Overview

Dataset statistics

Number of variables34
Number of observations54
Missing cells814
Missing cells (%)44.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.1 KiB
Average record size in memory267.4 B

Variable types

Numeric2
Categorical24
DateTime1
Boolean1
Unsupported6

Alerts

Duplicar has constant value "False"Constant
Referencia externa has constant value "test_response"Constant
Actual-Gasolina has constant value "1.0"Constant
Actual-Híbrido has constant value "1.0"Constant
Actual-Otro has constant value "1.0"Constant
Futuro-Gasolina has constant value "1.0"Constant
Futuro-Diesel has constant value "1.0"Constant
Futuro-Híbrido has constant value "1.0"Constant
Futuro-Otro has constant value "1.0"Constant
Tiempo necesario para completar (segundos) is highly overall correlated with Edad and 4 other fieldsHigh correlation
Estado de respuesta is highly overall correlated with Selecciona el vehículo que concuerda con 1 año de ingresos: and 1 other fieldsHigh correlation
Dirección IP is highly overall correlated with RegiónHigh correlation
Código de país is highly overall correlated with RegiónHigh correlation
Región is highly overall correlated with Dirección IP and 1 other fieldsHigh correlation
Edad is highly overall correlated with Tiempo necesario para completar (segundos) and 1 other fieldsHigh correlation
Miembros en la familia is highly overall correlated with Tiempo necesario para completar (segundos)High correlation
Lugar de vivienda is highly overall correlated with Tiempo necesario para completar (segundos) and 1 other fieldsHigh correlation
Género is highly overall correlated with Tiempo necesario para completar (segundos)High correlation
¿Tiene vehículo propio? is highly overall correlated with Edad and 1 other fieldsHigh correlation
¿El vehículo fue comprado con financiamiento? is highly overall correlated with Tiempo necesario para completar (segundos)High correlation
Selecciona el vehículo que quisiera comprar: is highly overall correlated with ¿Tiene usted relación con actividades ligadas al agro?High correlation
Selecciona el vehículo que concuerda con 1 año de ingresos: is highly overall correlated with Estado de respuesta and 1 other fieldsHigh correlation
¿Tiene usted relación con actividades ligadas al agro? is highly overall correlated with Estado de respuesta and 2 other fieldsHigh correlation
Seq. Número is highly imbalanced (72.2%)Imbalance
Código de país is highly imbalanced (65.1%)Imbalance
Lugar de vivienda is highly imbalanced (53.1%)Imbalance
Referencia externa has 53 (98.1%) missing valuesMissing
Variable personalizada 1 has 54 (100.0%) missing valuesMissing
Variable personalizada 2 has 54 (100.0%) missing valuesMissing
Variable personalizada 3 has 54 (100.0%) missing valuesMissing
Variable personalizada 4 has 54 (100.0%) missing valuesMissing
Variable personalizada 5 has 54 (100.0%) missing valuesMissing
Región has 1 (1.9%) missing valuesMissing
Edad has 15 (27.8%) missing valuesMissing
Miembros en la familia has 14 (25.9%) missing valuesMissing
Lugar de vivienda has 14 (25.9%) missing valuesMissing
Género has 14 (25.9%) missing valuesMissing
¿Tiene vehículo propio? has 1 (1.9%) missing valuesMissing
¿El vehículo fue comprado con financiamiento? has 23 (42.6%) missing valuesMissing
¿Quisiera comprar un nuevo vehículo? has 12 (22.2%) missing valuesMissing
Selecciona el más parecido al vehículo actual: has 14 (25.9%) missing valuesMissing
Actual-Gasolina has 21 (38.9%) missing valuesMissing
Actual-Diesel has 54 (100.0%) missing valuesMissing
Actual-Híbrido has 49 (90.7%) missing valuesMissing
Actual-Otro has 52 (96.3%) missing valuesMissing
Selecciona el vehículo que quisiera comprar: has 4 (7.4%) missing valuesMissing
Futuro-Gasolina has 42 (77.8%) missing valuesMissing
Futuro-Diesel has 50 (92.6%) missing valuesMissing
Futuro-Híbrido has 24 (44.4%) missing valuesMissing
Futuro-Otro has 50 (92.6%) missing valuesMissing
Selecciona el vehículo que concuerda con 1 año de ingresos: has 3 (5.6%) missing valuesMissing
¿Tiene usted relación con actividades ligadas al agro? has 34 (63.0%) missing valuesMissing
Variable personalizada 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Variable personalizada 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Variable personalizada 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Variable personalizada 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Variable personalizada 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Actual-Diesel is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-01-19 00:42:13.942275
Analysis finished2023-01-19 00:42:21.467725
Duration7.53 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

ID de respuesta
Real number (ℝ)

Distinct50
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0121202 × 108
Minimum1.0101424 × 108
Maximum1.0126044 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2023-01-18T19:42:21.667152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.0101424 × 108
5-th percentile1.0114005 × 108
Q11.0115516 × 108
median1.012433 × 108
Q31.0124492 × 108
95-th percentile1.0125518 × 108
Maximum1.0126044 × 108
Range246193
Interquartile range (IQR)89751.25

Descriptive statistics

Standard deviation54214.014
Coefficient of variation (CV)0.00053564796
Kurtosis1.5381988
Mean1.0121202 × 108
Median Absolute Deviation (MAD)2601.5
Skewness-1.3644503
Sum5.4654493 × 109
Variance2.9391593 × 109
MonotonicityIncreasing
2023-01-18T19:42:22.014474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101255175 3
 
5.6%
101243067 2
 
3.7%
101242836 2
 
3.7%
101014244 1
 
1.9%
101244644 1
 
1.9%
101243480 1
 
1.9%
101243538 1
 
1.9%
101243698 1
 
1.9%
101243734 1
 
1.9%
101243767 1
 
1.9%
Other values (40) 40
74.1%
ValueCountFrequency (%)
101014244 1
1.9%
101140002 1
1.9%
101140024 1
1.9%
101140066 1
1.9%
101140127 1
1.9%
101140190 1
1.9%
101140566 1
1.9%
101140628 1
1.9%
101140695 1
1.9%
101140730 1
1.9%
ValueCountFrequency (%)
101260437 1
 
1.9%
101255175 3
5.6%
101254877 1
 
1.9%
101254261 1
 
1.9%
101251738 1
 
1.9%
101251532 1
 
1.9%
101247555 1
 
1.9%
101245935 1
 
1.9%
101245866 1
 
1.9%
101245413 1
 
1.9%

Estado de respuesta
Categorical

Distinct2
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size560.0 B
Terminated
32 
Completed
22 

Length

Max length10
Median length10
Mean length9.5925926
Min length9

Characters and Unicode

Total characters518
Distinct characters13
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 rowCompleted
2nd rowCompleted
3rd rowCompleted
4th rowTerminated
5th rowTerminated

Common Values

ValueCountFrequency (%)
Terminated 32
59.3%
Completed 22
40.7%

Length

2023-01-18T19:42:22.264465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:22.500072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
terminated 32
59.3%
completed 22
40.7%

Most occurring characters

ValueCountFrequency (%)
e 108
20.8%
m 54
10.4%
t 54
10.4%
d 54
10.4%
T 32
 
6.2%
r 32
 
6.2%
i 32
 
6.2%
n 32
 
6.2%
a 32
 
6.2%
C 22
 
4.2%
Other values (3) 66
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 464
89.6%
Uppercase Letter 54
 
10.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 108
23.3%
m 54
11.6%
t 54
11.6%
d 54
11.6%
r 32
 
6.9%
i 32
 
6.9%
n 32
 
6.9%
a 32
 
6.9%
o 22
 
4.7%
p 22
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
T 32
59.3%
C 22
40.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 518
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 108
20.8%
m 54
10.4%
t 54
10.4%
d 54
10.4%
T 32
 
6.2%
r 32
 
6.2%
i 32
 
6.2%
n 32
 
6.2%
a 32
 
6.2%
C 22
 
4.2%
Other values (3) 66
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 108
20.8%
m 54
10.4%
t 54
10.4%
d 54
10.4%
T 32
 
6.2%
r 32
 
6.2%
i 32
 
6.2%
n 32
 
6.2%
a 32
 
6.2%
C 22
 
4.2%
Other values (3) 66
12.7%

Dirección IP
Categorical

Distinct43
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Memory size560.0 B
200.118.16.170
5 
45.70.239.132
 
3
181.55.68.101
 
3
186.155.14.78
 
2
179.33.236.55
 
2
Other values (38)
39 

Length

Max length15
Median length14
Mean length13.351852
Min length11

Characters and Unicode

Total characters721
Distinct characters11
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

Unique37 ?
Unique (%)68.5%

Sample

1st row200.118.16.170
2nd row200.118.16.170
3rd row186.84.22.150
4th row161.18.115.222
5th row190.60.254.216

Common Values

ValueCountFrequency (%)
200.118.16.170 5
 
9.3%
45.70.239.132 3
 
5.6%
181.55.68.101 3
 
5.6%
186.155.14.78 2
 
3.7%
179.33.236.55 2
 
3.7%
186.28.129.159 2
 
3.7%
172.226.13.224 1
 
1.9%
204.48.78.10 1
 
1.9%
181.61.156.71 1
 
1.9%
190.1.193.95 1
 
1.9%
Other values (33) 33
61.1%

Length

2023-01-18T19:42:22.735699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
200.118.16.170 5
 
9.3%
181.55.68.101 3
 
5.6%
45.70.239.132 3
 
5.6%
186.155.14.78 2
 
3.7%
179.33.236.55 2
 
3.7%
186.28.129.159 2
 
3.7%
161.18.121.114 1
 
1.9%
161.18.115.222 1
 
1.9%
190.60.254.216 1
 
1.9%
181.61.61.133 1
 
1.9%
Other values (33) 33
61.1%

Most occurring characters

ValueCountFrequency (%)
1 168
23.3%
. 162
22.5%
2 68
9.4%
5 53
 
7.4%
0 47
 
6.5%
8 47
 
6.5%
6 47
 
6.5%
9 41
 
5.7%
3 33
 
4.6%
7 29
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 559
77.5%
Other Punctuation 162
 
22.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 168
30.1%
2 68
12.2%
5 53
 
9.5%
0 47
 
8.4%
8 47
 
8.4%
6 47
 
8.4%
9 41
 
7.3%
3 33
 
5.9%
7 29
 
5.2%
4 26
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 721
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 168
23.3%
. 162
22.5%
2 68
9.4%
5 53
 
7.4%
0 47
 
6.5%
8 47
 
6.5%
6 47
 
6.5%
9 41
 
5.7%
3 33
 
4.6%
7 29
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 168
23.3%
. 162
22.5%
2 68
9.4%
5 53
 
7.4%
0 47
 
6.5%
8 47
 
6.5%
6 47
 
6.5%
9 41
 
5.7%
3 33
 
4.6%
7 29
 
4.0%

Marca de tiempo (mm/dd/yyyy)
Date

Distinct50
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size560.0 B
Minimum2023-01-03 15:48:25
Maximum2023-01-14 18:00:16
2023-01-18T19:42:23.020483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-18T19:42:23.380177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Duplicar
Boolean

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size182.0 B
False
54 
ValueCountFrequency (%)
False 54
100.0%
2023-01-18T19:42:23.631726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Tiempo necesario para completar (segundos)
Real number (ℝ)

Distinct45
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.48148
Minimum42
Maximum2843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2023-01-18T19:42:23.868613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile48.9
Q160.5
median83.5
Q3131.5
95-th percentile242
Maximum2843
Range2801
Interquartile range (IQR)71

Descriptive statistics

Standard deviation376.94911
Coefficient of variation (CV)2.3936091
Kurtosis51.261514
Mean157.48148
Median Absolute Deviation (MAD)28.5
Skewness7.0768986
Sum8504
Variance142090.63
MonotonicityNot monotonic
2023-01-18T19:42:24.184476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
60 3
 
5.6%
214 3
 
5.6%
77 2
 
3.7%
96 2
 
3.7%
242 2
 
3.7%
55 2
 
3.7%
81 2
 
3.7%
67 1
 
1.9%
118 1
 
1.9%
93 1
 
1.9%
Other values (35) 35
64.8%
ValueCountFrequency (%)
42 1
1.9%
44 1
1.9%
45 1
1.9%
51 1
1.9%
52 1
1.9%
54 1
1.9%
55 2
3.7%
56 1
1.9%
57 1
1.9%
58 1
1.9%
ValueCountFrequency (%)
2843 1
 
1.9%
246 1
 
1.9%
242 2
3.7%
224 1
 
1.9%
219 1
 
1.9%
214 3
5.6%
161 1
 
1.9%
154 1
 
1.9%
144 1
 
1.9%
140 1
 
1.9%

Seq. Número
Categorical

Distinct3
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size560.0 B
1
50 
2
 
3
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 50
92.6%
2 3
 
5.6%
3 1
 
1.9%

Length

2023-01-18T19:42:24.459171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:24.639740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 50
92.6%
2 3
 
5.6%
3 1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
1 50
92.6%
2 3
 
5.6%
3 1
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 50
92.6%
2 3
 
5.6%
3 1
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 54
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 50
92.6%
2 3
 
5.6%
3 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 50
92.6%
2 3
 
5.6%
3 1
 
1.9%

Referencia externa
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing53
Missing (%)98.1%
Memory size560.0 B
test_response
1 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
Distinct characters8
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

Unique1 ?
Unique (%)100.0%

Sample

1st rowtest_response

Common Values

ValueCountFrequency (%)
test_response 1
 
1.9%
(Missing) 53
98.1%

Length

2023-01-18T19:42:24.815408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:25.000994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
test_response 1
100.0%

Most occurring characters

ValueCountFrequency (%)
e 3
23.1%
s 3
23.1%
t 2
15.4%
_ 1
 
7.7%
r 1
 
7.7%
p 1
 
7.7%
o 1
 
7.7%
n 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12
92.3%
Connector Punctuation 1
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3
25.0%
s 3
25.0%
t 2
16.7%
r 1
 
8.3%
p 1
 
8.3%
o 1
 
8.3%
n 1
 
8.3%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12
92.3%
Common 1
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3
25.0%
s 3
25.0%
t 2
16.7%
r 1
 
8.3%
p 1
 
8.3%
o 1
 
8.3%
n 1
 
8.3%
Common
ValueCountFrequency (%)
_ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3
23.1%
s 3
23.1%
t 2
15.4%
_ 1
 
7.7%
r 1
 
7.7%
p 1
 
7.7%
o 1
 
7.7%
n 1
 
7.7%

Variable personalizada 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54
Missing (%)100.0%
Memory size560.0 B

Variable personalizada 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54
Missing (%)100.0%
Memory size560.0 B

Variable personalizada 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54
Missing (%)100.0%
Memory size560.0 B

Variable personalizada 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54
Missing (%)100.0%
Memory size560.0 B

Variable personalizada 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54
Missing (%)100.0%
Memory size560.0 B

Código de país
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size560.0 B
CO
46 
EC
 
4
JP
 
1
AR
 
1
CA
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters108
Distinct characters8
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

Unique4 ?
Unique (%)7.4%

Sample

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

Common Values

ValueCountFrequency (%)
CO 46
85.2%
EC 4
 
7.4%
JP 1
 
1.9%
AR 1
 
1.9%
CA 1
 
1.9%
AU 1
 
1.9%

Length

2023-01-18T19:42:25.175893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:25.427230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
co 46
85.2%
ec 4
 
7.4%
jp 1
 
1.9%
ar 1
 
1.9%
ca 1
 
1.9%
au 1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
C 51
47.2%
O 46
42.6%
E 4
 
3.7%
A 3
 
2.8%
J 1
 
0.9%
P 1
 
0.9%
R 1
 
0.9%
U 1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 51
47.2%
O 46
42.6%
E 4
 
3.7%
A 3
 
2.8%
J 1
 
0.9%
P 1
 
0.9%
R 1
 
0.9%
U 1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 51
47.2%
O 46
42.6%
E 4
 
3.7%
A 3
 
2.8%
J 1
 
0.9%
P 1
 
0.9%
R 1
 
0.9%
U 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 51
47.2%
O 46
42.6%
E 4
 
3.7%
A 3
 
2.8%
J 1
 
0.9%
P 1
 
0.9%
R 1
 
0.9%
U 1
 
0.9%

Región
Categorical

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)22.6%
Missing1
Missing (%)1.9%
Memory size560.0 B
DC
36 
TOL
4 
P
4 
ANT
 
1
QUI
 
1
Other values (7)
7 

Length

Max length3
Median length2
Mean length2.1132075
Min length1

Characters and Unicode

Total characters112
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

Unique9 ?
Unique (%)17.0%

Sample

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

Common Values

ValueCountFrequency (%)
DC 36
66.7%
TOL 4
 
7.4%
P 4
 
7.4%
ANT 1
 
1.9%
QUI 1
 
1.9%
RIS 1
 
1.9%
B 1
 
1.9%
QC 1
 
1.9%
VAC 1
 
1.9%
CUN 1
 
1.9%
Other values (2) 2
 
3.7%

Length

2023-01-18T19:42:25.664869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dc 36
67.9%
tol 4
 
7.5%
p 4
 
7.5%
ant 1
 
1.9%
qui 1
 
1.9%
ris 1
 
1.9%
b 1
 
1.9%
qc 1
 
1.9%
vac 1
 
1.9%
cun 1
 
1.9%
Other values (2) 2
 
3.8%

Most occurring characters

ValueCountFrequency (%)
C 39
34.8%
D 37
33.0%
L 6
 
5.4%
T 5
 
4.5%
O 5
 
4.5%
P 4
 
3.6%
Q 3
 
2.7%
A 2
 
1.8%
N 2
 
1.8%
U 2
 
1.8%
Other values (5) 7
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 112
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 39
34.8%
D 37
33.0%
L 6
 
5.4%
T 5
 
4.5%
O 5
 
4.5%
P 4
 
3.6%
Q 3
 
2.7%
A 2
 
1.8%
N 2
 
1.8%
U 2
 
1.8%
Other values (5) 7
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 39
34.8%
D 37
33.0%
L 6
 
5.4%
T 5
 
4.5%
O 5
 
4.5%
P 4
 
3.6%
Q 3
 
2.7%
A 2
 
1.8%
N 2
 
1.8%
U 2
 
1.8%
Other values (5) 7
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 39
34.8%
D 37
33.0%
L 6
 
5.4%
T 5
 
4.5%
O 5
 
4.5%
P 4
 
3.6%
Q 3
 
2.7%
A 2
 
1.8%
N 2
 
1.8%
U 2
 
1.8%
Other values (5) 7
 
6.2%

Edad
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)12.8%
Missing15
Missing (%)27.8%
Memory size560.0 B
2.0
17 
3.0
10 
1.0
6 
4.0
4 
5.0
2 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters117
Distinct characters7
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 row2.0
2nd row2.0
3rd row3.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 17
31.5%
3.0 10
18.5%
1.0 6
 
11.1%
4.0 4
 
7.4%
5.0 2
 
3.7%
(Missing) 15
27.8%

Length

2023-01-18T19:42:25.969213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:26.266960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 17
43.6%
3.0 10
25.6%
1.0 6
 
15.4%
4.0 4
 
10.3%
5.0 2
 
5.1%

Most occurring characters

ValueCountFrequency (%)
. 39
33.3%
0 39
33.3%
2 17
14.5%
3 10
 
8.5%
1 6
 
5.1%
4 4
 
3.4%
5 2
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78
66.7%
Other Punctuation 39
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39
50.0%
2 17
21.8%
3 10
 
12.8%
1 6
 
7.7%
4 4
 
5.1%
5 2
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 117
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 39
33.3%
0 39
33.3%
2 17
14.5%
3 10
 
8.5%
1 6
 
5.1%
4 4
 
3.4%
5 2
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 39
33.3%
0 39
33.3%
2 17
14.5%
3 10
 
8.5%
1 6
 
5.1%
4 4
 
3.4%
5 2
 
1.7%

Miembros en la familia
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)5.0%
Missing14
Missing (%)25.9%
Memory size560.0 B
1.0
23 
2.0
17 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters120
Distinct characters4
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 row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 23
42.6%
2.0 17
31.5%
(Missing) 14
25.9%

Length

2023-01-18T19:42:26.523787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:26.782282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 23
57.5%
2.0 17
42.5%

Most occurring characters

ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
1 23
19.2%
2 17
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
66.7%
Other Punctuation 40
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
50.0%
1 23
28.7%
2 17
21.2%
Other Punctuation
ValueCountFrequency (%)
. 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
1 23
19.2%
2 17
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
1 23
19.2%
2 17
14.2%

Lugar de vivienda
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)5.0%
Missing14
Missing (%)25.9%
Memory size560.0 B
1.0
36 
2.0
4 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters120
Distinct characters4
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 row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 36
66.7%
2.0 4
 
7.4%
(Missing) 14
 
25.9%

Length

2023-01-18T19:42:27.019022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:27.281015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 36
90.0%
2.0 4
 
10.0%

Most occurring characters

ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
1 36
30.0%
2 4
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
66.7%
Other Punctuation 40
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
50.0%
1 36
45.0%
2 4
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
1 36
30.0%
2 4
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
1 36
30.0%
2 4
 
3.3%

Género
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)7.5%
Missing14
Missing (%)25.9%
Memory size560.0 B
2.0
28 
1.0
11 
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters120
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

Unique1 ?
Unique (%)2.5%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 28
51.9%
1.0 11
 
20.4%
3.0 1
 
1.9%
(Missing) 14
25.9%

Length

2023-01-18T19:42:27.524071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:27.852454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 28
70.0%
1.0 11
 
27.5%
3.0 1
 
2.5%

Most occurring characters

ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
2 28
23.3%
1 11
 
9.2%
3 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
66.7%
Other Punctuation 40
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
50.0%
2 28
35.0%
1 11
 
13.8%
3 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
2 28
23.3%
1 11
 
9.2%
3 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
2 28
23.3%
1 11
 
9.2%
3 1
 
0.8%

¿Tiene vehículo propio?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)3.8%
Missing1
Missing (%)1.9%
Memory size560.0 B
1.0
39 
2.0
14 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters159
Distinct characters4
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 row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 39
72.2%
2.0 14
 
25.9%
(Missing) 1
 
1.9%

Length

2023-01-18T19:42:28.117429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:28.411467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 39
73.6%
2.0 14
 
26.4%

Most occurring characters

ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
1 39
24.5%
2 14
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 106
66.7%
Other Punctuation 53
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53
50.0%
1 39
36.8%
2 14
 
13.2%
Other Punctuation
ValueCountFrequency (%)
. 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
1 39
24.5%
2 14
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 53
33.3%
0 53
33.3%
1 39
24.5%
2 14
 
8.8%

¿El vehículo fue comprado con financiamiento?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)6.5%
Missing23
Missing (%)42.6%
Memory size560.0 B
2.0
18 
1.0
13 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters93
Distinct characters4
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 row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 18
33.3%
1.0 13
24.1%
(Missing) 23
42.6%

Length

2023-01-18T19:42:28.630598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:28.897366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 18
58.1%
1.0 13
41.9%

Most occurring characters

ValueCountFrequency (%)
. 31
33.3%
0 31
33.3%
2 18
19.4%
1 13
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
66.7%
Other Punctuation 31
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31
50.0%
2 18
29.0%
1 13
21.0%
Other Punctuation
ValueCountFrequency (%)
. 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 31
33.3%
0 31
33.3%
2 18
19.4%
1 13
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 31
33.3%
0 31
33.3%
2 18
19.4%
1 13
14.0%

¿Quisiera comprar un nuevo vehículo?
Categorical

Distinct2
Distinct (%)4.8%
Missing12
Missing (%)22.2%
Memory size560.0 B
1.0
23 
2.0
19 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters126
Distinct characters4
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 row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 23
42.6%
2.0 19
35.2%
(Missing) 12
22.2%

Length

2023-01-18T19:42:29.105160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:29.340408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 23
54.8%
2.0 19
45.2%

Most occurring characters

ValueCountFrequency (%)
. 42
33.3%
0 42
33.3%
1 23
18.3%
2 19
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84
66.7%
Other Punctuation 42
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42
50.0%
1 23
27.4%
2 19
22.6%
Other Punctuation
ValueCountFrequency (%)
. 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 42
33.3%
0 42
33.3%
1 23
18.3%
2 19
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 42
33.3%
0 42
33.3%
1 23
18.3%
2 19
15.1%

Selecciona el más parecido al vehículo actual:
Categorical

Distinct4
Distinct (%)10.0%
Missing14
Missing (%)25.9%
Memory size560.0 B
2.0
19 
3.0
17 
1.0
3 
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters120
Distinct characters6
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

Unique1 ?
Unique (%)2.5%

Sample

1st row1.0
2nd row2.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 19
35.2%
3.0 17
31.5%
1.0 3
 
5.6%
4.0 1
 
1.9%
(Missing) 14
25.9%

Length

2023-01-18T19:42:29.575521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:30.099574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 19
47.5%
3.0 17
42.5%
1.0 3
 
7.5%
4.0 1
 
2.5%

Most occurring characters

ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
2 19
15.8%
3 17
14.2%
1 3
 
2.5%
4 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
66.7%
Other Punctuation 40
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
50.0%
2 19
23.8%
3 17
21.2%
1 3
 
3.8%
4 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
2 19
15.8%
3 17
14.2%
1 3
 
2.5%
4 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 40
33.3%
0 40
33.3%
2 19
15.8%
3 17
14.2%
1 3
 
2.5%
4 1
 
0.8%

Actual-Gasolina
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.0%
Missing21
Missing (%)38.9%
Memory size560.0 B
1.0
33 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters99
Distinct characters3
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 row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 33
61.1%
(Missing) 21
38.9%

Length

2023-01-18T19:42:30.376065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:30.619878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 33
100.0%

Most occurring characters

ValueCountFrequency (%)
1 33
33.3%
. 33
33.3%
0 33
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66
66.7%
Other Punctuation 33
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 33
50.0%
0 33
50.0%
Other Punctuation
ValueCountFrequency (%)
. 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 33
33.3%
. 33
33.3%
0 33
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33
33.3%
. 33
33.3%
0 33
33.3%

Actual-Diesel
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54
Missing (%)100.0%
Memory size560.0 B

Actual-Híbrido
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing49
Missing (%)90.7%
Memory size560.0 B
1.0
5 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
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 row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5
 
9.3%
(Missing) 49
90.7%

Length

2023-01-18T19:42:30.828537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:31.078269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 5
50.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
33.3%
. 5
33.3%
0 5
33.3%

Actual-Otro
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing52
Missing (%)96.3%
Memory size560.0 B
1.0
2 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
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

Common Values

ValueCountFrequency (%)
1.0 2
 
3.7%
(Missing) 52
96.3%

Length

2023-01-18T19:42:31.249712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:31.515925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2
100.0%

Most occurring characters

ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2
33.3%
. 2
33.3%
0 2
33.3%

Selecciona el vehículo que quisiera comprar:
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)8.0%
Missing4
Missing (%)7.4%
Memory size560.0 B
3.0
33 
2.0
8 
4.0
5 
1.0
4 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters150
Distinct characters6
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 row3.0
2nd row3.0
3rd row4.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 33
61.1%
2.0 8
 
14.8%
4.0 5
 
9.3%
1.0 4
 
7.4%
(Missing) 4
 
7.4%

Length

2023-01-18T19:42:31.734154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:32.046220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 33
66.0%
2.0 8
 
16.0%
4.0 5
 
10.0%
1.0 4
 
8.0%

Most occurring characters

ValueCountFrequency (%)
. 50
33.3%
0 50
33.3%
3 33
22.0%
2 8
 
5.3%
4 5
 
3.3%
1 4
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100
66.7%
Other Punctuation 50
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50
50.0%
3 33
33.0%
2 8
 
8.0%
4 5
 
5.0%
1 4
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 50
33.3%
0 50
33.3%
3 33
22.0%
2 8
 
5.3%
4 5
 
3.3%
1 4
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 50
33.3%
0 50
33.3%
3 33
22.0%
2 8
 
5.3%
4 5
 
3.3%
1 4
 
2.7%

Futuro-Gasolina
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)8.3%
Missing42
Missing (%)77.8%
Memory size560.0 B
1.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36
Distinct characters3
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 row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12
 
22.2%
(Missing) 42
77.8%

Length

2023-01-18T19:42:32.271132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:32.491001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12
100.0%

Most occurring characters

ValueCountFrequency (%)
1 12
33.3%
. 12
33.3%
0 12
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24
66.7%
Other Punctuation 12
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
50.0%
0 12
50.0%
Other Punctuation
ValueCountFrequency (%)
. 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
33.3%
. 12
33.3%
0 12
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
33.3%
. 12
33.3%
0 12
33.3%

Futuro-Diesel
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)25.0%
Missing50
Missing (%)92.6%
Memory size560.0 B
1.0
4 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters3
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 row1.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.0 4
 
7.4%
(Missing) 50
92.6%

Length

2023-01-18T19:42:32.648720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:32.886885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4
50.0%
0 4
50.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Futuro-Híbrido
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.3%
Missing24
Missing (%)44.4%
Memory size560.0 B
1.0
30 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters3
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 row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 30
55.6%
(Missing) 24
44.4%

Length

2023-01-18T19:42:33.075464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:33.280762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 30
100.0%

Most occurring characters

ValueCountFrequency (%)
1 30
33.3%
. 30
33.3%
0 30
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
66.7%
Other Punctuation 30
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30
50.0%
0 30
50.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30
33.3%
. 30
33.3%
0 30
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30
33.3%
. 30
33.3%
0 30
33.3%

Futuro-Otro
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)25.0%
Missing50
Missing (%)92.6%
Memory size560.0 B
1.0
4 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters3
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 row1.0
4th row1.0

Common Values

ValueCountFrequency (%)
1.0 4
 
7.4%
(Missing) 50
92.6%

Length

2023-01-18T19:42:33.480373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:33.708208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8
66.7%
Other Punctuation 4
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4
50.0%
0 4
50.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4
33.3%
. 4
33.3%
0 4
33.3%

Selecciona el vehículo que concuerda con 1 año de ingresos:
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)7.8%
Missing3
Missing (%)5.6%
Memory size560.0 B
2.0
17 
3.0
16 
1.0
14 
4.0
4 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters153
Distinct characters6
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 row3.0
2nd row4.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 17
31.5%
3.0 16
29.6%
1.0 14
25.9%
4.0 4
 
7.4%
(Missing) 3
 
5.6%

Length

2023-01-18T19:42:33.937680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:34.244464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 17
33.3%
3.0 16
31.4%
1.0 14
27.5%
4.0 4
 
7.8%

Most occurring characters

ValueCountFrequency (%)
. 51
33.3%
0 51
33.3%
2 17
 
11.1%
3 16
 
10.5%
1 14
 
9.2%
4 4
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102
66.7%
Other Punctuation 51
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51
50.0%
2 17
 
16.7%
3 16
 
15.7%
1 14
 
13.7%
4 4
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 153
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 51
33.3%
0 51
33.3%
2 17
 
11.1%
3 16
 
10.5%
1 14
 
9.2%
4 4
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 51
33.3%
0 51
33.3%
2 17
 
11.1%
3 16
 
10.5%
1 14
 
9.2%
4 4
 
2.6%

¿Tiene usted relación con actividades ligadas al agro?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)10.0%
Missing34
Missing (%)63.0%
Memory size560.0 B
2.0
17 
1.0
3 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60
Distinct characters4
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 row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 17
31.5%
1.0 3
 
5.6%
(Missing) 34
63.0%

Length

2023-01-18T19:42:34.482346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-18T19:42:34.732815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 17
85.0%
1.0 3
 
15.0%

Most occurring characters

ValueCountFrequency (%)
. 20
33.3%
0 20
33.3%
2 17
28.3%
1 3
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40
66.7%
Other Punctuation 20
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20
50.0%
2 17
42.5%
1 3
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 20
33.3%
0 20
33.3%
2 17
28.3%
1 3
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 20
33.3%
0 20
33.3%
2 17
28.3%
1 3
 
5.0%

Interactions

2023-01-18T19:42:17.904516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-18T19:42:17.404871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-18T19:42:18.142512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-18T19:42:17.654346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-01-18T19:42:34.966652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ID de respuestaTiempo necesario para completar (segundos)Estado de respuestaDirección IPSeq. NúmeroCódigo de paísRegiónEdadMiembros en la familiaLugar de viviendaGénero¿Tiene vehículo propio?¿El vehículo fue comprado con financiamiento?¿Quisiera comprar un nuevo vehículo?Selecciona el más parecido al vehículo actual:Selecciona el vehículo que quisiera comprar:Selecciona el vehículo que concuerda con 1 año de ingresos:¿Tiene usted relación con actividades ligadas al agro?
ID de respuesta1.0000.1200.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.1730.0000.3310.0000.0000.000
Tiempo necesario para completar (segundos)0.1201.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.0001.0000.0000.0000.2170.0000.000
Estado de respuesta0.0940.0001.0000.2400.0880.0890.0850.2640.0000.0640.2640.3510.0000.0000.0000.0000.9791.000
Dirección IP0.0000.0000.2401.0000.0000.4790.5180.0000.1880.3970.3430.2260.0000.2920.1850.1770.1700.123
Seq. Número0.0000.0000.0880.0001.0000.2450.0000.2690.0940.0000.0000.0000.0000.1740.0000.0000.1730.000
Código de país0.0000.0000.0890.4790.2451.0000.9240.0000.0000.0000.0000.0000.0000.0000.0000.3250.0000.084
Región0.0000.0000.0850.5180.0000.9241.0000.2020.2830.0000.0000.0000.0000.0470.0000.4390.1250.000
Edad0.0001.0000.2640.0000.2690.0000.2021.0000.2180.0000.2640.5200.0910.3280.0770.0000.1170.000
Miembros en la familia0.0001.0000.0000.1880.0940.0000.2830.2181.0000.1250.0000.0000.0540.0000.2280.1960.1830.000
Lugar de vivienda0.0001.0000.0640.3970.0000.0000.0000.0000.1251.0000.4580.0000.0000.0000.0000.0000.2371.000
Género0.0001.0000.2640.3430.0000.0000.0000.2640.0000.4581.0000.1990.0000.0000.0000.0000.0000.000
¿Tiene vehículo propio?0.0000.0000.3510.2260.0000.0000.0000.5200.0000.0000.1991.0000.0000.0000.0000.0000.5740.000
¿El vehículo fue comprado con financiamiento?0.1731.0000.0000.0000.0000.0000.0000.0910.0540.0000.0000.0001.0000.0540.1820.0000.2350.000
¿Quisiera comprar un nuevo vehículo?0.0000.0000.0000.2920.1740.0000.0470.3280.0000.0000.0000.0000.0541.0000.2400.0000.0000.000
Selecciona el más parecido al vehículo actual:0.3310.0000.0000.1850.0000.0000.0000.0770.2280.0000.0000.0000.1820.2401.0000.2580.2370.480
Selecciona el vehículo que quisiera comprar:0.0000.2170.0000.1770.0000.3250.4390.0000.1960.0000.0000.0000.0000.0000.2581.0000.1560.704
Selecciona el vehículo que concuerda con 1 año de ingresos:0.0000.0000.9790.1700.1730.0000.1250.1170.1830.2370.0000.5740.2350.0000.2370.1561.0000.222
¿Tiene usted relación con actividades ligadas al agro?0.0000.0001.0000.1230.0000.0840.0000.0000.0001.0000.0000.0000.0000.0000.4800.7040.2221.000

Missing values

2023-01-18T19:42:18.788544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-18T19:42:19.752159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-18T19:42:20.613754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ID de respuestaEstado de respuestaDirección IPMarca de tiempo (mm/dd/yyyy)DuplicarTiempo necesario para completar (segundos)Seq. NúmeroReferencia externaVariable personalizada 1Variable personalizada 2Variable personalizada 3Variable personalizada 4Variable personalizada 5Código de paísRegiónEdadMiembros en la familiaLugar de viviendaGénero¿Tiene vehículo propio?¿El vehículo fue comprado con financiamiento?¿Quisiera comprar un nuevo vehículo?Selecciona el más parecido al vehículo actual:Actual-GasolinaActual-DieselActual-HíbridoActual-OtroSelecciona el vehículo que quisiera comprar:Futuro-GasolinaFuturo-DieselFuturo-HíbridoFuturo-OtroSelecciona el vehículo que concuerda con 1 año de ingresos:¿Tiene usted relación con actividades ligadas al agro?
0101014244Completed200.118.16.1702023-01-03 15:48:25False541test_responseNaNNaNNaNNaNNaNCODCNaNNaNNaNNaN1.0NaN1.01.01.0NaNNaNNaN3.0NaNNaNNaNNaNNaNNaN
1101140002Completed200.118.16.1702023-01-09 19:44:47False451NaNNaNNaNNaNNaNNaNCODCNaNNaNNaNNaN1.0NaN1.02.01.0NaNNaNNaN3.0NaNNaN1.0NaN3.02.0
2101140024Completed186.84.22.1502023-01-09 19:48:02False841NaNNaNNaNNaNNaNNaNCODCNaNNaNNaNNaN1.0NaN1.04.01.0NaNNaNNaN4.0NaNNaN1.0NaN4.01.0
3101140066Terminated161.18.115.2222023-01-09 19:54:46False771NaNNaNNaNNaNNaNNaNCODCNaNNaNNaNNaN1.0NaN2.02.01.0NaNNaNNaN3.0NaNNaN1.0NaN2.0NaN
4101140127Terminated190.60.254.2162023-01-09 20:03:32False851NaNNaNNaNNaNNaNNaNCODCNaNNaNNaNNaN2.0NaN2.03.01.0NaNNaNNaN2.0NaNNaN1.0NaN2.0NaN
5101140190Completed181.61.61.1332023-01-09 20:10:38False1171NaNNaNNaNNaNNaNNaNCODCNaNNaNNaNNaN1.0NaN1.03.01.0NaNNaNNaN3.0NaNNaN1.0NaN3.01.0
6101140566Completed186.155.14.782023-01-09 20:44:27False28431NaNNaNNaNNaNNaNNaNCODCNaNNaNNaNNaN1.0NaN1.02.01.0NaNNaNNaN2.0NaNNaN1.0NaN3.02.0
7101140628Terminated186.155.14.782023-01-09 20:50:24False551NaNNaNNaNNaNNaNNaNCODCNaNNaNNaNNaN1.0NaN1.02.01.0NaNNaNNaN3.0NaNNaN1.0NaN2.0NaN
8101140695Completed186.102.11.592023-01-09 20:56:28False511NaNNaNNaNNaNNaNNaNCODCNaNNaNNaNNaN1.0NaN2.03.01.0NaNNaNNaN3.01.0NaNNaNNaN3.02.0
9101140730Terminated191.111.6.1232023-01-09 20:59:56False1321NaNNaNNaNNaNNaNNaNCODCNaNNaNNaNNaN2.0NaN2.02.01.0NaNNaNNaN3.01.0NaNNaNNaN1.0NaN
ID de respuestaEstado de respuestaDirección IPMarca de tiempo (mm/dd/yyyy)DuplicarTiempo necesario para completar (segundos)Seq. NúmeroReferencia externaVariable personalizada 1Variable personalizada 2Variable personalizada 3Variable personalizada 4Variable personalizada 5Código de paísRegiónEdadMiembros en la familiaLugar de viviendaGénero¿Tiene vehículo propio?¿El vehículo fue comprado con financiamiento?¿Quisiera comprar un nuevo vehículo?Selecciona el más parecido al vehículo actual:Actual-GasolinaActual-DieselActual-HíbridoActual-OtroSelecciona el vehículo que quisiera comprar:Futuro-GasolinaFuturo-DieselFuturo-HíbridoFuturo-OtroSelecciona el vehículo que concuerda con 1 año de ingresos:¿Tiene usted relación con actividades ligadas al agro?
44101245935Completed186.155.18.772023-01-13 20:52:33False821NaNNaNNaNNaNNaNNaNCODC2.02.01.01.01.02.02.03.01.0NaNNaNNaN3.0NaNNaN1.0NaN3.02.0
45101247555Completed200.118.16.1702023-01-13 22:31:50False831NaNNaNNaNNaNNaNNaNCODC2.02.01.01.01.01.01.02.01.0NaNNaNNaN3.0NaNNaN1.0NaN3.02.0
46101251532Terminated191.156.181.762023-01-14 06:15:16False691NaNNaNNaNNaNNaNNaNCODC2.01.02.02.02.0NaNNaNNaNNaNNaNNaNNaN1.0NaNNaN1.01.01.0NaN
47101251738Terminated144.134.162.322023-01-14 06:34:06False961NaNNaNNaNNaNNaNNaNAUQLD2.01.01.02.01.02.01.02.0NaNNaNNaN1.02.0NaNNaNNaN1.01.0NaN
48101254261Terminated191.95.49.1642023-01-14 09:22:47False961NaNNaNNaNNaNNaNNaNCODC2.01.01.02.02.0NaNNaNNaNNaNNaNNaNNaN3.0NaN1.0NaNNaN1.0NaN
49101254877Completed186.82.84.2122023-01-14 09:56:15False661NaNNaNNaNNaNNaNNaNCOBOL2.02.01.02.01.01.02.03.01.0NaNNaNNaN2.01.0NaNNaNNaN3.02.0
50101255175Completed45.70.239.1322023-01-14 10:11:04False2141NaNNaNNaNNaNNaNNaNECP3.02.01.02.01.01.02.03.01.0NaNNaNNaN4.0NaNNaN1.0NaN4.01.0
51101255175Completed45.70.239.1322023-01-14 10:11:04False2142NaNNaNNaNNaNNaNNaNECP3.01.01.02.01.02.02.03.0NaNNaNNaNNaN3.0NaNNaNNaNNaN3.02.0
52101255175Completed45.70.239.1322023-01-14 10:11:04False2143NaNNaNNaNNaNNaNNaNECP4.01.01.01.01.02.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
53101260437Terminated157.100.137.1112023-01-14 18:00:16False581NaNNaNNaNNaNNaNNaNECP2.01.01.02.01.02.01.03.01.0NaNNaNNaN4.0NaN1.0NaNNaN2.0NaN