Overview

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

pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
month is highly correlated with contact and 1 other fieldsHigh correlation
contact is highly correlated with monthHigh correlation
housing is highly correlated with monthHigh correlation
age is highly correlated with jobHigh correlation
job is highly correlated with age and 1 other fieldsHigh correlation
education is highly correlated with jobHigh correlation
housing is highly correlated with monthHigh correlation
contact is highly correlated with monthHigh correlation
day is highly correlated with monthHigh correlation
month is highly correlated with housing and 2 other fieldsHigh correlation
pdays is highly correlated with poutcomeHigh correlation
poutcome is highly correlated with pdaysHigh correlation
previous is highly skewed (γ1 = 41.84645447) Skewed
balance has 3514 (7.8%) zeros Zeros
previous has 36954 (81.7%) zeros Zeros

Reproduction

Analysis started2022-09-06 19:05:17.680115
Analysis finished2022-09-06 19:05:27.880709
Duration10.2 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

HIGH CORRELATION

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
2022-09-06T19:05:27.943062image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:05:28.067251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
322085
 
4.6%
311996
 
4.4%
331972
 
4.4%
341930
 
4.3%
351894
 
4.2%
361806
 
4.0%
301757
 
3.9%
371696
 
3.8%
391487
 
3.3%
381466
 
3.2%
Other values (67)27122
60.0%
ValueCountFrequency (%)
1812
 
< 0.1%
1935
 
0.1%
2050
 
0.1%
2179
 
0.2%
22129
 
0.3%
23202
 
0.4%
24302
 
0.7%
25527
1.2%
26805
1.8%
27909
2.0%
ValueCountFrequency (%)
952
 
< 0.1%
941
 
< 0.1%
932
 
< 0.1%
922
 
< 0.1%
902
 
< 0.1%
893
 
< 0.1%
882
 
< 0.1%
874
< 0.1%
869
< 0.1%
855
< 0.1%

job
Categorical

HIGH CORRELATION

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 length9.485545553
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

Common Values

ValueCountFrequency (%)
blue-collar9732
21.5%
management9458
20.9%
technician7597
16.8%
admin.5171
11.4%
services4154
9.2%
retired2264
 
5.0%
self-employed1579
 
3.5%
entrepreneur1487
 
3.3%
unemployed1303
 
2.9%
housemaid1240
 
2.7%
Other values (2)1226
 
2.7%

Length

2022-09-06T19:05:28.195326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
blue-collar9732
21.5%
management9458
20.9%
technician7597
16.8%
admin5171
11.4%
services4154
9.2%
retired2264
 
5.0%
self-employed1579
 
3.5%
entrepreneur1487
 
3.3%
unemployed1303
 
2.9%
housemaid1240
 
2.7%
Other values (2)1226
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e64550
15.1%
n45360
10.6%
a42656
9.9%
l33657
 
7.8%
c29080
 
6.8%
m28209
 
6.6%
i28023
 
6.5%
r22875
 
5.3%
t22682
 
5.3%
u14988
 
3.5%
Other values (14)96771
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter412369
96.2%
Dash Punctuation11311
 
2.6%
Other Punctuation5171
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e64550
15.7%
n45360
11.0%
a42656
10.3%
l33657
8.2%
c29080
 
7.1%
m28209
 
6.8%
i28023
 
6.8%
r22875
 
5.5%
t22682
 
5.5%
u14988
 
3.6%
Other values (12)80289
19.5%
Dash Punctuation
ValueCountFrequency (%)
-11311
100.0%
Other Punctuation
ValueCountFrequency (%)
.5171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin412369
96.2%
Common16482
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e64550
15.7%
n45360
11.0%
a42656
10.3%
l33657
8.2%
c29080
 
7.1%
m28209
 
6.8%
i28023
 
6.8%
r22875
 
5.5%
t22682
 
5.5%
u14988
 
3.6%
Other values (12)80289
19.5%
Common
ValueCountFrequency (%)
-11311
68.6%
.5171
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII428851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e64550
15.1%
n45360
10.6%
a42656
9.9%
l33657
 
7.8%
c29080
 
6.8%
m28209
 
6.6%
i28023
 
6.5%
r22875
 
5.3%
t22682
 
5.3%
u14988
 
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 length6.832275331
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

Common Values

ValueCountFrequency (%)
married27214
60.2%
single12790
28.3%
divorced5207
 
11.5%

Length

2022-09-06T19:05:28.303818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:05:28.410123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
married27214
60.2%
single12790
28.3%
divorced5207
 
11.5%

Most occurring characters

ValueCountFrequency (%)
r59635
19.3%
i45211
14.6%
e45211
14.6%
d37628
12.2%
m27214
8.8%
a27214
8.8%
s12790
 
4.1%
n12790
 
4.1%
g12790
 
4.1%
l12790
 
4.1%
Other values (3)15621
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter308894
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r59635
19.3%
i45211
14.6%
e45211
14.6%
d37628
12.2%
m27214
8.8%
a27214
8.8%
s12790
 
4.1%
n12790
 
4.1%
g12790
 
4.1%
l12790
 
4.1%
Other values (3)15621
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin308894
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r59635
19.3%
i45211
14.6%
e45211
14.6%
d37628
12.2%
m27214
8.8%
a27214
8.8%
s12790
 
4.1%
n12790
 
4.1%
g12790
 
4.1%
l12790
 
4.1%
Other values (3)15621
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII308894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r59635
19.3%
i45211
14.6%
e45211
14.6%
d37628
12.2%
m27214
8.8%
a27214
8.8%
s12790
 
4.1%
n12790
 
4.1%
g12790
 
4.1%
l12790
 
4.1%
Other values (3)15621
 
5.1%

education
Categorical

HIGH CORRELATION

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 length8.320585698
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

Common Values

ValueCountFrequency (%)
secondary23202
51.3%
tertiary13301
29.4%
primary6851
 
15.2%
unknown1857
 
4.1%

Length

2022-09-06T19:05:28.502623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:05:28.607850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
secondary23202
51.3%
tertiary13301
29.4%
primary6851
 
15.2%
unknown1857
 
4.1%

Most occurring characters

ValueCountFrequency (%)
r63506
16.9%
a43354
11.5%
y43354
11.5%
e36503
9.7%
n28773
7.6%
t26602
7.1%
o25059
 
6.7%
s23202
 
6.2%
c23202
 
6.2%
d23202
 
6.2%
Other values (6)39425
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter376182
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r63506
16.9%
a43354
11.5%
y43354
11.5%
e36503
9.7%
n28773
7.6%
t26602
7.1%
o25059
 
6.7%
s23202
 
6.2%
c23202
 
6.2%
d23202
 
6.2%
Other values (6)39425
10.5%

Most occurring scripts

ValueCountFrequency (%)
Latin376182
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r63506
16.9%
a43354
11.5%
y43354
11.5%
e36503
9.7%
n28773
7.6%
t26602
7.1%
o25059
 
6.7%
s23202
 
6.2%
c23202
 
6.2%
d23202
 
6.2%
Other values (6)39425
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII376182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r63506
16.9%
a43354
11.5%
y43354
11.5%
e36503
9.7%
n28773
7.6%
t26602
7.1%
o25059
 
6.7%
s23202
 
6.2%
c23202
 
6.2%
d23202
 
6.2%
Other values (6)39425
10.5%

default
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
False
44396 
True
 
815
ValueCountFrequency (%)
False44396
98.2%
True815
 
1.8%
2022-09-06T19:05:28.698174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

balance
Real number (ℝ)

ZEROS

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
2022-09-06T19:05:28.902533image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:05:29.022134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03514
 
7.8%
1195
 
0.4%
2156
 
0.3%
4139
 
0.3%
3134
 
0.3%
5113
 
0.2%
688
 
0.2%
881
 
0.2%
2375
 
0.2%
769
 
0.2%
Other values (7158)40647
89.9%
ValueCountFrequency (%)
-80191
< 0.1%
-68471
< 0.1%
-40571
< 0.1%
-33721
< 0.1%
-33131
< 0.1%
-30581
< 0.1%
-28271
< 0.1%
-27121
< 0.1%
-26041
< 0.1%
-22821
< 0.1%
ValueCountFrequency (%)
1021271
< 0.1%
984171
< 0.1%
812042
< 0.1%
711881
< 0.1%
667211
< 0.1%
666531
< 0.1%
643431
< 0.1%
596491
< 0.1%
589321
< 0.1%
585441
< 0.1%

housing
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
True
25130 
False
20081 
ValueCountFrequency (%)
True25130
55.6%
False20081
44.4%
2022-09-06T19:05:29.132151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

loan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
False
37967 
True
7244 
ValueCountFrequency (%)
False37967
84.0%
True7244
 
16.0%
2022-09-06T19:05:29.211038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

contact
Categorical

HIGH CORRELATION
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 length7.77629338
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

Common Values

ValueCountFrequency (%)
cellular29285
64.8%
unknown13020
28.8%
telephone2906
 
6.4%

Length

2022-09-06T19:05:29.296958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:05:29.404456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
cellular29285
64.8%
unknown13020
28.8%
telephone2906
 
6.4%

Most occurring characters

ValueCountFrequency (%)
l90761
25.8%
u42305
12.0%
n41966
11.9%
e38003
10.8%
c29285
 
8.3%
a29285
 
8.3%
r29285
 
8.3%
o15926
 
4.5%
k13020
 
3.7%
w13020
 
3.7%
Other values (3)8718
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter351574
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l90761
25.8%
u42305
12.0%
n41966
11.9%
e38003
10.8%
c29285
 
8.3%
a29285
 
8.3%
r29285
 
8.3%
o15926
 
4.5%
k13020
 
3.7%
w13020
 
3.7%
Other values (3)8718
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin351574
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l90761
25.8%
u42305
12.0%
n41966
11.9%
e38003
10.8%
c29285
 
8.3%
a29285
 
8.3%
r29285
 
8.3%
o15926
 
4.5%
k13020
 
3.7%
w13020
 
3.7%
Other values (3)8718
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII351574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l90761
25.8%
u42305
12.0%
n41966
11.9%
e38003
10.8%
c29285
 
8.3%
a29285
 
8.3%
r29285
 
8.3%
o15926
 
4.5%
k13020
 
3.7%
w13020
 
3.7%
Other values (3)8718
 
2.5%

day
Real number (ℝ≥0)

HIGH CORRELATION

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
2022-09-06T19:05:29.492565image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:05:29.593086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
202752
 
6.1%
182308
 
5.1%
212026
 
4.5%
171939
 
4.3%
61932
 
4.3%
51910
 
4.2%
141848
 
4.1%
81842
 
4.1%
281830
 
4.0%
71817
 
4.0%
Other values (21)25007
55.3%
ValueCountFrequency (%)
1322
 
0.7%
21293
2.9%
31079
2.4%
41445
3.2%
51910
4.2%
61932
4.3%
71817
4.0%
81842
4.1%
91561
3.5%
10524
 
1.2%
ValueCountFrequency (%)
31643
 
1.4%
301566
3.5%
291745
3.9%
281830
4.0%
271121
2.5%
261035
2.3%
25840
1.9%
24447
 
1.0%
23939
2.1%
22905
2.0%

month
Categorical

HIGH CORRELATION
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 (%)
may13766
30.4%
jul6895
15.3%
aug6247
13.8%
jun5341
 
11.8%
nov3970
 
8.8%
apr2932
 
6.5%
feb2649
 
5.9%
jan1403
 
3.1%
oct738
 
1.6%
sep579
 
1.3%
Other values (2)691
 
1.5%

Length

2022-09-06T19:05:29.695253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may13766
30.4%
jul6895
15.3%
aug6247
13.8%
jun5341
 
11.8%
nov3970
 
8.8%
apr2932
 
6.5%
feb2649
 
5.9%
jan1403
 
3.1%
oct738
 
1.6%
sep579
 
1.3%
Other values (2)691
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a24825
18.3%
u18483
13.6%
m14243
10.5%
y13766
10.1%
j13639
10.1%
n10714
7.9%
l6895
 
5.1%
g6247
 
4.6%
o4708
 
3.5%
v3970
 
2.9%
Other values (9)18143
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter135633
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a24825
18.3%
u18483
13.6%
m14243
10.5%
y13766
10.1%
j13639
10.1%
n10714
7.9%
l6895
 
5.1%
g6247
 
4.6%
o4708
 
3.5%
v3970
 
2.9%
Other values (9)18143
13.4%

Most occurring scripts

ValueCountFrequency (%)
Latin135633
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a24825
18.3%
u18483
13.6%
m14243
10.5%
y13766
10.1%
j13639
10.1%
n10714
7.9%
l6895
 
5.1%
g6247
 
4.6%
o4708
 
3.5%
v3970
 
2.9%
Other values (9)18143
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII135633
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a24825
18.3%
u18483
13.6%
m14243
10.5%
y13766
10.1%
j13639
10.1%
n10714
7.9%
l6895
 
5.1%
g6247
 
4.6%
o4708
 
3.5%
v3970
 
2.9%
Other values (9)18143
13.4%

duration
Real number (ℝ≥0)

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
2022-09-06T19:05:29.797596image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:05:29.927882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124188
 
0.4%
90184
 
0.4%
89177
 
0.4%
104175
 
0.4%
122175
 
0.4%
114175
 
0.4%
136174
 
0.4%
139174
 
0.4%
112174
 
0.4%
121173
 
0.4%
Other values (1563)43442
96.1%
ValueCountFrequency (%)
03
 
< 0.1%
12
 
< 0.1%
23
 
< 0.1%
34
 
< 0.1%
415
 
< 0.1%
535
0.1%
645
0.1%
773
0.2%
885
0.2%
977
0.2%
ValueCountFrequency (%)
49181
< 0.1%
38811
< 0.1%
37851
< 0.1%
34221
< 0.1%
33661
< 0.1%
33221
< 0.1%
32841
< 0.1%
32531
< 0.1%
31831
< 0.1%
31021
< 0.1%

campaign
Real number (ℝ≥0)

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
2022-09-06T19:05:30.053918image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:05:30.172227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
117544
38.8%
212505
27.7%
35521
 
12.2%
43522
 
7.8%
51764
 
3.9%
61291
 
2.9%
7735
 
1.6%
8540
 
1.2%
9327
 
0.7%
10266
 
0.6%
Other values (38)1196
 
2.6%
ValueCountFrequency (%)
117544
38.8%
212505
27.7%
35521
 
12.2%
43522
 
7.8%
51764
 
3.9%
61291
 
2.9%
7735
 
1.6%
8540
 
1.2%
9327
 
0.7%
10266
 
0.6%
ValueCountFrequency (%)
631
 
< 0.1%
581
 
< 0.1%
551
 
< 0.1%
511
 
< 0.1%
502
< 0.1%
461
 
< 0.1%
441
 
< 0.1%
433
< 0.1%
412
< 0.1%
391
 
< 0.1%

pdays
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

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
2022-09-06T19:05:30.300631image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:05:30.419768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-136954
81.7%
182167
 
0.4%
92147
 
0.3%
91126
 
0.3%
183126
 
0.3%
181117
 
0.3%
37099
 
0.2%
18485
 
0.2%
36477
 
0.2%
9574
 
0.2%
Other values (549)7239
 
16.0%
ValueCountFrequency (%)
-136954
81.7%
115
 
< 0.1%
237
 
0.1%
31
 
< 0.1%
42
 
< 0.1%
511
 
< 0.1%
610
 
< 0.1%
77
 
< 0.1%
825
 
0.1%
912
 
< 0.1%
ValueCountFrequency (%)
8711
< 0.1%
8541
< 0.1%
8501
< 0.1%
8421
< 0.1%
8381
< 0.1%
8311
< 0.1%
8281
< 0.1%
8261
< 0.1%
8081
< 0.1%
8051
< 0.1%

previous
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
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
2022-09-06T19:05:30.535943image/svg+xmlMatplotlib v3.5.3, 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
2022-09-06T19:05:30.640814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
036954
81.7%
12772
 
6.1%
22106
 
4.7%
31142
 
2.5%
4714
 
1.6%
5459
 
1.0%
6277
 
0.6%
7205
 
0.5%
8129
 
0.3%
992
 
0.2%
Other values (31)361
 
0.8%
ValueCountFrequency (%)
036954
81.7%
12772
 
6.1%
22106
 
4.7%
31142
 
2.5%
4714
 
1.6%
5459
 
1.0%
6277
 
0.6%
7205
 
0.5%
8129
 
0.3%
992
 
0.2%
ValueCountFrequency (%)
2751
< 0.1%
581
< 0.1%
551
< 0.1%
511
< 0.1%
411
< 0.1%
401
< 0.1%
382
< 0.1%
372
< 0.1%
351
< 0.1%
321
< 0.1%

poutcome
Categorical

HIGH CORRELATION

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 length6.91860388
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

Common Values

ValueCountFrequency (%)
unknown36959
81.7%
failure4901
 
10.8%
other1840
 
4.1%
success1511
 
3.3%

Length

2022-09-06T19:05:30.863574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-06T19:05:30.966654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
unknown36959
81.7%
failure4901
 
10.8%
other1840
 
4.1%
success1511
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n110877
35.4%
u43371
 
13.9%
o38799
 
12.4%
k36959
 
11.8%
w36959
 
11.8%
e8252
 
2.6%
r6741
 
2.2%
f4901
 
1.6%
a4901
 
1.6%
i4901
 
1.6%
Other values (5)16136
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter312797
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n110877
35.4%
u43371
 
13.9%
o38799
 
12.4%
k36959
 
11.8%
w36959
 
11.8%
e8252
 
2.6%
r6741
 
2.2%
f4901
 
1.6%
a4901
 
1.6%
i4901
 
1.6%
Other values (5)16136
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Latin312797
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n110877
35.4%
u43371
 
13.9%
o38799
 
12.4%
k36959
 
11.8%
w36959
 
11.8%
e8252
 
2.6%
r6741
 
2.2%
f4901
 
1.6%
a4901
 
1.6%
i4901
 
1.6%
Other values (5)16136
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII312797
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n110877
35.4%
u43371
 
13.9%
o38799
 
12.4%
k36959
 
11.8%
w36959
 
11.8%
e8252
 
2.6%
r6741
 
2.2%
f4901
 
1.6%
a4901
 
1.6%
i4901
 
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 (%)
False39922
88.3%
True5289
 
11.7%
2022-09-06T19:05:31.054387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Interactions

2022-09-06T19:05:26.439808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:05:21.468634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:05:22.290286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:05:23.208494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-06T19:05:23.986090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/</