Overview

Brought to you by YData

Dataset statistics

Number of variables	85
Number of observations	34
Missing cells	1732
Missing cells (%)	59.9%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	22.6 KiB
Average record size in memory	680.0 B

Variable types

Text	13
Unsupported	1
Numeric	17
Categorical	54

Alerts

`Country_Gibraltar` has constant value "1.0"	Constant
`Country_Sierra Leone` has constant value "2.0"	Constant
`Country_Botswana` has constant value "3.0"	Constant
`Country_Zimbabwe` has constant value "1.0"	Constant
`Country_Portugal` has constant value "1.0"	Constant
`Country_Cape Verde` has constant value "2.0"	Constant
`Country_Saint Helena, Ascension and Tristan da Cunha` has constant value "1.0"	Constant
`Country_India` has constant value "2.0"	Constant
`Country_Somalia` has constant value "1.0"	Constant
`Country_Guinea-Bissau` has constant value "1.0"	Constant
`Country_Malawi` has constant value "6.0"	Constant
`Country_Ghana` has constant value "1.0"	Constant
`Country_Liberia` has constant value "1.0"	Constant
`Country_The Gambia` has constant value "1.0"	Constant
`Country_Ethiopia` has constant value "9.0"	Constant
`Country_Malta` has constant value "6.0"	Constant
`Country_Gabon` has constant value "1.0"	Constant
`Country_Germany` has constant value "1.0"	Constant
`Country_Angola` has constant value "2.0"	Constant
`Country_Namibia` has constant value "1.0"	Constant
`Country_Sahrawi Arab Democratic Republic` has constant value "4.0"	Constant
`Country_Sweden` has constant value "1.0"	Constant
`Country_Ukraine` has constant value "1.0"	Constant
`Country_Russia` has constant value "1.0"	Constant
`Arrival Age` is highly overall correlated with `Country_Côte d'Ivoire` and 18 other fields	High correlation
`Country_Algeria` is highly overall correlated with `Country_France` and 8 other fields	High correlation
`Country_Côte d'Ivoire` is highly overall correlated with `Arrival Age` and 13 other fields	High correlation
`Country_Democratic Republic of the Congo` is highly overall correlated with `Arrival Age` and 13 other fields	High correlation
`Country_Egypt` is highly overall correlated with `Country_Libya` and 17 other fields	High correlation
`Country_France` is highly overall correlated with `Arrival Age` and 15 other fields	High correlation
`Country_Guinea` is highly overall correlated with `Country_Morocco` and 16 other fields	High correlation
`Country_Kenya` is highly overall correlated with `Arrival Age` and 13 other fields	High correlation
`Country_Libya` is highly overall correlated with `Country_Algeria` and 13 other fields	High correlation
`Country_Mali` is highly overall correlated with `Arrival Age` and 21 other fields	High correlation
`Country_Mauritania` is highly overall correlated with `Arrival Age` and 14 other fields	High correlation
`Country_Morocco` is highly overall correlated with `Country_Guinea` and 5 other fields	High correlation
`Country_Mozambique` is highly overall correlated with `Arrival Age` and 7 other fields	High correlation
`Country_Niger` is highly overall correlated with `Arrival Age` and 15 other fields	High correlation
`Country_Nigeria` is highly overall correlated with `Country_Niger` and 8 other fields	High correlation
`Country_Senegal` is highly overall correlated with `Arrival Age` and 16 other fields	High correlation
`Country_South Africa` is highly overall correlated with `Arrival Age` and 11 other fields	High correlation
`Country_Spain` is highly overall correlated with `Country_Mozambique` and 9 other fields	High correlation
`Country_Tanzania` is highly overall correlated with `Country_Egypt` and 8 other fields	High correlation
`Country_Tunisia` is highly overall correlated with `Arrival Age` and 16 other fields	High correlation
`Country_United Kingdom` is highly overall correlated with `Arrival Age` and 10 other fields	High correlation
`Country_Zambia` is highly overall correlated with `Arrival Age` and 17 other fields	High correlation
`Departure Age` is highly overall correlated with `Arrival Age` and 17 other fields	High correlation
`Difficulty_Climate` is highly overall correlated with `Country_Guinea` and 7 other fields	High correlation
`Difficulty_Fatigue/Illness` is highly overall correlated with `Arrival Age` and 15 other fields	High correlation
`Difficulty_Humans` is highly overall correlated with `Arrival Age` and 18 other fields	High correlation
`Difficulty_Nature` is highly overall correlated with `Arrival Age` and 15 other fields	High correlation
`Difficulty_Thirst/Hunger` is highly overall correlated with `Arrival Age` and 19 other fields	High correlation
`Difficulty_Transport` is highly overall correlated with `Country_Guinea` and 8 other fields	High correlation
`Hierarchical_Cluster` is highly overall correlated with `Country_Côte d'Ivoire` and 21 other fields	High correlation
`KMeans_Cluster` is highly overall correlated with `Country_Algeria` and 21 other fields	High correlation
`Language` is highly overall correlated with `Country_Côte d'Ivoire` and 17 other fields	High correlation
`Money` is highly overall correlated with `Country_Côte d'Ivoire` and 20 other fields	High correlation
`Nationality` is highly overall correlated with `Country_Côte d'Ivoire` and 17 other fields	High correlation
`Number of images` is highly overall correlated with `Country_Côte d'Ivoire` and 16 other fields	High correlation
`Number of steps` is highly overall correlated with `Country_Côte d'Ivoire` and 16 other fields	High correlation
`Start Year` is highly overall correlated with `Country_Côte d'Ivoire` and 16 other fields	High correlation
`Steps with null coordinates in %` is highly overall correlated with `Country_Algeria` and 17 other fields	High correlation
`Total distance traveled (km)` is highly overall correlated with `Country_Côte d'Ivoire` and 19 other fields	High correlation
`Transport_Boat` is highly overall correlated with `Country_Côte d'Ivoire` and 14 other fields	High correlation
`Transport_Camelid` is highly overall correlated with `Country_Guinea` and 16 other fields	High correlation
`Transport_Caravan` is highly overall correlated with `Country_Algeria` and 9 other fields	High correlation
`Transport_Equid` is highly overall correlated with `Country_Algeria` and 12 other fields	High correlation
`Transport_Slow ground vehicle` is highly overall correlated with `Country_Guinea` and 8 other fields	High correlation
`Transport_Train` is highly overall correlated with `Country_Egypt` and 8 other fields	High correlation
`Transport_Walking` is highly overall correlated with `Country_Egypt` and 15 other fields	High correlation
`Travel duration in days` is highly overall correlated with `Arrival Age` and 19 other fields	High correlation
`Transport_Train` has 25 (73.5%) missing values	Missing
`Transport_Equid` has 22 (64.7%) missing values	Missing
`Country_France` has 32 (94.1%) missing values	Missing
`Country_Algeria` has 24 (70.6%) missing values	Missing
`Transport_Slow ground vehicle` has 25 (73.5%) missing values	Missing
`Transport_Boat` has 10 (29.4%) missing values	Missing
`Difficulty_Climate` has 28 (82.4%) missing values	Missing
`Difficulty_Transport` has 29 (85.3%) missing values	Missing
`Difficulty_Nature` has 28 (82.4%) missing values	Missing
`Country_Tunisia` has 31 (91.2%) missing values	Missing
`Country_Morocco` has 27 (79.4%) missing values	Missing
`Country_Libya` has 27 (79.4%) missing values	Missing
`Transport_Caravan` has 25 (73.5%) missing values	Missing
`Transport_Camelid` has 22 (64.7%) missing values	Missing
`Country_United Kingdom` has 28 (82.4%) missing values	Missing
`Country_Nigeria` has 29 (85.3%) missing values	Missing
`Country_Niger` has 31 (91.2%) missing values	Missing
`Difficulty_Humans` has 30 (88.2%) missing values	Missing
`Country_Spain` has 29 (85.3%) missing values	Missing
`Country_Gibraltar` has 31 (91.2%) missing values	Missing
`Transport_Walking` has 20 (58.8%) missing values	Missing
`Difficulty_Fatigue/Illness` has 30 (88.2%) missing values	Missing
`Difficulty_Thirst/Hunger` has 29 (85.3%) missing values	Missing
`Country_Sierra Leone` has 33 (97.1%) missing values	Missing
`Country_Guinea` has 29 (85.3%) missing values	Missing
`Country_Mali` has 29 (85.3%) missing values	Missing
`Country_Côte d'Ivoire` has 32 (94.1%) missing values	Missing
`Country_Senegal` has 31 (91.2%) missing values	Missing
`Country_Egypt` has 30 (88.2%) missing values	Missing
`Country_Kenya` has 32 (94.1%) missing values	Missing
`Country_Tanzania` has 28 (82.4%) missing values	Missing
`Country_Mozambique` has 27 (79.4%) missing values	Missing
`Country_Democratic Republic of the Congo` has 32 (94.1%) missing values	Missing
`Country_Zambia` has 32 (94.1%) missing values	Missing
`Country_Botswana` has 33 (97.1%) missing values	Missing
`Country_South Africa` has 30 (88.2%) missing values	Missing
`Country_Zimbabwe` has 32 (94.1%) missing values	Missing
`Country_Portugal` has 31 (91.2%) missing values	Missing
`Country_Cape Verde` has 33 (97.1%) missing values	Missing
`Country_Saint Helena, Ascension and Tristan da Cunha` has 33 (97.1%) missing values	Missing
`Country_India` has 33 (97.1%) missing values	Missing
`Country_Somalia` has 33 (97.1%) missing values	Missing
`Country_Guinea-Bissau` has 33 (97.1%) missing values	Missing
`Country_Malawi` has 33 (97.1%) missing values	Missing
`Country_Mauritania` has 32 (94.1%) missing values	Missing
`Country_Ghana` has 33 (97.1%) missing values	Missing
`Country_Liberia` has 33 (97.1%) missing values	Missing
`Country_The Gambia` has 33 (97.1%) missing values	Missing
`Country_Ethiopia` has 33 (97.1%) missing values	Missing
`Country_Malta` has 33 (97.1%) missing values	Missing
`Country_Gabon` has 33 (97.1%) missing values	Missing
`Country_Germany` has 33 (97.1%) missing values	Missing
`Country_Angola` has 33 (97.1%) missing values	Missing
`Country_Namibia` has 33 (97.1%) missing values	Missing
`Country_Sahrawi Arab Democratic Republic` has 33 (97.1%) missing values	Missing
`Country_Sweden` has 33 (97.1%) missing values	Missing
`Country_Ukraine` has 33 (97.1%) missing values	Missing
`Country_Russia` has 33 (97.1%) missing values	Missing
`Country_France` is uniformly distributed	Uniform
`Country_Tunisia` is uniformly distributed	Uniform
`Country_Niger` is uniformly distributed	Uniform
`Country_Côte d'Ivoire` is uniformly distributed	Uniform
`Country_Senegal` is uniformly distributed	Uniform
`Country_Kenya` is uniformly distributed	Uniform
`Country_Democratic Republic of the Congo` is uniformly distributed	Uniform
`Country_Zambia` is uniformly distributed	Uniform
`Country_Mauritania` is uniformly distributed	Uniform
`Title` has unique values	Unique
`URL` has unique values	Unique
`Goal` has unique values	Unique
`Total distance traveled (km)` has unique values	Unique
`Year of the Journey` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`Departure Age` has 10 (29.4%) zeros	Zeros
`Arrival Age` has 14 (41.2%) zeros	Zeros
`Travel duration in days` has 14 (41.2%) zeros	Zeros
`Number of images` has 10 (29.4%) zeros	Zeros
`Steps with null coordinates in %` has 3 (8.8%) zeros	Zeros
`Total distance traveled (km)` has 1 (2.9%) zeros	Zeros

Reproduction

Analysis started	2025-03-20 12:59:29.594953
Analysis finished	2025-03-20 13:00:36.541195
Duration	1 minute and 6.95 seconds
Software version	ydata-profiling vv4.15.0
Download configuration	config.json

Title
Text

Unique

Distinct	34
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	228
Median length	61.5
Mean length	58.823529
Min length	6

Characters and Unicode

Total characters	2000
Distinct characters	74
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	34 ?
Unique (%)	100.0%

Sample

1st row	A Journal of the First Voyage of Vasco da Gama 1497-1499
2nd row	A Popular Account of Dr. Livingstone's Expedition to the Zambesi and Its Tributaries
3rd row	A biographical memoir of the late Dr. Walter Oudney, Captain Hugh Clapperton, both of the Royal Navy, and Major Alex. Gordon Laing, all of whom died amid their active and enterprising endeavours to explore the interior of Africa
4th row	Afrikanska reseminnen, Äfventyr och Intryck från En utflykt till de Svartes Världsdel
5th row	Au Hoggar

Value	Count	Frequency (%)
the	24	6.9%
of	21	6.1%
in	14	4.0%
and	14	4.0%
africa	13	3.8%
to	12	3.5%
sahara	9	2.6%
au	8	2.3%
central	6	1.7%
travels	5	1.4%
Other values (156)	220	63.6%

Most occurring characters

Value	Count	Frequency (%)
	312	15.6%
a	160	8.0%
e	146	7.3%
r	134	6.7%
o	132	6.6%
t	106	5.3%
i	96	4.8%
n	94	4.7%
s	72	3.6%
h	58	2.9%
Other values (64)	690	34.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2000	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	312	15.6%
a	160	8.0%
e	146	7.3%
r	134	6.7%
o	132	6.6%
t	106	5.3%
i	96	4.8%
n	94	4.7%
s	72	3.6%
h	58	2.9%
Other values (64)	690	34.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2000	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	312	15.6%
a	160	8.0%
e	146	7.3%
r	134	6.7%
o	132	6.6%
t	106	5.3%
i	96	4.8%
n	94	4.7%
s	72	3.6%
h	58	2.9%
Other values (64)	690	34.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2000	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	312	15.6%
a	160	8.0%
e	146	7.3%
r	134	6.7%
o	132	6.6%
t	106	5.3%
i	96	4.8%
n	94	4.7%
s	72	3.6%
h	58	2.9%
Other values (64)	690	34.5%

Year of the Journey
Unsupported

Rejected Unsupported

Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Start Year
Real number (ℝ)

High correlation

Distinct	29
Distinct (%)	85.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1863.8529

Minimum	1497
Maximum	1922
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	1497
5-th percentile	1809.45
Q1	1849.25
median	1874
Q3	1909.25
95-th percentile	1921.35
Maximum	1922
Range	425
Interquartile range (IQR)	60

Descriptive statistics

Standard deviation	73.534215
Coefficient of variation (CV)	0.039452799
Kurtosis	19.371902
Mean	1863.8529
Median Absolute Deviation (MAD)	27
Skewness	-3.9356189
Sum	63371
Variance	5407.2807
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=29)

Value	Count	Frequency (%)
1915	2	5.9%
1824	2	5.9%
1922	2	5.9%
1910	2	5.9%
1849	2	5.9%
1497	1	2.9%
1853	1	2.9%
1845	1	2.9%
1879	1	2.9%
1880	1	2.9%
Other values (19)	19	55.9%

Value	Count	Frequency (%)
1497	1	2.9%
1788	1	2.9%
1821	1	2.9%
1824	2	5.9%
1827	1	2.9%
1845	1	2.9%
1849	2	5.9%
1850	1	2.9%
1853	1	2.9%
1856	1	2.9%

Value	Count	Frequency (%)
1922	2	5.9%
1921	1	2.9%
1915	2	5.9%
1914	1	2.9%
1912	1	2.9%
1910	2	5.9%
1907	1	2.9%
1898	1	2.9%
1893	1	2.9%
1890	1	2.9%

Author
Text

Distinct	25
Distinct (%)	73.5%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	71
Median length	23.5
Mean length	19.735294
Min length	10

Characters and Unicode

Total characters	671
Distinct characters	51
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	20 ?
Unique (%)	58.8%

Sample

1st row	Vasco da Gama
2nd row	David Livingstone
3rd row	Thomas Nelson (publisher)
4th row	Georg Edvard von Alfthan
5th row	Conrad Kilian

Value	Count	Frequency (%)
david	4	4.2%
livingstone	4	4.2%
rené	3	3.2%
caillié	3	3.2%
angus	3	3.2%
buchanan	3	3.2%
richardson	2	2.1%
austin	2	2.1%
the	2	2.1%
of	2	2.1%
Other values (64)	67	70.5%

Most occurring characters

Value	Count	Frequency (%)
	61	9.1%
a	55	8.2%
i	53	7.9%
n	48	7.2%
r	43	6.4%
o	39	5.8%
e	37	5.5%
s	35	5.2%
t	30	4.5%
d	24	3.6%
Other values (41)	246	36.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	671	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	61	9.1%
a	55	8.2%
i	53	7.9%
n	48	7.2%
r	43	6.4%
o	39	5.8%
e	37	5.5%
s	35	5.2%
t	30	4.5%
d	24	3.6%
Other values (41)	246	36.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	671	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	61	9.1%
a	55	8.2%
i	53	7.9%
n	48	7.2%
r	43	6.4%
o	39	5.8%
e	37	5.5%
s	35	5.2%
t	30	4.5%
d	24	3.6%
Other values (41)	246	36.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	671	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	61	9.1%
a	55	8.2%
i	53	7.9%
n	48	7.2%
r	43	6.4%
o	39	5.8%
e	37	5.5%
s	35	5.2%
t	30	4.5%
d	24	3.6%
Other values (41)	246	36.7%

Date of birth
Text

Distinct	25
Distinct (%)	73.5%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	13
Median length	10
Mean length	8.9411765
Min length	0

Characters and Unicode

Total characters	304
Distinct characters	22
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	20 ?
Unique (%)	58.8%

Sample

1st row	1469
2nd row	19/03/1813
3rd row
4th row	17/06/1856
5th row	23/08/1898

Value	Count	Frequency (%)
19/03/1813	4	11.8%
1886	3	8.8%
19/11/1799	3	8.8%
13/04/1848	2	5.9%
2/07/1809	2	5.9%
27/09/1883	1	2.9%
17/06/1856	1	2.9%
23/08/1898	1	2.9%
23/11/1860	1	2.9%
23/09/1870	1	2.9%
Other values (15)	15	44.1%

Most occurring characters

Value	Count	Frequency (%)
1	69	22.7%
/	56	18.4%
8	39	12.8%
0	28	9.2%
3	22	7.2%
9	21	6.9%
7	14	4.6%
2	14	4.6%
6	12	3.9%
4	12	3.9%
Other values (12)	17	5.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	304	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	69	22.7%
/	56	18.4%
8	39	12.8%
0	28	9.2%
3	22	7.2%
9	21	6.9%
7	14	4.6%
2	14	4.6%
6	12	3.9%
4	12	3.9%
Other values (12)	17	5.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	304	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	69	22.7%
/	56	18.4%
8	39	12.8%
0	28	9.2%
3	22	7.2%
9	21	6.9%
7	14	4.6%
2	14	4.6%
6	12	3.9%
4	12	3.9%
Other values (12)	17	5.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	304	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	69	22.7%
/	56	18.4%
8	39	12.8%
0	28	9.2%
3	22	7.2%
9	21	6.9%
7	14	4.6%
2	14	4.6%
6	12	3.9%
4	12	3.9%
Other values (12)	17	5.6%

Place of birth
Text

Distinct	22
Distinct (%)	64.7%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	45
Median length	30
Mean length	20.882353
Min length	0

Characters and Unicode

Total characters	710
Distinct characters	50
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	16 ?
Unique (%)	47.1%

Sample

1st row	Sines (Portugal)
2nd row	Blantyre (Scotland)
3rd row
4th row	Helsinki (Finland)
5th row	Desaignes (Ardèche, France)

Value	Count	Frequency (%)
france	8	10.1%
scotland	7	8.9%
germany	5	6.3%
london	4	5.1%
blantyre	4	5.1%
islands	3	3.8%
states	3	3.8%
united	3	3.8%
kirkwall	3	3.8%
mauzé-sur-le-mignon	3	3.8%
Other values (32)	36	45.6%

Most occurring characters

Value	Count	Frequency (%)
n	70	9.9%
a	58	8.2%
e	57	8.0%
	46	6.5%
r	36	5.1%
l	36	5.1%
i	34	4.8%
t	29	4.1%
s	28	3.9%
(	27	3.8%
Other values (40)	289	40.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	710	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	70	9.9%
a	58	8.2%
e	57	8.0%
	46	6.5%
r	36	5.1%
l	36	5.1%
i	34	4.8%
t	29	4.1%
s	28	3.9%
(	27	3.8%
Other values (40)	289	40.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	710	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	70	9.9%
a	58	8.2%
e	57	8.0%
	46	6.5%
r	36	5.1%
l	36	5.1%
i	34	4.8%
t	29	4.1%
s	28	3.9%
(	27	3.8%
Other values (40)	289	40.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	710	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	70	9.9%
a	58	8.2%
e	57	8.0%
	46	6.5%
r	36	5.1%
l	36	5.1%
i	34	4.8%
t	29	4.1%
s	28	3.9%
(	27	3.8%
Other values (40)	289	40.7%

Date of death
Text

Distinct	25
Distinct (%)	73.5%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	13
Median length	11.5
Mean length	8.9117647
Min length	0

Characters and Unicode

Total characters	303
Distinct characters	22
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	20 ?
Unique (%)	58.8%

Sample

1st row	24/12/1524
2nd row	1/05/1873
3rd row
4th row	23/03/1901
5th row	29/04/1950

Value	Count	Frequency (%)
1/05/1873	4	11.8%
1954	3	8.8%
17/05/1838	3	8.8%
1/03/1925	2	5.9%
4/03/1851	2	5.9%
22/08/1914	1	2.9%
23/03/1901	1	2.9%
29/04/1950	1	2.9%
14/11/1925	1	2.9%
16/01/1958	1	2.9%
Other values (15)	15	44.1%

Most occurring characters

Value	Count	Frequency (%)
1	64	21.1%
/	58	19.1%
0	31	10.2%
8	22	7.3%
5	21	6.9%
9	21	6.9%
3	19	6.3%
4	16	5.3%
2	16	5.3%
7	12	4.0%
Other values (12)	23	7.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	303	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	64	21.1%
/	58	19.1%
0	31	10.2%
8	22	7.3%
5	21	6.9%
9	21	6.9%
3	19	6.3%
4	16	5.3%
2	16	5.3%
7	12	4.0%
Other values (12)	23	7.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	303	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	64	21.1%
/	58	19.1%
0	31	10.2%
8	22	7.3%
5	21	6.9%
9	21	6.9%
3	19	6.3%
4	16	5.3%
2	16	5.3%
7	12	4.0%
Other values (12)	23	7.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	303	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	64	21.1%
/	58	19.1%
0	31	10.2%
8	22	7.3%
5	21	6.9%
9	21	6.9%
3	19	6.3%
4	16	5.3%
2	16	5.3%
7	12	4.0%
Other values (12)	23	7.6%

Place of death
Text

Distinct	21
Distinct (%)	61.8%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	38
Median length	23
Mean length	17.529412
Min length	0

Characters and Unicode

Total characters	596
Distinct characters	47
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	15 ?
Unique (%)	44.1%

Sample

1st row	Kochi (India)
2nd row	Lake Bangweulu (Zambia)
3rd row
4th row	Artjärvi (Finland)
5th row	Grenoble (France)

Value	Count	Frequency (%)
not	6	8.0%
specified	6	8.0%
france	5	6.7%
lake	4	5.3%
bangweulu	4	5.3%
zambia	4	5.3%
la	4	5.3%
gripperie-saint-symphorien	3	4.0%
kukawa	2	2.7%
england	2	2.7%
Other values (30)	35	46.7%

Most occurring characters

Value	Count	Frequency (%)
a	56	9.4%
e	52	8.7%
i	50	8.4%
	42	7.0%
n	33	5.5%
r	31	5.2%
(	23	3.9%
)	23	3.9%
o	22	3.7%
s	20	3.4%
Other values (37)	244	40.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	596	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	56	9.4%
e	52	8.7%
i	50	8.4%
	42	7.0%
n	33	5.5%
r	31	5.2%
(	23	3.9%
)	23	3.9%
o	22	3.7%
s	20	3.4%
Other values (37)	244	40.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	596	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	56	9.4%
e	52	8.7%
i	50	8.4%
	42	7.0%
n	33	5.5%
r	31	5.2%
(	23	3.9%
)	23	3.9%
o	22	3.7%
s	20	3.4%
Other values (37)	244	40.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	596	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	56	9.4%
e	52	8.7%
i	50	8.4%
	42	7.0%
n	33	5.5%
r	31	5.2%
(	23	3.9%
)	23	3.9%
o	22	3.7%
s	20	3.4%
Other values (37)	244	40.9%

Nationality
Categorical

High correlation

Distinct	11
Distinct (%)	32.4%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

French	8
Scottish	7
British	6
American	3
Finnish	2
Other values (6)	8

Length

Max length	15
Median length	13
Mean length	7.3529412
Min length	0

Characters and Unicode

Total characters	250
Distinct characters	25
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4 ?
Unique (%)	11.8%

Sample

1st row	Portuguese
2nd row	Scottish
3rd row
4th row	Finnish
5th row	French

Common Values

Value	Count	Frequency (%)
French	8	23.5%
Scottish	7	20.6%
British	6	17.6%
American	3	8.8%
Finnish	2	5.9%
German	2	5.9%
Austrian	2	5.9%
Portuguese	1	2.9%
	1	2.9%
Franco-American	1	2.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
french	8	23.5%
scottish	7	20.6%
british	6	17.6%
american	3	8.8%
finnish	2	5.9%
german	2	5.9%
austrian	2	5.9%
portuguese	1	2.9%
franco-american	1	2.9%
not	1	2.9%

Most occurring characters

Value	Count	Frequency (%)
i	31	12.4%
t	24	9.6%
r	24	9.6%
h	23	9.2%
n	21	8.4%
c	21	8.4%
s	19	7.6%
e	18	7.2%
F	11	4.4%
o	10	4.0%
Other values (15)	48	19.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	250	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	31	12.4%
t	24	9.6%
r	24	9.6%
h	23	9.2%
n	21	8.4%
c	21	8.4%
s	19	7.6%
e	18	7.2%
F	11	4.4%
o	10	4.0%
Other values (15)	48	19.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	250	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	31	12.4%
t	24	9.6%
r	24	9.6%
h	23	9.2%
n	21	8.4%
c	21	8.4%
s	19	7.6%
e	18	7.2%
F	11	4.4%
o	10	4.0%
Other values (15)	48	19.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	250	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	31	12.4%
t	24	9.6%
r	24	9.6%
h	23	9.2%
n	21	8.4%
c	21	8.4%
s	19	7.6%
e	18	7.2%
F	11	4.4%
o	10	4.0%
Other values (15)	48	19.2%

Activities
Text

Distinct	21
Distinct (%)	61.8%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	80
Median length	43
Mean length	30.411765
Min length	8

Characters and Unicode

Total characters	1034
Distinct characters	28
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	15 ?
Unique (%)	44.1%

Sample

1st row	explorer, aristocrat
2nd row	explorer, writer, geographer, missionary
3rd row	explorer, traveler, writer
4th row	aristocrat, military, writer
5th row	geologist, explorer

Value	Count	Frequency (%)
explorer	22	20.6%
writer	17	15.9%
geographer	8	7.5%
traveler	5	4.7%
military	5	4.7%
academic	4	3.7%
missionary	4	3.7%
photographer	3	2.8%
geologist	3	2.8%
filmmaker	3	2.8%
Other values (25)	33	30.8%

Most occurring characters

Value	Count	Frequency (%)
r	144	13.9%
e	119	11.5%
i	85	8.2%
o	78	7.5%
	73	7.1%
t	70	6.8%
,	69	6.7%
a	60	5.8%
l	55	5.3%
p	46	4.4%
Other values (18)	235	22.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1034	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
r	144	13.9%
e	119	11.5%
i	85	8.2%
o	78	7.5%
	73	7.1%
t	70	6.8%
,	69	6.7%
a	60	5.8%
l	55	5.3%
p	46	4.4%
Other values (18)	235	22.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1034	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
r	144	13.9%
e	119	11.5%
i	85	8.2%
o	78	7.5%
	73	7.1%
t	70	6.8%
,	69	6.7%
a	60	5.8%
l	55	5.3%
p	46	4.4%
Other values (18)	235	22.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1034	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
r	144	13.9%
e	119	11.5%
i	85	8.2%
o	78	7.5%
	73	7.1%
t	70	6.8%
,	69	6.7%
a	60	5.8%
l	55	5.3%
p	46	4.4%
Other values (18)	235	22.7%

Language
Categorical

High correlation

Distinct	6
Distinct (%)	17.6%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

English	23
French	7
Swedish	1
Dutch	1
German	1

Length

Max length	7
Median length	7
Mean length	6.7058824
Min length	5

Characters and Unicode

Total characters	228
Distinct characters	20
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4 ?
Unique (%)	11.8%

Sample

1st row	English
2nd row	English
3rd row	English
4th row	Swedish
5th row	French

Common Values

Value	Count	Frequency (%)
English	23	67.6%
French	7	20.6%
Swedish	1	2.9%
Dutch	1	2.9%
German	1	2.9%
Finnish	1	2.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
english	23	67.6%
french	7	20.6%
swedish	1	2.9%
dutch	1	2.9%
german	1	2.9%
finnish	1	2.9%

Most occurring characters

Value	Count	Frequency (%)
h	33	14.5%
n	33	14.5%
i	26	11.4%
s	25	11.0%
E	23	10.1%
g	23	10.1%
l	23	10.1%
e	9	3.9%
c	8	3.5%
r	8	3.5%
Other values (10)	17	7.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	228	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
h	33	14.5%
n	33	14.5%
i	26	11.4%
s	25	11.0%
E	23	10.1%
g	23	10.1%
l	23	10.1%
e	9	3.9%
c	8	3.5%
r	8	3.5%
Other values (10)	17	7.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	228	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
h	33	14.5%
n	33	14.5%
i	26	11.4%
s	25	11.0%
E	23	10.1%
g	23	10.1%
l	23	10.1%
e	9	3.9%
c	8	3.5%
r	8	3.5%
Other values (10)	17	7.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	228	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
h	33	14.5%
n	33	14.5%
i	26	11.4%
s	25	11.0%
E	23	10.1%
g	23	10.1%
l	23	10.1%
e	9	3.9%
c	8	3.5%
r	8	3.5%
Other values (10)	17	7.5%

URL
Text

Unique

Distinct	34
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	62
Median length	62
Mean length	61.441176
Min length	57

Characters and Unicode

Total characters	2089
Distinct characters	32
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	34 ?
Unique (%)	100.0%

Sample

1st row	https://www.gutenberg.org/cache/epub/46440/pg46440-images.html
2nd row	https://www.gutenberg.org/cache/epub/2519/pg2519-images.html
3rd row	https://www.gutenberg.org/cache/epub/72209/pg72209-images.html
4th row	https://www.gutenberg.org/cache/epub/62623/pg62623-images.html
5th row	https://www.gutenberg.org/cache/epub/73291/pg73291-images.html

Value	Count	Frequency (%)
https://www.gutenberg.org/cache/epub/46440/pg46440-images.html	1	2.9%
https://www.gutenberg.org/cache/epub/16117/pg16117-images.html	1	2.9%
https://www.gutenberg.org/cache/epub/70171/pg70171-images.html	1	2.9%
https://www.gutenberg.org/cache/epub/17164/pg17164-images.html	1	2.9%
https://www.gutenberg.org/cache/epub/1039/pg1039-images.html	1	2.9%
https://www.gutenberg.org/cache/epub/71199/pg71199-images.html	1	2.9%
https://www.gutenberg.org/cache/epub/14142/pg14142-images.html	1	2.9%
https://www.gutenberg.org/cache/epub/49591/pg49591-images.html	1	2.9%
https://www.gutenberg.org/cache/epub/45396/pg45396-images.html	1	2.9%
https://www.gutenberg.org/cache/epub/70221/pg70221-images.html	1	2.9%
Other values (24)	24	70.6%

Most occurring characters

Value	Count	Frequency (%)
/	204	9.8%
g	164	7.9%
e	164	7.9%
t	136	6.5%
h	105	5.0%
w	102	4.9%
.	102	4.9%
p	96	4.6%
s	68	3.3%
r	68	3.3%
Other values (22)	880	42.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2089	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
/	204	9.8%
g	164	7.9%
e	164	7.9%
t	136	6.5%
h	105	5.0%
w	102	4.9%
.	102	4.9%
p	96	4.6%
s	68	3.3%
r	68	3.3%
Other values (22)	880	42.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2089	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
/	204	9.8%
g	164	7.9%
e	164	7.9%
t	136	6.5%
h	105	5.0%
w	102	4.9%
.	102	4.9%
p	96	4.6%
s	68	3.3%
r	68	3.3%
Other values (22)	880	42.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2089	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
/	204	9.8%
g	164	7.9%
e	164	7.9%
t	136	6.5%
h	105	5.0%
w	102	4.9%
.	102	4.9%
p	96	4.6%
s	68	3.3%
r	68	3.3%
Other values (22)	880	42.1%

Citation
Text

Distinct	32
Distinct (%)	94.1%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	26
Median length	22.5
Mean length	17.764706
Min length	9

Characters and Unicode

Total characters	604
Distinct characters	35
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	30 ?
Unique (%)	88.2%

Sample

1st row	ravenstein2017journal
2nd row	livingstone1875popular
3rd row	nelson2024biographical
4th row	von1892afrikanska
5th row	kilian1925hoggar

Value	Count	Frequency (%)
caillie1830travels	2	5.9%
lenz1886timbouctou	2	5.9%
nelson2024biographical	1	2.9%
von1892afrikanska	1	2.9%
kilian1925hoggar	1	2.9%
le1890sahara	1	2.9%
cannon1913botanical	1	2.9%
buchanan1921exploration	1	2.9%
grogan1900cape	1	2.9%
de2023het	1	2.9%
Other values (22)	22	64.7%

Most occurring characters

Value	Count	Frequency (%)
a	57	9.4%
n	42	7.0%
o	39	6.5%
i	38	6.3%
r	36	6.0%
e	34	5.6%
1	33	5.5%
t	31	5.1%
l	30	5.0%
s	28	4.6%
Other values (25)	236	39.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	604	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	57	9.4%
n	42	7.0%
o	39	6.5%
i	38	6.3%
r	36	6.0%
e	34	5.6%
1	33	5.5%
t	31	5.1%
l	30	5.0%
s	28	4.6%
Other values (25)	236	39.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	604	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	57	9.4%
n	42	7.0%
o	39	6.5%
i	38	6.3%
r	36	6.0%
e	34	5.6%
1	33	5.5%
t	31	5.1%
l	30	5.0%
s	28	4.6%
Other values (25)	236	39.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	604	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	57	9.4%
n	42	7.0%
o	39	6.5%
i	38	6.3%
r	36	6.0%
e	34	5.6%
1	33	5.5%
t	31	5.1%
l	30	5.0%
s	28	4.6%
Other values (25)	236	39.1%

Departure date
Text

Distinct	32
Distinct (%)	94.1%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	92
Median length	10
Mean length	17.117647
Min length	10

Characters and Unicode

Total characters	582
Distinct characters	45
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	30 ?
Unique (%)	88.2%

Sample

1st row	08/07/1497
2nd row	15/06/1852
3rd row	27/08/1825
4th row	01/07/1890
5th row	08/01/1922

Value	Count	Frequency (%)
non	8	10.3%
mais	3	3.8%
spécifié	3	3.8%
spécifiée	3	3.8%
à	2	2.6%
information	2	2.6%
en	2	2.6%
le	2	2.6%
19/04/1827	2	2.6%
disponible	2	2.6%
Other values (49)	49	62.8%

Most occurring characters

Value	Count	Frequency (%)
/	52	8.9%
1	49	8.4%
	44	7.6%
0	43	7.4%
8	28	4.8%
n	25	4.3%
e	24	4.1%
i	24	4.1%
s	22	3.8%
o	21	3.6%
Other values (35)	250	43.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	582	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
/	52	8.9%
1	49	8.4%
	44	7.6%
0	43	7.4%
8	28	4.8%
n	25	4.3%
e	24	4.1%
i	24	4.1%
s	22	3.8%
o	21	3.6%
Other values (35)	250	43.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	582	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
/	52	8.9%
1	49	8.4%
	44	7.6%
0	43	7.4%
8	28	4.8%
n	25	4.3%
e	24	4.1%
i	24	4.1%
s	22	3.8%
o	21	3.6%
Other values (35)	250	43.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	582	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
/	52	8.9%
1	49	8.4%
	44	7.6%
0	43	7.4%
8	28	4.8%
n	25	4.3%
e	24	4.1%
i	24	4.1%
s	22	3.8%
o	21	3.6%
Other values (35)	250	43.0%

Departure Age
Real number (ℝ)

High correlation Zeros

Distinct	17
Distinct (%)	50.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	24.264706

Minimum	0
Maximum	73
Zeros	10
Zeros (%)	29.4%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	28
Q3	33.75
95-th percentile	53.7
Maximum	73
Range	73
Interquartile range (IQR)	33.75

Descriptive statistics

Standard deviation	18.529761
Coefficient of variation (CV)	0.76365076
Kurtosis	0.056249614
Mean	24.264706
Median Absolute Deviation (MAD)	7
Skewness	0.20361249
Sum	825
Variance	343.35205
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=17)

Value	Count	Frequency (%)
0	10	29.4%
28	4	11.8%
27	3	8.8%
31	2	5.9%
32	2	5.9%
35	2	5.9%
40	1	2.9%
55	1	2.9%
53	1	2.9%
36	1	2.9%
Other values (7)	7	20.6%

Value	Count	Frequency (%)
0	10	29.4%
23	1	2.9%
24	1	2.9%
26	1	2.9%
27	3	8.8%
28	4	11.8%
31	2	5.9%
32	2	5.9%
33	1	2.9%
34	1	2.9%

Value	Count	Frequency (%)
73	1	2.9%
55	1	2.9%
53	1	2.9%
40	1	2.9%
39	1	2.9%
36	1	2.9%
35	2	5.9%
34	1	2.9%
33	1	2.9%
32	2	5.9%

Arrival date
Text

Distinct	20
Distinct (%)	58.8%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	13
Median length	10
Mean length	11.235294
Min length	10

Characters and Unicode

Total characters	382
Distinct characters	22
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	18 ?
Unique (%)	52.9%

Sample

1st row	25/04/1499
2nd row	19/03/1866
3rd row	Not specified
4th row	01/04/1891
5th row	08/02/1922

Value	Count	Frequency (%)
not	14	29.2%
specified	14	29.2%
07/09/1828	2	4.2%
19/03/1866	1	2.1%
01/04/1891	1	2.1%
08/02/1922	1	2.1%
14/07/1889	1	2.1%
06/08/1920	1	2.1%
19/06/1910	1	2.1%
20/05/1856	1	2.1%
Other values (11)	11	22.9%

Most occurring characters

Value	Count	Frequency (%)
1	42	11.0%
/	40	10.5%
0	32	8.4%
e	28	7.3%
i	28	7.3%
8	22	5.8%
2	17	4.5%
9	15	3.9%
d	14	3.7%
o	14	3.7%
Other values (12)	130	34.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	382	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	42	11.0%
/	40	10.5%
0	32	8.4%
e	28	7.3%
i	28	7.3%
8	22	5.8%
2	17	4.5%
9	15	3.9%
d	14	3.7%
o	14	3.7%
Other values (12)	130	34.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	382	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	42	11.0%
/	40	10.5%
0	32	8.4%
e	28	7.3%
i	28	7.3%
8	22	5.8%
2	17	4.5%
9	15	3.9%
d	14	3.7%
o	14	3.7%
Other values (12)	130	34.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	382	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	42	11.0%
/	40	10.5%
0	32	8.4%
e	28	7.3%
i	28	7.3%
8	22	5.8%
2	17	4.5%
9	15	3.9%
d	14	3.7%
o	14	3.7%
Other values (12)	130	34.0%

Arrival Age
Real number (ℝ)

High correlation Zeros

Distinct	16
Distinct (%)	47.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	20.147059

Minimum	0
Maximum	59
Zeros	14
Zeros (%)	41.2%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	27.5
Q3	31.75
95-th percentile	53.7
Maximum	59
Range	59
Interquartile range (IQR)	31.75

Descriptive statistics

Standard deviation	18.769951
Coefficient of variation (CV)	0.93164719
Kurtosis	-1.0317815
Mean	20.147059
Median Absolute Deviation (MAD)	20.5
Skewness	0.25502823
Sum	685
Variance	352.31105
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=16)

Value	Count	Frequency (%)
0	14	41.2%
28	4	11.8%
29	2	5.9%
34	2	5.9%
53	1	2.9%
23	1	2.9%
26	1	2.9%
43	1	2.9%
35	1	2.9%
27	1	2.9%
Other values (6)	6	17.6%

Value	Count	Frequency (%)
0	14	41.2%
23	1	2.9%
26	1	2.9%
27	1	2.9%
28	4	11.8%
29	2	5.9%
30	1	2.9%
31	1	2.9%
32	1	2.9%
33	1	2.9%

Value	Count	Frequency (%)
59	1	2.9%
55	1	2.9%
53	1	2.9%
43	1	2.9%
35	1	2.9%
34	2	5.9%
33	1	2.9%
32	1	2.9%
31	1	2.9%
30	1	2.9%

Travel duration in days
Real number (ℝ)

High correlation Zeros

Distinct	20
Distinct (%)	58.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	452.73529

Minimum	0
Maximum	5025
Zeros	14
Zeros (%)	41.2%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	62.5
Q3	463.5
95-th percentile	1843
Maximum	5025
Range	5025
Interquartile range (IQR)	463.5

Descriptive statistics

Standard deviation	976.14773
Coefficient of variation (CV)	2.1561114
Kurtosis	15.069364
Mean	452.73529
Median Absolute Deviation (MAD)	62.5
Skewness	3.6317266
Sum	15393
Variance	952864.38
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=20)

Value	Count	Frequency (%)
0	14	41.2%
507	2	5.9%
999	1	2.9%
1316	1	2.9%
710	1	2.9%
142	1	2.9%
232	1	2.9%
182	1	2.9%
333	1	2.9%
1465	1	2.9%
Other values (10)	10	29.4%

Value	Count	Frequency (%)
0	14	41.2%
15	1	2.9%
31	1	2.9%
36	1	2.9%
89	1	2.9%
122	1	2.9%
142	1	2.9%
182	1	2.9%
207	1	2.9%
232	1	2.9%

Value	Count	Frequency (%)
5025	1	2.9%
2545	1	2.9%
1465	1	2.9%
1316	1	2.9%
999	1	2.9%
710	1	2.9%
656	1	2.9%
507	2	5.9%
333	1	2.9%
274	1	2.9%

Money
Categorical

High correlation

Distinct	16
Distinct (%)	47.1%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

	19
The book mentions that he left with little money and had to find ways to earn a living during his journey	1
Enough money for a guide, camels, and food	1
Large sum withdrawn from Kano bank, mostly silver coins due to higher local value	1
An annual salary of 100 pounds as a missionary	1
Other values (11)	11

Length

Max length	138
Median length	0
Mean length	32.294118
Min length	0

Characters and Unicode

Total characters	1098
Distinct characters	50
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	15 ?
Unique (%)	44.1%

Sample

1st row
2nd row
3rd row
4th row	The book mentions that he left with little money and had to find ways to earn a living during his journey
5th row

Common Values

Value	Count	Frequency (%)
	19	55.9%
The book mentions that he left with little money and had to find ways to earn a living during his journey	1	2.9%
Enough money for a guide, camels, and food	1	2.9%
Large sum withdrawn from Kano bank, mostly silver coins due to higher local value	1	2.9%
An annual salary of 100 pounds as a missionary	1	2.9%
He mentions spending about 150 pounds on forced gifts until September 19, 1850	1	2.9%
He traded goods like cloth, beads, gunpowder, and ivory. Financial dealings included Mohamad bin Saleh and a 4,000-rupee check to Dr. Kirk	1	2.9%
Bills of exchange and minimize cash for security reasons	1	2.9%
Upon arriving in Timbuktu, he has about 800 francs from the sale of his camels, a small remainder of about 500 francs, and some cloth	1	2.9%
A few thousand francs	1	2.9%
Other values (6)	6	17.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
and	9	4.6%
a	8	4.1%
he	7	3.6%
of	6	3.1%
francs	6	3.1%
the	5	2.6%
for	4	2.0%
his	4	2.0%
to	4	2.0%
about	3	1.5%
Other values (124)	140	71.4%

Most occurring characters

Value	Count	Frequency (%)
	181	16.5%
e	83	7.6%
a	75	6.8%
n	74	6.7%
o	66	6.0%
s	63	5.7%
i	60	5.5%
t	53	4.8%
r	51	4.6%
l	40	3.6%
Other values (40)	352	32.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1098	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	181	16.5%
e	83	7.6%
a	75	6.8%
n	74	6.7%
o	66	6.0%
s	63	5.7%
i	60	5.5%
t	53	4.8%
r	51	4.6%
l	40	3.6%
Other values (40)	352	32.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1098	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	181	16.5%
e	83	7.6%
a	75	6.8%
n	74	6.7%
o	66	6.0%
s	63	5.7%
i	60	5.5%
t	53	4.8%
r	51	4.6%
l	40	3.6%
Other values (40)	352	32.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1098	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	181	16.5%
e	83	7.6%
a	75	6.8%
n	74	6.7%
o	66	6.0%
s	63	5.7%
i	60	5.5%
t	53	4.8%
r	51	4.6%
l	40	3.6%
Other values (40)	352	32.1%

Goal
Text

Unique

Distinct	34
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	274
Median length	86
Mean length	97.264706
Min length	0

Characters and Unicode

Total characters	3307
Distinct characters	54
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	34 ?
Unique (%)	100.0%

Sample

1st row	Discover a sea route to India
2nd row	To expand knowledge of East and Central Africa's geography, minerals, and agriculture, improve relations with locals, encourage industrial activities and land cultivation for export to England, and help end the slave trade by providing a more profitable economic alternative
3rd row	Exploring interior Africa, particularly tracing the course and trying to determine the source of the Niger River
4th row	The author mentions traveling to escape personal issues and discover new horizons
5th row	Scientific mission in Central Sahara, focused on geology, botany, and morphology

Value	Count	Frequency (%)
the	47	9.0%
and	38	7.3%
to	25	4.8%
of	16	3.1%
sahara	10	1.9%
explore	10	1.9%
in	8	1.5%
africa	7	1.3%
a	6	1.1%
was	6	1.1%
Other values (255)	350	66.9%

Most occurring characters

Value	Count	Frequency (%)
	490	14.8%
e	284	8.6%
a	259	7.8%
o	228	6.9%
i	214	6.5%
t	205	6.2%
r	200	6.0%
n	186	5.6%
s	153	4.6%
l	130	3.9%
Other values (44)	958	29.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3307	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	490	14.8%
e	284	8.6%
a	259	7.8%
o	228	6.9%
i	214	6.5%
t	205	6.2%
r	200	6.0%
n	186	5.6%
s	153	4.6%
l	130	3.9%
Other values (44)	958	29.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3307	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	490	14.8%
e	284	8.6%
a	259	7.8%
o	228	6.9%
i	214	6.5%
t	205	6.2%
r	200	6.0%
n	186	5.6%
s	153	4.6%
l	130	3.9%
Other values (44)	958	29.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3307	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	490	14.8%
e	284	8.6%
a	259	7.8%
o	228	6.9%
i	214	6.5%
t	205	6.2%
r	200	6.0%
n	186	5.6%
s	153	4.6%
l	130	3.9%
Other values (44)	958	29.0%

Success
Text

Distinct	29
Distinct (%)	85.3%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

Length

Max length	350
Median length	136.5
Mean length	106.44118
Min length	0

Characters and Unicode

Total characters	3619
Distinct characters	57
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	26 ?
Unique (%)	76.5%

Sample

1st row	Not specified
2nd row	While Livingstone and his companions explored vast lands, established relations with local chiefs, and gathered valuable geographical and scientific information, their efforts to foster trade and encourage indigenous industry were hindered by factors such as the ongoing slave trade, tribal rivalries, and natural obstacles like cataracts and drought
3rd row	Oudney died during the expedition before determining the course and source of the Niger River. However, he gathered significant information on Central Africa before his death
4th row
5th row	Yes, Kilian successfully completed his scientific mission, reporting numerous geological, botanical, and ethnographic observations and data

Value	Count	Frequency (%)
the	39	7.0%
and	36	6.5%
of	18	3.2%
to	16	2.9%
his	16	2.9%
in	10	1.8%
he	10	1.8%
author	6	1.1%
a	6	1.1%
before	6	1.1%
Other values (284)	391	70.6%

Most occurring characters

Value	Count	Frequency (%)
	523	14.5%
e	363	10.0%
a	261	7.2%
i	250	6.9%
n	222	6.1%
t	212	5.9%
o	207	5.7%
s	197	5.4%
r	181	5.0%
d	151	4.2%
Other values (47)	1052	29.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3619	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	523	14.5%
e	363	10.0%
a	261	7.2%
i	250	6.9%
n	222	6.1%
t	212	5.9%
o	207	5.7%
s	197	5.4%
r	181	5.0%
d	151	4.2%
Other values (47)	1052	29.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3619	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	523	14.5%
e	363	10.0%
a	261	7.2%
i	250	6.9%
n	222	6.1%
t	212	5.9%
o	207	5.7%
s	197	5.4%
r	181	5.0%
d	151	4.2%
Other values (47)	1052	29.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3619	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	523	14.5%
e	363	10.0%
a	261	7.2%
i	250	6.9%
n	222	6.1%
t	212	5.9%
o	207	5.7%
s	197	5.4%
r	181	5.0%
d	151	4.2%
Other values (47)	1052	29.1%

Number of steps
Real number (ℝ)

High correlation

Distinct	24
Distinct (%)	70.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	22.676471

Minimum	5
Maximum	78
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	5
5-th percentile	7.65
Q1	10
median	16.5
Q3	25.75
95-th percentile	55.4
Maximum	78
Range	73
Interquartile range (IQR)	15.75

Descriptive statistics

Standard deviation	17.728116
Coefficient of variation (CV)	0.78178462
Kurtosis	1.7691181
Mean	22.676471
Median Absolute Deviation (MAD)	7.5
Skewness	1.5151682
Sum	771
Variance	314.2861
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=24)

Value	Count	Frequency (%)
10	4	11.8%
8	3	8.8%
14	3	8.8%
13	2	5.9%
18	2	5.9%
9	2	5.9%
17	1	2.9%
54	1	2.9%
48	1	2.9%
44	1	2.9%
Other values (14)	14	41.2%

Value	Count	Frequency (%)
5	1	2.9%
7	1	2.9%
8	3	8.8%
9	2	5.9%
10	4	11.8%
13	2	5.9%
14	3	8.8%
16	1	2.9%
17	1	2.9%
18	2	5.9%

Value	Count	Frequency (%)
78	1	2.9%
58	1	2.9%
54	1	2.9%
51	1	2.9%
48	1	2.9%
44	1	2.9%
40	1	2.9%
32	1	2.9%
26	1	2.9%
25	1	2.9%

Number of images
Real number (ℝ)

High correlation Zeros

Distinct	20
Distinct (%)	58.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	12.294118

Minimum	0
Maximum	82
Zeros	10
Zeros (%)	29.4%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	6
Q3	15.5
95-th percentile	43.65
Maximum	82
Range	82
Interquartile range (IQR)	15.5

Descriptive statistics

Standard deviation	18.088217
Coefficient of variation (CV)	1.4712904
Kurtosis	6.3349328
Mean	12.294118
Median Absolute Deviation (MAD)	6
Skewness	2.3660582
Sum	418
Variance	327.1836
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=20)

Value	Count	Frequency (%)
0	10	29.4%
6	4	11.8%
3	3	8.8%
14	1	2.9%
34	1	2.9%
5	1	2.9%
7	1	2.9%
16	1	2.9%
37	1	2.9%
10	1	2.9%
Other values (10)	10	29.4%

Value	Count	Frequency (%)
0	10	29.4%
1	1	2.9%
3	3	8.8%
4	1	2.9%
5	1	2.9%
6	4	11.8%
7	1	2.9%
9	1	2.9%
10	1	2.9%
13	1	2.9%

Value	Count	Frequency (%)
82	1	2.9%
56	1	2.9%
37	1	2.9%
34	1	2.9%
33	1	2.9%
25	1	2.9%
22	1	2.9%
17	1	2.9%
16	1	2.9%
14	1	2.9%

Steps with null coordinates in %
Real number (ℝ)

High correlation Zeros

Distinct	27
Distinct (%)	79.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	35.127059

Minimum	0
Maximum	92.31
Zeros	3
Zeros (%)	8.8%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	0
5-th percentile	0
Q1	20
median	32.5
Q3	49.5
95-th percentile	74.74
Maximum	92.31
Range	92.31
Interquartile range (IQR)	29.5

Descriptive statistics

Standard deviation	24.018021
Coefficient of variation (CV)	0.68374701
Kurtosis	-0.49180169
Mean	35.127059
Median Absolute Deviation (MAD)	16.5
Skewness	0.35955375
Sum	1194.32
Variance	576.86535
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=27)

Value	Count	Frequency (%)
20	4	11.8%
0	3	8.8%
50	2	5.9%
27.78	2	5.9%
78.12	1	2.9%
64.81	1	2.9%
72.92	1	2.9%
21.43	1	2.9%
47.73	1	2.9%
35	1	2.9%
Other values (17)	17	50.0%

Value	Count	Frequency (%)
0	3	8.8%
4.76	1	2.9%
5.88	1	2.9%
7.69	1	2.9%
11.11	1	2.9%
12.5	1	2.9%
20	4	11.8%
21.43	1	2.9%
25	1	2.9%
27.78	2	5.9%

Value	Count	Frequency (%)
92.31	1	2.9%
78.12	1	2.9%
72.92	1	2.9%
64.81	1	2.9%
60.26	1	2.9%
58.62	1	2.9%
57.14	1	2.9%
50	2	5.9%
48	1	2.9%
47.73	1	2.9%

Total distance traveled (km)
Real number (ℝ)

High correlation Unique Zeros

Distinct	34
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5794.7106

Minimum	0
Maximum	35819.58
Zeros	1
Zeros (%)	2.9%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	0
5-th percentile	280.952
Q1	1587.9975
median	3093.99
Q3	6165.1625
95-th percentile	18439.98
Maximum	35819.58
Range	35819.58
Interquartile range (IQR)	4577.165

Descriptive statistics

Standard deviation	7414.4268
Coefficient of variation (CV)	1.2795163
Kurtosis	7.7542214
Mean	5794.7106
Median Absolute Deviation (MAD)	1950.175
Skewness	2.5716488
Sum	197020.16
Variance	54973724
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=34)

Value	Count	Frequency (%)
35819.58	1	2.9%
4795.91	1	2.9%
505.91	1	2.9%
10422.71	1	2.9%
1128.15	1	2.9%
2272.64	1	2.9%
11009.18	1	2.9%
2766.24	1	2.9%
3862.91	1	2.9%
6332.11	1	2.9%
Other values (24)	24	70.6%

Value	Count	Frequency (%)
0	1	2.9%
102.54	1	2.9%
377.02	1	2.9%
505.91	1	2.9%
641.25	1	2.9%
1028.81	1	2.9%
1128.15	1	2.9%
1159.48	1	2.9%
1513.94	1	2.9%
1810.17	1	2.9%

Value	Count	Frequency (%)
35819.58	1	2.9%
22288	1	2.9%
16367.97	1	2.9%
15793.63	1	2.9%
11009.18	1	2.9%
10648.58	1	2.9%
10422.71	1	2.9%
8723.39	1	2.9%
6332.11	1	2.9%
5664.32	1	2.9%

Transport_Train
Categorical

High correlation Missing

Distinct	5
Distinct (%)	55.6%
Missing	25
Missing (%)	73.5%
Memory size	544.0 B

3.0	3
1.0	2
2.0	2
4.0	1
10.0	1

Length

Max length	4
Median length	3
Mean length	3.1111111
Min length	3

Characters and Unicode

Total characters	28
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	22.2%

Sample

1st row	3.0
2nd row	4.0
3rd row	1.0
4th row	3.0
5th row	1.0

Common Values

Value	Count	Frequency (%)
3.0	3	8.8%
1.0	2	5.9%
2.0	2	5.9%
4.0	1	2.9%
10.0	1	2.9%
(Missing)	25	73.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
3.0	3	33.3%
1.0	2	22.2%
2.0	2	22.2%
4.0	1	11.1%
10.0	1	11.1%

Most occurring characters

Value	Count	Frequency (%)
0	10	35.7%
.	9	32.1%
3	3	10.7%
1	3	10.7%
2	2	7.1%
4	1	3.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	28	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	10	35.7%
.	9	32.1%
3	3	10.7%
1	3	10.7%
2	2	7.1%
4	1	3.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	28	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	10	35.7%
.	9	32.1%
3	3	10.7%
1	3	10.7%
2	2	7.1%
4	1	3.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	28	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	10	35.7%
.	9	32.1%
3	3	10.7%
1	3	10.7%
2	2	7.1%
4	1	3.6%

Transport_Equid
Real number (ℝ)

High correlation Missing

Distinct	9
Distinct (%)	75.0%
Missing	22
Missing (%)	64.7%
Infinite	0
Infinite (%)	0.0%
Mean	6.5

Minimum	1
Maximum	33
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1.75
median	4.5
Q3	6.25
95-th percentile	20.35
Maximum	33
Range	32
Interquartile range (IQR)	4.5

Descriptive statistics

Standard deviation	8.7853183
Coefficient of variation (CV)	1.3515874
Kurtosis	9.104337
Mean	6.5
Median Absolute Deviation (MAD)	2.5
Skewness	2.8954507
Sum	78
Variance	77.181818
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
1	3	8.8%
5	2	5.9%
4	1	2.9%
7	1	2.9%
10	1	2.9%
2	1	2.9%
33	1	2.9%
3	1	2.9%
6	1	2.9%
(Missing)	22	64.7%

Value	Count	Frequency (%)
1	3	8.8%
2	1	2.9%
3	1	2.9%
4	1	2.9%
5	2	5.9%
6	1	2.9%
7	1	2.9%
10	1	2.9%
33	1	2.9%

Value	Count	Frequency (%)
33	1	2.9%
10	1	2.9%
7	1	2.9%
6	1	2.9%
5	2	5.9%
4	1	2.9%
3	1	2.9%
2	1	2.9%
1	3	8.8%

Country_France
Categorical

High correlation Missing Uniform

Distinct	2
Distinct (%)	100.0%
Missing	32
Missing (%)	94.1%
Memory size	544.0 B

1.0	1
7.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	6
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	100.0%

Sample

1st row	1.0
2nd row	7.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
7.0	1	2.9%
(Missing)	32	94.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	50.0%
7.0	1	50.0%

Most occurring characters

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
1	1	16.7%
7	1	16.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
1	1	16.7%
7	1	16.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
1	1	16.7%
7	1	16.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
1	1	16.7%
7	1	16.7%

Country_Algeria
Real number (ℝ)

High correlation Missing

Distinct	7
Distinct (%)	70.0%
Missing	24
Missing (%)	70.6%
Infinite	0
Infinite (%)	0.0%
Mean	6.7

Minimum	1
Maximum	13
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2.75
median	8
Q3	9.75
95-th percentile	11.65
Maximum	13
Range	12
Interquartile range (IQR)	7

Descriptive statistics

Standard deviation	4.2439499
Coefficient of variation (CV)	0.63342536
Kurtosis	-1.327882
Mean	6.7
Median Absolute Deviation (MAD)	2.5
Skewness	-0.24137129
Sum	67
Variance	18.011111
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
10	2	5.9%
1	2	5.9%
9	2	5.9%
5	1	2.9%
7	1	2.9%
2	1	2.9%
13	1	2.9%
(Missing)	24	70.6%

Value	Count	Frequency (%)
1	2	5.9%
2	1	2.9%
5	1	2.9%
7	1	2.9%
9	2	5.9%
10	2	5.9%
13	1	2.9%

Value	Count	Frequency (%)
13	1	2.9%
10	2	5.9%
9	2	5.9%
7	1	2.9%
5	1	2.9%
2	1	2.9%
1	2	5.9%

Transport_Slow ground vehicle
Real number (ℝ)

High correlation Missing

Distinct	6
Distinct (%)	66.7%
Missing	25
Missing (%)	73.5%
Infinite	0
Infinite (%)	0.0%
Mean	4.1111111

Minimum	1
Maximum	11
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	5
95-th percentile	9.4
Maximum	11
Range	10
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	3.29562
Coefficient of variation (CV)	0.80163729
Kurtosis	1.2225643
Mean	4.1111111
Median Absolute Deviation (MAD)	2
Skewness	1.2213817
Sum	37
Variance	10.861111
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
2	2	5.9%
5	2	5.9%
1	2	5.9%
3	1	2.9%
7	1	2.9%
11	1	2.9%
(Missing)	25	73.5%

Value	Count	Frequency (%)
1	2	5.9%
2	2	5.9%
3	1	2.9%
5	2	5.9%
7	1	2.9%
11	1	2.9%

Value	Count	Frequency (%)
11	1	2.9%
7	1	2.9%
5	2	5.9%
3	1	2.9%
2	2	5.9%
1	2	5.9%

Transport_Boat
Real number (ℝ)

High correlation Missing

Distinct	11
Distinct (%)	45.8%
Missing	10
Missing (%)	29.4%
Infinite	0
Infinite (%)	0.0%
Mean	6.375

Minimum	1
Maximum	24
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	5.5
Q3	9
95-th percentile	17
Maximum	24
Range	23
Interquartile range (IQR)	8

Descriptive statistics

Standard deviation	6.1135096
Coefficient of variation (CV)	0.9589819
Kurtosis	1.809148
Mean	6.375
Median Absolute Deviation (MAD)	4
Skewness	1.3950848
Sum	153
Variance	37.375
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
1	7	20.6%
9	3	8.8%
6	3	8.8%
2	3	8.8%
17	2	5.9%
24	1	2.9%
10	1	2.9%
4	1	2.9%
7	1	2.9%
11	1	2.9%
(Missing)	10	29.4%

Value	Count	Frequency (%)
1	7	20.6%
2	3	8.8%
4	1	2.9%
5	1	2.9%
6	3	8.8%
7	1	2.9%
9	3	8.8%
10	1	2.9%
11	1	2.9%
17	2	5.9%

Value	Count	Frequency (%)
24	1	2.9%
17	2	5.9%
11	1	2.9%
10	1	2.9%
9	3	8.8%
7	1	2.9%
6	3	8.8%
5	1	2.9%
4	1	2.9%
2	3	8.8%

Difficulty_Climate
Categorical

High correlation Missing

Distinct	4
Distinct (%)	66.7%
Missing	28
Missing (%)	82.4%
Memory size	544.0 B

3.0	3
1.0	1
5.0	1
6.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	18
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	50.0%

Sample

1st row	3.0
2nd row	3.0
3rd row	1.0
4th row	5.0
5th row	3.0

Common Values

Value	Count	Frequency (%)
3.0	3	8.8%
1.0	1	2.9%
5.0	1	2.9%
6.0	1	2.9%
(Missing)	28	82.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
3.0	3	50.0%
1.0	1	16.7%
5.0	1	16.7%
6.0	1	16.7%

Most occurring characters

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
3	3	16.7%
1	1	5.6%
5	1	5.6%
6	1	5.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
3	3	16.7%
1	1	5.6%
5	1	5.6%
6	1	5.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
3	3	16.7%
1	1	5.6%
5	1	5.6%
6	1	5.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
3	3	16.7%
1	1	5.6%
5	1	5.6%
6	1	5.6%

Difficulty_Transport
Categorical

High correlation Missing

Distinct	3
Distinct (%)	60.0%
Missing	29
Missing (%)	85.3%
Memory size	544.0 B

1.0	2
9.0	2
3.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	15
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	20.0%

Sample

1st row	1.0
2nd row	3.0
3rd row	9.0
4th row	9.0
5th row	1.0

Common Values

Value	Count	Frequency (%)
1.0	2	5.9%
9.0	2	5.9%
3.0	1	2.9%
(Missing)	29	85.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	2	40.0%
9.0	2	40.0%
3.0	1	20.0%

Most occurring characters

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	2	13.3%
9	2	13.3%
3	1	6.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	2	13.3%
9	2	13.3%
3	1	6.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	2	13.3%
9	2	13.3%
3	1	6.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	2	13.3%
9	2	13.3%
3	1	6.7%

Difficulty_Nature
Categorical

High correlation Missing

Distinct	4
Distinct (%)	66.7%
Missing	28
Missing (%)	82.4%
Memory size	544.0 B

1.0	2
2.0	2
3.0	1
8.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	18
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	33.3%

Sample

1st row	3.0
2nd row	1.0
3rd row	2.0
4th row	1.0
5th row	2.0

Common Values

Value	Count	Frequency (%)
1.0	2	5.9%
2.0	2	5.9%
3.0	1	2.9%
8.0	1	2.9%
(Missing)	28	82.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	2	33.3%
2.0	2	33.3%
3.0	1	16.7%
8.0	1	16.7%

Most occurring characters

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	2	11.1%
2	2	11.1%
3	1	5.6%
8	1	5.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	2	11.1%
2	2	11.1%
3	1	5.6%
8	1	5.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	2	11.1%
2	2	11.1%
3	1	5.6%
8	1	5.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	2	11.1%
2	2	11.1%
3	1	5.6%
8	1	5.6%

Country_Tunisia
Categorical

High correlation Missing Uniform

Distinct	3
Distinct (%)	100.0%
Missing	31
Missing (%)	91.2%
Memory size	544.0 B

8.0	1
3.0	1
1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	9
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	100.0%

Sample

1st row	8.0
2nd row	3.0
3rd row	1.0

Common Values

Value	Count	Frequency (%)
8.0	1	2.9%
3.0	1	2.9%
1.0	1	2.9%
(Missing)	31	91.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
8.0	1	33.3%
3.0	1	33.3%
1.0	1	33.3%

Most occurring characters

Value	Count	Frequency (%)
.	3	33.3%
0	3	33.3%
8	1	11.1%
3	1	11.1%
1	1	11.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	3	33.3%
0	3	33.3%
8	1	11.1%
3	1	11.1%
1	1	11.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	3	33.3%
0	3	33.3%
8	1	11.1%
3	1	11.1%
1	1	11.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	3	33.3%
0	3	33.3%
8	1	11.1%
3	1	11.1%
1	1	11.1%

Country_Morocco
Real number (ℝ)

High correlation Missing

Distinct	6
Distinct (%)	85.7%
Missing	27
Missing (%)	79.4%
Infinite	0
Infinite (%)	0.0%
Mean	6.1428571

Minimum	1
Maximum	22
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1.5
median	4
Q3	6.5
95-th percentile	17.8
Maximum	22
Range	21
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	7.4258237
Coefficient of variation (CV)	1.208855
Kurtosis	4.5687455
Mean	6.1428571
Median Absolute Deviation (MAD)	3
Skewness	2.0737045
Sum	43
Variance	55.142857
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
1	2	5.9%
4	1	2.9%
8	1	2.9%
22	1	2.9%
5	1	2.9%
2	1	2.9%
(Missing)	27	79.4%

Value	Count	Frequency (%)
1	2	5.9%
2	1	2.9%
4	1	2.9%
5	1	2.9%
8	1	2.9%
22	1	2.9%

Value	Count	Frequency (%)
22	1	2.9%
8	1	2.9%
5	1	2.9%
4	1	2.9%
2	1	2.9%
1	2	5.9%

Country_Libya
Real number (ℝ)

High correlation Missing

Distinct	6
Distinct (%)	85.7%
Missing	27
Missing (%)	79.4%
Infinite	0
Infinite (%)	0.0%
Mean	4.1428571

Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1.5
median	3
Q3	5
95-th percentile	10.2
Maximum	12
Range	11
Interquartile range (IQR)	3.5

Descriptive statistics

Standard deviation	3.8913824
Coefficient of variation (CV)	0.9392992
Kurtosis	2.8239231
Mean	4.1428571
Median Absolute Deviation (MAD)	2
Skewness	1.6553268
Sum	29
Variance	15.142857
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
1	2	5.9%
2	1	2.9%
3	1	2.9%
6	1	2.9%
4	1	2.9%
12	1	2.9%
(Missing)	27	79.4%

Value	Count	Frequency (%)
1	2	5.9%
2	1	2.9%
3	1	2.9%
4	1	2.9%
6	1	2.9%
12	1	2.9%

Value	Count	Frequency (%)
12	1	2.9%
6	1	2.9%
4	1	2.9%
3	1	2.9%
2	1	2.9%
1	2	5.9%

Transport_Caravan
Real number (ℝ)

High correlation Missing

Distinct	8
Distinct (%)	88.9%
Missing	25
Missing (%)	73.5%
Infinite	0
Infinite (%)	0.0%
Mean	14

Minimum	1
Maximum	70
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	4
Q3	8
95-th percentile	54.4
Maximum	70
Range	69
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	22.989128
Coefficient of variation (CV)	1.6420806
Kurtosis	5.0432986
Mean	14
Median Absolute Deviation (MAD)	3
Skewness	2.2665538
Sum	126
Variance	528.5
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
1	2	5.9%
2	1	2.9%
70	1	2.9%
8	1	2.9%
31	1	2.9%
4	1	2.9%
6	1	2.9%
3	1	2.9%
(Missing)	25	73.5%

Value	Count	Frequency (%)
1	2	5.9%
2	1	2.9%
3	1	2.9%
4	1	2.9%
6	1	2.9%
8	1	2.9%
31	1	2.9%
70	1	2.9%

Value	Count	Frequency (%)
70	1	2.9%
31	1	2.9%
8	1	2.9%
6	1	2.9%
4	1	2.9%
3	1	2.9%
2	1	2.9%
1	2	5.9%

Transport_Camelid
Real number (ℝ)

High correlation Missing

Distinct	9
Distinct (%)	75.0%
Missing	22
Missing (%)	64.7%
Infinite	0
Infinite (%)	0.0%
Mean	9.3333333

Minimum	1
Maximum	43
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1.75
median	7.5
Q3	9.75
95-th percentile	27.6
Maximum	43
Range	42
Interquartile range (IQR)	8

Descriptive statistics

Standard deviation	11.562741
Coefficient of variation (CV)	1.2388651
Kurtosis	7.4618806
Mean	9.3333333
Median Absolute Deviation (MAD)	5
Skewness	2.5448766
Sum	112
Variance	133.69697
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
1	3	8.8%
9	2	5.9%
4	1	2.9%
2	1	2.9%
15	1	2.9%
8	1	2.9%
7	1	2.9%
43	1	2.9%
12	1	2.9%
(Missing)	22	64.7%

Value	Count	Frequency (%)
1	3	8.8%
2	1	2.9%
4	1	2.9%
7	1	2.9%
8	1	2.9%
9	2	5.9%
12	1	2.9%
15	1	2.9%
43	1	2.9%

Value	Count	Frequency (%)
43	1	2.9%
15	1	2.9%
12	1	2.9%
9	2	5.9%
8	1	2.9%
7	1	2.9%
4	1	2.9%
2	1	2.9%
1	3	8.8%

Country_United Kingdom
Categorical

High correlation Missing

Distinct	2
Distinct (%)	33.3%
Missing	28
Missing (%)	82.4%
Memory size	544.0 B

1.0	5
2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	18
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	16.7%

Sample

1st row	1.0
2nd row	1.0
3rd row	1.0
4th row	1.0
5th row	1.0

Common Values

Value	Count	Frequency (%)
1.0	5	14.7%
2.0	1	2.9%
(Missing)	28	82.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	5	83.3%
2.0	1	16.7%

Most occurring characters

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	5	27.8%
2	1	5.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	5	27.8%
2	1	5.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	5	27.8%
2	1	5.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	5	27.8%
2	1	5.6%

Country_Nigeria
Categorical

High correlation Missing

Distinct	3
Distinct (%)	60.0%
Missing	29
Missing (%)	85.3%
Memory size	544.0 B

1.0	2
2.0	2
3.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	15
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	20.0%

Sample

1st row	1.0
2nd row	3.0
3rd row	2.0
4th row	2.0
5th row	1.0

Common Values

Value	Count	Frequency (%)
1.0	2	5.9%
2.0	2	5.9%
3.0	1	2.9%
(Missing)	29	85.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	2	40.0%
2.0	2	40.0%
3.0	1	20.0%

Most occurring characters

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	2	13.3%
2	2	13.3%
3	1	6.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	2	13.3%
2	2	13.3%
3	1	6.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	2	13.3%
2	2	13.3%
3	1	6.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	2	13.3%
2	2	13.3%
3	1	6.7%

Country_Niger
Categorical

High correlation Missing Uniform

Distinct	3
Distinct (%)	100.0%
Missing	31
Missing (%)	91.2%
Memory size	544.0 B

24.0	1
2.0	1
3.0	1

Length

Max length	4
Median length	3
Mean length	3.3333333
Min length	3

Characters and Unicode

Total characters	10
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	100.0%

Sample

1st row	24.0
2nd row	2.0
3rd row	3.0

Common Values

Value	Count	Frequency (%)
24.0	1	2.9%
2.0	1	2.9%
3.0	1	2.9%
(Missing)	31	91.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
24.0	1	33.3%
2.0	1	33.3%
3.0	1	33.3%

Most occurring characters

Value	Count	Frequency (%)
.	3	30.0%
0	3	30.0%
2	2	20.0%
4	1	10.0%
3	1	10.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	10	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	3	30.0%
0	3	30.0%
2	2	20.0%
4	1	10.0%
3	1	10.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	10	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	3	30.0%
0	3	30.0%
2	2	20.0%
4	1	10.0%
3	1	10.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	10	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	3	30.0%
0	3	30.0%
2	2	20.0%
4	1	10.0%
3	1	10.0%

Difficulty_Humans
Categorical

High correlation Missing

Distinct	3
Distinct (%)	75.0%
Missing	30
Missing (%)	88.2%
Memory size	544.0 B

6.0	2
1.0	1
4.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	12
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	50.0%

Sample

1st row	1.0
2nd row	6.0
3rd row	6.0
4th row	4.0

Common Values

Value	Count	Frequency (%)
6.0	2	5.9%
1.0	1	2.9%
4.0	1	2.9%
(Missing)	30	88.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
6.0	2	50.0%
1.0	1	25.0%
4.0	1	25.0%

Most occurring characters

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
6	2	16.7%
1	1	8.3%
4	1	8.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
6	2	16.7%
1	1	8.3%
4	1	8.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
6	2	16.7%
1	1	8.3%
4	1	8.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
6	2	16.7%
1	1	8.3%
4	1	8.3%

Country_Spain
Categorical

High correlation Missing

Distinct	2
Distinct (%)	40.0%
Missing	29
Missing (%)	85.3%
Memory size	544.0 B

1.0	4
9.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	15
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	20.0%

Sample

1st row	1.0
2nd row	1.0
3rd row	1.0
4th row	1.0
5th row	9.0

Common Values

Value	Count	Frequency (%)
1.0	4	11.8%
9.0	1	2.9%
(Missing)	29	85.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	4	80.0%
9.0	1	20.0%

Most occurring characters

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	4	26.7%
9	1	6.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	4	26.7%
9	1	6.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	4	26.7%
9	1	6.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	4	26.7%
9	1	6.7%

Country_Gibraltar
Categorical

Constant Missing

Distinct	1
Distinct (%)	33.3%
Missing	31
Missing (%)	91.2%
Memory size	544.0 B

1.0	3

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	9
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1.0
2nd row	1.0
3rd row	1.0

Common Values

Value	Count	Frequency (%)
1.0	3	8.8%
(Missing)	31	91.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	3	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	3	33.3%
.	3	33.3%
0	3	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	3	33.3%
.	3	33.3%
0	3	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	3	33.3%
.	3	33.3%
0	3	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	3	33.3%
.	3	33.3%
0	3	33.3%

Transport_Walking
Real number (ℝ)

High correlation Missing

Distinct	9
Distinct (%)	64.3%
Missing	20
Missing (%)	58.8%
Infinite	0
Infinite (%)	0.0%
Mean	7.7857143

Minimum	1
Maximum	40
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	544.0 B

Quantile statistics

Minimum	1
5-th percentile	1
Q1	4.25
median	6
Q3	6.75
95-th percentile	21.15
Maximum	40
Range	39
Interquartile range (IQR)	2.5

Descriptive statistics

Standard deviation	9.6808977
Coefficient of variation (CV)	1.2434181
Kurtosis	11.245328
Mean	7.7857143
Median Absolute Deviation (MAD)	1.5
Skewness	3.2173041
Sum	109
Variance	93.71978
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
6	4	11.8%
1	2	5.9%
5	2	5.9%
7	1	2.9%
2	1	2.9%
11	1	2.9%
9	1	2.9%
40	1	2.9%
4	1	2.9%
(Missing)	20	58.8%

Value	Count	Frequency (%)
1	2	5.9%
2	1	2.9%
4	1	2.9%
5	2	5.9%
6	4	11.8%
7	1	2.9%
9	1	2.9%
11	1	2.9%
40	1	2.9%

Value	Count	Frequency (%)
40	1	2.9%
11	1	2.9%
9	1	2.9%
7	1	2.9%
6	4	11.8%
5	2	5.9%
4	1	2.9%
2	1	2.9%
1	2	5.9%

Difficulty_Fatigue/Illness
Categorical

High correlation Missing

Distinct	3
Distinct (%)	75.0%
Missing	30
Missing (%)	88.2%
Memory size	544.0 B

1.0	2
2.0	1
8.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	12
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	50.0%

Sample

1st row	2.0
2nd row	1.0
3rd row	1.0
4th row	8.0

Common Values

Value	Count	Frequency (%)
1.0	2	5.9%
2.0	1	2.9%
8.0	1	2.9%
(Missing)	30	88.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	2	50.0%
2.0	1	25.0%
8.0	1	25.0%

Most occurring characters

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
1	2	16.7%
2	1	8.3%
8	1	8.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
1	2	16.7%
2	1	8.3%
8	1	8.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
1	2	16.7%
2	1	8.3%
8	1	8.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
1	2	16.7%
2	1	8.3%
8	1	8.3%

Difficulty_Thirst/Hunger
Categorical

High correlation Missing

Distinct	3
Distinct (%)	60.0%
Missing	29
Missing (%)	85.3%
Memory size	544.0 B

1.0	3
2.0	1
3.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	15
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	40.0%

Sample

1st row	1.0
2nd row	1.0
3rd row	1.0
4th row	2.0
5th row	3.0

Common Values

Value	Count	Frequency (%)
1.0	3	8.8%
2.0	1	2.9%
3.0	1	2.9%
(Missing)	29	85.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	3	60.0%
2.0	1	20.0%
3.0	1	20.0%

Most occurring characters

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	3	20.0%
2	1	6.7%
3	1	6.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	3	20.0%
2	1	6.7%
3	1	6.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	3	20.0%
2	1	6.7%
3	1	6.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	15	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	5	33.3%
0	5	33.3%
1	3	20.0%
2	1	6.7%
3	1	6.7%

Country_Sierra Leone
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	2.0

Common Values

Value	Count	Frequency (%)
2.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
2.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Guinea
Categorical

High correlation Missing

Distinct	3
Distinct (%)	60.0%
Missing	29
Missing (%)	85.3%
Memory size	544.0 B

3.0	3
6.0	1
11.0	1

Length

Max length	4
Median length	3
Mean length	3.2
Min length	3

Characters and Unicode

Total characters	16
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	40.0%

Sample

1st row	6.0
2nd row	3.0
3rd row	11.0
4th row	3.0
5th row	3.0

Common Values

Value	Count	Frequency (%)
3.0	3	8.8%
6.0	1	2.9%
11.0	1	2.9%
(Missing)	29	85.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
3.0	3	60.0%
6.0	1	20.0%
11.0	1	20.0%

Most occurring characters

Value	Count	Frequency (%)
.	5	31.2%
0	5	31.2%
3	3	18.8%
1	2	12.5%
6	1	6.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	16	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	5	31.2%
0	5	31.2%
3	3	18.8%
1	2	12.5%
6	1	6.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	16	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	5	31.2%
0	5	31.2%
3	3	18.8%
1	2	12.5%
6	1	6.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	16	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	5	31.2%
0	5	31.2%
3	3	18.8%
1	2	12.5%
6	1	6.2%

Country_Mali
Categorical

High correlation Missing

Distinct	4
Distinct (%)	80.0%
Missing	29
Missing (%)	85.3%
Memory size	544.0 B

3.0	2
18.0	1
5.0	1
2.0	1

Length

Max length	4
Median length	3
Mean length	3.2
Min length	3

Characters and Unicode

Total characters	16
Distinct characters	7
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	60.0%

Sample

1st row	18.0
2nd row	5.0
3rd row	3.0
4th row	3.0
5th row	2.0

Common Values

Value	Count	Frequency (%)
3.0	2	5.9%
18.0	1	2.9%
5.0	1	2.9%
2.0	1	2.9%
(Missing)	29	85.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
3.0	2	40.0%
18.0	1	20.0%
5.0	1	20.0%
2.0	1	20.0%

Most occurring characters

Value	Count	Frequency (%)
.	5	31.2%
0	5	31.2%
3	2	12.5%
1	1	6.2%
8	1	6.2%
5	1	6.2%
2	1	6.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	16	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	5	31.2%
0	5	31.2%
3	2	12.5%
1	1	6.2%
8	1	6.2%
5	1	6.2%
2	1	6.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	16	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	5	31.2%
0	5	31.2%
3	2	12.5%
1	1	6.2%
8	1	6.2%
5	1	6.2%
2	1	6.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	16	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	5	31.2%
0	5	31.2%
3	2	12.5%
1	1	6.2%
8	1	6.2%
5	1	6.2%
2	1	6.2%

Country_Côte d'Ivoire
Categorical

High correlation Missing Uniform

Distinct	2
Distinct (%)	100.0%
Missing	32
Missing (%)	94.1%
Memory size	544.0 B

1.0	1
2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	6
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	100.0%

Sample

1st row	1.0
2nd row	2.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
2.0	1	2.9%
(Missing)	32	94.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	50.0%
2.0	1	50.0%

Most occurring characters

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
1	1	16.7%
2	1	16.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
1	1	16.7%
2	1	16.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
1	1	16.7%
2	1	16.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
1	1	16.7%
2	1	16.7%

Country_Senegal
Categorical

High correlation Missing Uniform

Distinct	3
Distinct (%)	100.0%
Missing	31
Missing (%)	91.2%
Memory size	544.0 B

1.0	1
4.0	1
5.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	9
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	100.0%

Sample

1st row	1.0
2nd row	4.0
3rd row	5.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
4.0	1	2.9%
5.0	1	2.9%
(Missing)	31	91.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	33.3%
4.0	1	33.3%
5.0	1	33.3%

Most occurring characters

Value	Count	Frequency (%)
.	3	33.3%
0	3	33.3%
1	1	11.1%
4	1	11.1%
5	1	11.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	3	33.3%
0	3	33.3%
1	1	11.1%
4	1	11.1%
5	1	11.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	3	33.3%
0	3	33.3%
1	1	11.1%
4	1	11.1%
5	1	11.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	3	33.3%
0	3	33.3%
1	1	11.1%
4	1	11.1%
5	1	11.1%

Country_Egypt
Categorical

High correlation Missing

Distinct	2
Distinct (%)	50.0%
Missing	30
Missing (%)	88.2%
Memory size	544.0 B

1.0	3
2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	12
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	25.0%

Sample

1st row	1.0
2nd row	2.0
3rd row	1.0
4th row	1.0

Common Values

Value	Count	Frequency (%)
1.0	3	8.8%
2.0	1	2.9%
(Missing)	30	88.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	3	75.0%
2.0	1	25.0%

Most occurring characters

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
1	3	25.0%
2	1	8.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
1	3	25.0%
2	1	8.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
1	3	25.0%
2	1	8.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
1	3	25.0%
2	1	8.3%

Country_Kenya
Categorical

High correlation Missing Uniform

Distinct	2
Distinct (%)	100.0%
Missing	32
Missing (%)	94.1%
Memory size	544.0 B

3.0	1
2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	6
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	100.0%

Sample

1st row	3.0
2nd row	2.0

Common Values

Value	Count	Frequency (%)
3.0	1	2.9%
2.0	1	2.9%
(Missing)	32	94.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
3.0	1	50.0%
2.0	1	50.0%

Most occurring characters

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
3	1	16.7%
2	1	16.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
3	1	16.7%
2	1	16.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
3	1	16.7%
2	1	16.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
3	1	16.7%
2	1	16.7%

Country_Tanzania
Categorical

High correlation Missing

Distinct	4
Distinct (%)	66.7%
Missing	28
Missing (%)	82.4%
Memory size	544.0 B

1.0	2
3.0	2
4.0	1
2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	18
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	33.3%

Sample

1st row	1.0
2nd row	1.0
3rd row	3.0
4th row	3.0
5th row	4.0

Common Values

Value	Count	Frequency (%)
1.0	2	5.9%
3.0	2	5.9%
4.0	1	2.9%
2.0	1	2.9%
(Missing)	28	82.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	2	33.3%
3.0	2	33.3%
4.0	1	16.7%
2.0	1	16.7%

Most occurring characters

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	2	11.1%
3	2	11.1%
4	1	5.6%
2	1	5.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	2	11.1%
3	2	11.1%
4	1	5.6%
2	1	5.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	2	11.1%
3	2	11.1%
4	1	5.6%
2	1	5.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	18	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	6	33.3%
0	6	33.3%
1	2	11.1%
3	2	11.1%
4	1	5.6%
2	1	5.6%

Country_Mozambique
Categorical

High correlation Missing

Distinct	4
Distinct (%)	57.1%
Missing	27
Missing (%)	79.4%
Memory size	544.0 B

3.0	3
2.0	2
6.0	1
1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	21
Distinct characters	6
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	28.6%

Sample

1st row	3.0
2nd row	6.0
3rd row	3.0
4th row	2.0
5th row	3.0

Common Values

Value	Count	Frequency (%)
3.0	3	8.8%
2.0	2	5.9%
6.0	1	2.9%
1.0	1	2.9%
(Missing)	27	79.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
3.0	3	42.9%
2.0	2	28.6%
6.0	1	14.3%
1.0	1	14.3%

Most occurring characters

Value	Count	Frequency (%)
.	7	33.3%
0	7	33.3%
3	3	14.3%
2	2	9.5%
6	1	4.8%
1	1	4.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	21	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	7	33.3%
0	7	33.3%
3	3	14.3%
2	2	9.5%
6	1	4.8%
1	1	4.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	21	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	7	33.3%
0	7	33.3%
3	3	14.3%
2	2	9.5%
6	1	4.8%
1	1	4.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	21	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	7	33.3%
0	7	33.3%
3	3	14.3%
2	2	9.5%
6	1	4.8%
1	1	4.8%

Country_Democratic Republic of the Congo
Categorical

High correlation Missing Uniform

Distinct	2
Distinct (%)	100.0%
Missing	32
Missing (%)	94.1%
Memory size	544.0 B

8.0	1
2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	6
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	100.0%

Sample

1st row	8.0
2nd row	2.0

Common Values

Value	Count	Frequency (%)
8.0	1	2.9%
2.0	1	2.9%
(Missing)	32	94.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
8.0	1	50.0%
2.0	1	50.0%

Most occurring characters

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
8	1	16.7%
2	1	16.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
8	1	16.7%
2	1	16.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
8	1	16.7%
2	1	16.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
8	1	16.7%
2	1	16.7%

Country_Zambia
Categorical

High correlation Missing Uniform

Distinct	2
Distinct (%)	100.0%
Missing	32
Missing (%)	94.1%
Memory size	544.0 B

2.0	1
1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	6
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	100.0%

Sample

1st row	2.0
2nd row	1.0

Common Values

Value	Count	Frequency (%)
2.0	1	2.9%
1.0	1	2.9%
(Missing)	32	94.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
2.0	1	50.0%
1.0	1	50.0%

Most occurring characters

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
2	1	16.7%
1	1	16.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
2	1	16.7%
1	1	16.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
2	1	16.7%
1	1	16.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	2	33.3%
0	2	33.3%
2	1	16.7%
1	1	16.7%

Country_Botswana
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

3.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	3.0

Common Values

Value	Count	Frequency (%)
3.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
3.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
3	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	1	33.3%
.	1	33.3%
0	1	33.3%

Country_South Africa
Categorical

High correlation Missing

Distinct	3
Distinct (%)	75.0%
Missing	30
Missing (%)	88.2%
Memory size	544.0 B

4.0	2
3.0	1
1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	12
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	50.0%

Sample

1st row	3.0
2nd row	1.0
3rd row	4.0
4th row	4.0

Common Values

Value	Count	Frequency (%)
4.0	2	5.9%
3.0	1	2.9%
1.0	1	2.9%
(Missing)	30	88.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
4.0	2	50.0%
3.0	1	25.0%
1.0	1	25.0%

Most occurring characters

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
4	2	16.7%
3	1	8.3%
1	1	8.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
4	2	16.7%
3	1	8.3%
1	1	8.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
4	2	16.7%
3	1	8.3%
1	1	8.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
.	4	33.3%
0	4	33.3%
4	2	16.7%
3	1	8.3%
1	1	8.3%

Country_Zimbabwe
Categorical

Constant Missing

Distinct	1
Distinct (%)	50.0%
Missing	32
Missing (%)	94.1%
Memory size	544.0 B

1.0	2

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	6
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1.0
2nd row	1.0

Common Values

Value	Count	Frequency (%)
1.0	2	5.9%
(Missing)	32	94.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	2	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	2	33.3%
.	2	33.3%
0	2	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	2	33.3%
.	2	33.3%
0	2	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	2	33.3%
.	2	33.3%
0	2	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	6	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	2	33.3%
.	2	33.3%
0	2	33.3%

Country_Portugal
Categorical

Constant Missing

Distinct	1
Distinct (%)	33.3%
Missing	31
Missing (%)	91.2%
Memory size	544.0 B

1.0	3

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	9
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1.0
2nd row	1.0
3rd row	1.0

Common Values

Value	Count	Frequency (%)
1.0	3	8.8%
(Missing)	31	91.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	3	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	3	33.3%
.	3	33.3%
0	3	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	3	33.3%
.	3	33.3%
0	3	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	3	33.3%
.	3	33.3%
0	3	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	9	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	3	33.3%
.	3	33.3%
0	3	33.3%

Country_Cape Verde
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	2.0

Common Values

Value	Count	Frequency (%)
2.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
2.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Saint Helena, Ascension and Tristan da Cunha
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_India
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	2.0

Common Values

Value	Count	Frequency (%)
2.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
2.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Somalia
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Guinea-Bissau
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Malawi
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

6.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	6.0

Common Values

Value	Count	Frequency (%)
6.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
6.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
6	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
6	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
6	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
6	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Mauritania
Categorical

High correlation Missing Uniform

Distinct	2
Distinct (%)	100.0%
Missing	32
Missing (%)	94.1%
Memory size	544.0 B

23.0	1
2.0	1

Length

Max length	4
Median length	3.5
Mean length	3.5
Min length	3

Characters and Unicode

Total characters	7
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	100.0%

Sample

1st row	23.0
2nd row	2.0

Common Values

Value	Count	Frequency (%)
23.0	1	2.9%
2.0	1	2.9%
(Missing)	32	94.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
23.0	1	50.0%
2.0	1	50.0%

Most occurring characters

Value	Count	Frequency (%)
2	2	28.6%
.	2	28.6%
0	2	28.6%
3	1	14.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	7	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	2	28.6%
.	2	28.6%
0	2	28.6%
3	1	14.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	7	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	2	28.6%
.	2	28.6%
0	2	28.6%
3	1	14.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	7	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	2	28.6%
.	2	28.6%
0	2	28.6%
3	1	14.3%

Country_Ghana
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Liberia
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_The Gambia
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Ethiopia
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

9.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	9.0

Common Values

Value	Count	Frequency (%)
9.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
9.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
9	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
9	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
9	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
9	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Malta
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

6.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	6.0

Common Values

Value	Count	Frequency (%)
6.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
6.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
6	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
6	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
6	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
6	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Gabon
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Germany
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Angola
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

2.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	2.0

Common Values

Value	Count	Frequency (%)
2.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
2.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Namibia
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Sahrawi Arab Democratic Republic
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

4.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	4.0

Common Values

Value	Count	Frequency (%)
4.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
4.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
4	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
4	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
4	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
4	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Sweden
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Ukraine
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Country_Russia
Categorical

Constant Missing

Distinct	1
Distinct (%)	100.0%
Missing	33
Missing (%)	97.1%
Memory size	544.0 B

1.0	1

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	3
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	100.0%

Sample

1st row	1.0

Common Values

Value	Count	Frequency (%)
1.0	1	2.9%
(Missing)	33	97.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1.0	1	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
1	1	33.3%
.	1	33.3%
0	1	33.3%

Hierarchical_Cluster
Categorical

High correlation

Distinct	5
Distinct (%)	14.7%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

2	24
3	6
1	2
4	1
5	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	34
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	5.9%

Sample

1st row	1
2nd row	1
3rd row	2
4th row	2
5th row	2

Common Values

Value	Count	Frequency (%)
2	24	70.6%
3	6	17.6%
1	2	5.9%
4	1	2.9%
5	1	2.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
2	24	70.6%
3	6	17.6%
1	2	5.9%
4	1	2.9%
5	1	2.9%

Most occurring characters

Value	Count	Frequency (%)
2	24	70.6%
3	6	17.6%
1	2	5.9%
4	1	2.9%
5	1	2.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	34	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	24	70.6%
3	6	17.6%
1	2	5.9%
4	1	2.9%
5	1	2.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	34	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	24	70.6%
3	6	17.6%
1	2	5.9%
4	1	2.9%
5	1	2.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	34	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	24	70.6%
3	6	17.6%
1	2	5.9%
4	1	2.9%
5	1	2.9%

KMeans_Cluster
Categorical

High correlation

Distinct	5
Distinct (%)	14.7%
Missing	0
Missing (%)	0.0%
Memory size	544.0 B

3	24
0	5
1	3
4	1
2	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	34
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	5.9%

Sample

1st row	4
2nd row	3
3rd row	3
4th row	3
5th row	3

Common Values

Value	Count	Frequency (%)
3	24	70.6%
0	5	14.7%
1	3	8.8%
4	1	2.9%
2	1	2.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
3	24	70.6%
0	5	14.7%
1	3	8.8%
4	1	2.9%
2	1	2.9%

Most occurring characters

Value	Count	Frequency (%)
3	24	70.6%
0	5	14.7%
1	3	8.8%
4	1	2.9%
2	1	2.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	34	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	24	70.6%
0	5	14.7%
1	3	8.8%
4	1	2.9%
2	1	2.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	34	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	24	70.6%
0	5	14.7%
1	3	8.8%
4	1	2.9%
2	1	2.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	34	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	24	70.6%
0	5	14.7%
1	3	8.8%
4	1	2.9%
2	1	2.9%

Interactions

Correlations

	Arrival Age	Country_Algeria	Country_Côte d'Ivoire	Country_Democratic Republic of the Congo	Country_Egypt	Country_France	Country_Guinea	Country_Kenya	Country_Libya	Country_Mali	Country_Mauritania	Country_Morocco	Country_Mozambique	Country_Niger	Country_Nigeria	Country_Senegal	Country_South Africa	Country_Spain	Country_Tanzania	Country_Tunisia	Country_United Kingdom	Country_Zambia	Departure Age	Difficulty_Climate	Difficulty_Fatigue/Illness	Difficulty_Humans	Difficulty_Nature	Difficulty_Thirst/Hunger	Difficulty_Transport	Hierarchical_Cluster	KMeans_Cluster	Language	Money	Nationality	Number of images	Number of steps	Start Year	Steps with null coordinates in %	Total distance traveled (km)	Transport_Boat	Transport_Camelid	Transport_Caravan	Transport_Equid	Transport_Slow ground vehicle	Transport_Train	Transport_Walking	Travel duration in days
Arrival Age	1.000	-0.496	1.000	1.000	0.000	1.000	0.333	1.000	0.206	0.577	1.000	0.145	0.775	1.000	0.000	1.000	1.000	0.000	0.000	1.000	0.866	1.000	0.643	0.000	1.000	1.000	0.866	0.816	0.000	0.185	0.406	0.000	0.094	0.000	0.172	-0.073	0.077	-0.028	0.139	-0.133	-0.412	-0.162	-0.235	-0.397	0.000	0.133	0.900
Country_Algeria	-0.496	1.000	0.000	0.000	0.000	1.000	NaN	0.000	-1.000	1.000	NaN	NaN	0.000	NaN	NaN	NaN	0.000	NaN	0.000	NaN	NaN	0.000	-0.494	0.000	NaN	1.000	0.000	NaN	0.000	0.000	0.515	0.433	0.265	0.316	0.088	0.495	0.006	-0.583	-0.355	-0.287	0.429	-1.000	0.564	0.189	0.000	NaN	-0.535
Country_Côte d'Ivoire	1.000	0.000	1.000	NaN	0.000	0.000	NaN	0.000	0.000	NaN	0.000	NaN	0.000	0.000	0.000	0.000	0.000	NaN	0.000	0.000	NaN	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.000	NaN	NaN	0.000	NaN	NaN	1.000
Country_Democratic Republic of the Congo	1.000	0.000	NaN	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	NaN	NaN	0.000	NaN	NaN	1.000	NaN	NaN	0.000	NaN	NaN	0.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.000	0.000	0.000	0.000	NaN	NaN	1.000
Country_Egypt	0.000	0.000	0.000	0.000	1.000	0.000	0.000	NaN	1.000	0.000	0.000	0.000	NaN	0.000	NaN	0.000	NaN	NaN	1.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	1.000	0.707	0.000	1.000	1.000	1.000	0.707	1.000	1.000	1.000	0.000	1.000	1.000	NaN	1.000	1.000	1.000
Country_France	1.000	1.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	NaN	0.000	0.000	0.000	0.000	1.000	NaN	0.000	NaN	NaN	0.000	NaN	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	NaN	0.000	0.000	1.000	NaN	1.000	0.000	1.000
Country_Guinea	0.333	NaN	NaN	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	NaN	0.000	0.000	0.000	0.000	0.000	0.000	0.333	1.000	1.000	1.000	1.000	1.000	1.000	0.333	0.816	0.333	1.000	0.000	0.000	0.000	0.000	0.816	0.816	0.577	1.000	1.000	1.000	1.000	NaN	0.000	0.577
Country_Kenya	1.000	0.000	0.000	0.000	NaN	0.000	0.000	1.000	0.000	0.000	0.000	0.000	NaN	0.000	0.000	0.000	NaN	NaN	NaN	0.000	NaN	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.000	0.000	0.000	0.000	NaN	NaN	1.000
Country_Libya	0.206	-1.000	0.000	0.000	1.000	0.000	0.000	0.000	1.000	0.000	0.000	NaN	0.000	NaN	0.000	0.000	0.000	NaN	0.000	1.000	NaN	0.000	0.517	NaN	0.000	0.000	NaN	0.000	NaN	0.632	0.000	0.000	0.866	0.000	0.774	0.090	-0.703	0.649	-0.559	-0.544	1.000	1.000	-0.866	NaN	NaN	NaN	0.206
Country_Mali	0.577	1.000	NaN	0.000	0.000	0.000	0.000	0.000	0.000	1.000	NaN	1.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.577	1.000	1.000	1.000	1.000	1.000	1.000	0.000	0.000	0.577	1.000	0.816	0.577	1.000	0.577	0.000	0.000	0.000	1.000	1.000	1.000	1.000	NaN	1.000	0.000
Country_Mauritania	1.000	NaN	0.000	0.000	0.000	0.000	0.000	0.000	0.000	NaN	1.000	1.000	0.000	0.000	0.000	NaN	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	NaN	1.000	NaN	0.000	NaN	0.000	NaN	1.000
Country_Morocco	0.145	NaN	NaN	0.000	0.000	0.000	1.000	0.000	NaN	1.000	1.000	1.000	0.000	0.000	0.000	NaN	0.000	0.000	0.000	NaN	0.000	0.000	0.330	NaN	0.000	0.000	NaN	0.000	NaN	0.424	0.000	0.000	0.000	0.430	0.376	0.000	-0.342	0.216	-0.360	-0.351	0.500	-0.400	0.700	NaN	NaN	0.500	0.036
Country_Mozambique	0.775	0.000	0.000	0.000	NaN	0.000	0.000	NaN	0.000	0.000	0.000	0.000	1.000	0.000	0.000	NaN	0.000	1.000	1.000	0.000	1.000	NaN	0.866	NaN	NaN	NaN	NaN	1.000	NaN	0.200	0.000	0.000	0.000	0.000	0.000	0.500	0.000	0.236	0.000	0.000	NaN	0.000	NaN	0.000	1.000	0.000	0.430
Country_Niger	1.000	NaN	0.000	0.000	0.000	0.000	0.000	0.000	NaN	0.000	0.000	0.000	0.000	1.000	1.000	0.000	0.000	0.000	0.000	NaN	NaN	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	NaN	NaN	0.000	NaN	0.000	1.000
Country_Nigeria	0.000	NaN	0.000	0.000	NaN	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000	1.000	0.000	0.000	NaN	0.000	NaN	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.816	1.000	0.000	0.577	0.000	0.000	0.000	0.000	0.816	0.000	0.000	1.000	1.000	1.000	0.000	NaN	0.000	0.000
Country_Senegal	1.000	NaN	0.000	0.000	0.000	0.000	NaN	0.000	0.000	1.000	NaN	NaN	NaN	0.000	0.000	1.000	NaN	NaN	NaN	0.000	0.000	0.000	1.000	NaN	NaN	NaN	NaN	NaN	NaN	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.000	0.000	1.000	0.000	NaN	1.000
Country_South Africa	1.000	0.000	0.000	0.000	NaN	0.000	0.000	NaN	0.000	0.000	0.000	0.000	0.000	0.000	0.000	NaN	1.000	1.000	0.000	0.000	NaN	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.707	0.707	0.000	0.000	1.000	0.707	0.000	1.000	1.000	1.000	0.000	0.000	0.000	NaN	NaN	NaN	1.000	1.000
Country_Spain	0.000	NaN	NaN	NaN	NaN	NaN	0.000	NaN	NaN	0.000	0.000	0.000	1.000	0.000	NaN	NaN	1.000	1.000	NaN	0.000	NaN	0.000	0.000	NaN	0.000	NaN	NaN	0.000	NaN	0.816	0.000	0.000	1.000	1.000	0.577	0.000	0.000	0.000	0.577	0.577	0.000	NaN	1.000	NaN	1.000	0.000	0.000
Country_Tanzania	0.000	0.000	0.000	NaN	1.000	0.000	0.000	NaN	0.000	0.000	0.000	0.000	1.000	0.000	0.000	NaN	0.000	NaN	1.000	0.000	1.000	NaN	0.000	1.000	NaN	0.000	NaN	1.000	NaN	0.382	0.177	0.000	0.382	0.000	0.707	0.000	0.000	0.000	0.000	0.612	NaN	0.000	NaN	NaN	1.000	0.816	0.000
Country_Tunisia	1.000	NaN	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	NaN	0.000	NaN	NaN	0.000	0.000	0.000	0.000	1.000	0.000	0.000	1.000	NaN	0.000	0.000	NaN	0.000	NaN	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.000	1.000	NaN	NaN	0.000	1.000
Country_United Kingdom	0.866	NaN	NaN	NaN	1.000	0.000	0.000	NaN	NaN	0.000	0.000	0.000	1.000	NaN	1.000	0.000	NaN	NaN	1.000	0.000	1.000	0.000	0.866	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.707	0.000	NaN	1.000	NaN	1.000	0.000	0.000	1.000
Country_Zambia	1.000	0.000	0.000	NaN	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	NaN	0.000	0.000	0.000	0.000	0.000	NaN	0.000	0.000	1.000	1.000	NaN	1.000	NaN	1.000	1.000	0.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.000	0.000	0.000	NaN	0.000	1.000	1.000
Departure Age	0.643	-0.494	1.000	1.000	0.000	1.000	0.333	1.000	0.517	0.577	1.000	0.330	0.866	1.000	0.000	1.000	1.000	0.000	0.000	1.000	0.866	1.000	1.000	0.000	0.000	0.000	0.612	0.577	0.000	0.094	0.343	0.393	0.000	0.000	0.028	-0.143	-0.046	0.212	-0.181	-0.137	0.122	0.077	-0.045	-0.397	0.000	-0.191	0.513
Difficulty_Climate	0.000	0.000	0.000	NaN	0.000	NaN	1.000	0.000	NaN	1.000	0.000	NaN	NaN	0.000	0.000	NaN	0.000	NaN	1.000	NaN	0.000	NaN	0.000	1.000	1.000	1.000	0.000	0.000	0.000	0.667	0.000	1.000	0.509	0.000	0.451	0.000	0.451	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Difficulty_Fatigue/Illness	1.000	NaN	0.000	NaN	0.000	0.000	1.000	0.000	0.000	1.000	0.000	0.000	NaN	0.000	0.000	NaN	0.000	0.000	NaN	0.000	0.000	1.000	0.000	1.000	1.000	1.000	1.000	0.000	1.000	1.000	0.000	1.000	1.000	0.000	0.707	1.000	0.000	0.000	0.000	0.000	1.000	0.000	NaN	1.000	NaN	1.000	0.000
Difficulty_Humans	1.000	1.000	0.000	0.000	0.000	NaN	1.000	0.000	0.000	1.000	0.000	0.000	NaN	0.000	0.000	NaN	0.000	NaN	0.000	0.000	0.000	NaN	0.000	1.000	1.000	1.000	1.000	1.000	0.000	1.000	1.000	1.000	1.000	1.000	0.000	1.000	0.000	0.000	0.000	0.000	1.000	0.000	1.000	0.000	1.000	1.000	1.000
Difficulty_Nature	0.866	0.000	0.000	NaN	0.000	NaN	1.000	0.000	NaN	1.000	0.000	NaN	NaN	0.000	0.000	NaN	0.000	NaN	NaN	NaN	0.000	1.000	0.612	0.000	1.000	1.000	1.000	0.000	0.000	0.612	0.612	1.000	0.866	0.000	0.204	0.866	0.000	0.612	0.000	0.333	1.000	0.000	0.000	0.000	0.000	1.000	0.565
Difficulty_Thirst/Hunger	0.816	NaN	0.000	NaN	0.000	0.000	1.000	0.000	0.000	1.000	0.000	0.000	1.000	0.000	0.000	NaN	0.000	0.000	1.000	0.000	0.000	1.000	0.577	0.000	0.000	1.000	0.000	1.000	1.000	0.577	1.000	1.000	1.000	1.000	0.000	0.816	1.000	0.000	0.816	0.577	1.000	0.000	NaN	1.000	NaN	0.000	0.000
Difficulty_Transport	0.000	0.000	0.000	0.000	0.000	NaN	1.000	0.000	NaN	1.000	0.000	NaN	NaN	0.000	0.000	NaN	0.000	NaN	NaN	NaN	0.000	0.000	0.000	0.000	1.000	0.000	0.000	1.000	1.000	0.000	0.000	1.000	0.000	0.816	0.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	1.000	0.577
Hierarchical_Cluster	0.185	0.000	1.000	1.000	1.000	1.000	0.333	1.000	0.632	0.000	1.000	0.424	0.200	1.000	0.816	1.000	0.707	0.816	0.382	1.000	1.000	1.000	0.094	0.667	1.000	1.000	0.612	0.577	0.000	1.000	0.588	0.000	0.120	0.000	0.243	0.388	0.318	0.098	0.000	0.000	0.000	0.319	0.186	0.000	0.398	0.660	0.440
KMeans_Cluster	0.406	0.515	1.000	1.000	1.000	1.000	0.816	1.000	0.000	0.000	1.000	0.000	0.000	1.000	1.000	1.000	0.707	0.000	0.177	1.000	0.000	1.000	0.343	0.000	0.000	1.000	0.612	1.000	0.000	0.588	1.000	0.000	0.182	0.486	0.526	0.000	0.524	0.248	0.376	0.453	0.000	1.000	0.000	0.380	0.000	0.608	0.467
Language	0.000	0.433	1.000	1.000	0.707	1.000	0.333	1.000	0.000	0.577	1.000	0.000	0.000	1.000	0.000	1.000	0.000	0.000	0.000	1.000	0.000	1.000	0.393	1.000	1.000	1.000	1.000	1.000	1.000	0.000	0.000	1.000	0.000	0.422	0.000	0.267	0.000	0.000	0.535	0.235	0.340	0.000	0.000	0.000	0.398	0.000	0.000
Money	0.094	0.265	1.000	1.000	0.000	1.000	1.000	1.000	0.866	1.000	1.000	0.000	0.000	1.000	0.577	1.000	0.000	1.000	0.382	1.000	0.000	1.000	0.000	0.509	1.000	1.000	0.866	1.000	0.000	0.120	0.182	0.000	1.000	0.000	0.000	0.183	0.000	0.000	0.000	0.000	0.656	0.000	0.000	0.000	0.172	0.597	0.470
Nationality	0.000	0.316	1.000	1.000	1.000	1.000	0.000	1.000	0.000	0.816	1.000	0.430	0.000	1.000	0.000	1.000	1.000	1.000	0.000	1.000	0.000	1.000	0.000	0.000	0.000	1.000	0.000	1.000	0.816	0.000	0.486	0.422	0.000	1.000	0.265	0.202	0.670	0.407	0.353	0.297	0.132	0.000	0.500	0.000	0.000	0.000	0.000
Number of images	0.172	0.088	1.000	1.000	1.000	1.000	0.000	1.000	0.774	0.577	1.000	0.376	0.000	1.000	0.000	1.000	0.707	0.577	0.707	1.000	0.000	1.000	0.028	0.451	0.707	0.000	0.204	0.000	0.000	0.243	0.526	0.000	0.000	0.265	1.000	-0.102	0.007	-0.294	-0.206	-0.213	-0.139	-0.060	-0.124	0.208	0.289	0.122	0.057
Number of steps	-0.073	0.495	1.000	1.000	1.000	1.000	0.000	1.000	0.090	1.000	1.000	0.000	0.500	1.000	0.000	1.000	0.000	0.000	0.000	1.000	0.000	1.000	-0.143	0.000	1.000	1.000	0.866	0.816	0.000	0.388	0.000	0.267	0.183	0.202	-0.102	1.000	-0.078	0.419	0.189	0.136	0.268	0.092	-0.058	-0.165	0.000	0.693	-0.029
Start Year	0.077	0.006	1.000	1.000	0.707	1.000	0.000	1.000	-0.703	0.577	1.000	-0.342	0.000	1.000	0.000	1.000	1.000	0.000	0.000	1.000	0.000	1.000	-0.046	0.451	0.000	0.000	0.000	1.000	0.000	0.318	0.524	0.000	0.000	0.670	0.007	-0.078	1.000	-0.357	0.046	0.017	-0.041	-0.092	0.236	-0.042	0.756	0.023	-0.176
Steps with null coordinates in %	-0.028	-0.583	1.000	1.000	1.000	1.000	0.816	1.000	0.649	0.000	1.000	0.216	0.236	1.000	0.816	1.000	1.000	0.000	0.000	1.000	0.000	1.000	0.212	0.000	0.000	0.000	0.612	0.000	0.000	0.098	0.248	0.000	0.000	0.407	-0.294	0.419	-0.357	1.000	-0.239	0.107	0.109	0.402	-0.065	-0.672	0.000	0.514	0.024
Total distance traveled (km)	0.139	-0.355	1.000	1.000	1.000	1.000	0.816	1.000	-0.559	0.000	1.000	-0.360	0.000	1.000	0.000	1.000	1.000	0.577	0.000	1.000	0.707	1.000	-0.181	0.000	0.000	0.000	0.000	0.816	0.000	0.000	0.376	0.535	0.000	0.353	-0.206	0.189	0.046	-0.239	1.000	0.519	-0.159	-0.184	-0.646	-0.532	0.000	0.053	0.173
Transport_Boat	-0.133	-0.287	1.000	1.000	1.000	NaN	0.577	1.000	-0.544	0.000	NaN	-0.351	0.000	1.000	0.000	1.000	0.000	0.577	0.612	1.000	0.000	1.000	-0.137	0.000	0.000	0.000	0.333	0.577	0.000	0.000	0.453	0.235	0.000	0.297	-0.213	0.136	0.017	0.107	0.519	1.000	0.155	-0.135	-0.295	-0.625	0.000	0.173	-0.038
Transport_Camelid	-0.412	0.429	0.000	0.000	0.000	0.000	1.000	0.000	1.000	1.000	1.000	0.500	NaN	1.000	1.000	1.000	0.000	0.000	NaN	1.000	NaN	0.000	0.122	0.000	1.000	1.000	1.000	1.000	1.000	0.000	0.000	0.340	0.656	0.132	-0.139	0.268	-0.041	0.109	-0.159	0.155	1.000	-0.500	-0.400	-0.316	1.000	0.105	-0.487
Transport_Caravan	-0.162	-1.000	NaN	0.000	1.000	0.000	1.000	0.000	1.000	1.000	NaN	-0.400	0.000	NaN	1.000	0.000	0.000	NaN	0.000	0.000	1.000	0.000	0.077	0.000	0.000	0.000	0.000	0.000	0.000	0.319	1.000	0.000	0.000	0.000	-0.060	0.092	-0.092	0.402	-0.184	-0.135	-0.500	1.000	-0.211	NaN	NaN	1.000	-0.031
Transport_Equid	-0.235	0.564	NaN	0.000	1.000	1.000	1.000	0.000	-0.866	1.000	0.000	0.700	NaN	NaN	1.000	0.000	NaN	1.000	NaN	1.000	NaN	0.000	-0.045	0.000	NaN	1.000	0.000	NaN	0.000	0.186	0.000	0.000	0.000	0.500	-0.124	-0.058	0.236	-0.065	-0.646	-0.295	-0.400	-0.211	1.000	-0.211	0.000	0.211	-0.329
Transport_Slow ground vehicle	-0.397	0.189	0.000	0.000	NaN	NaN	1.000	0.000	NaN	1.000	NaN	NaN	0.000	0.000	0.000	1.000	NaN	NaN	NaN	NaN	1.000	NaN	-0.397	0.000	1.000	0.000	0.000	1.000	0.000	0.000	0.380	0.000	0.000	0.000	0.208	-0.165	-0.042	-0.672	-0.532	-0.625	-0.316	NaN	-0.211	1.000	0.333	-0.316	-0.326
Transport_Train	0.000	0.000	NaN	NaN	1.000	1.000	NaN	NaN	NaN	NaN	0.000	NaN	1.000	NaN	NaN	0.000	NaN	1.000	1.000	NaN	0.000	0.000	0.000	0.000	NaN	1.000	0.000	NaN	0.000	0.398	0.000	0.398	0.172	0.000	0.289	0.000	0.756	0.000	0.000	0.000	1.000	NaN	0.000	0.333	1.000	0.000	1.000
Transport_Walking	0.133	NaN	NaN	NaN	1.000	0.000	0.000	NaN	NaN	1.000	NaN	0.500	0.000	0.000	0.000	NaN	1.000	0.000	0.816	0.000	0.000	1.000	-0.191	0.000	1.000	1.000	1.000	0.000	1.000	0.660	0.608	0.000	0.597	0.000	0.122	0.693	0.023	0.514	0.053	0.173	0.105	1.000	0.211	-0.316	0.000	1.000	0.032
Travel duration in days	0.900	-0.535	1.000	1.000	1.000	1.000	0.577	1.000	0.206	0.000	1.000	0.036	0.430	1.000	0.000	1.000	1.000	0.000	0.000	1.000	1.000	1.000	0.513	0.000	0.000	1.000	0.565	0.000	0.577	0.440	0.467	0.000	0.470	0.000	0.057	-0.029	-0.176	0.024	0.173	-0.038	-0.487	-0.031	-0.329	-0.326	1.000	0.032	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

	Title	Year of the Journey	Start Year	Author	Date of birth	Place of birth	Date of death	Place of death	Nationality	Activities	Language	URL	Citation	Departure date	Departure Age	Arrival date	Arrival Age	Travel duration in days	Money	Goal	Success	Number of steps	Number of images	Steps with null coordinates in %	Total distance traveled (km)	Transport_Train	Transport_Equid	Country_France	Country_Algeria	Transport_Slow ground vehicle	Transport_Boat	Difficulty_Climate	Difficulty_Transport	Difficulty_Nature	Country_Tunisia	Country_Morocco	Country_Libya	Transport_Caravan	Transport_Camelid	Country_United Kingdom	Country_Nigeria	Country_Niger	Difficulty_Humans	Country_Spain	Country_Gibraltar	Transport_Walking	Difficulty_Fatigue/Illness	Difficulty_Thirst/Hunger	Country_Sierra Leone	Country_Guinea	Country_Mali	Country_Côte d'Ivoire	Country_Senegal	Country_Egypt	Country_Kenya	Country_Tanzania	Country_Mozambique	Country_Democratic Republic of the Congo	Country_Zambia	Country_Botswana	Country_South Africa	Country_Zimbabwe	Country_Portugal	Country_Cape Verde	Country_Saint Helena, Ascension and Tristan da Cunha	Country_India	Country_Somalia	Country_Guinea-Bissau	Country_Malawi	Country_Mauritania	Country_Ghana	Country_Liberia	Country_The Gambia	Country_Ethiopia	Country_Malta	Country_Gabon	Country_Germany	Country_Angola	Country_Namibia	Country_Sahrawi Arab Democratic Republic	Country_Sweden	Country_Ukraine	Country_Russia	Hierarchical_Cluster	KMeans_Cluster
19	A Journal of the First Voyage of Vasco da Gama 1497-1499	1497	1497	Vasco da Gama	1469	Sines (Portugal)	24/12/1524	Kochi (India)	Portuguese	explorer, aristocrat	English	https://www.gutenberg.org/cache/epub/46440/pg46440-images.html	ravenstein2017journal	08/07/1497	28	25/04/1499	29	656		Discover a sea route to India	Not specified	24	25	25.00	35819.58	NaN	NaN	NaN	NaN	NaN	24.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0	NaN	3.0	NaN	NaN	NaN	3.0	NaN	1.0	2.0	1.0	2.0	1.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1	4
18	A Popular Account of Dr. Livingstone's Expedition to the Zambesi and Its Tributaries	1858-1864	1858	David Livingstone	19/03/1813	Blantyre (Scotland)	1/05/1873	Lake Bangweulu (Zambia)	Scottish	explorer, writer, geographer, missionary	English	https://www.gutenberg.org/cache/epub/2519/pg2519-images.html	livingstone1875popular	15/06/1852	39	19/03/1866	53	5025		To expand knowledge of East and Central Africa's geography, minerals, and agriculture, improve relations with locals, encourage industrial activities and land cultivation for export to England, and help end the slave trade by providing a more profitable economic alternative	While Livingstone and his companions explored vast lands, established relations with local chiefs, and gathered valuable geographical and scientific information, their efforts to foster trade and encourage indigenous industry were hindered by factors such as the ongoing slave trade, tribal rivalries, and natural obstacles like cataracts and drought	16	0	43.75	6332.11	NaN	NaN	NaN	NaN	NaN	10.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	6.0	NaN	NaN	NaN	1.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1	3
26	A biographical memoir of the late Dr. Walter Oudney, Captain Hugh Clapperton, both of the Royal Navy, and Major Alex. Gordon Laing, all of whom died amid their active and enterprising endeavours to explore the interior of Africa	1821	1821	Thomas Nelson (publisher)						explorer, traveler, writer	English	https://www.gutenberg.org/cache/epub/72209/pg72209-images.html	nelson2024biographical	27/08/1825	0	Not specified	0	0		Exploring interior Africa, particularly tracing the course and trying to determine the source of the Niger River	Oudney died during the expedition before determining the course and source of the Niger River. However, he gathered significant information on Central Africa before his death	5	1	20.00	5664.32	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	2.0	NaN	1.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	3
31	Afrikanska reseminnen, Äfventyr och Intryck från En utflykt till de Svartes Världsdel	1890-1891	1890	Georg Edvard von Alfthan	17/06/1856	Helsinki (Finland)	23/03/1901	Artjärvi (Finland)	Finnish	aristocrat, military, writer	Swedish	https://www.gutenberg.org/cache/epub/62623/pg62623-images.html	von1892afrikanska	01/07/1890	34	01/04/1891	34	274	The book mentions that he left with little money and had to find ways to earn a living during his journey	The author mentions traveling to escape personal issues and discover new horizons		25	0	48.00	22288.00	3.0	1.0	NaN	NaN	2.0	9.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	7.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	1.0	3.0	NaN	NaN	NaN	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1.0	1.0	2	3
33	Au Hoggar	1922	1922	Conrad Kilian	23/08/1898	Desaignes (Ardèche, France)	29/04/1950	Grenoble (France)	French	geologist, explorer	French	https://www.gutenberg.org/cache/epub/73291/pg73291-images.html	kilian1925hoggar	08/01/1922	23	08/02/1922	23	31		Scientific mission in Central Sahara, focused on geology, botany, and morphology	Yes, Kilian successfully completed his scientific mission, reporting numerous geological, botanical, and ethnographic observations and data	9	0	44.44	641.25	NaN	NaN	NaN	5.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	9.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	3
0	Au Sahara	1889	1889	Hugues Le Roux	23/11/1860	Le Havre (France)	14/11/1925	Paris	French	journalist, politician, writer	French	https://www.gutenberg.org/cache/epub/70754/pg70754-images.html	le1890sahara	08/06/1889	28	14/07/1889	28	36	Enough money for a guide, camels, and food	Exploring the Sahara and documenting local cultures and customs	The author achieved his goal through detailed descriptions of places, encounters, and reflections	9	56	11.11	2043.19	4.0	5.0	1.0	7.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	0
4	Botanical features of the Algerian Sahara	1910	1910	William Austin Cannon	23/09/1870	Washington Township (Michigan, United States)	16/01/1958	Not specified	American	botanist	English	https://www.gutenberg.org/cache/epub/70581/pg70581-images.html	cannon1913botanical	Information non disponible	0	Not specified	0	0		Study the physiological traits of plants in southern Algeria and examine the roots of notable native species	The author conducted field observations and studies as planned	10	82	0.00	1028.81	NaN	NaN	NaN	10.0	5.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	0
14	Exploration de l'Aïr	1921	1921	Angus Buchanan	1886	Kirkwall (Orkney Islands, Scotland)	1954	Not specified	Scottish	explorer, photographer, filmmaker	English	https://www.gutenberg.org/cache/epub/70323/pg70323-images.html	buchanan1921exploration	12/01/1920	33	06/08/1920	34	207	Large sum withdrawn from Kano bank, mostly silver coins due to higher local value	Buchanan's expedition aimed to link zoological geography between Algeria and Nigeria, focusing on the Aïr Massif in the Sahara, addressing gaps in naturalist research	He crossed the Aïr region, collecting numerous animal and plant specimens, many new to science, and advanced zoological knowledge of Africa	51	17	47.06	1810.17	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3.0	24.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	3
20	From the Cape to Cairo: The First Traverse of Africa from South to North	1898	1898	Ewart Scott Grogan	12/12/1874	London	16/08/1967	Cape Town (South Africa)	British	explorer	English	https://www.gutenberg.org/cache/epub/45396/pg45396-images.html	grogan1900cape	Non spécifiée - Quelque temps avant Octobre	0	Not specified	0	0		Travel across South Africa from south to north	Not specified	19	4	47.37	10648.58	1.0	NaN	NaN	NaN	1.0	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	6.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	6.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	3
27	Het land der Bagas en de Rio-Nuñez	1885	1885	Grégoire-Gaspard-Félix Coffinières de Nordeck	3/09/1811	Castelnaudary (France)	7/01/1887	Paris	French	military	Dutch	https://www.gutenberg.org/cache/epub/16117/pg16117-images.html	de2023het	21/04/1885	73	Not specified	0	0		The author's goal was to negotiate with local chiefs along the Rio-Nuñez river to have them recognize King Nalou Yoera Towel as their overlord and place the region under French protectorate	The author successfully negotiated with several Baga chiefs and Landoemans, convincing them to recognize King Nalou and the French protectorate. He also played a key role in safely returning Mahmadi, the sister of Prince Nalou Dinah Salifoe, who had sought refuge in Baga to escape a forced marriage	14	6	57.14	102.54	NaN	NaN	NaN	NaN	NaN	9.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	NaN	NaN	NaN	6.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	3

	Title	Year of the Journey	Start Year	Author	Date of birth	Place of birth	Date of death	Place of death	Nationality	Activities	Language	URL	Citation	Departure date	Departure Age	Arrival date	Arrival Age	Travel duration in days	Money	Goal	Success	Number of steps	Number of images	Steps with null coordinates in %	Total distance traveled (km)	Transport_Train	Transport_Equid	Country_France	Country_Algeria	Transport_Slow ground vehicle	Transport_Boat	Difficulty_Climate	Difficulty_Transport	Difficulty_Nature	Country_Tunisia	Country_Morocco	Country_Libya	Transport_Caravan	Transport_Camelid	Country_United Kingdom	Country_Nigeria	Country_Niger	Difficulty_Humans	Country_Spain	Country_Gibraltar	Transport_Walking	Difficulty_Fatigue/Illness	Difficulty_Thirst/Hunger	Country_Sierra Leone	Country_Guinea	Country_Mali	Country_Côte d'Ivoire	Country_Senegal	Country_Egypt	Country_Kenya	Country_Tanzania	Country_Mozambique	Country_Democratic Republic of the Congo	Country_Zambia	Country_Botswana	Country_South Africa	Country_Zimbabwe	Country_Portugal	Country_Cape Verde	Country_Saint Helena, Ascension and Tristan da Cunha	Country_India	Country_Somalia	Country_Guinea-Bissau	Country_Malawi	Country_Mauritania	Country_Ghana	Country_Liberia	Country_The Gambia	Country_Ethiopia	Country_Malta	Country_Gabon	Country_Germany	Country_Angola	Country_Namibia	Country_Sahrawi Arab Democratic Republic	Country_Sweden	Country_Ukraine	Country_Russia	Hierarchical_Cluster	KMeans_Cluster
5	Through Spain to the Sahara	1868	1868	Matilda Betham-Edwards	4/03/1836	Westerfield (Suffolk, England)	4/01/1919	Hastings (England)	British	poet, writer	English	https://www.gutenberg.org/cache/epub/56260/pg56260-images.html	betham1868through	Information non disponible	0	Not specified	0	0		Explore Spain and the Algerian Sahara, especially Velázquez's paintings and Moorish ruins	The book describes the author's experiences in various places in Spain and the Sahara	26	37	0.00	2766.24	10.0	1.0	7.0	9.0	11.0	1.0	5.0	9.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	6.0	9.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4	0
6	Through Timbuctu and across the great Sahara	1912	1912	Austin Hubert Wightwick Haywood	7/03/1878	Ranikhet (India)	28/03/1965	Peaslake (England)	British	military	English	https://www.gutenberg.org/cache/epub/70221/pg70221-images.html	haywood1913through	06/01/1911	32	07/07/1911	33	182	Bills of exchange and minimize cash for security reasons	Explore the Niger River from its source to Timbuktu, then cross the Sahara to Algiers, focusing on hunting and local culture	He reached Algiers, then Tombouctou, his main landmark, and continued north through the Sahara to Biskra, where he took a train to Algiers	40	16	20.00	4795.91	3.0	2.0	NaN	9.0	1.0	9.0	3.0	9.0	2.0	NaN	NaN	NaN	NaN	15.0	NaN	NaN	NaN	6.0	NaN	NaN	9.0	1.0	2.0	2.0	3.0	18.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	3
22	Timbouctou, voyage au Maroc au Sahara et au Soudan, Tome 2 (de 2)	1880	1880	Oskar Lenz	13/04/1848	Leipzig (Germany)	1/03/1925	Sooß (Austria)	Austrian	explorer, mineralogist, ethnologist, geographer, academic, geologist, africanist	French	https://www.gutenberg.org/cache/epub/74286/pg74286-images.html	lenz1886timbouctou	04/04/1880	31	22/11/1880	32	232	Upon arriving in Timbuktu, he has about 800 francs from the sale of his camels, a small remainder of about 500 francs, and some cloth	Explore the Sahara and reach the city of Timbuktu	Yes	20	7	35.00	3862.91	NaN	NaN	NaN	1.0	5.0	6.0	NaN	NaN	NaN	NaN	1.0	NaN	NaN	8.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.0	NaN	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	3
21	Timbouctou, voyage au Maroc, au Sahara et au Soudan, Tome 1 (de 2)	1879	1879	Oskar Lenz	13/04/1848	Leipzig (Germany)	1/03/1925	Sooß (Austria)	Austrian	explorer, mineralogist, ethnologist, geographer, academic, geologist, africanist	French	https://www.gutenberg.org/cache/epub/74285/pg74285-images.html	lenz1886timbouctou	13/11/1879	31	03/04/1880	31	142	A few thousand francs	Explore Morocco and reach Timbuktu	Not specified	44	5	47.73	1159.48	NaN	33.0	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	22.0	NaN	1.0	9.0	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	3
7	Travels and discoveries in North and Central Africa	1849	1849	Heinrich Barth	16/02/1821	Hamburg (Germany)	25/11/1865	Berlin	German	explorer, philologist, academic, geographer, writer, historian, archaeologist	English	https://www.gutenberg.org/files/73138/73138-h/73138-h.htm	barth1890travels	30/12/1849	28	10/12/1851	30	710	£200 / significant extortions	Explore North and Central Africa, focusing on Tripoli, the Sahara, Bornu Kingdom, and Lake Chad areas, aiming to build friendly ties with local leaders and observe cultures, languages, and geography	The author visits many desired places and gathers substantial information, as shown in his detailed writings	14	34	21.43	3547.08	NaN	3.0	NaN	NaN	NaN	1.0	NaN	NaN	NaN	3.0	NaN	4.0	NaN	7.0	NaN	1.0	3.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	3
8	Travels in the Great Desert of Sahara, in the Years of 1845 and 1846	1845	1845	James Richardson	2/07/1809	London	4/03/1851	Kukawa (Nigeria)	British	explorer	English	https://www.gutenberg.org/cache/epub/22094/pg22094-images.html	richardson2022travels	10/05/1845	35	Not specified	0	0	£50, nearly half spent on gifts. He admits the budget was insufficient and a proper trip should have cost at least £100	The author's main goal was to study the extent and functioning of the trans-Saharan slave trade, as well as document the life and customs of Saharan tribes, particularly the Tuaregs	The author gathers valuable information on the slave trade in the Sahara and interacts with those involved, while also providing detailed descriptions of Tuareg tribes, their customs, and way of life	48	6	72.92	2858.26	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	1.0	NaN	12.0	NaN	43.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	3
11	Travels through Central Africa to Timbuctoo and across the Great Desert to Morocco performed in the year 1824-1828, in Two Volumes, Vol. I	1824	1824	René Caillié	19/11/1799	Mauzé-sur-le-Mignon (France)	17/05/1838	La Gripperie-Saint-Symphorien (France)	French	explorer, traveler, writer	English	https://www.gutenberg.org/cache/epub/69847/pg69847-images.html	caillie1830travels	03/08/1824	24	11/03/1828	28	1316	2000 francs, mostly invested in goods for trade	To be the first European to reach Tombouctou and return alive, driven by a desire for exploration and discovery	He reached Timbuktu, spent two weeks there, and returned safely through the Sahara to Morocco	54	3	64.81	2714.89	NaN	NaN	NaN	NaN	2.0	6.0	6.0	1.0	8.0	NaN	NaN	NaN	NaN	1.0	NaN	NaN	NaN	4.0	NaN	NaN	40.0	8.0	3.0	NaN	11.0	3.0	NaN	5.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5	2
9	Travels through Central Africa to Timbuctoo and across the Great Desert to Morocco performed in the year 1824-1828, in Two Volumes, Vol. II	1824	1824	René Caillié	19/11/1799	Mauzé-sur-le-Mignon (France)	17/05/1838	La Gripperie-Saint-Symphorien (France)	French	explorer, traveler, writer	English	https://www.gutenberg.org/cache/epub/70011/pg70011-images.html	caillie1830travels	19/04/1827	27	07/09/1828	28	507	He exhausted all his resources for the trip and arrived in Toulon penniless	Explore Central Africa and visit Timbuktu	He reached Timbuktu and returned alive	18	3	27.78	3920.16	NaN	5.0	NaN	NaN	NaN	2.0	NaN	NaN	NaN	NaN	5.0	NaN	6.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.0	NaN	NaN	NaN	3.0	3.0	2.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	3
10	Un été dans le Sahara	1853	1853	Eugène Fromentin	24/11/1820	La Rochelle (France)	27/08/1876	La Rochelle (France)	French	painter, writer, art historian	French	https://www.gutenberg.org/files/37914/37914-h/37914-h.htm	fromentin1888ete	22/05/1853	32	Not specified	0	0	A theft where he loses part of his money, keeping only five francs, and is later reimbursed half of the stolen amount			18	0	27.78	377.02	NaN	6.0	NaN	13.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	3
12	Voyage d'un faux musulman à travers l'Afrique	1827	1827	René Caillié	19/11/1799	Mauzé-sur-le-Mignon (France)	17/05/1838	La Gripperie-Saint-Symphorien (France)	French	explorer, traveler, writer	French	https://www.gutenberg.org/cache/epub/65530/pg65530-images.html	caillie2023voyage	19/04/1827	27	07/09/1828	28	507	2000 francs	Reach Timbuktu via the Senegal-Gambia route to claim the prize offered by the Paris Geographical Society	Yes	10	3	30.00	3770.28	NaN	1.0	NaN	NaN	NaN	2.0	NaN	NaN	NaN	NaN	2.0	NaN	3.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4.0	NaN	NaN	NaN	3.0	2.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	3

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