
39
 
10.5%
38
 
10.2%
38
 
10.2%
38
 
10.2%
10
 
2.7%
9
 
2.4%
8
 
2.2%
7
 
1.9%
7
 
1.9%
7
 
1.9%
Other values (87) 170
45.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 371
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
39
 
10.5%
38
 
10.2%
38
 
10.2%
38
 
10.2%
10
 
2.7%
9
 
2.4%
8
 
2.2%
7
 
1.9%
7
 
1.9%
7
 
1.9%
Other values (87) 170
45.8%

1990
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)100.0%
Missing20
Missing (%)52.6%
Infinite0
Infinite (%)0.0%
Mean1.8361566 × 109
Minimum16966000
Maximum2.2689433 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:14.100776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16966000
5-th percentile18350166
Q133164302
median1.759919 × 108
Q37.3190563 × 108
95-th percentile7.675136 × 109
Maximum2.2689433 × 1010
Range2.2672467 × 1010
Interquartile range (IQR)6.9874132 × 108

Descriptive statistics

Standard deviation5.3360728 × 109
Coefficient of variation (CV)2.9061099
Kurtosis15.961096
Mean1.8361566 × 109
Median Absolute Deviation (MAD)1.5821169 × 108
Skewness3.9352743
Sum3.3050818 × 1010
Variance2.8473673 × 1019
MonotonicityNot monotonic
2023-12-12T15:28:14.229828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
370000000 1
 
2.6%
1092411062 1
 
2.6%
46070729 1
 
2.6%
125045491 1
 
2.6%
206241066 1
 
2.6%
528903053 1
 
2.6%
38575367 1
 
2.6%
145742738 1
 
2.6%
22689432968 1
 
2.6%
5025554166 1
 
2.6%
Other values (8) 8
 
21.1%
(Missing) 20
52.6%
ValueCountFrequency (%)
16966000 1
2.6%
18594431 1
2.6%
20445217 1
2.6%
25388572 1
2.6%
31360613 1
2.6%
38575367 1
2.6%
46070729 1
2.6%
125045491 1
2.6%
145742738 1
2.6%
206241066 1
2.6%
ValueCountFrequency (%)
22689432968 1
2.6%
5025554166 1
2.6%
1477440947 1
2.6%
1092411062 1
2.6%
799573150 1
2.6%
528903053 1
2.6%
393072617 1
2.6%
370000000 1
2.6%
206241066 1
2.6%
145742738 1
2.6%

1991
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing16
Missing (%)42.1%
Infinite0
Infinite (%)0.0%
Mean1.8463869 × 109
Minimum17367000
Maximum2.6979748 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:14.348008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17367000
5-th percentile23301323
Q156052754
median2.7248139 × 108
Q38.9758204 × 108
95-th percentile4.8774468 × 109
Maximum2.6979748 × 1010
Range2.6962381 × 1010
Interquartile range (IQR)8.4152928 × 108

Descriptive statistics

Standard deviation5.7200907 × 109
Coefficient of variation (CV)3.0979914
Kurtosis20.166149
Mean1.8463869 × 109
Median Absolute Deviation (MAD)2.4470123 × 108
Skewness4.4315314
Sum4.0620511 × 1010
Variance3.2719438 × 1019
MonotonicityNot monotonic
2023-12-12T15:28:14.507322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
43866306 1
 
2.6%
160000000 1
 
2.6%
1075921590 1
 
2.6%
51351568 1
 
2.6%
141977921 1
 
2.6%
418837913 1
 
2.6%
680340000 1
 
2.6%
70156311 1
 
2.6%
174962789 1
 
2.6%
370000000 1
 
2.6%
Other values (12) 12
31.6%
(Missing) 16
42.1%
ValueCountFrequency (%)
17367000 1
2.6%
23251819 1
2.6%
24241903 1
2.6%
31318417 1
2.6%
43866306 1
2.6%
51351568 1
2.6%
70156311 1
2.6%
101194000 1
2.6%
141977921 1
2.6%
160000000 1
2.6%
ValueCountFrequency (%)
26979748000 1
2.6%
5039027661 1
2.6%
1807411000 1
2.6%
1436046000 1
2.6%
1075921590 1
2.6%
969996047 1
2.6%
680340000 1
2.6%
557031000 1
2.6%
446463711 1
2.6%
418837913 1
2.6%

1992
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing15
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean2.2451598 × 109
Minimum18067000
Maximum3.3200029 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:14.651170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18067000
5-th percentile25566771
Q11.0869457 × 108
median3.7 × 108
Q31.1866989 × 109
95-th percentile5.0897919 × 109
Maximum3.3200029 × 1010
Range3.3181962 × 1010
Interquartile range (IQR)1.0780044 × 109

Descriptive statistics

Standard deviation6.8523108 × 109
Coefficient of variation (CV)3.052037
Kurtosis21.431783
Mean2.2451598 × 109
Median Absolute Deviation (MAD)3.4279589 × 108
Skewness4.5743026
Sum5.1638676 × 1010
Variance4.6954164 × 1019
MonotonicityNot monotonic
2023-12-12T15:28:14.795915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5393858793 1
 
2.6%
250000000 1
 
2.6%
1705515000 1
 
2.6%
61174247 1
 
2.6%
200102429 1
 
2.6%
495207298 1
 
2.6%
984091055 1
 
2.6%
151816140 1
 
2.6%
227626237 1
 
2.6%
370000000 1
 
2.6%
Other values (13) 13
34.2%
(Missing) 15
39.5%
ValueCountFrequency (%)
18067000 1
2.6%
25384845 1
2.6%
27204109 1
2.6%
38398851 1
2.6%
61174247 1
2.6%
65573004 1
2.6%
151816140 1
2.6%
200102429 1
2.6%
227626237 1
2.6%
232149000 1
2.6%
ValueCountFrequency (%)
33200029000 1
2.6%
5393858793 1
2.6%
2353190000 1
2.6%
1773313000 1
2.6%
1705515000 1
2.6%
1250629000 1
2.6%
1122768860 1
2.6%
1121911007 1
2.6%
984091055 1
2.6%
570666865 1
2.6%

1993
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing14
Missing (%)36.8%
Infinite0
Infinite (%)0.0%
Mean2.5838451 × 109
Minimum22220000
Maximum3.805 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:15.002715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22220000
5-th percentile24552580
Q169356829
median3.35647 × 108
Q31.4925649 × 109
95-th percentile7.4569799 × 109
Maximum3.805 × 1010
Range3.802778 × 1010
Interquartile range (IQR)1.4232081 × 109

Descriptive statistics

Standard deviation7.7540672 × 109
Coefficient of variation (CV)3.0009798
Kurtosis21.302839
Mean2.5838451 × 109
Median Absolute Deviation (MAD)3.0971873 × 108
Skewness4.5360406
Sum6.2012283 × 1010
Variance6.0125558 × 1019
MonotonicityNot monotonic
2023-12-12T15:28:15.181330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1291663000 1
 
2.6%
22220000 1
 
2.6%
381000000 1
 
2.6%
2104932000 1
 
2.6%
70138124 1
 
2.6%
235075260 1
 
2.6%
449747493 1
 
2.6%
1533107000 1
 
2.6%
172634985 1
 
2.6%
264668383 1
 
2.6%
Other values (14) 14
36.8%
(Missing) 14
36.8%
ValueCountFrequency (%)
22220000 1
2.6%
23963000 1
2.6%
27893536 1
2.6%
28830961 1
2.6%
44265000 1
2.6%
67012943 1
2.6%
70138124 1
2.6%
83384889 1
2.6%
172634985 1
2.6%
235075260 1
2.6%
ValueCountFrequency (%)
38050000000 1
2.6%
8342245847 1
2.6%
2440473000 1
2.6%
2415752780 1
2.6%
2104932000 1
2.6%
1533107000 1
2.6%
1479050855 1
2.6%
1470473309 1
2.6%
1291663000 1
2.6%
723457099 1
2.6%

1994
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing17
Missing (%)44.7%
Infinite0
Infinite (%)0.0%
Mean3.6719455 × 109
Minimum28576000
Maximum4.325 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:15.370772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28576000
5-th percentile31125772
Q183413453
median1.1586567 × 109
Q32.569119 × 109
95-th percentile1.1086611 × 1010
Maximum4.325 × 1010
Range4.3221424 × 1010
Interquartile range (IQR)2.4857055 × 109

Descriptive statistics

Standard deviation9.4035786 × 109
Coefficient of variation (CV)2.5609254
Kurtosis17.726303
Mean3.6719455 × 109
Median Absolute Deviation (MAD)1.1076207 × 109
Skewness4.1154391
Sum7.7110856 × 1010
Variance8.842729 × 1019
MonotonicityNot monotonic
2023-12-12T15:28:15.530386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
11086611174 1
 
2.6%
4534472607 1
 
2.6%
79276641 1
 
2.6%
1463428250 1
 
2.6%
178679947 1
 
2.6%
116600366 1
 
2.6%
1534070000 1
 
2.6%
2504597000 1
 
2.6%
2732135544 1
 
2.6%
31125772 1
 
2.6%
Other values (11) 11
28.9%
(Missing) 17
44.7%
ValueCountFrequency (%)
28576000 1
2.6%
31125772 1
2.6%
43407694 1
2.6%
51035970 1
2.6%
79276641 1
2.6%
83413453 1
2.6%
116600366 1
2.6%
178679947 1
2.6%
362081000 1
2.6%
787634425 1
2.6%
ValueCountFrequency (%)
43250000000 1
2.6%
11086611174 1
2.6%
4534472607 1
2.6%
2741187462 1
2.6%
2732135544 1
2.6%
2569118999 1
2.6%
2504597000 1
2.6%
1774746795 1
2.6%
1534070000 1
2.6%
1463428250 1
2.6%

1995
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing14
Missing (%)36.8%
Infinite0
Infinite (%)0.0%
Mean3.7667931 × 109
Minimum31000000
Maximum4.9987915 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:15.686195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31000000
5-th percentile41621447
Q11.1002249 × 108
median1.2859762 × 109
Q32.0919012 × 109
95-th percentile1.0412955 × 1010
Maximum4.9987915 × 1010
Range4.9956915 × 1010
Interquartile range (IQR)1.9818787 × 109

Descriptive statistics

Standard deviation1.0153749 × 1010
Coefficient of variation (CV)2.695595
Kurtosis20.838448
Mean3.7667931 × 109
Median Absolute Deviation (MAD)1.1800098 × 109
Skewness4.46748
Sum9.0403034 × 1010
Variance1.0309861 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:15.848428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
440656580 1
 
2.6%
5384068672 1
 
2.6%
97854079 1
 
2.6%
1364136431 1
 
2.6%
125825362 1
 
2.6%
114078633 1
 
2.6%
1869525000 1
 
2.6%
2757230000 1
 
2.6%
1779763349 1
 
2.6%
39906605 1
 
2.6%
Other values (14) 14
36.8%
(Missing) 14
36.8%
ValueCountFrequency (%)
31000000 1
2.6%
39906605 1
2.6%
51338884 1
2.6%
53943800 1
2.6%
88523203 1
2.6%
97854079 1
2.6%
114078633 1
2.6%
125825362 1
2.6%
419355849 1
2.6%
440656580 1
2.6%
ValueCountFrequency (%)
49987915000 1
2.6%
11300405437 1
2.6%
5384068672 1
2.6%
4528506185 1
2.6%
2986982421 1
2.6%
2757230000 1
2.6%
1870124889 1
2.6%
1869525000 1
2.6%
1779763349 1
2.6%
1543200000 1
2.6%

1996
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing15
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean4.51689 × 109
Minimum33000000
Maximum5.79621 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:16.019861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33000000
5-th percentile51195636
Q11.255607 × 108
median1.5661677 × 109
Q32.8619988 × 109
95-th percentile1.2010375 × 1010
Maximum5.79621 × 1010
Range5.79291 × 1010
Interquartile range (IQR)2.7364381 × 109

Descriptive statistics

Standard deviation1.2007632 × 1010
Coefficient of variation (CV)2.6583849
Kurtosis20.013101
Mean4.51689 × 109
Median Absolute Deviation (MAD)1.4564839 × 109
Skewness4.3771248
Sum1.0388847 × 1011
Variance1.4418323 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:16.156986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
12619191247 1
 
2.6%
141437607 1
 
2.6%
400000000 1
 
2.6%
109683796 1
 
2.6%
2186636953 1
 
2.6%
3172914000 1
 
2.6%
1592692856 1
 
2.6%
50936818 1
 
2.6%
97805460 1
 
2.6%
330509642 1
 
2.6%
Other values (13) 13
34.2%
(Missing) 15
39.5%
ValueCountFrequency (%)
33000000 1
2.6%
50936818 1
2.6%
53525000 1
2.6%
79210999 1
2.6%
97805460 1
2.6%
109683796 1
2.6%
141437607 1
2.6%
330509642 1
2.6%
400000000 1
2.6%
610090966 1
2.6%
ValueCountFrequency (%)
57962100000 1
2.6%
12619191247 1
2.6%
6531024869 1
2.6%
5417300144 1
2.6%
4113634129 1
2.6%
3172914000 1
2.6%
2551083601 1
2.6%
2186636953 1
2.6%
1758712280 1
2.6%
1592692856 1
2.6%

1997
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing15
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean5.1422101 × 109
Minimum34500000
Maximum6.75786 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:16.364038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34500000
5-th percentile56193062
Q11.5762609 × 108
median1.500004 × 109
Q33.6371642 × 109
95-th percentile1.0411951 × 1010
Maximum6.75786 × 1010
Range6.75441 × 1010
Interquartile range (IQR)3.4795381 × 109

Descriptive statistics

Standard deviation1.3903964 × 1010
Coefficient of variation (CV)2.7038889
Kurtosis20.847224
Mean5.1422101 × 109
Median Absolute Deviation (MAD)1.3862725 × 109
Skewness4.4851475
Sum1.1827083 × 1011
Variance1.9332023 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:16.537365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10648592650 1
 
2.6%
178293691 1
 
2.6%
700000000 1
 
2.6%
136958492 1
 
2.6%
2853026844 1
 
2.6%
4085227000 1
 
2.6%
1772540986 1
 
2.6%
69416392 1
 
2.6%
94240400 1
 
2.6%
347992074 1
 
2.6%
Other values (13) 13
34.2%
(Missing) 15
39.5%
ValueCountFrequency (%)
34500000 1
2.6%
54723803 1
2.6%
69416392 1
2.6%
94240400 1
2.6%
113731497 1
2.6%
136958492 1
2.6%
178293691 1
2.6%
347992074 1
2.6%
700000000 1
2.6%
792065088 1
2.6%
ValueCountFrequency (%)
67578600000 1
2.6%
10648592650 1
2.6%
8282172222 1
2.6%
5926020028 1
2.6%
5271765783 1
2.6%
4085227000 1
2.6%
3189101439 1
2.6%
2853026844 1
2.6%
1983469180 1
2.6%
1772540986 1
2.6%

1998
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing15
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean5.4898794 × 109
Minimum36000000
Maximum7.026357 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:16.715431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36000000
5-th percentile57171788
Q11.6579474 × 108
median1.2701177 × 109
Q33.6991125 × 109
95-th percentile1.2992014 × 1010
Maximum7.026357 × 1010
Range7.022757 × 1010
Interquartile range (IQR)3.5333177 × 109

Descriptive statistics

Standard deviation1.4519904 × 1010
Coefficient of variation (CV)2.6448493
Kurtosis20.219936
Mean5.4898794 × 109
Median Absolute Deviation (MAD)1.1731107 × 109
Skewness4.3996735
Sum1.2626723 × 1011
Variance2.1082761 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:16.860186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
13411150600 1
 
2.6%
183203476 1
 
2.6%
700000000 1
 
2.6%
131892501 1
 
2.6%
3356460981 1
 
2.6%
4041764000 1
 
2.6%
1270117672 1
 
2.6%
96761109 1
 
2.6%
97007000 1
 
2.6%
405314000 1
 
2.6%
Other values (13) 13
34.2%
(Missing) 15
39.5%
ValueCountFrequency (%)
36000000 1
2.6%
52772974 1
2.6%
96761109 1
2.6%
97007000 1
2.6%
131892501 1
2.6%
148386009 1
2.6%
183203476 1
2.6%
405314000 1
2.6%
700000000 1
2.6%
915217050 1
2.6%
ValueCountFrequency (%)
70263570000 1
2.6%
13411150600 1
2.6%
9219781367 1
2.6%
6291902740 1
2.6%
6218025132 1
2.6%
4041764000 1
2.6%
3356460981 1
2.6%
3097694529 1
2.6%
2336049043 1
2.6%
1524670289 1
2.6%

1999
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing15
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean6.3037016 × 109
Minimum36000000
Maximum8.01378 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:17.029449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36000000
5-th percentile60045490
Q11.774918 × 108
median1.1765555 × 109
Q33.7384163 × 109
95-th percentile2.2665927 × 1010
Maximum8.01378 × 1010
Range8.01018 × 1010
Interquartile range (IQR)3.5609245 × 109

Descriptive statistics

Standard deviation1.6921554 × 1010
Coefficient of variation (CV)2.6843838
Kurtosis18.28009
Mean6.3037016 × 109
Median Absolute Deviation (MAD)1.0787318 × 109
Skewness4.1635387
Sum1.4498514 × 1011
Variance2.86339 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:17.222008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
23969697525 1
 
2.6%
195985176 1
 
2.6%
700000000 1
 
2.6%
148017553 1
 
2.6%
3514811549 1
 
2.6%
3962021000 1
 
2.6%
1322615243 1
 
2.6%
97823689 1
 
2.6%
99395280 1
 
2.6%
285923000 1
 
2.6%
Other values (13) 13
34.2%
(Missing) 15
39.5%
ValueCountFrequency (%)
36000000 1
2.6%
55847912 1
2.6%
97823689 1
2.6%
99395280 1
2.6%
148017553 1
2.6%
158998417 1
2.6%
195985176 1
2.6%
285923000 1
2.6%
700000000 1
2.6%
907168280 1
2.6%
ValueCountFrequency (%)
80137800000 1
2.6%
23969697525 1
2.6%
10931991306 1
2.6%
5146856691 1
2.6%
4691732164 1
2.6%
3962021000 1
2.6%
3514811549 1
2.6%
2772895825 1
2.6%
2410729000 1
2.6%
1322615243 1
2.6%

2000
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing14
Missing (%)36.8%
Infinite0
Infinite (%)0.0%
Mean6.427052 × 109
Minimum37500000
Maximum8.6474007 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:17.384493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37500000
5-th percentile44059520
Q12.3330376 × 108
median1.1380807 × 109
Q33.8506738 × 109
95-th percentile2.0595515 × 1010
Maximum8.6474007 × 1010
Range8.6436507 × 1010
Interquartile range (IQR)3.6173701 × 109

Descriptive statistics

Standard deviation1.774904 × 1010
Coefficient of variation (CV)2.7616145
Kurtosis19.969748
Mean6.427052 × 109
Median Absolute Deviation (MAD)1.0342391 × 109
Skewness4.3575828
Sum1.5424925 × 1011
Variance3.1502843 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:17.540862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
61058132 1
 
2.6%
245742075 1
 
2.6%
700000000 1
 
2.6%
195988835 1
 
2.6%
3595951909 1
 
2.6%
4389598000 1
 
2.6%
269698526 1
 
2.6%
1216836692 1
 
2.6%
146625000 1
 
2.6%
41059765 1
 
2.6%
Other values (14) 14
36.8%
(Missing) 14
36.8%
ValueCountFrequency (%)
37500000 1
2.6%
41059765 1
2.6%
61058132 1
2.6%
146625000 1
2.6%
169411348 1
2.6%
195988835 1
2.6%
245742075 1
2.6%
269698526 1
2.6%
308178564 1
2.6%
700000000 1
2.6%
ValueCountFrequency (%)
86474007200 1
2.6%
22053956838 1
2.6%
12331010477 1
2.6%
5824812829 1
2.6%
5223719962 1
2.6%
4389598000 1
2.6%
3671032464 1
2.6%
3595951909 1
2.6%
2254255339 1
2.6%
1629320750 1
2.6%

2001
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing15
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean6.9730822 × 109
Minimum39000000
Maximum9.41246 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:17.692760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39000000
5-th percentile69122178
Q12.5464833 × 108
median1.2887803 × 109
Q34.3739518 × 109
95-th percentile1.6286796 × 1010
Maximum9.41246 × 1010
Range9.40856 × 1010
Interquartile range (IQR)4.1193034 × 109

Descriptive statistics

Standard deviation1.9449058 × 1010
Coefficient of variation (CV)2.7891623
Kurtosis20.643362
Mean6.9730822 × 109
Median Absolute Deviation (MAD)1.2264033 × 109
Skewness4.4615341
Sum1.6038089 × 1011
Variance3.7826585 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:17.847884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
16705136586 1
 
2.6%
129828307 1
 
2.6%
3968386497 1
 
2.6%
4916555000 1
 
2.6%
514071021 1
 
2.6%
1158607649 1
 
2.6%
156835000 1
 
2.6%
157995056 1
 
2.6%
360349673 1
 
2.6%
311442517 1
 
2.6%
Other values (13) 13
34.2%
(Missing) 15
39.5%
ValueCountFrequency (%)
39000000 1
2.6%
62377053 1
2.6%
129828307 1
2.6%
156835000 1
2.6%
157995056 1
2.6%
197854148 1
2.6%
311442517 1
2.6%
360349673 1
2.6%
514071021 1
2.6%
1146648609 1
2.6%
ValueCountFrequency (%)
94124600000 1
2.6%
16705136586 1
2.6%
12521734721 1
2.6%
6517872231 1
2.6%
4916555000 1
2.6%
4779517025 1
2.6%
3968386497 1
2.6%
3624401228 1
2.6%
3547093870 1
2.6%
2521983000 1
2.6%

2002
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing15
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean7.5649621 × 109
Minimum40000000
Maximum1.0587667 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:18.002702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40000000
5-th percentile76584740
Q13.1401276 × 108
median1.3081267 × 109
Q34.2324424 × 109
95-th percentile1.8168674 × 1010
Maximum1.0587667 × 1011
Range1.0583667 × 1011
Interquartile range (IQR)3.9184296 × 109

Descriptive statistics

Standard deviation2.1907513 × 1010
Coefficient of variation (CV)2.8959184
Kurtosis20.78387
Mean7.5649621 × 109
Median Absolute Deviation (MAD)1.1364914 × 109
Skewness4.4830796
Sum1.7399413 × 1011
Variance4.7993913 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:18.179139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
18714536721 1
 
2.6%
171635315 1
 
2.6%
4115303437 1
 
2.6%
5844425185 1
 
2.6%
572485861 1
 
2.6%
1238183728 1
 
2.6%
176888000 1
 
2.6%
409958897 1
 
2.6%
399851164 1
 
2.6%
228174360 1
 
2.6%
Other values (13) 13
34.2%
(Missing) 15
39.5%
ValueCountFrequency (%)
40000000 1
2.6%
66023565 1
2.6%
171635315 1
2.6%
176888000 1
2.6%
190284010 1
2.6%
228174360 1
2.6%
399851164 1
2.6%
409958897 1
2.6%
572485861 1
2.6%
1168240246 1
2.6%
ValueCountFrequency (%)
105876671000 1
2.6%
18714536721 1
2.6%
13255911359 1
2.6%
6095435083 1
2.6%
5844425185 1
2.6%
4349581354 1
2.6%
4115303437 1
2.6%
3672631875 1
2.6%
2667559000 1
2.6%
1809116405 1
2.6%

2003
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing15
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean7.9506 × 109
Minimum45000000
Maximum1.114831 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:18.354286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45000000
5-th percentile78510519
Q13.7900536 × 108
median1.4477206 × 109
Q34.7335177 × 109
95-th percentile1.8396632 × 1010
Maximum1.114831 × 1011
Range1.114381 × 1011
Interquartile range (IQR)4.3545123 × 109

Descriptive statistics

Standard deviation2.3029211 × 1010
Coefficient of variation (CV)2.8965375
Kurtosis20.958531
Mean7.9506 × 109
Median Absolute Deviation (MAD)1.2664006 × 109
Skewness4.5067473
Sum1.828638 × 1011
Variance5.3034457 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:18.519982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
18971424287 1
 
2.6%
160795910 1
 
2.6%
4563517888 1
 
2.6%
6232912849 1
 
2.6%
661747530 1
 
2.6%
1593635528 1
 
2.6%
181320000 1
 
2.6%
922446420 1
 
2.6%
444242005 1
 
2.6%
313768718 1
 
2.6%
Other values (13) 13
34.2%
(Missing) 15
39.5%
ValueCountFrequency (%)
45000000 1
2.6%
69367698 1
2.6%
160795910 1
2.6%
181320000 1
2.6%
219597257 1
2.6%
313768718 1
2.6%
444242005 1
2.6%
661747530 1
2.6%
922446420 1
2.6%
1202107439 1
2.6%
ValueCountFrequency (%)
111483097967 1
2.6%
18971424287 1
2.6%
13223502495 1
2.6%
6232912849 1
2.6%
6088771942 1
2.6%
4903517434 1
2.6%
4563517888 1
2.6%
4091008260 1
2.6%
2551401180 1
2.6%
2122340440 1
2.6%

2004
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing15
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean8.0879481 × 109
Minimum46500000
Maximum1.1835605 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:18.661458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46500000
5-th percentile79356207
Q14.1359805 × 108
median1.4704551 × 109
Q34.5105013 × 109
95-th percentile1.5382334 × 1010
Maximum1.1835605 × 1011
Range1.1830955 × 1011
Interquartile range (IQR)4.0969032 × 109

Descriptive statistics

Standard deviation2.4384807 × 1010
Coefficient of variation (CV)3.0149559
Kurtosis21.530733
Mean8.0879481 × 109
Median Absolute Deviation (MAD)1.2853841 × 109
Skewness4.5849036
Sum1.8602281 × 1011
Variance5.946188 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:18.805530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
15590602871 1
 
2.6%
163283326 1
 
2.6%
4623845857 1
 
2.6%
5545119032 1
 
2.6%
707601734 1
 
2.6%
1470455116 1
 
2.6%
185071000 1
 
2.6%
737907150 1
 
2.6%
490381041 1
 
2.6%
336815056 1
 
2.6%
Other values (13) 13
34.2%
(Missing) 15
39.5%
ValueCountFrequency (%)
46500000 1
2.6%
70030972 1
2.6%
163283326 1
2.6%
185071000 1
2.6%
200341839 1
2.6%
336815056 1
2.6%
490381041 1
2.6%
707601734 1
2.6%
737907150 1
2.6%
1265215265 1
2.6%
ValueCountFrequency (%)
118356045454 1
2.6%
15590602871 1
2.6%
13507912077 1
2.6%
6533085727 1
2.6%
5545119032 1
2.6%
4623845857 1
2.6%
4397156729 1
2.6%
4238600000 1
2.6%
2431459180 1
2.6%
2228460529 1
2.6%

2005
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing18
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean9.7478493 × 109
Minimum47800000
Maximum1.3437038 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:18.940775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47800000
5-th percentile65629250
Q14.5475695 × 108
median1.8362875 × 109
Q35.2242042 × 109
95-th percentile2.0845263 × 1010
Maximum1.3437038 × 1011
Range1.3432258 × 1011
Interquartile range (IQR)4.7694472 × 109

Descriptive statistics

Standard deviation2.9624647 × 1010
Coefficient of variation (CV)3.0390957
Kurtosis19.091654
Mean9.7478493 × 109
Median Absolute Deviation (MAD)1.6033105 × 109
Skewness4.3323452
Sum1.9495699 × 1011
Variance8.7761971 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:19.453525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
66567632 1
 
2.6%
150551537 1
 
2.6%
5134610013 1
 
2.6%
707592543 1
 
2.6%
1441187673 1
 
2.6%
5492986677 1
 
2.6%
225020000 1
 
2.6%
626144000 1
 
2.6%
526031306 1
 
2.6%
134370379000 1
 
2.6%
Other values (10) 10
26.3%
(Missing) 18
47.4%
ValueCountFrequency (%)
47800000 1
2.6%
66567632 1
2.6%
150551537 1
2.6%
225020000 1
2.6%
240933898 1
2.6%
526031306 1
2.6%
626144000 1
2.6%
707592543 1
2.6%
1441187673 1
2.6%
1660788624 1
2.6%
ValueCountFrequency (%)
134370379000 1
2.6%
14870257317 1
2.6%
13058661676 1
2.6%
6327970343 1
2.6%
5492986677 1
2.6%
5134610013 1
2.6%
2796417000 1
2.6%
2692362000 1
2.6%
2508938415 1
2.6%
2011786322 1
2.6%

2006
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing16
Missing (%)42.1%
Infinite0
Infinite (%)0.0%
Mean9.1845287 × 109
Minimum45259465
Maximum1.4480761 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T15:28:19.586234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45259465
5-th percentile50114680
Q13.6375482 × 108
median1.3428846 × 109
Q34.50648 × 109
95-th percentile1.2484698 × 1010
Maximum1.4480761 × 1011
Range1.4476235 × 1011
Interquartile range (IQR)4.1427252 × 109

Descriptive statistics

Standard deviation3.0489625 × 1010
Coefficient of variation (CV)3.3196723
Kurtosis21.348223
Mean9.1845287 × 109
Median Absolute Deviation (MAD)1.2825473 × 109
Skewness4.5932561
Sum2.0205963 × 1011
Variance9.2961725 × 1020
MonotonicityNot monotonic
2023-12-12T15:28:19.778303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
561100262 1
 
2.6%
215130966 1
 
2.6%
5006226007 1
 
2.6%
616798756 1
 
2.6%
1293150627 1
 
2.6%
45259465 1
 
2.6%
626416370 1
 
2.6%
5906702766 1
 
2.6%
297973000 1
 
2.6%
669016792 1
 
2.6%
Other values (12) 12
31.6%
(Missing) 16
42.1%
ValueCountFrequency (%)
45259465 1
2.6%
49200000 1
2.6%
67493598 1
2.6%
215130966 1
2.6%
266835302 1
2.6%
297973000 1
2.6%
561100262 1
2.6%
616798756 1
2.6%
626416370 1
2.6%
669016792 1
2.6%
ValueCountFrequency (%)
144807610000 1
2.6%
12595344729 1
2.6%
10382406871 1
2.6%
7067249522 1
2.6%
5906702766 1
2.6%
5006226007 1
2.6%
3007242000 1
2.6%
2778585000 1
2.6%
2632588122 1
2.6%
1774683428 1
2.6%

Interactions

2023-12-12T15:28:10.379245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:39.323030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:41.176594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:42.855618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:44.907063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:46.928408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:48.625445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:50.286015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:52.519985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:54.580855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:56.481381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T15:28:04.046133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T15:28:10.637174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:39.572167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T15:28:02.405648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:04.223444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T15:28:08.437937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:10.753289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T15:27:40.581482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:42.199238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:43.933422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:46.170150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:47.988098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.679542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.818946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:53.870142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:55.872556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:57.766567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:59.921462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.722387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.365213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.526856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.478835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:09.578046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:11.632188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:40.667256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:42.293050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:44.027426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:46.293317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:48.082034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.759389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.925493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:53.980392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:55.968315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:57.884133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:00.006909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.813189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.452359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.612538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.604345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:09.685572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:11.757149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:40.764348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:42.398482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:44.444021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:46.418119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:48.186573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.868682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:52.031174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:54.119554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:56.074678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:58.320679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:00.113763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.900417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.559267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.718263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.730294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:09.832649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:11.870899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:40.870450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:42.523208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:44.555203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:46.540759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:48.294103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.973926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:52.140057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:54.238094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:56.180598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:58.449447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:00.210547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.976706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.682039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.812346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.837086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:09.986137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:11.977419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:40.973111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:42.642312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:44.671498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:46.662995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:48.388847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:50.072965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:52.260342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:54.369804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:56.277501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:58.571128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:00.302160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:02.062788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.827213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.906020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.960098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:10.117772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:12.073967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:41.086307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:42.745005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:44.787610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:46.795239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:48.506372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:50.174569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:52.391021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:54.472258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:56.369929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:58.708064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:00.405746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:02.151492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.957415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:06.029620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:08.087791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:10.252408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:28:19.926085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계명19901991199219931994199519961997199819992000200120022003200420052006
회계명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
19901.0001.0001.0001.0001.0001.0001.0001.0000.4940.4940.4940.4940.3960.3960.3960.3960.2930.293
19911.0001.0001.0001.0001.0001.0001.0001.0000.5620.5620.5620.5620.5230.5230.5230.5230.2930.293
19921.0001.0001.0001.0001.0001.0001.0001.0000.5760.5760.5760.5760.5450.5450.5450.5450.3960.396
19931.0001.0001.0001.0001.0001.0001.0001.0000.5760.5760.5760.5760.5620.5620.5620.5620.4550.494
19941.0001.0001.0001.0001.0001.0001.0001.0000.5970.5970.5970.5970.5870.5870.5870.5870.5230.523
19951.0001.0001.0001.0001.0001.0001.0001.0000.6180.6180.6180.6180.6120.6120.6120.6120.5760.576
19961.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
19971.0000.4940.5620.5760.5760.5970.6181.0001.0001.0001.0001.0001.0001.0001.0001.0000.9821.000
19981.0000.4940.5620.5760.5760.5970.6181.0001.0001.0001.0001.0001.0001.0001.0001.0000.9821.000
19991.0000.4940.5620.5760.5760.5970.6181.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20001.0000.4940.5620.5760.5760.5970.6181.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
20011.0000.3960.5230.5450.5620.5870.6121.0001.0001.0001.0001.0001.0001.0001.0001.0000.9831.000
20021.0000.3960.5230.5450.5620.5870.6121.0001.0001.0001.0001.0001.0001.0001.0001.0000.9831.000
20031.0000.3960.5230.5450.5620.5870.6121.0001.0001.0001.0001.0001.0001.0001.0001.0000.9831.000
20041.0000.3960.5230.5450.5620.5870.6121.0001.0001.0001.0001.0001.0001.0001.0001.0000.9831.000
20051.0000.2930.2930.3960.4550.5230.5761.0000.9820.9821.0001.0000.9830.9830.9830.9831.0001.000
20061.0000.2930.2930.3960.4940.5230.5761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T15:28:20.111253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
19901991199219931994199519961997199819992000200120022003200420052006
19901.0000.9960.9920.9900.7970.8790.8950.8450.8450.8450.8450.8670.8670.8170.8670.8100.810
19910.9961.0000.9900.9880.7880.8970.9180.8900.8950.8950.8900.8950.8950.8670.9020.8100.810
19920.9920.9901.0000.9900.8010.9040.9030.8750.8710.8710.8750.8680.8680.8680.8630.8500.850
19930.9900.9880.9901.0000.8060.8950.9240.9000.8960.8960.8860.8900.8810.8680.8770.8420.842
19940.7970.7880.8010.8061.0000.9840.9710.9530.9290.9410.9260.8820.8820.9030.8910.8810.867
19950.8790.8970.9040.8950.9841.0000.9940.9860.9770.9800.9760.9540.9490.9560.9510.9040.893
19960.8950.9180.9030.9240.9710.9941.0000.9970.9910.9930.9890.9650.9670.9700.9670.9380.929
19970.8450.8900.8750.9000.9530.9860.9971.0000.9940.9980.9930.9710.9700.9700.9680.9460.934
19980.8450.8950.8710.8960.9290.9770.9910.9941.0000.9960.9950.9840.9790.9740.9770.9710.956
19990.8450.8950.8710.8960.9410.9800.9930.9980.9961.0000.9910.9740.9710.9710.9710.9510.936
20000.8450.8900.8750.8860.9260.9760.9890.9930.9950.9911.0000.9790.9700.9590.9590.9590.940
20010.8670.8950.8680.8900.8820.9540.9650.9710.9840.9740.9791.0000.9930.9840.9850.9820.970
20020.8670.8950.8680.8810.8820.9490.9670.9700.9790.9710.9700.9931.0000.9940.9950.9890.982
20030.8170.8670.8680.8680.9030.9560.9700.9700.9740.9710.9590.9840.9941.0000.9970.9810.981
20040.8670.9020.8630.8770.8910.9510.9670.9680.9770.9710.9590.9850.9950.9971.0000.9820.984
20050.8100.8100.8500.8420.8810.9040.9380.9460.9710.9510.9590.9820.9890.9810.9821.0000.994
20060.8100.8100.8500.8420.8670.8930.9290.9340.9560.9360.9400.9700.9820.9810.9840.9941.000

Missing values

2023-12-12T15:28:12.580534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:28:12.844265image/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.
2023-12-12T15:28:13.042689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

회계명19901991199219931994199519961997199819992000200120022003200420052006
0일반회계226894329682697974800033200029000380500000004325000000049987915000579621000006757860000070263570000801378000008647400720094124600000105876671000111483097967118356045454134370379000144807610000
1재정투융자특별회계5025554166503902766153938587938342245847110866111741130040543712619191247<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2재정융자특별회계<NA><NA><NA><NA><NA><NA><NA>10648592650134111506002396969752522053956838167051365861871453672118971424287155906028711487025731710382406871
3국유재산관리특별회계<NA><NA><NA><NA>1158656702120781592315661677231701049281144144817812492974091299641032162982141116231058191370556890135412654420117863221392618543
4농어촌구조개선특별회계<NA><NA>112191100714790508552741187462452850618554173001445926020028629190274051468566915824812829651787223160954350836088771942653308572763279703437067249522
5농어촌특별세관리특별회계<NA><NA><NA><NA><NA>154320000015900230001500004025152467028911765555181629320750354709387018091164052122340440222846052926923620002778585000
6교통시설특별회계<NA><NA><NA><NA><NA><NA>6531024869828217222292197813671093199130612331010477125217347211325591135913223502495135079120771305866167612595344729
7등기특별회계<NA><NA><NA><NA>434076945394380079210999113731497148386009158998417169411348197854148190284010219597257200341839240933898266835302
8지방양여금관리특별회계<NA>5570310001250629000147047330917747467951870124889255108360131891014393097694529277289582536710324644779517025434958135449035174344397156729<NA><NA>
9교도작업특별회계1696600017367000180670002396300028576000310000003300000034500000360000003600000037500000390000004000000045000000465000004780000049200000
회계명19901991199219931994199519961997199819992000200120022003200420052006
28국립대학교부속병원특별회계145742738174962789227626237264668383178679947125825362<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
29국유임야관리특별회계3857536770156311151816140172634985<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
30산업재해보상보험특별회계528903053680340000984091055153310700014634282501364136431<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
31군용시설교외이전특별회계206241066418837913495207298449747493<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32사법시설등특별회계125045491141977921200102429235075260<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33체신보험특별회계460707295135156861174247701381247927664197854079141437607178293691183203476195985176245742075<NA><NA><NA><NA><NA><NA>
34도로사업특별회계1092411062107592159017055150002104932000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
35도시철도사업특별회계<NA>160000000250000000381000000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
36정부청사시설특별회계<NA><NA><NA>22220000<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
37도로등교통시설특별회계<NA><NA><NA><NA>45344726075384068672<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>