Overview

Dataset statistics

Number of variables17
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.7 KiB
Average record size in memory136.6 B

Variable types

Categorical9
Numeric7
Text1

Alerts

custo_inicial is highly overall correlated with custo_finalHigh correlation
custo_final is highly overall correlated with custo_inicialHigh correlation
esforco_estimado_inicial is highly overall correlated with esforco_estimado_realHigh correlation
esforco_estimado_real is highly overall correlated with esforco_estimado_inicialHigh correlation
situacao is highly overall correlated with motivoHigh correlation
motivo is highly overall correlated with situacaoHigh correlation
custo_final has unique valuesUnique
nome_projeto has unique valuesUnique

Reproduction

Analysis started2023-07-15 23:45:43.145797
Analysis finished2023-07-15 23:45:49.422417
Duration6.28 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

situacao
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sucesso
147 
falha
53 

Length

Max length7
Median length7
Mean length6.47
Min length5

Characters and Unicode

Total characters1294
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsucesso
2nd rowsucesso
3rd rowsucesso
4th rowfalha
5th rowfalha

Common Values

ValueCountFrequency (%)
sucesso 147
73.5%
falha 53
 
26.5%

Length

2023-07-15T20:45:49.519562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T20:45:49.650607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sucesso 147
73.5%
falha 53
 
26.5%

Most occurring characters

ValueCountFrequency (%)
s 441
34.1%
u 147
 
11.4%
c 147
 
11.4%
e 147
 
11.4%
o 147
 
11.4%
a 106
 
8.2%
f 53
 
4.1%
l 53
 
4.1%
h 53
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1294
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 441
34.1%
u 147
 
11.4%
c 147
 
11.4%
e 147
 
11.4%
o 147
 
11.4%
a 106
 
8.2%
f 53
 
4.1%
l 53
 
4.1%
h 53
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1294
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 441
34.1%
u 147
 
11.4%
c 147
 
11.4%
e 147
 
11.4%
o 147
 
11.4%
a 106
 
8.2%
f 53
 
4.1%
l 53
 
4.1%
h 53
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 441
34.1%
u 147
 
11.4%
c 147
 
11.4%
e 147
 
11.4%
o 147
 
11.4%
a 106
 
8.2%
f 53
 
4.1%
l 53
 
4.1%
h 53
 
4.1%

custo_inicial
Real number (ℝ)

HIGH CORRELATION 

Distinct199
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131852.64
Minimum16102
Maximum247801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-07-15T20:45:49.760122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum16102
5-th percentile27358.2
Q168918.75
median128578.5
Q3192254.5
95-th percentile239677.9
Maximum247801
Range231699
Interquartile range (IQR)123335.75

Descriptive statistics

Standard deviation69730.81
Coefficient of variation (CV)0.52885411
Kurtosis-1.2535527
Mean131852.64
Median Absolute Deviation (MAD)61539
Skewness0.055699888
Sum26370528
Variance4.8623859 × 109
MonotonicityNot monotonic
2023-07-15T20:45:49.881653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202276 2
 
1.0%
156795 1
 
0.5%
71528 1
 
0.5%
152491 1
 
0.5%
168320 1
 
0.5%
94216 1
 
0.5%
49989 1
 
0.5%
150971 1
 
0.5%
174274 1
 
0.5%
162659 1
 
0.5%
Other values (189) 189
94.5%
ValueCountFrequency (%)
16102 1
0.5%
16623 1
0.5%
17109 1
0.5%
17332 1
0.5%
17609 1
0.5%
19226 1
0.5%
23239 1
0.5%
23311 1
0.5%
23902 1
0.5%
25462 1
0.5%
ValueCountFrequency (%)
247801 1
0.5%
246991 1
0.5%
246597 1
0.5%
246558 1
0.5%
245308 1
0.5%
244823 1
0.5%
244559 1
0.5%
243196 1
0.5%
242889 1
0.5%
242849 1
0.5%

custo_final
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155154.3
Minimum29738
Maximum284123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-07-15T20:45:49.994170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum29738
5-th percentile53736.1
Q196927.25
median158304.5
Q3209754.75
95-th percentile268352.1
Maximum284123
Range254385
Interquartile range (IQR)112827.5

Descriptive statistics

Standard deviation68925.682
Coefficient of variation (CV)0.44423959
Kurtosis-1.1274184
Mean155154.3
Median Absolute Deviation (MAD)56063.5
Skewness0.059820086
Sum31030860
Variance4.7507497 × 109
MonotonicityNot monotonic
2023-07-15T20:45:50.111697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158614 1
 
0.5%
212991 1
 
0.5%
72156 1
 
0.5%
173656 1
 
0.5%
164858 1
 
0.5%
139414 1
 
0.5%
64153 1
 
0.5%
157641 1
 
0.5%
172593 1
 
0.5%
185346 1
 
0.5%
Other values (190) 190
95.0%
ValueCountFrequency (%)
29738 1
0.5%
32002 1
0.5%
33339 1
0.5%
40087 1
0.5%
40613 1
0.5%
40888 1
0.5%
45034 1
0.5%
47062 1
0.5%
51674 1
0.5%
51743 1
0.5%
ValueCountFrequency (%)
284123 1
0.5%
276970 1
0.5%
276761 1
0.5%
275712 1
0.5%
273376 1
0.5%
271259 1
0.5%
270831 1
0.5%
270365 1
0.5%
269871 1
0.5%
268354 1
0.5%

esforco_estimado_inicial
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.09
Minimum6
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-07-15T20:45:50.226232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q114
median25
Q335
95-th percentile45
Maximum47
Range41
Interquartile range (IQR)21

Descriptive statistics

Standard deviation11.878442
Coefficient of variation (CV)0.47343332
Kurtosis-1.166149
Mean25.09
Median Absolute Deviation (MAD)11
Skewness0.15480893
Sum5018
Variance141.09739
MonotonicityNot monotonic
2023-07-15T20:45:50.335266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
12 11
 
5.5%
20 9
 
4.5%
19 9
 
4.5%
13 8
 
4.0%
26 8
 
4.0%
36 8
 
4.0%
35 7
 
3.5%
6 7
 
3.5%
11 7
 
3.5%
30 6
 
3.0%
Other values (32) 120
60.0%
ValueCountFrequency (%)
6 7
3.5%
7 2
 
1.0%
8 6
3.0%
9 2
 
1.0%
10 4
 
2.0%
11 7
3.5%
12 11
5.5%
13 8
4.0%
14 5
2.5%
15 2
 
1.0%
ValueCountFrequency (%)
47 3
1.5%
46 5
2.5%
45 4
2.0%
44 3
1.5%
43 4
2.0%
42 3
1.5%
41 4
2.0%
40 2
 
1.0%
39 4
2.0%
38 5
2.5%

esforco_estimado_real
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.57
Minimum6
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-07-15T20:45:50.445793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q120
median30
Q340.25
95-th percentile51
Maximum59
Range53
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation12.547201
Coefficient of variation (CV)0.41044165
Kurtosis-0.92567175
Mean30.57
Median Absolute Deviation (MAD)10
Skewness0.10444104
Sum6114
Variance157.43226
MonotonicityNot monotonic
2023-07-15T20:45:50.567324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 8
 
4.0%
44 7
 
3.5%
36 7
 
3.5%
42 7
 
3.5%
19 7
 
3.5%
37 7
 
3.5%
17 6
 
3.0%
24 6
 
3.0%
20 6
 
3.0%
21 6
 
3.0%
Other values (41) 133
66.5%
ValueCountFrequency (%)
6 2
 
1.0%
8 1
 
0.5%
9 1
 
0.5%
10 5
2.5%
11 2
 
1.0%
12 2
 
1.0%
13 5
2.5%
14 4
2.0%
15 3
1.5%
16 4
2.0%
ValueCountFrequency (%)
59 1
 
0.5%
58 1
 
0.5%
56 2
1.0%
55 1
 
0.5%
54 1
 
0.5%
52 2
1.0%
51 3
1.5%
50 3
1.5%
49 1
 
0.5%
48 3
1.5%

numero_integrantes_equipe
Real number (ℝ)

Distinct18
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.13
Minimum3
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-07-15T20:45:50.669854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median9
Q314
95-th percentile19
Maximum20
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.8487785
Coefficient of variation (CV)0.47865533
Kurtosis-0.81911758
Mean10.13
Median Absolute Deviation (MAD)4
Skewness0.48439497
Sum2026
Variance23.510653
MonotonicityNot monotonic
2023-07-15T20:45:50.763368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
8 24
12.0%
7 17
 
8.5%
5 17
 
8.5%
9 14
 
7.0%
4 14
 
7.0%
6 12
 
6.0%
14 12
 
6.0%
11 12
 
6.0%
10 11
 
5.5%
19 11
 
5.5%
Other values (8) 56
28.0%
ValueCountFrequency (%)
3 9
 
4.5%
4 14
7.0%
5 17
8.5%
6 12
6.0%
7 17
8.5%
8 24
12.0%
9 14
7.0%
10 11
5.5%
11 12
6.0%
12 7
 
3.5%
ValueCountFrequency (%)
20 7
3.5%
19 11
5.5%
18 4
 
2.0%
17 5
2.5%
16 7
3.5%
15 8
4.0%
14 12
6.0%
13 9
4.5%
12 7
3.5%
11 12
6.0%

metodologia
Categorical

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
agile
98 
tradicional
51 
personalizada
37 
outras
14 

Length

Max length13
Median length11
Mean length8.08
Min length5

Characters and Unicode

Total characters1616
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtradicional
2nd rowagile
3rd rowagile
4th rowtradicional
5th rowtradicional

Common Values

ValueCountFrequency (%)
agile 98
49.0%
tradicional 51
25.5%
personalizada 37
 
18.5%
outras 14
 
7.0%

Length

2023-07-15T20:45:50.876901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T20:45:50.999425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
agile 98
49.0%
tradicional 51
25.5%
personalizada 37
 
18.5%
outras 14
 
7.0%

Most occurring characters

ValueCountFrequency (%)
a 325
20.1%
i 237
14.7%
l 186
11.5%
e 135
8.4%
r 102
 
6.3%
o 102
 
6.3%
g 98
 
6.1%
d 88
 
5.4%
n 88
 
5.4%
t 65
 
4.0%
Other values (5) 190
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1616
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 325
20.1%
i 237
14.7%
l 186
11.5%
e 135
8.4%
r 102
 
6.3%
o 102
 
6.3%
g 98
 
6.1%
d 88
 
5.4%
n 88
 
5.4%
t 65
 
4.0%
Other values (5) 190
11.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1616
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 325
20.1%
i 237
14.7%
l 186
11.5%
e 135
8.4%
r 102
 
6.3%
o 102
 
6.3%
g 98
 
6.1%
d 88
 
5.4%
n 88
 
5.4%
t 65
 
4.0%
Other values (5) 190
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 325
20.1%
i 237
14.7%
l 186
11.5%
e 135
8.4%
r 102
 
6.3%
o 102
 
6.3%
g 98
 
6.1%
d 88
 
5.4%
n 88
 
5.4%
t 65
 
4.0%
Other values (5) 190
11.8%

uso_melhores_praticas
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sim
162 
nao
38 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters600
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsim
2nd rowsim
3rd rownao
4th rownao
5th rowsim

Common Values

ValueCountFrequency (%)
sim 162
81.0%
nao 38
 
19.0%

Length

2023-07-15T20:45:51.094951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T20:45:51.200473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sim 162
81.0%
nao 38
 
19.0%

Most occurring characters

ValueCountFrequency (%)
s 162
27.0%
i 162
27.0%
m 162
27.0%
n 38
 
6.3%
a 38
 
6.3%
o 38
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 600
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 162
27.0%
i 162
27.0%
m 162
27.0%
n 38
 
6.3%
a 38
 
6.3%
o 38
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 162
27.0%
i 162
27.0%
m 162
27.0%
n 38
 
6.3%
a 38
 
6.3%
o 38
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 162
27.0%
i 162
27.0%
m 162
27.0%
n 38
 
6.3%
a 38
 
6.3%
o 38
 
6.3%

praticas_inovadoras
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sim
107 
nao
93 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters600
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownao
2nd rownao
3rd rownao
4th rowsim
5th rownao

Common Values

ValueCountFrequency (%)
sim 107
53.5%
nao 93
46.5%

Length

2023-07-15T20:45:51.285998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T20:45:51.383525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sim 107
53.5%
nao 93
46.5%

Most occurring characters

ValueCountFrequency (%)
s 107
17.8%
i 107
17.8%
m 107
17.8%
n 93
15.5%
a 93
15.5%
o 93
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 600
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 107
17.8%
i 107
17.8%
m 107
17.8%
n 93
15.5%
a 93
15.5%
o 93
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 107
17.8%
i 107
17.8%
m 107
17.8%
n 93
15.5%
a 93
15.5%
o 93
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 107
17.8%
i 107
17.8%
m 107
17.8%
n 93
15.5%
a 93
15.5%
o 93
15.5%

nome_projeto
Text

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-07-15T20:45:51.600556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.46
Min length9

Characters and Unicode

Total characters2092
Distinct characters17
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

Unique200 ?
Unique (%)100.0%

Sample

1st rowProjeto 1
2nd rowProjeto 2
3rd rowProjeto 3
4th rowProjeto 4
5th rowProjeto 5
ValueCountFrequency (%)
projeto 200
50.0%
12 1
 
0.2%
24 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
Other values (191) 191
47.8%
2023-07-15T20:45:51.952661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 400
19.1%
P 200
9.6%
j 200
9.6%
e 200
9.6%
t 200
9.6%
200
9.6%
r 200
9.6%
1 140
 
6.7%
2 41
 
2.0%
4 40
 
1.9%
Other values (7) 271
13.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1200
57.4%
Decimal Number 492
23.5%
Uppercase Letter 200
 
9.6%
Space Separator 200
 
9.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 140
28.5%
2 41
 
8.3%
4 40
 
8.1%
6 40
 
8.1%
5 40
 
8.1%
3 40
 
8.1%
9 40
 
8.1%
8 40
 
8.1%
7 40
 
8.1%
0 31
 
6.3%
Lowercase Letter
ValueCountFrequency (%)
o 400
33.3%
j 200
16.7%
e 200
16.7%
t 200
16.7%
r 200
16.7%
Uppercase Letter
ValueCountFrequency (%)
P 200
100.0%
Space Separator
ValueCountFrequency (%)
200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1400
66.9%
Common 692
33.1%

Most frequent character per script

Common
ValueCountFrequency (%)
200
28.9%
1 140
20.2%
2 41
 
5.9%
4 40
 
5.8%
6 40
 
5.8%
5 40
 
5.8%
3 40
 
5.8%
9 40
 
5.8%
8 40
 
5.8%
7 40
 
5.8%
Latin
ValueCountFrequency (%)
o 400
28.6%
P 200
14.3%
j 200
14.3%
e 200
14.3%
t 200
14.3%
r 200
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2092
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 400
19.1%
P 200
9.6%
j 200
9.6%
e 200
9.6%
t 200
9.6%
200
9.6%
r 200
9.6%
1 140
 
6.7%
2 41
 
2.0%
4 40
 
1.9%
Other values (7) 271
13.0%

gerente
Categorical

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Joaquim
51 
Marcio
39 
Rodrigo
23 
Marcelo
22 
Sophia
21 
Other values (3)
44 

Length

Max length8
Median length7
Mean length6.825
Min length6

Characters and Unicode

Total characters1365
Distinct characters21
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 rowJoaquim
2nd rowJoaquim
3rd rowCrissia
4th rowMarcio
5th rowSophia

Common Values

ValueCountFrequency (%)
Joaquim 51
25.5%
Marcio 39
19.5%
Rodrigo 23
11.5%
Marcelo 22
11.0%
Sophia 21
10.5%
Crissia 19
 
9.5%
Clarissa 15
 
7.5%
Priscila 10
 
5.0%

Length

2023-07-15T20:45:52.076180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T20:45:52.215696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
joaquim 51
25.5%
marcio 39
19.5%
rodrigo 23
11.5%
marcelo 22
11.0%
sophia 21
10.5%
crissia 19
 
9.5%
clarissa 15
 
7.5%
priscila 10
 
5.0%

Most occurring characters

ValueCountFrequency (%)
i 207
15.2%
a 192
14.1%
o 179
13.1%
r 128
9.4%
s 78
 
5.7%
c 71
 
5.2%
M 61
 
4.5%
J 51
 
3.7%
q 51
 
3.7%
u 51
 
3.7%
Other values (11) 296
21.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1165
85.3%
Uppercase Letter 200
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 207
17.8%
a 192
16.5%
o 179
15.4%
r 128
11.0%
s 78
 
6.7%
c 71
 
6.1%
q 51
 
4.4%
u 51
 
4.4%
m 51
 
4.4%
l 47
 
4.0%
Other values (5) 110
9.4%
Uppercase Letter
ValueCountFrequency (%)
M 61
30.5%
J 51
25.5%
C 34
17.0%
R 23
 
11.5%
S 21
 
10.5%
P 10
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1365
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 207
15.2%
a 192
14.1%
o 179
13.1%
r 128
9.4%
s 78
 
5.7%
c 71
 
5.2%
M 61
 
4.5%
J 51
 
3.7%
q 51
 
3.7%
u 51
 
3.7%
Other values (11) 296
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1365
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 207
15.2%
a 192
14.1%
o 179
13.1%
r 128
9.4%
s 78
 
5.7%
c 71
 
5.2%
M 61
 
4.5%
J 51
 
3.7%
q 51
 
3.7%
u 51
 
3.7%
Other values (11) 296
21.7%

frequencia_mudanca_requisitos
Categorical

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
semanal
44 
quinzenal
43 
mensal
30 
semestral
28 
anual
23 
Other values (2)
32 

Length

Max length10
Median length9
Mean length7.45
Min length5

Characters and Unicode

Total characters1490
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquinzenal
2nd rowsemanal
3rd rowquinzenal
4th rowquinzenal
5th rowdiaria

Common Values

ValueCountFrequency (%)
semanal 44
22.0%
quinzenal 43
21.5%
mensal 30
15.0%
semestral 28
14.0%
anual 23
11.5%
diaria 18
9.0%
trimestral 14
 
7.0%

Length

2023-07-15T20:45:52.343327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T20:45:52.470366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
semanal 44
22.0%
quinzenal 43
21.5%
mensal 30
15.0%
semestral 28
14.0%
anual 23
11.5%
diaria 18
9.0%
trimestral 14
 
7.0%

Most occurring characters

ValueCountFrequency (%)
a 285
19.1%
e 187
12.6%
n 183
12.3%
l 182
12.2%
s 144
9.7%
m 116
7.8%
i 93
 
6.2%
r 74
 
5.0%
u 66
 
4.4%
t 56
 
3.8%
Other values (3) 104
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1490
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 285
19.1%
e 187
12.6%
n 183
12.3%
l 182
12.2%
s 144
9.7%
m 116
7.8%
i 93
 
6.2%
r 74
 
5.0%
u 66
 
4.4%
t 56
 
3.8%
Other values (3) 104
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1490
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 285
19.1%
e 187
12.6%
n 183
12.3%
l 182
12.2%
s 144
9.7%
m 116
7.8%
i 93
 
6.2%
r 74
 
5.0%
u 66
 
4.4%
t 56
 
3.8%
Other values (3) 104
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 285
19.1%
e 187
12.6%
n 183
12.3%
l 182
12.2%
s 144
9.7%
m 116
7.8%
i 93
 
6.2%
r 74
 
5.0%
u 66
 
4.4%
t 56
 
3.8%
Other values (3) 104
 
7.0%

maturidade_equipe
Real number (ℝ)

Distinct10
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.15
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-07-15T20:45:52.576888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7741629
Coefficient of variation (CV)0.53867241
Kurtosis-1.1252968
Mean5.15
Median Absolute Deviation (MAD)2
Skewness0.12128685
Sum1030
Variance7.6959799
MonotonicityNot monotonic
2023-07-15T20:45:52.664412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 28
14.0%
4 27
13.5%
3 25
12.5%
1 24
12.0%
6 19
9.5%
2 17
8.5%
9 16
8.0%
5 15
7.5%
8 15
7.5%
10 14
7.0%
ValueCountFrequency (%)
1 24
12.0%
2 17
8.5%
3 25
12.5%
4 27
13.5%
5 15
7.5%
6 19
9.5%
7 28
14.0%
8 15
7.5%
9 16
8.0%
10 14
7.0%
ValueCountFrequency (%)
10 14
7.0%
9 16
8.0%
8 15
7.5%
7 28
14.0%
6 19
9.5%
5 15
7.5%
4 27
13.5%
3 25
12.5%
2 17
8.5%
1 24
12.0%

area_projeto
Categorical

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
aplicativos
49 
saude
40 
financas
31 
telecomunicacoes
31 
educacao
25 

Length

Max length16
Median length15
Mean length10.215
Min length5

Characters and Unicode

Total characters2043
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaplicativos
2nd rowaplicativos
3rd rowaplicativos
4th rowaplicativos
5th rowautomobilistica

Common Values

ValueCountFrequency (%)
aplicativos 49
24.5%
saude 40
20.0%
financas 31
15.5%
telecomunicacoes 31
15.5%
educacao 25
12.5%
automobilistica 24
12.0%

Length

2023-07-15T20:45:52.757931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T20:45:52.874452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
aplicativos 49
24.5%
saude 40
20.0%
financas 31
15.5%
telecomunicacoes 31
15.5%
educacao 25
12.5%
automobilistica 24
12.0%

Most occurring characters

ValueCountFrequency (%)
a 329
16.1%
c 247
12.1%
i 232
11.4%
o 184
9.0%
s 175
8.6%
e 158
7.7%
t 128
 
6.3%
u 120
 
5.9%
l 104
 
5.1%
n 93
 
4.6%
Other values (6) 273
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2043
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 329
16.1%
c 247
12.1%
i 232
11.4%
o 184
9.0%
s 175
8.6%
e 158
7.7%
t 128
 
6.3%
u 120
 
5.9%
l 104
 
5.1%
n 93
 
4.6%
Other values (6) 273
13.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2043
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 329
16.1%
c 247
12.1%
i 232
11.4%
o 184
9.0%
s 175
8.6%
e 158
7.7%
t 128
 
6.3%
u 120
 
5.9%
l 104
 
5.1%
n 93
 
4.6%
Other values (6) 273
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 329
16.1%
c 247
12.1%
i 232
11.4%
o 184
9.0%
s 175
8.6%
e 158
7.7%
t 128
 
6.3%
u 120
 
5.9%
l 104
 
5.1%
n 93
 
4.6%
Other values (6) 273
13.4%

tipo_projeto
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
novo projeto
127 
evolucao
40 
manutencao
33 

Length

Max length12
Median length12
Mean length10.87
Min length8

Characters and Unicode

Total characters2174
Distinct characters14
Distinct categories2 ?
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 rowmanutencao
2nd rownovo projeto
3rd rowevolucao
4th rowevolucao
5th rownovo projeto

Common Values

ValueCountFrequency (%)
novo projeto 127
63.5%
evolucao 40
 
20.0%
manutencao 33
 
16.5%

Length

2023-07-15T20:45:52.993973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T20:45:53.119497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
novo 127
38.8%
projeto 127
38.8%
evolucao 40
 
12.2%
manutencao 33
 
10.1%

Most occurring characters

ValueCountFrequency (%)
o 621
28.6%
e 200
 
9.2%
n 193
 
8.9%
v 167
 
7.7%
t 160
 
7.4%
127
 
5.8%
p 127
 
5.8%
r 127
 
5.8%
j 127
 
5.8%
a 106
 
4.9%
Other values (4) 219
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2047
94.2%
Space Separator 127
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 621
30.3%
e 200
 
9.8%
n 193
 
9.4%
v 167
 
8.2%
t 160
 
7.8%
p 127
 
6.2%
r 127
 
6.2%
j 127
 
6.2%
a 106
 
5.2%
u 73
 
3.6%
Other values (3) 146
 
7.1%
Space Separator
ValueCountFrequency (%)
127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2047
94.2%
Common 127
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 621
30.3%
e 200
 
9.8%
n 193
 
9.4%
v 167
 
8.2%
t 160
 
7.8%
p 127
 
6.2%
r 127
 
6.2%
j 127
 
6.2%
a 106
 
5.2%
u 73
 
3.6%
Other values (3) 146
 
7.1%
Common
ValueCountFrequency (%)
127
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 621
28.6%
e 200
 
9.2%
n 193
 
8.9%
v 167
 
7.7%
t 160
 
7.4%
127
 
5.8%
p 127
 
5.8%
r 127
 
5.8%
j 127
 
5.8%
a 106
 
4.9%
Other values (4) 219
 
10.1%

motivo
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
feedback contínuo
36 
mudancas de escopo frequentes
27 
envolvimento dos stakeholders
26 
equipe qualificada
18 
comunicação eficaz
17 
Other values (9)
76 

Length

Max length29
Median length27
Mean length21.335
Min length13

Characters and Unicode

Total characters4267
Distinct characters28
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 rowfeedback contínuo
2nd rowfeedback contínuo
3rd rowfeedback contínuo
4th rowescopo mal definido
5th rowmudancas de escopo frequentes

Common Values

ValueCountFrequency (%)
feedback contínuo 36
18.0%
mudancas de escopo frequentes 27
13.5%
envolvimento dos stakeholders 26
13.0%
equipe qualificada 18
9.0%
comunicação eficaz 17
8.5%
baixo turnover da equipe 16
8.0%
flexibilidade 16
8.0%
liderança forte 14
 
7.0%
escopo mal definido 11
 
5.5%
ambiente de trabalho positivo 4
 
2.0%
Other values (4) 15
7.5%

Length

2023-07-15T20:45:53.223024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
escopo 38
 
7.1%
feedback 36
 
6.8%
contínuo 36
 
6.8%
de 34
 
6.4%
equipe 34
 
6.4%
mudancas 27
 
5.1%
frequentes 27
 
5.1%
stakeholders 26
 
4.9%
dos 26
 
4.9%
envolvimento 26
 
4.9%
Other values (26) 223
41.8%

Most occurring characters

ValueCountFrequency (%)
e 543
12.7%
o 389
 
9.1%
a 335
 
7.9%
333
 
7.8%
i 276
 
6.5%
n 263
 
6.2%
d 257
 
6.0%
c 237
 
5.6%
s 211
 
4.9%
u 190
 
4.5%
Other values (18) 1233
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3934
92.2%
Space Separator 333
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 543
13.8%
o 389
 
9.9%
a 335
 
8.5%
i 276
 
7.0%
n 263
 
6.7%
d 257
 
6.5%
c 237
 
6.0%
s 211
 
5.4%
u 190
 
4.8%
t 172
 
4.4%
Other values (17) 1061
27.0%
Space Separator
ValueCountFrequency (%)
333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3934
92.2%
Common 333
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 543
13.8%
o 389
 
9.9%
a 335
 
8.5%
i 276
 
7.0%
n 263
 
6.7%
d 257
 
6.5%
c 237
 
6.0%
s 211
 
5.4%
u 190
 
4.8%
t 172
 
4.4%
Other values (17) 1061
27.0%
Common
ValueCountFrequency (%)
333
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4179
97.9%
None 88
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 543
13.0%
o 389
 
9.3%
a 335
 
8.0%
333
 
8.0%
i 276
 
6.6%
n 263
 
6.3%
d 257
 
6.1%
c 237
 
5.7%
s 211
 
5.0%
u 190
 
4.5%
Other values (15) 1145
27.4%
None
ValueCountFrequency (%)
í 36
40.9%
ç 31
35.2%
ã 21
23.9%

riscos_inicias
Real number (ℝ)

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.365
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-07-15T20:45:53.319538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q37
95-th percentile8
Maximum10
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7311149
Coefficient of variation (CV)0.3226682
Kurtosis-0.58896926
Mean5.365
Median Absolute Deviation (MAD)1
Skewness0.19203429
Sum1073
Variance2.9967588
MonotonicityNot monotonic
2023-07-15T20:45:53.569098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 42
21.0%
6 38
19.0%
4 36
18.0%
7 28
14.0%
3 25
12.5%
8 18
9.0%
9 6
 
3.0%
2 6
 
3.0%
10 1
 
0.5%
ValueCountFrequency (%)
2 6
 
3.0%
3 25
12.5%
4 36
18.0%
5 42
21.0%
6 38
19.0%
7 28
14.0%
8 18
9.0%
9 6
 
3.0%
10 1
 
0.5%
ValueCountFrequency (%)
10 1
 
0.5%
9 6
 
3.0%
8 18
9.0%
7 28
14.0%
6 38
19.0%
5 42
21.0%
4 36
18.0%
3 25
12.5%
2 6
 
3.0%

Interactions

2023-07-15T20:45:48.192389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:44.307756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:44.923504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.538174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.261765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.890911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:47.585568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:48.288901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:44.411272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.010019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.619179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.352298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.985427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:47.667092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:48.381416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:44.489788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.089541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.827228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.435827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:47.081952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:47.748610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:48.466944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:44.567937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.168077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.902741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.514826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:47.170467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:47.834122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:48.558461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:44.654462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.255602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.989259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.599351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:47.269987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:47.924844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:48.657990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:44.746977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.355136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.088784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.700868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:47.384514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:48.019356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:48.745508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:44.827987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:45.438650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.169311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:46.786387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:47.485040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T20:45:48.098866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-15T20:45:53.677622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
custo_inicialcusto_finalesforco_estimado_inicialesforco_estimado_realnumero_integrantes_equipematuridade_equiperiscos_iniciassituacaometodologiauso_melhores_praticaspraticas_inovadorasgerentefrequencia_mudanca_requisitosarea_projetotipo_projetomotivo
custo_inicial1.0000.971-0.090-0.1000.0100.0060.0440.0970.0400.0000.0510.0000.1120.0000.1050.000
custo_final0.9711.000-0.078-0.0950.0000.0170.0440.0760.0000.0000.0000.0000.0000.0920.1180.121
esforco_estimado_inicial-0.090-0.0781.0000.941-0.1280.0100.0120.2160.0880.0000.0000.0000.0000.0000.1000.058
esforco_estimado_real-0.100-0.0950.9411.000-0.128-0.0070.0060.2200.0000.0860.1020.0610.0000.0000.0000.105
numero_integrantes_equipe0.0100.000-0.128-0.1281.000-0.0790.0650.1710.2280.1870.0000.0000.0900.0000.0000.120
maturidade_equipe0.0060.0170.010-0.007-0.0791.000-0.1010.2180.1990.0000.2990.1170.1060.1100.0000.079
riscos_inicias0.0440.0440.0120.0060.065-0.1011.0000.0000.0000.0870.0000.1350.0000.0000.1470.000
situacao0.0970.0760.2160.2200.1710.2180.0001.0000.2920.0690.2370.4160.4840.0000.0000.969
metodologia0.0400.0000.0880.0000.2280.1990.0000.2921.0000.0000.1000.0000.1060.0310.0000.205
uso_melhores_praticas0.0000.0000.0000.0860.1870.0000.0870.0690.0001.0000.0680.1380.0780.0000.1400.304
praticas_inovadoras0.0510.0000.0000.1020.0000.2990.0000.2370.1000.0681.0000.2090.0000.0000.0280.265
gerente0.0000.0000.0000.0610.0000.1170.1350.4160.0000.1380.2091.0000.1070.0000.0790.174
frequencia_mudanca_requisitos0.1120.0000.0000.0000.0900.1060.0000.4840.1060.0780.0000.1071.0000.0000.1190.180
area_projeto0.0000.0920.0000.0000.0000.1100.0000.0000.0310.0000.0000.0000.0001.0000.1070.000
tipo_projeto0.1050.1180.1000.0000.0000.0000.1470.0000.0000.1400.0280.0790.1190.1071.0000.194
motivo0.0000.1210.0580.1050.1200.0790.0000.9690.2050.3040.2650.1740.1800.0000.1941.000

Missing values

2023-07-15T20:45:49.037624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-15T20:45:49.296166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

situacaocusto_inicialcusto_finalesforco_estimado_inicialesforco_estimado_realnumero_integrantes_equipemetodologiauso_melhores_praticaspraticas_inovadorasnome_projetogerentefrequencia_mudanca_requisitosmaturidade_equipearea_projetotipo_projetomotivoriscos_inicias
0sucesso15679515861420296tradicionalsimnaoProjeto 1Joaquimquinzenal3aplicativosmanutencaofeedback contínuo4
1sucesso9496910234237474agilesimnaoProjeto 2Joaquimsemanal7aplicativosnovo projetofeedback contínuo5
2sucesso166485165711323914agilenaonaoProjeto 3Crissiaquinzenal9aplicativosevolucaofeedback contínuo8
3falha21976425682419243tradicionalnaosimProjeto 4Marcioquinzenal5aplicativosevolucaoescopo mal definido6
4falha3254765740111010tradicionalsimnaoProjeto 5Sophiadiaria3automobilisticanovo projetomudancas de escopo frequentes5
5falha7208175923263514agilenaonaoProjeto 6Marciodiaria2saudemanutencaomudancas de escopo frequentes6
6sucesso167685197949201820agilesimsimProjeto 7Marceloquinzenal10educacaomanutencaofeedback contínuo6
7falha676888940581312tradicionalsimnaoProjeto 8Marciodiaria1financasnovo projetomudancas de escopo frequentes7
8sucesso12182016453920294agilesimnaoProjeto 9Marciosemanal8financasmanutencaofeedback contínuo7
9sucesso9750610444731408tradicionalsimsimProjeto 10Rodrigoanual1aplicativosnovo projetofeedback contínuo8
situacaocusto_inicialcusto_finalesforco_estimado_inicialesforco_estimado_realnumero_integrantes_equipemetodologiauso_melhores_praticaspraticas_inovadorasnome_projetogerentefrequencia_mudanca_requisitosmaturidade_equipearea_projetotipo_projetomotivoriscos_inicias
190falha446245174328274agilesimsimProjeto 191Marciodiaria7saudeevolucaocomunicacao ineficaz8
191sucesso20149420791025379agilenaosimProjeto 192Sophiasemestral1telecomunicacoesevolucaofeedback contínuo6
192sucesso111243117987896agilesimnaoProjeto 193Marceloanual9educacaonovo projetofeedback contínuo3
193falha200481223079313713agilenaosimProjeto 194Marciodiaria7telecomunicacoesnovo projetomudancas de escopo frequentes7
194sucesso15335016701235448agilenaonaoProjeto 195Sophiasemanal10financasnovo projetofeedback contínuo3
195sucesso807321042739164agilenaosimProjeto 196Marcelodiaria7aplicativosnovo projetofeedback contínuo4
196falha394798263036365personalizadasimsimProjeto 197Clarissamensal5saudenovo projetogestao inadequada de riscos6
197sucesso8507598046223116tradicionalnaonaoProjeto 198Sophiatrimestral3saudenovo projetofeedback contínuo2
198falha18767720507321279personalizadanaosimProjeto 199Clarissamensal5saudeevolucaogestao inadequada de riscos5
199sucesso5387485537404013personalizadasimnaoProjeto 200Sophiasemestral9financasnovo projetofeedback contínuo3