Overview

Dataset statistics

Number of variables8
Number of observations19798
Missing cells0
Missing cells (%)0.0%
Total size in memory1.2 MiB
Average record size in memory64.0 B

Variable types

Numeric2
Text6

Variable descriptions

ald_business_unitsub-sector of ald_sector
ald_sectorasset production sector
indicatorprice indicator
pricecost of production of 1 unit
scenarioname of the scenario
scenario_geographyregional geography of a scenario
unitunit of production
yearyear

Alerts

scenario_geography has constant value ""Constant

Reproduction

Analysis started2024-04-15 12:05:09.863428
Analysis finished2024-04-15 12:05:09.943624
Duration0.08 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

year
Real number (ℝ)

year

Distinct79
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2056.558895
Minimum2022
Maximum2100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size154.8 KiB
2024-04-15T14:05:10.048439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2022
5-th percentile2024
Q12036
median2053
Q32077
95-th percentile2096
Maximum2100
Range78
Interquartile range (IQR)41

Descriptive statistics

Standard deviation23.12936361
Coefficient of variation (CV)0.01124663323
Kurtosis-1.183663301
Mean2056.558895
Median Absolute Deviation (MAD)19
Skewness0.2769240436
Sum40715753
Variance534.9674612
MonotonicityNot monotonic
2024-04-15T14:05:10.219103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 332
 
1.7%
2033 332
 
1.7%
2023 332
 
1.7%
2041 332
 
1.7%
2040 332
 
1.7%
2039 332
 
1.7%
2038 332
 
1.7%
2037 332
 
1.7%
2036 332
 
1.7%
2035 332
 
1.7%
Other values (69) 16478
83.2%
ValueCountFrequency (%)
2022 332
1.7%
2023 332
1.7%
2024 332
1.7%
2025 332
1.7%
2026 332
1.7%
ValueCountFrequency (%)
2100 207
1.0%
2099 207
1.0%
2098 207
1.0%
2097 207
1.0%
2096 207
1.0%

scenario
Text

name of the scenario

Distinct42
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size154.8 KiB
2024-04-15T14:05:10.453218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length26
Median length21
Mean length16.79568643
Min length8

Characters and Unicode

Total characters332521
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 (%)
ngfs2023remind_ld 711
 
3.6%
ngfs2023remind_cp 711
 
3.6%
ngfs2023remind_ndc 711
 
3.6%
ngfs2023gcam_dt 711
 
3.6%
oxford2021_fast 711
 
3.6%
ngfs2023remind_nz2050 711
 
3.6%
ngfs2023gcam_b2ds 711
 
3.6%
ngfs2023message_b2ds 711
 
3.6%
ngfs2023remind_b2ds 711
 
3.6%
ngfs2023gcam_cp 711
 
3.6%
Other values (32) 12688
64.1%
2024-04-15T14:05:10.889070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 43688
13.1%
S 29991
 
9.0%
G 25473
 
7.7%
N 25066
 
7.5%
0 24760
 
7.4%
_ 20320
 
6.1%
E 18390
 
5.5%
F 17405
 
5.2%
3 16744
 
5.0%
M 14931
 
4.5%
Other values (33) 95753
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 332521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 43688
13.1%
S 29991
 
9.0%
G 25473
 
7.7%
N 25066
 
7.5%
0 24760
 
7.4%
_ 20320
 
6.1%
E 18390
 
5.5%
F 17405
 
5.2%
3 16744
 
5.0%
M 14931
 
4.5%
Other values (33) 95753
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 332521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 43688
13.1%
S 29991
 
9.0%
G 25473
 
7.7%
N 25066
 
7.5%
0 24760
 
7.4%
_ 20320
 
6.1%
E 18390
 
5.5%
F 17405
 
5.2%
3 16744
 
5.0%
M 14931
 
4.5%
Other values (33) 95753
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 332521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 43688
13.1%
S 29991
 
9.0%
G 25473
 
7.7%
N 25066
 
7.5%
0 24760
 
7.4%
_ 20320
 
6.1%
E 18390
 
5.5%
F 17405
 
5.2%
3 16744
 
5.0%
M 14931
 
4.5%
Other values (33) 95753
28.8%

scenario_geography
Text

CONSTANT 

regional geography of a scenario

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size154.8 KiB
2024-04-15T14:05:11.030391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters118788
Distinct characters5
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 rowGlobal
2nd rowGlobal
3rd rowGlobal
4th rowGlobal
5th rowGlobal
ValueCountFrequency (%)
global 19798
100.0%
2024-04-15T14:05:11.263296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 39596
33.3%
G 19798
16.7%
o 19798
16.7%
b 19798
16.7%
a 19798
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 118788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 39596
33.3%
G 19798
16.7%
o 19798
16.7%
b 19798
16.7%
a 19798
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 118788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 39596
33.3%
G 19798
16.7%
o 19798
16.7%
b 19798
16.7%
a 19798
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 118788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 39596
33.3%
G 19798
16.7%
o 19798
16.7%
b 19798
16.7%
a 19798
16.7%

ald_sector
Text

asset production sector

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size154.8 KiB
2024-04-15T14:05:11.393928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.503788261
Min length4

Characters and Unicode

Total characters108964
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 rowPower
2nd rowPower
3rd rowPower
4th rowPower
5th rowPower
ValueCountFrequency (%)
power 12468
63.0%
oil&gas 4156
 
21.0%
coal 2078
 
10.5%
automotive 748
 
3.8%
steel 348
 
1.8%
2024-04-15T14:05:11.661760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 16042
14.7%
e 13912
12.8%
P 12468
11.4%
w 12468
11.4%
r 12468
11.4%
l 6582
6.0%
a 6234
 
5.7%
i 4904
 
4.5%
s 4156
 
3.8%
G 4156
 
3.8%
Other values (9) 15574
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108964
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 16042
14.7%
e 13912
12.8%
P 12468
11.4%
w 12468
11.4%
r 12468
11.4%
l 6582
6.0%
a 6234
 
5.7%
i 4904
 
4.5%
s 4156
 
3.8%
G 4156
 
3.8%
Other values (9) 15574
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108964
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 16042
14.7%
e 13912
12.8%
P 12468
11.4%
w 12468
11.4%
r 12468
11.4%
l 6582
6.0%
a 6234
 
5.7%
i 4904
 
4.5%
s 4156
 
3.8%
G 4156
 
3.8%
Other values (9) 15574
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108964
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 16042
14.7%
e 13912
12.8%
P 12468
11.4%
w 12468
11.4%
r 12468
11.4%
l 6582
6.0%
a 6234
 
5.7%
i 4904
 
4.5%
s 4156
 
3.8%
G 4156
 
3.8%
Other values (9) 15574
14.3%

ald_business_unit
Text

sub-sector of ald_sector

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size154.8 KiB
2024-04-15T14:05:11.853567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length13
Median length8
Mean length6.647994747
Min length3

Characters and Unicode

Total characters131617
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 rowCoalCap
2nd rowCoalCap
3rd rowCoalCap
4th rowCoalCap
5th rowCoalCap
ValueCountFrequency (%)
coalcap 2078
10.5%
nuclearcap 2078
10.5%
oilcap 2078
10.5%
renewablescap 2078
10.5%
coal 2078
10.5%
gas 2078
10.5%
oil 2078
10.5%
gascap 2078
10.5%
hydrocap 2078
10.5%
fuelcell 187
 
0.9%
Other values (9) 909
4.6%
2024-04-15T14:05:12.162684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 24936
18.9%
C 16998
12.9%
l 13216
10.0%
p 12468
9.5%
e 8873
 
6.7%
o 6234
 
4.7%
s 6234
 
4.7%
i 4530
 
3.4%
r 4530
 
3.4%
O 4330
 
3.3%
Other values (20) 29268
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 24936
18.9%
C 16998
12.9%
l 13216
10.0%
p 12468
9.5%
e 8873
 
6.7%
o 6234
 
4.7%
s 6234
 
4.7%
i 4530
 
3.4%
r 4530
 
3.4%
O 4330
 
3.3%
Other values (20) 29268
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 24936
18.9%
C 16998
12.9%
l 13216
10.0%
p 12468
9.5%
e 8873
 
6.7%
o 6234
 
4.7%
s 6234
 
4.7%
i 4530
 
3.4%
r 4530
 
3.4%
O 4330
 
3.3%
Other values (20) 29268
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 24936
18.9%
C 16998
12.9%
l 13216
10.0%
p 12468
9.5%
e 8873
 
6.7%
o 6234
 
4.7%
s 6234
 
4.7%
i 4530
 
3.4%
r 4530
 
3.4%
O 4330
 
3.3%
Other values (20) 29268
22.2%

indicator
Text

price indicator

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size154.8 KiB
2024-04-15T14:05:12.287126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters98990
Distinct characters6
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 rowprice
2nd rowprice
3rd rowprice
4th rowprice
5th rowprice
ValueCountFrequency (%)
price 19798
100.0%
2024-04-15T14:05:12.538975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 19798
20.0%
i 19798
20.0%
c 19798
20.0%
e 19798
20.0%
p 14821
15.0%
P 4977
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 19798
20.0%
i 19798
20.0%
c 19798
20.0%
e 19798
20.0%
p 14821
15.0%
P 4977
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 19798
20.0%
i 19798
20.0%
c 19798
20.0%
e 19798
20.0%
p 14821
15.0%
P 4977
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 19798
20.0%
i 19798
20.0%
c 19798
20.0%
e 19798
20.0%
p 14821
15.0%
P 4977
 
5.0%

unit
Text

unit of production

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size154.8 KiB
2024-04-15T14:05:12.661812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.041468835
Min length2

Characters and Unicode

Total characters99811
Distinct characters16
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 row$/MWh
2nd row$/MWh
3rd row$/MWh
4th row$/MWh
5th row$/MWh
ValueCountFrequency (%)
mwh 12468
63.0%
gj 4156
 
21.0%
tonnes 1659
 
8.4%
dummy 748
 
3.8%
usd/tonne 419
 
2.1%
ton 348
 
1.8%
2024-04-15T14:05:12.926028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 18212
18.2%
$ 17793
17.8%
M 12468
12.5%
W 12468
12.5%
h 12468
12.5%
n 4504
 
4.5%
G 4156
 
4.2%
J 4156
 
4.2%
t 2426
 
2.4%
o 2426
 
2.4%
Other values (6) 8734
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99811
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 18212
18.2%
$ 17793
17.8%
M 12468
12.5%
W 12468
12.5%
h 12468
12.5%
n 4504
 
4.5%
G 4156
 
4.2%
J 4156
 
4.2%
t 2426
 
2.4%
o 2426
 
2.4%
Other values (6) 8734
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99811
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 18212
18.2%
$ 17793
17.8%
M 12468
12.5%
W 12468
12.5%
h 12468
12.5%
n 4504
 
4.5%
G 4156
 
4.2%
J 4156
 
4.2%
t 2426
 
2.4%
o 2426
 
2.4%
Other values (6) 8734
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99811
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 18212
18.2%
$ 17793
17.8%
M 12468
12.5%
W 12468
12.5%
h 12468
12.5%
n 4504
 
4.5%
G 4156
 
4.2%
J 4156
 
4.2%
t 2426
 
2.4%
o 2426
 
2.4%
Other values (6) 8734
8.8%

price
Real number (ℝ)

cost of production of 1 unit

Distinct6888
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.02644996
Minimum-85.60901441
Maximum1110.422928
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)< 0.1%
Memory size154.8 KiB
2024-04-15T14:05:13.074843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-85.60901441
5-th percentile3.259404319
Q141.63985345
median58.86579362
Q391.90251874
95-th percentile122.9972922
Maximum1110.422928
Range1196.031942
Interquartile range (IQR)50.2626653

Descriptive statistics

Standard deviation95.37650987
Coefficient of variation (CV)1.342830874
Kurtosis53.74279125
Mean71.02644996
Median Absolute Deviation (MAD)32.57841892
Skewness6.574293046
Sum1406181.656
Variance9096.678635
MonotonicityNot monotonic
2024-04-15T14:05:13.220579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.88135608 1610
 
8.1%
101.6949155 1610
 
8.1%
1 748
 
3.8%
101.6949155 207
 
1.0%
52.88135608 207
 
1.0%
62.85369299 46
 
0.2%
62.13181824 46
 
0.2%
59.19644898 46
 
0.2%
59.2811808 46
 
0.2%
59.38379444 46
 
0.2%
Other values (6878) 15186
76.7%
ValueCountFrequency (%)
-85.60901441 1
< 0.1%
-54.84588155 1
< 0.1%
-41.48753913 1
< 0.1%
-30.40574015 1
< 0.1%
-24.0827487 1
< 0.1%
ValueCountFrequency (%)
1110.422928 1
< 0.1%
1110.262878 1
< 0.1%
1110.240018 1
< 0.1%
1109.942918 1
< 0.1%
1109.783007 1
< 0.1%