Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory43.3 B

Variable types

DateTime1
Text1
Numeric1
Categorical2

Alerts

Visit_Date_YM has constant value ""Constant
FILE_NAME has constant value ""Constant
BASE_YMD has constant value ""Constant
Domestic_Visitor_CO has 20 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:04:21.553541
Analysis finished2023-12-10 10:04:22.257844
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Visit_Date_YM
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2018-12-01 00:00:00
Maximum2018-12-01 00:00:00
2023-12-10T19:04:22.394865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:22.585804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Country_CD
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:04:23.064407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters200
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st rowAF
2nd rowBH
3rd rowBD
4th rowBT
5th rowBN
ValueCountFrequency (%)
tm 2
 
2.0%
th 2
 
2.0%
ee 1
 
1.0%
pe 1
 
1.0%
af 1
 
1.0%
pa 1
 
1.0%
az 1
 
1.0%
am 1
 
1.0%
at 1
 
1.0%
al 1
 
1.0%
Other values (88) 88
88.0%
2023-12-10T19:04:23.746938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 16
 
8.0%
B 14
 
7.0%
T 13
 
6.5%
A 13
 
6.5%
E 12
 
6.0%
K 10
 
5.0%
L 10
 
5.0%
I 10
 
5.0%
N 9
 
4.5%
H 9
 
4.5%
Other values (16) 84
42.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 16
 
8.0%
B 14
 
7.0%
T 13
 
6.5%
A 13
 
6.5%
E 12
 
6.0%
K 10
 
5.0%
L 10
 
5.0%
I 10
 
5.0%
N 9
 
4.5%
H 9
 
4.5%
Other values (16) 84
42.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 200
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 16
 
8.0%
B 14
 
7.0%
T 13
 
6.5%
A 13
 
6.5%
E 12
 
6.0%
K 10
 
5.0%
L 10
 
5.0%
I 10
 
5.0%
N 9
 
4.5%
H 9
 
4.5%
Other values (16) 84
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 16
 
8.0%
B 14
 
7.0%
T 13
 
6.5%
A 13
 
6.5%
E 12
 
6.0%
K 10
 
5.0%
L 10
 
5.0%
I 10
 
5.0%
N 9
 
4.5%
H 9
 
4.5%
Other values (16) 84
42.0%

Domestic_Visitor_CO
Real number (ℝ)

ZEROS 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278.94
Minimum0
Maximum10091
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:23.982476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q347.25
95-th percentile1173.65
Maximum10091
Range10091
Interquartile range (IQR)46.25

Descriptive statistics

Standard deviation1156.3762
Coefficient of variation (CV)4.145609
Kurtosis55.117903
Mean278.94
Median Absolute Deviation (MAD)3
Skewness7.0022568
Sum27894
Variance1337205.9
MonotonicityNot monotonic
2023-12-10T19:04:24.206626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 20
20.0%
1 17
17.0%
2 8
 
8.0%
3 8
 
8.0%
4 4
 
4.0%
5 3
 
3.0%
6 2
 
2.0%
26 2
 
2.0%
17 2
 
2.0%
9 2
 
2.0%
Other values (32) 32
32.0%
ValueCountFrequency (%)
0 20
20.0%
1 17
17.0%
2 8
 
8.0%
3 8
 
8.0%
4 4
 
4.0%
5 3
 
3.0%
6 2
 
2.0%
9 2
 
2.0%
16 1
 
1.0%
17 2
 
2.0%
ValueCountFrequency (%)
10091 1
1.0%
4679 1
1.0%
2960 1
1.0%
1294 1
1.0%
1186 1
1.0%
1173 1
1.0%
832 1
1.0%
751 1
1.0%
735 1
1.0%
646 1
1.0%

FILE_NAME
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KC_FOREIGNER_VISITOR_2019
100 

Length

Max length25
Median length25
Mean length25
Min length25

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_FOREIGNER_VISITOR_2019
2nd rowKC_FOREIGNER_VISITOR_2019
3rd rowKC_FOREIGNER_VISITOR_2019
4th rowKC_FOREIGNER_VISITOR_2019
5th rowKC_FOREIGNER_VISITOR_2019

Common Values

ValueCountFrequency (%)
KC_FOREIGNER_VISITOR_2019 100
100.0%

Length

2023-12-10T19:04:24.417747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:24.565511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_foreigner_visitor_2019 100
100.0%

BASE_YMD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 100
100.0%

Length

2023-12-10T19:04:24.702995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:24.843918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 100
100.0%

Interactions

2023-12-10T19:04:21.758064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:04:24.933960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Country_CDDomestic_Visitor_CO
Country_CD1.0000.000
Domestic_Visitor_CO0.0001.000

Missing values

2023-12-10T19:04:21.994119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:04:22.185719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Visit_Date_YMCountry_CDDomestic_Visitor_COFILE_NAMEBASE_YMD
02018-12AF5KC_FOREIGNER_VISITOR_20192019
12018-12BH0KC_FOREIGNER_VISITOR_20192019
22018-12BD352KC_FOREIGNER_VISITOR_20192019
32018-12BT0KC_FOREIGNER_VISITOR_20192019
42018-12BN0KC_FOREIGNER_VISITOR_20192019
52018-12MM735KC_FOREIGNER_VISITOR_20192019
62018-12KH646KC_FOREIGNER_VISITOR_20192019
72018-12LK270KC_FOREIGNER_VISITOR_20192019
82018-12CN10091KC_FOREIGNER_VISITOR_20192019
92018-12TH89KC_FOREIGNER_VISITOR_20192019
Visit_Date_YMCountry_CDDomestic_Visitor_COFILE_NAMEBASE_YMD
902018-12IT17KC_FOREIGNER_VISITOR_20192019
912018-12KV0KC_FOREIGNER_VISITOR_20192019
922018-12LV2KC_FOREIGNER_VISITOR_20192019
932018-12LU0KC_FOREIGNER_VISITOR_20192019
942018-12LT1KC_FOREIGNER_VISITOR_20192019
952018-12MK0KC_FOREIGNER_VISITOR_20192019
962018-12MT1KC_FOREIGNER_VISITOR_20192019
972018-12MD2KC_FOREIGNER_VISITOR_20192019
982018-12ME0KC_FOREIGNER_VISITOR_20192019
992018-12NL4KC_FOREIGNER_VISITOR_20192019