Overview

Dataset statistics

Number of variables20
Number of observations66
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory120.0 B

Variable types

Categorical5
Numeric9
Boolean6

Alerts

freq has constant value "512"Constant
util_type has constant value "Power Transmission"Constant
temp has constant value "29"Constant
SwingWarning has constant value "False"Constant
StrikeAlert has constant value "False"Constant
ant_mode_Peak has constant value "True"Constant
Sonde_Line_Line has constant value "True"Constant
Success is highly overall correlated with Ticketstart_StopeventsHigh correlation
Ticketstart_Stopevents is highly overall correlated with Success and 5 other fieldsHigh correlation
bargraph is highly overall correlated with Ticketstart_Stopevents and 4 other fieldsHigh correlation
cur is highly overall correlated with depthHigh correlation
depth is highly overall correlated with curHigh correlation
disp_sig_str is highly overall correlated with Ticketstart_Stopevents and 4 other fieldsHigh correlation
gain is highly overall correlated with op_mode_Active and 1 other fieldsHigh correlation
op_mode_Active is highly overall correlated with Ticketstart_Stopevents and 4 other fieldsHigh correlation
op_mode_MENU is highly overall correlated with Ticketstart_Stopevents and 4 other fieldsHigh correlation
t_p_a is highly overall correlated with Ticketstart_Stopevents and 2 other fieldsHigh correlation
op_mode_Active is highly imbalanced (56.1%)Imbalance
op_mode_MENU is highly imbalanced (56.1%)Imbalance
comp_disp has unique valuesUnique
depth has 56 (84.8%) zerosZeros
cur has 56 (84.8%) zerosZeros
key_b_e has 57 (86.4%) zerosZeros
disp_sig_str has 1 (1.5%) zerosZeros
t_p_a has 1 (1.5%) zerosZeros
bargraph has 2 (3.0%) zerosZeros

Reproduction

Analysis started2024-08-27 18:34:20.256047
Analysis finished2024-08-27 18:34:26.042999
Duration5.79 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

freq
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
512
66 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters198
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row512
2nd row512
3rd row512
4th row512
5th row512

Common Values

ValueCountFrequency (%)
512 66
100.0%

Length

2024-08-27T19:34:26.115730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-27T19:34:26.180864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
512 66
100.0%

Most occurring characters

ValueCountFrequency (%)
5 66
33.3%
1 66
33.3%
2 66
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 198
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 66
33.3%
1 66
33.3%
2 66
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 198
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 66
33.3%
1 66
33.3%
2 66
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 66
33.3%
1 66
33.3%
2 66
33.3%

util_type
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
Power Transmission
66 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters1188
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 rowPower Transmission
2nd rowPower Transmission
3rd rowPower Transmission
4th rowPower Transmission
5th rowPower Transmission

Common Values

ValueCountFrequency (%)
Power Transmission 66
100.0%

Length

2024-08-27T19:34:26.241944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-27T19:34:26.299807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
power 66
50.0%
transmission 66
50.0%

Most occurring characters

ValueCountFrequency (%)
s 198
16.7%
o 132
11.1%
r 132
11.1%
n 132
11.1%
i 132
11.1%
P 66
 
5.6%
w 66
 
5.6%
e 66
 
5.6%
66
 
5.6%
T 66
 
5.6%
Other values (2) 132
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 990
83.3%
Uppercase Letter 132
 
11.1%
Space Separator 66
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 198
20.0%
o 132
13.3%
r 132
13.3%
n 132
13.3%
i 132
13.3%
w 66
 
6.7%
e 66
 
6.7%
a 66
 
6.7%
m 66
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
P 66
50.0%
T 66
50.0%
Space Separator
ValueCountFrequency (%)
66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1122
94.4%
Common 66
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 198
17.6%
o 132
11.8%
r 132
11.8%
n 132
11.8%
i 132
11.8%
P 66
 
5.9%
w 66
 
5.9%
e 66
 
5.9%
T 66
 
5.9%
a 66
 
5.9%
Common
ValueCountFrequency (%)
66
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 198
16.7%
o 132
11.1%
r 132
11.1%
n 132
11.1%
i 132
11.1%
P 66
 
5.6%
w 66
 
5.6%
e 66
 
5.6%
66
 
5.6%
T 66
 
5.6%
Other values (2) 132
11.1%

Ticketstart_Stopevents
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
1
32 
0
28 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 32
48.5%
0 28
42.4%
2 6
 
9.1%

Length

2024-08-27T19:34:26.361157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-27T19:34:26.426714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 32
48.5%
0 28
42.4%
2 6
 
9.1%

Most occurring characters

ValueCountFrequency (%)
1 32
48.5%
0 28
42.4%
2 6
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 32
48.5%
0 28
42.4%
2 6
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 66
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 32
48.5%
0 28
42.4%
2 6
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 32
48.5%
0 28
42.4%
2 6
 
9.1%

depth
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13908779
Minimum0
Maximum1.04388
Zeros56
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size660.0 B
2024-08-27T19:34:26.490787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.971606
Maximum1.04388
Range1.04388
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.33434013
Coefficient of variation (CV)2.4038065
Kurtosis2.3923297
Mean0.13908779
Median Absolute Deviation (MAD)0
Skewness2.0532655
Sum9.179794
Variance0.11178332
MonotonicityNot monotonic
2024-08-27T19:34:26.564069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 56
84.8%
1.04388 1
 
1.5%
0.956948 1
 
1.5%
0.893898 1
 
1.5%
0.911289 1
 
1.5%
0.976492 1
 
1.5%
0.763505 1
 
1.5%
1.01849 1
 
1.5%
1.03427 1
 
1.5%
0.705514 1
 
1.5%
ValueCountFrequency (%)
0 56
84.8%
0.705514 1
 
1.5%
0.763505 1
 
1.5%
0.875508 1
 
1.5%
0.893898 1
 
1.5%
0.911289 1
 
1.5%
0.956948 1
 
1.5%
0.976492 1
 
1.5%
1.01849 1
 
1.5%
1.03427 1
 
1.5%
ValueCountFrequency (%)
1.04388 1
1.5%
1.03427 1
1.5%
1.01849 1
1.5%
0.976492 1
1.5%
0.956948 1
1.5%
0.911289 1
1.5%
0.893898 1
1.5%
0.875508 1
1.5%
0.763505 1
1.5%
0.705514 1
1.5%

cur
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0042524045
Minimum0
Maximum0.0313765
Zeros56
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size660.0 B
2024-08-27T19:34:26.629348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.030470675
Maximum0.0313765
Range0.0313765
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.010204027
Coefficient of variation (CV)2.3995898
Kurtosis2.3301906
Mean0.0042524045
Median Absolute Deviation (MAD)0
Skewness2.0414992
Sum0.2806587
Variance0.00010412216
MonotonicityNot monotonic
2024-08-27T19:34:26.696012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 56
84.8%
0.0305159 1
 
1.5%
0.0313765 1
 
1.5%
0.030753 1
 
1.5%
0.0311449 1
 
1.5%
0.024615 1
 
1.5%
0.0234595 1
 
1.5%
0.0258647 1
 
1.5%
0.0271461 1
 
1.5%
0.0254481 1
 
1.5%
ValueCountFrequency (%)
0 56
84.8%
0.0234595 1
 
1.5%
0.024615 1
 
1.5%
0.0254481 1
 
1.5%
0.0258647 1
 
1.5%
0.0271461 1
 
1.5%
0.030335 1
 
1.5%
0.0305159 1
 
1.5%
0.030753 1
 
1.5%
0.0311449 1
 
1.5%
ValueCountFrequency (%)
0.0313765 1
1.5%
0.0311449 1
1.5%
0.030753 1
1.5%
0.0305159 1
1.5%
0.030335 1
1.5%
0.0271461 1
1.5%
0.0258647 1
1.5%
0.0254481 1
1.5%
0.024615 1
1.5%
0.0234595 1
1.5%

gain
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.939394
Minimum54
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size660.0 B
2024-08-27T19:34:26.758511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile54
Q154
median75
Q377
95-th percentile78
Maximum78
Range24
Interquartile range (IQR)23

Descriptive statistics

Standard deviation10.597356
Coefficient of variation (CV)0.1559825
Kurtosis-1.7927403
Mean67.939394
Median Absolute Deviation (MAD)2.5
Skewness-0.44587384
Sum4484
Variance112.30396
MonotonicityNot monotonic
2024-08-27T19:34:26.818369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
54 18
27.3%
77 16
24.2%
75 16
24.2%
78 7
 
10.6%
57 7
 
10.6%
76 1
 
1.5%
59 1
 
1.5%
ValueCountFrequency (%)
54 18
27.3%
57 7
 
10.6%
59 1
 
1.5%
75 16
24.2%
76 1
 
1.5%
77 16
24.2%
78 7
 
10.6%
ValueCountFrequency (%)
78 7
 
10.6%
77 16
24.2%
76 1
 
1.5%
75 16
24.2%
59 1
 
1.5%
57 7
 
10.6%
54 18
27.3%

key_b_e
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4393939
Minimum0
Maximum64
Zeros57
Zeros (%)86.4%
Negative0
Negative (%)0.0%
Memory size660.0 B
2024-08-27T19:34:26.876486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum64
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9558953
Coefficient of variation (CV)5.5272536
Kurtosis61.377429
Mean1.4393939
Median Absolute Deviation (MAD)0
Skewness7.7211914
Sum95
Variance63.29627
MonotonicityNot monotonic
2024-08-27T19:34:26.937227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 57
86.4%
4 4
 
6.1%
1 2
 
3.0%
8 1
 
1.5%
5 1
 
1.5%
64 1
 
1.5%
ValueCountFrequency (%)
0 57
86.4%
1 2
 
3.0%
4 4
 
6.1%
5 1
 
1.5%
8 1
 
1.5%
64 1
 
1.5%
ValueCountFrequency (%)
64 1
 
1.5%
8 1
 
1.5%
5 1
 
1.5%
4 4
 
6.1%
1 2
 
3.0%
0 57
86.4%

temp
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
29
66 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters132
Distinct characters2
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 row29
2nd row29
3rd row29
4th row29
5th row29

Common Values

ValueCountFrequency (%)
29 66
100.0%

Length

2024-08-27T19:34:27.009512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-27T19:34:27.066509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
29 66
100.0%

Most occurring characters

ValueCountFrequency (%)
2 66
50.0%
9 66
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 66
50.0%
9 66
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 66
50.0%
9 66
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 66
50.0%
9 66
50.0%

Success
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size660.0 B
0
34 
1
32 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66
Distinct characters2
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 row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34
51.5%
1 32
48.5%

Length

2024-08-27T19:34:27.135388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-27T19:34:27.196805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 34
51.5%
1 32
48.5%

Most occurring characters

ValueCountFrequency (%)
0 34
51.5%
1 32
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34
51.5%
1 32
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common 66
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34
51.5%
1 32
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34
51.5%
1 32
48.5%

SwingWarning
Boolean

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size198.0 B
False
66 
ValueCountFrequency (%)
False 66
100.0%
2024-08-27T19:34:27.250782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

StrikeAlert
Boolean

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size198.0 B
False
66 
ValueCountFrequency (%)
False 66
100.0%
2024-08-27T19:34:27.301692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

comp_disp
Real number (ℝ)

UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.639394
Minimum4
Maximum177.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size660.0 B
2024-08-27T19:34:27.367157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10.55
Q153.95
median94.9
Q3140.15
95-th percentile174.1
Maximum177.4
Range173.4
Interquartile range (IQR)86.2

Descriptive statistics

Standard deviation52.778541
Coefficient of variation (CV)0.55768047
Kurtosis-1.2977607
Mean94.639394
Median Absolute Deviation (MAD)45
Skewness-0.077142343
Sum6246.2
Variance2785.5744
MonotonicityNot monotonic
2024-08-27T19:34:27.453189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.6 1
 
1.5%
139.8 1
 
1.5%
102.2 1
 
1.5%
159.4 1
 
1.5%
165 1
 
1.5%
118.6 1
 
1.5%
57.8 1
 
1.5%
156.8 1
 
1.5%
4 1
 
1.5%
6.6 1
 
1.5%
Other values (56) 56
84.8%
ValueCountFrequency (%)
4 1
1.5%
6.6 1
1.5%
9.6 1
1.5%
10.2 1
1.5%
11.6 1
1.5%
17.6 1
1.5%
18.6 1
1.5%
20.4 1
1.5%
21 1
1.5%
34.6 1
1.5%
ValueCountFrequency (%)
177.4 1
1.5%
175.8 1
1.5%
174.4 1
1.5%
174.2 1
1.5%
173.8 1
1.5%
165 1
1.5%
163.8 1
1.5%
159.4 1
1.5%
158.4 1
1.5%
156.8 1
1.5%

disp_sig_str
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean373.58182
Minimum0
Maximum999
Zeros1
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size660.0 B
2024-08-27T19:34:27.547722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.95
Q1123.55
median418.6
Q3518.9
95-th percentile775.2
Maximum999
Range999
Interquartile range (IQR)395.35

Descriptive statistics

Standard deviation258.23222
Coefficient of variation (CV)0.69123336
Kurtosis-0.4221925
Mean373.58182
Median Absolute Deviation (MAD)162.3
Skewness0.23886575
Sum24656.4
Variance66683.878
MonotonicityNot monotonic
2024-08-27T19:34:27.641986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
464 6
 
9.1%
382.8 1
 
1.5%
642.6 1
 
1.5%
520.6 1
 
1.5%
415.4 1
 
1.5%
252.4 1
 
1.5%
513.8 1
 
1.5%
489.8 1
 
1.5%
354.4 1
 
1.5%
169.8 1
 
1.5%
Other values (51) 51
77.3%
ValueCountFrequency (%)
0 1
1.5%
1.8 1
1.5%
4.8 1
1.5%
9.6 1
1.5%
15 1
1.5%
17.2 1
1.5%
22.2 1
1.5%
29.8 1
1.5%
33.6 1
1.5%
35.2 1
1.5%
ValueCountFrequency (%)
999 1
1.5%
991.6 1
1.5%
920.6 1
1.5%
787.4 1
1.5%
738.6 1
1.5%
673.8 1
1.5%
665.2 1
1.5%
658.2 1
1.5%
648.2 1
1.5%
642.6 1
1.5%

t_b_a
Real number (ℝ)

Distinct54
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0484848
Minimum-16.4
Maximum19
Zeros0
Zeros (%)0.0%
Negative21
Negative (%)31.8%
Memory size660.0 B
2024-08-27T19:34:27.726263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-16.4
5-th percentile-8.7
Q1-1.35
median4
Q37.5
95-th percentile15.15
Maximum19
Range35.4
Interquartile range (IQR)8.85

Descriptive statistics

Standard deviation7.2026543
Coefficient of variation (CV)2.3626997
Kurtosis0.42971082
Mean3.0484848
Median Absolute Deviation (MAD)4.7
Skewness-0.36342842
Sum201.2
Variance51.878228
MonotonicityNot monotonic
2024-08-27T19:34:27.812924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.2 3
 
4.5%
8.6 2
 
3.0%
7.2 2
 
3.0%
16 2
 
3.0%
3 2
 
3.0%
5.8 2
 
3.0%
1.2 2
 
3.0%
6 2
 
3.0%
4.2 2
 
3.0%
7.6 2
 
3.0%
Other values (44) 45
68.2%
ValueCountFrequency (%)
-16.4 1
1.5%
-16 1
1.5%
-11.2 1
1.5%
-9.2 1
1.5%
-7.2 1
1.5%
-6 1
1.5%
-5.4 1
1.5%
-4.8 1
1.5%
-4.4 1
1.5%
-4 1
1.5%
ValueCountFrequency (%)
19 1
1.5%
16.2 1
1.5%
16 2
3.0%
12.6 1
1.5%
12.2 1
1.5%
11.8 1
1.5%
10.6 1
1.5%
10.4 1
1.5%
10.2 1
1.5%
9.6 1
1.5%

t_p_a
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8242424
Minimum-10.4
Maximum16
Zeros1
Zeros (%)1.5%
Negative28
Negative (%)42.4%
Memory size660.0 B
2024-08-27T19:34:27.901888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-10.4
5-th percentile-5.6
Q1-2.55
median1.2
Q34.5
95-th percentile13.4
Maximum16
Range26.4
Interquartile range (IQR)7.05

Descriptive statistics

Standard deviation5.7007978
Coefficient of variation (CV)3.1250221
Kurtosis-0.027556051
Mean1.8242424
Median Absolute Deviation (MAD)3.7
Skewness0.57802466
Sum120.4
Variance32.499096
MonotonicityNot monotonic
2024-08-27T19:34:27.986356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3.2 4
 
6.1%
1.2 3
 
4.5%
-3.2 3
 
4.5%
3.4 3
 
4.5%
3.6 2
 
3.0%
-4.4 2
 
3.0%
-1.8 2
 
3.0%
-1.6 2
 
3.0%
1.8 2
 
3.0%
-0.2 2
 
3.0%
Other values (39) 41
62.1%
ValueCountFrequency (%)
-10.4 1
 
1.5%
-6.8 1
 
1.5%
-6 1
 
1.5%
-5.8 1
 
1.5%
-5 1
 
1.5%
-4.8 1
 
1.5%
-4.4 2
3.0%
-4 2
3.0%
-3.6 1
 
1.5%
-3.2 3
4.5%
ValueCountFrequency (%)
16 1
1.5%
14.2 2
3.0%
13.8 1
1.5%
12.2 1
1.5%
11.6 1
1.5%
10.6 1
1.5%
9 1
1.5%
8.6 1
1.5%
8.4 1
1.5%
8 1
1.5%

bargraph
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.591515
Minimum0
Maximum79
Zeros2
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size660.0 B
2024-08-27T19:34:28.074022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99
Q111.07
median32.66
Q342.53
95-th percentile61.41
Maximum79
Range79
Interquartile range (IQR)31.46

Descriptive statistics

Standard deviation20.580441
Coefficient of variation (CV)0.69548452
Kurtosis-0.4167654
Mean29.591515
Median Absolute Deviation (MAD)16.94
Skewness0.28113319
Sum1953.04
Variance423.55454
MonotonicityNot monotonic
2024-08-27T19:34:28.163409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 6
 
9.1%
79 2
 
3.0%
53.04 2
 
3.0%
0 2
 
3.0%
42.68 2
 
3.0%
20.4 1
 
1.5%
29.68 1
 
1.5%
32.32 1
 
1.5%
26.44 1
 
1.5%
41.08 1
 
1.5%
Other values (47) 47
71.2%
ValueCountFrequency (%)
0 2
3.0%
0.08 1
1.5%
0.96 1
1.5%
1.08 1
1.5%
1.16 1
1.5%
1.36 1
1.5%
1.48 1
1.5%
2.56 1
1.5%
2.8 1
1.5%
4 1
1.5%
ValueCountFrequency (%)
79 2
3.0%
74.4 1
1.5%
62.64 1
1.5%
57.72 1
1.5%
53.04 2
3.0%
52.44 1
1.5%
52.16 1
1.5%
51.12 1
1.5%
50.92 1
1.5%
50.8 1
1.5%

ant_mode_Peak
Boolean

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size198.0 B
True
66 
ValueCountFrequency (%)
True 66
100.0%
2024-08-27T19:34:28.257940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

op_mode_Active
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size198.0 B
True
60 
False
 
6
ValueCountFrequency (%)
True 60
90.9%
False 6
 
9.1%
2024-08-27T19:34:28.340465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

op_mode_MENU
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size198.0 B
False
60 
True
 
6
ValueCountFrequency (%)
False 60
90.9%
True 6
 
9.1%
2024-08-27T19:34:28.408154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Sonde_Line_Line
Boolean

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size198.0 B
True
66 
ValueCountFrequency (%)
True 66
100.0%
2024-08-27T19:34:28.465099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Interactions

2024-08-27T19:34:25.066864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:20.727487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.246579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.783672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.368337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.957446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.498576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.021464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.547207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:25.126497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:20.783294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.308227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.833030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.426711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.017182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.558638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.082068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.601565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:25.191607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:20.842340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.366229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.891507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.485485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.079197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.616639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.143075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.659393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:25.242708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:20.894898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.419202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.937616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.552675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.131189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.669495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.197544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.708582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:25.307068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:20.952904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.478810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.092496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.628560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.191802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.727949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.251914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.763525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:25.365858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.013720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.543166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.145315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.718464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.252209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.785775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.313869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.825724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:25.425743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.068368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.600982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.195084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.779163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.311396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.841798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.366590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.879918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:25.484372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.127134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.660999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.250756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.843862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.376075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.900324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.429514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.933732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:25.544300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.184243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:21.716956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.308161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:22.899498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.433203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:23.956411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.480904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-27T19:34:24.988684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-08-27T19:34:28.516216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
SuccessTicketstart_Stopeventsbargraphcomp_dispcurdepthdisp_sig_strgainkey_b_eop_mode_Activeop_mode_MENUt_b_at_p_a
Success1.0000.9920.5000.1810.1590.1720.4980.3930.0480.2230.2230.104-0.429
Ticketstart_Stopevents0.9921.0000.5190.2680.0300.0410.5450.1830.1930.9920.9920.140-0.723
bargraph0.5000.5191.0000.4480.3930.3760.9490.4620.2280.5020.502-0.066-0.506
comp_disp0.1810.2680.4481.0000.0750.0300.4450.272-0.0760.2860.286-0.413-0.338
cur0.1590.0300.3930.0751.0000.9930.4070.2590.1950.0000.000-0.061-0.102
depth0.1720.0410.3760.0300.9931.0000.3900.2550.2070.0000.000-0.033-0.121
disp_sig_str0.4980.5450.9490.4450.4070.3901.0000.4680.2480.5020.502-0.103-0.556
gain0.3930.1830.4620.2720.2590.2550.4681.0000.0800.8910.891-0.167-0.406
key_b_e0.0480.1930.228-0.0760.1950.2070.2480.0801.0000.3550.355-0.172-0.291
op_mode_Active0.2230.9920.5020.2860.0000.0000.5020.8910.3551.0000.907-0.0590.423
op_mode_MENU0.2230.9920.5020.2860.0000.0000.5020.8910.3550.9071.0000.059-0.423
t_b_a0.1040.140-0.066-0.413-0.061-0.033-0.103-0.167-0.172-0.0590.0591.0000.046
t_p_a-0.429-0.723-0.506-0.338-0.102-0.121-0.556-0.406-0.2910.423-0.4230.0461.000

Missing values

2024-08-27T19:34:25.776778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-27T19:34:25.935307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

frequtil_typeTicketstart_Stopeventsdepthcurgainkey_b_etempSuccessSwingWarningStrikeAlertcomp_dispdisp_sig_strt_b_at_p_abargraphant_mode_Peakop_mode_Activeop_mode_MENUSonde_Line_Line
0512Power Transmission00.00.0540290FalseFalse17.622.2-3.43.60.96TrueTrueFalseTrue
1512Power Transmission00.00.0540290FalseFalse137.458.0-3.29.05.44TrueTrueFalseTrue
2512Power Transmission00.00.0540290FalseFalse91.667.42.45.84.00TrueTrueFalseTrue
3512Power Transmission00.00.0540290FalseFalse146.8318.65.68.635.40TrueTrueFalseTrue
4512Power Transmission00.00.0540290FalseFalse46.0328.47.610.615.32TrueTrueFalseTrue
5512Power Transmission00.00.0540290FalseFalse62.017.29.08.01.36TrueTrueFalseTrue
6512Power Transmission00.00.0540290FalseFalse52.477.06.811.65.24TrueTrueFalseTrue
7512Power Transmission00.00.0540290FalseFalse66.64.86.212.20.08TrueTrueFalseTrue
8512Power Transmission00.00.0540290FalseFalse66.20.07.214.20.00TrueTrueFalseTrue
9512Power Transmission00.00.0540290FalseFalse46.240.05.014.26.36TrueTrueFalseTrue
frequtil_typeTicketstart_Stopeventsdepthcurgainkey_b_etempSuccessSwingWarningStrikeAlertcomp_dispdisp_sig_strt_b_at_p_abargraphant_mode_Peakop_mode_Activeop_mode_MENUSonde_Line_Line
56512Power Transmission10.00.0750291FalseFalse158.4920.6-3.0-3.079.0TrueTrueFalseTrue
57512Power Transmission10.00.0750291FalseFalse142.0999.0-1.8-2.879.0TrueTrueFalseTrue
58512Power Transmission10.00.0594291FalseFalse140.0991.60.6-1.674.4TrueTrueFalseTrue
59512Power Transmission10.00.0575291FalseFalse140.2508.4-4.4-4.037.2TrueTrueFalseTrue
60512Power Transmission20.00.0571290FalseFalse142.8464.00.8-10.435.0TrueFalseTrueTrue
61512Power Transmission20.00.0570290FalseFalse135.8464.05.8-6.035.0TrueFalseTrueTrue
62512Power Transmission20.00.0570290FalseFalse119.2464.010.4-3.235.0TrueFalseTrueTrue
63512Power Transmission20.00.0570290FalseFalse98.2464.019.0-5.835.0TrueFalseTrueTrue
64512Power Transmission20.00.0570290FalseFalse125.6464.0-3.2-0.635.0TrueFalseTrueTrue
65512Power Transmission20.00.05764290FalseFalse113.2464.0-0.8-6.835.0TrueFalseTrueTrue