Overview

Dataset statistics

Number of variables5
Number of observations119808
Missing cells0
Missing cells (%)0.0%
Total size in memory4.6 MiB
Average record size in memory40.0 B

Variable types

Text3
Numeric2

Variable descriptions

ald_business_unitsub-sector of ald_sector
capacity_factorratio by which a capacity is converted into production.
scenarioname of the scenario
scenario_geographyregional geography of a scenario
yearyear

Alerts

capacity_factor has 8888 (7.4%) zerosZeros

Reproduction

Analysis started2024-04-15 12:05:21.593548
Analysis finished2024-04-15 12:05:21.732322
Duration0.14 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

scenario
Text

name of the scenario

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size936.1 KiB
2024-04-15T14:05:21.874750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length22
Median length20
Mean length16.58698918
Min length8

Characters and Unicode

Total characters1987254
Distinct characters37
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_SDS
2nd rowWEO2021_SDS
3rd rowWEO2021_SDS
4th rowWEO2021_SDS
5th rowWEO2021_SDS
ValueCountFrequency (%)
ipr2023_baseline 6090
 
5.1%
ipr2023_fps 6090
 
5.1%
ngfs2023gcam_nz2050 5688
 
4.7%
ngfs2023gcam_cp 5688
 
4.7%
ngfs2023gcam_fw 5688
 
4.7%
ngfs2023gcam_ndc 5688
 
4.7%
ngfs2023gcam_dt 5688
 
4.7%
ngfs2023gcam_ld 5688
 
4.7%
ngfs2023gcam_b2ds 5688
 
4.7%
ngfs2023remind_dt 4740
 
4.0%
Other values (24) 63072
52.6%
2024-04-15T14:05:22.346193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 268956
13.5%
S 175362
 
8.8%
N 159642
 
8.0%
G 159264
 
8.0%
0 152076
 
7.7%
_ 122376
 
6.2%
F 116058
 
5.8%
3 108924
 
5.5%
M 96222
 
4.8%
E 94866
 
4.8%
Other values (27) 533508
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1987254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 268956
13.5%
S 175362
 
8.8%
N 159642
 
8.0%
G 159264
 
8.0%
0 152076
 
7.7%
_ 122376
 
6.2%
F 116058
 
5.8%
3 108924
 
5.5%
M 96222
 
4.8%
E 94866
 
4.8%
Other values (27) 533508
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1987254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 268956
13.5%
S 175362
 
8.8%
N 159642
 
8.0%
G 159264
 
8.0%
0 152076
 
7.7%
_ 122376
 
6.2%
F 116058
 
5.8%
3 108924
 
5.5%
M 96222
 
4.8%
E 94866
 
4.8%
Other values (27) 533508
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1987254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 268956
13.5%
S 175362
 
8.8%
N 159642
 
8.0%
G 159264
 
8.0%
0 152076
 
7.7%
_ 122376
 
6.2%
F 116058
 
5.8%
3 108924
 
5.5%
M 96222
 
4.8%
E 94866
 
4.8%
Other values (27) 533508
26.8%

scenario_geography
Text

regional geography of a scenario

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size936.1 KiB
2024-04-15T14:05:22.648447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length22
Median length18
Mean length9.341897035
Min length2

Characters and Unicode

Total characters1119234
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 12820
10.7%
china 10410
8.7%
reformingeconomies 9954
8.3%
oecdandeu 9954
8.3%
middleeastandafrica 9954
8.3%
latinamerica 9954
8.3%
asia 9954
8.3%
japan 7672
 
6.4%
india 7672
 
6.4%
unitedstates 7672
 
6.4%
Other values (31) 23792
19.9%
2024-04-15T14:05:23.066722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 127898
 
11.4%
i 112264
 
10.0%
n 95552
 
8.5%
e 77802
 
7.0%
d 71292
 
6.4%
A 61376
 
5.5%
o 57566
 
5.1%
t 55072
 
4.9%
s 53912
 
4.8%
c 44500
 
4.0%
Other values (29) 362000
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1119234
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 127898
 
11.4%
i 112264
 
10.0%
n 95552
 
8.5%
e 77802
 
7.0%
d 71292
 
6.4%
A 61376
 
5.5%
o 57566
 
5.1%
t 55072
 
4.9%
s 53912
 
4.8%
c 44500
 
4.0%
Other values (29) 362000
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1119234
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 127898
 
11.4%
i 112264
 
10.0%
n 95552
 
8.5%
e 77802
 
7.0%
d 71292
 
6.4%
A 61376
 
5.5%
o 57566
 
5.1%
t 55072
 
4.9%
s 53912
 
4.8%
c 44500
 
4.0%
Other values (29) 362000
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1119234
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 127898
 
11.4%
i 112264
 
10.0%
n 95552
 
8.5%
e 77802
 
7.0%
d 71292
 
6.4%
A 61376
 
5.5%
o 57566
 
5.1%
t 55072
 
4.9%
s 53912
 
4.8%
c 44500
 
4.0%
Other values (29) 362000
32.3%

ald_business_unit
Text

sub-sector of ald_sector

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size936.1 KiB
2024-04-15T14:05:23.251928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length13
Median length9.5
Mean length8.365100828
Min length6

Characters and Unicode

Total characters1002206
Distinct characters33
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 19096
15.9%
hydrocap 19096
15.9%
gascap 19096
15.9%
nuclearcap 19096
15.9%
oilcap 19096
15.9%
renewablescap 19096
15.9%
biomasscap 1218
 
1.0%
solarcap 1218
 
1.0%
onwindcap 1218
 
1.0%
offwindcap 1218
 
1.0%
Other values (6) 360
 
0.3%
2024-04-15T14:05:23.567671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 198268
19.8%
C 138544
13.8%
p 119448
11.9%
l 77602
 
7.7%
e 76384
 
7.6%
s 40628
 
4.1%
o 40628
 
4.1%
r 39410
 
3.9%
n 22750
 
2.3%
i 22750
 
2.3%
Other values (23) 225794
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1002206
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 198268
19.8%
C 138544
13.8%
p 119448
11.9%
l 77602
 
7.7%
e 76384
 
7.6%
s 40628
 
4.1%
o 40628
 
4.1%
r 39410
 
3.9%
n 22750
 
2.3%
i 22750
 
2.3%
Other values (23) 225794
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1002206
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 198268
19.8%
C 138544
13.8%
p 119448
11.9%
l 77602
 
7.7%
e 76384
 
7.6%
s 40628
 
4.1%
o 40628
 
4.1%
r 39410
 
3.9%
n 22750
 
2.3%
i 22750
 
2.3%
Other values (23) 225794
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1002206
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 198268
19.8%
C 138544
13.8%
p 119448
11.9%
l 77602
 
7.7%
e 76384
 
7.6%
s 40628
 
4.1%
o 40628
 
4.1%
r 39410
 
3.9%
n 22750
 
2.3%
i 22750
 
2.3%
Other values (23) 225794
22.5%

year
Real number (ℝ)

year

Distinct80
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2055.87505
Minimum2021
Maximum2100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size936.1 KiB
2024-04-15T14:05:23.734682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2021
5-th percentile2024
Q12035
median2052
Q32076
95-th percentile2096
Maximum2100
Range79
Interquartile range (IQR)41

Descriptive statistics

Standard deviation23.35979019
Coefficient of variation (CV)0.01136245619
Kurtosis-1.194863645
Mean2055.87505
Median Absolute Deviation (MAD)19
Skewness0.3036264903
Sum246310278
Variance545.6797975
MonotonicityNot monotonic
2024-04-15T14:05:23.900840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 2184
 
1.8%
2032 2184
 
1.8%
2023 2184
 
1.8%
2040 2184
 
1.8%
2039 2184
 
1.8%
2038 2184
 
1.8%
2037 2184
 
1.8%
2036 2184
 
1.8%
2035 2184
 
1.8%
2034 2184
 
1.8%
Other values (70) 97968
81.8%
ValueCountFrequency (%)
2021 12
 
< 0.1%
2022 2184
1.8%
2023 2184
1.8%
2024 2184
1.8%
2025 2184
1.8%
ValueCountFrequency (%)
2100 1230
1.0%
2099 1230
1.0%
2098 1230
1.0%
2097 1230
1.0%
2096 1230
1.0%

capacity_factor
Real number (ℝ)

ZEROS 

ratio by which a capacity is converted into production.

Distinct81591
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4399129151
Minimum0
Maximum1
Zeros8888
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size936.1 KiB
2024-04-15T14:05:24.065949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2503956826
median0.3849102617
Q30.6665467365
95-th percentile0.8483013396
Maximum1
Range1
Interquartile range (IQR)0.416151054

Descriptive statistics

Standard deviation0.2657513697
Coefficient of variation (CV)0.6040999493
Kurtosis-1.008876789
Mean0.4399129151
Median Absolute Deviation (MAD)0.1760115187
Skewness0.1887513051
Sum52705.08653
Variance0.07062379051
MonotonicityNot monotonic
2024-04-15T14:05:24.240602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8888
 
7.4%
0.2535047025 2744
 
2.3%
0.2535047025 784
 
0.7%
0.3407007705 553
 
0.5%
0.2508640285 392
 
0.3%
1 299
 
0.2%
0.636920856 180
 
0.2%
0.4477501457 147
 
0.1%
0.461395189 120
 
0.1%
0.4277891854 107
 
0.1%
Other values (81581) 105594
88.1%
ValueCountFrequency (%)
0 8888
7.4%
1.431037907 × 10-71
 
< 0.1%
2.854079446 × 10-71
 
< 0.1%
4.269191454 × 10-71
 
< 0.1%
5.676440024 × 10-71
 
< 0.1%
ValueCountFrequency (%)
1 299
0.2%
0.9981747661 2
 
< 0.1%
0.9961213632 1
 
< 0.1%
0.9956474099 1
 
< 0.1%
0.9951028753 1
 
< 0.1%