Overview

Dataset statistics

Number of variables19
Number of observations4462
Missing cells6093
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory662.5 KiB
Average record size in memory152.0 B

Variable types

Numeric7
Categorical10
DateTime1
Unsupported1

Warnings

Nome has a high cardinality: 377 distinct values High cardinality
Sobrenome has a high cardinality: 1098 distinct values High cardinality
E-mail has a high cardinality: 1110 distinct values High cardinality
df_index is highly correlated with NotaHigh correlation
Nota is highly correlated with df_indexHigh correlation
Q.9 has 2658 (59.6%) missing values Missing
Q.10 has 3435 (77.0%) missing values Missing
df_index is uniformly distributed Uniform
Sobrenome is uniformly distributed Uniform
E-mail is uniformly distributed Uniform
Tempo is an unsupported type, check if it needs cleaning or further analysis Unsupported
Q.1 has 889 (19.9%) zeros Zeros
Q.5 has 1792 (40.2%) zeros Zeros
Q.8 has 1361 (30.5%) zeros Zeros
Q.9 has 750 (16.8%) zeros Zeros
Q.10 has 122 (2.7%) zeros Zeros

Reproduction

Analysis started2021-07-05 15:50:22.918645
Analysis finished2021-07-05 15:50:30.789191
Duration7.87 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM

Distinct3753
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50
Minimum0
Maximum100
Zeros5
Zeros (%)0.1%
Memory size35.0 KiB
2021-07-05T12:50:30.885489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.9705
Q125
median50
Q375
95-th percentile95.0295
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.9030988
Coefficient of variation (CV)0.5780619759
Kurtosis-1.200005904
Mean50
Median Absolute Deviation (MAD)25
Skewness-5.190158757 × 1017
Sum223100
Variance835.38912
MonotocityNot monotonic
2021-07-05T12:50:30.992696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
0.1%
1005
 
0.1%
504
 
0.1%
23.584
 
0.1%
76.424
 
0.1%
52.843
 
0.1%
38.213
 
0.1%
14.33
 
0.1%
82.463
 
0.1%
1.423
 
0.1%
Other values (3743)4425
99.2%
ValueCountFrequency (%)
05
0.1%
0.11
 
< 0.1%
0.112
 
< 0.1%
0.121
 
< 0.1%
0.131
 
< 0.1%
ValueCountFrequency (%)
1005
0.1%
99.91
 
< 0.1%
99.892
 
< 0.1%
99.881
 
< 0.1%
99.871
 
< 0.1%

Nome
Categorical

HIGH CARDINALITY

Distinct377
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
Gabriel
 
207
Lucas
 
168
Guilherme
 
158
Pedro
 
113
Joao
 
101
Other values (372)
3715 

Length

Max length10
Median length6
Mean length6.202151502
Min length2

Characters and Unicode

Total characters27674
Distinct characters48
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)0.9%

Sample

1st rowFernanda
2nd rowJessica
3rd rowMatheus
4th rowArthur
5th rowGabriel
ValueCountFrequency (%)
Gabriel207
 
4.6%
Lucas168
 
3.8%
Guilherme158
 
3.5%
Pedro113
 
2.5%
Joao101
 
2.3%
Vinicius96
 
2.2%
Gustavo86
 
1.9%
Felipe84
 
1.9%
Ana81
 
1.8%
Gabriela78
 
1.7%
Other values (367)3290
73.7%
2021-07-05T12:50:31.334449image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gabriel207
 
4.6%
lucas168
 
3.8%
guilherme158
 
3.5%
pedro113
 
2.5%
joao101
 
2.3%
vinicius96
 
2.2%
gustavo86
 
1.9%
felipe84
 
1.9%
ana81
 
1.8%
gabriela78
 
1.7%
Other values (367)3290
73.7%

Most occurring characters

ValueCountFrequency (%)
a4132
14.9%
i2786
 
10.1%
e2390
 
8.6%
r2082
 
7.5%
o1805
 
6.5%
n1649
 
6.0%
l1615
 
5.8%
u1351
 
4.9%
s1043
 
3.8%
c808
 
2.9%
Other values (38)8013
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23212
83.9%
Uppercase Letter4462
 
16.1%

Most frequent character per category

ValueCountFrequency (%)
G675
15.1%
L514
11.5%
M392
 
8.8%
J338
 
7.6%
A320
 
7.2%
V279
 
6.3%
R249
 
5.6%
B247
 
5.5%
P222
 
5.0%
F183
 
4.1%
Other values (14)1043
23.4%
ValueCountFrequency (%)
a4132
17.8%
i2786
12.0%
e2390
10.3%
r2082
9.0%
o1805
7.8%
n1649
 
7.1%
l1615
 
7.0%
u1351
 
5.8%
s1043
 
4.5%
c808
 
3.5%
Other values (14)3551
15.3%

Most occurring scripts

ValueCountFrequency (%)
Latin27674
100.0%

Most frequent character per script

ValueCountFrequency (%)
a4132
14.9%
i2786
 
10.1%
e2390
 
8.6%
r2082
 
7.5%
o1805
 
6.5%
n1649
 
6.0%
l1615
 
5.8%
u1351
 
4.9%
s1043
 
3.8%
c808
 
2.9%
Other values (38)8013
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII27674
100.0%

Most frequent character per block

ValueCountFrequency (%)
a4132
14.9%
i2786
 
10.1%
e2390
 
8.6%
r2082
 
7.5%
o1805
 
6.5%
n1649
 
6.0%
l1615
 
5.8%
u1351
 
4.9%
s1043
 
3.8%
c808
 
2.9%
Other values (38)8013
29.0%

Sobrenome
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1098
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
de Oliveira
 
11
da Silva Santos
 
10
Luisa Lopes Martins
 
10
Allucci Goncalves
 
10
de Melo Tavari
 
10
Other values (1093)
4411 

Length

Max length42
Median length16
Mean length17.56185567
Min length4

Characters and Unicode

Total characters78361
Distinct characters53
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)3.8%

Sample

1st rowGarcia Rozendo
2nd rowGoncalves da Silva
3rd rowde Oliveira
4th rowAlves Guilhen
5th rowUezu Cazer
ValueCountFrequency (%)
de Oliveira11
 
0.2%
da Silva Santos10
 
0.2%
Luisa Lopes Martins10
 
0.2%
Allucci Goncalves10
 
0.2%
de Melo Tavari10
 
0.2%
da Silva Oliveira10
 
0.2%
Modesto Martins10
 
0.2%
Fondato de Souza9
 
0.2%
Rodrigues Ramos9
 
0.2%
Sofientini Ribeiro9
 
0.2%
Other values (1088)4364
97.8%
2021-07-05T12:50:31.552444image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de924
 
7.5%
silva702
 
5.7%
da549
 
4.5%
santos412
 
3.4%
oliveira255
 
2.1%
dos251
 
2.0%
souza227
 
1.9%
rodrigues149
 
1.2%
lima138
 
1.1%
alves137
 
1.1%
Other values (1045)8501
69.4%

Most occurring characters

ValueCountFrequency (%)
a9581
12.2%
7783
 
9.9%
e6721
 
8.6%
i6589
 
8.4%
o5973
 
7.6%
r4903
 
6.3%
s4018
 
5.1%
n3541
 
4.5%
l3067
 
3.9%
d2810
 
3.6%
Other values (43)23375
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter59891
76.4%
Uppercase Letter10687
 
13.6%
Space Separator7783
 
9.9%

Most frequent character per category

ValueCountFrequency (%)
S1905
17.8%
M958
 
9.0%
C894
 
8.4%
A824
 
7.7%
F639
 
6.0%
L587
 
5.5%
B570
 
5.3%
P563
 
5.3%
G562
 
5.3%
R555
 
5.2%
Other values (16)2630
24.6%
ValueCountFrequency (%)
a9581
16.0%
e6721
11.2%
i6589
11.0%
o5973
10.0%
r4903
8.2%
s4018
 
6.7%
n3541
 
5.9%
l3067
 
5.1%
d2810
 
4.7%
t2341
 
3.9%
Other values (16)10347
17.3%
ValueCountFrequency (%)
7783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin70578
90.1%
Common7783
 
9.9%

Most frequent character per script

ValueCountFrequency (%)
a9581
13.6%
e6721
 
9.5%
i6589
 
9.3%
o5973
 
8.5%
r4903
 
6.9%
s4018
 
5.7%
n3541
 
5.0%
l3067
 
4.3%
d2810
 
4.0%
t2341
 
3.3%
Other values (42)21034
29.8%
ValueCountFrequency (%)
7783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII78361
100.0%

Most frequent character per block

ValueCountFrequency (%)
a9581
12.2%
7783
 
9.9%
e6721
 
8.6%
i6589
 
8.4%
o5973
 
7.6%
r4903
 
6.3%
s4018
 
5.1%
n3541
 
4.5%
l3067
 
3.9%
d2810
 
3.6%
Other values (43)23375
29.8%

E-mail
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1110
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
bianca.tavari@aluno.ufabc.edu.br
 
10
lopes.martins@aluno.ufabc.edu.br
 
10
henrique.allucci@aluno.ufabc.edu.br
 
10
modesto.gabriela@aluno.ufabc.edu.br
 
10
thierry.garre@aluno.ufabc.edu.br
 
9
Other values (1105)
4413 

Length

Max length39
Median length32
Mean length31.56275213
Min length21

Characters and Unicode

Total characters140833
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)3.8%

Sample

1st rowgarcia.f@aluno.ufabc.edu.br
2nd rowgoncalves.j@aluno.ufabc.edu.br
3rd rowmatheus.oliveira1@aluno.ufabc.edu.br
4th rowarthur.guilhen@aluno.ufabc.edu.br
5th rowgabriel.cazer@aluno.ufabc.edu.br
ValueCountFrequency (%)
bianca.tavari@aluno.ufabc.edu.br10
 
0.2%
lopes.martins@aluno.ufabc.edu.br10
 
0.2%
henrique.allucci@aluno.ufabc.edu.br10
 
0.2%
modesto.gabriela@aluno.ufabc.edu.br10
 
0.2%
thierry.garre@aluno.ufabc.edu.br9
 
0.2%
costa.vitor@aluno.ufabc.edu.br9
 
0.2%
otavio.fondato@aluno.ufabc.edu.br9
 
0.2%
beatriz.sofientini@aluno.ufabc.edu.br9
 
0.2%
vanessa.cecilia@aluno.ufabc.edu.br9
 
0.2%
rhanna.rayza@aluno.ufabc.edu.br9
 
0.2%
Other values (1100)4368
97.9%
2021-07-05T12:50:31.768833image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bianca.tavari@aluno.ufabc.edu.br10
 
0.2%
lopes.martins@aluno.ufabc.edu.br10
 
0.2%
henrique.allucci@aluno.ufabc.edu.br10
 
0.2%
modesto.gabriela@aluno.ufabc.edu.br10
 
0.2%
thierry.garre@aluno.ufabc.edu.br9
 
0.2%
costa.vitor@aluno.ufabc.edu.br9
 
0.2%
otavio.fondato@aluno.ufabc.edu.br9
 
0.2%
beatriz.sofientini@aluno.ufabc.edu.br9
 
0.2%
vanessa.cecilia@aluno.ufabc.edu.br9
 
0.2%
rhanna.rayza@aluno.ufabc.edu.br9
 
0.2%
Other values (1100)4368
97.9%

Most occurring characters

ValueCountFrequency (%)
.17836
12.7%
a16839
12.0%
u15327
10.9%
b10071
 
7.2%
e9056
 
6.4%
r8640
 
6.1%
o8553
 
6.1%
l7740
 
5.5%
n7618
 
5.4%
c6395
 
4.5%
Other values (20)32758
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter118515
84.2%
Other Punctuation22298
 
15.8%
Decimal Number20
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a16839
14.2%
u15327
12.9%
b10071
8.5%
e9056
7.6%
r8640
 
7.3%
o8553
 
7.2%
l7740
 
6.5%
n7618
 
6.4%
c6395
 
5.4%
d5794
 
4.9%
Other values (16)22482
19.0%
ValueCountFrequency (%)
.17836
80.0%
@4462
 
20.0%
ValueCountFrequency (%)
115
75.0%
25
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin118515
84.2%
Common22318
 
15.8%

Most frequent character per script

ValueCountFrequency (%)
a16839
14.2%
u15327
12.9%
b10071
8.5%
e9056
7.6%
r8640
 
7.3%
o8553
 
7.2%
l7740
 
6.5%
n7618
 
6.4%
c6395
 
5.4%
d5794
 
4.9%
Other values (16)22482
19.0%
ValueCountFrequency (%)
.17836
79.9%
@4462
 
20.0%
115
 
0.1%
25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII140833
100.0%

Most frequent character per block

ValueCountFrequency (%)
.17836
12.7%
a16839
12.0%
u15327
10.9%
b10071
 
7.2%
e9056
 
6.4%
r8640
 
6.1%
o8553
 
6.1%
l7740
 
5.5%
n7618
 
5.4%
c6395
 
4.5%
Other values (20)32758
23.3%

Avaliação
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
Teste 1
1027 
Teste 2
917 
Teste 3
896 
Teste 4
845 
Teste 5
777 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters31234
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTeste 1
2nd rowTeste 1
3rd rowTeste 1
4th rowTeste 1
5th rowTeste 1
ValueCountFrequency (%)
Teste 11027
23.0%
Teste 2917
20.6%
Teste 3896
20.1%
Teste 4845
18.9%
Teste 5777
17.4%
2021-07-05T12:50:31.934378image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-07-05T12:50:31.988426image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
teste4462
50.0%
11027
 
11.5%
2917
 
10.3%
3896
 
10.0%
4845
 
9.5%
5777
 
8.7%

Most occurring characters

ValueCountFrequency (%)
e8924
28.6%
T4462
14.3%
s4462
14.3%
t4462
14.3%
4462
14.3%
11027
 
3.3%
2917
 
2.9%
3896
 
2.9%
4845
 
2.7%
5777
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17848
57.1%
Uppercase Letter4462
 
14.3%
Space Separator4462
 
14.3%
Decimal Number4462
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
11027
23.0%
2917
20.6%
3896
20.1%
4845
18.9%
5777
17.4%
ValueCountFrequency (%)
e8924
50.0%
s4462
25.0%
t4462
25.0%
ValueCountFrequency (%)
T4462
100.0%
ValueCountFrequency (%)
4462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22310
71.4%
Common8924
 
28.6%

Most frequent character per script

ValueCountFrequency (%)
4462
50.0%
11027
 
11.5%
2917
 
10.3%
3896
 
10.0%
4845
 
9.5%
5777
 
8.7%
ValueCountFrequency (%)
e8924
40.0%
T4462
20.0%
s4462
20.0%
t4462
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII31234
100.0%

Most frequent character per block

ValueCountFrequency (%)
e8924
28.6%
T4462
14.3%
s4462
14.3%
t4462
14.3%
4462
14.3%
11027
 
3.3%
2917
 
2.9%
3896
 
2.9%
4845
 
2.7%
5777
 
2.5%
Distinct4048
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
Minimum2021-05-24 04:26:00
Maximum2021-06-28 03:02:00
2021-07-05T12:50:32.077570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:32.183807image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Tempo
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size35.0 KiB

Dia de início
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
Dia 7
949 
Dia 6
748 
Dia 5
596 
Dia 4
568 
Dia 3
424 
Other values (4)
1177 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters22310
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDia 7
2nd rowDia 6
3rd rowDia 7
4th rowDia 4
5th rowDia 4
ValueCountFrequency (%)
Dia 7949
21.3%
Dia 6748
16.8%
Dia 5596
13.4%
Dia 4568
12.7%
Dia 3424
9.5%
Dia 1415
9.3%
Dia 2374
 
8.4%
Dia 8367
 
8.2%
Dia 921
 
0.5%
2021-07-05T12:50:32.361526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-07-05T12:50:32.417821image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
dia4462
50.0%
7949
 
10.6%
6748
 
8.4%
5596
 
6.7%
4568
 
6.4%
3424
 
4.8%
1415
 
4.7%
2374
 
4.2%
8367
 
4.1%
921
 
0.2%

Most occurring characters

ValueCountFrequency (%)
D4462
20.0%
i4462
20.0%
a4462
20.0%
4462
20.0%
7949
 
4.3%
6748
 
3.4%
5596
 
2.7%
4568
 
2.5%
3424
 
1.9%
1415
 
1.9%
Other values (3)762
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8924
40.0%
Uppercase Letter4462
20.0%
Space Separator4462
20.0%
Decimal Number4462
20.0%

Most frequent character per category

ValueCountFrequency (%)
7949
21.3%
6748
16.8%
5596
13.4%
4568
12.7%
3424
9.5%
1415
9.3%
2374
 
8.4%
8367
 
8.2%
921
 
0.5%
ValueCountFrequency (%)
i4462
50.0%
a4462
50.0%
ValueCountFrequency (%)
D4462
100.0%
ValueCountFrequency (%)
4462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13386
60.0%
Common8924
40.0%

Most frequent character per script

ValueCountFrequency (%)
4462
50.0%
7949
 
10.6%
6748
 
8.4%
5596
 
6.7%
4568
 
6.4%
3424
 
4.8%
1415
 
4.7%
2374
 
4.2%
8367
 
4.1%
921
 
0.2%
ValueCountFrequency (%)
D4462
33.3%
i4462
33.3%
a4462
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII22310
100.0%

Most frequent character per block

ValueCountFrequency (%)
D4462
20.0%
i4462
20.0%
a4462
20.0%
4462
20.0%
7949
 
4.3%
6748
 
3.4%
5596
 
2.7%
4568
 
2.5%
3424
 
1.9%
1415
 
1.9%
Other values (3)762
 
3.4%

Nota
Real number (ℝ≥0)

HIGH CORRELATION

Distinct790
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.43888615
Minimum0.01
Maximum10
Zeros0
Zeros (%)0.0%
Memory size35.0 KiB
2021-07-05T12:50:32.513018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.25
Q13.37
median5.575
Q37.62
95-th percentile9.33
Maximum10
Range9.99
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation2.5826927
Coefficient of variation (CV)0.4748569154
Kurtosis-1.030744695
Mean5.43888615
Median Absolute Deviation (MAD)2.085
Skewness-0.1619349544
Sum24268.31
Variance6.670301584
MonotocityNot monotonic
2021-07-05T12:50:32.623518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.575
 
1.7%
1.2555
 
1.2%
6.2553
 
1.2%
548
 
1.1%
3.7548
 
1.1%
1036
 
0.8%
8.0236
 
0.8%
9.6434
 
0.8%
7.534
 
0.8%
8.7533
 
0.7%
Other values (780)4010
89.9%
ValueCountFrequency (%)
0.012
< 0.1%
0.032
< 0.1%
0.043
0.1%
0.051
 
< 0.1%
0.061
 
< 0.1%
ValueCountFrequency (%)
1036
0.8%
9.8410
 
0.2%
9.813
 
0.1%
9.82
 
< 0.1%
9.7914
 
0.3%

Q.1
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6523240699
Minimum0
Maximum1.25
Zeros889
Zeros (%)19.9%
Memory size35.0 KiB
2021-07-05T12:50:32.718986image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.56
Q31.25
95-th percentile1.25
Maximum1.25
Range1.25
Interquartile range (IQR)1.17

Descriptive statistics

Standard deviation0.5213205086
Coefficient of variation (CV)0.7991741109
Kurtosis-1.73799907
Mean0.6523240699
Median Absolute Deviation (MAD)0.55
Skewness-0.03595730968
Sum2910.67
Variance0.2717750727
MonotocityNot monotonic
2021-07-05T12:50:32.813954image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1.251339
30.0%
0889
19.9%
1.11516
 
11.6%
0.13260
 
5.8%
0.42179
 
4.0%
0.38143
 
3.2%
0.71140
 
3.1%
0.06137
 
3.1%
0.2599
 
2.2%
184
 
1.9%
Other values (18)676
15.2%
ValueCountFrequency (%)
0889
19.9%
0.0456
 
1.3%
0.06137
 
3.1%
0.0840
 
0.9%
0.13260
 
5.8%
ValueCountFrequency (%)
1.251339
30.0%
1.11516
 
11.6%
184
 
1.9%
0.948
 
0.2%
0.8321
 
0.5%

Q.2
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
1.25
1658 
0.0
1434 
1.0
787 
1.11
583 

Length

Max length4
Median length4
Mean length3.502241147
Min length3

Characters and Unicode

Total characters15627
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
1.251658
37.2%
0.01434
32.1%
1.0787
17.6%
1.11583
 
13.1%
2021-07-05T12:50:32.982696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-07-05T12:50:33.035348image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.251658
37.2%
0.01434
32.1%
1.0787
17.6%
1.11583
 
13.1%

Most occurring characters

ValueCountFrequency (%)
.4462
28.6%
14194
26.8%
03655
23.4%
21658
 
10.6%
51658
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11165
71.4%
Other Punctuation4462
 
28.6%

Most frequent character per category

ValueCountFrequency (%)
14194
37.6%
03655
32.7%
21658
 
14.8%
51658
 
14.8%
ValueCountFrequency (%)
.4462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15627
100.0%

Most frequent character per script

ValueCountFrequency (%)
.4462
28.6%
14194
26.8%
03655
23.4%
21658
 
10.6%
51658
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII15627
100.0%

Most frequent character per block

ValueCountFrequency (%)
.4462
28.6%
14194
26.8%
03655
23.4%
21658
 
10.6%
51658
 
10.6%

Q.3
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0.0
2185 
1.25
1368 
1.0
574 
1.11
335 

Length

Max length4
Median length3
Mean length3.381667414
Min length3

Characters and Unicode

Total characters15089
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.02185
49.0%
1.251368
30.7%
1.0574
 
12.9%
1.11335
 
7.5%
2021-07-05T12:50:33.190517image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-07-05T12:50:33.243820image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.02185
49.0%
1.251368
30.7%
1.0574
 
12.9%
1.11335
 
7.5%

Most occurring characters

ValueCountFrequency (%)
04944
32.8%
.4462
29.6%
12947
19.5%
21368
 
9.1%
51368
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10627
70.4%
Other Punctuation4462
29.6%

Most frequent character per category

ValueCountFrequency (%)
04944
46.5%
12947
27.7%
21368
 
12.9%
51368
 
12.9%
ValueCountFrequency (%)
.4462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15089
100.0%

Most frequent character per script

ValueCountFrequency (%)
04944
32.8%
.4462
29.6%
12947
19.5%
21368
 
9.1%
51368
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15089
100.0%

Most frequent character per block

ValueCountFrequency (%)
04944
32.8%
.4462
29.6%
12947
19.5%
21368
 
9.1%
51368
 
9.1%

Q.4
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
1.25
1765 
0.0
1558 
1.11
576 
1.0
563 

Length

Max length4
Median length4
Mean length3.524652622
Min length3

Characters and Unicode

Total characters15727
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
1.251765
39.6%
0.01558
34.9%
1.11576
 
12.9%
1.0563
 
12.6%
2021-07-05T12:50:33.403555image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-07-05T12:50:33.456347image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.251765
39.6%
0.01558
34.9%
1.11576
 
12.9%
1.0563
 
12.6%

Most occurring characters

ValueCountFrequency (%)
.4462
28.4%
14056
25.8%
03679
23.4%
21765
 
11.2%
51765
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11265
71.6%
Other Punctuation4462
 
28.4%

Most frequent character per category

ValueCountFrequency (%)
14056
36.0%
03679
32.7%
21765
15.7%
51765
15.7%
ValueCountFrequency (%)
.4462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15727
100.0%

Most frequent character per script

ValueCountFrequency (%)
.4462
28.4%
14056
25.8%
03679
23.4%
21765
 
11.2%
51765
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII15727
100.0%

Most frequent character per block

ValueCountFrequency (%)
.4462
28.4%
14056
25.8%
03679
23.4%
21765
 
11.2%
51765
 
11.2%

Q.5
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5741237113
Minimum0
Maximum1.25
Zeros1792
Zeros (%)40.2%
Memory size35.0 KiB
2021-07-05T12:50:33.514893image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.47
Q31.25
95-th percentile1.25
Maximum1.25
Range1.25
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation0.5527033933
Coefficient of variation (CV)0.9626904139
Kurtosis-1.830292548
Mean0.5741237113
Median Absolute Deviation (MAD)0.47
Skewness0.1031944546
Sum2561.74
Variance0.305481041
MonotocityNot monotonic
2021-07-05T12:50:33.603541image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
01792
40.2%
1.251158
26.0%
1510
 
11.4%
1.11246
 
5.5%
0.16112
 
2.5%
0.5286
 
1.9%
0.8982
 
1.8%
0.2168
 
1.5%
0.160
 
1.3%
0.4755
 
1.2%
Other values (10)293
 
6.6%
ValueCountFrequency (%)
01792
40.2%
0.0535
 
0.8%
0.160
 
1.3%
0.16112
 
2.5%
0.2168
 
1.5%
ValueCountFrequency (%)
1.251158
26.0%
1.11246
 
5.5%
1.0441
 
0.9%
1510
11.4%
0.8982
 
1.8%

Q.6
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0.0
1925 
1.25
1768 
1.0
423 
1.11
346 

Length

Max length4
Median length3
Mean length3.473778575
Min length3

Characters and Unicode

Total characters15500
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.01925
43.1%
1.251768
39.6%
1.0423
 
9.5%
1.11346
 
7.8%
2021-07-05T12:50:33.974077image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-07-05T12:50:34.031747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.01925
43.1%
1.251768
39.6%
1.0423
 
9.5%
1.11346
 
7.8%

Most occurring characters

ValueCountFrequency (%)
.4462
28.8%
04273
27.6%
13229
20.8%
21768
 
11.4%
51768
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11038
71.2%
Other Punctuation4462
28.8%

Most frequent character per category

ValueCountFrequency (%)
04273
38.7%
13229
29.3%
21768
16.0%
51768
16.0%
ValueCountFrequency (%)
.4462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15500
100.0%

Most frequent character per script

ValueCountFrequency (%)
.4462
28.8%
04273
27.6%
13229
20.8%
21768
 
11.4%
51768
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII15500
100.0%

Most frequent character per block

ValueCountFrequency (%)
.4462
28.8%
04273
27.6%
13229
20.8%
21768
 
11.4%
51768
 
11.4%

Q.7
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.0 KiB
0.0
1749 
1.25
1586 
1.0
667 
1.11
460 

Length

Max length4
Median length3
Mean length3.458538772
Min length3

Characters and Unicode

Total characters15432
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.01749
39.2%
1.251586
35.5%
1.0667
 
14.9%
1.11460
 
10.3%
2021-07-05T12:50:34.187945image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-07-05T12:50:34.241576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.01749
39.2%
1.251586
35.5%
1.0667
 
14.9%
1.11460
 
10.3%

Most occurring characters

ValueCountFrequency (%)
.4462
28.9%
04165
27.0%
13633
23.5%
21586
 
10.3%
51586
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10970
71.1%
Other Punctuation4462
28.9%

Most frequent character per category

ValueCountFrequency (%)
04165
38.0%
13633
33.1%
21586
 
14.5%
51586
 
14.5%
ValueCountFrequency (%)
.4462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15432
100.0%

Most frequent character per script

ValueCountFrequency (%)
.4462
28.9%
04165
27.0%
13633
23.5%
21586
 
10.3%
51586
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15432
100.0%

Most frequent character per block

ValueCountFrequency (%)
.4462
28.9%
04165
27.0%
13633
23.5%
21586
 
10.3%
51586
 
10.3%

Q.8
Real number (ℝ≥0)

ZEROS

Distinct31
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5539892425
Minimum0
Maximum1.25
Zeros1361
Zeros (%)30.5%
Memory size35.0 KiB
2021-07-05T12:50:34.305521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.49
Q31
95-th percentile1.25
Maximum1.25
Range1.25
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.489015084
Coefficient of variation (CV)0.8827158481
Kurtosis-1.571820457
Mean0.5539892425
Median Absolute Deviation (MAD)0.49
Skewness0.1685936632
Sum2471.9
Variance0.2391357524
MonotocityNot monotonic
2021-07-05T12:50:34.407591image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
01361
30.5%
1.25790
17.7%
1462
 
10.4%
0.83419
 
9.4%
0.42202
 
4.5%
1.11180
 
4.0%
0.28146
 
3.3%
0.56136
 
3.0%
0.2290
 
2.0%
0.1872
 
1.6%
Other values (21)604
13.5%
ValueCountFrequency (%)
01361
30.5%
0.0449
 
1.1%
0.0962
 
1.4%
0.1357
 
1.3%
0.1872
 
1.6%
ValueCountFrequency (%)
1.25790
17.7%
1.11180
 
4.0%
1.071
 
< 0.1%
1462
10.4%
0.9415
 
0.3%

Q.9
Real number (ℝ≥0)

MISSING
ZEROS

Distinct14
Distinct (%)0.8%
Missing2658
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean0.437921286
Minimum0
Maximum1.11
Zeros750
Zeros (%)16.8%
Memory size35.0 KiB
2021-07-05T12:50:34.501409image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.32
Q30.95
95-th percentile1
Maximum1.11
Range1.11
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.4393534457
Coefficient of variation (CV)1.003270359
Kurtosis-1.725452032
Mean0.437921286
Median Absolute Deviation (MAD)0.32
Skewness0.2495310676
Sum790.01
Variance0.1930314503
MonotocityNot monotonic
2021-07-05T12:50:34.584854image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0750
 
16.8%
1354
 
7.9%
0.95128
 
2.9%
0.79116
 
2.6%
0.16111
 
2.5%
0.3272
 
1.6%
0.6361
 
1.4%
0.4847
 
1.1%
1.1144
 
1.0%
0.9337
 
0.8%
Other values (4)84
 
1.9%
(Missing)2658
59.6%
ValueCountFrequency (%)
0750
16.8%
0.16111
 
2.5%
0.1922
 
0.5%
0.3272
 
1.6%
0.3727
 
0.6%
ValueCountFrequency (%)
1.1144
 
1.0%
1354
7.9%
0.95128
 
2.9%
0.9337
 
0.8%
0.79116
 
2.6%

Q.10
Real number (ℝ≥0)

MISSING
ZEROS

Distinct21
Distinct (%)2.0%
Missing3435
Missing (%)77.0%
Infinite0
Infinite (%)0.0%
Mean0.3689873418
Minimum0
Maximum1
Zeros122
Zeros (%)2.7%
Memory size35.0 KiB
2021-07-05T12:50:34.675982image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.15
median0.3
Q30.6
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.2892818069
Coefficient of variation (CV)0.7839884302
Kurtosis-0.49568247
Mean0.3689873418
Median Absolute Deviation (MAD)0.2
Skewness0.6354346041
Sum378.95
Variance0.08368396378
MonotocityNot monotonic
2021-07-05T12:50:34.768037image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.2125
 
2.8%
0122
 
2.7%
0.6108
 
2.4%
0.2585
 
1.9%
176
 
1.7%
0.474
 
1.7%
0.6568
 
1.5%
0.1568
 
1.5%
0.0553
 
1.2%
0.153
 
1.2%
Other values (11)195
 
4.4%
(Missing)3435
77.0%
ValueCountFrequency (%)
0122
2.7%
0.0553
1.2%
0.153
1.2%
0.1568
1.5%
0.2125
2.8%
ValueCountFrequency (%)
176
1.7%
0.951
 
< 0.1%
0.93
 
0.1%
0.851
 
< 0.1%
0.838
0.9%

Interactions

2021-07-05T12:50:26.173210image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:26.287358image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:26.385000image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:26.475290image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:26.562883image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:26.652547image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:26.740236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:26.825203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:26.911737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:26.998246image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.084718image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.177352image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.266081image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.358485image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.447024image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.536465image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.625286image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.710831image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.795967image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.885992image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:27.988482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:28.199588image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:28.301194image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:28.387812image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:28.475345image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:28.568103image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:28.656299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:28.745848image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:28.838005image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:28.924383image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.011799image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.110357image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.201799image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.291133image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.377924image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.462528image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.546928image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.631383image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.733595image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.818795image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.903135image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-07-05T12:50:29.987669image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-07-05T12:50:34.856247image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-05T12:50:34.984855image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-05T12:50:35.112329image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-05T12:50:35.245634image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-05T12:50:35.372706image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-05T12:50:30.195848image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-05T12:50:30.462348image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-07-05T12:50:30.598998image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-07-05T12:50:30.680699image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexNomeSobrenomeE-mailAvaliaçãoInícioTempoDia de inícioNotaQ.1Q.2Q.3Q.4Q.5Q.6Q.7Q.8Q.9Q.10
00.00FernandaGarcia Rozendogarcia.f@aluno.ufabc.edu.brTeste 12021-05-30 12:42:000 days 13:03:00Dia 70.010.460.01.00.00.00.00.00.00.00.55
10.10JessicaGoncalves da Silvagoncalves.j@aluno.ufabc.edu.brTeste 12021-05-29 22:51:001 days 02:41:00Dia 60.010.460.01.00.00.00.00.00.00.00.55
20.19Matheusde Oliveiramatheus.oliveira1@aluno.ufabc.edu.brTeste 12021-05-30 17:27:000 days 01:44:00Dia 70.030.130.00.00.00.00.00.00.00.00.15
30.29ArthurAlves Guilhenarthur.guilhen@aluno.ufabc.edu.brTeste 12021-05-27 08:28:003 days 05:42:00Dia 40.030.170.00.00.00.00.00.00.00.00.20
40.39GabrielUezu Cazergabriel.cazer@aluno.ufabc.edu.brTeste 12021-05-27 22:07:003 days 00:52:00Dia 40.040.040.00.00.00.00.00.00.00.00.00
50.49Nathande Oliveira Silvanathan.s@aluno.ufabc.edu.brTeste 12021-05-29 17:37:001 days 10:41:00Dia 60.040.040.00.00.00.00.00.00.00.00.00
60.58MarcusVinicius Alves de Oliveiramarcus.oliveira@aluno.ufabc.edu.brTeste 12021-05-26 15:14:004 days 07:31:00Dia 30.070.420.00.00.00.00.00.00.00.00.35
70.68GeovannaCassia Fonseca Azevedogeovanna.azevedo@aluno.ufabc.edu.brTeste 12021-05-29 12:41:001 days 15:37:00Dia 60.080.080.00.00.00.00.00.00.00.00.00
80.78IgorMesquita de Sousa Carneiroigor.carneiro@aluno.ufabc.edu.brTeste 12021-05-31 02:52:000 days 01:26:00Dia 80.080.080.00.00.00.00.00.00.00.00.00
90.88AndreLuiz Oliveira Basccheraandre.bascchera@aluno.ufabc.edu.brTeste 12021-05-27 21:35:002 days 19:44:00Dia 40.120.080.00.00.00.00.00.00.00.00.20

Last rows

df_indexNomeSobrenomeE-mailAvaliaçãoInícioTempoDia de inícioNotaQ.1Q.2Q.3Q.4Q.5Q.6Q.7Q.8Q.9Q.10
445298.84BrunoHenrrico Ancelaniancelani.bruno@aluno.ufabc.edu.brTeste 52021-06-27 15:30:000 days 08:21:00Dia 79.841.111.111.111.111.111.111.111.110.95NaN
445398.97ViniciusHiguchivinicius.higuchi@aluno.ufabc.edu.brTeste 52021-06-26 15:43:001 days 10:45:00Dia 69.841.111.111.111.111.111.111.111.110.95NaN
445499.10FelipeAraujo de Limaf.araujo@aluno.ufabc.edu.brTeste 52021-06-26 13:01:001 days 00:18:00Dia 69.841.111.111.111.111.111.111.111.110.95NaN
445599.23BrunaRibeiro Abreu Vasconcelosbruna.vasconcelos@aluno.ufabc.edu.brTeste 52021-06-22 11:02:005 days 12:03:00Dia 29.841.111.111.111.111.111.111.111.110.95NaN
445699.36OtavioFondato de Souzaotavio.fondato@aluno.ufabc.edu.brTeste 52021-06-25 12:30:002 days 15:09:00Dia 59.841.111.111.111.111.111.111.111.110.95NaN
445799.48RicardoHelder Rodrigues Gomesricardo.helder@aluno.ufabc.edu.brTeste 52021-06-23 15:04:004 days 06:02:00Dia 39.841.111.111.111.111.111.111.111.110.95NaN
445899.61GabrielFogaca Nascimentog.fogaca@aluno.ufabc.edu.brTeste 52021-06-24 12:22:003 days 02:23:00Dia 410.001.111.111.111.111.111.111.111.111.11NaN
445999.74LeonardoGoncalves Maciell.maciel@aluno.ufabc.edu.brTeste 52021-06-21 08:04:006 days 12:04:00Dia 110.001.111.111.111.111.111.111.111.111.11NaN
446099.87BrunoSilva Simaobruno.simao@aluno.ufabc.edu.brTeste 52021-06-21 04:10:006 days 20:22:00Dia 110.001.111.111.111.111.111.111.111.111.11NaN
4461100.00LeticiaSantos Bicalholeticia.bicalho@aluno.ufabc.edu.brTeste 52021-06-25 16:19:002 days 06:53:00Dia 510.001.111.111.111.111.111.111.111.111.11NaN