Overview

Brought to you by Baobao Tang

Dataset statistics

Number of variables28
Number of observations79302
Missing cells745
Missing cells (%)< 0.1%
Duplicate rows15
Duplicate rows (%)< 0.1%
Total size in memory16.5 MiB
Average record size in memory218.3 B

Variable types

Numeric12
Categorical12
Text3
DateTime1

Dataset

DescriptionExploratory Data Analysis for OB Report - AWB.xlsx
AuthorBaobao Tang
URL

Alerts

Year has constant value "2023"Constant
Leg Origin has constant value "MIA"Constant
Dataset has 15 (< 0.1%) duplicate rowsDuplicates
Commodity has a high cardinality: 512 distinct valuesHigh cardinality
AWB Prefix is highly overall correlated with Airway Bill NumberHigh correlation
Airway Bill Number is highly overall correlated with AWB Prefix and 2 other fieldsHigh correlation
Bellies - Freighters is highly overall correlated with LEG and 4 other fieldsHigh correlation
KG Chg Operational is highly overall correlated with KG OB Chg and 4 other fieldsHigh correlation
KG OB Chg is highly overall correlated with KG Chg Operational and 4 other fieldsHigh correlation
KG OB Gross is highly overall correlated with KG Chg Operational and 4 other fieldsHigh correlation
KG Volumetric is highly overall correlated with KG Chg Operational and 4 other fieldsHigh correlation
LEG is highly overall correlated with Bellies - Freighters and 4 other fieldsHigh correlation
Leg Destination is highly overall correlated with Bellies - Freighters and 4 other fieldsHigh correlation
Month is highly overall correlated with Airway Bill Number and 1 other fieldsHigh correlation
OD Network Destination is highly overall correlated with LEG and 2 other fieldsHigh correlation
OD Real Destination is highly overall correlated with LEG and 2 other fieldsHigh correlation
Operating Flight Number is highly overall correlated with Bellies - Freighters and 1 other fieldsHigh correlation
Pieces is highly overall correlated with KG Chg Operational and 4 other fieldsHigh correlation
ProductCode is highly overall correlated with Bellies - FreightersHigh correlation
Route Owner is highly overall correlated with Bellies - Freighters and 3 other fieldsHigh correlation
Volume (m3) is highly overall correlated with KG Chg Operational and 4 other fieldsHigh correlation
Week is highly overall correlated with Airway Bill Number and 1 other fieldsHigh correlation
Route Owner is highly imbalanced (71.1%)Imbalance
Bellies - Freighters is highly imbalanced (53.7%)Imbalance
Commodity is highly imbalanced (65.9%)Imbalance
OD Network Origin is highly imbalanced (91.8%)Imbalance

Reproduction

Analysis started2024-09-19 03:12:26.597818
Analysis finished2024-09-19 03:12:35.519369
Duration8.92 seconds

Variables

Day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.735177
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:35.548878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6871954
Coefficient of variation (CV)0.55208756
Kurtosis-1.177198
Mean15.735177
Median Absolute Deviation (MAD)7
Skewness-0.016842829
Sum1247831
Variance75.467364
MonotonicityNot monotonic
2024-09-18T23:12:35.597782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11 3103
 
3.9%
18 3058
 
3.9%
22 2994
 
3.8%
25 2959
 
3.7%
15 2887
 
3.6%
8 2821
 
3.6%
4 2807
 
3.5%
26 2760
 
3.5%
2 2757
 
3.5%
16 2747
 
3.5%
Other values (21) 50409
63.6%
ValueCountFrequency (%)
1 2614
3.3%
2 2757
3.5%
3 2115
2.7%
4 2807
3.5%
5 2523
3.2%
6 2496
3.1%
7 2175
2.7%
8 2821
3.6%
9 2594
3.3%
10 2441
3.1%
ValueCountFrequency (%)
31 1079
 
1.4%
30 2209
2.8%
29 2537
3.2%
28 2376
3.0%
27 2688
3.4%
26 2760
3.5%
25 2959
3.7%
24 2273
2.9%
23 2715
3.4%
22 2994
3.8%

Month
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6223172
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:35.639126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2811078
Coefficient of variation (CV)0.49349876
Kurtosis-1.230866
Mean4.6223172
Median Absolute Deviation (MAD)2
Skewness-0.052522368
Sum366559
Variance5.203453
MonotonicityNot monotonic
2024-09-18T23:12:35.681143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8 10707
13.5%
7 10606
13.4%
3 10547
13.3%
6 10152
12.8%
4 9883
12.5%
5 9551
12.0%
2 8965
11.3%
1 8891
11.2%
ValueCountFrequency (%)
1 8891
11.2%
2 8965
11.3%
3 10547
13.3%
4 9883
12.5%
5 9551
12.0%
6 10152
12.8%
7 10606
13.4%
8 10707
13.5%
ValueCountFrequency (%)
8 10707
13.5%
7 10606
13.4%
6 10152
12.8%
5 9551
12.0%
4 9883
12.5%
3 10547
13.3%
2 8965
11.3%
1 8891
11.2%

Year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
2023
79302 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters317208
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 row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 79302
100.0%

Length

2024-09-18T23:12:35.727198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T23:12:35.768363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 79302
100.0%

Most occurring characters

ValueCountFrequency (%)
2 158604
50.0%
0 79302
25.0%
3 79302
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 317208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 158604
50.0%
0 79302
25.0%
3 79302
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 317208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 158604
50.0%
0 79302
25.0%
3 79302
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 317208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 158604
50.0%
0 79302
25.0%
3 79302
25.0%

Week
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.079065
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:35.808957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median19
Q328
95-th percentile34
Maximum36
Range35
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.9501198
Coefficient of variation (CV)0.52152031
Kurtosis-1.2120527
Mean19.079065
Median Absolute Deviation (MAD)9
Skewness-0.031603535
Sum1513008
Variance99.004883
MonotonicityNot monotonic
2024-09-18T23:12:35.861744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
27 2539
 
3.2%
30 2534
 
3.2%
33 2529
 
3.2%
32 2521
 
3.2%
29 2501
 
3.2%
12 2500
 
3.2%
24 2497
 
3.1%
14 2431
 
3.1%
17 2422
 
3.1%
31 2409
 
3.0%
Other values (26) 54419
68.6%
ValueCountFrequency (%)
1 156
 
0.2%
2 1895
2.4%
3 2070
2.6%
4 2163
2.7%
5 2269
2.9%
6 2133
2.7%
7 2345
3.0%
8 2088
2.6%
9 2392
3.0%
10 2308
2.9%
ValueCountFrequency (%)
36 1062
1.3%
35 2404
3.0%
34 2360
3.0%
33 2529
3.2%
32 2521
3.2%
31 2409
3.0%
30 2534
3.2%
29 2501
3.2%
28 2216
2.8%
27 2539
3.2%

AWB Prefix
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean676.28516
Minimum1
Maximum996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:35.918518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile160
Q1729
median729
Q3729
95-th percentile729
Maximum996
Range995
Interquartile range (IQR)0

Descriptive statistics

Standard deviation173.54668
Coefficient of variation (CV)0.25661761
Kurtosis7.085297
Mean676.28516
Median Absolute Deviation (MAD)0
Skewness-2.9358276
Sum53630766
Variance30118.451
MonotonicityNot monotonic
2024-09-18T23:12:36.019415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
729 70850
89.3%
160 2640
 
3.3%
1 1224
 
1.5%
47 906
 
1.1%
202 798
 
1.0%
403 443
 
0.6%
810 350
 
0.4%
133 305
 
0.4%
805 235
 
0.3%
125 226
 
0.3%
Other values (40) 1325
 
1.7%
ValueCountFrequency (%)
1 1224
1.5%
14 7
 
< 0.1%
16 49
 
0.1%
20 13
 
< 0.1%
23 29
 
< 0.1%
45 4
 
< 0.1%
47 906
1.1%
57 29
 
< 0.1%
74 28
 
< 0.1%
75 65
 
0.1%
ValueCountFrequency (%)
996 14
 
< 0.1%
976 105
 
0.1%
973 2
 
< 0.1%
932 85
 
0.1%
930 2
 
< 0.1%
906 59
 
0.1%
881 13
 
< 0.1%
873 24
 
< 0.1%
817 33
 
< 0.1%
810 350
0.4%

Operating Flight Number
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3721.2247
Minimum3
Maximum9955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:36.074754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile31
Q14041
median4085
Q34159
95-th percentile4257
Maximum9955
Range9952
Interquartile range (IQR)118

Descriptive statistics

Standard deviation1194.1402
Coefficient of variation (CV)0.32089978
Kurtosis5.3060834
Mean3721.2247
Median Absolute Deviation (MAD)46
Skewness-2.6881705
Sum2.9510056 × 108
Variance1425970.8
MonotonicityNot monotonic
2024-09-18T23:12:36.130948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4039 5586
 
7.0%
4257 4720
 
6.0%
4055 4076
 
5.1%
4087 3580
 
4.5%
4169 3084
 
3.9%
31 3043
 
3.8%
4171 2803
 
3.5%
4099 2728
 
3.4%
4091 2716
 
3.4%
4071 2317
 
2.9%
Other values (110) 44649
56.3%
ValueCountFrequency (%)
3 93
 
0.1%
5 315
 
0.4%
7 1279
1.6%
9 76
 
0.1%
31 3043
3.8%
39 32
 
< 0.1%
99 237
 
0.3%
105 133
 
0.2%
107 7
 
< 0.1%
127 1112
 
1.4%
ValueCountFrequency (%)
9955 1
 
< 0.1%
5907 1
 
< 0.1%
5903 2
 
< 0.1%
5077 4
 
< 0.1%
4299 329
0.4%
4291 16
 
< 0.1%
4289 24
 
< 0.1%
4287 168
 
0.2%
4285 453
0.6%
4283 636
0.8%

Route Owner
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
QT
71102 
AV
 
6343
TA
 
1437
6R
 
420

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters158604
Distinct characters6
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 rowQT
2nd rowQT
3rd rowQT
4th rowQT
5th rowQT

Common Values

ValueCountFrequency (%)
QT 71102
89.7%
AV 6343
 
8.0%
TA 1437
 
1.8%
6R 420
 
0.5%

Length

2024-09-18T23:12:36.179100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T23:12:36.217047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
qt 71102
89.7%
av 6343
 
8.0%
ta 1437
 
1.8%
6r 420
 
0.5%

Most occurring characters

ValueCountFrequency (%)
T 72539
45.7%
Q 71102
44.8%
A 7780
 
4.9%
V 6343
 
4.0%
6 420
 
0.3%
R 420
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 158604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 72539
45.7%
Q 71102
44.8%
A 7780
 
4.9%
V 6343
 
4.0%
6 420
 
0.3%
R 420
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 158604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 72539
45.7%
Q 71102
44.8%
A 7780
 
4.9%
V 6343
 
4.0%
6 420
 
0.3%
R 420
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 158604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 72539
45.7%
Q 71102
44.8%
A 7780
 
4.9%
V 6343
 
4.0%
6 420
 
0.3%
R 420
 
0.3%

Leg Origin
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
MIA
79302 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters237906
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 rowMIA
2nd rowMIA
3rd rowMIA
4th rowMIA
5th rowMIA

Common Values

ValueCountFrequency (%)
MIA 79302
100.0%

Length

2024-09-18T23:12:36.258188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T23:12:36.292762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mia 79302
100.0%

Most occurring characters

ValueCountFrequency (%)
M 79302
33.3%
I 79302
33.3%
A 79302
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 79302
33.3%
I 79302
33.3%
A 79302
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 79302
33.3%
I 79302
33.3%
A 79302
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 79302
33.3%
I 79302
33.3%
A 79302
33.3%

Leg Destination
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
BOG
17019 
MDE
6627 
GYE
5322 
SAL
5288 
GUA
4826 
Other values (19)
40220 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters237906
Distinct characters21
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 rowCWB
2nd rowASU
3rd rowGUA
4th rowBOG
5th rowGYE

Common Values

ValueCountFrequency (%)
BOG 17019
21.5%
MDE 6627
 
8.4%
GYE 5322
 
6.7%
SAL 5288
 
6.7%
GUA 4826
 
6.1%
UIO 4302
 
5.4%
CWB 4076
 
5.1%
ASU 4072
 
5.1%
SDQ 4049
 
5.1%
SJO 3804
 
4.8%
Other values (14) 19917
25.1%

Length

2024-09-18T23:12:36.329688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bog 17019
21.5%
mde 6627
 
8.4%
gye 5322
 
6.7%
sal 5288
 
6.7%
gua 4826
 
6.1%
uio 4302
 
5.4%
cwb 4076
 
5.1%
asu 4072
 
5.1%
sdq 4049
 
5.1%
sjo 3804
 
4.8%
Other values (14) 19917
25.1%

Most occurring characters

ValueCountFrequency (%)
O 30967
13.0%
G 29971
12.6%
B 22657
9.5%
A 20969
8.8%
E 18746
 
7.9%
S 17238
 
7.2%
D 13707
 
5.8%
M 13473
 
5.7%
U 13200
 
5.5%
L 9066
 
3.8%
Other values (11) 47912
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 30967
13.0%
G 29971
12.6%
B 22657
9.5%
A 20969
8.8%
E 18746
 
7.9%
S 17238
 
7.2%
D 13707
 
5.8%
M 13473
 
5.7%
U 13200
 
5.5%
L 9066
 
3.8%
Other values (11) 47912
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 30967
13.0%
G 29971
12.6%
B 22657
9.5%
A 20969
8.8%
E 18746
 
7.9%
S 17238
 
7.2%
D 13707
 
5.8%
M 13473
 
5.7%
U 13200
 
5.5%
L 9066
 
3.8%
Other values (11) 47912
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 30967
13.0%
G 29971
12.6%
B 22657
9.5%
A 20969
8.8%
E 18746
 
7.9%
S 17238
 
7.2%
D 13707
 
5.8%
M 13473
 
5.7%
U 13200
 
5.5%
L 9066
 
3.8%
Other values (11) 47912
20.1%

LEG
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
MIABOG
17019 
MIAMDE
6627 
MIAGYE
5322 
MIASAL
5288 
MIAGUA
4826 
Other values (19)
40220 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters475812
Distinct characters21
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 rowMIACWB
2nd rowMIAASU
3rd rowMIAGUA
4th rowMIABOG
5th rowMIAGYE

Common Values

ValueCountFrequency (%)
MIABOG 17019
21.5%
MIAMDE 6627
 
8.4%
MIAGYE 5322
 
6.7%
MIASAL 5288
 
6.7%
MIAGUA 4826
 
6.1%
MIAUIO 4302
 
5.4%
MIACWB 4076
 
5.1%
MIAASU 4072
 
5.1%
MIASDQ 4049
 
5.1%
MIASJO 3804
 
4.8%
Other values (14) 19917
25.1%

Length

2024-09-18T23:12:36.370607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
miabog 17019
21.5%
miamde 6627
 
8.4%
miagye 5322
 
6.7%
miasal 5288
 
6.7%
miagua 4826
 
6.1%
miauio 4302
 
5.4%
miacwb 4076
 
5.1%
miaasu 4072
 
5.1%
miasdq 4049
 
5.1%
miasjo 3804
 
4.8%
Other values (14) 19917
25.1%

Most occurring characters

ValueCountFrequency (%)
A 100271
21.1%
M 92775
19.5%
I 84673
17.8%
O 30967
 
6.5%
G 29971
 
6.3%
B 22657
 
4.8%
E 18746
 
3.9%
S 17238
 
3.6%
D 13707
 
2.9%
U 13200
 
2.8%
Other values (11) 51607
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 475812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 100271
21.1%
M 92775
19.5%
I 84673
17.8%
O 30967
 
6.5%
G 29971
 
6.3%
B 22657
 
4.8%
E 18746
 
3.9%
S 17238
 
3.6%
D 13707
 
2.9%
U 13200
 
2.8%
Other values (11) 51607
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 475812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 100271
21.1%
M 92775
19.5%
I 84673
17.8%
O 30967
 
6.5%
G 29971
 
6.3%
B 22657
 
4.8%
E 18746
 
3.9%
S 17238
 
3.6%
D 13707
 
2.9%
U 13200
 
2.8%
Other values (11) 51607
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 475812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 100271
21.1%
M 92775
19.5%
I 84673
17.8%
O 30967
 
6.5%
G 29971
 
6.3%
B 22657
 
4.8%
E 18746
 
3.9%
S 17238
 
3.6%
D 13707
 
2.9%
U 13200
 
2.8%
Other values (11) 51607
10.8%

Pieces
Real number (ℝ)

HIGH CORRELATION 

Distinct389
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.681811
Minimum1
Maximum1127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:36.417293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310
95-th percentile51
Maximum1127
Range1126
Interquartile range (IQR)9

Descriptive statistics

Standard deviation30.506709
Coefficient of variation (CV)2.6114708
Kurtosis214.46307
Mean11.681811
Median Absolute Deviation (MAD)2
Skewness10.973131
Sum926391
Variance930.65929
MonotonicityNot monotonic
2024-09-18T23:12:36.472630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 27655
34.9%
2 9976
 
12.6%
3 5775
 
7.3%
4 4246
 
5.4%
5 3309
 
4.2%
6 2735
 
3.4%
7 2005
 
2.5%
8 1873
 
2.4%
10 1586
 
2.0%
9 1551
 
2.0%
Other values (379) 18591
23.4%
ValueCountFrequency (%)
1 27655
34.9%
2 9976
 
12.6%
3 5775
 
7.3%
4 4246
 
5.4%
5 3309
 
4.2%
6 2735
 
3.4%
7 2005
 
2.5%
8 1873
 
2.4%
9 1551
 
2.0%
10 1586
 
2.0%
ValueCountFrequency (%)
1127 1
< 0.1%
1000 1
< 0.1%
959 1
< 0.1%
928 1
< 0.1%
903 1
< 0.1%
851 1
< 0.1%
824 1
< 0.1%
814 1
< 0.1%
809 1
< 0.1%
798 1
< 0.1%

KG OB Chg
Real number (ℝ)

HIGH CORRELATION 

Distinct9381
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean892.32101
Minimum0.5
Maximum56800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:36.524773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile3
Q158.5
median257
Q3852
95-th percentile3618
Maximum56800
Range56799.5
Interquartile range (IQR)793.5

Descriptive statistics

Standard deviation2164.9667
Coefficient of variation (CV)2.4262196
Kurtosis143.45042
Mean892.32101
Median Absolute Deviation (MAD)242
Skewness9.3190106
Sum70762841
Variance4687080.9
MonotonicityNot monotonic
2024-09-18T23:12:36.576826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1773
 
2.2%
1 1540
 
1.9%
3 865
 
1.1%
4 839
 
1.1%
5 828
 
1.0%
100 773
 
1.0%
7 652
 
0.8%
6 557
 
0.7%
10 501
 
0.6%
12 429
 
0.5%
Other values (9371) 70545
89.0%
ValueCountFrequency (%)
0.5 6
 
< 0.1%
0.6323133027 1
 
< 0.1%
1 1540
1.9%
1.205681818 1
 
< 0.1%
1.333 1
 
< 0.1%
1.5 90
 
0.1%
1.989411765 1
 
< 0.1%
2 1773
2.2%
2.078651685 1
 
< 0.1%
2.409836066 1
 
< 0.1%
ValueCountFrequency (%)
56800 1
< 0.1%
54968 1
< 0.1%
54267.5 1
< 0.1%
53117.5 1
< 0.1%
52382 1
< 0.1%
52058.5 1
< 0.1%
51949 1
< 0.1%
50177 1
< 0.1%
49094 1
< 0.1%
49093 1
< 0.1%

KG OB Gross
Real number (ℝ)

HIGH CORRELATION 

Distinct7309
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean765.52645
Minimum0.2
Maximum56800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:36.627435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q145
median202
Q3704
95-th percentile3244
Maximum56800
Range56799.8
Interquartile range (IQR)659

Descriptive statistics

Standard deviation1987.4129
Coefficient of variation (CV)2.5961388
Kurtosis186.17082
Mean765.52645
Median Absolute Deviation (MAD)192
Skewness10.718516
Sum60707779
Variance3949810.1
MonotonicityNot monotonic
2024-09-18T23:12:36.678659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2706
 
3.4%
2 1806
 
2.3%
3 1272
 
1.6%
4 1010
 
1.3%
5 890
 
1.1%
6 804
 
1.0%
7 686
 
0.9%
8 595
 
0.8%
10 579
 
0.7%
9 524
 
0.7%
Other values (7299) 68430
86.3%
ValueCountFrequency (%)
0.2 3
 
< 0.1%
0.22 1
 
< 0.1%
0.3 1
 
< 0.1%
0.4 6
< 0.1%
0.42 1
 
< 0.1%
0.5 9
< 0.1%
0.54 1
 
< 0.1%
0.59 2
 
< 0.1%
0.6 10
< 0.1%
0.65 1
 
< 0.1%
ValueCountFrequency (%)
56800 1
< 0.1%
54943.01 1
< 0.1%
54172 1
< 0.1%
53117 1
< 0.1%
51949 1
< 0.1%
51785 1
< 0.1%
50177 1
< 0.1%
49094 1
< 0.1%
49093 1
< 0.1%
48467 1
< 0.1%

Bellies - Freighters
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
Freighters
71529 
Bellies
7773 

Length

Max length10
Median length10
Mean length9.7059469
Min length7

Characters and Unicode

Total characters769701
Distinct characters10
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 rowFreighters
2nd rowFreighters
3rd rowFreighters
4th rowFreighters
5th rowFreighters

Common Values

ValueCountFrequency (%)
Freighters 71529
90.2%
Bellies 7773
 
9.8%

Length

2024-09-18T23:12:36.731433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T23:12:36.774032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
freighters 71529
90.2%
bellies 7773
 
9.8%

Most occurring characters

ValueCountFrequency (%)
e 158604
20.6%
r 143058
18.6%
i 79302
10.3%
s 79302
10.3%
F 71529
9.3%
g 71529
9.3%
h 71529
9.3%
t 71529
9.3%
l 15546
 
2.0%
B 7773
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 769701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 158604
20.6%
r 143058
18.6%
i 79302
10.3%
s 79302
10.3%
F 71529
9.3%
g 71529
9.3%
h 71529
9.3%
t 71529
9.3%
l 15546
 
2.0%
B 7773
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 769701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 158604
20.6%
r 143058
18.6%
i 79302
10.3%
s 79302
10.3%
F 71529
9.3%
g 71529
9.3%
h 71529
9.3%
t 71529
9.3%
l 15546
 
2.0%
B 7773
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 769701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 158604
20.6%
r 143058
18.6%
i 79302
10.3%
s 79302
10.3%
F 71529
9.3%
g 71529
9.3%
h 71529
9.3%
t 71529
9.3%
l 15546
 
2.0%
B 7773
 
1.0%

ProductCode
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)< 0.1%
Missing643
Missing (%)0.8%
Memory size619.7 KiB
GCR
47737 
CAC
9175 
DGR
7556 
AOG
5695 
COM
 
3667
Other values (10)
4829 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters235977
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDGR
2nd rowCAC
3rd rowHUM
4th rowGCR
5th rowGCR

Common Values

ValueCountFrequency (%)
GCR 47737
60.2%
CAC 9175
 
11.6%
DGR 7556
 
9.5%
AOG 5695
 
7.2%
COM 3667
 
4.6%
PER 2760
 
3.5%
PIL 910
 
1.1%
HUM 570
 
0.7%
XCS 236
 
0.3%
AVI 214
 
0.3%
Other values (5) 139
 
0.2%
(Missing) 643
 
0.8%

Length

2024-09-18T23:12:36.813269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gcr 47737
60.7%
cac 9175
 
11.7%
dgr 7556
 
9.6%
aog 5695
 
7.2%
com 3667
 
4.7%
per 2760
 
3.5%
pil 910
 
1.2%
hum 570
 
0.7%
xcs 236
 
0.3%
avi 214
 
0.3%
Other values (5) 139
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 70028
29.7%
G 60988
25.8%
R 58060
24.6%
A 15177
 
6.4%
O 9382
 
4.0%
D 7556
 
3.2%
M 4244
 
1.8%
P 3678
 
1.6%
E 2760
 
1.2%
I 1124
 
0.5%
Other values (6) 2980
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 235977
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 70028
29.7%
G 60988
25.8%
R 58060
24.6%
A 15177
 
6.4%
O 9382
 
4.0%
D 7556
 
3.2%
M 4244
 
1.8%
P 3678
 
1.6%
E 2760
 
1.2%
I 1124
 
0.5%
Other values (6) 2980
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 235977
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 70028
29.7%
G 60988
25.8%
R 58060
24.6%
A 15177
 
6.4%
O 9382
 
4.0%
D 7556
 
3.2%
M 4244
 
1.8%
P 3678
 
1.6%
E 2760
 
1.2%
I 1124
 
0.5%
Other values (6) 2980
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 235977
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 70028
29.7%
G 60988
25.8%
R 58060
24.6%
A 15177
 
6.4%
O 9382
 
4.0%
D 7556
 
3.2%
M 4244
 
1.8%
P 3678
 
1.6%
E 2760
 
1.2%
I 1124
 
0.5%
Other values (6) 2980
 
1.3%
Distinct340
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:36.974148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length5.5585736
Min length3

Characters and Unicode

Total characters440806
Distinct characters24
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

Unique142 ?
Unique (%)0.2%

Sample

1st rowDGR
2nd rowCAC
3rd rowHUM
4th rowGCR
5th rowSTD,GCR
ValueCountFrequency (%)
std,gcr 32511
41.0%
gcr 10604
 
13.4%
cac 9175
 
11.6%
dgr 7627
 
9.6%
aog 5231
 
6.6%
com,gcr 3453
 
4.4%
std,gcr,nsc 1565
 
2.0%
col,std,per 818
 
1.0%
pip 692
 
0.9%
hum 570
 
0.7%
Other values (330) 7056
 
8.9%
2024-09-18T23:12:37.164910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 79454
18.0%
G 66034
15.0%
R 63571
14.4%
, 50725
11.5%
D 45915
10.4%
S 42331
9.6%
T 38939
8.8%
A 15539
 
3.5%
O 11418
 
2.6%
P 5814
 
1.3%
Other values (14) 21066
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 440806
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 79454
18.0%
G 66034
15.0%
R 63571
14.4%
, 50725
11.5%
D 45915
10.4%
S 42331
9.6%
T 38939
8.8%
A 15539
 
3.5%
O 11418
 
2.6%
P 5814
 
1.3%
Other values (14) 21066
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 440806
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 79454
18.0%
G 66034
15.0%
R 63571
14.4%
, 50725
11.5%
D 45915
10.4%
S 42331
9.6%
T 38939
8.8%
A 15539
 
3.5%
O 11418
 
2.6%
P 5814
 
1.3%
Other values (14) 21066
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 440806
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 79454
18.0%
G 66034
15.0%
R 63571
14.4%
, 50725
11.5%
D 45915
10.4%
S 42331
9.6%
T 38939
8.8%
A 15539
 
3.5%
O 11418
 
2.6%
P 5814
 
1.3%
Other values (14) 21066
 
4.8%

Commodity
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct512
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size175.2 KiB
9985
44092 
9997
8735 
4105
5365 
9724
 
4176
9731
 
2160
Other values (507)
14774 

Length

Max length4
Median length4
Mean length3.9877935
Min length1

Characters and Unicode

Total characters316240
Distinct characters18
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

Unique155 ?
Unique (%)0.2%

Sample

1st row5506
2nd row9997
3rd row9091
4th row9985
5th row9985

Common Values

ValueCountFrequency (%)
9985 44092
55.6%
9997 8735
 
11.0%
4105 5365
 
6.8%
9724 4176
 
5.3%
9731 2160
 
2.7%
6200 1689
 
2.1%
4406 736
 
0.9%
6500 693
 
0.9%
4711 609
 
0.8%
3480 605
 
0.8%
Other values (502) 10442
 
13.2%

Length

2024-09-18T23:12:37.229385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9985 44092
55.6%
9997 8735
 
11.0%
4105 5365
 
6.8%
9724 4176
 
5.3%
9731 2160
 
2.7%
6200 1689
 
2.1%
4406 736
 
0.9%
6500 693
 
0.9%
4711 609
 
0.8%
3480 605
 
0.8%
Other values (502) 10442
 
13.2%

Most occurring characters

ValueCountFrequency (%)
9 130432
41.2%
5 52730
16.7%
8 46209
 
14.6%
7 19117
 
6.0%
0 16616
 
5.3%
4 14982
 
4.7%
1 14692
 
4.6%
2 9442
 
3.0%
6 6052
 
1.9%
3 5521
 
1.7%
Other values (8) 447
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 316240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 130432
41.2%
5 52730
16.7%
8 46209
 
14.6%
7 19117
 
6.0%
0 16616
 
5.3%
4 14982
 
4.7%
1 14692
 
4.6%
2 9442
 
3.0%
6 6052
 
1.9%
3 5521
 
1.7%
Other values (8) 447
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 316240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 130432
41.2%
5 52730
16.7%
8 46209
 
14.6%
7 19117
 
6.0%
0 16616
 
5.3%
4 14982
 
4.7%
1 14692
 
4.6%
2 9442
 
3.0%
6 6052
 
1.9%
3 5521
 
1.7%
Other values (8) 447
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 316240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 130432
41.2%
5 52730
16.7%
8 46209
 
14.6%
7 19117
 
6.0%
0 16616
 
5.3%
4 14982
 
4.7%
1 14692
 
4.6%
2 9442
 
3.0%
6 6052
 
1.9%
3 5521
 
1.7%
Other values (8) 447
 
0.1%
Distinct248
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:37.336309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters237906
Distinct characters26
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

Unique61 ?
Unique (%)0.1%

Sample

1st rowMIA
2nd rowMIA
3rd rowMIA
4th rowXMN
5th rowMIA
ValueCountFrequency (%)
mia 64856
81.8%
pvg 1185
 
1.5%
xmn 1130
 
1.4%
pek 1086
 
1.4%
iah 679
 
0.9%
lhr 524
 
0.7%
ord 501
 
0.6%
mex 463
 
0.6%
vcp 454
 
0.6%
dfw 428
 
0.5%
Other values (238) 7996
 
10.1%
2024-09-18T23:12:37.504254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 67805
28.5%
A 67219
28.3%
I 66684
28.0%
P 3331
 
1.4%
G 2740
 
1.2%
E 2460
 
1.0%
H 2303
 
1.0%
X 2179
 
0.9%
D 2091
 
0.9%
R 2034
 
0.9%
Other values (16) 19060
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 67805
28.5%
A 67219
28.3%
I 66684
28.0%
P 3331
 
1.4%
G 2740
 
1.2%
E 2460
 
1.0%
H 2303
 
1.0%
X 2179
 
0.9%
D 2091
 
0.9%
R 2034
 
0.9%
Other values (16) 19060
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 67805
28.5%
A 67219
28.3%
I 66684
28.0%
P 3331
 
1.4%
G 2740
 
1.2%
E 2460
 
1.0%
H 2303
 
1.0%
X 2179
 
0.9%
D 2091
 
0.9%
R 2034
 
0.9%
Other values (16) 19060
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 67805
28.5%
A 67219
28.3%
I 66684
28.0%
P 3331
 
1.4%
G 2740
 
1.2%
E 2460
 
1.0%
H 2303
 
1.0%
X 2179
 
0.9%
D 2091
 
0.9%
R 2034
 
0.9%
Other values (16) 19060
 
8.0%

OD Real Destination
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
BOG
14925 
MDE
7769 
UIO
6025 
GUA
5008 
SAL
4603 
Other values (32)
40972 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters237906
Distinct characters25
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

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowCWB
2nd rowASU
3rd rowGUA
4th rowBOG
5th rowUIO

Common Values

ValueCountFrequency (%)
BOG 14925
18.8%
MDE 7769
 
9.8%
UIO 6025
 
7.6%
GUA 5008
 
6.3%
SAL 4603
 
5.8%
VCP 4588
 
5.8%
SJO 3962
 
5.0%
GYE 3958
 
5.0%
SDQ 3705
 
4.7%
MVD 3028
 
3.8%
Other values (27) 21731
27.4%

Length

2024-09-18T23:12:37.567969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bog 14925
18.8%
mde 7769
 
9.8%
uio 6025
 
7.6%
gua 5008
 
6.3%
sal 4603
 
5.8%
vcp 4588
 
5.8%
sjo 3962
 
5.0%
gye 3958
 
5.0%
sdq 3705
 
4.7%
mvd 3028
 
3.8%
Other values (27) 21731
27.4%

Most occurring characters

ValueCountFrequency (%)
O 28640
12.0%
G 25409
10.7%
S 18767
 
7.9%
B 18586
 
7.8%
A 17120
 
7.2%
E 15536
 
6.5%
M 15435
 
6.5%
U 14935
 
6.3%
D 14505
 
6.1%
L 11450
 
4.8%
Other values (15) 57523
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 28640
12.0%
G 25409
10.7%
S 18767
 
7.9%
B 18586
 
7.8%
A 17120
 
7.2%
E 15536
 
6.5%
M 15435
 
6.5%
U 14935
 
6.3%
D 14505
 
6.1%
L 11450
 
4.8%
Other values (15) 57523
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 28640
12.0%
G 25409
10.7%
S 18767
 
7.9%
B 18586
 
7.8%
A 17120
 
7.2%
E 15536
 
6.5%
M 15435
 
6.5%
U 14935
 
6.3%
D 14505
 
6.1%
L 11450
 
4.8%
Other values (15) 57523
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 28640
12.0%
G 25409
10.7%
S 18767
 
7.9%
B 18586
 
7.8%
A 17120
 
7.2%
E 15536
 
6.5%
M 15435
 
6.5%
U 14935
 
6.3%
D 14505
 
6.1%
L 11450
 
4.8%
Other values (15) 57523
24.2%

OD Network Origin
Categorical

IMBALANCE 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
MIA
75910 
LHR
 
555
VCP
 
454
GRU
 
366
MAD
 
351
Other values (30)
 
1666

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters237906
Distinct characters26
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

Unique3 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
MIA 75910
95.7%
LHR 555
 
0.7%
VCP 454
 
0.6%
GRU 366
 
0.5%
MAD 351
 
0.4%
BOG 254
 
0.3%
UIO 246
 
0.3%
MDE 143
 
0.2%
EZE 139
 
0.2%
SCL 127
 
0.2%
Other values (25) 757
 
1.0%

Length

2024-09-18T23:12:37.609332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mia 75910
95.7%
lhr 555
 
0.7%
vcp 454
 
0.6%
gru 366
 
0.5%
mad 351
 
0.4%
bog 254
 
0.3%
uio 246
 
0.3%
mde 143
 
0.2%
eze 139
 
0.2%
scl 127
 
0.2%
Other values (25) 757
 
1.0%

Most occurring characters

ValueCountFrequency (%)
M 76663
32.2%
A 76326
32.1%
I 76314
32.1%
R 921
 
0.4%
L 857
 
0.4%
C 731
 
0.3%
D 672
 
0.3%
O 656
 
0.3%
G 643
 
0.3%
U 622
 
0.3%
Other values (16) 3501
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 76663
32.2%
A 76326
32.1%
I 76314
32.1%
R 921
 
0.4%
L 857
 
0.4%
C 731
 
0.3%
D 672
 
0.3%
O 656
 
0.3%
G 643
 
0.3%
U 622
 
0.3%
Other values (16) 3501
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 76663
32.2%
A 76326
32.1%
I 76314
32.1%
R 921
 
0.4%
L 857
 
0.4%
C 731
 
0.3%
D 672
 
0.3%
O 656
 
0.3%
G 643
 
0.3%
U 622
 
0.3%
Other values (16) 3501
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 76663
32.2%
A 76326
32.1%
I 76314
32.1%
R 921
 
0.4%
L 857
 
0.4%
C 731
 
0.3%
D 672
 
0.3%
O 656
 
0.3%
G 643
 
0.3%
U 622
 
0.3%
Other values (16) 3501
 
1.5%

OD Network Destination
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
BOG
14947 
MDE
7749 
UIO
6026 
GUA
5007 
SAL
4604 
Other values (30)
40969 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters237906
Distinct characters22
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCWB
2nd rowASU
3rd rowGUA
4th rowBOG
5th rowUIO

Common Values

ValueCountFrequency (%)
BOG 14947
18.8%
MDE 7749
 
9.8%
UIO 6026
 
7.6%
GUA 5007
 
6.3%
SAL 4604
 
5.8%
VCP 4555
 
5.7%
GYE 3957
 
5.0%
SJO 3942
 
5.0%
SDQ 3701
 
4.7%
MVD 3060
 
3.9%
Other values (25) 21754
27.4%

Length

2024-09-18T23:12:37.652335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bog 14947
18.8%
mde 7749
 
9.8%
uio 6026
 
7.6%
gua 5007
 
6.3%
sal 4604
 
5.8%
vcp 4555
 
5.7%
gye 3957
 
5.0%
sjo 3942
 
5.0%
sdq 3701
 
4.7%
mvd 3060
 
3.9%
Other values (25) 21754
27.4%

Most occurring characters

ValueCountFrequency (%)
O 28660
12.0%
G 25416
10.7%
S 18725
 
7.9%
B 18599
 
7.8%
A 17098
 
7.2%
E 15520
 
6.5%
M 15490
 
6.5%
U 14928
 
6.3%
D 14552
 
6.1%
L 11470
 
4.8%
Other values (12) 57448
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 28660
12.0%
G 25416
10.7%
S 18725
 
7.9%
B 18599
 
7.8%
A 17098
 
7.2%
E 15520
 
6.5%
M 15490
 
6.5%
U 14928
 
6.3%
D 14552
 
6.1%
L 11470
 
4.8%
Other values (12) 57448
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 28660
12.0%
G 25416
10.7%
S 18725
 
7.9%
B 18599
 
7.8%
A 17098
 
7.2%
E 15520
 
6.5%
M 15490
 
6.5%
U 14928
 
6.3%
D 14552
 
6.1%
L 11470
 
4.8%
Other values (12) 57448
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 237906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 28660
12.0%
G 25416
10.7%
S 18725
 
7.9%
B 18599
 
7.8%
A 17098
 
7.2%
E 15520
 
6.5%
M 15490
 
6.5%
U 14928
 
6.3%
D 14552
 
6.1%
L 11470
 
4.8%
Other values (12) 57448
24.1%

Airway Bill Number
Real number (ℝ)

HIGH CORRELATION 

Distinct78798
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7671095 × 1010
Minimum1.3069979 × 108
Maximum9.9612717 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:37.701161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.3069979 × 108
5-th percentile1.6056014 × 1010
Q17.2941526 × 1010
median7.2941767 × 1010
Q37.2942016 × 1010
95-th percentile7.2947241 × 1010
Maximum9.9612717 × 1010
Range9.9482017 × 1010
Interquartile range (IQR)489946.5

Descriptive statistics

Standard deviation1.7352089 × 1010
Coefficient of variation (CV)0.25641803
Kurtosis7.0836618
Mean6.7671095 × 1010
Median Absolute Deviation (MAD)246004
Skewness-2.9356186
Sum5.3664532 × 1015
Variance3.0109498 × 1020
MonotonicityNot monotonic
2024-09-18T23:12:37.758804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.294687497 × 10103
 
< 0.1%
7.294143811 × 10103
 
< 0.1%
7.294200908 × 10103
 
< 0.1%
7.294743217 × 10103
 
< 0.1%
7.294125522 × 10102
 
< 0.1%
2.023023032 × 10102
 
< 0.1%
7.294212536 × 10102
 
< 0.1%
7.294134278 × 10102
 
< 0.1%
7.294189501 × 10102
 
< 0.1%
7.294196226 × 10102
 
< 0.1%
Other values (78788) 79278
> 99.9%
ValueCountFrequency (%)
130699793 1
< 0.1%
130699830 1
< 0.1%
130906282 1
< 0.1%
130970881 1
< 0.1%
131039411 1
< 0.1%
131039433 1
< 0.1%
131042852 1
< 0.1%
131084874 1
< 0.1%
131084900 1
< 0.1%
131417422 1
< 0.1%
ValueCountFrequency (%)
9.96127172 × 10101
< 0.1%
9.96126112 × 10101
< 0.1%
9.961260382 × 10101
< 0.1%
9.961255786 × 10101
< 0.1%
9.961247806 × 10101
< 0.1%
9.961244774 × 10101
< 0.1%
9.961232388 × 10101
< 0.1%
9.961227529 × 10101
< 0.1%
9.961227366 × 10101
< 0.1%
9.961225519 × 10101
< 0.1%
Distinct243
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
Minimum2023-01-01 00:00:00
Maximum2023-08-31 00:00:00
2024-09-18T23:12:37.812633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:37.864134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Agency
Text

Distinct1465
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:37.972459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length73
Median length49
Mean length18.406509
Min length3

Characters and Unicode

Total characters1459673
Distinct characters74
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

Unique444 ?
Unique (%)0.6%

Sample

1st rowHERCO FREIGHT FORWARDERS INC
2nd rowINTERLINEO - UPS
3rd rowSOUTHERN CROSS TRADING INC
4th rowINTERLINEO - CATHAY PACIFIC
5th rowFLASHCARGO GROUP CORP
ValueCountFrequency (%)
inc 13645
 
6.1%
11240
 
5.0%
cargo 10382
 
4.6%
logistics 8513
 
3.8%
interlineo 7368
 
3.3%
avianca 6279
 
2.8%
international 4954
 
2.2%
corp 4846
 
2.2%
llc 4550
 
2.0%
freight 4335
 
1.9%
Other values (1610) 148367
66.1%
2024-09-18T23:12:38.159686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
147677
10.1%
A 140000
 
9.6%
I 138162
 
9.5%
R 107549
 
7.4%
N 101167
 
6.9%
E 100758
 
6.9%
O 97702
 
6.7%
C 92840
 
6.4%
S 85621
 
5.9%
T 80909
 
5.5%
Other values (64) 367288
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1459673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
147677
10.1%
A 140000
 
9.6%
I 138162
 
9.5%
R 107549
 
7.4%
N 101167
 
6.9%
E 100758
 
6.9%
O 97702
 
6.7%
C 92840
 
6.4%
S 85621
 
5.9%
T 80909
 
5.5%
Other values (64) 367288
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1459673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
147677
10.1%
A 140000
 
9.6%
I 138162
 
9.5%
R 107549
 
7.4%
N 101167
 
6.9%
E 100758
 
6.9%
O 97702
 
6.7%
C 92840
 
6.4%
S 85621
 
5.9%
T 80909
 
5.5%
Other values (64) 367288
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1459673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
147677
10.1%
A 140000
 
9.6%
I 138162
 
9.5%
R 107549
 
7.4%
N 101167
 
6.9%
E 100758
 
6.9%
O 97702
 
6.7%
C 92840
 
6.4%
S 85621
 
5.9%
T 80909
 
5.5%
Other values (64) 367288
25.2%

Volume (m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct36460
Distinct (%)46.0%
Missing51
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.9458124
Minimum0
Maximum461.39077
Zeros40
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:38.225010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.016379672
Q10.26639467
median1.2904542
Q34.5460491
95-th percentile21.609678
Maximum461.39077
Range461.39077
Interquartile range (IQR)4.2796544

Descriptive statistics

Standard deviation12.166427
Coefficient of variation (CV)2.4599452
Kurtosis175.76605
Mean4.9458124
Median Absolute Deviation (MAD)1.2222356
Skewness9.67206
Sum391960.57
Variance148.02195
MonotonicityNot monotonic
2024-09-18T23:12:38.279582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.005999880002 1729
 
2.2%
0.01199976 1053
 
1.3%
9.899802004 743
 
0.9%
0.01799964001 672
 
0.8%
0.02399952001 544
 
0.7%
0.03599928001 451
 
0.6%
0.02999940001 433
 
0.5%
0.04199916002 334
 
0.4%
0.04799904002 301
 
0.4%
0.05999880002 247
 
0.3%
Other values (36450) 72744
91.7%
ValueCountFrequency (%)
0 40
0.1%
0.0001199976 2
 
< 0.1%
0.0004199916002 1
 
< 0.1%
0.0005399892002 1
 
< 0.1%
0.0008399832003 3
 
< 0.1%
0.0009599808004 3
 
< 0.1%
0.0011399772 1
 
< 0.1%
0.001199976 1
 
< 0.1%
0.001319973601 1
 
< 0.1%
0.001379972401 1
 
< 0.1%
ValueCountFrequency (%)
461.3907722 1
< 0.1%
425.7843643 1
< 0.1%
413.9917202 1
< 0.1%
376.4215516 1
< 0.1%
353.0929381 1
< 0.1%
314.2838543 1
< 0.1%
272.3945521 1
< 0.1%
271.7945641 1
< 0.1%
267.8600228 1
< 0.1%
262.1947561 1
< 0.1%

KG Volumetric
Real number (ℝ)

HIGH CORRELATION 

Distinct37082
Distinct (%)46.8%
Missing51
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean824.31854
Minimum0
Maximum76900
Zeros40
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:38.333084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.73
Q144.4
median215.08
Q3757.69
95-th percentile3601.685
Maximum76900
Range76900
Interquartile range (IQR)713.29

Descriptive statistics

Standard deviation2027.7784
Coefficient of variation (CV)2.4599452
Kurtosis175.76605
Mean824.31854
Median Absolute Deviation (MAD)203.71
Skewness9.67206
Sum65328069
Variance4111885.4
MonotonicityIncreasing
2024-09-18T23:12:38.392155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1729
 
2.2%
2 1053
 
1.3%
1650 743
 
0.9%
3 672
 
0.8%
4 544
 
0.7%
6 451
 
0.6%
5 433
 
0.5%
7 334
 
0.4%
8 300
 
0.4%
10 247
 
0.3%
Other values (37072) 72745
91.7%
ValueCountFrequency (%)
0 40
0.1%
0.02 2
 
< 0.1%
0.07 1
 
< 0.1%
0.09 1
 
< 0.1%
0.14 3
 
< 0.1%
0.16 3
 
< 0.1%
0.19 1
 
< 0.1%
0.2 1
 
< 0.1%
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
ValueCountFrequency (%)
76900 1
< 0.1%
70965.48 1
< 0.1%
69000 1
< 0.1%
62738.18 1
< 0.1%
58850 1
< 0.1%
52381.69 1
< 0.1%
45400 1
< 0.1%
45300 1
< 0.1%
44644.23 1
< 0.1%
43700 1
< 0.1%

Cargo Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size619.7 KiB
Others
41422 
Volume
20475 
Dense
17405 

Length

Max length6
Median length6
Mean length5.7805226
Min length5

Characters and Unicode

Total characters458407
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDense
2nd rowOthers
3rd rowDense
4th rowOthers
5th rowOthers

Common Values

ValueCountFrequency (%)
Others 41422
52.2%
Volume 20475
25.8%
Dense 17405
21.9%

Length

2024-09-18T23:12:38.486171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T23:12:38.527168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
others 41422
52.2%
volume 20475
25.8%
dense 17405
21.9%

Most occurring characters

ValueCountFrequency (%)
e 96707
21.1%
s 58827
12.8%
O 41422
9.0%
t 41422
9.0%
h 41422
9.0%
r 41422
9.0%
V 20475
 
4.5%
o 20475
 
4.5%
l 20475
 
4.5%
u 20475
 
4.5%
Other values (3) 55285
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 458407
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 96707
21.1%
s 58827
12.8%
O 41422
9.0%
t 41422
9.0%
h 41422
9.0%
r 41422
9.0%
V 20475
 
4.5%
o 20475
 
4.5%
l 20475
 
4.5%
u 20475
 
4.5%
Other values (3) 55285
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 458407
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 96707
21.1%
s 58827
12.8%
O 41422
9.0%
t 41422
9.0%
h 41422
9.0%
r 41422
9.0%
V 20475
 
4.5%
o 20475
 
4.5%
l 20475
 
4.5%
u 20475
 
4.5%
Other values (3) 55285
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 458407
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 96707
21.1%
s 58827
12.8%
O 41422
9.0%
t 41422
9.0%
h 41422
9.0%
r 41422
9.0%
V 20475
 
4.5%
o 20475
 
4.5%
l 20475
 
4.5%
u 20475
 
4.5%
Other values (3) 55285
12.1%

KG Chg Operational
Real number (ℝ)

HIGH CORRELATION 

Distinct28662
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean945.32825
Minimum0.33
Maximum76900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.7 KiB
2024-09-18T23:12:38.575574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile3
Q158
median262.3
Q3898
95-th percentile4077.0226
Maximum76900
Range76899.67
Interquartile range (IQR)840

Descriptive statistics

Standard deviation2277.5988
Coefficient of variation (CV)2.4093206
Kurtosis162.02157
Mean945.32825
Median Absolute Deviation (MAD)248.3
Skewness9.5737907
Sum74966421
Variance5187456.4
MonotonicityNot monotonic
2024-09-18T23:12:38.634484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1904
 
2.4%
2 1190
 
1.5%
3 820
 
1.0%
4 693
 
0.9%
6 569
 
0.7%
5 562
 
0.7%
1650 428
 
0.5%
7 414
 
0.5%
8 413
 
0.5%
10 360
 
0.5%
Other values (28652) 71949
90.7%
ValueCountFrequency (%)
0.33 1
 
< 0.1%
0.4 1
 
< 0.1%
0.5 2
 
< 0.1%
0.6 1
 
< 0.1%
0.6321309656 1
 
< 0.1%
0.78 1
 
< 0.1%
0.9 1
 
< 0.1%
0.96 2
 
< 0.1%
1 1904
2.4%
1.00002 9
 
< 0.1%
ValueCountFrequency (%)
76900 1
< 0.1%
70965.48 1
< 0.1%
69000 1
< 0.1%
62738.18 1
< 0.1%
58850 1
< 0.1%
56800 1
< 0.1%
54943.01 1
< 0.1%
54172 1
< 0.1%
53117 1
< 0.1%
52381.69 1
< 0.1%

Interactions

2024-09-18T23:12:34.410477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:28.835663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.326615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.884059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.382150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.859072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.391199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.869718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.389860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.875350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.366943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.884268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.454162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:28.878752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.366666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.924584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.421327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.899264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.430273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.909943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.428952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.915717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.405167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.928872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.498724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:28.920883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.429722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.966412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.462199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.981095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.471448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.952120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.471764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.959016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.446499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.974236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.542376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:28.962447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.473219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.008785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.503540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.022859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.513775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.994890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.513245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.000468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.487103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.019517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.584610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.002485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.513075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.047844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.541399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.063110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.550342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.032201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.551659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.039785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.569552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.062805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.626161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.041891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.554495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.088369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.581520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.102635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.589296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.070959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.590442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.079427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.607799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.107772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.669629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.080425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.594586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.127920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.618659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.143196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.626195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.108820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.630657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.118807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.645385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.148763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.711037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.119334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.635667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.169146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.656544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.183347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.666298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.147622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.670066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.157253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.683612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.190583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.796321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.158265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.676541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.210707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.695334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.222535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.704471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.184922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.710227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.197937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.722315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.231640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.839897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.199255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.717278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.253228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.734707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.264565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.744413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.266414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.751217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.238463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.760591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.276371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.879656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.236766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.755920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.291624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.772929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.303572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.782101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.303429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.788129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.277801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.798219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.316760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.926786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.281191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:29.838989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.337893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:30.815390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.348283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:31.827216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.347093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:32.831125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.321249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:33.840367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T23:12:34.363686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-18T23:12:38.686188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AWB PrefixAirway Bill NumberBellies - FreightersCargo TypeDayKG Chg OperationalKG OB ChgKG OB GrossKG VolumetricLEGLeg DestinationMonthOD Network DestinationOD Network OriginOD Real DestinationOperating Flight NumberPiecesProductCodeRoute OwnerVolume (m3)Week
AWB Prefix1.0000.5360.0950.066-0.0060.0490.0440.0310.0740.0710.071-0.0260.0990.1960.099-0.0210.0030.0810.0630.074-0.027
Airway Bill Number0.5361.0000.0950.0660.0600.0480.0430.0360.0640.0710.0710.5570.0990.1960.0990.003-0.0050.0810.0630.0640.559
Bellies - Freighters0.0950.0951.0000.2770.0150.0330.0410.0390.0290.6020.6020.0290.5000.0610.5000.9990.0350.7630.9990.0290.021
Cargo Type0.0660.0660.2771.0000.0040.0810.1040.0860.0770.1420.1420.0160.1610.0900.1610.1950.0510.2650.1960.0770.015
Day-0.0060.0600.0150.0041.0000.0050.0020.0020.0050.0690.0690.0030.0420.0240.0370.0120.0000.0080.0270.0050.127
KG Chg Operational0.0490.0480.0330.0810.0051.0000.9950.9800.9820.0600.0600.0250.1710.0000.1730.2190.6120.0180.0180.9820.025
KG OB Chg0.0440.0430.0410.1040.0020.9951.0000.9810.9740.0670.0670.0220.1780.0000.1800.2200.6040.0240.0220.9740.022
KG OB Gross0.0310.0360.0390.0860.0020.9800.9811.0000.9480.0660.0660.0230.1850.0000.1870.2090.6060.0190.0210.9480.023
KG Volumetric0.0740.0640.0290.0770.0050.9820.9740.9481.0000.0550.0550.0250.1480.0000.1500.2170.6170.0180.0151.0000.024
LEG0.0710.0710.6020.1420.0690.0600.0670.0660.0551.0001.0000.1030.7450.0810.7450.3010.0550.1870.6010.0550.092
Leg Destination0.0710.0710.6020.1420.0690.0600.0670.0660.0551.0001.0000.1030.7450.0810.7450.3010.0550.1870.6010.0550.092
Month-0.0260.5570.0290.0160.0030.0250.0220.0230.0250.1030.1031.0000.0460.0320.0390.0280.0130.0300.0780.0250.992
OD Network Destination0.0990.0990.5000.1610.0420.1710.1780.1850.1480.7450.7450.0461.0000.1420.9680.2510.0780.2040.4630.1480.043
OD Network Origin0.1960.1960.0610.0900.0240.0000.0000.0000.0000.0810.0810.0320.1421.0000.0650.0240.0000.1100.0590.0000.029
OD Real Destination0.0990.0990.5000.1610.0370.1730.1800.1870.1500.7450.7450.0390.9680.0651.0000.2500.0780.2040.4630.1500.037
Operating Flight Number-0.0210.0030.9990.1950.0120.2190.2200.2090.2170.3010.3010.0280.2510.0240.2501.0000.1010.3820.5780.2170.027
Pieces0.003-0.0050.0350.0510.0000.6120.6040.6060.6170.0550.0550.0130.0780.0000.0780.1011.0000.0130.0190.6170.012
ProductCode0.0810.0810.7630.2650.0080.0180.0240.0190.0180.1870.1870.0300.2040.1100.2040.3820.0131.0000.4550.0180.026
Route Owner0.0630.0630.9990.1960.0270.0180.0220.0210.0150.6010.6010.0780.4630.0590.4630.5780.0190.4551.0000.0150.070
Volume (m3)0.0740.0640.0290.0770.0050.9820.9740.9481.0000.0550.0550.0250.1480.0000.1500.2170.6170.0180.0151.0000.024
Week-0.0270.5590.0210.0150.1270.0250.0220.0230.0240.0920.0920.9920.0430.0290.0370.0270.0120.0260.0700.0241.000

Missing values

2024-09-18T23:12:35.023651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-18T23:12:35.236076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-18T23:12:35.461578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DayMonthYearWeekAWB PrefixOperating Flight NumberRoute OwnerLeg OriginLeg DestinationLEGPiecesKG OB ChgKG OB GrossBellies - FreightersProductCodeCommodity UnificadoCommodityOD Real OriginOD Real DestinationOD Network OriginOD Network DestinationAirway Bill NumberFlight_dateAgencyVolume (m3)KG VolumetricCargo TypeKG Chg Operational
0291202357294055QTMIACWBMIACWB1132.0132.0FreightersDGRDGR5506MIACWBMIACWB729414710652023-01-29HERCO FREIGHT FORWARDERS INC0.00.0Dense132.0
12042023174064087QTMIAASUMIAASU64755.0754.0FreightersCACCAC9997MIAASUMIAASU406015113552023-04-20INTERLINEO - UPS0.00.0Others754.0
2111202337294069QTMIAGUAMIAGUA1150.0150.0FreightersHUMHUM9091MIAGUAMIAGUA729416035312023-01-11SOUTHERN CROSS TRADING INC0.00.0Dense150.0
31482023341604281QTMIABOGMIABOG41337.0337.0FreightersGCRGCR9985XMNBOGMIABOG160551551922023-08-14INTERLINEO - CATHAY PACIFIC0.00.0Others337.0
42742023187294039QTMIAGYEMIAGYE7263.0263.0FreightersGCRSTD,GCR9985MIAUIOMIAUIO729416881242023-04-27FLASHCARGO GROUP CORP0.00.0Others263.0
5232202397294171QTMIASJOMIASJO1268.5122.0FreightersGCRPRI,GCR8575MIASJOMIASJO729417262962023-02-23KUEHNE & NAGEL0.00.0Volume122.0
6652023197294071QTMIAPTYMIAPTY241612.5134.0FreightersGCRSTD,GCR4711VCPPTYVCPPTY729467674532023-05-06VENTANA CARGO DO BRASIL0.00.0Others134.0
792202377294171QTMIASJOMIASJO1280.0130.0FreightersGCRSTD,GCR9985MIASJOMIASJO729414813662023-02-09MAGNUM FREIGHT CORPORATION0.00.0Volume130.0
83082023367294071QTMIAPTYMIAPTY3101.0101.0FreightersCACCAC9997MIABOGMIABOG729419534352023-08-30COLOMBIANA DE CARGA0.00.0Others101.0
92532023137294129QTMIAMDEMIAMDE3146.082.0FreightersGCRSTD,GCR4119UIOMDEUIOMDE729472418912023-03-25AEROGAL0.00.0Others82.0
DayMonthYearWeekAWB PrefixOperating Flight NumberRoute OwnerLeg OriginLeg DestinationLEGPiecesKG OB ChgKG OB GrossBellies - FreightersProductCodeCommodity UnificadoCommodityOD Real OriginOD Real DestinationOD Network OriginOD Network DestinationAirway Bill NumberFlight_dateAgencyVolume (m3)KG VolumetricCargo TypeKG Chg Operational
79292772023281604085QTMIASDQMIASDQ2172.0172.0FreightersGCRGCR9985PVGSDQMIASDQ160567113622023-07-07INTERLINEO - CATHAY PACIFICNaNNaNDense172.0
79293192202387294041QTMIABOGMIABOG250.020.0FreightersDGRDGR4406DFWLIMMIALIM729413435842023-02-19DHL GLOBAL FORWARDINGNaNNaNOthers20.0
79294972023287294141QTMIABOGMIABOG3897.0644.2FreightersPILPIP6502OMALIMMIALIM729419268022023-07-09DSVNaNNaNVolume644.2
79295672023287294171QTMIASJOMIASJO15211.0124.0FreightersGCRGCR9985MIAVCPMIASJO729419438602023-07-06HERMES AVIATION LLCNaNNaNVolume124.0
792961642023162024055QTMIACWBMIACWB328.012.0FreightersGCRGCR9985FLRCWBMADCWB202303320462023-04-16ALBINI ANDPITIGLIANINaNNaNOthers12.0
792971682023347294051QTMIAVCPMIAVCP102623.02623.0FreightersDGRDGR3077ORDVCPMIAVCP729421697022023-08-16HERMES AVIATION LLCNaNNaNDense2623.0
792982332023137294091QTMIAMVDMIAMVD13.51.0FreightersGCRSTD,GCR6010IADMVDMIAMVD729413909042023-03-23EXPEDITORSNaNNaNOthers1.0
792991862023257294047QTMIAAGTMIAAGT19.51.0FreightersDGRDGR5506DFWVCPMIAVCP729412912022023-06-18DB SCHENKERNaNNaNOthers1.0
79300962023247294083QTMIASDQMIASDQ6324.5305.0FreightersGCRSTD,EAP,GCR9985CWBSDQVCPSDQ729467785612023-06-09GEODIS WILSONNaNNaNVolume305.0
79301452023197294071QTMIAPTYMIAPTY1236.0236.0FreightersGCRGCR4711YHMPTYMIAPTY729483377302023-05-04CARGOLUTIONS INCNaNNaNDense236.0
DayMonthYearWeekAWB PrefixOperating Flight NumberRoute OwnerLeg OriginLeg DestinationLEGPiecesKG OB ChgKG OB GrossBellies - FreightersProductCodeCommodity UnificadoCommodityOD Real OriginOD Real DestinationOD Network OriginOD Network DestinationAirway Bill NumberFlight_dateAgencyVolume (m3)KG VolumetricCargo TypeKG Chg Operational
458382552023227294225QTMIACLOMIACLO1305.000000248.00FreightersGCRSTD,GCR9985MIACLOMIACLO729420342152023-05-25FAST CARGO CORP1.828523304.76000Volume304.76000
272723082023362354257QTMIASALMIASAL8135.000000135.00FreightersNaNGCR0CANSALMIASAL235292894552023-08-30INTERLINEO - TURKISH AIRLINES0.59014898.36000Others135.00000
1663581202327294099QTMIAEZEMIAEZE528.50000022.00FreightersDGRDGR9735MIAMVDMIAMVD729401710142023-01-08USA CARGO & COURIER0.17309728.85000Others28.85000
58539862023247294133QTMIABOGMIABOG12354.000000316.00FreightersGCRSTD,GCR9985MIAGYEMIAGYE729419106432023-06-08GLOVAL SHIPPING USA LLC4.240355706.74000Others706.74000
8062362023237294073QTMIAPTYMIAPTY38.5000003.00FreightersDGRDGR9735MIAPTYMIAPTY729420435622023-06-03TLA LOGISTICS CORP0.0394796.58000Others6.58000
5166782202377294069QTMIAGUAMIAGUA1446.000000177.00FreightersGCRSTD,GCR9985MIAGUAMIAGUA729415472142023-02-08TALATRANS WORLDWIDE CORPORATION2.675346445.90000Volume445.90000
49801281202357294211QTMIAMDEMIAMDE1394.000000306.00FreightersDGRDGR2631MIAMDEMIAMDE729414981922023-01-28AIR MARINE FORWARDING2.364253394.05000Volume394.05000
649022942023187294191QTMIABOGMIABOG351277.000000748.00FreightersCACCAC9997MIABOGMIABOG729418533462023-04-29LATIN LOGISTICS6.9883601164.75000Others1164.75000
493131382023337294257QTMIASALMIASAL2381.000000198.00FreightersGCRGCR9985MIASALMIASAL729421342712023-08-13EXPEDITORS2.291414381.91000Volume381.91000
225171942023177294257QTMIASALMIASAL361.00000061.00FreightersCOMCOM,GCR9724MIASALMIASAL729418103702023-04-19COMAT AERONAUTICO MIA0.36599361.00000Others61.00000
745192262023267294139QTMIABOGMIABOG52667.0000002667.00FreightersCACCAC9997MIABOGMIABOG729416999432023-06-22LARS COURIER19.3736733229.01000Dense3229.01000
770481132023117294085QTMIASDQMIASDQ513181.0000003164.00FreightersCACCAC9997MIASDQMIASDQ729416685352023-03-11ATALLAH BUSINESS GROUP30.7648655127.58000Volume5127.58000
49361452023207294087QTMIAASUMIAASU13.5000001.00FreightersPERCOL,STD,GCR,PER8575MIASCLMIASCL729415105122023-05-14KUEHNE & NAGEL0.0189003.15000Others3.15000
641241762023257294145QTMIABOGMIABOG291092.000000748.00FreightersDGRDGR5506MIABOGMIABOG729421120222023-06-17MAGNUM FREIGHT CORPORATION6.5192301086.56000Others1086.56000
43995362023237294171QTMIASJOMIASJO2274.50000093.00FreightersGCRSTD,GCR9985MIASJOMIASJO729418355552023-06-03GROUND CARGO TRANSPORTATION1.646007274.34000Others274.34000
597142782023353194087QTMIAASUMIAASU1181677.0000001676.00FreightersPERSTD,CRT,PER9731BOMASUMIAASU319015009252023-08-27INTERLINEO - FITS AVIATION4.642527773.77000Others1676.00000
1870482023327294035QTMIAUIOMIAUIO11.0000001.00FreightersAOGAOG4105MIAUIOMIAUIO729420964712023-08-04COMAT AERONAUTICO MIA0.0060001.00000Others1.00000
26352252202397294201QTMIABOGMIABOG190.00000071.00FreightersDGRDGR2631MIABOGMIABOG729417134642023-02-25ACCESS INTERNATIONAL SERVICES CORP0.53950989.92000Volume89.92000
4745152202367294087QTMIAASUMIAASU3349.500000285.00FreightersPERCOL,STD,PER9731MIAASUMIAASU729415171622023-02-05KUEHNE & NAGEL2.018600336.44000Volume336.44000
62378291202357294087QTMIAASUMIAASU131148.0000001098.00FreightersGCRGCR9985MIAASUMIAASU729415786702023-01-29DSV5.665327944.24000Volume1098.00000
63171362023237294047QTMIAAGTMIAAGT31739.0000001739.00FreightersGCRSTD,GCR9985MIAVCPMIAVCP729420403902023-06-03EXPEDITORS6.0368991006.17000Dense1739.00000
547422262023267294171QTMIASJOMIASJO2542.000000441.00FreightersGCRSTD,GCR9985MIASJOMIASJO729420532812023-06-22FREIGHT LATAM LLC3.244855540.82000Volume540.82000
451371762023257294137QTMIABOGMIABOG2404.000000404.00FreightersGCRSTD,GCR9985MIABOGMIABOG729418882542023-06-17ROYALTY EXIMPORT1.762645293.78000Dense404.00000
42847682023327294141QTMIABOGMIABOG1257.000000196.00FreightersPERSTD,CRT,PER9731MIALIMMIALIM729421333462023-08-06EXPEDITORS1.542269257.05000Volume257.05000
35031762023257294039QTMIAGYEMIAGYE12.0000001.00FreightersAOGAOG4105MIAUIOMIAUIO729418155502023-06-17COMAT AERONAUTICO MIA0.0120002.00000Others2.00000
53744982023337294123QTMIAMDEMIAMDE3506.500000329.00FreightersGCRSTD,GCR9985MIAMDEMIAMDE729422216162023-08-09AMERIMPEX3.038879506.49000Volume506.49000
34924242023147294099QTMIAEZEMIAEZE3168.000000159.00FreightersGCRGCR9985MIAMVDMIAMVD729415024362023-04-02UPS SCS0.986260164.38000Volume164.38000
18814942023157294091QTMIAMVDMIAMVD239.50000032.00FreightersGCRSTD,GCR9985MIAMVDMIAMVD729403801412023-04-09FIRST CARGO CORP0.23651539.42000Others39.42000
41561141202337294067QTMIAMIDMIAMID11424.000000424.00FreightersGCRSTD,GCR9985MIASJOMIASJO729415003142023-01-14FLASHCARGO GROUP CORP1.442611240.44000Others424.00000
649061752023217294099QTMIAEZEMIAEZE181198.0000001198.00FreightersGCRSTD,GCR9985MIAVCPMIAVCP729417429602023-05-17DHL GLOBAL FORWARDING6.9904601165.10000Dense1198.00000
424252162023267294099QTMIAEZEMIAEZE1126.00000049.00FreightersGCRGCR,NSC9985MIAVCPMIAVCP729420375162023-06-21DB SCHENKER1.510770251.80000Others251.80000
640972772023317294083QTMIASDQMIASDQ21664.000000664.00FreightersNaNCOU9997MIASDQMIASDQ729421506142023-07-27RENAISSANCE INTERNATIONAL TRADING INC6.5064501084.43000Others1084.43000
602391642023167294055QTMIACWBMIACWB11371.0000001371.00FreightersGCRSTD,GCR4711ATLCWBMIACWB729417626152023-04-16NETWORK CARGO4.819584803.28000Dense1371.00000
554202542023187294053QTMIAEZEMIAEZE21550.0000001550.00FreightersDGRDGR1133MIAEZEMIAEZE729417548822023-04-25JNC LOGISTICS SERVICES INC3.399292566.56000Dense1550.00000
40604181202347294099QTMIAEZEMIAEZE10230.000000118.00FreightersGCRGCR9985MIAVCPMIAVCP729415592762023-01-18SUPPLY CHAIN SHIPPING LLC1.369473228.25000Others228.25000
662272362023267294257QTMIASALMIASAL181310.000000909.00FreightersGCRSTD,GCR9985MIASALMIASAL729420695212023-06-23UPS SCS7.8564831309.44000Volume1309.44000
3594810820233372940776RMIAGUAMIAGUA1175.000000166.00FreightersHUMHUM9091MIAGUAMIAGUA729418831702023-08-10TAMPA TRAFICO1.049679174.95000Volume174.95000
69754242023147294099QTMIAEZEMIAEZE211959.0000001959.00FreightersGCRSTD,GCR9985MIAMVDMIAMVD729417395852023-04-02JAUSER CARGO10.4871301747.89000Dense1959.00000
667901352023207294137QTMIABOGMIABOG41075.0000001075.00FreightersCACCAC9997MIABOGMIABOG729419001102023-05-13A TIEMPO CARGO INC8.3329931388.86000Dense1388.86000
657651742023177294237QTMIAUIOMIAUIO461312.0000001312.00FreightersCACCAC9997MIAUIOMIAUIO729418450202023-04-17CARGO MASTER LOGISTICS INC7.5215101253.61000Others1312.00000
6935262202397294091QTMIAMVDMIAMVD15.0000002.00FreightersGCRSTD,GCR9985MIAMVDMIAMVD729412350732023-02-26NIPPON EXPRESS USA INC0.0302395.04000Others5.04000
462562472023317294089QTMIAASUMIAASU8346.000000346.00FreightersGCRGCR9985PVGASUMIAASU729483967842023-07-24ALL LINK LOGISTICS SHANGHAI1.878982313.17000Others346.00000
32174832023117294257QTMIASALMIASAL2150.000000150.00FreightersGCRGCR9985MIASALMIASAL729416992062023-03-08TRANS EXPRESS INC0.818384136.40000Dense150.00000
283522282023357294145QTMIABOGMIABOG275.50000031.00FreightersCACCAC9997MIABOGMIABOG729421064552023-08-22UNION CARGO INTERNACIONAL LTDA0.627947104.66000Others104.66000
63950952023207294083QTMIASDQMIASDQ11073.0000001073.00FreightersGCRSTD,GCR6805MDESDQMDESDQ729439988502023-05-09TRANSTAINER SAS6.4378711073.00000Dense1073.00000
140443620232314039QTMIAGYEMIAGYE329.00000028.00FreightersGCRSTD,GCR9985MIAGYEMIAGYE1742111262023-06-03INTERLINEO - AMERICAN AIRLINES0.11567819.28000Others28.00000
407891752023217294057QTMIAMAOMIAMAO1780.000000780.00FreightersGCRGCR9985MIAMAOMIAMAO729418695202023-05-17SAMSUNG1.384892230.82000Dense780.00000
636843052023237294001QTMIABOGMIABOG31064.0000001064.00FreightersGCRSTD,GCR9985MIABOGMIABOG729420476002023-05-30RUSH AIRWAYS INC6.3127741052.15000Dense1064.00000
48284151202337294283QTMIABOGMIABOG11078.0000001078.00FreightersGCRGCR9985MIABOGMIABOG729414066912023-01-15GLOBAL CARGO ALLIANCE/COLBOX I2.134397355.74000Dense1078.00000
33583662023247294231QTMIAUIOMIAUIO6104.000000104.00FreightersCACCAC9997MIAUIOMIAUIO729420058062023-06-06SFS CARGO EXPRESS INC0.906462151.08000Others151.08000
14589211202347294225QTMIACLOMIACLO327.00000027.00FreightersGCRSTD,GCR9985MIAMDEMIAMDE729415593352023-01-21SUPPLY CHAIN SHIPPING LLC0.12743721.24000Others27.00000
691082462023267294171QTMIASJOMIASJO11100.0000001100.00FreightersCACCAC9992MIASJOMIASJO729420716762023-06-24AEROPOST INTL SERV9.8998021650.00000Dense1650.00000
56273202311729127AVMIABOGMIABOG11.0000001.00BelliesAOGAOG4105MIABOGMIABOG729416421452023-03-07AVIANCA0.0060001.00000Others1.00000
59514320231272931AVMIAMDEMIAMDE21.0000001.00BelliesAOGAOG4105MIAMDEMIAMDE729416441862023-03-14AVIANCA0.0060001.00000Others1.00000
65879142023147294151QTMIABOGMIABOG601269.0000001153.00FreightersGCRGCR9985MIABOGMIABOG729415022412023-04-01UPS SCS7.6139681269.02000Others1269.02000
29173672023287294171QTMIASJOMIASJO1110.00000086.00FreightersAVIAVI1001MIASJOMIASJO729418836342023-07-06TAMPA TRAFICO0.660947110.16000Volume110.16000
447721582023347294101QTMIACLOMIACLO1288.50000068.00FreightersGCRGCR9797MIASCLMIASCL729422744222023-08-15CONTROL TOWERS INTERNATIONAL INC1.728925288.16000Volume288.16000
54904262202397294041QTMIABOGMIABOG28573.000000573.00FreightersGCRSTD,GCR9985MIALIMMIALIM729414894902023-02-26SAMSUNG3.275994546.01000Others573.00000
570841132023117294085QTMIASDQMIASDQ2851.500000851.00FreightersGCRGCR9985MIASDQMIASDQ729417606002023-03-11NETWORK CARGO3.799784633.31000Volume851.00000
755312822023107294039QTMIAGYEMIAGYE384418.5000004418.00FreightersGCRSTD,GCR9985MIAGYEMIAGYE729415768022023-02-28DSV22.6314873771.99000Volume4418.00000
741082352023227294093QTMIACLOMIACLO173032.0000001536.00FreightersGCRGCR9985MIASCLMIASCL729417967162023-05-23GRUPO DELPA CORP18.1944563032.47000Volume3032.47000
289942242023177294039QTMIAGYEMIAGYE1179.000000179.00FreightersGCRSTD,GCR,NSC9797SLCGYEMIAGYE729417528852023-04-22DSV0.654647109.11000Dense179.00000
9208156202325729127AVMIABOGMIABOG14.0000004.00BelliesCOMCOM,GCR9724MIABOGMIABOG729419715912023-06-15AVIANCA0.0479998.00000Others8.00000
12626972023287294055QTMIACWBMIACWB115.00000015.00FreightersGCRSTD,GCR,NSC9985LHRCWBLHRCWB729484624112023-07-09DB SCHENKER0.08999815.00000Others15.00000
311701062023241604039QTMIAGYEMIAGYE4127.50000058.00FreightersGCRSTD,GCR9985XMNGYEMIAGYE160575460052023-06-10INTERLINEO - CATHAY PACIFIC0.763125127.19000Others127.19000
3079392202377294141QTMIABOGMIABOG1125.000000124.00FreightersPERPER,PES9731MIABOGMIABOG729415486512023-02-09ROYALTY EXIMPORT0.747525124.59000Volume124.59000
21129142202381604039QTMIAGYEMIAGYE145.00000021.00FreightersDGRDGR6200HGHUIOMIAUIO160520745512023-02-14INTERLINEO - CATHAY PACIFIC0.30983451.64000Others51.64000
44085192202387294087QTMIAASUMIAASU1276.159722228.00FreightersGCRGCR9985MIAEZEMIAEZE729414381102023-02-19CH ROBINSON1.656953276.16434Volume276.16434
567962442023187294225QTMIACLOMIACLO30622.000000577.00FreightersGCRSTD,GCR9985MIACLOMIACLO729417031302023-04-24GLOBAL CARGO ALLIANCE/COLBOX I3.729525621.60000Others621.60000
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Duplicate rows

Most frequently occurring

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