Sunday, July 30, 2023

INTERACTIVE DATA PROFILING : DATA ANALYSIS BEST PRACTICES : POST 3

How To . . .
Interpret Data profiling report

Pandas Profiling Report

Overview

Dataset statistics

Number of variables10
Number of observations97498
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 MiB
Average record size in memory80.0 B

Variable types

Categorical6
DateTime1
Numeric3

Alerts

ID Ticket has a high cardinality: 97498 distinct valuesHigh cardinality
Severity is highly imbalanced (74.9%)Imbalance
ID Ticket is uniformly distributedUniform
ID Ticket has unique valuesUnique
Resolution Time (Days) has 25071 (25.7%) zerosZeros

Reproduction

Analysis started2023-07-28 17:31:25.751127
Analysis finished2023-07-28 17:31:41.087460
Duration15.34 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

ID Ticket
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct97498
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size761.8 KiB
GDDENR-5042564453
 
1
TDLTSR-6543590870
 
1
TDLTSR-6743525276
 
1
TDLTSR-6643795007
 
1
TDLTSR-6643672052
 
1
Other values (97493)
97493 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters1657466
Distinct characters23
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

Unique97498 ?
Unique (%)100.0%

Sample

1st rowGDDENR-5042564453
2nd rowGDDENR-8042508060
3rd rowGDDESR-1342539995
4th rowGDDTSR-5942488006
5th rowGDLEER-0042524120

Common Values

ValueCountFrequency (%)
GDDENR-5042564453 1
 
< 0.1%
TDLTSR-6543590870 1
 
< 0.1%
TDLTSR-6743525276 1
 
< 0.1%
TDLTSR-6643795007 1
 
< 0.1%
TDLTSR-6643672052 1
 
< 0.1%
TDLTSR-6643670597 1
 
< 0.1%
TDLTSR-6543823716 1
 
< 0.1%
TDLTSR-6543775787 1
 
< 0.1%
TDLTSR-6543742069 1
 
< 0.1%
TDLTSR-6543637734 1
 
< 0.1%
Other values (97488) 97488
> 99.9%

Length

2023-07-28T23:01:41.527244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gddenr-5042564453 1
 
< 0.1%
gdleer-2342666259 1
 
< 0.1%
gdleer-0142608095 1
 
< 0.1%
gdleer-0242564650 1
 
< 0.1%
gdleer-0542574815 1
 
< 0.1%
gdleer-0842457219 1
 
< 0.1%
gdleer-1242542213 1
 
< 0.1%
gdleer-1342611596 1
 
< 0.1%
gdleer-7342441622 1
 
< 0.1%
gdleer-1442518153 1
 
< 0.1%
Other values (97488) 97488
> 99.9%

Most occurring characters

ValueCountFrequency (%)
4 187129
 
11.3%
3 130592
 
7.9%
T 112170
 
6.8%
0 97943
 
5.9%
- 97498
 
5.9%
2 95170
 
5.7%
L 90048
 
5.4%
R 85244
 
5.1%
1 82403
 
5.0%
5 78179
 
4.7%
Other values (13) 601090
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 974980
58.8%
Uppercase Letter 584988
35.3%
Dash Punctuation 97498
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 112170
19.2%
L 90048
15.4%
R 85244
14.6%
S 60126
10.3%
E 53581
9.2%
H 35549
 
6.1%
D 29766
 
5.1%
N 29193
 
5.0%
G 29063
 
5.0%
K 27709
 
4.7%
Other values (2) 32539
 
5.6%
Decimal Number
ValueCountFrequency (%)
4 187129
19.2%
3 130592
13.4%
0 97943
10.0%
2 95170
9.8%
1 82403
8.5%
5 78179
8.0%
6 76785
7.9%
7 76283
7.8%
8 75910
7.8%
9 74586
 
7.7%
Dash Punctuation
ValueCountFrequency (%)
- 97498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1072478
64.7%
Latin 584988
35.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 112170
19.2%
L 90048
15.4%
R 85244
14.6%
S 60126
10.3%
E 53581
9.2%
H 35549
 
6.1%
D 29766
 
5.1%
N 29193
 
5.0%
G 29063
 
5.0%
K 27709
 
4.7%
Other values (2) 32539
 
5.6%
Common
ValueCountFrequency (%)
4 187129
17.4%
3 130592
12.2%
0 97943
9.1%
- 97498
9.1%
2 95170
8.9%
1 82403
7.7%
5 78179
7.3%
6 76785
7.2%
7 76283
7.1%
8 75910
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1657466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 187129
 
11.3%
3 130592
 
7.9%
T 112170
 
6.8%
0 97943
 
5.9%
- 97498
 
5.9%
2 95170
 
5.7%
L 90048
 
5.4%
R 85244
 
5.1%
1 82403
 
5.0%
5 78179
 
4.7%
Other values (13) 601090
36.3%

Fecha
Date

Distinct1827
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size761.8 KiB
Minimum2016-01-01 00:00:00
Maximum2020-12-31 00:00:00
2023-07-28T23:01:41.861077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T23:01:42.213681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Employee ID
Real number (ℝ)

Distinct2000
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean999.28502
Minimum1
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size761.8 KiB
2023-07-28T23:01:42.634991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101
Q1500
median999
Q31499
95-th percentile1901
Maximum2000
Range1999
Interquartile range (IQR)999

Descriptive statistics

Standard deviation577.40151
Coefficient of variation (CV)0.57781464
Kurtosis-1.1983956
Mean999.28502
Median Absolute Deviation (MAD)500
Skewness0.0059080822
Sum97428291
Variance333392.51
MonotonicityNot monotonic
2023-07-28T23:01:42.966582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
754 73
 
0.1%
285 73
 
0.1%
636 71
 
0.1%
1341 70
 
0.1%
523 69
 
0.1%
79 69
 
0.1%
1448 68
 
0.1%
442 68
 
0.1%
482 68
 
0.1%
326 68
 
0.1%
Other values (1990) 96801
99.3%
ValueCountFrequency (%)
1 41
< 0.1%
2 49
0.1%
3 47
< 0.1%
4 55
0.1%
5 50
0.1%
6 49
0.1%
7 39
< 0.1%
8 60
0.1%
9 48
< 0.1%
10 48
< 0.1%
ValueCountFrequency (%)
2000 51
0.1%
1999 40
< 0.1%
1998 46
< 0.1%
1997 52
0.1%
1996 45
< 0.1%
1995 43
< 0.1%
1994 40
< 0.1%
1993 48
< 0.1%
1992 40
< 0.1%
1991 50
0.1%

Agent ID
Real number (ℝ)

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.468328
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size761.8 KiB
2023-07-28T23:01:43.643738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median26
Q338
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.449695
Coefficient of variation (CV)0.56735941
Kurtosis-1.2023269
Mean25.468328
Median Absolute Deviation (MAD)13
Skewness-0.0014041438
Sum2483111
Variance208.79369
MonotonicityNot monotonic
2023-07-28T23:01:43.984708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 2027
 
2.1%
39 2026
 
2.1%
3 2021
 
2.1%
35 2007
 
2.1%
24 2003
 
2.1%
5 2000
 
2.1%
15 1991
 
2.0%
4 1988
 
2.0%
31 1987
 
2.0%
19 1984
 
2.0%
Other values (40) 77464
79.5%
ValueCountFrequency (%)
1 1969
2.0%
2 1968
2.0%
3 2021
2.1%
4 1988
2.0%
5 2000
2.1%
6 1949
2.0%
7 1935
2.0%
8 1960
2.0%
9 1949
2.0%
10 1974
2.0%
ValueCountFrequency (%)
50 1949
2.0%
49 1890
1.9%
48 2027
2.1%
47 1933
2.0%
46 1950
2.0%
45 1929
2.0%
44 1943
2.0%
43 1897
1.9%
42 1945
2.0%
41 1966
2.0%

Request Category
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size761.8 KiB
System
39002 
Login Access
29193 
Software
19570 
Hardware
9733 

Length

Max length12
Median length8
Mean length8.3976287
Min length6

Characters and Unicode

Total characters818752
Distinct characters20
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 rowLogin Access
2nd rowLogin Access
3rd rowSystem
4th rowSystem
5th rowSoftware

Common Values

ValueCountFrequency (%)
System 39002
40.0%
Login Access 29193
29.9%
Software 19570
20.1%
Hardware 9733
 
10.0%

Length

2023-07-28T23:01:44.226566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-28T23:01:44.527420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
system 39002
30.8%
login 29193
23.0%
access 29193
23.0%
software 19570
15.4%
hardware 9733
 
7.7%

Most occurring characters

ValueCountFrequency (%)
e 97498
11.9%
s 97388
 
11.9%
S 58572
 
7.2%
t 58572
 
7.2%
c 58386
 
7.1%
o 48763
 
6.0%
r 39036
 
4.8%
a 39036
 
4.8%
m 39002
 
4.8%
y 39002
 
4.8%
Other values (10) 243497
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 662868
81.0%
Uppercase Letter 126691
 
15.5%
Space Separator 29193
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 97498
14.7%
s 97388
14.7%
t 58572
8.8%
c 58386
8.8%
o 48763
7.4%
r 39036
 
5.9%
a 39036
 
5.9%
m 39002
 
5.9%
y 39002
 
5.9%
w 29303
 
4.4%
Other values (5) 116882
17.6%
Uppercase Letter
ValueCountFrequency (%)
S 58572
46.2%
A 29193
23.0%
L 29193
23.0%
H 9733
 
7.7%
Space Separator
ValueCountFrequency (%)
29193
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 789559
96.4%
Common 29193
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 97498
12.3%
s 97388
12.3%
S 58572
 
7.4%
t 58572
 
7.4%
c 58386
 
7.4%
o 48763
 
6.2%
r 39036
 
4.9%
a 39036
 
4.9%
m 39002
 
4.9%
y 39002
 
4.9%
Other values (9) 214304
27.1%
Common
ValueCountFrequency (%)
29193
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 818752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 97498
11.9%
s 97388
 
11.9%
S 58572
 
7.2%
t 58572
 
7.2%
c 58386
 
7.1%
o 48763
 
6.0%
r 39036
 
4.8%
a 39036
 
4.8%
m 39002
 
4.8%
y 39002
 
4.8%
Other values (10) 243497
29.7%

Issue Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size761.8 KiB
IT Request
73220 
IT Error
24278 

Length

Max length10
Median length10
Mean length9.5019795
Min length8

Characters and Unicode

Total characters926424
Distinct characters12
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 rowIT Error
2nd rowIT Error
3rd rowIT Error
4th rowIT Request
5th rowIT Error

Common Values

ValueCountFrequency (%)
IT Request 73220
75.1%
IT Error 24278
 
24.9%

Length

2023-07-28T23:01:44.812420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-28T23:01:45.044227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
it 97498
50.0%
request 73220
37.5%
error 24278
 
12.5%

Most occurring characters

ValueCountFrequency (%)
e 146440
15.8%
I 97498
10.5%
T 97498
10.5%
97498
10.5%
R 73220
7.9%
q 73220
7.9%
u 73220
7.9%
s 73220
7.9%
t 73220
7.9%
r 72834
7.9%
Other values (2) 48556
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 536432
57.9%
Uppercase Letter 292494
31.6%
Space Separator 97498
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 146440
27.3%
q 73220
13.6%
u 73220
13.6%
s 73220
13.6%
t 73220
13.6%
r 72834
13.6%
o 24278
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
I 97498
33.3%
T 97498
33.3%
R 73220
25.0%
E 24278
 
8.3%
Space Separator
ValueCountFrequency (%)
97498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 828926
89.5%
Common 97498
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 146440
17.7%
I 97498
11.8%
T 97498
11.8%
R 73220
8.8%
q 73220
8.8%
u 73220
8.8%
s 73220
8.8%
t 73220
8.8%
r 72834
8.8%
E 24278
 
2.9%
Common
ValueCountFrequency (%)
97498
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 926424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 146440
15.8%
I 97498
10.5%
T 97498
10.5%
97498
10.5%
R 73220
7.9%
q 73220
7.9%
u 73220
7.9%
s 73220
7.9%
t 73220
7.9%
r 72834
7.9%
Other values (2) 48556
 
5.2%

Severity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size761.8 KiB
2 - Normal
88656 
3 - Mayor
 
4836
1 - Minor
 
2258
4 - Urgent
 
1392
0 - Unclasified
 
356

Length

Max length15
Median length10
Mean length9.9454963
Min length9

Characters and Unicode

Total characters969666
Distinct characters25
Distinct categories5 ?
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 row0 - Unclasified
2nd row0 - Unclasified
3rd row0 - Unclasified
4th row0 - Unclasified
5th row2 - Normal

Common Values

ValueCountFrequency (%)
2 - Normal 88656
90.9%
3 - Mayor 4836
 
5.0%
1 - Minor 2258
 
2.3%
4 - Urgent 1392
 
1.4%
0 - Unclasified 356
 
0.4%

Length

2023-07-28T23:01:45.244814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-28T23:01:45.824989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
97498
33.3%
2 88656
30.3%
normal 88656
30.3%
3 4836
 
1.7%
mayor 4836
 
1.7%
1 2258
 
0.8%
minor 2258
 
0.8%
4 1392
 
0.5%
urgent 1392
 
0.5%
0 356
 
0.1%

Most occurring characters

ValueCountFrequency (%)
194996
20.1%
- 97498
10.1%
r 97142
10.0%
o 95750
9.9%
a 93848
9.7%
l 89012
9.2%
2 88656
9.1%
N 88656
9.1%
m 88656
9.1%
M 7094
 
0.7%
Other values (15) 28358
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 482176
49.7%
Space Separator 194996
20.1%
Dash Punctuation 97498
 
10.1%
Decimal Number 97498
 
10.1%
Uppercase Letter 97498
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 97142
20.1%
o 95750
19.9%
a 93848
19.5%
l 89012
18.5%
m 88656
18.4%
y 4836
 
1.0%
n 4006
 
0.8%
i 2970
 
0.6%
e 1748
 
0.4%
g 1392
 
0.3%
Other values (5) 2816
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 88656
90.9%
3 4836
 
5.0%
1 2258
 
2.3%
4 1392
 
1.4%
0 356
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 88656
90.9%
M 7094
 
7.3%
U 1748
 
1.8%
Space Separator
ValueCountFrequency (%)
194996
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 97498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 579674
59.8%
Common 389992
40.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 97142
16.8%
o 95750
16.5%
a 93848
16.2%
l 89012
15.4%
N 88656
15.3%
m 88656
15.3%
M 7094
 
1.2%
y 4836
 
0.8%
n 4006
 
0.7%
i 2970
 
0.5%
Other values (8) 7704
 
1.3%
Common
ValueCountFrequency (%)
194996
50.0%
- 97498
25.0%
2 88656
22.7%
3 4836
 
1.2%
1 2258
 
0.6%
4 1392
 
0.4%
0 356
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 969666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
194996
20.1%
- 97498
10.1%
r 97142
10.0%
o 95750
9.9%
a 93848
9.7%
l 89012
9.2%
2 88656
9.1%
N 88656
9.1%
m 88656
9.1%
M 7094
 
0.7%
Other values (15) 28358
 
2.9%

Priority
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size761.8 KiB
3 - High
35549 
0 - Unassiged
29410 
1 - Low
16694 
2 - Mid
15845 

Length

Max length13
Median length8
Mean length9.1744959
Min length7

Characters and Unicode

Total characters894495
Distinct characters20
Distinct categories5 ?
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 row0 - Unassiged
2nd row0 - Unassiged
3rd row0 - Unassiged
4th row0 - Unassiged
5th row0 - Unassiged

Common Values

ValueCountFrequency (%)
3 - High 35549
36.5%
0 - Unassiged 29410
30.2%
1 - Low 16694
17.1%
2 - Mid 15845
16.3%

Length

2023-07-28T23:01:46.098760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-28T23:01:46.383929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
97498
33.3%
3 35549
 
12.2%
high 35549
 
12.2%
0 29410
 
10.1%
unassiged 29410
 
10.1%
1 16694
 
5.7%
low 16694
 
5.7%
2 15845
 
5.4%
mid 15845
 
5.4%

Most occurring characters

ValueCountFrequency (%)
194996
21.8%
- 97498
10.9%
i 80804
 
9.0%
g 64959
 
7.3%
s 58820
 
6.6%
d 45255
 
5.1%
3 35549
 
4.0%
H 35549
 
4.0%
h 35549
 
4.0%
e 29410
 
3.3%
Other values (10) 216106
24.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 407005
45.5%
Space Separator 194996
21.8%
Dash Punctuation 97498
 
10.9%
Decimal Number 97498
 
10.9%
Uppercase Letter 97498
 
10.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 80804
19.9%
g 64959
16.0%
s 58820
14.5%
d 45255
11.1%
h 35549
8.7%
e 29410
 
7.2%
a 29410
 
7.2%
n 29410
 
7.2%
o 16694
 
4.1%
w 16694
 
4.1%
Decimal Number
ValueCountFrequency (%)
3 35549
36.5%
0 29410
30.2%
1 16694
17.1%
2 15845
16.3%
Uppercase Letter
ValueCountFrequency (%)
H 35549
36.5%
U 29410
30.2%
L 16694
17.1%
M 15845
16.3%
Space Separator
ValueCountFrequency (%)
194996
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 97498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 504503
56.4%
Common 389992
43.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 80804
16.0%
g 64959
12.9%
s 58820
11.7%
d 45255
9.0%
H 35549
7.0%
h 35549
7.0%
e 29410
 
5.8%
a 29410
 
5.8%
n 29410
 
5.8%
U 29410
 
5.8%
Other values (4) 65927
13.1%
Common
ValueCountFrequency (%)
194996
50.0%
- 97498
25.0%
3 35549
 
9.1%
0 29410
 
7.5%
1 16694
 
4.3%
2 15845
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 894495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
194996
21.8%
- 97498
10.9%
i 80804
 
9.0%
g 64959
 
7.3%
s 58820
 
6.6%
d 45255
 
5.1%
3 35549
 
4.0%
H 35549
 
4.0%
h 35549
 
4.0%
e 29410
 
3.3%
Other values (10) 216106
24.2%

Resolution Time (Days)
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5531498
Minimum0
Maximum21
Zeros25071
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size761.8 KiB
2023-07-28T23:01:46.684782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q37
95-th percentile14
Maximum21
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3655179
Coefficient of variation (CV)0.95879074
Kurtosis0.018514197
Mean4.5531498
Median Absolute Deviation (MAD)3
Skewness0.85081825
Sum443923
Variance19.057746
MonotonicityNot monotonic
2023-07-28T23:01:47.192778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 25071
25.7%
1 9277
 
9.5%
5 8789
 
9.0%
6 7802
 
8.0%
7 6582
 
6.8%
2 6466
 
6.6%
3 6200
 
6.4%
4 4919
 
5.0%
8 4850
 
5.0%
10 3899
 
4.0%
Other values (12) 13643
14.0%
ValueCountFrequency (%)
0 25071
25.7%
1 9277
 
9.5%
2 6466
 
6.6%
3 6200
 
6.4%
4 4919
 
5.0%
5 8789
 
9.0%
6 7802
 
8.0%
7 6582
 
6.8%
8 4850
 
5.0%
9 3739
 
3.8%
ValueCountFrequency (%)
21 2
 
< 0.1%
20 2
 
< 0.1%
19 130
 
0.1%
18 124
 
0.1%
17 554
 
0.6%
16 1167
1.2%
15 1360
1.4%
14 1566
1.6%
13 1712
1.8%
12 1555
1.6%

Satisfaction Rate
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size761.8 KiB
5
50770 
4
27562 
1
9907 
3
7282 
2
 
1977

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters97498
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 row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 50770
52.1%
4 27562
28.3%
1 9907
 
10.2%
3 7282
 
7.5%
2 1977
 
2.0%

Length

2023-07-28T23:01:48.041093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-28T23:01:48.356614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
5 50770
52.1%
4 27562
28.3%
1 9907
 
10.2%
3 7282
 
7.5%
2 1977
 
2.0%

Most occurring characters

ValueCountFrequency (%)
5 50770
52.1%
4 27562
28.3%
1 9907
 
10.2%
3 7282
 
7.5%
2 1977
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97498
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 50770
52.1%
4 27562
28.3%
1 9907
 
10.2%
3 7282
 
7.5%
2 1977
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 50770
52.1%
4 27562
28.3%
1 9907
 
10.2%
3 7282
 
7.5%
2 1977
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 50770
52.1%
4 27562
28.3%
1 9907
 
10.2%
3 7282
 
7.5%
2 1977
 
2.0%

Interactions

2023-07-28T23:01:36.130663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T23:01:33.489036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T23:01:34.956588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T23:01:36.607859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T23:01:33.989163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T23:01:35.297277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T23:01:37.160135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T23:01:34.502033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-28T23:01:35.801784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-28T23:01:48.667578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Employee IDAgent IDResolution Time (Days)Request CategoryIssue TypeSeverityPrioritySatisfaction Rate
Employee ID1.000-0.000-0.0060.0070.0040.0050.0250.004
Agent ID-0.0001.000-0.0080.0000.0060.0020.0000.065
Resolution Time (Days)-0.006-0.0081.0000.4740.2280.0600.1990.024
Request Category0.0070.0000.4741.0000.0010.0000.0040.000
Issue Type0.0040.0060.2280.0011.0000.1330.0000.004
Severity0.0050.0020.0600.0000.1331.0000.0300.013
Priority0.0250.0000.1990.0040.0000.0301.0000.007
Satisfaction Rate0.0040.0650.0240.0000.0040.0130.0071.000

Missing values

2023-07-28T23:01:38.058363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-28T23:01:39.858877image/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.

Sample

ID TicketFechaEmployee IDAgent IDRequest CategoryIssue TypeSeverityPriorityResolution Time (Days)Satisfaction Rate
0GDDENR-50425644532016-07-1317354Login AccessIT Error0 - Unclasified0 - Unassiged05
1GDDENR-80425080602016-05-18156610Login AccessIT Error0 - Unclasified0 - Unassiged05
2GDDESR-13425399952016-06-1856929SystemIT Error0 - Unclasified0 - Unassiged35
3GDDTSR-59424880062016-04-2832040SystemIT Request0 - Unclasified0 - Unassiged95
4GDLEER-00425241202016-06-03184231SoftwareIT Error2 - Normal0 - Unassiged05
5GDLEER-01426080952016-08-265920SoftwareIT Error2 - Normal0 - Unassiged11
6GDLEER-02425646502016-07-13117536SoftwareIT Error2 - Normal0 - Unassiged21
7GDLEER-05425748152016-07-2356118SoftwareIT Error2 - Normal0 - Unassiged55
8GDLEER-08424572192016-03-287112SoftwareIT Error2 - Normal0 - Unassiged85
9GDLEER-12425422132016-06-21183142SoftwareIT Error2 - Normal0 - Unassiged25
ID TicketFechaEmployee IDAgent IDRequest CategoryIssue TypeSeverityPriorityResolution Time (Days)Satisfaction Rate
97488TWRTSR-35439597482020-05-08141447SystemIT Request1 - Minor1 - Low54
97489TWRTSR-45441640652020-11-2928620SystemIT Request1 - Minor1 - Low55
97490TWRTSR-49440358502020-07-2393518SystemIT Request1 - Minor1 - Low95
97491TWRTSR-63440494202020-08-0613024SystemIT Request3 - Mayor1 - Low14
97492TWRTSR-79439734152020-05-224914SystemIT Request1 - Minor1 - Low194
97493TWRTSR-85438831202020-02-2211421SystemIT Request3 - Mayor1 - Low115
97494TWRTSR-87440970392020-09-2322340SystemIT Request1 - Minor1 - Low74
97495TWRTSR-96438467682020-01-162567SystemIT Request3 - Mayor1 - Low135
97496TWRTSR-99441389062020-11-0310609SystemIT Request1 - Minor1 - Low95
97497TWRTST-86439861622020-06-04187641SystemIT Request1 - Minor1 - Low64

Report generated by YData.

By No comments:
Subscribe to: Comments (Atom)