Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations6663
Missing cells11345
Missing cells (%)9.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory344.6 B

Variable types

Text3
Numeric10
Boolean2
Unsupported1
Categorical1
DateTime1

Alerts

position_source has constant value "0" Constant
timestamp has constant value "2025-06-22 00:47:32.596053" Constant
baro_altitude is highly overall correlated with geo_altitude and 1 other fieldsHigh correlation
geo_altitude is highly overall correlated with baro_altitude and 2 other fieldsHigh correlation
last_contact is highly overall correlated with time_positionHigh correlation
on_ground is highly overall correlated with geo_altitudeHigh correlation
time_position is highly overall correlated with last_contactHigh correlation
velocity is highly overall correlated with baro_altitude and 1 other fieldsHigh correlation
on_ground is highly imbalanced (58.3%) Imbalance
spi is highly imbalanced (92.5%) Imbalance
callsign has 123 (1.8%) missing values Missing
baro_altitude has 597 (9.0%) missing values Missing
vertical_rate has 569 (8.5%) missing values Missing
sensors has 6663 (100.0%) missing values Missing
geo_altitude has 645 (9.7%) missing values Missing
squawk has 2569 (38.6%) missing values Missing
icao24 has unique values Unique
sensors is an unsupported type, check if it needs cleaning or further analysis Unsupported
velocity has 132 (2.0%) zeros Zeros
vertical_rate has 2358 (35.4%) zeros Zeros

Reproduction

Analysis started2025-06-22 00:50:10.415487
Analysis finished2025-06-22 00:50:31.358258
Duration20.94 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

icao24
Text

Unique 

Distinct6663
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
2025-06-21T19:50:31.746199image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters39978
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

Unique6663 ?
Unique (%)100.0%

Sample

1st rowe8027b
2nd rowa61867
3rd rowaa9321
4th rowa4b205
5th row80162e
ValueCountFrequency (%)
e8027b 1
 
< 0.1%
a66a86 1
 
< 0.1%
aa9321 1
 
< 0.1%
a4b205 1
 
< 0.1%
80162e 1
 
< 0.1%
80162f 1
 
< 0.1%
a09294 1
 
< 0.1%
a41ef3 1
 
< 0.1%
acdfa6 1
 
< 0.1%
51111d 1
 
< 0.1%
Other values (6653) 6653
99.8%
2025-06-21T19:50:32.354403image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 5900
14.8%
0 3231
 
8.1%
8 2903
 
7.3%
c 2845
 
7.1%
4 2797
 
7.0%
7 2652
 
6.6%
1 2417
 
6.0%
6 2198
 
5.5%
2 2087
 
5.2%
b 2081
 
5.2%
Other values (6) 10867
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5900
14.8%
0 3231
 
8.1%
8 2903
 
7.3%
c 2845
 
7.1%
4 2797
 
7.0%
7 2652
 
6.6%
1 2417
 
6.0%
6 2198
 
5.5%
2 2087
 
5.2%
b 2081
 
5.2%
Other values (6) 10867
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5900
14.8%
0 3231
 
8.1%
8 2903
 
7.3%
c 2845
 
7.1%
4 2797
 
7.0%
7 2652
 
6.6%
1 2417
 
6.0%
6 2198
 
5.5%
2 2087
 
5.2%
b 2081
 
5.2%
Other values (6) 10867
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5900
14.8%
0 3231
 
8.1%
8 2903
 
7.3%
c 2845
 
7.1%
4 2797
 
7.0%
7 2652
 
6.6%
1 2417
 
6.0%
6 2198
 
5.5%
2 2087
 
5.2%
b 2081
 
5.2%
Other values (6) 10867
27.2%

callsign
Text

Missing 

Distinct6527
Distinct (%)99.8%
Missing123
Missing (%)1.8%
Memory size368.0 KiB
2025-06-21T19:50:32.785516image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters52320
Distinct characters37
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

Unique6526 ?
Unique (%)99.8%

Sample

1st rowLAN533
2nd rowN492K
3rd rowUAL97
4th rowN401TD
5th rowAIC2965
ValueCountFrequency (%)
daa 3
 
< 0.1%
tow 2
 
< 0.1%
lan533 1
 
< 0.1%
aal2556 1
 
< 0.1%
aic2965 1
 
< 0.1%
aic2720 1
 
< 0.1%
n136mf 1
 
< 0.1%
n365av 1
 
< 0.1%
aal2752 1
 
< 0.1%
tvs2055 1
 
< 0.1%
Other values (6521) 6521
99.8%
2025-06-21T19:50:33.332082image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11278
21.6%
A 4211
 
8.0%
1 2909
 
5.6%
2 2764
 
5.3%
3 2245
 
4.3%
5 2119
 
4.1%
4 2053
 
3.9%
7 1945
 
3.7%
6 1902
 
3.6%
0 1780
 
3.4%
Other values (27) 19114
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11278
21.6%
A 4211
 
8.0%
1 2909
 
5.6%
2 2764
 
5.3%
3 2245
 
4.3%
5 2119
 
4.1%
4 2053
 
3.9%
7 1945
 
3.7%
6 1902
 
3.6%
0 1780
 
3.4%
Other values (27) 19114
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11278
21.6%
A 4211
 
8.0%
1 2909
 
5.6%
2 2764
 
5.3%
3 2245
 
4.3%
5 2119
 
4.1%
4 2053
 
3.9%
7 1945
 
3.7%
6 1902
 
3.6%
0 1780
 
3.4%
Other values (27) 19114
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11278
21.6%
A 4211
 
8.0%
1 2909
 
5.6%
2 2764
 
5.3%
3 2245
 
4.3%
5 2119
 
4.1%
4 2053
 
3.9%
7 1945
 
3.7%
6 1902
 
3.6%
0 1780
 
3.4%
Other values (27) 19114
36.5%
Distinct88
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size390.9 KiB
2025-06-21T19:50:33.662367image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length26
Median length13
Mean length11.051328
Min length4

Characters and Unicode

Total characters73635
Distinct characters50
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

Unique9 ?
Unique (%)0.1%

Sample

1st rowChile
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowIndia
ValueCountFrequency (%)
united 4138
36.6%
states 3869
34.2%
canada 313
 
2.8%
australia 268
 
2.4%
china 199
 
1.8%
kingdom 198
 
1.8%
japan 173
 
1.5%
turkey 145
 
1.3%
india 127
 
1.1%
republic 100
 
0.9%
Other values (91) 1776
15.7%
2025-06-21T19:50:34.168620image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 12535
17.0%
e 9294
12.6%
a 7736
10.5%
i 6024
8.2%
n 6012
8.2%
d 5177
7.0%
4643
 
6.3%
s 4523
 
6.1%
U 4148
 
5.6%
S 3999
 
5.4%
Other values (40) 9544
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73635
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 12535
17.0%
e 9294
12.6%
a 7736
10.5%
i 6024
8.2%
n 6012
8.2%
d 5177
7.0%
4643
 
6.3%
s 4523
 
6.1%
U 4148
 
5.6%
S 3999
 
5.4%
Other values (40) 9544
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73635
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 12535
17.0%
e 9294
12.6%
a 7736
10.5%
i 6024
8.2%
n 6012
8.2%
d 5177
7.0%
4643
 
6.3%
s 4523
 
6.1%
U 4148
 
5.6%
S 3999
 
5.4%
Other values (40) 9544
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73635
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 12535
17.0%
e 9294
12.6%
a 7736
10.5%
i 6024
8.2%
n 6012
8.2%
d 5177
7.0%
4643
 
6.3%
s 4523
 
6.1%
U 4148
 
5.6%
S 3999
 
5.4%
Other values (40) 9544
13.0%

time_position
Real number (ℝ)

High correlation 

Distinct373
Distinct (%)5.6%
Missing59
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1.7505532 × 109
Minimum1.7505468 × 109
Maximum1.7505532 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.2 KiB
2025-06-21T19:50:34.512661image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1.7505468 × 109
5-th percentile1.750553 × 109
Q11.7505532 × 109
median1.7505532 × 109
Q31.7505532 × 109
95-th percentile1.7505532 × 109
Maximum1.7505532 × 109
Range6395
Interquartile range (IQR)3

Descriptive statistics

Standard deviation198.7653
Coefficient of variation (CV)1.1354428 × 10-7
Kurtosis402.76818
Mean1.7505532 × 109
Median Absolute Deviation (MAD)1
Skewness-17.582392
Sum1.1560653 × 1013
Variance39507.644
MonotonicityNot monotonic
2025-06-21T19:50:34.710685image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1750553236 2544
38.2%
1750553237 2154
32.3%
1750553235 231
 
3.5%
1750553234 110
 
1.7%
1750553232 62
 
0.9%
1750553233 61
 
0.9%
1750553228 39
 
0.6%
1750553231 38
 
0.6%
1750553230 34
 
0.5%
1750553229 33
 
0.5%
Other values (363) 1298
19.5%
(Missing) 59
 
0.9%
ValueCountFrequency (%)
1750546842 1
< 0.1%
1750547797 1
< 0.1%
1750548709 1
< 0.1%
1750549029 1
< 0.1%
1750549053 1
< 0.1%
1750549709 1
< 0.1%
1750549894 1
< 0.1%
1750550057 1
< 0.1%
1750550516 1
< 0.1%
1750550649 1
< 0.1%
ValueCountFrequency (%)
1750553237 2154
32.3%
1750553236 2544
38.2%
1750553235 231
 
3.5%
1750553234 110
 
1.7%
1750553233 61
 
0.9%
1750553232 62
 
0.9%
1750553231 38
 
0.6%
1750553230 34
 
0.5%
1750553229 33
 
0.5%
1750553228 39
 
0.6%

last_contact
Real number (ℝ)

High correlation 

Distinct291
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7505532 × 109
Minimum1.7505529 × 109
Maximum1.7505532 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.2 KiB
2025-06-21T19:50:34.983223image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1.7505529 × 109
5-th percentile1.7505531 × 109
Q11.7505532 × 109
median1.7505532 × 109
Q31.7505532 × 109
95-th percentile1.7505532 × 109
Maximum1.7505532 × 109
Range315
Interquartile range (IQR)2

Descriptive statistics

Standard deviation58.143274
Coefficient of variation (CV)3.3214228 × 10-8
Kurtosis9.5349579
Mean1.7505532 × 109
Median Absolute Deviation (MAD)1
Skewness-3.1749366
Sum1.1663936 × 1013
Variance3380.6403
MonotonicityNot monotonic
2025-06-21T19:50:35.189287image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1750553237 2948
44.2%
1750553236 2036
30.6%
1750553235 171
 
2.6%
1750553234 108
 
1.6%
1750553233 61
 
0.9%
1750553232 56
 
0.8%
1750553231 33
 
0.5%
1750553228 31
 
0.5%
1750553230 27
 
0.4%
1750553229 25
 
0.4%
Other values (281) 1167
 
17.5%
ValueCountFrequency (%)
1750552922 2
< 0.1%
1750552923 1
 
< 0.1%
1750552924 2
< 0.1%
1750552925 2
< 0.1%
1750552926 2
< 0.1%
1750552927 4
0.1%
1750552928 4
0.1%
1750552929 2
< 0.1%
1750552930 3
< 0.1%
1750552931 2
< 0.1%
ValueCountFrequency (%)
1750553237 2948
44.2%
1750553236 2036
30.6%
1750553235 171
 
2.6%
1750553234 108
 
1.6%
1750553233 61
 
0.9%
1750553232 56
 
0.8%
1750553231 33
 
0.5%
1750553230 27
 
0.4%
1750553229 25
 
0.4%
1750553228 31
 
0.5%

longitude
Real number (ℝ)

Distinct6547
Distinct (%)99.1%
Missing59
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean-37.026506
Minimum-173.9391
Maximum177.4456
Zeros0
Zeros (%)0.0%
Negative4615
Negative (%)69.3%
Memory size52.2 KiB
2025-06-21T19:50:35.391218image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-173.9391
5-th percentile-121.81914
Q1-97.5779
median-80.9744
Q324.454125
95-th percentile144.69374
Maximum177.4456
Range351.3847
Interquartile range (IQR)122.03203

Descriptive statistics

Standard deviation90.726243
Coefficient of variation (CV)-2.4503053
Kurtosis-0.44186241
Mean-37.026506
Median Absolute Deviation (MAD)25.9598
Skewness1.0160607
Sum-244523.04
Variance8231.2512
MonotonicityNot monotonic
2025-06-21T19:50:35.582730image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-84.4321 3
 
< 0.1%
-87.9309 3
 
< 0.1%
-84.4379 3
 
< 0.1%
-122.3062 3
 
< 0.1%
-96.7506 2
 
< 0.1%
-83.9808 2
 
< 0.1%
-84.4346 2
 
< 0.1%
-84.5513 2
 
< 0.1%
149.0505 2
 
< 0.1%
140.3965 2
 
< 0.1%
Other values (6537) 6580
98.8%
(Missing) 59
 
0.9%
ValueCountFrequency (%)
-173.9391 1
< 0.1%
-173.4554 1
< 0.1%
-173.0243 1
< 0.1%
-172.5567 1
< 0.1%
-171.626 1
< 0.1%
-168.3876 1
< 0.1%
-167.5077 1
< 0.1%
-167.1695 1
< 0.1%
-166.635 1
< 0.1%
-164.1511 1
< 0.1%
ValueCountFrequency (%)
177.4456 1
< 0.1%
177.0308 1
< 0.1%
176.9613 1
< 0.1%
176.7977 1
< 0.1%
176.7735 1
< 0.1%
176.5565 1
< 0.1%
176.3925 1
< 0.1%
176.2877 2
< 0.1%
176.2163 1
< 0.1%
176.2152 1
< 0.1%

latitude
Real number (ℝ)

Distinct6494
Distinct (%)98.3%
Missing59
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean31.593744
Minimum-45.6392
Maximum71.2872
Zeros0
Zeros (%)0.0%
Negative541
Negative (%)8.1%
Memory size52.2 KiB
2025-06-21T19:50:35.791357image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-45.6392
5-th percentile-30.9399
Q130.48935
median36.2175
Q341.51615
95-th percentile51.21981
Maximum71.2872
Range116.9264
Interquartile range (IQR)11.0268

Descriptive statistics

Standard deviation20.559621
Coefficient of variation (CV)0.65074974
Kurtosis4.6207072
Mean31.593744
Median Absolute Deviation (MAD)5.4179
Skewness-2.1777567
Sum208645.09
Variance422.698
MonotonicityNot monotonic
2025-06-21T19:50:35.990965image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.6358 4
 
0.1%
41.8931 3
 
< 0.1%
38.9492 3
 
< 0.1%
33.6454 3
 
< 0.1%
43.6748 2
 
< 0.1%
40.5407 2
 
< 0.1%
37.5871 2
 
< 0.1%
40.2308 2
 
< 0.1%
43.5372 2
 
< 0.1%
42.3606 2
 
< 0.1%
Other values (6484) 6579
98.7%
(Missing) 59
 
0.9%
ValueCountFrequency (%)
-45.6392 1
< 0.1%
-45.2627 1
< 0.1%
-44.6819 1
< 0.1%
-44.4811 1
< 0.1%
-44.397 1
< 0.1%
-44.293 1
< 0.1%
-43.4856 1
< 0.1%
-43.4853 1
< 0.1%
-43.4852 1
< 0.1%
-43.472 1
< 0.1%
ValueCountFrequency (%)
71.2872 1
< 0.1%
70.9865 1
< 0.1%
65.5997 1
< 0.1%
64.9685 1
< 0.1%
64.5289 1
< 0.1%
64.4162 1
< 0.1%
64.3574 1
< 0.1%
64.1922 1
< 0.1%
64.1906 1
< 0.1%
64.1854 1
< 0.1%

baro_altitude
Real number (ℝ)

High correlation  Missing 

Distinct1255
Distinct (%)20.7%
Missing597
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean7156.5882
Minimum-129.54
Maximum38191.44
Zeros9
Zeros (%)0.1%
Negative15
Negative (%)0.2%
Memory size52.2 KiB
2025-06-21T19:50:36.207868image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-129.54
5-th percentile304.8
Q12514.6
median9144
Q310965.18
95-th percentile11887.2
Maximum38191.44
Range38320.98
Interquartile range (IQR)8450.58

Descriptive statistics

Standard deviation4317.7504
Coefficient of variation (CV)0.60332525
Kurtosis-0.51211853
Mean7156.5882
Median Absolute Deviation (MAD)2438.4
Skewness-0.31080044
Sum43411864
Variance18642968
MonotonicityNot monotonic
2025-06-21T19:50:36.426528image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10972.8 393
 
5.9%
10668 328
 
4.9%
10363.2 278
 
4.2%
11277.6 267
 
4.0%
11582.4 236
 
3.5%
10058.4 152
 
2.3%
11887.2 120
 
1.8%
9753.6 112
 
1.7%
12192 83
 
1.2%
9448.8 63
 
0.9%
Other values (1245) 4034
60.5%
(Missing) 597
 
9.0%
ValueCountFrequency (%)
-129.54 1
 
< 0.1%
-99.06 1
 
< 0.1%
-91.44 1
 
< 0.1%
-53.34 3
 
< 0.1%
-38.1 1
 
< 0.1%
-22.86 5
0.1%
-15.24 3
 
< 0.1%
0 9
0.1%
7.62 3
 
< 0.1%
15.24 3
 
< 0.1%
ValueCountFrequency (%)
38191.44 1
 
< 0.1%
37459.92 1
 
< 0.1%
28925.52 1
 
< 0.1%
17495.52 1
 
< 0.1%
17221.2 1
 
< 0.1%
14935.2 1
 
< 0.1%
14333.22 1
 
< 0.1%
14325.6 6
 
0.1%
13723.62 1
 
< 0.1%
13716 19
0.3%

on_ground
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
False
6101 
True
 
562
ValueCountFrequency (%)
False 6101
91.6%
True 562
 
8.4%
2025-06-21T19:50:36.605265image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

velocity
Real number (ℝ)

High correlation  Zeros 

Distinct4891
Distinct (%)73.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean168.89813
Minimum0
Maximum1589.39
Zeros132
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size52.2 KiB
2025-06-21T19:50:36.768950image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.12
Q182.51
median212.48
Q3237.63
95-th percentile260.68
Maximum1589.39
Range1589.39
Interquartile range (IQR)155.12

Descriptive statistics

Standard deviation88.622874
Coefficient of variation (CV)0.524712
Kurtosis8.768745
Mean168.89813
Median Absolute Deviation (MAD)39.27
Skewness-0.033603253
Sum1125030.4
Variance7854.0139
MonotonicityNot monotonic
2025-06-21T19:50:36.968155image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
 
2.0%
0.06 26
 
0.4%
4.12 22
 
0.3%
7.72 18
 
0.3%
1.29 16
 
0.2%
5.92 14
 
0.2%
4.63 14
 
0.2%
1.03 13
 
0.2%
6.69 13
 
0.2%
8.23 12
 
0.2%
Other values (4881) 6381
95.8%
ValueCountFrequency (%)
0 132
2.0%
0.06 26
 
0.4%
0.13 1
 
< 0.1%
0.26 3
 
< 0.1%
0.32 4
 
0.1%
0.39 3
 
< 0.1%
0.45 8
 
0.1%
0.51 4
 
0.1%
0.64 7
 
0.1%
0.73 1
 
< 0.1%
ValueCountFrequency (%)
1589.39 1
< 0.1%
433.13 1
< 0.1%
315.71 1
< 0.1%
312.35 1
< 0.1%
311.61 1
< 0.1%
310.7 1
< 0.1%
309.73 1
< 0.1%
306.51 1
< 0.1%
302.82 1
< 0.1%
301.72 1
< 0.1%

heading
Real number (ℝ)

Distinct5558
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.67662
Minimum0
Maximum359.87
Zeros22
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size52.2 KiB
2025-06-21T19:50:37.203060image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.424
Q190
median184.11
Q3273.085
95-th percentile336.286
Maximum359.87
Range359.87
Interquartile range (IQR)183.085

Descriptive statistics

Standard deviation102.73743
Coefficient of variation (CV)0.56240053
Kurtosis-1.2851625
Mean182.67662
Median Absolute Deviation (MAD)91.4
Skewness-0.063374067
Sum1217174.3
Variance10554.979
MonotonicityNot monotonic
2025-06-21T19:50:37.427418image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
270 38
 
0.6%
90 36
 
0.5%
180 24
 
0.4%
0 22
 
0.3%
315 16
 
0.2%
135 14
 
0.2%
300.94 11
 
0.2%
45 10
 
0.2%
59.06 10
 
0.2%
67.5 10
 
0.2%
Other values (5548) 6472
97.1%
ValueCountFrequency (%)
0 22
0.3%
0.15 1
 
< 0.1%
0.39 1
 
< 0.1%
0.4 1
 
< 0.1%
0.43 2
 
< 0.1%
0.47 2
 
< 0.1%
0.51 1
 
< 0.1%
0.52 1
 
< 0.1%
0.56 1
 
< 0.1%
0.77 1
 
< 0.1%
ValueCountFrequency (%)
359.87 1
< 0.1%
359.77 2
< 0.1%
359.73 1
< 0.1%
359.72 1
< 0.1%
359.64 2
< 0.1%
359.59 2
< 0.1%
359.49 1
< 0.1%
359.33 1
< 0.1%
359.15 1
< 0.1%
358.9 1
< 0.1%

vertical_rate
Real number (ℝ)

Missing  Zeros 

Distinct117
Distinct (%)1.9%
Missing569
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean-0.094207417
Minimum-19.51
Maximum23.41
Zeros2358
Zeros (%)35.4%
Negative2111
Negative (%)31.7%
Memory size52.2 KiB
2025-06-21T19:50:37.629653image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-19.51
5-th percentile-8.13
Q1-0.65
median0
Q30.33
95-th percentile8.78
Maximum23.41
Range42.92
Interquartile range (IQR)0.98

Descriptive statistics

Standard deviation4.6091028
Coefficient of variation (CV)-48.925052
Kurtosis2.7885142
Mean-0.094207417
Median Absolute Deviation (MAD)0.33
Skewness0.37591954
Sum-574.1
Variance21.243828
MonotonicityNot monotonic
2025-06-21T19:50:37.972633image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2358
35.4%
-0.33 573
 
8.6%
0.33 465
 
7.0%
-0.65 98
 
1.5%
-3.58 93
 
1.4%
-4.88 87
 
1.3%
0.65 82
 
1.2%
-5.2 82
 
1.2%
-0.98 71
 
1.1%
-3.9 68
 
1.0%
Other values (107) 2117
31.8%
(Missing) 569
 
8.5%
ValueCountFrequency (%)
-19.51 1
 
< 0.1%
-17.88 1
 
< 0.1%
-17.56 2
< 0.1%
-17.23 1
 
< 0.1%
-16.91 1
 
< 0.1%
-16.58 3
< 0.1%
-16.26 2
< 0.1%
-15.93 1
 
< 0.1%
-15.28 1
 
< 0.1%
-14.96 3
< 0.1%
ValueCountFrequency (%)
23.41 1
 
< 0.1%
20.81 1
 
< 0.1%
19.83 1
 
< 0.1%
19.18 1
 
< 0.1%
18.86 1
 
< 0.1%
18.21 3
< 0.1%
17.88 2
< 0.1%
17.56 3
< 0.1%
17.23 3
< 0.1%
16.91 3
< 0.1%

sensors
Unsupported

Missing  Rejected  Unsupported 

Missing6663
Missing (%)100.0%
Memory size52.2 KiB

geo_altitude
Real number (ℝ)

High correlation  Missing 

Distinct1569
Distinct (%)26.1%
Missing645
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean7510.4217
Minimum-22.86
Maximum29016.96
Zeros1
Zeros (%)< 0.1%
Negative4
Negative (%)0.1%
Memory size52.2 KiB
2025-06-21T19:50:38.156481image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-22.86
5-th percentile335.28
Q12630.805
median9574.53
Q311361.42
95-th percentile12458.7
Maximum29016.96
Range29039.82
Interquartile range (IQR)8730.615

Descriptive statistics

Standard deviation4490.3366
Coefficient of variation (CV)0.59788076
Kurtosis-1.3229781
Mean7510.4217
Median Absolute Deviation (MAD)2495.55
Skewness-0.4383782
Sum45197718
Variance20163123
MonotonicityNot monotonic
2025-06-21T19:50:38.357523image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11643.36 29
 
0.4%
11635.74 25
 
0.4%
11277.6 23
 
0.3%
11597.64 23
 
0.3%
10988.04 23
 
0.3%
10972.8 22
 
0.3%
11612.88 22
 
0.3%
11658.6 21
 
0.3%
11628.12 19
 
0.3%
11033.76 18
 
0.3%
Other values (1559) 5793
86.9%
(Missing) 645
 
9.7%
ValueCountFrequency (%)
-22.86 3
 
< 0.1%
-15.24 1
 
< 0.1%
0 1
 
< 0.1%
7.62 4
0.1%
30.48 7
0.1%
38.1 3
 
< 0.1%
45.72 5
0.1%
53.34 4
0.1%
60.96 8
0.1%
68.58 8
0.1%
ValueCountFrequency (%)
29016.96 1
< 0.1%
18455.64 1
< 0.1%
18158.46 1
< 0.1%
15582.9 1
< 0.1%
15011.4 1
< 0.1%
15003.78 1
< 0.1%
14973.3 1
< 0.1%
14912.34 1
< 0.1%
14904.72 1
< 0.1%
14813.28 1
< 0.1%

squawk
Real number (ℝ)

Missing 

Distinct2436
Distinct (%)59.5%
Missing2569
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean3527.9851
Minimum4
Maximum7777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.2 KiB
2025-06-21T19:50:38.557365image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile571
Q11644
median3176.5
Q35423
95-th percentile7321
Maximum7777
Range7773
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation2152.8142
Coefficient of variation (CV)0.61021067
Kurtosis-1.0585915
Mean3527.9851
Median Absolute Deviation (MAD)1717.5
Skewness0.39551836
Sum14443571
Variance4634608.8
MonotonicityNot monotonic
2025-06-21T19:50:38.762366image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 141
 
2.1%
1000 22
 
0.3%
3000 10
 
0.2%
3103 6
 
0.1%
2652 5
 
0.1%
3223 5
 
0.1%
1765 5
 
0.1%
1077 5
 
0.1%
2111 5
 
0.1%
6270 5
 
0.1%
Other values (2426) 3885
58.3%
(Missing) 2569
38.6%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
13 1
< 0.1%
20 2
< 0.1%
21 1
< 0.1%
24 1
< 0.1%
27 1
< 0.1%
52 1
< 0.1%
61 1
< 0.1%
65 1
< 0.1%
ValueCountFrequency (%)
7777 4
0.1%
7770 1
 
< 0.1%
7762 1
 
< 0.1%
7760 1
 
< 0.1%
7757 1
 
< 0.1%
7756 2
< 0.1%
7754 2
< 0.1%
7753 1
 
< 0.1%
7750 1
 
< 0.1%
7745 1
 
< 0.1%

spi
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
False
6602 
True
 
61
ValueCountFrequency (%)
False 6602
99.1%
True 61
 
0.9%
2025-06-21T19:50:38.927532image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

position_source
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size325.5 KiB
0
6663 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6663
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 row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6663
100.0%

Length

2025-06-21T19:50:39.070577image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-21T19:50:39.205666image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6663
100.0%

Most occurring characters

ValueCountFrequency (%)
0 6663
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6663
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6663
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6663
100.0%

timestamp
Date

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.2 KiB
Minimum2025-06-22 00:47:32.596053
Maximum2025-06-22 00:47:32.596053
Invalid dates0
Invalid dates (%)0.0%
2025-06-21T19:50:39.319174image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:39.510069image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2025-06-21T19:50:28.481879image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:11.573434image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:13.512550image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:16.362442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:18.155005image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:20.059512image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:21.789528image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:23.420400image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:25.058596image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:26.827945image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:28.645979image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:11.914208image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:13.796704image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:16.631087image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:18.318381image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:20.233783image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:21.957400image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:23.626798image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:25.211543image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:26.973368image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:28.802336image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:12.081269image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:14.102903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:16.891618image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:18.476128image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:20.400284image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:22.133233image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:23.793215image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:25.368015image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:27.139169image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:28.962142image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:12.240273image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:14.448847image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:17.051092image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:18.621020image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:20.568888image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:22.292253image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:23.945698image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:25.519955image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:27.292593image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:29.115131image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:12.386300image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:14.769432image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:17.192982image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:18.796187image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:20.718478image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:22.441833image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:24.097573image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:25.662871image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:27.437681image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:29.296841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:12.572557image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:15.108563image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:17.368148image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:19.044939image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:20.898429image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:22.612157image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:24.272372image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:25.889435image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:27.610282image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:29.539395image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:12.730463image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:15.371023image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:17.522733image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:19.210300image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:21.085453image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:22.785701image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:24.441586image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:26.047291image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:27.769634image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:29.698324image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:12.887675image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:15.672074image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:17.683038image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:19.363306image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:21.299412image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:22.951149image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:24.599927image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:26.193194image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:27.979289image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:29.852415image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:13.041110image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:15.910492image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:17.829430image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:19.526234image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:21.462336image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:23.088954image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:24.742848image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:26.330789image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:28.156200image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:30.006244image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:13.183876image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:16.120466image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:18.000218image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:19.699557image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:21.621917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:23.249046image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:24.907917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:26.505680image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-06-21T19:50:28.319218image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-06-21T19:50:39.631133image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
baro_altitudegeo_altitudeheadinglast_contactlatitudelongitudeon_groundspisquawktime_positionvelocityvertical_rate
baro_altitude1.0000.9930.0210.1250.127-0.0030.1800.0150.0690.1150.7790.155
geo_altitude0.9931.0000.0210.1280.0860.0101.0000.0410.0630.1200.7880.156
heading0.0210.0211.0000.024-0.010-0.0500.0460.0000.000-0.013-0.076-0.011
last_contact0.1250.1280.0241.0000.086-0.0890.2010.0590.0170.8830.2420.056
latitude0.1270.086-0.0100.0861.000-0.3360.0500.0610.0840.0730.054-0.031
longitude-0.0030.010-0.050-0.089-0.3361.0000.0780.036-0.041-0.0990.0350.036
on_ground0.1801.0000.0460.2010.0500.0781.0000.0150.0000.0250.3910.000
spi0.0150.0410.0000.0590.0610.0360.0151.0000.0310.0410.0000.000
squawk0.0690.0630.0000.0170.084-0.0410.0000.0311.0000.0220.0580.005
time_position0.1150.120-0.0130.8830.073-0.0990.0250.0410.0221.0000.2360.055
velocity0.7790.788-0.0760.2420.0540.0350.3910.0000.0580.2361.0000.162
vertical_rate0.1550.156-0.0110.056-0.0310.0360.0000.0000.0050.0550.1621.000

Missing values

2025-06-21T19:50:30.258128image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-21T19:50:30.699402image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-21T19:50:31.175136image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

icao24callsignorigin_countrytime_positionlast_contactlongitudelatitudebaro_altitudeon_groundvelocityheadingvertical_ratesensorsgeo_altitudesquawkspiposition_sourcetimestamp
0e8027bLAN533Chile1.750553e+091750553236-73.813640.6487NaNTrue0.64312.19NaNNaNNaNNaNFalse02025-06-22 00:47:32.596053
1a61867N492KUnited States1.750553e+091750553236-156.665258.684230.48False27.66136.51-1.95NaN38.10NaNFalse02025-06-22 00:47:32.596053
2aa9321UAL97United States1.750553e+091750553237153.1309-27.3742NaNTrue11.3228.12NaNNaNNaN1110.0False02025-06-22 00:47:32.596053
3a4b205N401TDUnited States1.750553e+091750553236-74.158640.6293182.88False56.86241.35-0.65NaN213.361200.0False02025-06-22 00:47:32.596053
480162eAIC2965India1.750553e+09175055323676.151727.93525105.40False166.6740.87-7.48NaN5326.38NaNFalse02025-06-22 00:47:32.596053
580162fAIC2720India1.750553e+09175055305077.068928.5464358.14False72.49103.54-3.25NaN205.74NaNFalse02025-06-22 00:47:32.596053
6a09294N136MFUnited States1.750553e+091750552935-84.293438.0391548.64False68.7680.960.33NaN563.88NaNFalse02025-06-22 00:47:32.596053
7a41ef3N365AVUnited States1.750553e+091750553237-115.279944.891312199.62False254.66340.28-0.65NaN12359.64NaNFalse02025-06-22 00:47:32.596053
8acdfa6AAL2752United States1.750553e+091750553237-97.023832.8451807.72False96.49158.095.20NaN822.96NaNFalse02025-06-22 00:47:32.596053
951111dTVS2055Estonia1.750553e+09175055300018.170349.7330403.86False67.32226.55-3.25NaN533.401000.0False02025-06-22 00:47:32.596053
icao24callsignorigin_countrytime_positionlast_contactlongitudelatitudebaro_altitudeon_groundvelocityheadingvertical_ratesensorsgeo_altitudesquawkspiposition_sourcetimestamp
6653aa7833DAL2260United States1.750553e+091750553237-111.647835.536611269.98False248.41123.850.33NaN11841.48NaNFalse02025-06-22 00:47:32.596053
6654c00741WJA1427Canada1.750553e+091750553117-104.599747.183310972.80False227.31307.830.00NaN11346.181676.0False02025-06-22 00:47:32.596053
66554b1902EDW11Switzerland1.750553e+091750553030-120.472451.068210058.40False246.5349.060.00NaN10149.8424.0False02025-06-22 00:47:32.596053
6656abc0e4SWA3857United States1.750553e+091750553236-104.658739.97612392.68False98.77180.00-4.55NaN2362.205034.0False02025-06-22 00:47:32.596053
6657a1c28eN212SPUnited States1.750553e+091750553198-81.455928.4312213.36False56.78355.32-0.98NaN236.22NaNFalse02025-06-22 00:47:32.596053
6658a0c7b3ABX3111United States1.750553e+091750553237-111.963033.923910675.62False269.7784.530.00NaN11231.886776.0False02025-06-22 00:47:32.596053
66594b1900EDW7VSwitzerland1.750553e+091750553236-120.344149.166810271.76False225.4555.691.30NaN10355.586144.0False02025-06-22 00:47:32.596053
6660a6d83cDAL863United States1.750553e+091750553237-88.769740.934610363.20False227.15272.860.00NaN11010.902105.0False02025-06-22 00:47:32.596053
6661a09a01N138MEUnited States1.750553e+091750553233-122.053037.98710.00False20.71333.430.00NaN-22.86NaNFalse02025-06-22 00:47:32.596053
6662c00734WJA671Canada1.750553e+091750553236-90.910747.038110972.80False208.89288.080.00NaN11490.964532.0False02025-06-22 00:47:32.596053