Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations45211
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.2 MiB
Average record size in memory677.2 B

Variable types

Numeric7
Categorical6
Boolean4

Alerts

contact is highly overall correlated with monthHigh correlation
housing is highly overall correlated with monthHigh correlation
month is highly overall correlated with contact and 1 other fieldsHigh correlation
pdays is highly overall correlated with poutcome and 1 other fieldsHigh correlation
poutcome is highly overall correlated with pdaysHigh correlation
previous is highly overall correlated with pdaysHigh correlation
default is highly imbalanced (87.0%) Imbalance
poutcome is highly imbalanced (53.1%) Imbalance
previous is highly skewed (γ1 = 41.84645447) Skewed
balance has 3514 (7.8%) zeros Zeros
previous has 36954 (81.7%) zeros Zeros

Reproduction

Analysis started2024-10-29 15:20:45.488341
Analysis finished2024-10-29 15:20:52.737476
Duration7.25 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.93621
Minimum18
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2024-10-29T15:20:52.816916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27
Q133
median39
Q348
95-th percentile59
Maximum95
Range77
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.618762
Coefficient of variation (CV)0.25939778
Kurtosis0.31957038
Mean40.93621
Median Absolute Deviation (MAD)7
Skewness0.68481793
Sum1850767
Variance112.75811
MonotonicityNot monotonic
2024-10-29T15:20:52.968793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 2085
 
4.6%
31 1996
 
4.4%
33 1972
 
4.4%
34 1930
 
4.3%
35 1894
 
4.2%
36 1806
 
4.0%
30 1757
 
3.9%
37 1696
 
3.8%
39 1487
 
3.3%
38 1466
 
3.2%
Other values (67) 27122
60.0%
ValueCountFrequency (%)
18 12
 
< 0.1%
19 35
 
0.1%
20 50
 
0.1%
21 79
 
0.2%
22 129
 
0.3%
23 202
 
0.4%
24 302
 
0.7%
25 527
1.2%
26 805
1.8%
27 909
2.0%
ValueCountFrequency (%)
95 2
 
< 0.1%
94 1
 
< 0.1%
93 2
 
< 0.1%
92 2
 
< 0.1%
90 2
 
< 0.1%
89 3
 
< 0.1%
88 2
 
< 0.1%
87 4
< 0.1%
86 9
< 0.1%
85 5
< 0.1%

job
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
blue-collar
9732 
management
9458 
technician
7597 
admin.
5171 
services
4154 
Other values (7)
9099 

Length

Max length13
Median length12
Mean length9.4855456
Min length6

Characters and Unicode

Total characters428851
Distinct characters24
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 rowmanagement
2nd rowtechnician
3rd rowentrepreneur
4th rowblue-collar
5th rowunknown

Common Values

ValueCountFrequency (%)
blue-collar 9732
21.5%
management 9458
20.9%
technician 7597
16.8%
admin. 5171
11.4%
services 4154
9.2%
retired 2264
 
5.0%
self-employed 1579
 
3.5%
entrepreneur 1487
 
3.3%
unemployed 1303
 
2.9%
housemaid 1240
 
2.7%
Other values (2) 1226
 
2.7%

Length

2024-10-29T15:20:53.125551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
blue-collar 9732
21.5%
management 9458
20.9%
technician 7597
16.8%
admin 5171
11.4%
services 4154
9.2%
retired 2264
 
5.0%
self-employed 1579
 
3.5%
entrepreneur 1487
 
3.3%
unemployed 1303
 
2.9%
housemaid 1240
 
2.7%
Other values (2) 1226
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 64550
15.1%
n 45360
10.6%
a 42656
9.9%
l 33657
 
7.8%
c 29080
 
6.8%
m 28209
 
6.6%
i 28023
 
6.5%
r 22875
 
5.3%
t 22682
 
5.3%
u 14988
 
3.5%
Other values (14) 96771
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 428851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 64550
15.1%
n 45360
10.6%
a 42656
9.9%
l 33657
 
7.8%
c 29080
 
6.8%
m 28209
 
6.6%
i 28023
 
6.5%
r 22875
 
5.3%
t 22682
 
5.3%
u 14988
 
3.5%
Other values (14) 96771
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 428851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 64550
15.1%
n 45360
10.6%
a 42656
9.9%
l 33657
 
7.8%
c 29080
 
6.8%
m 28209
 
6.6%
i 28023
 
6.5%
r 22875
 
5.3%
t 22682
 
5.3%
u 14988
 
3.5%
Other values (14) 96771
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 428851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 64550
15.1%
n 45360
10.6%
a 42656
9.9%
l 33657
 
7.8%
c 29080
 
6.8%
m 28209
 
6.6%
i 28023
 
6.5%
r 22875
 
5.3%
t 22682
 
5.3%
u 14988
 
3.5%
Other values (14) 96771
22.6%

marital
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
married
27214 
single
12790 
divorced
5207 

Length

Max length8
Median length7
Mean length6.8322753
Min length6

Characters and Unicode

Total characters308894
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 rowmarried
2nd rowsingle
3rd rowmarried
4th rowmarried
5th rowsingle

Common Values

ValueCountFrequency (%)
married 27214
60.2%
single 12790
28.3%
divorced 5207
 
11.5%

Length

2024-10-29T15:20:53.269544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T15:20:53.383510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
married 27214
60.2%
single 12790
28.3%
divorced 5207
 
11.5%

Most occurring characters

ValueCountFrequency (%)
r 59635
19.3%
e 45211
14.6%
i 45211
14.6%
d 37628
12.2%
m 27214
8.8%
a 27214
8.8%
s 12790
 
4.1%
n 12790
 
4.1%
g 12790
 
4.1%
l 12790
 
4.1%
Other values (3) 15621
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 308894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 59635
19.3%
e 45211
14.6%
i 45211
14.6%
d 37628
12.2%
m 27214
8.8%
a 27214
8.8%
s 12790
 
4.1%
n 12790
 
4.1%
g 12790
 
4.1%
l 12790
 
4.1%
Other values (3) 15621
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 308894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 59635
19.3%
e 45211
14.6%
i 45211
14.6%
d 37628
12.2%
m 27214
8.8%
a 27214
8.8%
s 12790
 
4.1%
n 12790
 
4.1%
g 12790
 
4.1%
l 12790
 
4.1%
Other values (3) 15621
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 308894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 59635
19.3%
e 45211
14.6%
i 45211
14.6%
d 37628
12.2%
m 27214
8.8%
a 27214
8.8%
s 12790
 
4.1%
n 12790
 
4.1%
g 12790
 
4.1%
l 12790
 
4.1%
Other values (3) 15621
 
5.1%

education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
secondary
23202 
tertiary
13301 
primary
6851 
unknown
 
1857

Length

Max length9
Median length9
Mean length8.3205857
Min length7

Characters and Unicode

Total characters376182
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 rowtertiary
2nd rowsecondary
3rd rowsecondary
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
secondary 23202
51.3%
tertiary 13301
29.4%
primary 6851
 
15.2%
unknown 1857
 
4.1%

Length

2024-10-29T15:20:53.507110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T15:20:53.619049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
secondary 23202
51.3%
tertiary 13301
29.4%
primary 6851
 
15.2%
unknown 1857
 
4.1%

Most occurring characters

ValueCountFrequency (%)
r 63506
16.9%
a 43354
11.5%
y 43354
11.5%
e 36503
9.7%
n 28773
7.6%
t 26602
7.1%
o 25059
 
6.7%
s 23202
 
6.2%
d 23202
 
6.2%
c 23202
 
6.2%
Other values (6) 39425
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 376182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 63506
16.9%
a 43354
11.5%
y 43354
11.5%
e 36503
9.7%
n 28773
7.6%
t 26602
7.1%
o 25059
 
6.7%
s 23202
 
6.2%
d 23202
 
6.2%
c 23202
 
6.2%
Other values (6) 39425
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 376182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 63506
16.9%
a 43354
11.5%
y 43354
11.5%
e 36503
9.7%
n 28773
7.6%
t 26602
7.1%
o 25059
 
6.7%
s 23202
 
6.2%
d 23202
 
6.2%
c 23202
 
6.2%
Other values (6) 39425
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 376182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 63506
16.9%
a 43354
11.5%
y 43354
11.5%
e 36503
9.7%
n 28773
7.6%
t 26602
7.1%
o 25059
 
6.7%
s 23202
 
6.2%
d 23202
 
6.2%
c 23202
 
6.2%
Other values (6) 39425
10.5%

default
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
False
44396 
True
 
815
ValueCountFrequency (%)
False 44396
98.2%
True 815
 
1.8%
2024-10-29T15:20:53.712504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

balance
Real number (ℝ)

Zeros 

Distinct7168
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1362.2721
Minimum-8019
Maximum102127
Zeros3514
Zeros (%)7.8%
Negative3766
Negative (%)8.3%
Memory size353.3 KiB
2024-10-29T15:20:53.825147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-8019
5-th percentile-172
Q172
median448
Q31428
95-th percentile5768
Maximum102127
Range110146
Interquartile range (IQR)1356

Descriptive statistics

Standard deviation3044.7658
Coefficient of variation (CV)2.2350644
Kurtosis140.75155
Mean1362.2721
Median Absolute Deviation (MAD)448
Skewness8.3603083
Sum61589682
Variance9270599
MonotonicityNot monotonic
2024-10-29T15:20:53.973139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3514
 
7.8%
1 195
 
0.4%
2 156
 
0.3%
4 139
 
0.3%
3 134
 
0.3%
5 113
 
0.2%
6 88
 
0.2%
8 81
 
0.2%
23 75
 
0.2%
10 69
 
0.2%
Other values (7158) 40647
89.9%
ValueCountFrequency (%)
-8019 1
< 0.1%
-6847 1
< 0.1%
-4057 1
< 0.1%
-3372 1
< 0.1%
-3313 1
< 0.1%
-3058 1
< 0.1%
-2827 1
< 0.1%
-2712 1
< 0.1%
-2604 1
< 0.1%
-2282 1
< 0.1%
ValueCountFrequency (%)
102127 1
< 0.1%
98417 1
< 0.1%
81204 2
< 0.1%
71188 1
< 0.1%
66721 1
< 0.1%
66653 1
< 0.1%
64343 1
< 0.1%
59649 1
< 0.1%
58932 1
< 0.1%
58544 1
< 0.1%

housing
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
True
25130 
False
20081 
ValueCountFrequency (%)
True 25130
55.6%
False 20081
44.4%
2024-10-29T15:20:54.086153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

loan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
False
37967 
True
7244 
ValueCountFrequency (%)
False 37967
84.0%
True 7244
 
16.0%
2024-10-29T15:20:54.168116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

contact
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
cellular
29285 
unknown
13020 
telephone
 
2906

Length

Max length9
Median length8
Mean length7.7762934
Min length7

Characters and Unicode

Total characters351574
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 rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
cellular 29285
64.8%
unknown 13020
28.8%
telephone 2906
 
6.4%

Length

2024-10-29T15:20:54.287845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T15:20:54.398584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 29285
64.8%
unknown 13020
28.8%
telephone 2906
 
6.4%

Most occurring characters

ValueCountFrequency (%)
l 90761
25.8%
u 42305
12.0%
n 41966
11.9%
e 38003
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15926
 
4.5%
k 13020
 
3.7%
w 13020
 
3.7%
Other values (3) 8718
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 351574
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 90761
25.8%
u 42305
12.0%
n 41966
11.9%
e 38003
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15926
 
4.5%
k 13020
 
3.7%
w 13020
 
3.7%
Other values (3) 8718
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 351574
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 90761
25.8%
u 42305
12.0%
n 41966
11.9%
e 38003
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15926
 
4.5%
k 13020
 
3.7%
w 13020
 
3.7%
Other values (3) 8718
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 351574
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 90761
25.8%
u 42305
12.0%
n 41966
11.9%
e 38003
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15926
 
4.5%
k 13020
 
3.7%
w 13020
 
3.7%
Other values (3) 8718
 
2.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.806419
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2024-10-29T15:20:54.504841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median16
Q321
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.3224762
Coefficient of variation (CV)0.52652509
Kurtosis-1.0598974
Mean15.806419
Median Absolute Deviation (MAD)7
Skewness0.093079014
Sum714624
Variance69.263609
MonotonicityNot monotonic
2024-10-29T15:20:54.634423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 2752
 
6.1%
18 2308
 
5.1%
21 2026
 
4.5%
17 1939
 
4.3%
6 1932
 
4.3%
5 1910
 
4.2%
14 1848
 
4.1%
8 1842
 
4.1%
28 1830
 
4.0%
7 1817
 
4.0%
Other values (21) 25007
55.3%
ValueCountFrequency (%)
1 322
 
0.7%
2 1293
2.9%
3 1079
2.4%
4 1445
3.2%
5 1910
4.2%
6 1932
4.3%
7 1817
4.0%
8 1842
4.1%
9 1561
3.5%
10 524
 
1.2%
ValueCountFrequency (%)
31 643
 
1.4%
30 1566
3.5%
29 1745
3.9%
28 1830
4.0%
27 1121
2.5%
26 1035
2.3%
25 840
1.9%
24 447
 
1.0%
23 939
2.1%
22 905
2.0%

month
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
may
13766 
jul
6895 
aug
6247 
jun
5341 
nov
3970 
Other values (7)
8992 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters135633
Distinct characters19
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 rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may 13766
30.4%
jul 6895
15.3%
aug 6247
13.8%
jun 5341
 
11.8%
nov 3970
 
8.8%
apr 2932
 
6.5%
feb 2649
 
5.9%
jan 1403
 
3.1%
oct 738
 
1.6%
sep 579
 
1.3%
Other values (2) 691
 
1.5%

Length

2024-10-29T15:20:54.867122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 13766
30.4%
jul 6895
15.3%
aug 6247
13.8%
jun 5341
 
11.8%
nov 3970
 
8.8%
apr 2932
 
6.5%
feb 2649
 
5.9%
jan 1403
 
3.1%
oct 738
 
1.6%
sep 579
 
1.3%
Other values (2) 691
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a 24825
18.3%
u 18483
13.6%
m 14243
10.5%
y 13766
10.1%
j 13639
10.1%
n 10714
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4708
 
3.5%
v 3970
 
2.9%
Other values (9) 18143
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135633
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 24825
18.3%
u 18483
13.6%
m 14243
10.5%
y 13766
10.1%
j 13639
10.1%
n 10714
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4708
 
3.5%
v 3970
 
2.9%
Other values (9) 18143
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135633
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 24825
18.3%
u 18483
13.6%
m 14243
10.5%
y 13766
10.1%
j 13639
10.1%
n 10714
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4708
 
3.5%
v 3970
 
2.9%
Other values (9) 18143
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135633
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 24825
18.3%
u 18483
13.6%
m 14243
10.5%
y 13766
10.1%
j 13639
10.1%
n 10714
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4708
 
3.5%
v 3970
 
2.9%
Other values (9) 18143
13.4%

duration
Real number (ℝ)

Distinct1573
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.16308
Minimum0
Maximum4918
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2024-10-29T15:20:54.996636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1103
median180
Q3319
95-th percentile751
Maximum4918
Range4918
Interquartile range (IQR)216

Descriptive statistics

Standard deviation257.52781
Coefficient of variation (CV)0.99753928
Kurtosis18.153915
Mean258.16308
Median Absolute Deviation (MAD)93
Skewness3.1443181
Sum11671811
Variance66320.574
MonotonicityNot monotonic
2024-10-29T15:20:55.148521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124 188
 
0.4%
90 184
 
0.4%
89 177
 
0.4%
104 175
 
0.4%
114 175
 
0.4%
122 175
 
0.4%
136 174
 
0.4%
112 174
 
0.4%
139 174
 
0.4%
121 173
 
0.4%
Other values (1563) 43442
96.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 2
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 15
 
< 0.1%
5 35
0.1%
6 45
0.1%
7 73
0.2%
8 85
0.2%
9 77
0.2%
ValueCountFrequency (%)
4918 1
< 0.1%
3881 1
< 0.1%
3785 1
< 0.1%
3422 1
< 0.1%
3366 1
< 0.1%
3322 1
< 0.1%
3284 1
< 0.1%
3253 1
< 0.1%
3183 1
< 0.1%
3102 1
< 0.1%

campaign
Real number (ℝ)

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7638407
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2024-10-29T15:20:55.289145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile8
Maximum63
Range62
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0980209
Coefficient of variation (CV)1.1209115
Kurtosis39.249651
Mean2.7638407
Median Absolute Deviation (MAD)1
Skewness4.8986502
Sum124956
Variance9.5977334
MonotonicityNot monotonic
2024-10-29T15:20:55.437507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 17544
38.8%
2 12505
27.7%
3 5521
 
12.2%
4 3522
 
7.8%
5 1764
 
3.9%
6 1291
 
2.9%
7 735
 
1.6%
8 540
 
1.2%
9 327
 
0.7%
10 266
 
0.6%
Other values (38) 1196
 
2.6%
ValueCountFrequency (%)
1 17544
38.8%
2 12505
27.7%
3 5521
 
12.2%
4 3522
 
7.8%
5 1764
 
3.9%
6 1291
 
2.9%
7 735
 
1.6%
8 540
 
1.2%
9 327
 
0.7%
10 266
 
0.6%
ValueCountFrequency (%)
63 1
 
< 0.1%
58 1
 
< 0.1%
55 1
 
< 0.1%
51 1
 
< 0.1%
50 2
< 0.1%
46 1
 
< 0.1%
44 1
 
< 0.1%
43 3
< 0.1%
41 2
< 0.1%
39 1
 
< 0.1%

pdays
Real number (ℝ)

High correlation 

Distinct559
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.197828
Minimum-1
Maximum871
Zeros0
Zeros (%)0.0%
Negative36954
Negative (%)81.7%
Memory size353.3 KiB
2024-10-29T15:20:55.588520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile317
Maximum871
Range872
Interquartile range (IQR)0

Descriptive statistics

Standard deviation100.12875
Coefficient of variation (CV)2.4908994
Kurtosis6.9351952
Mean40.197828
Median Absolute Deviation (MAD)0
Skewness2.6157155
Sum1817384
Variance10025.766
MonotonicityNot monotonic
2024-10-29T15:20:55.734868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 36954
81.7%
182 167
 
0.4%
92 147
 
0.3%
183 126
 
0.3%
91 126
 
0.3%
181 117
 
0.3%
370 99
 
0.2%
184 85
 
0.2%
364 77
 
0.2%
95 74
 
0.2%
Other values (549) 7239
 
16.0%
ValueCountFrequency (%)
-1 36954
81.7%
1 15
 
< 0.1%
2 37
 
0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 11
 
< 0.1%
6 10
 
< 0.1%
7 7
 
< 0.1%
8 25
 
0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
871 1
< 0.1%
854 1
< 0.1%
850 1
< 0.1%
842 1
< 0.1%
838 1
< 0.1%
831 1
< 0.1%
828 1
< 0.1%
826 1
< 0.1%
808 1
< 0.1%
805 1
< 0.1%

previous
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58032337
Minimum0
Maximum275
Zeros36954
Zeros (%)81.7%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2024-10-29T15:20:55.867310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum275
Range275
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.303441
Coefficient of variation (CV)3.9692371
Kurtosis4506.8607
Mean0.58032337
Median Absolute Deviation (MAD)0
Skewness41.846454
Sum26237
Variance5.3058406
MonotonicityNot monotonic
2024-10-29T15:20:55.997421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 36954
81.7%
1 2772
 
6.1%
2 2106
 
4.7%
3 1142
 
2.5%
4 714
 
1.6%
5 459
 
1.0%
6 277
 
0.6%
7 205
 
0.5%
8 129
 
0.3%
9 92
 
0.2%
Other values (31) 361
 
0.8%
ValueCountFrequency (%)
0 36954
81.7%
1 2772
 
6.1%
2 2106
 
4.7%
3 1142
 
2.5%
4 714
 
1.6%
5 459
 
1.0%
6 277
 
0.6%
7 205
 
0.5%
8 129
 
0.3%
9 92
 
0.2%
ValueCountFrequency (%)
275 1
< 0.1%
58 1
< 0.1%
55 1
< 0.1%
51 1
< 0.1%
41 1
< 0.1%
40 1
< 0.1%
38 2
< 0.1%
37 2
< 0.1%
35 1
< 0.1%
32 1
< 0.1%

poutcome
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
unknown
36959 
failure
4901 
other
 
1840
success
 
1511

Length

Max length7
Median length7
Mean length6.9186039
Min length5

Characters and Unicode

Total characters312797
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 rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 36959
81.7%
failure 4901
 
10.8%
other 1840
 
4.1%
success 1511
 
3.3%

Length

2024-10-29T15:20:56.136508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-29T15:20:56.247972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 36959
81.7%
failure 4901
 
10.8%
other 1840
 
4.1%
success 1511
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n 110877
35.4%
u 43371
 
13.9%
o 38799
 
12.4%
k 36959
 
11.8%
w 36959
 
11.8%
e 8252
 
2.6%
r 6741
 
2.2%
a 4901
 
1.6%
f 4901
 
1.6%
l 4901
 
1.6%
Other values (5) 16136
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 312797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 110877
35.4%
u 43371
 
13.9%
o 38799
 
12.4%
k 36959
 
11.8%
w 36959
 
11.8%
e 8252
 
2.6%
r 6741
 
2.2%
a 4901
 
1.6%
f 4901
 
1.6%
l 4901
 
1.6%
Other values (5) 16136
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 312797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 110877
35.4%
u 43371
 
13.9%
o 38799
 
12.4%
k 36959
 
11.8%
w 36959
 
11.8%
e 8252
 
2.6%
r 6741
 
2.2%
a 4901
 
1.6%
f 4901
 
1.6%
l 4901
 
1.6%
Other values (5) 16136
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 312797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 110877
35.4%
u 43371
 
13.9%
o 38799
 
12.4%
k 36959
 
11.8%
w 36959
 
11.8%
e 8252
 
2.6%
r 6741
 
2.2%
a 4901
 
1.6%
f 4901
 
1.6%
l 4901
 
1.6%
Other values (5) 16136
 
5.2%

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
False
39922 
True
5289 
ValueCountFrequency (%)
False 39922
88.3%
True 5289
 
11.7%
2024-10-29T15:20:56.339147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2024-10-29T15:20:51.457834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:47.214789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:47.938066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:48.710820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.409351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:50.095252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:50.791960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:51.562217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:47.325504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:48.042189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:48.816958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.513875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:50.201161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:50.893773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:51.657919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:47.427218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:48.138343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:48.916147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.611255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:50.300036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:50.988047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:51.756798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:47.532536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:48.239558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.016127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.710980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:50.401964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:51.084679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:51.956846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:47.634474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:48.418439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.114683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.806535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:50.499383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:51.180111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:52.056273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:47.739905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:48.519941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.216851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.907237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:50.600001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:51.276707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:52.146152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:47.836033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:48.612709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.310712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:49.999136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:50.693182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-29T15:20:51.364708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-29T15:20:56.423162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agebalancecampaigncontactdaydefaultdurationeducationhousingjobloanmaritalmonthpdayspoutcomepreviousy
age1.0000.0960.0370.164-0.0090.021-0.0330.1350.2250.2420.0640.3290.095-0.0170.068-0.0120.155
balance0.0961.000-0.0310.0340.0010.0480.0430.0460.0600.0330.0750.0170.0570.0700.0220.0800.058
campaign0.037-0.0311.0000.0320.1400.015-0.1080.0110.0270.0120.0100.0060.058-0.1120.047-0.1080.048
contact0.1640.0340.0321.0000.0910.0230.0250.1230.2130.1500.0150.0450.5120.2000.2070.0080.151
day-0.0090.0010.1400.0911.0000.013-0.0580.0390.1070.0410.0480.0320.284-0.0920.079-0.0880.073
default0.0210.0480.0150.0230.0131.0000.0000.0140.0030.0330.0770.0180.0570.0350.0400.0000.022
duration-0.0330.043-0.1080.025-0.0580.0001.0000.0000.0000.0120.0160.0110.0190.0290.0140.0310.364
education0.1350.0460.0110.1230.0390.0140.0001.0000.1190.4580.0800.1210.1090.0460.0350.0060.072
housing0.2250.0600.0270.2130.1070.0030.0000.1191.0000.2810.0410.0200.5040.1680.1430.0000.139
job0.2420.0330.0120.1500.0410.0330.0120.4580.2811.0000.1050.2050.1090.0430.0620.0000.135
loan0.0640.0750.0100.0150.0480.0770.0160.0800.0410.1051.0000.0520.1820.0300.0550.0080.068
marital0.3290.0170.0060.0450.0320.0180.0110.1210.0200.2050.0521.0000.0710.0260.0280.0000.066
month0.0950.0570.0580.5120.2840.0570.0190.1090.5040.1090.1820.0711.0000.1770.2140.0140.260
pdays-0.0170.070-0.1120.200-0.0920.0350.0290.0460.1680.0430.0300.0260.1771.0000.5710.9860.192
poutcome0.0680.0220.0470.2070.0790.0400.0140.0350.1430.0620.0550.0280.2140.5711.0000.0320.312
previous-0.0120.080-0.1080.008-0.0880.0000.0310.0060.0000.0000.0080.0000.0140.9860.0321.0000.011
y0.1550.0580.0480.1510.0730.0220.3640.0720.1390.1350.0680.0660.2600.1920.3120.0111.000

Missing values

2024-10-29T15:20:52.307101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-29T15:20:52.587241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomey
058managementmarriedtertiaryno2143yesnounknown5may2611-10unknownno
144techniciansinglesecondaryno29yesnounknown5may1511-10unknownno
233entrepreneurmarriedsecondaryno2yesyesunknown5may761-10unknownno
347blue-collarmarriedunknownno1506yesnounknown5may921-10unknownno
433unknownsingleunknownno1nonounknown5may1981-10unknownno
535managementmarriedtertiaryno231yesnounknown5may1391-10unknownno
628managementsingletertiaryno447yesyesunknown5may2171-10unknownno
742entrepreneurdivorcedtertiaryyes2yesnounknown5may3801-10unknownno
858retiredmarriedprimaryno121yesnounknown5may501-10unknownno
943techniciansinglesecondaryno593yesnounknown5may551-10unknownno
agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomey
4520153managementmarriedtertiaryno583nonocellular17nov22611844successyes
4520234admin.singlesecondaryno557nonocellular17nov2241-10unknownyes
4520323studentsingletertiaryno113nonocellular17nov2661-10unknownyes
4520473retiredmarriedsecondaryno2850nonocellular17nov3001408failureyes
4520525techniciansinglesecondaryno505noyescellular17nov3862-10unknownyes
4520651technicianmarriedtertiaryno825nonocellular17nov9773-10unknownyes
4520771retireddivorcedprimaryno1729nonocellular17nov4562-10unknownyes
4520872retiredmarriedsecondaryno5715nonocellular17nov112751843successyes
4520957blue-collarmarriedsecondaryno668nonotelephone17nov5084-10unknownno
4521037entrepreneurmarriedsecondaryno2971nonocellular17nov361218811otherno