Overview

Dataset statistics

Number of variables9
Number of observations157978
Missing cells0
Missing cells (%)0.0%
Total size in memory10.8 MiB
Average record size in memory72.0 B

Variable types

Text7
Numeric2

Variable descriptions

ald_business_unitsub-sector of ald_sector
ald_sectorasset production sector
directiondirection of the evolution of the scenario trajectory
fair_share_percevolution of scenario trajectory for a given technology
scenarioname of the scenario
scenario_geographyregional geography of a scenario
scenario_typetype of scenario
unitsunit of production
yearyear

Alerts

fair_share_perc has 5729 (3.6%) zerosZeros

Reproduction

Analysis started2024-04-15 12:05:16.782808
Analysis finished2024-04-15 12:05:17.257459
Duration0.47 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

scenario_geography
Text

regional geography of a scenario

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2024-04-15T14:05:17.388154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length13
Mean length9.71615668
Min length2

Characters and Unicode

Total characters1534939
Distinct characters39
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdvancedEconomies
2nd rowAdvancedEconomies
3rd rowAdvancedEconomies
4th rowAdvancedEconomies
5th rowAdvancedEconomies
ValueCountFrequency (%)
global 19798
12.5%
china 15453
9.8%
latinamerica 14931
9.5%
reformingeconomies 14931
9.5%
oecdandeu 14931
9.5%
middleeastandafrica 14931
9.5%
asia 14931
9.5%
india 11332
7.2%
unitedstates 10991
7.0%
southeastasia 5499
 
3.5%
Other values (28) 20250
12.8%
2024-04-15T14:05:17.737277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 162130
 
10.6%
a 162026
 
10.6%
n 126453
 
8.2%
e 111369
 
7.3%
d 105428
 
6.9%
A 86014
 
5.6%
o 80346
 
5.2%
s 77647
 
5.1%
t 76763
 
5.0%
c 64763
 
4.2%
Other values (29) 482000
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1534939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 162130
 
10.6%
a 162026
 
10.6%
n 126453
 
8.2%
e 111369
 
7.3%
d 105428
 
6.9%
A 86014
 
5.6%
o 80346
 
5.2%
s 77647
 
5.1%
t 76763
 
5.0%
c 64763
 
4.2%
Other values (29) 482000
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1534939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 162130
 
10.6%
a 162026
 
10.6%
n 126453
 
8.2%
e 111369
 
7.3%
d 105428
 
6.9%
A 86014
 
5.6%
o 80346
 
5.2%
s 77647
 
5.1%
t 76763
 
5.0%
c 64763
 
4.2%
Other values (29) 482000
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1534939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 162130
 
10.6%
a 162026
 
10.6%
n 126453
 
8.2%
e 111369
 
7.3%
d 105428
 
6.9%
A 86014
 
5.6%
o 80346
 
5.2%
s 77647
 
5.1%
t 76763
 
5.0%
c 64763
 
4.2%
Other values (29) 482000
31.4%

scenario
Text

name of the scenario

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2024-04-15T14:05:17.971134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length26
Median length21
Mean length16.75703579
Min length8

Characters and Unicode

Total characters2647243
Distinct characters43
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowWEO2021_APS
2nd rowWEO2021_APS
3rd rowWEO2021_APS
4th rowWEO2021_APS
5th rowWEO2021_APS
ValueCountFrequency (%)
ngfs2023gcam_ld 7821
 
5.0%
ngfs2023gcam_nz2050 7821
 
5.0%
ngfs2023gcam_ndc 7821
 
5.0%
ngfs2023gcam_fw 7821
 
5.0%
ngfs2023gcam_dt 7821
 
5.0%
ngfs2023gcam_b2ds 7821
 
5.0%
ngfs2023gcam_cp 7821
 
5.0%
ngfs2023remind_ndc 6399
 
4.1%
ngfs2023remind_dt 6399
 
4.1%
ngfs2023remind_nz2050 6399
 
4.1%
Other values (32) 84034
53.2%
2024-04-15T14:05:18.345522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 354176
13.4%
S 251223
 
9.5%
G 224553
 
8.5%
N 218458
 
8.3%
0 197068
 
7.4%
F 161078
 
6.1%
_ 158500
 
6.0%
3 144223
 
5.4%
M 134379
 
5.1%
E 132198
 
5.0%
Other values (33) 671387
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2647243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 354176
13.4%
S 251223
 
9.5%
G 224553
 
8.5%
N 218458
 
8.3%
0 197068
 
7.4%
F 161078
 
6.1%
_ 158500
 
6.0%
3 144223
 
5.4%
M 134379
 
5.1%
E 132198
 
5.0%
Other values (33) 671387
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2647243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 354176
13.4%
S 251223
 
9.5%
G 224553
 
8.5%
N 218458
 
8.3%
0 197068
 
7.4%
F 161078
 
6.1%
_ 158500
 
6.0%
3 144223
 
5.4%
M 134379
 
5.1%
E 132198
 
5.0%
Other values (33) 671387
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2647243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 354176
13.4%
S 251223
 
9.5%
G 224553
 
8.5%
N 218458
 
8.3%
0 197068
 
7.4%
F 161078
 
6.1%
_ 158500
 
6.0%
3 144223
 
5.4%
M 134379
 
5.1%
E 132198
 
5.0%
Other values (33) 671387
25.4%

ald_sector
Text

asset production sector

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2024-04-15T14:05:18.495161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.394776488
Min length4

Characters and Unicode

Total characters852256
Distinct characters19
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowCoal
2nd rowCoal
3rd rowCoal
4th rowCoal
5th rowCoal
ValueCountFrequency (%)
power 104976
66.4%
oil&gas 33484
 
21.2%
coal 16742
 
10.6%
automotive 2428
 
1.5%
steel 348
 
0.2%
2024-04-15T14:05:18.780661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 126574
14.9%
e 108100
12.7%
P 104976
12.3%
w 104976
12.3%
r 104976
12.3%
l 50574
 
5.9%
a 50226
 
5.9%
i 35912
 
4.2%
s 33484
 
3.9%
G 33484
 
3.9%
Other values (9) 98974
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 852256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 126574
14.9%
e 108100
12.7%
P 104976
12.3%
w 104976
12.3%
r 104976
12.3%
l 50574
 
5.9%
a 50226
 
5.9%
i 35912
 
4.2%
s 33484
 
3.9%
G 33484
 
3.9%
Other values (9) 98974
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 852256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 126574
14.9%
e 108100
12.7%
P 104976
12.3%
w 104976
12.3%
r 104976
12.3%
l 50574
 
5.9%
a 50226
 
5.9%
i 35912
 
4.2%
s 33484
 
3.9%
G 33484
 
3.9%
Other values (9) 98974
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 852256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 126574
14.9%
e 108100
12.7%
P 104976
12.3%
w 104976
12.3%
r 104976
12.3%
l 50574
 
5.9%
a 50226
 
5.9%
i 35912
 
4.2%
s 33484
 
3.9%
G 33484
 
3.9%
Other values (9) 98974
11.6%

units
Text

unit of production

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2024-04-15T14:05:18.914394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length20
Median length2
Mean length3.09522845
Min length2

Characters and Unicode

Total characters488978
Distinct characters30
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowMtce
2nd rowMtce
3rd rowMtce
4th rowMtce
5th rowMtce
ValueCountFrequency (%)
gw 104028
65.1%
ej/yr 44793
28.0%
ej 2118
 
1.3%
pj 1914
 
1.2%
vehicles 1760
 
1.1%
thousands 1760
 
1.1%
mtce 783
 
0.5%
bcm 783
 
0.5%
mb/d 783
 
0.5%
k*veh 668
 
0.4%
2024-04-15T14:05:19.190050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 104028
21.3%
W 104028
21.3%
J 48825
10.0%
E 46911
9.6%
/ 45924
9.4%
y 45141
9.2%
r 45141
9.2%
s 5280
 
1.1%
e 4971
 
1.0%
h 4188
 
0.9%
Other values (20) 34541
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 488978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 104028
21.3%
W 104028
21.3%
J 48825
10.0%
E 46911
9.6%
/ 45924
9.4%
y 45141
9.2%
r 45141
9.2%
s 5280
 
1.1%
e 4971
 
1.0%
h 4188
 
0.9%
Other values (20) 34541
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 488978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 104028
21.3%
W 104028
21.3%
J 48825
10.0%
E 46911
9.6%
/ 45924
9.4%
y 45141
9.2%
r 45141
9.2%
s 5280
 
1.1%
e 4971
 
1.0%
h 4188
 
0.9%
Other values (20) 34541
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 488978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 104028
21.3%
W 104028
21.3%
J 48825
10.0%
E 46911
9.6%
/ 45924
9.4%
y 45141
9.2%
r 45141
9.2%
s 5280
 
1.1%
e 4971
 
1.0%
h 4188
 
0.9%
Other values (20) 34541
 
7.1%

ald_business_unit
Text

sub-sector of ald_sector

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2024-04-15T14:05:19.370042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length13
Median length8
Mean length6.707623846
Min length3

Characters and Unicode

Total characters1059657
Distinct characters30
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowCoal
2nd rowCoal
3rd rowCoal
4th rowCoal
5th rowCoal
ValueCountFrequency (%)
coalcap 17496
11.1%
hydrocap 17496
11.1%
nuclearcap 17496
11.1%
oilcap 17496
11.1%
renewablescap 17496
11.1%
gascap 17496
11.1%
coal 16742
10.6%
oil 16742
10.6%
gas 16742
10.6%
electric 607
 
0.4%
Other values (9) 2169
 
1.4%
2024-04-15T14:05:19.676148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 208444
19.7%
C 140428
13.3%
l 105896
10.0%
p 104976
9.9%
e 71805
 
6.8%
s 51734
 
4.9%
o 51734
 
4.9%
r 36206
 
3.4%
i 35452
 
3.3%
O 34412
 
3.2%
Other values (20) 218570
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1059657
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 208444
19.7%
C 140428
13.3%
l 105896
10.0%
p 104976
9.9%
e 71805
 
6.8%
s 51734
 
4.9%
o 51734
 
4.9%
r 36206
 
3.4%
i 35452
 
3.3%
O 34412
 
3.2%
Other values (20) 218570
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1059657
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 208444
19.7%
C 140428
13.3%
l 105896
10.0%
p 104976
9.9%
e 71805
 
6.8%
s 51734
 
4.9%
o 51734
 
4.9%
r 36206
 
3.4%
i 35452
 
3.3%
O 34412
 
3.2%
Other values (20) 218570
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1059657
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 208444
19.7%
C 140428
13.3%
l 105896
10.0%
p 104976
9.9%
e 71805
 
6.8%
s 51734
 
4.9%
o 51734
 
4.9%
r 36206
 
3.4%
i 35452
 
3.3%
O 34412
 
3.2%
Other values (20) 218570
20.6%

year
Real number (ℝ)

year

Distinct79
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2057.431244
Minimum2022
Maximum2100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-04-15T14:05:19.834578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2022
5-th percentile2025
Q12037
median2055
Q32078
95-th percentile2096
Maximum2100
Range78
Interquartile range (IQR)41

Descriptive statistics

Standard deviation23.12096684
Coefficient of variation (CV)0.01123778348
Kurtosis-1.205400544
Mean2057.431244
Median Absolute Deviation (MAD)20
Skewness0.2213973896
Sum325028873
Variance534.5791075
MonotonicityNot monotonic
2024-04-15T14:05:20.010718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 2516
 
1.6%
2033 2516
 
1.6%
2023 2516
 
1.6%
2041 2516
 
1.6%
2040 2516
 
1.6%
2039 2516
 
1.6%
2038 2516
 
1.6%
2037 2516
 
1.6%
2036 2516
 
1.6%
2035 2516
 
1.6%
Other values (69) 132818
84.1%
ValueCountFrequency (%)
2022 2516
1.6%
2023 2516
1.6%
2024 2516
1.6%
2025 2516
1.6%
2026 2516
1.6%
ValueCountFrequency (%)
2100 1719
1.1%
2099 1719
1.1%
2098 1719
1.1%
2097 1719
1.1%
2096 1719
1.1%

direction
Text

direction of the evolution of the scenario trajectory

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2024-04-15T14:05:20.175559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.340863918
Min length9

Characters and Unicode

Total characters1475651
Distinct characters10
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowdeclining
2nd rowdeclining
3rd rowdeclining
4th rowdeclining
5th rowdeclining
ValueCountFrequency (%)
declining 104129
65.9%
increasing 53849
34.1%
2024-04-15T14:05:20.437512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 315956
21.4%
n 315956
21.4%
e 157978
10.7%
c 157978
10.7%
g 157978
10.7%
d 104129
 
7.1%
l 104129
 
7.1%
r 53849
 
3.6%
a 53849
 
3.6%
s 53849
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1475651
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 315956
21.4%
n 315956
21.4%
e 157978
10.7%
c 157978
10.7%
g 157978
10.7%
d 104129
 
7.1%
l 104129
 
7.1%
r 53849
 
3.6%
a 53849
 
3.6%
s 53849
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1475651
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 315956
21.4%
n 315956
21.4%
e 157978
10.7%
c 157978
10.7%
g 157978
10.7%
d 104129
 
7.1%
l 104129
 
7.1%
r 53849
 
3.6%
a 53849
 
3.6%
s 53849
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1475651
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 315956
21.4%
n 315956
21.4%
e 157978
10.7%
c 157978
10.7%
g 157978
10.7%
d 104129
 
7.1%
l 104129
 
7.1%
r 53849
 
3.6%
a 53849
 
3.6%
s 53849
 
3.6%

fair_share_perc
Real number (ℝ)

ZEROS 

evolution of scenario trajectory for a given technology

Distinct133931
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite19
Infinite (%)< 0.1%
Meaninf
Minimum-1
Maximuminf
Zeros5729
Zeros (%)3.6%
Negative85528
Negative (%)54.1%
Memory size1.2 MiB
2024-04-15T14:05:20.592026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.9999939785
Q1-0.7343296528
median-0.01177703098
Q30.1326488552
95-th percentile2.917161133
Maximuminf
Rangeinf
Interquartile range (IQR)0.866978508

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)0.4681358892
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2024-04-15T14:05:20.820839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5729
 
3.6%
-1 4652
 
2.9%
-0.999839363 392
 
0.2%
-0.9999930387 392
 
0.2%
-0.9999927347 392
 
0.2%
-0.9999938501 392
 
0.2%
-0.9999751781 392
 
0.2%
-0.9999969976 392
 
0.2%
-0.9999960085 392
 
0.2%
-0.9999926465 392
 
0.2%
Other values (133921) 144461
91.4%
ValueCountFrequency (%)
-1 4652
2.9%
-1 1
 
< 0.1%
-1 1
 
< 0.1%
-1 1
 
< 0.1%
-1 1
 
< 0.1%
ValueCountFrequency (%)
inf 19
< 0.1%
63.59908176 1
 
< 0.1%
63.13370321 1
 
< 0.1%
62.71706401 1
 
< 0.1%
62.12055619 1
 
< 0.1%

scenario_type
Text

type of scenario

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2024-04-15T14:05:20.951070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length8
Median length5
Mean length6.287521047
Min length5

Characters and Unicode

Total characters993290
Distinct characters11
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowbaseline
2nd rowbaseline
3rd rowbaseline
4th rowbaseline
5th rowbaseline
ValueCountFrequency (%)
shock 90178
57.1%
baseline 67800
42.9%
2024-04-15T14:05:21.212423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 157978
15.9%
e 135600
13.7%
h 90178
9.1%
o 90178
9.1%
c 90178
9.1%
k 90178
9.1%
b 67800
6.8%
a 67800
6.8%
l 67800
6.8%
i 67800
6.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 993290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 157978
15.9%
e 135600
13.7%
h 90178
9.1%
o 90178
9.1%
c 90178
9.1%
k 90178
9.1%
b 67800
6.8%
a 67800
6.8%
l 67800
6.8%
i 67800
6.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 993290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 157978
15.9%
e 135600
13.7%
h 90178
9.1%
o 90178
9.1%
c 90178
9.1%
k 90178
9.1%
b 67800
6.8%
a 67800
6.8%
l 67800
6.8%
i 67800
6.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 993290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 157978
15.9%
e 135600
13.7%
h 90178
9.1%
o 90178
9.1%
c 90178
9.1%
k 90178
9.1%
b 67800
6.8%
a 67800
6.8%
l 67800
6.8%
i 67800
6.8%