Overview

Brought to you by YData

Dataset statistics

Number of variables39
Number of observations64
Missing cells1270
Missing cells (%)50.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.6 KiB
Average record size in memory314.0 B

Variable types

Numeric25
Categorical14

Alerts

Average precipitation in depth (mm per year) has constant value "2348.0" Constant
Droughts, floods, extreme temperatures (% of population, average 1990-2009) has constant value "0.806384865170547" Constant
Ease of doing business rank (1=most business-friendly regulations) has constant value "95.0" Constant
Access to electricity (% of population) is highly overall correlated with Agricultural irrigated land (% of total agricultural land) and 28 other fieldsHigh correlation
Agricultural irrigated land (% of total agricultural land) is highly overall correlated with Access to electricity (% of population) and 21 other fieldsHigh correlation
Agricultural land (% of land area) is highly overall correlated with Access to electricity (% of population) and 33 other fieldsHigh correlation
Agricultural land (sq. km) is highly overall correlated with Access to electricity (% of population) and 33 other fieldsHigh correlation
Agriculture, forestry, and fishing, value added (% of GDP) is highly overall correlated with Access to electricity (% of population) and 33 other fieldsHigh correlation
Annual freshwater withdrawals, total (% of internal resources) is highly overall correlated with Access to electricity (% of population) and 24 other fieldsHigh correlation
Annual freshwater withdrawals, total (billion cubic meters) is highly overall correlated with Access to electricity (% of population) and 24 other fieldsHigh correlation
Arable land (% of land area) is highly overall correlated with Access to electricity (% of population) and 28 other fieldsHigh correlation
Cereal yield (kg per hectare) is highly overall correlated with Access to electricity (% of population) and 32 other fieldsHigh correlation
Foreign direct investment, net inflows (% of GDP) is highly overall correlated with Agricultural land (% of land area) and 21 other fieldsHigh correlation
Forest area (% of land area) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Forest area (sq. km) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Land area where elevation is below 5 meters (% of total land area) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Marine protected areas (% of territorial waters) is highly overall correlated with Access to electricity (% of population) and 21 other fieldsHigh correlation
Mortality rate, under-5 (per 1,000 live births) is highly overall correlated with Access to electricity (% of population) and 33 other fieldsHigh correlation
Population growth (annual %) is highly overall correlated with Access to electricity (% of population) and 29 other fieldsHigh correlation
Population in urban agglomerations of more than 1 million (% of total population) is highly overall correlated with Agricultural irrigated land (% of total agricultural land) and 25 other fieldsHigh correlation
Population living in areas where elevation is below 5 meters (% of total population) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Population, total is highly overall correlated with Access to electricity (% of population) and 32 other fieldsHigh correlation
Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Prevalence of underweight, weight for age (% of children under 5) is highly overall correlated with Access to electricity (% of population) and 31 other fieldsHigh correlation
Primary completion rate, total (% of relevant age group) is highly overall correlated with Agricultural irrigated land (% of total agricultural land) and 12 other fieldsHigh correlation
Renewable energy consumption (% of total final energy consumption) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Rural land area where elevation is below 5 meters (% of total land area) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Rural land area where elevation is below 5 meters (sq. km) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Rural population living in areas where elevation is below 5 meters (% of total population) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
School enrollment, primary and secondary (gross), gender parity index (GPI) is highly overall correlated with Agricultural irrigated land (% of total agricultural land) and 10 other fieldsHigh correlation
Terrestrial and marine protected areas (% of total territorial area) is highly overall correlated with Access to electricity (% of population) and 21 other fieldsHigh correlation
Terrestrial protected areas (% of total land area) is highly overall correlated with Access to electricity (% of population) and 21 other fieldsHigh correlation
Urban land area where elevation is below 5 meters (% of total land area) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Urban land area where elevation is below 5 meters (sq. km) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Urban population is highly overall correlated with Access to electricity (% of population) and 29 other fieldsHigh correlation
Urban population (% of total population) is highly overall correlated with Agricultural irrigated land (% of total agricultural land) and 29 other fieldsHigh correlation
Urban population growth (annual %) is highly overall correlated with Agricultural irrigated land (% of total agricultural land) and 24 other fieldsHigh correlation
Urban population living in areas where elevation is below 5 meters (% of total population) is highly overall correlated with Access to electricity (% of population) and 27 other fieldsHigh correlation
Year is highly overall correlated with Access to electricity (% of population) and 32 other fieldsHigh correlation
Agricultural land (sq. km) has 3 (4.7%) missing values Missing
Agricultural land (% of land area) has 2 (3.1%) missing values Missing
Arable land (% of land area) has 2 (3.1%) missing values Missing
Rural land area where elevation is below 5 meters (sq. km) has 61 (95.3%) missing values Missing
Rural land area where elevation is below 5 meters (% of total land area) has 61 (95.3%) missing values Missing
Urban land area where elevation is below 5 meters (sq. km) has 61 (95.3%) missing values Missing
Urban land area where elevation is below 5 meters (% of total land area) has 61 (95.3%) missing values Missing
Land area where elevation is below 5 meters (% of total land area) has 61 (95.3%) missing values Missing
Forest area (sq. km) has 31 (48.4%) missing values Missing
Forest area (% of land area) has 31 (48.4%) missing values Missing
Agricultural irrigated land (% of total agricultural land) has 58 (90.6%) missing values Missing
Average precipitation in depth (mm per year) has 3 (4.7%) missing values Missing
Cereal yield (kg per hectare) has 2 (3.1%) missing values Missing
Foreign direct investment, net inflows (% of GDP) has 10 (15.6%) missing values Missing
Access to electricity (% of population) has 34 (53.1%) missing values Missing
Renewable energy consumption (% of total final energy consumption) has 32 (50.0%) missing values Missing
Droughts, floods, extreme temperatures (% of population, average 1990-2009) has 63 (98.4%) missing values Missing
Rural population living in areas where elevation is below 5 meters (% of total population) has 61 (95.3%) missing values Missing
Urban population living in areas where elevation is below 5 meters (% of total population) has 61 (95.3%) missing values Missing
Population living in areas where elevation is below 5 meters (% of total population) has 61 (95.3%) missing values Missing
Annual freshwater withdrawals, total (billion cubic meters) has 48 (75.0%) missing values Missing
Annual freshwater withdrawals, total (% of internal resources) has 48 (75.0%) missing values Missing
Terrestrial protected areas (% of total land area) has 57 (89.1%) missing values Missing
Marine protected areas (% of territorial waters) has 57 (89.1%) missing values Missing
Terrestrial and marine protected areas (% of total territorial area) has 57 (89.1%) missing values Missing
Ease of doing business rank (1=most business-friendly regulations) has 63 (98.4%) missing values Missing
School enrollment, primary and secondary (gross), gender parity index (GPI) has 35 (54.7%) missing values Missing
Primary completion rate, total (% of relevant age group) has 36 (56.2%) missing values Missing
Mortality rate, under-5 (per 1,000 live births) has 1 (1.6%) missing values Missing
Prevalence of underweight, weight for age (% of children under 5) has 51 (79.7%) missing values Missing
Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population) has 56 (87.5%) missing values Missing
Population growth (annual %) has 1 (1.6%) missing values Missing
Urban population growth (annual %) has 1 (1.6%) missing values Missing
Year is uniformly distributed Uniform
Rural land area where elevation is below 5 meters (sq. km) is uniformly distributed Uniform
Rural land area where elevation is below 5 meters (% of total land area) is uniformly distributed Uniform
Urban land area where elevation is below 5 meters (sq. km) is uniformly distributed Uniform
Urban land area where elevation is below 5 meters (% of total land area) is uniformly distributed Uniform
Land area where elevation is below 5 meters (% of total land area) is uniformly distributed Uniform
Rural population living in areas where elevation is below 5 meters (% of total population) is uniformly distributed Uniform
Urban population living in areas where elevation is below 5 meters (% of total population) is uniformly distributed Uniform
Population living in areas where elevation is below 5 meters (% of total population) is uniformly distributed Uniform
Year has unique values Unique
Population in urban agglomerations of more than 1 million (% of total population) has unique values Unique
Agriculture, forestry, and fishing, value added (% of GDP) has unique values Unique
Population, total has unique values Unique
Urban population has unique values Unique

Reproduction

Analysis started2025-02-05 13:52:43.283122
Analysis finished2025-02-05 13:53:41.669495
Duration58.39 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Year
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991.5
Minimum1960
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:41.809036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1960
5-th percentile1963.15
Q11975.75
median1991.5
Q32007.25
95-th percentile2019.85
Maximum2023
Range63
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation18.618987
Coefficient of variation (CV)0.0093492276
Kurtosis-1.2
Mean1991.5
Median Absolute Deviation (MAD)16
Skewness0
Sum127456
Variance346.66667
MonotonicityNot monotonic
2025-02-05T21:53:41.926612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2021 1
 
1.6%
2020 1
 
1.6%
2019 1
 
1.6%
2018 1
 
1.6%
2017 1
 
1.6%
2016 1
 
1.6%
2015 1
 
1.6%
2014 1
 
1.6%
2013 1
 
1.6%
2012 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
1960 1
1.6%
1961 1
1.6%
1962 1
1.6%
1963 1
1.6%
1964 1
1.6%
1965 1
1.6%
1966 1
1.6%
1967 1
1.6%
1968 1
1.6%
1969 1
1.6%
ValueCountFrequency (%)
2023 1
1.6%
2022 1
1.6%
2021 1
1.6%
2020 1
1.6%
2019 1
1.6%
2018 1
1.6%
2017 1
1.6%
2016 1
1.6%
2015 1
1.6%
2014 1
1.6%

Agricultural land (sq. km)
Real number (ℝ)

High correlation  Missing 

Distinct59
Distinct (%)96.7%
Missing3
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean106539.02
Minimum77130
Maximum126830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:42.046613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum77130
5-th percentile81300
Q196740
median110650
Q3115800
95-th percentile126130
Maximum126830
Range49700
Interquartile range (IQR)19060

Descriptive statistics

Standard deviation15315.487
Coefficient of variation (CV)0.14375473
Kurtosis-0.82621595
Mean106539.02
Median Absolute Deviation (MAD)9450
Skewness-0.60475537
Sum6498880
Variance2.3456415 × 108
MonotonicityNot monotonic
2025-02-05T21:53:42.163612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121000 2
 
3.1%
110150 2
 
3.1%
126360 1
 
1.6%
126130 1
 
1.6%
125900 1
 
1.6%
125270 1
 
1.6%
125560 1
 
1.6%
124980 1
 
1.6%
124690 1
 
1.6%
124300 1
 
1.6%
Other values (49) 49
76.6%
(Missing) 3
 
4.7%
ValueCountFrequency (%)
77130 1
1.6%
77920 1
1.6%
78720 1
1.6%
81300 1
1.6%
81320 1
1.6%
81520 1
1.6%
82530 1
1.6%
82790 1
1.6%
82810 1
1.6%
83050 1
1.6%
ValueCountFrequency (%)
126830 1
1.6%
126590 1
1.6%
126360 1
1.6%
126130 1
1.6%
125900 1
1.6%
125560 1
1.6%
125270 1
1.6%
124980 1
1.6%
124690 1
1.6%
124300 1
1.6%

Agricultural land (% of land area)
Real number (ℝ)

High correlation  Missing 

Distinct59
Distinct (%)95.2%
Missing2
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean35.839006
Minimum25.859988
Maximum42.536137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:42.266612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25.859988
5-th percentile27.258432
Q132.793373
median37.168394
Q339.365127
95-th percentile42.374652
Maximum42.536137
Range16.676149
Interquartile range (IQR)6.5717544

Descriptive statistics

Standard deviation5.1698537
Coefficient of variation (CV)0.14425215
Kurtosis-0.81159432
Mean35.839006
Median Absolute Deviation (MAD)3.2447932
Skewness-0.61196682
Sum2222.0183
Variance26.727387
MonotonicityNot monotonic
2025-02-05T21:53:42.385623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.5361371 2
 
3.1%
40.58087668 2
 
3.1%
36.94201295 2
 
3.1%
42.3013717 1
 
1.6%
42.2242345 1
 
1.6%
42.01294564 1
 
1.6%
42.11020559 1
 
1.6%
41.91568568 1
 
1.6%
41.81842573 1
 
1.6%
41.68762786 1
 
1.6%
Other values (49) 49
76.6%
(Missing) 2
 
3.1%
ValueCountFrequency (%)
25.85998793 1
1.6%
26.12485751 1
1.6%
26.39307986 1
1.6%
27.25809696 1
1.6%
27.26480252 1
1.6%
27.33185811 1
1.6%
27.67048884 1
1.6%
27.7576611 1
1.6%
27.76436666 1
1.6%
27.84483337 1
1.6%
ValueCountFrequency (%)
42.5361371 2
3.1%
42.45564611 1
1.6%
42.3785089 1
1.6%
42.3013717 1
1.6%
42.2242345 1
1.6%
42.11020559 1
1.6%
42.01294564 1
1.6%
41.91568568 1
1.6%
41.81842573 1
1.6%
41.68762786 1
1.6%

Arable land (% of land area)
Real number (ℝ)

High correlation  Missing 

Distinct51
Distinct (%)82.3%
Missing2
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean17.426432
Minimum15.570308
Maximum18.747694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:42.499281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.570308
5-th percentile15.763093
Q116.580307
median17.54536
Q318.278163
95-th percentile18.747694
Maximum18.747694
Range3.1773865
Interquartile range (IQR)1.6978569

Descriptive statistics

Standard deviation1.0055858
Coefficient of variation (CV)0.05770463
Kurtosis-1.1970298
Mean17.426432
Median Absolute Deviation (MAD)0.83174028
Skewness-0.25225871
Sum1080.4388
Variance1.0112028
MonotonicityNot monotonic
2025-02-05T21:53:42.613285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.74769427 10
 
15.6%
18.27816346 2
 
3.1%
15.75806343 2
 
3.1%
18.44585304 1
 
1.6%
17.74155683 1
 
1.6%
17.28879498 1
 
1.6%
16.78572626 1
 
1.6%
17.17141228 1
 
1.6%
16.66834356 1
 
1.6%
16.55096086 1
 
1.6%
Other values (41) 41
64.1%
(Missing) 2
 
3.1%
ValueCountFrequency (%)
15.57030779 1
1.6%
15.65748005 1
1.6%
15.75806343 2
3.1%
15.85864682 1
1.6%
15.92730443 1
1.6%
15.9592302 1
1.6%
16.09334138 1
1.6%
16.09711929 1
1.6%
16.16039697 1
1.6%
16.22745256 1
1.6%
ValueCountFrequency (%)
18.74769427 10
15.6%
18.71415635 1
 
1.6%
18.44585304 1
 
1.6%
18.40225375 1
 
1.6%
18.37877721 1
 
1.6%
18.31170138 1
 
1.6%
18.27816346 2
 
3.1%
18.24462555 1
 
1.6%
18.22785659 1
 
1.6%
18.17754972 1
 
1.6%

Rural land area where elevation is below 5 meters (sq. km)
Categorical

High correlation  Missing  Uniform 

Distinct3
Distinct (%)100.0%
Missing61
Missing (%)95.3%
Memory size640.0 B
12170.141244
12445.3683085
12570.8102114

Length

Max length13
Median length13
Mean length12.666667
Min length12

Characters and Unicode

Total characters38
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

Unique3 ?
Unique (%)100.0%

Sample

1st row12170.141244
2nd row12445.3683085
3rd row12570.8102114

Common Values

ValueCountFrequency (%)
12170.141244 1
 
1.6%
12445.3683085 1
 
1.6%
12570.8102114 1
 
1.6%
(Missing) 61
95.3%

Length

2025-02-05T21:53:42.720709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:42.797340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12170.141244 1
33.3%
12445.3683085 1
33.3%
12570.8102114 1
33.3%

Most occurring characters

ValueCountFrequency (%)
1 9
23.7%
4 6
15.8%
2 5
13.2%
0 4
10.5%
. 3
 
7.9%
5 3
 
7.9%
8 3
 
7.9%
7 2
 
5.3%
3 2
 
5.3%
6 1
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9
23.7%
4 6
15.8%
2 5
13.2%
0 4
10.5%
. 3
 
7.9%
5 3
 
7.9%
8 3
 
7.9%
7 2
 
5.3%
3 2
 
5.3%
6 1
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9
23.7%
4 6
15.8%
2 5
13.2%
0 4
10.5%
. 3
 
7.9%
5 3
 
7.9%
8 3
 
7.9%
7 2
 
5.3%
3 2
 
5.3%
6 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9
23.7%
4 6
15.8%
2 5
13.2%
0 4
10.5%
. 3
 
7.9%
5 3
 
7.9%
8 3
 
7.9%
7 2
 
5.3%
3 2
 
5.3%
6 1
 
2.6%

Rural land area where elevation is below 5 meters (% of total land area)
Categorical

High correlation  Missing  Uniform 

Distinct3
Distinct (%)100.0%
Missing61
Missing (%)95.3%
Memory size640.0 B
4.1356910066334
4.22921942776347
4.27184744162947

Length

Max length16
Median length16
Mean length15.666667
Min length15

Characters and Unicode

Total characters47
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

Unique3 ?
Unique (%)100.0%

Sample

1st row4.1356910066334
2nd row4.22921942776347
3rd row4.27184744162947

Common Values

ValueCountFrequency (%)
4.1356910066334 1
 
1.6%
4.22921942776347 1
 
1.6%
4.27184744162947 1
 
1.6%
(Missing) 61
95.3%

Length

2025-02-05T21:53:42.886038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:42.946605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4.1356910066334 1
33.3%
4.22921942776347 1
33.3%
4.27184744162947 1
33.3%

Most occurring characters

ValueCountFrequency (%)
4 10
21.3%
2 6
12.8%
7 6
12.8%
6 5
10.6%
1 5
10.6%
9 4
 
8.5%
3 4
 
8.5%
. 3
 
6.4%
0 2
 
4.3%
5 1
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 10
21.3%
2 6
12.8%
7 6
12.8%
6 5
10.6%
1 5
10.6%
9 4
 
8.5%
3 4
 
8.5%
. 3
 
6.4%
0 2
 
4.3%
5 1
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 10
21.3%
2 6
12.8%
7 6
12.8%
6 5
10.6%
1 5
10.6%
9 4
 
8.5%
3 4
 
8.5%
. 3
 
6.4%
0 2
 
4.3%
5 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 10
21.3%
2 6
12.8%
7 6
12.8%
6 5
10.6%
1 5
10.6%
9 4
 
8.5%
3 4
 
8.5%
. 3
 
6.4%
0 2
 
4.3%
5 1
 
2.1%

Urban land area where elevation is below 5 meters (sq. km)
Categorical

High correlation  Missing  Uniform 

Distinct3
Distinct (%)100.0%
Missing61
Missing (%)95.3%
Memory size640.0 B
1511.452095905
1236.225031424
1110.783128511

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters42
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

Unique3 ?
Unique (%)100.0%

Sample

1st row1511.452095905
2nd row1236.225031424
3rd row1110.783128511

Common Values

ValueCountFrequency (%)
1511.452095905 1
 
1.6%
1236.225031424 1
 
1.6%
1110.783128511 1
 
1.6%
(Missing) 61
95.3%

Length

2025-02-05T21:53:43.027605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:43.087606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1511.452095905 1
33.3%
1236.225031424 1
33.3%
1110.783128511 1
33.3%

Most occurring characters

ValueCountFrequency (%)
1 11
26.2%
5 6
14.3%
2 6
14.3%
0 4
 
9.5%
. 3
 
7.1%
4 3
 
7.1%
3 3
 
7.1%
9 2
 
4.8%
8 2
 
4.8%
6 1
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 11
26.2%
5 6
14.3%
2 6
14.3%
0 4
 
9.5%
. 3
 
7.1%
4 3
 
7.1%
3 3
 
7.1%
9 2
 
4.8%
8 2
 
4.8%
6 1
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 11
26.2%
5 6
14.3%
2 6
14.3%
0 4
 
9.5%
. 3
 
7.1%
4 3
 
7.1%
3 3
 
7.1%
9 2
 
4.8%
8 2
 
4.8%
6 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 11
26.2%
5 6
14.3%
2 6
14.3%
0 4
 
9.5%
. 3
 
7.1%
4 3
 
7.1%
3 3
 
7.1%
9 2
 
4.8%
8 2
 
4.8%
6 1
 
2.4%

Urban land area where elevation is below 5 meters (% of total land area)
Categorical

High correlation  Missing  Uniform 

Distinct3
Distinct (%)100.0%
Missing61
Missing (%)95.3%
Memory size640.0 B
0.513625825260924
0.420097404141512
0.37746939027301

Length

Max length17
Median length17
Mean length16.666667
Min length16

Characters and Unicode

Total characters50
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

Unique3 ?
Unique (%)100.0%

Sample

1st row0.513625825260924
2nd row0.420097404141512
3rd row0.37746939027301

Common Values

ValueCountFrequency (%)
0.513625825260924 1
 
1.6%
0.420097404141512 1
 
1.6%
0.37746939027301 1
 
1.6%
(Missing) 61
95.3%

Length

2025-02-05T21:53:43.166012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:43.226012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.513625825260924 1
33.3%
0.420097404141512 1
33.3%
0.37746939027301 1
33.3%

Most occurring characters

ValueCountFrequency (%)
0 9
18.0%
2 7
14.0%
4 6
12.0%
1 5
10.0%
5 4
8.0%
7 4
8.0%
3 4
8.0%
9 4
8.0%
. 3
 
6.0%
6 3
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9
18.0%
2 7
14.0%
4 6
12.0%
1 5
10.0%
5 4
8.0%
7 4
8.0%
3 4
8.0%
9 4
8.0%
. 3
 
6.0%
6 3
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9
18.0%
2 7
14.0%
4 6
12.0%
1 5
10.0%
5 4
8.0%
7 4
8.0%
3 4
8.0%
9 4
8.0%
. 3
 
6.0%
6 3
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9
18.0%
2 7
14.0%
4 6
12.0%
1 5
10.0%
5 4
8.0%
7 4
8.0%
3 4
8.0%
9 4
8.0%
. 3
 
6.0%
6 3
 
6.0%

Land area where elevation is below 5 meters (% of total land area)
Categorical

High correlation  Missing  Uniform 

Distinct3
Distinct (%)100.0%
Missing61
Missing (%)95.3%
Memory size640.0 B
4.64931683189432
4.64931683190498
4.64931683190248

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters48
Distinct characters9
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

Unique3 ?
Unique (%)100.0%

Sample

1st row4.64931683189432
2nd row4.64931683190498
3rd row4.64931683190248

Common Values

ValueCountFrequency (%)
4.64931683189432 1
 
1.6%
4.64931683190498 1
 
1.6%
4.64931683190248 1
 
1.6%
(Missing) 61
95.3%

Length

2025-02-05T21:53:43.307013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:43.366012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4.64931683189432 1
33.3%
4.64931683190498 1
33.3%
4.64931683190248 1
33.3%

Most occurring characters

ValueCountFrequency (%)
4 9
18.8%
3 7
14.6%
9 7
14.6%
6 6
12.5%
1 6
12.5%
8 6
12.5%
. 3
 
6.2%
2 2
 
4.2%
0 2
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 9
18.8%
3 7
14.6%
9 7
14.6%
6 6
12.5%
1 6
12.5%
8 6
12.5%
. 3
 
6.2%
2 2
 
4.2%
0 2
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 9
18.8%
3 7
14.6%
9 7
14.6%
6 6
12.5%
1 6
12.5%
8 6
12.5%
. 3
 
6.2%
2 2
 
4.2%
0 2
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 9
18.8%
3 7
14.6%
9 7
14.6%
6 6
12.5%
1 6
12.5%
8 6
12.5%
. 3
 
6.2%
2 2
 
4.2%
0 2
 
4.2%

Forest area (sq. km)
Real number (ℝ)

High correlation  Missing 

Distinct33
Distinct (%)100.0%
Missing31
Missing (%)48.4%
Infinite0
Infinite (%)0.0%
Mean72209.795
Minimum68397.2
Maximum77788.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:43.449011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum68397.2
5-th percentile68818.468
Q170141.5
median71683.98
Q374031.7
95-th percentile77036.82
Maximum77788.1
Range9390.9
Interquartile range (IQR)3890.2

Descriptive statistics

Standard deviation2693.6192
Coefficient of variation (CV)0.037302684
Kurtosis-0.70204916
Mean72209.795
Median Absolute Deviation (MAD)1878.17
Skewness0.57728482
Sum2382923.2
Variance7255584.2
MonotonicityNot monotonic
2025-02-05T21:53:43.553011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
72234.8 1
 
1.6%
71885.9 1
 
1.6%
71537 1
 
1.6%
70839.3 1
 
1.6%
71188.1 1
 
1.6%
70141.5 1
 
1.6%
69792.64 1
 
1.6%
69443.78 1
 
1.6%
70490.4 1
 
1.6%
68746.06 1
 
1.6%
Other values (23) 23
35.9%
(Missing) 31
48.4%
ValueCountFrequency (%)
68397.2 1
1.6%
68746.06 1
1.6%
68866.74 1
1.6%
69094.92 1
1.6%
69336.28 1
1.6%
69443.78 1
1.6%
69792.64 1
1.6%
69805.82 1
1.6%
70141.5 1
1.6%
70275.36 1
1.6%
ValueCountFrequency (%)
77788.1 1
1.6%
77318.55 1
1.6%
76849 1
1.6%
76379.45 1
1.6%
75909.9 1
1.6%
75440.35 1
1.6%
74970.8 1
1.6%
74501.25 1
1.6%
74031.7 1
1.6%
73562.15 1
1.6%

Forest area (% of land area)
Real number (ℝ)

High correlation  Missing 

Distinct33
Distinct (%)100.0%
Missing31
Missing (%)48.4%
Infinite0
Infinite (%)0.0%
Mean24.21766
Minimum22.938995
Maximum26.088507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:43.646500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22.938995
5-th percentile23.080279
Q123.523996
median24.041312
Q324.828688
95-th percentile25.836543
Maximum26.088507
Range3.149512
Interquartile range (IQR)1.304692

Descriptive statistics

Standard deviation0.9033837
Coefficient of variation (CV)0.037302684
Kurtosis-0.70204916
Mean24.21766
Median Absolute Deviation (MAD)0.62989905
Skewness0.57728482
Sum799.18276
Variance0.8161021
MonotonicityNot monotonic
2025-02-05T21:53:43.936714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
24.22604554 1
 
1.6%
24.10903176 1
 
1.6%
23.99201798 1
 
1.6%
23.75802395 1
 
1.6%
23.87500419 1
 
1.6%
23.52399638 1
 
1.6%
23.40699601 1
 
1.6%
23.28999564 1
 
1.6%
23.64101016 1
 
1.6%
23.0559949 1
 
1.6%
Other values (23) 23
35.9%
(Missing) 31
48.4%
ValueCountFrequency (%)
22.93899453 1
1.6%
23.0559949 1
1.6%
23.09646846 1
1.6%
23.17299527 1
1.6%
23.25394238 1
1.6%
23.28999564 1
1.6%
23.40699601 1
1.6%
23.41141631 1
1.6%
23.52399638 1
1.6%
23.56889023 1
1.6%
ValueCountFrequency (%)
26.08850656 1
1.6%
25.93102928 1
1.6%
25.773552 1
1.6%
25.61607472 1
1.6%
25.45859744 1
1.6%
25.30112017 1
1.6%
25.14364289 1
1.6%
24.98616561 1
1.6%
24.82868833 1
1.6%
24.67121105 1
1.6%

Agricultural irrigated land (% of total agricultural land)
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)100.0%
Missing58
Missing (%)90.6%
Infinite0
Infinite (%)0.0%
Mean8.8996877
Minimum8.2273113
Maximum9.3966942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:44.017715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.2273113
5-th percentile8.2538168
Q18.5168609
median9.0874409
Q39.2262885
95-th percentile9.363997
Maximum9.3966942
Range1.1693829
Interquartile range (IQR)0.70942759

Descriptive statistics

Standard deviation0.49503141
Coefficient of variation (CV)0.055623459
Kurtosis-1.7164615
Mean8.8996877
Median Absolute Deviation (MAD)0.24385889
Skewness-0.74206024
Sum53.398126
Variance0.2450561
MonotonicityNot monotonic
2025-02-05T21:53:44.097234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9.265905383 1
 
1.6%
9.107438017 1
 
1.6%
9.396694215 1
 
1.6%
9.067443797 1
 
1.6%
8.227311281 1
 
1.6%
8.333333333 1
 
1.6%
(Missing) 58
90.6%
ValueCountFrequency (%)
8.227311281 1
1.6%
8.333333333 1
1.6%
9.067443797 1
1.6%
9.107438017 1
1.6%
9.265905383 1
1.6%
9.396694215 1
1.6%
ValueCountFrequency (%)
9.396694215 1
1.6%
9.265905383 1
1.6%
9.107438017 1
1.6%
9.067443797 1
1.6%
8.333333333 1
1.6%
8.227311281 1
1.6%

Average precipitation in depth (mm per year)
Categorical

Constant  Missing 

Distinct1
Distinct (%)1.6%
Missing3
Missing (%)4.7%
Memory size640.0 B
2348.0
61 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters366
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 row2348.0
2nd row2348.0
3rd row2348.0
4th row2348.0
5th row2348.0

Common Values

ValueCountFrequency (%)
2348.0 61
95.3%
(Missing) 3
 
4.7%

Length

2025-02-05T21:53:44.182750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:44.234750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2348.0 61
100.0%

Most occurring characters

ValueCountFrequency (%)
2 61
16.7%
3 61
16.7%
4 61
16.7%
8 61
16.7%
. 61
16.7%
0 61
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 61
16.7%
3 61
16.7%
4 61
16.7%
8 61
16.7%
. 61
16.7%
0 61
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 61
16.7%
3 61
16.7%
4 61
16.7%
8 61
16.7%
. 61
16.7%
0 61
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 61
16.7%
3 61
16.7%
4 61
16.7%
8 61
16.7%
. 61
16.7%
0 61
16.7%

Cereal yield (kg per hectare)
Real number (ℝ)

High correlation  Missing 

Distinct62
Distinct (%)100.0%
Missing2
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean2261.3758
Minimum996.3
Maximum3834.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:44.305997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum996.3
5-th percentile1036.19
Q11387.375
median2102.35
Q33216.875
95-th percentile3735.62
Maximum3834.8
Range2838.5
Interquartile range (IQR)1829.5

Descriptive statistics

Standard deviation936.91613
Coefficient of variation (CV)0.41431244
Kurtosis-1.3228763
Mean2261.3758
Median Absolute Deviation (MAD)823.1
Skewness0.28453038
Sum140205.3
Variance877811.84
MonotonicityNot monotonic
2025-02-05T21:53:44.421567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3769.4 1
 
1.6%
3834.8 1
 
1.6%
3670.5 1
 
1.6%
3692.3 1
 
1.6%
3529 1
 
1.6%
3737.9 1
 
1.6%
3637.4 1
 
1.6%
3531.8 1
 
1.6%
3492.6 1
 
1.6%
3340.6 1
 
1.6%
Other values (52) 52
81.2%
(Missing) 2
 
3.1%
ValueCountFrequency (%)
996.3 1
1.6%
1025.3 1
1.6%
1030.2 1
1.6%
1035.7 1
1.6%
1045.5 1
1.6%
1062.8 1
1.6%
1105.4 1
1.6%
1113.1 1
1.6%
1163.9 1
1.6%
1186 1
1.6%
ValueCountFrequency (%)
3834.8 1
1.6%
3821.6 1
1.6%
3769.4 1
1.6%
3737.9 1
1.6%
3692.3 1
1.6%
3670.5 1
1.6%
3637.4 1
1.6%
3556.1 1
1.6%
3531.8 1
1.6%
3529 1
1.6%

Foreign direct investment, net inflows (% of GDP)
Real number (ℝ)

High correlation  Missing 

Distinct54
Distinct (%)100.0%
Missing10
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean1.2483923
Minimum-0.28766763
Maximum3.1223878
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)3.1%
Memory size640.0 B
2025-02-05T21:53:44.536564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.28766763
5-th percentile0.024220673
Q10.51517553
median1.1055049
Q31.9790426
95-th percentile2.9286959
Maximum3.1223878
Range3.4100554
Interquartile range (IQR)1.4638671

Descriptive statistics

Standard deviation0.91221634
Coefficient of variation (CV)0.7307129
Kurtosis-0.86893105
Mean1.2483923
Median Absolute Deviation (MAD)0.73325505
Skewness0.32149814
Sum67.413183
Variance0.83213865
MonotonicityNot monotonic
2025-02-05T21:53:44.654567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.885863632 1
 
1.6%
3.040788553 1
 
1.6%
2.868338261 1
 
1.6%
3.122387784 1
 
1.6%
2.598508035 1
 
1.6%
2.301174986 1
 
1.6%
1.929375228 1
 
1.6%
1.316426384 1
 
1.6%
1.227630007 1
 
1.6%
0.856963003 1
 
1.6%
Other values (44) 44
68.8%
(Missing) 10
 
15.6%
ValueCountFrequency (%)
-0.2876676272 1
1.6%
-0.0137582234 1
1.6%
0.0224227483 1
1.6%
0.02518878601 1
1.6%
0.03432348351 1
1.6%
0.03790929187 1
1.6%
0.04521485787 1
1.6%
0.2577887168 1
1.6%
0.2780780734 1
1.6%
0.3736700074 1
1.6%
ValueCountFrequency (%)
3.122387784 1
1.6%
3.070111177 1
1.6%
3.040788553 1
1.6%
2.868338261 1
1.6%
2.598508035 1
1.6%
2.347509723 1
1.6%
2.301174986 1
1.6%
2.174705331 1
1.6%
2.16907027 1
1.6%
2.135679994 1
1.6%

Access to electricity (% of population)
Real number (ℝ)

High correlation  Missing 

Distinct30
Distinct (%)100.0%
Missing34
Missing (%)53.1%
Infinite0
Infinite (%)0.0%
Mean82.587473
Minimum65.4
Maximum97.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:44.755205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum65.4
5-th percentile68.520108
Q174.95
median83.8
Q389.625
95-th percentile95.905
Maximum97.5
Range32.1
Interquartile range (IQR)14.675

Descriptive statistics

Standard deviation9.5708383
Coefficient of variation (CV)0.11588729
Kurtosis-1.2089509
Mean82.587473
Median Absolute Deviation (MAD)8.15
Skewness-0.14202892
Sum2477.6242
Variance91.600945
MonotonicityNot monotonic
2025-02-05T21:53:44.848208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
96.4 1
 
1.6%
97.5 1
 
1.6%
94.2 1
 
1.6%
93 1
 
1.6%
92 1
 
1.6%
95.3 1
 
1.6%
89.8 1
 
1.6%
87.5 1
 
1.6%
87.1 1
 
1.6%
87.2 1
 
1.6%
Other values (20) 20
31.2%
(Missing) 34
53.1%
ValueCountFrequency (%)
65.4 1
1.6%
68.01855469 1
1.6%
69.13311768 1
1.6%
70.24495697 1
1.6%
71.3 1
1.6%
71.35339355 1
1.6%
71.87417093 1
1.6%
74.7 1
1.6%
75.7 1
1.6%
76.6 1
1.6%
ValueCountFrequency (%)
97.5 1
1.6%
96.4 1
1.6%
95.3 1
1.6%
94.8 1
1.6%
94.2 1
1.6%
93 1
1.6%
92 1
1.6%
89.8 1
1.6%
89.1 1
1.6%
87.6 1
1.6%

Renewable energy consumption (% of total final energy consumption)
Real number (ℝ)

High correlation  Missing 

Distinct29
Distinct (%)90.6%
Missing32
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean34.334375
Minimum26.9
Maximum51.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:44.970086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.9
5-th percentile27.71
Q130.875
median33
Q334.1
95-th percentile48.51
Maximum51.3
Range24.4
Interquartile range (IQR)3.225

Descriptive statistics

Standard deviation6.2292551
Coefficient of variation (CV)0.18142911
Kurtosis1.729538
Mean34.334375
Median Absolute Deviation (MAD)1.9
Skewness1.5085708
Sum1098.7
Variance38.803619
MonotonicityNot monotonic
2025-02-05T21:53:45.064985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
34.1 2
 
3.1%
33.9 2
 
3.1%
32.4 2
 
3.1%
28 1
 
1.6%
26.9 1
 
1.6%
28.6 1
 
1.6%
27.8 1
 
1.6%
27.6 1
 
1.6%
30.8 1
 
1.6%
33.1 1
 
1.6%
Other values (19) 19
29.7%
(Missing) 32
50.0%
ValueCountFrequency (%)
26.9 1
1.6%
27.6 1
1.6%
27.8 1
1.6%
28 1
1.6%
28.6 1
1.6%
29.1 1
1.6%
30.2 1
1.6%
30.8 1
1.6%
30.9 1
1.6%
31.3 1
1.6%
ValueCountFrequency (%)
51.3 1
1.6%
49.5 1
1.6%
47.7 1
1.6%
44.1 1
1.6%
43.1 1
1.6%
37.8 1
1.6%
35.8 1
1.6%
34.1 2
3.1%
33.9 2
3.1%
33.8 1
1.6%
Distinct1
Distinct (%)100.0%
Missing63
Missing (%)98.4%
Memory size640.0 B
0.806384865170547

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters17
Distinct characters9
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

Unique1 ?
Unique (%)100.0%

Sample

1st row0.806384865170547

Common Values

ValueCountFrequency (%)
0.806384865170547 1
 
1.6%
(Missing) 63
98.4%

Length

2025-02-05T21:53:45.162654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:45.210655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.806384865170547 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3
17.6%
8 3
17.6%
6 2
11.8%
5 2
11.8%
4 2
11.8%
7 2
11.8%
. 1
 
5.9%
3 1
 
5.9%
1 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3
17.6%
8 3
17.6%
6 2
11.8%
5 2
11.8%
4 2
11.8%
7 2
11.8%
. 1
 
5.9%
3 1
 
5.9%
1 1
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3
17.6%
8 3
17.6%
6 2
11.8%
5 2
11.8%
4 2
11.8%
7 2
11.8%
. 1
 
5.9%
3 1
 
5.9%
1 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3
17.6%
8 3
17.6%
6 2
11.8%
5 2
11.8%
4 2
11.8%
7 2
11.8%
. 1
 
5.9%
3 1
 
5.9%
1 1
 
5.9%
Distinct3
Distinct (%)100.0%
Missing61
Missing (%)95.3%
Memory size640.0 B
4.58854136068604
5.46857869539386
5.77252195933699

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters48
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

Unique3 ?
Unique (%)100.0%

Sample

1st row4.58854136068604
2nd row5.46857869539386
3rd row5.77252195933699

Common Values

ValueCountFrequency (%)
4.58854136068604 1
 
1.6%
5.46857869539386 1
 
1.6%
5.77252195933699 1
 
1.6%
(Missing) 61
95.3%

Length

2025-02-05T21:53:45.273665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:45.333674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4.58854136068604 1
33.3%
5.46857869539386 1
33.3%
5.77252195933699 1
33.3%

Most occurring characters

ValueCountFrequency (%)
5 8
16.7%
6 7
14.6%
8 6
12.5%
9 6
12.5%
3 5
10.4%
4 4
8.3%
. 3
 
6.2%
7 3
 
6.2%
1 2
 
4.2%
0 2
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 8
16.7%
6 7
14.6%
8 6
12.5%
9 6
12.5%
3 5
10.4%
4 4
8.3%
. 3
 
6.2%
7 3
 
6.2%
1 2
 
4.2%
0 2
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 8
16.7%
6 7
14.6%
8 6
12.5%
9 6
12.5%
3 5
10.4%
4 4
8.3%
. 3
 
6.2%
7 3
 
6.2%
1 2
 
4.2%
0 2
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 8
16.7%
6 7
14.6%
8 6
12.5%
9 6
12.5%
3 5
10.4%
4 4
8.3%
. 3
 
6.2%
7 3
 
6.2%
1 2
 
4.2%
0 2
 
4.2%
Distinct3
Distinct (%)100.0%
Missing61
Missing (%)95.3%
Memory size640.0 B
5.93585399053803
5.57578866221652
6.16434308911466

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters48
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

Unique3 ?
Unique (%)100.0%

Sample

1st row5.93585399053803
2nd row5.57578866221652
3rd row6.16434308911466

Common Values

ValueCountFrequency (%)
5.93585399053803 1
 
1.6%
5.57578866221652 1
 
1.6%
6.16434308911466 1
 
1.6%
(Missing) 61
95.3%

Length

2025-02-05T21:53:45.414122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:45.474146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5.93585399053803 1
33.3%
5.57578866221652 1
33.3%
6.16434308911466 1
33.3%

Most occurring characters

ValueCountFrequency (%)
5 8
16.7%
6 7
14.6%
3 6
12.5%
8 5
10.4%
9 4
8.3%
1 4
8.3%
. 3
 
6.2%
2 3
 
6.2%
0 3
 
6.2%
4 3
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 8
16.7%
6 7
14.6%
3 6
12.5%
8 5
10.4%
9 4
8.3%
1 4
8.3%
. 3
 
6.2%
2 3
 
6.2%
0 3
 
6.2%
4 3
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 8
16.7%
6 7
14.6%
3 6
12.5%
8 5
10.4%
9 4
8.3%
1 4
8.3%
. 3
 
6.2%
2 3
 
6.2%
0 3
 
6.2%
4 3
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 8
16.7%
6 7
14.6%
3 6
12.5%
8 5
10.4%
9 4
8.3%
1 4
8.3%
. 3
 
6.2%
2 3
 
6.2%
0 3
 
6.2%
4 3
 
6.2%
Distinct3
Distinct (%)100.0%
Missing61
Missing (%)95.3%
Memory size640.0 B
10.5243953512241
11.0443673576104
11.9368650484517

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters48
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

Unique3 ?
Unique (%)100.0%

Sample

1st row10.5243953512241
2nd row11.0443673576104
3rd row11.9368650484517

Common Values

ValueCountFrequency (%)
10.5243953512241 1
 
1.6%
11.0443673576104 1
 
1.6%
11.9368650484517 1
 
1.6%
(Missing) 61
95.3%

Length

2025-02-05T21:53:45.554168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:45.613169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
10.5243953512241 1
33.3%
11.0443673576104 1
33.3%
11.9368650484517 1
33.3%

Most occurring characters

ValueCountFrequency (%)
1 9
18.8%
4 7
14.6%
5 6
12.5%
3 5
10.4%
0 4
8.3%
6 4
8.3%
. 3
 
6.2%
2 3
 
6.2%
7 3
 
6.2%
9 2
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9
18.8%
4 7
14.6%
5 6
12.5%
3 5
10.4%
0 4
8.3%
6 4
8.3%
. 3
 
6.2%
2 3
 
6.2%
7 3
 
6.2%
9 2
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9
18.8%
4 7
14.6%
5 6
12.5%
3 5
10.4%
0 4
8.3%
6 4
8.3%
. 3
 
6.2%
2 3
 
6.2%
7 3
 
6.2%
9 2
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9
18.8%
4 7
14.6%
5 6
12.5%
3 5
10.4%
0 4
8.3%
6 4
8.3%
. 3
 
6.2%
2 3
 
6.2%
7 3
 
6.2%
9 2
 
4.2%
Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.03111
Minimum8.9777544
Maximum14.745903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:45.701178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.9777544
5-th percentile9.3528332
Q113.135276
median13.757987
Q313.957706
95-th percentile14.473594
Maximum14.745903
Range5.7681486
Interquartile range (IQR)0.82242976

Descriptive statistics

Standard deviation1.6928434
Coefficient of variation (CV)0.12990784
Kurtosis0.46874084
Mean13.03111
Median Absolute Deviation (MAD)0.28964127
Skewness-1.4171711
Sum833.99103
Variance2.8657187
MonotonicityNot monotonic
2025-02-05T21:53:45.816189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.1687351 1
 
1.6%
14.0513244 1
 
1.6%
13.9739861 1
 
1.6%
13.91075511 1
 
1.6%
13.85265217 1
 
1.6%
13.80195884 1
 
1.6%
13.75889216 1
 
1.6%
13.7351221 1
 
1.6%
13.73397552 1
 
1.6%
13.76577741 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
8.977754364 1
1.6%
9.093618118 1
1.6%
9.211286302 1
1.6%
9.333552102 1
1.6%
9.462092634 1
1.6%
9.596189045 1
1.6%
9.738936978 1
1.6%
9.888999033 1
1.6%
10.0469152 1
1.6%
10.21299618 1
1.6%
ValueCountFrequency (%)
14.74590299 1
1.6%
14.63928878 1
1.6%
14.58514982 1
1.6%
14.47549758 1
1.6%
14.46280407 1
1.6%
14.42503098 1
1.6%
14.31481136 1
1.6%
14.29933278 1
1.6%
14.29098517 1
1.6%
14.1687351 1
1.6%

Annual freshwater withdrawals, total (billion cubic meters)
Real number (ℝ)

High correlation  Missing 

Distinct16
Distinct (%)100.0%
Missing48
Missing (%)75.0%
Infinite0
Infinite (%)0.0%
Mean85.301327
Minimum78.89
Maximum92.157811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:45.919200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum78.89
5-th percentile80.8445
Q183.077
median84.4395
Q387.201817
95-th percentile91.882913
Maximum92.157811
Range13.267811
Interquartile range (IQR)4.1248173

Descriptive statistics

Standard deviation3.6357688
Coefficient of variation (CV)0.042622652
Kurtosis-0.067298044
Mean85.301327
Median Absolute Deviation (MAD)2.3155
Skewness0.40907834
Sum1364.8212
Variance13.218815
MonotonicityNot monotonic
2025-02-05T21:53:46.004215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
89.00036353 1
 
1.6%
87.47726918 1
 
1.6%
92.15781079 1
 
1.6%
85.99451398 1
 
1.6%
87.11 1
 
1.6%
86.4 1
 
1.6%
84.432 1
 
1.6%
91.79128 1
 
1.6%
84.447 1
 
1.6%
84.248 1
 
1.6%
Other values (6) 6
 
9.4%
(Missing) 48
75.0%
ValueCountFrequency (%)
78.89 1
1.6%
81.496 1
1.6%
81.556 1
1.6%
82.489 1
1.6%
83.273 1
1.6%
84.059 1
1.6%
84.248 1
1.6%
84.432 1
1.6%
84.447 1
1.6%
85.99451398 1
1.6%
ValueCountFrequency (%)
92.15781079 1
1.6%
91.79128 1
1.6%
89.00036353 1
1.6%
87.47726918 1
1.6%
87.11 1
1.6%
86.4 1
1.6%
85.99451398 1
1.6%
84.447 1
1.6%
84.432 1
1.6%
84.248 1
1.6%

Annual freshwater withdrawals, total (% of internal resources)
Real number (ℝ)

High correlation  Missing 

Distinct16
Distinct (%)100.0%
Missing48
Missing (%)75.0%
Infinite0
Infinite (%)0.0%
Mean17.80821
Minimum16.469729
Maximum19.239626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:46.077222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16.469729
5-th percentile16.877766
Q117.343841
median17.628288
Q318.204972
95-th percentile19.182236
Maximum19.239626
Range2.7698979
Interquartile range (IQR)0.86113096

Descriptive statistics

Standard deviation0.75903315
Coefficient of variation (CV)0.042622652
Kurtosis-0.067298044
Mean17.80821
Median Absolute Deviation (MAD)0.48340292
Skewness0.40907834
Sum284.93136
Variance0.57613132
MonotonicityNot monotonic
2025-02-05T21:53:46.153685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
18.58045168 1
 
1.6%
18.26247791 1
 
1.6%
19.23962647 1
 
1.6%
17.95292567 1
 
1.6%
18.18580376 1
 
1.6%
18.03757829 1
 
1.6%
17.62672234 1
 
1.6%
19.16310647 1
 
1.6%
17.62985386 1
 
1.6%
17.58830898 1
 
1.6%
Other values (6) 6
 
9.4%
(Missing) 48
75.0%
ValueCountFrequency (%)
16.4697286 1
1.6%
17.01377871 1
1.6%
17.0263048 1
1.6%
17.22108559 1
1.6%
17.38475992 1
1.6%
17.54885177 1
1.6%
17.58830898 1
1.6%
17.62672234 1
1.6%
17.62985386 1
1.6%
17.95292567 1
1.6%
ValueCountFrequency (%)
19.23962647 1
1.6%
19.16310647 1
1.6%
18.58045168 1
1.6%
18.26247791 1
1.6%
18.18580376 1
1.6%
18.03757829 1
1.6%
17.95292567 1
1.6%
17.62985386 1
1.6%
17.62672234 1
1.6%
17.58830898 1
1.6%

Terrestrial protected areas (% of total land area)
Categorical

High correlation  Missing 

Distinct5
Distinct (%)71.4%
Missing57
Missing (%)89.1%
Memory size640.0 B
15.37247
15.3166593209145
15.86868954
15.32
15.8686895

Length

Max length16
Median length11
Mean length10.571429
Min length5

Characters and Unicode

Total characters74
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

Unique3 ?
Unique (%)42.9%

Sample

1st row15.86868954
2nd row15.37247
3rd row15.37247
4th row15.3166593209145
5th row15.3166593209145

Common Values

ValueCountFrequency (%)
15.37247 2
 
3.1%
15.3166593209145 2
 
3.1%
15.86868954 1
 
1.6%
15.32 1
 
1.6%
15.8686895 1
 
1.6%
(Missing) 57
89.1%

Length

2025-02-05T21:53:46.240688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:46.310696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15.37247 2
28.6%
15.3166593209145 2
28.6%
15.86868954 1
14.3%
15.32 1
14.3%
15.8686895 1
14.3%

Most occurring characters

ValueCountFrequency (%)
5 13
17.6%
1 11
14.9%
6 8
10.8%
. 7
9.5%
3 7
9.5%
9 6
8.1%
8 6
8.1%
2 5
 
6.8%
4 5
 
6.8%
7 4
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 13
17.6%
1 11
14.9%
6 8
10.8%
. 7
9.5%
3 7
9.5%
9 6
8.1%
8 6
8.1%
2 5
 
6.8%
4 5
 
6.8%
7 4
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 13
17.6%
1 11
14.9%
6 8
10.8%
. 7
9.5%
3 7
9.5%
9 6
8.1%
8 6
8.1%
2 5
 
6.8%
4 5
 
6.8%
7 4
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 13
17.6%
1 11
14.9%
6 8
10.8%
. 7
9.5%
3 7
9.5%
9 6
8.1%
8 6
8.1%
2 5
 
6.8%
4 5
 
6.8%
7 4
 
5.4%

Marine protected areas (% of territorial waters)
Categorical

High correlation  Missing 

Distinct5
Distinct (%)71.4%
Missing57
Missing (%)89.1%
Memory size640.0 B
1.159064
1.15906292876311
1.744403958
1.159055757
1.744404

Length

Max length16
Median length11
Mean length11.142857
Min length8

Characters and Unicode

Total characters78
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

Unique3 ?
Unique (%)42.9%

Sample

1st row1.744403958
2nd row1.159064
3rd row1.159064
4th row1.15906292876311
5th row1.15906292876311

Common Values

ValueCountFrequency (%)
1.159064 2
 
3.1%
1.15906292876311 2
 
3.1%
1.744403958 1
 
1.6%
1.159055757 1
 
1.6%
1.744404 1
 
1.6%
(Missing) 57
89.1%

Length

2025-02-05T21:53:46.411721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:46.486585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.159064 2
28.6%
1.15906292876311 2
28.6%
1.744403958 1
14.3%
1.159055757 1
14.3%
1.744404 1
14.3%

Most occurring characters

ValueCountFrequency (%)
1 16
20.5%
5 9
11.5%
4 9
11.5%
9 8
10.3%
. 7
9.0%
0 7
9.0%
6 6
 
7.7%
7 6
 
7.7%
2 4
 
5.1%
8 3
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 16
20.5%
5 9
11.5%
4 9
11.5%
9 8
10.3%
. 7
9.0%
0 7
9.0%
6 6
 
7.7%
7 6
 
7.7%
2 4
 
5.1%
8 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 16
20.5%
5 9
11.5%
4 9
11.5%
9 8
10.3%
. 7
9.0%
0 7
9.0%
6 6
 
7.7%
7 6
 
7.7%
2 4
 
5.1%
8 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 16
20.5%
5 9
11.5%
4 9
11.5%
9 8
10.3%
. 7
9.0%
0 7
9.0%
6 6
 
7.7%
7 6
 
7.7%
2 4
 
5.1%
8 3
 
3.8%
Distinct4
Distinct (%)57.1%
Missing57
Missing (%)89.1%
Memory size640.0 B
3.72208503336371
3.14922432473144
3.14140839390029
3.14138638190187

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters112
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

Unique1 ?
Unique (%)14.3%

Sample

1st row3.72208503336371
2nd row3.14922432473144
3rd row3.14922432473144
4th row3.14140839390029
5th row3.14140839390029

Common Values

ValueCountFrequency (%)
3.72208503336371 2
 
3.1%
3.14922432473144 2
 
3.1%
3.14140839390029 2
 
3.1%
3.14138638190187 1
 
1.6%
(Missing) 57
89.1%

Length

2025-02-05T21:53:46.718872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:46.781872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.72208503336371 2
28.6%
3.14922432473144 2
28.6%
3.14140839390029 2
28.6%
3.14138638190187 1
14.3%

Most occurring characters

ValueCountFrequency (%)
3 25
22.3%
4 15
13.4%
1 14
12.5%
2 12
10.7%
0 11
9.8%
9 9
 
8.0%
. 7
 
6.2%
7 7
 
6.2%
8 7
 
6.2%
6 3
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 25
22.3%
4 15
13.4%
1 14
12.5%
2 12
10.7%
0 11
9.8%
9 9
 
8.0%
. 7
 
6.2%
7 7
 
6.2%
8 7
 
6.2%
6 3
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 25
22.3%
4 15
13.4%
1 14
12.5%
2 12
10.7%
0 11
9.8%
9 9
 
8.0%
. 7
 
6.2%
7 7
 
6.2%
8 7
 
6.2%
6 3
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 25
22.3%
4 15
13.4%
1 14
12.5%
2 12
10.7%
0 11
9.8%
9 9
 
8.0%
. 7
 
6.2%
7 7
 
6.2%
8 7
 
6.2%
6 3
 
2.7%
Distinct1
Distinct (%)100.0%
Missing63
Missing (%)98.4%
Memory size640.0 B
95.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
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

Unique1 ?
Unique (%)100.0%

Sample

1st row95.0

Common Values

ValueCountFrequency (%)
95.0 1
 
1.6%
(Missing) 63
98.4%

Length

2025-02-05T21:53:46.868882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T21:53:46.916944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
95.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
9 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 1
25.0%
5 1
25.0%
. 1
25.0%
0 1
25.0%

Agriculture, forestry, and fishing, value added (% of GDP)
Real number (ℝ)

High correlation  Unique 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.536709
Minimum8.8203237
Maximum27.630003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:46.990873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8.8203237
5-th percentile9.7129939
Q113.500264
median19.133582
Q323.899201
95-th percentile26.921615
Maximum27.630003
Range18.809679
Interquartile range (IQR)10.398937

Descriptive statistics

Standard deviation5.7934743
Coefficient of variation (CV)0.31254061
Kurtosis-1.3514675
Mean18.536709
Median Absolute Deviation (MAD)5.2317336
Skewness-0.060603352
Sum1186.3494
Variance33.564345
MonotonicityNot monotonic
2025-02-05T21:53:47.109507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.06916402 1
 
1.6%
10.18531103 1
 
1.6%
8.820323747 1
 
1.6%
9.650140358 1
 
1.6%
10.18295413 1
 
1.6%
10.20512553 1
 
1.6%
10.99649915 1
 
1.6%
12.27168162 1
 
1.6%
12.4734251 1
 
1.6%
13.09580701 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
8.820323747 1
1.6%
9.39675018 1
1.6%
9.551768044 1
1.6%
9.650140358 1
1.6%
10.06916402 1
1.6%
10.18295413 1
1.6%
10.18531103 1
1.6%
10.20512553 1
1.6%
10.99649915 1
1.6%
12.27168162 1
1.6%
ValueCountFrequency (%)
27.63000311 1
1.6%
27.19911785 1
1.6%
27.05289607 1
1.6%
26.94469022 1
1.6%
26.79085744 1
1.6%
26.35887827 1
1.6%
26.21745069 1
1.6%
26.12838646 1
1.6%
25.91656233 1
1.6%
25.38149293 1
1.6%
Distinct29
Distinct (%)100.0%
Missing35
Missing (%)54.7%
Infinite0
Infinite (%)0.0%
Mean1.0198836
Minimum0.99769002
Maximum1.03883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:47.214922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.99769002
5-th percentile1.00215
Q11.01531
median1.0221601
Q31.0258089
95-th percentile1.030262
Maximum1.03883
Range0.04114002
Interquartile range (IQR)0.010498881

Descriptive statistics

Standard deviation0.0095261275
Coefficient of variation (CV)0.0093404064
Kurtosis0.36618276
Mean1.0198836
Median Absolute Deviation (MAD)0.0043600798
Skewness-0.69528371
Sum29.576625
Variance9.0747106 × 10-5
MonotonicityNot monotonic
2025-02-05T21:53:47.304612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1.026121378 1
 
1.6%
1.025194764 1
 
1.6%
1.02580893 1
 
1.6%
1.018489957 1
 
1.6%
1.023869872 1
 
1.6%
1.008820057 1
 
1.6%
1.007279992 1
 
1.6%
1.015310049 1
 
1.6%
1.017799973 1
 
1.6%
1.021319985 1
 
1.6%
Other values (19) 19
29.7%
(Missing) 35
54.7%
ValueCountFrequency (%)
0.997690022 1
1.6%
0.9987300038 1
1.6%
1.007279992 1
1.6%
1.008059978 1
1.6%
1.008540034 1
1.6%
1.008820057 1
1.6%
1.013290048 1
1.6%
1.015310049 1
1.6%
1.017060041 1
1.6%
1.017799973 1
1.6%
ValueCountFrequency (%)
1.038830042 1
1.6%
1.030310035 1
1.6%
1.030189991 1
1.6%
1.028749943 1
1.6%
1.027840018 1
1.6%
1.026379943 1
1.6%
1.026121378 1
1.6%
1.02580893 1
1.6%
1.025660038 1
1.6%
1.025194764 1
1.6%

Primary completion rate, total (% of relevant age group)
Real number (ℝ)

High correlation  Missing 

Distinct28
Distinct (%)100.0%
Missing36
Missing (%)56.2%
Infinite0
Infinite (%)0.0%
Mean91.791113
Minimum80.705971
Maximum110.88219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:47.394610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum80.705971
5-th percentile85.286971
Q187.67412
median89.614731
Q394.496147
95-th percentile103.33195
Maximum110.88219
Range30.176216
Interquartile range (IQR)6.8220272

Descriptive statistics

Standard deviation6.6502039
Coefficient of variation (CV)0.072449322
Kurtosis1.365976
Mean91.791113
Median Absolute Deviation (MAD)3.3757401
Skewness1.1248282
Sum2570.1512
Variance44.225212
MonotonicityNot monotonic
2025-02-05T21:53:47.484610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
98.11262512 1
 
1.6%
101.023819 1
 
1.6%
110.8821869 1
 
1.6%
91.10198212 1
 
1.6%
104.4364548 1
 
1.6%
97.59192657 1
 
1.6%
91.51695251 1
 
1.6%
91.53292847 1
 
1.6%
89.28318787 1
 
1.6%
89.9462738 1
 
1.6%
Other values (18) 18
28.1%
(Missing) 36
56.2%
ValueCountFrequency (%)
80.70597076 1
1.6%
85.2811203 1
1.6%
85.2978363 1
1.6%
85.38350677 1
1.6%
86.18753815 1
1.6%
86.55445862 1
1.6%
87.31276703 1
1.6%
87.79457092 1
1.6%
87.9826889 1
1.6%
88.23085022 1
1.6%
ValueCountFrequency (%)
110.8821869 1
1.6%
104.4364548 1
1.6%
101.2807236 1
1.6%
101.023819 1
1.6%
98.11262512 1
1.6%
97.59192657 1
1.6%
94.89774323 1
1.6%
94.3622818 1
1.6%
93.02021027 1
1.6%
92.96073151 1
1.6%

Mortality rate, under-5 (per 1,000 live births)
Real number (ℝ)

High correlation  Missing 

Distinct57
Distinct (%)90.5%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean57.173016
Minimum27.5
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:47.585611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27.5
5-th percentile27.9
Q131.75
median52.8
Q381.7
95-th percentile93.9
Maximum102
Range74.5
Interquartile range (IQR)49.95

Descriptive statistics

Standard deviation25.118251
Coefficient of variation (CV)0.43933751
Kurtosis-1.6218648
Mean57.173016
Median Absolute Deviation (MAD)24.5
Skewness0.16690084
Sum3601.9
Variance630.92652
MonotonicityNot monotonic
2025-02-05T21:53:47.704612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.9 4
 
6.2%
28 3
 
4.7%
81.8 2
 
3.1%
28.1 1
 
1.6%
28.7 1
 
1.6%
28.3 1
 
1.6%
29.6 1
 
1.6%
30.2 1
 
1.6%
30.8 1
 
1.6%
29.1 1
 
1.6%
Other values (47) 47
73.4%
ValueCountFrequency (%)
27.5 1
 
1.6%
27.9 4
6.2%
28 3
4.7%
28.1 1
 
1.6%
28.3 1
 
1.6%
28.7 1
 
1.6%
29.1 1
 
1.6%
29.6 1
 
1.6%
30.2 1
 
1.6%
30.8 1
 
1.6%
ValueCountFrequency (%)
102 1
1.6%
98.9 1
1.6%
96.3 1
1.6%
94.1 1
1.6%
92.1 1
1.6%
90.2 1
1.6%
88.4 1
1.6%
86.7 1
1.6%
85.3 1
1.6%
84 1
1.6%

Prevalence of underweight, weight for age (% of children under 5)
Real number (ℝ)

High correlation  Missing 

Distinct13
Distinct (%)100.0%
Missing51
Missing (%)79.7%
Infinite0
Infinite (%)0.0%
Mean23.161538
Minimum16.7
Maximum29.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:47.796801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16.7
5-th percentile18.02
Q119.9
median20.8
Q328.3
95-th percentile29.84
Maximum29.9
Range13.2
Interquartile range (IQR)8.4

Descriptive statistics

Standard deviation4.7896657
Coefficient of variation (CV)0.20679394
Kurtosis-1.6048571
Mean23.161538
Median Absolute Deviation (MAD)1.9
Skewness0.43117276
Sum301.1
Variance22.940897
MonotonicityNot monotonic
2025-02-05T21:53:47.879804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
16.7 1
 
1.6%
18.9 1
 
1.6%
19 1
 
1.6%
21.4 1
 
1.6%
19.9 1
 
1.6%
20.2 1
 
1.6%
20.6 1
 
1.6%
20.8 1
 
1.6%
28.3 1
 
1.6%
26.3 1
 
1.6%
Other values (3) 3
 
4.7%
(Missing) 51
79.7%
ValueCountFrequency (%)
16.7 1
1.6%
18.9 1
1.6%
19 1
1.6%
19.9 1
1.6%
20.2 1
1.6%
20.6 1
1.6%
20.8 1
1.6%
21.4 1
1.6%
26.3 1
1.6%
28.3 1
1.6%
ValueCountFrequency (%)
29.9 1
1.6%
29.8 1
1.6%
29.3 1
1.6%
28.3 1
1.6%
26.3 1
1.6%
21.4 1
1.6%
20.8 1
1.6%
20.6 1
1.6%
20.2 1
1.6%
19.9 1
1.6%

Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)87.5%
Missing56
Missing (%)87.5%
Infinite0
Infinite (%)0.0%
Mean9.7125
Minimum3
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:47.957804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15.625
median11.1
Q313.825
95-th percentile14.76
Maximum14.9
Range11.9
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation4.9180244
Coefficient of variation (CV)0.5063603
Kurtosis-1.5868728
Mean9.7125
Median Absolute Deviation (MAD)3.6
Skewness-0.51929689
Sum77.7
Variance24.186964
MonotonicityNot monotonic
2025-02-05T21:53:48.036801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 2
 
3.1%
6.5 1
 
1.6%
10.9 1
 
1.6%
11.3 1
 
1.6%
14.9 1
 
1.6%
13.6 1
 
1.6%
14.5 1
 
1.6%
(Missing) 56
87.5%
ValueCountFrequency (%)
3 2
3.1%
6.5 1
1.6%
10.9 1
1.6%
11.3 1
1.6%
13.6 1
1.6%
14.5 1
1.6%
14.9 1
1.6%
ValueCountFrequency (%)
14.9 1
1.6%
14.5 1
1.6%
13.6 1
1.6%
11.3 1
1.6%
10.9 1
1.6%
6.5 1
1.6%
3 2
3.1%

Population growth (annual %)
Real number (ℝ)

High correlation  Missing 

Distinct63
Distinct (%)100.0%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean2.2470625
Minimum0.76047922
Maximum3.1820051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:48.135802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.76047922
5-th percentile1.1525782
Q11.9109891
median2.3548365
Q32.6656086
95-th percentile3.1027929
Maximum3.1820051
Range2.4215259
Interquartile range (IQR)0.75461951

Descriptive statistics

Standard deviation0.60655904
Coefficient of variation (CV)0.26993421
Kurtosis-0.11678836
Mean2.2470625
Median Absolute Deviation (MAD)0.37497516
Skewness-0.66570788
Sum141.56494
Variance0.36791387
MonotonicityNot monotonic
2025-02-05T21:53:48.246697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9056603109 1
 
1.6%
1.145514086 1
 
1.6%
1.216155346 1
 
1.6%
1.236860333 1
 
1.6%
1.288301867 1
 
1.6%
1.341907095 1
 
1.6%
1.478753843 1
 
1.6%
1.642832838 1
 
1.6%
1.879715797 1
 
1.6%
1.942262379 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
0.760479216 1
1.6%
0.8100009987 1
1.6%
0.9056603109 1
1.6%
1.145514086 1
1.6%
1.216155346 1
1.6%
1.236860333 1
1.6%
1.288301867 1
1.6%
1.341907095 1
1.6%
1.478753843 1
1.6%
1.642832838 1
1.6%
ValueCountFrequency (%)
3.182005086 1
1.6%
3.17829225 1
1.6%
3.150003131 1
1.6%
3.107874517 1
1.6%
3.057058505 1
1.6%
2.994839693 1
1.6%
2.943272892 1
1.6%
2.913819199 1
1.6%
2.895240349 1
1.6%
2.892100016 1
1.6%

Population, total
Real number (ℝ)

High correlation  Unique 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67997815
Minimum27891897
Maximum1.148912 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:48.357693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27891897
5-th percentile30819976
Q142931322
median65154187
Q391481162
95-th percentile1.1188978 × 108
Maximum1.148912 × 108
Range86999302
Interquartile range (IQR)48549839

Descriptive statistics

Standard deviation27445956
Coefficient of variation (CV)0.40362997
Kurtosis-1.2950255
Mean67997815
Median Absolute Deviation (MAD)24167322
Skewness0.21484973
Sum4.3518601 × 109
Variance7.532805 × 1014
MonotonicityNot monotonic
2025-02-05T21:53:48.468569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113100950 1
 
1.6%
112081264 1
 
1.6%
110804683 1
 
1.6%
109465287 1
 
1.6%
108119693 1
 
1.6%
106735719 1
 
1.6%
105312992 1
 
1.6%
103767130 1
 
1.6%
102076336 1
 
1.6%
100175512 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
27891897 1
1.6%
28792621 1
1.6%
29723536 1
1.6%
30674731 1
1.6%
31643032 1
1.6%
32625316 1
1.6%
33617170 1
1.6%
34621320 1
1.6%
35638342 1
1.6%
36660799 1
1.6%
ValueCountFrequency (%)
114891199 1
1.6%
113964338 1
1.6%
113100950 1
1.6%
112081264 1
1.6%
110804683 1
1.6%
109465287 1
1.6%
108119693 1
1.6%
106735719 1
1.6%
105312992 1
1.6%
103767130 1
1.6%

Urban population growth (annual %)
Real number (ℝ)

High correlation  Missing 

Distinct63
Distinct (%)100.0%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean2.9869255
Minimum1.3730602
Maximum5.31716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:48.576952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.3730602
5-th percentile1.5011769
Q11.8675978
median2.372321
Q33.8683011
95-th percentile5.0756763
Maximum5.31716
Range3.9440998
Interquartile range (IQR)2.0007033

Descriptive statistics

Standard deviation1.2292308
Coefficient of variation (CV)0.41153716
Kurtosis-1.2659286
Mean2.9869255
Median Absolute Deviation (MAD)0.91825528
Skewness0.3695193
Sum188.1763
Variance1.5110084
MonotonicityNot monotonic
2025-02-05T21:53:48.696711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.486151734 1
 
1.6%
1.693333834 1
 
1.6%
1.730743897 1
 
1.6%
1.717686831 1
 
1.6%
1.732714292 1
 
1.6%
1.753727184 1
 
1.6%
1.892277316 1
 
1.6%
2.055893343 1
 
1.6%
2.296679825 1
 
1.6%
2.358774415 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
1.373060158 1
1.6%
1.454065726 1
1.6%
1.486151734 1
1.6%
1.495956673 1
1.6%
1.548159107 1
1.6%
1.580511533 1
1.6%
1.616580471 1
1.6%
1.673629618 1
1.6%
1.693333834 1
1.6%
1.717686831 1
1.6%
ValueCountFrequency (%)
5.317159953 1
1.6%
5.260844762 1
1.6%
5.209620472 1
1.6%
5.078435322 1
1.6%
5.050845138 1
1.6%
4.894043352 1
1.6%
4.88430958 1
1.6%
4.847553043 1
1.6%
4.687966161 1
1.6%
4.308832471 1
1.6%

Urban population
Real number (ℝ)

High correlation  Unique 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29702626
Minimum8450408
Maximum55477513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:48.816729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8450408
5-th percentile9590407
Q115382050
median30529617
Q341640823
95-th percentile53001658
Maximum55477513
Range47027105
Interquartile range (IQR)26258773

Descriptive statistics

Standard deviation14770706
Coefficient of variation (CV)0.49728622
Kurtosis-1.3205086
Mean29702626
Median Absolute Deviation (MAD)13522753
Skewness0.11858948
Sum1.900968 × 109
Variance2.1817377 × 1014
MonotonicityNot monotonic
2025-02-05T21:53:49.072735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53931057 1
 
1.6%
53135486 1
 
1.6%
52243300 1
 
1.6%
51346882 1
 
1.6%
50472435 1
 
1.6%
49605425 1
 
1.6%
48743065 1
 
1.6%
47829383 1
 
1.6%
46856101 1
 
1.6%
45792230 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
8450408 1
1.6%
8797585 1
1.6%
9159605 1
1.6%
9532786 1
1.6%
9916926 1
1.6%
10310905 1
1.6%
10713456 1
1.6%
11125561 1
1.6%
11547892 1
1.6%
11977816 1
1.6%
ValueCountFrequency (%)
55477513 1
1.6%
54676670 1
1.6%
53931057 1
1.6%
53135486 1
1.6%
52243300 1
1.6%
51346882 1
1.6%
50472435 1
1.6%
49605425 1
1.6%
48743065 1
1.6%
47829383 1
1.6%

Urban population (% of total population)
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.577375
Minimum30.297
Maximum48.287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size640.0 B
2025-02-05T21:53:49.184972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30.297
5-th percentile31.11645
Q135.82775
median45.4885
Q346.32625
95-th percentile47.36915
Maximum48.287
Range17.99
Interquartile range (IQR)10.4985

Descriptive statistics

Standard deviation6.0907092
Coefficient of variation (CV)0.14649095
Kurtosis-1.2329367
Mean41.577375
Median Absolute Deviation (MAD)1.579
Skewness-0.67992864
Sum2660.952
Variance37.096738
MonotonicityNot monotonic
2025-02-05T21:53:49.298868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.475 2
 
3.1%
47.408 1
 
1.6%
47.684 1
 
1.6%
47.149 1
 
1.6%
46.907 1
 
1.6%
46.682 1
 
1.6%
46.284 1
 
1.6%
46.093 1
 
1.6%
45.903 1
 
1.6%
45.712 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
30.297 1
1.6%
30.555 1
1.6%
30.816 1
1.6%
31.077 1
1.6%
31.34 1
1.6%
31.604 1
1.6%
31.869 1
1.6%
32.135 1
1.6%
32.403 1
1.6%
32.672 1
1.6%
ValueCountFrequency (%)
48.287 1
1.6%
47.977 1
1.6%
47.684 1
1.6%
47.408 1
1.6%
47.149 1
1.6%
46.986 1
1.6%
46.907 1
1.6%
46.901 1
1.6%
46.815 1
1.6%
46.73 1
1.6%

Interactions

2025-02-05T21:53:37.291836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:45.565763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:47.806889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:49.878433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:52.003009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:54.142781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:56.076622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:58.206963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:00.320773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:02.420176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:04.635572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:07.263991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:09.396706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:11.621454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:13.706170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:15.849281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:18.060133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:20.081703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:22.181738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:24.255122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:26.442593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:28.526522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:30.489584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:32.499584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:34.745649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:37.374930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:45.806242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:47.881898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:49.961453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:52.079009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:54.220781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:56.169080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:58.287247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:00.394771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:02.498210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:04.711947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:07.350858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:09.479739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:11.711040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:13.787684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:15.932792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:18.141143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:20.154712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:22.264737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:24.489625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:26.515608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:28.596521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:30.559723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:32.582584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:34.814670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:37.453669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:45.880503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:47.949909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:50.037452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:52.155024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:54.300781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:56.252350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:58.396626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:00.611411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:02.570808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:04.792949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:07.435864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:09.557914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:11.790829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:13.880384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:16.026853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:18.228651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:20.240034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:22.345747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:24.577629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:26.596609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:28.671524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:30.625628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:32.663584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:34.889590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:37.535669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:45.962345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:48.031993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:50.115467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:52.233286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:54.379781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:56.330345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:58.475194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:00.689997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:02.654258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:04.870948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:07.509821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:09.643914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:11.876588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:13.965044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:16.119283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:18.313384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:20.316703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:22.429759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:24.655626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:26.669608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:28.752522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:30.698559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:32.750586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:35.044592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:37.619954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:52:46.049343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-05T21:53:17.509811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-05T21:53:23.828624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:26.035566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:28.138804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:30.131798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:32.153755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:34.326935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:36.938035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-05T21:53:26.359593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:28.449522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:30.412564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:32.420587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:34.658648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-05T21:53:37.216279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-05T21:53:49.447770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Access to electricity (% of population)Agricultural irrigated land (% of total agricultural land)Agricultural land (% of land area)Agricultural land (sq. km)Agriculture, forestry, and fishing, value added (% of GDP)Annual freshwater withdrawals, total (% of internal resources)Annual freshwater withdrawals, total (billion cubic meters)Arable land (% of land area)Cereal yield (kg per hectare)Foreign direct investment, net inflows (% of GDP)Forest area (% of land area)Forest area (sq. km)Land area where elevation is below 5 meters (% of total land area)Marine protected areas (% of territorial waters)Mortality rate, under-5 (per 1,000 live births)Population growth (annual %)Population in urban agglomerations of more than 1 million (% of total population)Population living in areas where elevation is below 5 meters (% of total population)Population, totalPoverty headcount ratio at $2.15 a day (2017 PPP) (% of population)Prevalence of underweight, weight for age (% of children under 5)Primary completion rate, total (% of relevant age group)Renewable energy consumption (% of total final energy consumption)Rural land area where elevation is below 5 meters (% of total land area)Rural land area where elevation is below 5 meters (sq. km)Rural population living in areas where elevation is below 5 meters (% of total population)School enrollment, primary and secondary (gross), gender parity index (GPI)Terrestrial and marine protected areas (% of total territorial area)Terrestrial protected areas (% of total land area)Urban land area where elevation is below 5 meters (% of total land area)Urban land area where elevation is below 5 meters (sq. km)Urban populationUrban population (% of total population)Urban population growth (annual %)Urban population living in areas where elevation is below 5 meters (% of total population)Year
Access to electricity (% of population)1.0000.7710.9580.955-0.8710.9180.9180.7370.9680.233-0.533-0.5331.0000.632-0.954-0.895-0.3171.0000.976-0.922-0.8670.302-0.7621.0001.0001.000-0.3930.7750.6321.0001.0000.9760.206-0.4951.0000.976
Agricultural irrigated land (% of total agricultural land)0.7711.0000.8410.8410.7710.6000.6000.8410.086-0.429-0.714-0.7140.0000.000-0.7710.7140.8290.0000.771-1.000-1.0000.8000.7140.0000.0000.000-0.8000.0000.0000.0000.0000.771-0.6570.7710.0000.771
Agricultural land (% of land area)0.9580.8411.0001.000-0.9340.9270.9270.8050.9850.694-0.627-0.6271.0001.000-0.989-0.8560.6721.0000.991-0.922-0.8630.619-0.7361.0001.0001.0000.1051.0001.0001.0001.0000.9910.843-0.7431.0000.991
Agricultural land (sq. km)0.9550.8411.0001.000-0.9310.9270.9270.7960.9850.679-0.671-0.6711.0001.000-0.989-0.8490.6631.0000.991-0.922-0.8630.712-0.7361.0001.0001.0000.1051.0001.0001.0001.0000.9910.835-0.7301.0000.991
Agriculture, forestry, and fishing, value added (% of GDP)-0.8710.771-0.934-0.9311.000-0.856-0.856-0.743-0.942-0.7120.5130.5131.0001.0000.9340.770-0.6411.000-0.9380.8260.890-0.6090.8651.0001.0001.000-0.2591.0001.0001.0001.000-0.938-0.8170.7381.000-0.938
Annual freshwater withdrawals, total (% of internal resources)0.9180.6000.9270.927-0.8561.0001.0000.8820.8820.6680.6820.682NaN0.612-0.921-0.7060.659NaN0.929-0.986-0.5000.794-0.775NaNNaNNaN0.0280.6120.612NaNNaN0.9290.8500.100NaN0.929
Annual freshwater withdrawals, total (billion cubic meters)0.9180.6000.9270.927-0.8561.0001.0000.8820.8820.6680.6820.682NaN0.612-0.921-0.7060.659NaN0.929-0.986-0.5000.794-0.775NaNNaNNaN0.0280.6120.612NaNNaN0.9290.8500.100NaN0.929
Arable land (% of land area)0.7370.8410.8050.796-0.7430.8820.8821.0000.7830.465-0.270-0.2701.0001.000-0.785-0.5740.6641.0000.785-0.884-0.5970.131-0.2381.0001.0001.000-0.5311.0001.0001.0001.0000.7850.753-0.3861.0000.785
Cereal yield (kg per hectare)0.9680.0860.9850.985-0.9420.8820.8820.7831.0000.695-0.669-0.6691.0000.632-0.994-0.8720.6651.0000.996-0.922-0.9230.667-0.7661.0001.0001.0000.1390.7750.6321.0001.0000.9960.838-0.7761.0000.996
Foreign direct investment, net inflows (% of GDP)0.233-0.4290.6940.679-0.7120.6680.6680.4650.6951.0000.1160.1161.0000.433-0.711-0.6300.5681.0000.713-0.491-0.4290.252-0.4341.0001.0001.0000.1450.4710.4331.0001.0000.7130.742-0.6401.0000.713
Forest area (% of land area)-0.533-0.714-0.627-0.6710.5130.6820.682-0.270-0.6690.1161.0001.0001.0000.7070.6970.5780.7751.000-0.6740.0480.580-0.2880.4121.0001.0001.0000.3710.8660.7071.0001.000-0.6740.6790.3351.000-0.674
Forest area (sq. km)-0.533-0.714-0.627-0.6710.5130.6820.682-0.270-0.6690.1161.0001.0001.0000.7070.6970.5780.7751.000-0.6740.0480.580-0.2880.4121.0001.0001.0000.3710.8660.7071.0001.000-0.6740.6790.3351.000-0.674
Land area where elevation is below 5 meters (% of total land area)1.0000.0001.0001.0001.000NaNNaN1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.000
Marine protected areas (% of territorial waters)0.6320.0001.0001.0001.0000.6120.6121.0000.6320.4330.7070.7070.0001.0001.0000.4330.6320.0001.0001.0001.0001.0001.0000.0000.0000.0000.3820.8661.0000.0000.0000.0000.6321.0000.0000.632
Mortality rate, under-5 (per 1,000 live births)-0.954-0.771-0.989-0.9890.934-0.921-0.921-0.785-0.994-0.7110.6970.6971.0001.0001.0000.878-0.6801.000-0.9980.8730.843-0.6730.7801.0001.0001.000-0.0591.0001.0001.0001.000-0.998-0.8430.7701.000-0.998
Population growth (annual %)-0.8950.714-0.856-0.8490.770-0.706-0.706-0.574-0.872-0.6300.5780.5781.0000.4330.8781.000-0.4861.000-0.8860.7310.885-0.4750.7461.0001.0001.000-0.2070.6610.4331.0001.000-0.886-0.7140.8531.000-0.886
Population in urban agglomerations of more than 1 million (% of total population)-0.3170.8290.6720.663-0.6410.6590.6590.6640.6650.5680.7750.7751.0000.632-0.680-0.4861.0001.0000.696-0.4550.313-0.2260.4011.0001.0001.000-0.1490.0000.6321.0001.0000.6960.906-0.4191.0000.696
Population living in areas where elevation is below 5 meters (% of total population)1.0000.0001.0001.0001.000NaNNaN1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.000
Population, total0.9760.7710.9910.991-0.9380.9290.9290.7850.9960.713-0.674-0.6741.0001.000-0.998-0.8860.6961.0001.000-0.922-0.9230.492-0.7881.0001.0001.0000.1301.0001.0001.0001.0001.0000.855-0.7831.0001.000
Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)-0.922-1.000-0.922-0.9220.826-0.986-0.986-0.884-0.922-0.4910.0480.0481.0001.0000.8730.731-0.4551.000-0.9221.0000.738-0.4620.6021.0001.0001.0000.1161.0001.0001.0001.000-0.922-0.7070.1321.000-0.922
Prevalence of underweight, weight for age (% of children under 5)-0.867-1.000-0.863-0.8630.890-0.500-0.500-0.597-0.923-0.4290.5800.5801.0001.0000.8430.8850.3131.000-0.9230.7381.000-0.6430.7551.0001.0001.000-0.9001.0001.0001.0001.000-0.923-0.2360.6701.000-0.923
Primary completion rate, total (% of relevant age group)0.3020.8000.6190.712-0.6090.7940.7940.1310.6670.252-0.288-0.288NaN1.000-0.673-0.475-0.226NaN0.492-0.462-0.6431.000-0.644NaNNaNNaN0.4041.0001.000NaNNaN0.4920.321-0.367NaN0.492
Renewable energy consumption (% of total final energy consumption)-0.7620.714-0.736-0.7360.865-0.775-0.775-0.238-0.766-0.4340.4120.4121.0001.0000.7800.7460.4011.000-0.7880.6020.755-0.6441.0001.0001.0001.000-0.0561.0001.0001.0001.000-0.788-0.0620.5811.000-0.788
Rural land area where elevation is below 5 meters (% of total land area)1.0000.0001.0001.0001.000NaNNaN1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.000
Rural land area where elevation is below 5 meters (sq. km)1.0000.0001.0001.0001.000NaNNaN1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.000
Rural population living in areas where elevation is below 5 meters (% of total population)1.0000.0001.0001.0001.000NaNNaN1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.000
School enrollment, primary and secondary (gross), gender parity index (GPI)-0.393-0.8000.1050.105-0.2590.0280.028-0.5310.1390.1450.3710.3711.0000.382-0.059-0.207-0.1491.0000.1300.116-0.9000.404-0.0561.0001.0001.0001.0000.3820.3821.0001.0000.1300.060-0.3401.0000.130
Terrestrial and marine protected areas (% of total territorial area)0.7750.0001.0001.0001.0000.6120.6121.0000.7750.4710.8660.8660.0000.8661.0000.6610.0000.0001.0001.0001.0001.0001.0000.0000.0000.0000.3821.0000.8660.0000.0000.4240.7751.0000.0000.775
Terrestrial protected areas (% of total land area)0.6320.0001.0001.0001.0000.6120.6121.0000.6320.4330.7070.7070.0001.0001.0000.4330.6320.0001.0001.0001.0001.0001.0000.0000.0000.0000.3820.8661.0000.0000.0000.0000.6321.0000.0000.632
Urban land area where elevation is below 5 meters (% of total land area)1.0000.0001.0001.0001.000NaNNaN1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.000
Urban land area where elevation is below 5 meters (sq. km)1.0000.0001.0001.0001.000NaNNaN1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.000
Urban population0.9760.7710.9910.991-0.9380.9290.9290.7850.9960.713-0.674-0.6741.0000.000-0.998-0.8860.6961.0001.000-0.922-0.9230.492-0.7881.0001.0001.0000.1300.4240.0001.0001.0001.0000.855-0.7831.0001.000
Urban population (% of total population)0.206-0.6570.8430.835-0.8170.8500.8500.7530.8380.7420.6790.6791.0000.632-0.843-0.7140.9061.0000.855-0.707-0.2360.321-0.0621.0001.0001.0000.0600.7750.6321.0001.0000.8551.000-0.6461.0000.855
Urban population growth (annual %)-0.4950.771-0.743-0.7300.7380.1000.100-0.386-0.776-0.6400.3350.3351.0001.0000.7700.853-0.4191.000-0.7830.1320.670-0.3670.5811.0001.0001.000-0.3401.0001.0001.0001.000-0.783-0.6461.0001.000-0.783
Urban population living in areas where elevation is below 5 meters (% of total population)1.0000.0001.0001.0001.000NaNNaN1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.000
Year0.9760.7710.9910.991-0.9380.9290.9290.7850.9960.713-0.674-0.6741.0000.632-0.998-0.8860.6961.0001.000-0.922-0.9230.492-0.7881.0001.0001.0000.1300.7750.6321.0001.0001.0000.855-0.7831.0001.000

Missing values

2025-02-05T21:53:39.570021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-05T21:53:39.963383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-05T21:53:40.819483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

YearAgricultural land (sq. km)Agricultural land (% of land area)Arable land (% of land area)Rural land area where elevation is below 5 meters (sq. km)Rural land area where elevation is below 5 meters (% of total land area)Urban land area where elevation is below 5 meters (sq. km)Urban land area where elevation is below 5 meters (% of total land area)Land area where elevation is below 5 meters (% of total land area)Forest area (sq. km)Forest area (% of land area)Agricultural irrigated land (% of total agricultural land)Average precipitation in depth (mm per year)Cereal yield (kg per hectare)Foreign direct investment, net inflows (% of GDP)Access to electricity (% of population)Renewable energy consumption (% of total final energy consumption)Droughts, floods, extreme temperatures (% of population, average 1990-2009)Rural population living in areas where elevation is below 5 meters (% of total population)Urban population living in areas where elevation is below 5 meters (% of total population)Population living in areas where elevation is below 5 meters (% of total population)Population in urban agglomerations of more than 1 million (% of total population)Annual freshwater withdrawals, total (billion cubic meters)Annual freshwater withdrawals, total (% of internal resources)Terrestrial protected areas (% of total land area)Marine protected areas (% of territorial waters)Terrestrial and marine protected areas (% of total territorial area)Ease of doing business rank (1=most business-friendly regulations)Agriculture, forestry, and fishing, value added (% of GDP)School enrollment, primary and secondary (gross), gender parity index (GPI)Primary completion rate, total (% of relevant age group)Mortality rate, under-5 (per 1,000 live births)Prevalence of underweight, weight for age (% of children under 5)Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)Population growth (annual %)Population, totalUrban population growth (annual %)Urban populationUrban population (% of total population)
02021126830.042.53613718.747694NaNNaNNaNNaNNaN72234.8024.226046NaN2348.03834.83.04078997.528.0NaNNaNNaNNaN14.16873589.00036418.58045215.8686901.7444043.722085NaN10.0691641.02612191.10198227.916.73.00.905660113100950.01.48615253931057.047.684
12020126590.042.45564618.747694NaNNaNNaNNaNNaN71885.9024.109032NaN2348.03769.41.88586496.429.1NaNNaNNaNNaN14.05132487.47726918.26247815.3724701.1590643.149224NaN10.1853111.02387098.11262528.0NaNNaN1.145514112081264.01.69333453135486.047.408
22019126360.042.37850918.747694NaNNaNNaNNaNNaN71537.0023.992018NaN2348.03737.92.30117595.326.9NaNNaNNaNNaN13.97398685.99451417.95292615.3724701.1590643.14922495.08.8203241.025195101.02381928.018.9NaN1.216155110804683.01.73074452243300.047.149
32018126130.042.30137218.747694NaNNaNNaNNaNNaN71188.1023.875004NaN2348.03670.52.86833894.227.6NaNNaNNaNNaN13.91075592.15781119.23962615.3166591.1590633.141408NaN9.6501401.025809110.88218728.019.03.01.236860109465287.01.71768751346882.046.907
42017125900.042.22423418.747694NaNNaNNaNNaNNaN70839.3023.758024NaN2348.03692.33.12238893.027.8NaNNaNNaNNaN13.85265291.79128019.16310615.3166591.1590633.141408NaN10.1829541.018490104.43645527.9NaNNaN1.288302108119693.01.73271450472435.046.682
52016125560.042.11020618.747694NaNNaNNaNNaNNaN70490.4023.641010NaN2348.03529.02.59850892.028.6NaNNaNNaNNaN13.80195987.11000018.18580415.3200001.1590563.141386NaN10.2051261.008820NaN27.9NaNNaN1.341907106735719.01.75372749605425.046.475
62015125270.042.01294618.74769412170.1412444.1356911511.4520960.5136264.64931770141.5023.523996NaN2348.03556.11.84018089.130.8NaN4.5885415.93585410.52439513.75889286.40000018.037578NaNNaNNaNNaN10.9964991.007280NaN27.921.46.51.478754105312992.01.89227748743065.046.284
72014124980.041.91568618.747694NaNNaNNaNNaNNaN69792.6423.406996NaN2348.03637.41.92937589.832.4NaNNaNNaNNaN13.73512284.43200017.626722NaNNaNNaNNaN12.2716821.01531097.59192728.1NaNNaN1.642833103767130.02.05589347829383.046.093
82013124690.041.81842618.747694NaNNaNNaNNaNNaN69443.7823.289996NaN2348.03531.81.31642687.533.1NaNNaNNaNNaN13.73397684.44700017.629854NaNNaNNaNNaN12.473425NaNNaN28.319.9NaN1.879716102076336.02.29668046856101.045.903
92012124300.041.68762818.714156NaNNaNNaNNaNNaN69094.9223.172995NaN2348.03492.61.22763087.134.1NaNNaNNaNNaN13.76577784.24800017.588309NaNNaNNaNNaN13.095807NaNNaN28.7NaN10.91.942262100175512.02.35877445792230.045.712
YearAgricultural land (sq. km)Agricultural land (% of land area)Arable land (% of land area)Rural land area where elevation is below 5 meters (sq. km)Rural land area where elevation is below 5 meters (% of total land area)Urban land area where elevation is below 5 meters (sq. km)Urban land area where elevation is below 5 meters (% of total land area)Land area where elevation is below 5 meters (% of total land area)Forest area (sq. km)Forest area (% of land area)Agricultural irrigated land (% of total agricultural land)Average precipitation in depth (mm per year)Cereal yield (kg per hectare)Foreign direct investment, net inflows (% of GDP)Access to electricity (% of population)Renewable energy consumption (% of total final energy consumption)Droughts, floods, extreme temperatures (% of population, average 1990-2009)Rural population living in areas where elevation is below 5 meters (% of total population)Urban population living in areas where elevation is below 5 meters (% of total population)Population living in areas where elevation is below 5 meters (% of total population)Population in urban agglomerations of more than 1 million (% of total population)Annual freshwater withdrawals, total (billion cubic meters)Annual freshwater withdrawals, total (% of internal resources)Terrestrial protected areas (% of total land area)Marine protected areas (% of territorial waters)Terrestrial and marine protected areas (% of total territorial area)Ease of doing business rank (1=most business-friendly regulations)Agriculture, forestry, and fishing, value added (% of GDP)School enrollment, primary and secondary (gross), gender parity index (GPI)Primary completion rate, total (% of relevant age group)Mortality rate, under-5 (per 1,000 live births)Prevalence of underweight, weight for age (% of children under 5)Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)Population growth (annual %)Population, totalUrban population growth (annual %)Urban populationUrban population (% of total population)
54196783050.027.84483315.959230NaNNaNNaNNaNNaNNaNNaNNaN2348.01113.1NaNNaNNaNNaNNaNNaNNaN9.888999NaNNaNNaNNaNNaNNaN24.430640NaNNaN86.7NaNNaN2.94327334621320.03.77447311125561.032.135
55196681300.027.25809716.093341NaNNaNNaNNaNNaNNaNNaNNaN2348.01062.8NaNNaNNaNNaNNaNNaNNaN9.738937NaNNaNNaNNaNNaNNaN24.249555NaNNaN88.4NaNNaN2.99484033617170.03.82984510713456.031.869
56196581320.027.26480316.160397NaNNaNNaNNaNNaNNaNNaNNaN2348.01045.5NaNNaNNaNNaNNaNNaNNaN9.596189NaNNaNNaNNaNNaNNaN24.266986NaNNaN90.2NaNNaN3.05705932625316.03.89590810310905.031.604
57196481520.027.33185816.227453NaNNaNNaNNaNNaNNaNNaNNaN2348.01035.7NaNNaNNaNNaNNaNNaNNaN9.462093NaNNaNNaNNaNNaNNaN23.759728NaNNaN92.1NaNNaN3.10787531643032.03.9505989916926.031.340
58196378720.026.39308016.294508NaNNaNNaNNaNNaNNaNNaNNaN2348.01030.2NaNNaNNaNNaNNaNNaNNaN9.333552NaNNaNNaNNaNNaNNaN24.404562NaNNaN94.1NaNNaN3.15000330674731.03.9933969532786.031.077
59196277920.026.12485816.361564NaNNaNNaNNaNNaNNaNNaNNaN2348.01025.3NaNNaNNaNNaNNaNNaNNaN9.211286NaNNaNNaNNaNNaNNaN23.805024NaNNaN96.3NaNNaN3.18200529723536.04.0325809159605.030.816
60196177130.025.85998816.431972NaNNaNNaNNaNNaNNaNNaNNaN2348.0996.3NaNNaNNaNNaNNaNNaNNaN9.093618NaNNaNNaNNaNNaNNaN23.645382NaNNaN98.9NaNNaN3.17829228792621.04.0262538797585.030.555
612022NaN42.53613718.747694NaNNaNNaNNaNNaN72583.724.343059NaNNaN3821.62.34751094.8NaNNaNNaNNaNNaN14.314811NaNNaN15.868691.7444043.722085NaN9.551768NaN87.98268927.5NaNNaN0.760479113964338.01.37306054676670.047.977
622023NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.085411NaNNaNNaNNaNNaNNaN14.462804NaNNaNNaNNaNNaNNaN9.396750NaN80.705971NaNNaNNaN0.810001114891199.01.45406655477513.048.287
631960NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN8.977754NaNNaNNaNNaNNaNNaN23.708189NaNNaN102.0NaNNaNNaN27891897.0NaN8450408.030.297