Overview

Dataset statistics

Number of variables23
Number of observations675
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.3 KiB
Average record size in memory114.2 B

Variable types

Unsupported1
Categorical3
Numeric9
Boolean10

Alerts

util_type has constant value "5"Constant
StrikeAlert has constant value "False"Constant
Sonde_Line_Line has constant value "True"Constant
Ticketstart_Stopevents is highly overall correlated with op_mode_MENUHigh correlation
ant_mode_Peak is highly overall correlated with ant_mode_Peak+ and 5 other fieldsHigh correlation
ant_mode_Peak+ is highly overall correlated with ant_mode_Peak and 5 other fieldsHigh correlation
bargraph is highly overall correlated with ant_mode_Peak and 2 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 ant_mode_Peak and 2 other fieldsHigh correlation
gain is highly overall correlated with ant_mode_Peak and 5 other fieldsHigh correlation
op_mode_Active is highly overall correlated with ant_mode_Peak and 3 other fieldsHigh correlation
op_mode_CD is highly overall correlated with gainHigh correlation
op_mode_MENU is highly overall correlated with Ticketstart_Stopevents and 4 other fieldsHigh correlation
op_mode_Power is highly overall correlated with gainHigh correlation
t_b_a is highly overall correlated with tempHigh correlation
t_p_a is highly overall correlated with tempHigh correlation
temp is highly overall correlated with t_b_a and 1 other fieldsHigh correlation
SwingWarning is highly imbalanced (98.4%)Imbalance
ant_mode_Peak is highly imbalanced (50.6%)Imbalance
ant_mode_Peak+ is highly imbalanced (50.6%)Imbalance
op_mode_CD is highly imbalanced (94.8%)Imbalance
op_mode_Power is highly imbalanced (88.9%)Imbalance
op_mode_Radio is highly imbalanced (97.1%)Imbalance
freq is an unsupported type, check if it needs cleaning or further analysisUnsupported
depth has 642 (95.1%) zerosZeros
cur has 644 (95.4%) zerosZeros
key_b_e has 595 (88.1%) zerosZeros
disp_sig_str has 85 (12.6%) zerosZeros
t_b_a has 12 (1.8%) zerosZeros
bargraph has 86 (12.7%) zerosZeros

Reproduction

Analysis started2024-08-29 08:49:07.757294
Analysis finished2024-08-29 08:49:13.917327
Duration6.16 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

freq
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size5.4 KiB

util_type
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
5
675 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters675
Distinct characters1
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 (%)
5 675
100.0%

Length

2024-08-29T09:49:13.975165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T09:49:14.038161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
5 675
100.0%

Most occurring characters

ValueCountFrequency (%)
5 675
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 675
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 675
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 675
100.0%

Ticketstart_Stopevents
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
539 
1
104 
2
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters675
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 (%)
0 539
79.9%
1 104
 
15.4%
2 32
 
4.7%

Length

2024-08-29T09:49:14.101924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T09:49:14.165174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 539
79.9%
1 104
 
15.4%
2 32
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 539
79.9%
1 104
 
15.4%
2 32
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 539
79.9%
1 104
 
15.4%
2 32
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 539
79.9%
1 104
 
15.4%
2 32
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 539
79.9%
1 104
 
15.4%
2 32
 
4.7%

depth
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.091032464
Minimum0
Maximum8.69016
Zeros642
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-08-29T09:49:14.233461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8.69016
Range8.69016
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55987649
Coefficient of variation (CV)6.1502948
Kurtosis165.67196
Mean0.091032464
Median Absolute Deviation (MAD)0
Skewness11.514828
Sum61.446913
Variance0.31346168
MonotonicityNot monotonic
2024-08-29T09:49:14.315174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 642
95.1%
1.20541 1
 
0.1%
1.31335 1
 
0.1%
1.33727 1
 
0.1%
1.47256 1
 
0.1%
1.48714 1
 
0.1%
1.34847 1
 
0.1%
1.28051 1
 
0.1%
2.14041 1
 
0.1%
1.14331 1
 
0.1%
Other values (24) 24
 
3.6%
ValueCountFrequency (%)
0 642
95.1%
0.904017 1
 
0.1%
0.966796 1
 
0.1%
1.09262 1
 
0.1%
1.14331 1
 
0.1%
1.14627 1
 
0.1%
1.16742 1
 
0.1%
1.19378 1
 
0.1%
1.20541 1
 
0.1%
1.23145 1
 
0.1%
ValueCountFrequency (%)
8.69016 1
0.1%
8.68924 1
0.1%
2.41333 1
0.1%
2.14041 1
0.1%
1.81652 1
0.1%
1.77155 1
0.1%
1.75775 1
0.1%
1.73614 1
0.1%
1.69551 1
0.1%
1.68362 1
0.1%

cur
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.18116 × 10-5
Minimum0
Maximum0.00175714
Zeros644
Zeros (%)95.4%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-08-29T09:49:14.391210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.00175714
Range0.00175714
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00021233788
Coefficient of variation (CV)5.0784443
Kurtosis35.189008
Mean4.18116 × 10-5
Median Absolute Deviation (MAD)0
Skewness5.7491335
Sum0.02822283
Variance4.5087375 × 10-8
MonotonicityNot monotonic
2024-08-29T09:49:14.471127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 644
95.4%
0.000196033 1
 
0.1%
0.000513221 1
 
0.1%
0.000525948 1
 
0.1%
0.000502935 1
 
0.1%
0.000508199 1
 
0.1%
0.000611534 1
 
0.1%
0.000534079 1
 
0.1%
0.000669771 1
 
0.1%
0.000722389 1
 
0.1%
Other values (22) 22
 
3.3%
ValueCountFrequency (%)
0 644
95.4%
0.000164638 1
 
0.1%
0.000196033 1
 
0.1%
0.000502935 1
 
0.1%
0.000508199 1
 
0.1%
0.000513221 1
 
0.1%
0.000525194 1
 
0.1%
0.000525948 1
 
0.1%
0.000534079 1
 
0.1%
0.000611534 1
 
0.1%
ValueCountFrequency (%)
0.00175714 1
0.1%
0.0017489 1
0.1%
0.00170869 1
0.1%
0.00167929 1
0.1%
0.00154199 1
0.1%
0.00136531 1
0.1%
0.00133002 1
0.1%
0.00117972 1
0.1%
0.00115895 1
0.1%
0.00106481 1
0.1%

gain
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.985185
Minimum25
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-08-29T09:49:14.547835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile60
Q187
median90
Q390
95-th percentile101
Maximum134
Range109
Interquartile range (IQR)3

Descriptive statistics

Standard deviation12.794997
Coefficient of variation (CV)0.14880467
Kurtosis6.8451503
Mean85.985185
Median Absolute Deviation (MAD)0
Skewness-1.9796929
Sum58040
Variance163.71195
MonotonicityNot monotonic
2024-08-29T09:49:14.616385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
90 399
59.1%
60 77
 
11.4%
84 49
 
7.3%
87 32
 
4.7%
101 28
 
4.1%
91 25
 
3.7%
89 23
 
3.4%
94 20
 
3.0%
82 6
 
0.9%
25 5
 
0.7%
Other values (4) 11
 
1.6%
ValueCountFrequency (%)
25 5
 
0.7%
26 4
 
0.6%
60 77
 
11.4%
82 6
 
0.9%
84 49
 
7.3%
87 32
 
4.7%
89 23
 
3.4%
90 399
59.1%
91 25
 
3.7%
94 20
 
3.0%
ValueCountFrequency (%)
134 3
 
0.4%
121 1
 
0.1%
112 3
 
0.4%
101 28
 
4.1%
94 20
 
3.0%
91 25
 
3.7%
90 399
59.1%
89 23
 
3.4%
87 32
 
4.7%
84 49
 
7.3%

key_b_e
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1511111
Minimum0
Maximum64
Zeros595
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-08-29T09:49:14.678710image/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 deviation4.9864187
Coefficient of variation (CV)4.331831
Kurtosis81.946119
Mean1.1511111
Median Absolute Deviation (MAD)0
Skewness7.8893946
Sum777
Variance24.864372
MonotonicityNot monotonic
2024-08-29T09:49:14.744493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 595
88.1%
4 44
 
6.5%
16 14
 
2.1%
8 6
 
0.9%
1 5
 
0.7%
20 4
 
0.6%
64 2
 
0.3%
12 2
 
0.3%
36 1
 
0.1%
32 1
 
0.1%
ValueCountFrequency (%)
0 595
88.1%
1 5
 
0.7%
4 44
 
6.5%
8 6
 
0.9%
12 2
 
0.3%
16 14
 
2.1%
20 4
 
0.6%
24 1
 
0.1%
32 1
 
0.1%
36 1
 
0.1%
ValueCountFrequency (%)
64 2
 
0.3%
36 1
 
0.1%
32 1
 
0.1%
24 1
 
0.1%
20 4
 
0.6%
16 14
 
2.1%
12 2
 
0.3%
8 6
 
0.9%
4 44
6.5%
1 5
 
0.7%

temp
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
32
597 
31
78 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1350
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 row31
2nd row31
3rd row31
4th row31
5th row31

Common Values

ValueCountFrequency (%)
32 597
88.4%
31 78
 
11.6%

Length

2024-08-29T09:49:14.822037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T09:49:14.882630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
32 597
88.4%
31 78
 
11.6%

Most occurring characters

ValueCountFrequency (%)
3 675
50.0%
2 597
44.2%
1 78
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1350
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 675
50.0%
2 597
44.2%
1 78
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1350
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 675
50.0%
2 597
44.2%
1 78
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 675
50.0%
2 597
44.2%
1 78
 
5.8%

SwingWarning
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size807.0 B
False
674 
True
 
1
ValueCountFrequency (%)
False 674
99.9%
True 1
 
0.1%
2024-08-29T09:49:14.940920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

StrikeAlert
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size807.0 B
False
675 
ValueCountFrequency (%)
False 675
100.0%
2024-08-29T09:49:14.998430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

comp_disp
Real number (ℝ)

Distinct460
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.205333
Minimum1.8
Maximum177.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-08-29T09:49:15.069787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile17.34
Q140.9
median78.4
Q3123
95-th percentile162.26
Maximum177.2
Range175.4
Interquartile range (IQR)82.1

Descriptive statistics

Standard deviation47.578637
Coefficient of variation (CV)0.57182197
Kurtosis-1.1711434
Mean83.205333
Median Absolute Deviation (MAD)39.4
Skewness0.25827512
Sum56163.6
Variance2263.7267
MonotonicityNot monotonic
2024-08-29T09:49:15.161818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 7
 
1.0%
46.2 5
 
0.7%
71 5
 
0.7%
46 4
 
0.6%
106.8 4
 
0.6%
41 4
 
0.6%
44.4 4
 
0.6%
34.8 4
 
0.6%
98.8 4
 
0.6%
105.8 4
 
0.6%
Other values (450) 630
93.3%
ValueCountFrequency (%)
1.8 2
0.3%
3.6 1
0.1%
4.2 1
0.1%
4.4 1
0.1%
5.2 1
0.1%
5.4 2
0.3%
6.2 1
0.1%
6.4 1
0.1%
6.6 1
0.1%
7 1
0.1%
ValueCountFrequency (%)
177.2 1
 
0.1%
176.4 1
 
0.1%
175 1
 
0.1%
174.6 2
0.3%
174.4 1
 
0.1%
174 1
 
0.1%
173.8 3
0.4%
173.6 1
 
0.1%
172.2 1
 
0.1%
172 1
 
0.1%

disp_sig_str
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct490
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.62163
Minimum0
Maximum999
Zeros85
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-08-29T09:49:15.255403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.3
median146
Q3266.1
95-th percentile498.04
Maximum999
Range999
Interquartile range (IQR)226.8

Descriptive statistics

Standard deviation188.47709
Coefficient of variation (CV)1.0264428
Kurtosis4.3299795
Mean183.62163
Median Absolute Deviation (MAD)110
Skewness1.7822197
Sum123944.6
Variance35523.614
MonotonicityNot monotonic
2024-08-29T09:49:15.343053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85
 
12.6%
413 19
 
2.8%
169 12
 
1.8%
999 7
 
1.0%
9.8 3
 
0.4%
62.8 3
 
0.4%
55.2 3
 
0.4%
21.4 3
 
0.4%
61.4 3
 
0.4%
69.8 3
 
0.4%
Other values (480) 534
79.1%
ValueCountFrequency (%)
0 85
12.6%
0.6 1
 
0.1%
0.8 1
 
0.1%
1 1
 
0.1%
1.4 1
 
0.1%
2.4 1
 
0.1%
4 1
 
0.1%
4.8 1
 
0.1%
5 1
 
0.1%
5.2 1
 
0.1%
ValueCountFrequency (%)
999 7
1.0%
964.6 1
 
0.1%
955.8 1
 
0.1%
903.6 1
 
0.1%
868.6 1
 
0.1%
856 1
 
0.1%
839 1
 
0.1%
802.4 1
 
0.1%
802 1
 
0.1%
769.2 1
 
0.1%

t_b_a
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct138
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8743704
Minimum-34.8
Maximum175.8
Zeros12
Zeros (%)1.8%
Negative196
Negative (%)29.0%
Memory size5.4 KiB
2024-08-29T09:49:15.428953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-34.8
5-th percentile-5.4
Q1-0.6
median1.8
Q36.5
95-th percentile19
Maximum175.8
Range210.6
Interquartile range (IQR)7.1

Descriptive statistics

Standard deviation28.479658
Coefficient of variation (CV)3.6167536
Kurtosis27.3116
Mean7.8743704
Median Absolute Deviation (MAD)3.4
Skewness5.2135732
Sum5315.2
Variance811.0909
MonotonicityNot monotonic
2024-08-29T09:49:15.510947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 36
 
5.3%
0.4 23
 
3.4%
0.6 21
 
3.1%
0.8 18
 
2.7%
2.8 17
 
2.5%
1.8 15
 
2.2%
1.2 15
 
2.2%
1.4 14
 
2.1%
2 14
 
2.1%
18.8 13
 
1.9%
Other values (128) 489
72.4%
ValueCountFrequency (%)
-34.8 1
0.1%
-17.6 1
0.1%
-15.4 1
0.1%
-10.6 2
0.3%
-10.2 1
0.1%
-9.8 1
0.1%
-9.6 1
0.1%
-9.4 1
0.1%
-9.2 1
0.1%
-9 1
0.1%
ValueCountFrequency (%)
175.8 1
0.1%
174.4 1
0.1%
174.2 1
0.1%
173.8 2
0.3%
173.6 2
0.3%
173.4 1
0.1%
173.2 1
0.1%
173 1
0.1%
172.8 2
0.3%
172.6 1
0.1%

t_p_a
Real number (ℝ)

HIGH CORRELATION 

Distinct209
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.3460741
Minimum-71
Maximum30.4
Zeros0
Zeros (%)0.0%
Negative309
Negative (%)45.8%
Memory size5.4 KiB
2024-08-29T09:49:15.594025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-71
5-th percentile-32.4
Q1-11.2
median1
Q313.9
95-th percentile17.2
Maximum30.4
Range101.4
Interquartile range (IQR)25.1

Descriptive statistics

Standard deviation20.262185
Coefficient of variation (CV)-6.05551
Kurtosis1.79464
Mean-3.3460741
Median Absolute Deviation (MAD)12.8
Skewness-1.3425205
Sum-2258.6
Variance410.55614
MonotonicityNot monotonic
2024-08-29T09:49:15.679935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-32 50
 
7.4%
15.4 13
 
1.9%
15.2 13
 
1.9%
-70.8 12
 
1.8%
1.6 11
 
1.6%
0.2 10
 
1.5%
-0.6 10
 
1.5%
15 10
 
1.5%
16 10
 
1.5%
-0.8 10
 
1.5%
Other values (199) 526
77.9%
ValueCountFrequency (%)
-71 6
0.9%
-70.8 12
1.8%
-70.6 1
 
0.1%
-70.2 1
 
0.1%
-69.6 1
 
0.1%
-56.6 1
 
0.1%
-47.4 1
 
0.1%
-35.8 1
 
0.1%
-34 3
 
0.4%
-33.8 2
 
0.3%
ValueCountFrequency (%)
30.4 1
 
0.1%
23.6 1
 
0.1%
22 1
 
0.1%
21.4 3
0.4%
21 1
 
0.1%
20.4 1
 
0.1%
20 1
 
0.1%
19.8 1
 
0.1%
19.4 1
 
0.1%
19.2 2
0.3%

bargraph
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct409
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.623763
Minimum0
Maximum79
Zeros86
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2024-08-29T09:49:15.762736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median11.92
Q320.46
95-th percentile38.536
Maximum79
Range79
Interquartile range (IQR)16.96

Descriptive statistics

Standard deviation14.952217
Coefficient of variation (CV)1.0224603
Kurtosis4.371327
Mean14.623763
Median Absolute Deviation (MAD)8.52
Skewness1.7952681
Sum9871.04
Variance223.5688
MonotonicityNot monotonic
2024-08-29T09:49:15.855984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86
 
12.7%
33 19
 
2.8%
14 11
 
1.6%
79 7
 
1.0%
4 7
 
1.0%
4.08 5
 
0.7%
21.08 4
 
0.6%
16.92 4
 
0.6%
0.24 4
 
0.6%
4.16 4
 
0.6%
Other values (399) 524
77.6%
ValueCountFrequency (%)
0 86
12.7%
0.08 1
 
0.1%
0.2 2
 
0.3%
0.24 4
 
0.6%
0.28 2
 
0.3%
0.36 2
 
0.3%
0.4 2
 
0.3%
0.52 2
 
0.3%
0.56 2
 
0.3%
0.64 2
 
0.3%
ValueCountFrequency (%)
79 7
1.0%
78.28 1
 
0.1%
73.8 1
 
0.1%
73 1
 
0.1%
72.84 1
 
0.1%
68.36 1
 
0.1%
62.44 1
 
0.1%
61.16 2
 
0.3%
60.36 1
 
0.1%
59.72 1
 
0.1%

ant_mode_Peak
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size807.0 B
True
602 
False
73 
ValueCountFrequency (%)
True 602
89.2%
False 73
 
10.8%
2024-08-29T09:49:15.929388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

ant_mode_Peak+
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size807.0 B
False
602 
True
73 
ValueCountFrequency (%)
False 602
89.2%
True 73
 
10.8%
2024-08-29T09:49:15.986505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

op_mode_Active
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size807.0 B
True
554 
False
121 
ValueCountFrequency (%)
True 554
82.1%
False 121
 
17.9%
2024-08-29T09:49:16.041594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

op_mode_CD
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size807.0 B
False
671 
True
 
4
ValueCountFrequency (%)
False 671
99.4%
True 4
 
0.6%
2024-08-29T09:49:16.108191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

op_mode_MENU
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size807.0 B
False
570 
True
105 
ValueCountFrequency (%)
False 570
84.4%
True 105
 
15.6%
2024-08-29T09:49:16.171156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

op_mode_Power
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size807.0 B
False
665 
True
 
10
ValueCountFrequency (%)
False 665
98.5%
True 10
 
1.5%
2024-08-29T09:49:16.244078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

op_mode_Radio
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size807.0 B
False
673 
True
 
2
ValueCountFrequency (%)
False 673
99.7%
True 2
 
0.3%
2024-08-29T09:49:16.544526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Sonde_Line_Line
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size807.0 B
True
675 
ValueCountFrequency (%)
True 675
100.0%
2024-08-29T09:49:16.594897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Interactions

2024-08-29T09:49:12.997316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.167340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.766263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.400512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.978794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.584023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.184585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.911705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.434863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:13.059814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.227946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.831296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.459018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.036082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.649022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.245069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.965809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.495307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:13.132307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.310498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.903999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.527523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.105733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.722465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.310894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.029531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.567135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:13.195882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.378780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.970520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.587888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.167753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.785250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.541743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.085893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.630899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:13.272765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.451790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.064628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.659148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.230752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.849721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.608819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.143813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.694927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:13.355768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.520047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.133052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.728600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.298208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.922390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.673538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.206266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.757622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:13.421690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.583865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.201984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.791854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.362413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.988951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.729539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.262612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.818041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:13.480198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.640119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.261944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.847821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.435193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.050044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.789165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.314385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.871862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:13.544259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:08.698130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.326640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:09.906539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:10.503267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.113960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:11.846160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.370674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-08-29T09:49:12.926775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-08-29T09:49:16.644808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
SwingWarningTicketstart_Stopeventsant_mode_Peakant_mode_Peak+bargraphcomp_dispcurdepthdisp_sig_strgainkey_b_eop_mode_Activeop_mode_CDop_mode_MENUop_mode_Powerop_mode_Radiot_b_at_p_atemp
SwingWarning1.0000.0720.0000.000-0.0070.055-0.008-0.009-0.0060.013-0.0140.0000.0000.0000.0000.000-0.061-0.0180.000
Ticketstart_Stopevents0.0721.0000.1660.1660.412-0.0140.2820.2690.415-0.087-0.0710.4990.0000.5340.0290.000-0.0340.0470.173
ant_mode_Peak0.0000.1661.0000.9920.5270.0620.0760.0790.5280.583-0.4850.7380.0000.8040.0000.000-0.0580.3050.112
ant_mode_Peak+0.0000.1660.9921.000-0.527-0.062-0.076-0.079-0.528-0.5830.4850.7380.0000.8040.0000.0000.058-0.3050.112
bargraph-0.0070.4120.527-0.5271.000-0.1540.2120.2270.9840.309-0.1970.3250.3090.3410.2110.000-0.0120.0890.280
comp_disp0.055-0.0140.062-0.062-0.1541.0000.0700.067-0.1540.167-0.0440.1190.1620.1300.0960.147-0.2000.1550.297
cur-0.0080.2820.076-0.0760.2120.0701.0000.9690.2050.0460.0210.0000.0000.0000.0000.000-0.1320.0100.000
depth-0.0090.2690.079-0.0790.2270.0670.9691.0000.2210.0280.0360.0000.0000.0200.0790.000-0.136-0.0010.048
disp_sig_str-0.0060.4150.528-0.5280.984-0.1540.2050.2211.0000.302-0.2170.3490.1980.3550.3050.000-0.0080.0890.319
gain0.013-0.0870.583-0.5830.3090.1670.0460.0280.3021.000-0.2970.8470.9960.8080.9450.119-0.0230.2250.184
key_b_e-0.014-0.071-0.4850.485-0.197-0.0440.0210.036-0.217-0.2971.0000.2560.3280.1680.2600.3530.037-0.2850.000
op_mode_Active0.0000.4990.7380.7380.3250.1190.0000.0000.3490.8470.2561.0000.1350.9130.2440.071-0.0100.2370.158
op_mode_CD0.0000.0000.0000.0000.3090.1620.0000.0000.1980.9960.3280.1351.0000.0000.0000.000-0.072-0.0700.000
op_mode_MENU0.0000.5340.8040.8040.3410.1300.0000.0200.3550.8080.1680.9130.0001.0000.0000.0000.058-0.2030.144
op_mode_Power0.0000.0290.0000.0000.2110.0960.0000.0790.3050.9450.2600.2440.0000.0001.0000.000-0.091-0.0730.000
op_mode_Radio0.0000.0000.0000.0000.0000.1470.0000.0000.0000.1190.3530.0710.0000.0000.0001.000-0.017-0.0520.000
t_b_a-0.061-0.034-0.0580.058-0.012-0.200-0.132-0.136-0.008-0.0230.037-0.010-0.0720.058-0.091-0.0171.000-0.3540.506
t_p_a-0.0180.0470.305-0.3050.0890.1550.010-0.0010.0890.225-0.2850.237-0.070-0.203-0.073-0.052-0.3541.0000.816
temp0.0000.1730.1120.1120.2800.2970.0000.0480.3190.1840.0000.1580.0000.1440.0000.0000.5060.8161.000

Missing values

2024-08-29T09:49:13.654755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-29T09:49:13.841195image/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_etempSwingWarningStrikeAlertcomp_dispdisp_sig_strt_b_at_p_abargraphant_mode_Peakant_mode_Peak+op_mode_Activeop_mode_CDop_mode_MENUop_mode_Powerop_mode_RadioSonde_Line_Line
08192500.00.090031FalseFalse44.20.018.4-32.22.20TrueFalseTrueFalseFalseFalseFalseTrue
18192500.00.090031FalseFalse35.6194.018.8-32.018.08TrueFalseTrueFalseFalseFalseFalseTrue
28192500.00.090031FalseFalse35.0225.418.8-32.015.40TrueFalseTrueFalseFalseFalseFalseTrue
38192500.00.090031FalseFalse43.8212.818.8-32.018.16TrueFalseTrueFalseFalseFalseFalseTrue
48192500.00.090031FalseFalse29.8161.418.6-32.011.80TrueFalseTrueFalseFalseFalseFalseTrue
58192500.00.090031FalseFalse30.8240.219.0-32.019.20TrueFalseTrueFalseFalseFalseFalseTrue
68192500.00.090031FalseFalse39.0196.419.0-32.016.72TrueFalseTrueFalseFalseFalseFalseTrue
78192500.00.090031FalseFalse35.8272.819.0-32.019.80TrueFalseTrueFalseFalseFalseFalseTrue
88192500.00.090031FalseFalse32.8200.819.0-32.018.08TrueFalseTrueFalseFalseFalseFalseTrue
98192500.00.090031FalseFalse37.4296.819.0-32.020.20TrueFalseTrueFalseFalseFalseFalseTrue
frequtil_typeTicketstart_Stopeventsdepthcurgainkey_b_etempSwingWarningStrikeAlertcomp_dispdisp_sig_strt_b_at_p_abargraphant_mode_Peakant_mode_Peak+op_mode_Activeop_mode_CDop_mode_MENUop_mode_Powerop_mode_RadioSonde_Line_Line
66532768520.00.0101032FalseFalse51.4413.0-2.613.433.0TrueFalseFalseFalseTrueFalseFalseTrue
66632768520.00.0101032FalseFalse22.4413.06.68.833.0TrueFalseFalseFalseTrueFalseFalseTrue
66732768520.00.0101032FalseFalse74.0413.03.27.033.0TrueFalseFalseFalseTrueFalseFalseTrue
66832768520.00.0101032FalseFalse107.0413.0-1.6-0.633.0TrueFalseFalseFalseTrueFalseFalseTrue
66932768520.00.0101032FalseFalse41.0413.0-2.8-2.833.0TrueFalseFalseFalseTrueFalseFalseTrue
67032768520.00.0101432FalseFalse105.8413.04.4-10.433.0TrueFalseFalseFalseTrueFalseFalseTrue
67132768520.00.0101432FalseFalse165.2413.0-2.0-5.633.0TrueFalseFalseFalseTrueFalseFalseTrue
67232768520.00.0101432FalseFalse162.2413.07.6-8.433.0TrueFalseFalseFalseTrueFalseFalseTrue
67332768520.00.0101032FalseFalse156.4413.0-1.0-2.833.0TrueFalseFalseFalseTrueFalseFalseTrue
67432768520.00.0101032FalseFalse161.2413.05.03.233.0TrueFalseFalseFalseTrueFalseFalseTrue