Overview

Dataset statistics

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

Variable types

Numeric7
Text10

Alerts

previous is highly skewed (γ1 = 41.84645447)Skewed
balance has 3514 (7.8%) zerosZeros
previous has 36954 (81.7%) zerosZeros

Reproduction

Analysis started2023-06-11 22:16:59.255015
Analysis finished2023-06-11 22:17:00.057047
Duration0.8 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.93621021
Minimum18
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2023-06-11T15:17:00.232615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation10.61876204
Coefficient of variation (CV)0.2593977797
Kurtosis0.3195703759
Mean40.93621021
Median Absolute Deviation (MAD)7
Skewness0.6848179257
Sum1850767
Variance112.7581073
MonotonicityNot monotonic
2023-06-11T15:17:00.360530image/svg+xmlMatplotlib v3.7.1, 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%
ValueCountFrequency (%)
95 2
< 0.1%
94 1
< 0.1%
93 2
< 0.1%
92 2
< 0.1%
90 2
< 0.1%

job
Text

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2023-06-11T15:17:00.487773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.485545553
Min length6

Characters and Unicode

Total characters428851
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmanagement
2nd rowtechnician
3rd rowentrepreneur
4th rowblue-collar
5th rowunknown
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%
2023-06-11T15:17:00.706968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

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 (%)
Lowercase Letter 412369
96.2%
Dash Punctuation 11311
 
2.6%
Other Punctuation 5171
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 64550
15.7%
n 45360
11.0%
a 42656
10.3%
l 33657
8.2%
c 29080
 
7.1%
m 28209
 
6.8%
i 28023
 
6.8%
r 22875
 
5.5%
t 22682
 
5.5%
u 14988
 
3.6%
Other values (12) 80289
19.5%
Dash Punctuation
ValueCountFrequency (%)
- 11311
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 412369
96.2%
Common 16482
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 64550
15.7%
n 45360
11.0%
a 42656
10.3%
l 33657
8.2%
c 29080
 
7.1%
m 28209
 
6.8%
i 28023
 
6.8%
r 22875
 
5.5%
t 22682
 
5.5%
u 14988
 
3.6%
Other values (12) 80289
19.5%
Common
ValueCountFrequency (%)
- 11311
68.6%
. 5171
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 428851
100.0%

Most frequent character per block

ASCII
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
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2023-06-11T15:17:00.798789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.832275331
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
ValueCountFrequency (%)
married 27214
60.2%
single 12790
28.3%
divorced 5207
 
11.5%
2023-06-11T15:17:00.992330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 59635
19.3%
i 45211
14.6%
e 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 (%)
Lowercase Letter 308894
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 59635
19.3%
i 45211
14.6%
e 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 (%)
Latin 308894
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 59635
19.3%
i 45211
14.6%
e 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 (%)
ASCII 308894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 59635
19.3%
i 45211
14.6%
e 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
Text

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2023-06-11T15:17:01.085421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.320585698
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
ValueCountFrequency (%)
secondary 23202
51.3%
tertiary 13301
29.4%
primary 6851
 
15.2%
unknown 1857
 
4.1%
2023-06-11T15:17:01.281742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

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%
c 23202
 
6.2%
d 23202
 
6.2%
Other values (6) 39425
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 376182
100.0%

Most frequent character per category

Lowercase Letter
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%
c 23202
 
6.2%
d 23202
 
6.2%
Other values (6) 39425
10.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 376182
100.0%

Most frequent character per script

Latin
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%
c 23202
 
6.2%
d 23202
 
6.2%
Other values (6) 39425
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376182
100.0%

Most frequent character per block

ASCII
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%
c 23202
 
6.2%
d 23202
 
6.2%
Other values (6) 39425
10.5%

default
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2023-06-11T15:17:01.352445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.018026586
Min length2

Characters and Unicode

Total characters91237
Distinct characters5
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 rowno
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 44396
98.2%
yes 815
 
1.8%
2023-06-11T15:17:01.510285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 44396
48.7%
o 44396
48.7%
y 815
 
0.9%
e 815
 
0.9%
s 815
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91237
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 44396
48.7%
o 44396
48.7%
y 815
 
0.9%
e 815
 
0.9%
s 815
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 91237
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 44396
48.7%
o 44396
48.7%
y 815
 
0.9%
e 815
 
0.9%
s 815
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 44396
48.7%
o 44396
48.7%
y 815
 
0.9%
e 815
 
0.9%
s 815
 
0.9%

balance
Real number (ℝ)

Distinct7168
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1362.272058
Minimum-8019
Maximum102127
Zeros3514
Zeros (%)7.8%
Negative3766
Negative (%)8.3%
Memory size353.3 KiB
2023-06-11T15:17:01.621817image/svg+xmlMatplotlib v3.7.1, 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 deviation3044.765829
Coefficient of variation (CV)2.235064437
Kurtosis140.7515466
Mean1362.272058
Median Absolute Deviation (MAD)448
Skewness8.360308326
Sum61589682
Variance9270598.954
MonotonicityNot monotonic
2023-06-11T15:17:01.735355image/svg+xmlMatplotlib v3.7.1, 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%
7 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%
ValueCountFrequency (%)
102127 1
< 0.1%
98417 1
< 0.1%
81204 2
< 0.1%
71188 1
< 0.1%
66721 1
< 0.1%

housing
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2023-06-11T15:17:01.818550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.555838181
Min length2

Characters and Unicode

Total characters115552
Distinct characters5
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 rowyes
2nd rowyes
3rd rowyes
4th rowyes
5th rowno
ValueCountFrequency (%)
yes 25130
55.6%
no 20081
44.4%
2023-06-11T15:17:01.985282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
y 25130
21.7%
e 25130
21.7%
s 25130
21.7%
n 20081
17.4%
o 20081
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 115552
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 25130
21.7%
e 25130
21.7%
s 25130
21.7%
n 20081
17.4%
o 20081
17.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 115552
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
y 25130
21.7%
e 25130
21.7%
s 25130
21.7%
n 20081
17.4%
o 20081
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y 25130
21.7%
e 25130
21.7%
s 25130
21.7%
n 20081
17.4%
o 20081
17.4%

loan
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2023-06-11T15:17:02.059771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.160226494
Min length2

Characters and Unicode

Total characters97666
Distinct characters5
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 rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno
ValueCountFrequency (%)
no 37967
84.0%
yes 7244
 
16.0%
2023-06-11T15:17:02.228232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 37967
38.9%
o 37967
38.9%
y 7244
 
7.4%
e 7244
 
7.4%
s 7244
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 97666
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 37967
38.9%
o 37967
38.9%
y 7244
 
7.4%
e 7244
 
7.4%
s 7244
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 97666
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 37967
38.9%
o 37967
38.9%
y 7244
 
7.4%
e 7244
 
7.4%
s 7244
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 37967
38.9%
o 37967
38.9%
y 7244
 
7.4%
e 7244
 
7.4%
s 7244
 
7.4%

contact
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2023-06-11T15:17:02.316760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.77629338
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
ValueCountFrequency (%)
cellular 29285
64.8%
unknown 13020
28.8%
telephone 2906
 
6.4%
2023-06-11T15:17:02.513741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

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 (%)
Lowercase Letter 351574
100.0%

Most frequent character per category

Lowercase Letter
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 (%)
Latin 351574
100.0%

Most frequent character per script

Latin
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 (%)
ASCII 351574
100.0%

Most frequent character per block

ASCII
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%
Mean15.80641879
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2023-06-11T15:17:02.613204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation8.322476153
Coefficient of variation (CV)0.5265250948
Kurtosis-1.059897373
Mean15.80641879
Median Absolute Deviation (MAD)7
Skewness0.09307901402
Sum714624
Variance69.26360932
MonotonicityNot monotonic
2023-06-11T15:17:02.708945image/svg+xmlMatplotlib v3.7.1, 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%
ValueCountFrequency (%)
31 643
 
1.4%
30 1566
3.5%
29 1745
3.9%
28 1830
4.0%
27 1121
2.5%

month
Text

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2023-06-11T15:17:02.800818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

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
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%
2023-06-11T15:17:02.975285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

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 (%)
Lowercase Letter 135633
100.0%

Most frequent character per category

Lowercase Letter
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 (%)
Latin 135633
100.0%

Most frequent character per script

Latin
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 (%)
ASCII 135633
100.0%

Most frequent character per block

ASCII
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%
Mean258.1630798
Minimum0
Maximum4918
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2023-06-11T15:17:03.085453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation257.5278123
Coefficient of variation (CV)0.9975392782
Kurtosis18.15391527
Mean258.1630798
Median Absolute Deviation (MAD)93
Skewness3.144318099
Sum11671811
Variance66320.57409
MonotonicityNot monotonic
2023-06-11T15:17:03.205003image/svg+xmlMatplotlib v3.7.1, 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%
122 175
 
0.4%
114 175
 
0.4%
136 174
 
0.4%
139 174
 
0.4%
112 174
 
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%
ValueCountFrequency (%)
4918 1
< 0.1%
3881 1
< 0.1%
3785 1
< 0.1%
3422 1
< 0.1%
3366 1
< 0.1%

campaign
Real number (ℝ)

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.763840658
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2023-06-11T15:17:03.320331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation3.098020883
Coefficient of variation (CV)1.120911538
Kurtosis39.2496508
Mean2.763840658
Median Absolute Deviation (MAD)1
Skewness4.898650166
Sum124956
Variance9.597733393
MonotonicityNot monotonic
2023-06-11T15:17:03.431124image/svg+xmlMatplotlib v3.7.1, 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%
ValueCountFrequency (%)
63 1
< 0.1%
58 1
< 0.1%
55 1
< 0.1%
51 1
< 0.1%
50 2
< 0.1%

pdays
Real number (ℝ)

Distinct559
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.19782796
Minimum-1
Maximum871
Zeros0
Zeros (%)0.0%
Negative36954
Negative (%)81.7%
Memory size353.3 KiB
2023-06-11T15:17:03.552691image/svg+xmlMatplotlib v3.7.1, 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 deviation100.128746
Coefficient of variation (CV)2.490899411
Kurtosis6.93519521
Mean40.19782796
Median Absolute Deviation (MAD)0
Skewness2.615715474
Sum1817384
Variance10025.76577
MonotonicityNot monotonic
2023-06-11T15:17:03.665262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 36954
81.7%
182 167
 
0.4%
92 147
 
0.3%
91 126
 
0.3%
183 126
 
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%
ValueCountFrequency (%)
871 1
< 0.1%
854 1
< 0.1%
850 1
< 0.1%
842 1
< 0.1%
838 1
< 0.1%

previous
Real number (ℝ)

SKEWED  ZEROS 

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5803233726
Minimum0
Maximum275
Zeros36954
Zeros (%)81.7%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2023-06-11T15:17:03.772804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation2.303441045
Coefficient of variation (CV)3.969237073
Kurtosis4506.86066
Mean0.5803233726
Median Absolute Deviation (MAD)0
Skewness41.84645447
Sum26237
Variance5.305840647
MonotonicityNot monotonic
2023-06-11T15:17:03.871181image/svg+xmlMatplotlib v3.7.1, 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%
ValueCountFrequency (%)
275 1
< 0.1%
58 1
< 0.1%
55 1
< 0.1%
51 1
< 0.1%
41 1
< 0.1%

poutcome
Text

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2023-06-11T15:17:03.963531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.91860388
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
ValueCountFrequency (%)
unknown 36959
81.7%
failure 4901
 
10.8%
other 1840
 
4.1%
success 1511
 
3.3%
2023-06-11T15:17:04.153978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

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%
f 4901
 
1.6%
a 4901
 
1.6%
i 4901
 
1.6%
Other values (5) 16136
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 312797
100.0%

Most frequent character per category

Lowercase Letter
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%
f 4901
 
1.6%
a 4901
 
1.6%
i 4901
 
1.6%
Other values (5) 16136
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 312797
100.0%

Most frequent character per script

Latin
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%
f 4901
 
1.6%
a 4901
 
1.6%
i 4901
 
1.6%
Other values (5) 16136
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 312797
100.0%

Most frequent character per block

ASCII
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%
f 4901
 
1.6%
a 4901
 
1.6%
i 4901
 
1.6%
Other values (5) 16136
 
5.2%

y
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2023-06-11T15:17:04.316375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.116984805
Min length2

Characters and Unicode

Total characters95711
Distinct characters5
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 rowno
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 39922
88.3%
yes 5289
 
11.7%
2023-06-11T15:17:04.474466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 39922
41.7%
o 39922
41.7%
y 5289
 
5.5%
e 5289
 
5.5%
s 5289
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 95711
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 39922
41.7%
o 39922
41.7%
y 5289
 
5.5%
e 5289
 
5.5%
s 5289
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 95711
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 39922
41.7%
o 39922
41.7%
y 5289
 
5.5%
e 5289
 
5.5%
s 5289
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 39922
41.7%
o 39922
41.7%
y 5289
 
5.5%
e 5289
 
5.5%
s 5289
 
5.5%