Overview

Brought to you by Baobao Tang

Dataset statistics

Number of variables49
Number of observations4752
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory385.2 B

Variable types

Categorical14
Text1
Numeric29
DateTime5

Dataset

DescriptionExploratory Data Analysis for Load Factor Report.xlsx
AuthorBaobao Tang
URL

Alerts

LEG ORIGIN has constant value "MIA"Constant
DEPS has constant value "1"Constant
YEAR has constant value "2023"Constant
COUNTRY ORIGIN has constant value "US"Constant
REGION ORG has constant value "NAM"Constant
AC CATEGORY is highly overall correlated with AC TYPE and 22 other fieldsHigh correlation
AC TYPE is highly overall correlated with AC CATEGORY and 6 other fieldsHigh correlation
ACTUALPAYLOAD is highly overall correlated with AC CATEGORY and 22 other fieldsHigh correlation
ATK is highly overall correlated with AC CATEGORY and 29 other fieldsHigh correlation
ATK_FIS is highly overall correlated with AC CATEGORY and 27 other fieldsHigh correlation
ATK_VOL is highly overall correlated with AC CATEGORY and 29 other fieldsHigh correlation
AVAILABLE SEATS is highly overall correlated with AC CATEGORY and 24 other fieldsHigh correlation
BAGQTY OB is highly overall correlated with AC CATEGORY and 23 other fieldsHigh correlation
BAGWGT OB is highly overall correlated with AC CATEGORY and 23 other fieldsHigh correlation
CAP FIS (Kg) is highly overall correlated with AC CATEGORY and 24 other fieldsHigh correlation
CAP VOL (Kg) is highly overall correlated with AC CATEGORY and 24 other fieldsHigh correlation
CARRIER is highly overall correlated with AC CATEGORY and 27 other fieldsHigh correlation
COUNTRY DESTINATION is highly overall correlated with ATK and 11 other fieldsHigh correlation
DAY is highly overall correlated with MONTH and 1 other fieldsHigh correlation
HB is highly overall correlated with ATK and 7 other fieldsHigh correlation
HV is highly overall correlated with ATK and 6 other fieldsHigh correlation
KG CHG is highly overall correlated with AC CATEGORY and 22 other fieldsHigh correlation
KG CHG OPERATIONAL is highly overall correlated with AC CATEGORY and 22 other fieldsHigh correlation
KG GROSS is highly overall correlated with AC CATEGORY and 22 other fieldsHigh correlation
KM is highly overall correlated with ATK and 11 other fieldsHigh correlation
LEG is highly overall correlated with AC CATEGORY and 15 other fieldsHigh correlation
LEG DESTINATION is highly overall correlated with AC CATEGORY and 15 other fieldsHigh correlation
MAXALLOWEDPAYLOAD is highly overall correlated with AC CATEGORY and 24 other fieldsHigh correlation
MONTH is highly overall correlated with DAY and 1 other fieldsHigh correlation
OPERATING FLIGHT NUMBER is highly overall correlated with AC CATEGORY and 23 other fieldsHigh correlation
PAX OB is highly overall correlated with AC CATEGORY and 23 other fieldsHigh correlation
PAYLOAD IN CABIN is highly overall correlated with AC CATEGORY and 23 other fieldsHigh correlation
REGION DST is highly overall correlated with CARRIER and 6 other fieldsHigh correlation
REGION LEG is highly overall correlated with CARRIER and 6 other fieldsHigh correlation
ROUTE OWNER is highly overall correlated with AC CATEGORY and 28 other fieldsHigh correlation
RTK is highly overall correlated with ACTUALPAYLOAD and 21 other fieldsHigh correlation
RTK_FIS is highly overall correlated with ACTUALPAYLOAD and 21 other fieldsHigh correlation
RTK_VOL is highly overall correlated with ACTUALPAYLOAD and 21 other fieldsHigh correlation
UNDERLOAD is highly overall correlated with ATK and 13 other fieldsHigh correlation
VOL WGT is highly overall correlated with AC CATEGORY and 22 other fieldsHigh correlation
VOLUME is highly overall correlated with AC CATEGORY and 22 other fieldsHigh correlation
WEEK is highly overall correlated with DAY and 1 other fieldsHigh correlation
REGION DST is highly imbalanced (56.6%)Imbalance
REGION LEG is highly imbalanced (56.6%)Imbalance
KG CHG has 1478 (31.1%) zerosZeros
KG GROSS has 1423 (29.9%) zerosZeros
RTK has 1478 (31.1%) zerosZeros
RTK_FIS has 1423 (29.9%) zerosZeros
RTK_VOL has 1478 (31.1%) zerosZeros
UNDERLOAD has 82 (1.7%) zerosZeros
PAX OB has 2265 (47.7%) zerosZeros
BAGWGT OB has 2269 (47.7%) zerosZeros
ACTUALPAYLOAD has 1405 (29.6%) zerosZeros
VOLUME has 1483 (31.2%) zerosZeros
BAGQTY OB has 2269 (47.7%) zerosZeros
PAYLOAD IN CABIN has 2267 (47.7%) zerosZeros
AVAILABLE SEATS has 2263 (47.6%) zerosZeros
VOL WGT has 1489 (31.3%) zerosZeros
KG CHG OPERATIONAL has 1489 (31.3%) zerosZeros

Reproduction

Analysis started2024-09-19 01:50:04.157882
Analysis finished2024-09-19 01:50:40.629398
Duration36.47 seconds

Variables

CARRIER
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
QT
2263 
AV
1836 
TA
653 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9504
Distinct characters4
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 2263
47.6%
AV 1836
38.6%
TA 653
 
13.7%

Length

2024-09-18T21:50:40.657846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T21:50:40.699154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
qt 2263
47.6%
av 1836
38.6%
ta 653
 
13.7%

Most occurring characters

ValueCountFrequency (%)
T 2916
30.7%
A 2489
26.2%
Q 2263
23.8%
V 1836
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 2916
30.7%
A 2489
26.2%
Q 2263
23.8%
V 1836
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 2916
30.7%
A 2489
26.2%
Q 2263
23.8%
V 1836
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 2916
30.7%
A 2489
26.2%
Q 2263
23.8%
V 1836
19.3%

ROUTE OWNER
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
QT
2263 
AV
1743 
TA
746 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9504
Distinct characters4
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 2263
47.6%
AV 1743
36.7%
TA 746
 
15.7%

Length

2024-09-18T21:50:40.739440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T21:50:40.776556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
qt 2263
47.6%
av 1743
36.7%
ta 746
 
15.7%

Most occurring characters

ValueCountFrequency (%)
T 3009
31.7%
A 2489
26.2%
Q 2263
23.8%
V 1743
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 3009
31.7%
A 2489
26.2%
Q 2263
23.8%
V 1743
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 3009
31.7%
A 2489
26.2%
Q 2263
23.8%
V 1743
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 3009
31.7%
A 2489
26.2%
Q 2263
23.8%
V 1743
18.3%

TAIL
Text

Distinct133
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:40.892399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.979798
Min length5

Characters and Unicode

Total characters28416
Distinct characters33
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 rowN332QT
2nd rowN330QT
3rd rowN330QT
4th rowN336QT
5th rowN334QT
ValueCountFrequency (%)
n336qt 287
 
6.0%
n334qt 271
 
5.7%
n335qt 268
 
5.6%
n330qt 235
 
4.9%
n332qt 225
 
4.7%
n331qt 216
 
4.5%
xa-uyr 172
 
3.6%
xa-efr 172
 
3.6%
xa-lrc 161
 
3.4%
xa-ggl 77
 
1.6%
Other values (123) 2668
56.1%
2024-09-18T21:50:41.075545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 3881
13.7%
3 3602
12.7%
A 2987
 
10.5%
V 2029
 
7.1%
T 1703
 
6.0%
Q 1502
 
5.3%
6 1302
 
4.6%
4 1250
 
4.4%
5 1199
 
4.2%
9 1116
 
3.9%
Other values (23) 7845
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 3881
13.7%
3 3602
12.7%
A 2987
 
10.5%
V 2029
 
7.1%
T 1703
 
6.0%
Q 1502
 
5.3%
6 1302
 
4.6%
4 1250
 
4.4%
5 1199
 
4.2%
9 1116
 
3.9%
Other values (23) 7845
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 3881
13.7%
3 3602
12.7%
A 2987
 
10.5%
V 2029
 
7.1%
T 1703
 
6.0%
Q 1502
 
5.3%
6 1302
 
4.6%
4 1250
 
4.4%
5 1199
 
4.2%
9 1116
 
3.9%
Other values (23) 7845
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 3881
13.7%
3 3602
12.7%
A 2987
 
10.5%
V 2029
 
7.1%
T 1703
 
6.0%
Q 1502
 
5.3%
6 1302
 
4.6%
4 1250
 
4.4%
5 1199
 
4.2%
9 1116
 
3.9%
Other values (23) 7845
27.6%

OPERATING FLIGHT NUMBER
Real number (ℝ)

HIGH CORRELATION 

Distinct154
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2062.447
Minimum3
Maximum5907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:41.146240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q139
median451
Q34099
95-th percentile4257
Maximum5907
Range5904
Interquartile range (IQR)4060

Descriptive statistics

Standard deviation1990.3331
Coefficient of variation (CV)0.96503478
Kurtosis-1.9719081
Mean2062.447
Median Absolute Deviation (MAD)448
Skewness0.075383158
Sum9800748
Variance3961425.7
MonotonicityNot monotonic
2024-09-18T21:50:41.203248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
397 241
 
5.1%
127 239
 
5.0%
7 237
 
5.0%
451 237
 
5.0%
5 235
 
4.9%
31 229
 
4.8%
9 199
 
4.2%
393 169
 
3.6%
3 159
 
3.3%
39 141
 
3.0%
Other values (144) 2666
56.1%
ValueCountFrequency (%)
3 159
3.3%
5 235
4.9%
7 237
5.0%
9 199
4.2%
28 2
 
< 0.1%
31 229
4.8%
39 141
3.0%
63 1
 
< 0.1%
99 34
 
0.7%
105 38
 
0.8%
ValueCountFrequency (%)
5907 1
 
< 0.1%
5903 1
 
< 0.1%
5137 1
 
< 0.1%
5079 1
 
< 0.1%
5077 3
0.1%
5076 1
 
< 0.1%
5027 1
 
< 0.1%
5015 1
 
< 0.1%
5007 1
 
< 0.1%
4299 7
0.1%

LEG ORIGIN
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
MIA
4752 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14256
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 4752
100.0%

Length

2024-09-18T21:50:41.252757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T21:50:41.286546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mia 4752
100.0%

Most occurring characters

ValueCountFrequency (%)
M 4752
33.3%
I 4752
33.3%
A 4752
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 4752
33.3%
I 4752
33.3%
A 4752
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 4752
33.3%
I 4752
33.3%
A 4752
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 4752
33.3%
I 4752
33.3%
A 4752
33.3%

LEG DESTINATION
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
BOG
1606 
MGA
650 
MDE
602 
CLO
213 
BAQ
193 
Other values (24)
1488 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14256
Distinct characters23
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 rowEZE
2nd rowBOG
3rd rowSDQ
4th rowCWB
5th rowAGT

Common Values

ValueCountFrequency (%)
BOG 1606
33.8%
MGA 650
13.7%
MDE 602
 
12.7%
CLO 213
 
4.5%
BAQ 193
 
4.1%
SAL 186
 
3.9%
UIO 163
 
3.4%
SDQ 145
 
3.1%
MAO 92
 
1.9%
CTG 91
 
1.9%
Other values (19) 811
17.1%

Length

2024-09-18T21:50:41.321742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bog 1606
33.8%
mga 650
13.7%
mde 602
 
12.7%
clo 213
 
4.5%
baq 193
 
4.1%
sal 186
 
3.9%
uio 163
 
3.4%
sdq 145
 
3.1%
mao 92
 
1.9%
ctg 91
 
1.9%
Other values (19) 811
17.1%

Most occurring characters

ValueCountFrequency (%)
G 2612
18.3%
O 2162
15.2%
B 1903
13.3%
M 1439
10.1%
A 1390
9.8%
E 882
 
6.2%
D 802
 
5.6%
S 524
 
3.7%
L 408
 
2.9%
C 385
 
2.7%
Other values (13) 1749
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 2612
18.3%
O 2162
15.2%
B 1903
13.3%
M 1439
10.1%
A 1390
9.8%
E 882
 
6.2%
D 802
 
5.6%
S 524
 
3.7%
L 408
 
2.9%
C 385
 
2.7%
Other values (13) 1749
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 2612
18.3%
O 2162
15.2%
B 1903
13.3%
M 1439
10.1%
A 1390
9.8%
E 882
 
6.2%
D 802
 
5.6%
S 524
 
3.7%
L 408
 
2.9%
C 385
 
2.7%
Other values (13) 1749
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 2612
18.3%
O 2162
15.2%
B 1903
13.3%
M 1439
10.1%
A 1390
9.8%
E 882
 
6.2%
D 802
 
5.6%
S 524
 
3.7%
L 408
 
2.9%
C 385
 
2.7%
Other values (13) 1749
12.3%

DATE
Date

Distinct241
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
Minimum2023-01-01 00:00:00
Maximum2023-08-29 00:00:00
2024-09-18T21:50:41.369876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:41.424932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CAP VOL (Kg)
Real number (ℝ)

HIGH CORRELATION 

Distinct758
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31065.722
Minimum0
Maximum110000
Zeros26
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:41.479443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile957.01914
Q12544.9509
median12525.251
Q367483.83
95-th percentile67600
Maximum110000
Range110000
Interquartile range (IQR)64938.879

Descriptive statistics

Standard deviation30351.624
Coefficient of variation (CV)0.97701331
Kurtosis-1.4835259
Mean31065.722
Median Absolute Deviation (MAD)11947.439
Skewness0.32167653
Sum1.4762431 × 108
Variance9.2122106 × 108
MonotonicityNot monotonic
2024-09-18T21:50:41.536852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67600 1120
23.6%
45000 253
 
5.3%
47032 180
 
3.8%
59000 127
 
2.7%
44850 78
 
1.6%
110000 52
 
1.1%
45770 49
 
1.0%
11889.23778 43
 
0.9%
12525.2505 41
 
0.9%
13161.26322 35
 
0.7%
Other values (748) 2774
58.4%
ValueCountFrequency (%)
0 26
0.5%
1 2
 
< 0.1%
2 1
 
< 0.1%
8 1
 
< 0.1%
8.5 1
 
< 0.1%
21.5 1
 
< 0.1%
27 1
 
< 0.1%
52.5 1
 
< 0.1%
57.5 1
 
< 0.1%
58 1
 
< 0.1%
ValueCountFrequency (%)
110000 52
1.1%
70642.5 1
 
< 0.1%
69998.66883 1
 
< 0.1%
69920 1
 
< 0.1%
69663.5 1
 
< 0.1%
69473.78412 1
 
< 0.1%
69393 1
 
< 0.1%
68669.5 1
 
< 0.1%
68594 1
 
< 0.1%
67866 1
 
< 0.1%

CAP FIS (Kg)
Real number (ℝ)

HIGH CORRELATION 

Distinct2332
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30778.766
Minimum0
Maximum104000
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:41.640167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1694.295
Q14664.025
median19615.5
Q359923.613
95-th percentile65000
Maximum104000
Range104000
Interquartile range (IQR)55259.588

Descriptive statistics

Standard deviation27145.47
Coefficient of variation (CV)0.88195447
Kurtosis-1.4331906
Mean30778.766
Median Absolute Deviation (MAD)18421.65
Skewness0.31688409
Sum1.4626069 × 108
Variance7.3687653 × 108
MonotonicityNot monotonic
2024-09-18T21:50:41.697834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65000 972
 
20.5%
41000 252
 
5.3%
55000 223
 
4.7%
44000 179
 
3.8%
57000 137
 
2.9%
58000 97
 
2.0%
39000 78
 
1.6%
61000 51
 
1.1%
39800 50
 
1.1%
100000 42
 
0.9%
Other values (2322) 2671
56.2%
ValueCountFrequency (%)
0 5
0.1%
1 1
 
< 0.1%
13 1
 
< 0.1%
70 1
 
< 0.1%
161.1 1
 
< 0.1%
166.5 1
 
< 0.1%
179.1 1
 
< 0.1%
207.9 1
 
< 0.1%
227.7 1
 
< 0.1%
330.3 1
 
< 0.1%
ValueCountFrequency (%)
104000 10
 
0.2%
100000 42
 
0.9%
65000 972
20.5%
64800 12
 
0.3%
64750 20
 
0.4%
64500 1
 
< 0.1%
64000 7
 
0.1%
62792.98 1
 
< 0.1%
61432.6 1
 
< 0.1%
61430.94 1
 
< 0.1%

KG CHG
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2492
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14660.74
Minimum0
Maximum70642.5
Zeros1478
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:41.752129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median150
Q331123.5
95-th percentile58891.275
Maximum70642.5
Range70642.5
Interquartile range (IQR)31123.5

Descriptive statistics

Standard deviation21133.697
Coefficient of variation (CV)1.4415164
Kurtosis-0.34215018
Mean14660.74
Median Absolute Deviation (MAD)150
Skewness1.0925339
Sum69667835
Variance4.4663314 × 108
MonotonicityNot monotonic
2024-09-18T21:50:41.811146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1478
31.1%
1 32
 
0.7%
2 29
 
0.6%
8 18
 
0.4%
7 16
 
0.3%
14 16
 
0.3%
4 16
 
0.3%
3 15
 
0.3%
13 14
 
0.3%
12 13
 
0.3%
Other values (2482) 3105
65.3%
ValueCountFrequency (%)
0 1478
31.1%
1 32
 
0.7%
1.5 2
 
< 0.1%
2 29
 
0.6%
3 15
 
0.3%
3.5 4
 
0.1%
4 16
 
0.3%
5 13
 
0.3%
5.5 3
 
0.1%
6 9
 
0.2%
ValueCountFrequency (%)
70642.5 1
< 0.1%
69998.66883 1
< 0.1%
69920 1
< 0.1%
69663.5 1
< 0.1%
69473.78412 1
< 0.1%
69393 1
< 0.1%
68669.5 1
< 0.1%
68594 1
< 0.1%
67866 1
< 0.1%
67253.5 1
< 0.1%

KG GROSS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2356
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12724.603
Minimum0
Maximum62855
Zeros1423
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:41.866936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median151
Q326579.25
95-th percentile51176.167
Maximum62855
Range62855
Interquartile range (IQR)26579.25

Descriptive statistics

Standard deviation18296.073
Coefficient of variation (CV)1.4378502
Kurtosis-0.21956143
Mean12724.603
Median Absolute Deviation (MAD)151
Skewness1.1195529
Sum60467314
Variance3.3474628 × 108
MonotonicityNot monotonic
2024-09-18T21:50:41.923045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1423
29.9%
1 41
 
0.9%
2 32
 
0.7%
150 25
 
0.5%
7 22
 
0.5%
3 19
 
0.4%
8 19
 
0.4%
4 18
 
0.4%
20 16
 
0.3%
14 15
 
0.3%
Other values (2346) 3122
65.7%
ValueCountFrequency (%)
0 1423
29.9%
0.93 1
 
< 0.1%
1 41
 
0.9%
2 32
 
0.7%
3 19
 
0.4%
4 18
 
0.4%
5 13
 
0.3%
6 11
 
0.2%
7 22
 
0.5%
8 19
 
0.4%
ValueCountFrequency (%)
62855 1
< 0.1%
62792.98 1
< 0.1%
61432.6 1
< 0.1%
61430.94 1
< 0.1%
60994 1
< 0.1%
60726.209 1
< 0.1%
60547 1
< 0.1%
60413.24 1
< 0.1%
60372.71 1
< 0.1%
60343 1
< 0.1%

ATK
Real number (ℝ)

HIGH CORRELATION 

Distinct2185
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85278.917
Minimum0
Maximum496073.78
Zeros31
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:41.979113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1589.1754
Q15097.861
median29207.006
Q3132396
95-th percentile363448.6
Maximum496073.78
Range496073.78
Interquartile range (IQR)127298.14

Descriptive statistics

Standard deviation108732.97
Coefficient of variation (CV)1.2750275
Kurtosis2.4779457
Mean85278.917
Median Absolute Deviation (MAD)28182.283
Skewness1.6513553
Sum4.0524541 × 108
Variance1.1822858 × 1010
MonotonicityNot monotonic
2024-09-18T21:50:42.034975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164673.6 179
 
3.8%
109620 127
 
2.7%
114569.952 96
 
2.0%
143724 82
 
1.7%
195431.6 78
 
1.6%
151694.4 67
 
1.4%
105539.808 52
 
1.1%
267960 49
 
1.0%
125295.9233 49
 
1.0%
111607.6 46
 
1.0%
Other values (2175) 3927
82.6%
ValueCountFrequency (%)
0 31
0.7%
1.639 2
 
< 0.1%
3.278 1
 
< 0.1%
13.112 1
 
< 0.1%
13.9315 1
 
< 0.1%
31.668 1
 
< 0.1%
35.2385 1
 
< 0.1%
44.253 1
 
< 0.1%
86.0475 1
 
< 0.1%
94.2425 1
 
< 0.1%
ValueCountFrequency (%)
496073.7835 1
 
< 0.1%
487531.2 8
 
0.2%
487122.7303 1
 
< 0.1%
482839.2531 1
 
< 0.1%
481379.6 26
0.5%
473478.717 1
 
< 0.1%
472821.1216 1
 
< 0.1%
469531.5022 1
 
< 0.1%
468990.2478 1
 
< 0.1%
461334.635 1
 
< 0.1%

RTK
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2822
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48311.986
Minimum0
Maximum496073.78
Zeros1478
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:42.088902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median295.614
Q356978.488
95-th percentile325939.59
Maximum496073.78
Range496073.78
Interquartile range (IQR)56978.488

Descriptive statistics

Standard deviation94483.731
Coefficient of variation (CV)1.9556996
Kurtosis5.9529967
Mean48311.986
Median Absolute Deviation (MAD)295.614
Skewness2.5507886
Sum2.2957856 × 108
Variance8.9271753 × 109
MonotonicityNot monotonic
2024-09-18T21:50:42.146011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1478
31.1%
1.639 14
 
0.3%
2.436 14
 
0.3%
3.278 12
 
0.3%
12.18 10
 
0.2%
4.872 10
 
0.2%
286.825 9
 
0.2%
19.488 8
 
0.2%
48.72 7
 
0.1%
77.952 7
 
0.1%
Other values (2812) 3183
67.0%
ValueCountFrequency (%)
0 1478
31.1%
1.639 14
 
0.3%
1.651 2
 
< 0.1%
2.244 2
 
< 0.1%
2.436 14
 
0.3%
2.6355 1
 
< 0.1%
3.278 12
 
0.3%
3.302 3
 
0.1%
3.654 1
 
< 0.1%
4.488 4
 
0.1%
ValueCountFrequency (%)
496073.7835 1
< 0.1%
474838.9615 1
< 0.1%
472821.1216 1
< 0.1%
461334.635 1
< 0.1%
460816.1586 1
< 0.1%
456926.086 1
< 0.1%
454013.597 1
< 0.1%
452076.9963 1
< 0.1%
451385.1764 1
< 0.1%
447279.0249 1
< 0.1%

KM
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2501.5982
Minimum308.85999
Maximum7212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:42.194318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum308.85999
5-th percentile1639
Q11757
median2436
Q32436
95-th percentile6321
Maximum7212
Range6903.14
Interquartile range (IQR)679

Descriptive statistics

Standard deviation1224.6041
Coefficient of variation (CV)0.48952868
Kurtosis6.5926267
Mean2501.5982
Median Absolute Deviation (MAD)192
Skewness2.6422233
Sum11887595
Variance1499655.1
MonotonicityNot monotonic
2024-09-18T21:50:42.242398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2436 1606
33.8%
1639 650
13.7%
2244 602
 
12.7%
2510 213
 
4.5%
1757 193
 
4.1%
1651 186
 
3.9%
2891 163
 
3.4%
1366 145
 
3.1%
3878 92
 
1.9%
1780 91
 
1.9%
Other values (19) 811
17.1%
ValueCountFrequency (%)
308.8599854 2
 
< 0.1%
1098.05 17
 
0.4%
1354.54 2
 
< 0.1%
1366 145
 
3.1%
1639 650
13.7%
1641 89
 
1.9%
1651 186
 
3.9%
1752 28
 
0.6%
1757 193
 
4.1%
1780 91
 
1.9%
ValueCountFrequency (%)
7212 38
0.8%
7121 74
1.6%
6659 1
 
< 0.1%
6590.620117 65
1.4%
6497 11
 
0.2%
6321 63
1.3%
6155.069824 78
1.6%
4219 8
 
0.2%
3878 92
1.9%
3109 74
1.6%

AC CATEGORY
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
NB
2274 
FREIGHTERS
2263 
WB
 
215

Length

Max length10
Median length2
Mean length5.8097643
Min length2

Characters and Unicode

Total characters27608
Distinct characters11
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 (%)
NB 2274
47.9%
FREIGHTERS 2263
47.6%
WB 215
 
4.5%

Length

2024-09-18T21:50:42.295944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T21:50:42.340640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
nb 2274
47.9%
freighters 2263
47.6%
wb 215
 
4.5%

Most occurring characters

ValueCountFrequency (%)
R 4526
16.4%
E 4526
16.4%
B 2489
9.0%
N 2274
8.2%
F 2263
8.2%
I 2263
8.2%
G 2263
8.2%
H 2263
8.2%
T 2263
8.2%
S 2263
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 4526
16.4%
E 4526
16.4%
B 2489
9.0%
N 2274
8.2%
F 2263
8.2%
I 2263
8.2%
G 2263
8.2%
H 2263
8.2%
T 2263
8.2%
S 2263
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 4526
16.4%
E 4526
16.4%
B 2489
9.0%
N 2274
8.2%
F 2263
8.2%
I 2263
8.2%
G 2263
8.2%
H 2263
8.2%
T 2263
8.2%
S 2263
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 4526
16.4%
E 4526
16.4%
B 2489
9.0%
N 2274
8.2%
F 2263
8.2%
I 2263
8.2%
G 2263
8.2%
H 2263
8.2%
T 2263
8.2%
S 2263
8.2%

AC TYPE
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
A330F
1502 
320
733 
20F
661 
B767-200
333 
A300-600
249 
Other values (20)
1274 

Length

Max length8
Median length3
Mean length4.2925084
Min length3

Characters and Unicode

Total characters20398
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowA330F
2nd rowA330F
3rd rowA330F
4th rowA330F
5th rowA330F

Common Values

ValueCountFrequency (%)
A330F 1502
31.6%
320 733
15.4%
20F 661
13.9%
B767-200 333
 
7.0%
A300-600 249
 
5.2%
2NF 208
 
4.4%
787 173
 
3.6%
32N 148
 
3.1%
31J 95
 
2.0%
20B 66
 
1.4%
Other values (15) 584
 
12.3%

Length

2024-09-18T21:50:42.385228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a330f 1502
31.6%
320 733
15.4%
20f 661
13.9%
b767-200 333
 
7.0%
a300-600 249
 
5.2%
2nf 208
 
4.4%
787 173
 
3.6%
32n 148
 
3.1%
31j 95
 
2.0%
20b 66
 
1.4%
Other values (15) 584
 
12.3%

Most occurring characters

ValueCountFrequency (%)
3 4735
23.2%
0 4692
23.0%
F 2485
12.2%
2 2361
11.6%
A 1807
 
8.9%
7 1178
 
5.8%
6 644
 
3.2%
- 582
 
2.9%
B 527
 
2.6%
N 463
 
2.3%
Other values (12) 924
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 4735
23.2%
0 4692
23.0%
F 2485
12.2%
2 2361
11.6%
A 1807
 
8.9%
7 1178
 
5.8%
6 644
 
3.2%
- 582
 
2.9%
B 527
 
2.6%
N 463
 
2.3%
Other values (12) 924
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 4735
23.2%
0 4692
23.0%
F 2485
12.2%
2 2361
11.6%
A 1807
 
8.9%
7 1178
 
5.8%
6 644
 
3.2%
- 582
 
2.9%
B 527
 
2.6%
N 463
 
2.3%
Other values (12) 924
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 4735
23.2%
0 4692
23.0%
F 2485
12.2%
2 2361
11.6%
A 1807
 
8.9%
7 1178
 
5.8%
6 644
 
3.2%
- 582
 
2.9%
B 527
 
2.6%
N 463
 
2.3%
Other values (12) 924
 
4.5%

DEPS
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
1
4752 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4752
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 row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4752
100.0%

Length

2024-09-18T21:50:42.470323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T21:50:42.503047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4752
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4752
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4752
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4752
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4752
100.0%

HB
Real number (ℝ)

HIGH CORRELATION 

Distinct314
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6318287
Minimum1.1166667
Maximum9.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:42.542966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.1166667
5-th percentile2.4333333
Q12.8
median3.4166667
Q33.6875
95-th percentile7.8833333
Maximum9.4
Range8.2833333
Interquartile range (IQR)0.8875

Descriptive statistics

Standard deviation1.393841
Coefficient of variation (CV)0.3837849
Kurtosis6.1400004
Mean3.6318287
Median Absolute Deviation (MAD)0.4
Skewness2.5062569
Sum17258.45
Variance1.9427928
MonotonicityNot monotonic
2024-09-18T21:50:42.593283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 137
 
2.9%
3.416666667 115
 
2.4%
3.583333333 114
 
2.4%
3.333333333 111
 
2.3%
3.666666667 94
 
2.0%
3.516666667 81
 
1.7%
3.533333333 73
 
1.5%
3.466666667 70
 
1.5%
3.75 69
 
1.5%
3.35 68
 
1.4%
Other values (304) 3820
80.4%
ValueCountFrequency (%)
1.116666667 1
 
< 0.1%
1.75 1
 
< 0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
1.933333333 1
 
< 0.1%
1.95 2
< 0.1%
1.966666667 3
0.1%
1.983333333 1
 
< 0.1%
2 4
0.1%
2.016666667 3
0.1%
ValueCountFrequency (%)
9.4 1
< 0.1%
9.383333333 1
< 0.1%
9.3 1
< 0.1%
9.266666667 1
< 0.1%
9.216666667 1
< 0.1%
9.2 1
< 0.1%
9.183333333 2
< 0.1%
9.166666667 1
< 0.1%
9.15 2
< 0.1%
9.133333333 1
< 0.1%

HV
Real number (ℝ)

HIGH CORRELATION 

Distinct284
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1707001
Minimum0.56666667
Maximum8.7666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:42.643131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.56666667
5-th percentile2.1
Q12.3333333
median2.9833333
Q33.15
95-th percentile7.45
Maximum8.7666667
Range8.2
Interquartile range (IQR)0.81666667

Descriptive statistics

Standard deviation1.3831305
Coefficient of variation (CV)0.43622245
Kurtosis6.5704151
Mean3.1707001
Median Absolute Deviation (MAD)0.26666667
Skewness2.626399
Sum15067.167
Variance1.9130501
MonotonicityNot monotonic
2024-09-18T21:50:42.694278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.083333333 183
 
3.9%
3 162
 
3.4%
2.916666667 129
 
2.7%
3.05 126
 
2.7%
3.166666667 107
 
2.3%
2.983333333 105
 
2.2%
3.116666667 104
 
2.2%
3.033333333 101
 
2.1%
3.066666667 101
 
2.1%
3.016666667 101
 
2.1%
Other values (274) 3533
74.3%
ValueCountFrequency (%)
0.5666666667 1
 
< 0.1%
0.5833333333 1
 
< 0.1%
0.6666666667 1
 
< 0.1%
1.616666667 1
 
< 0.1%
1.65 4
 
0.1%
1.666666667 9
0.2%
1.683333333 6
 
0.1%
1.7 13
0.3%
1.716666667 6
 
0.1%
1.733333333 17
0.4%
ValueCountFrequency (%)
8.766666667 1
 
< 0.1%
8.75 1
 
< 0.1%
8.7 2
< 0.1%
8.666666667 2
< 0.1%
8.65 1
 
< 0.1%
8.633333333 1
 
< 0.1%
8.616666667 3
0.1%
8.6 2
< 0.1%
8.583333333 2
< 0.1%
8.566666667 2
< 0.1%

LEG
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
MIABOG
1606 
MIAMGA
650 
MIAMDE
602 
MIACLO
213 
MIABAQ
193 
Other values (24)
1488 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters28512
Distinct characters23
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 rowMIAEZE
2nd rowMIABOG
3rd rowMIASDQ
4th rowMIACWB
5th rowMIAAGT

Common Values

ValueCountFrequency (%)
MIABOG 1606
33.8%
MIAMGA 650
13.7%
MIAMDE 602
 
12.7%
MIACLO 213
 
4.5%
MIABAQ 193
 
4.1%
MIASAL 186
 
3.9%
MIAUIO 163
 
3.4%
MIASDQ 145
 
3.1%
MIAMAO 92
 
1.9%
MIACTG 91
 
1.9%
Other values (19) 811
17.1%

Length

2024-09-18T21:50:42.740225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
miabog 1606
33.8%
miamga 650
13.7%
miamde 602
 
12.7%
miaclo 213
 
4.5%
miabaq 193
 
4.1%
miasal 186
 
3.9%
miauio 163
 
3.4%
miasdq 145
 
3.1%
miamao 92
 
1.9%
miactg 91
 
1.9%
Other values (19) 811
17.1%

Most occurring characters

ValueCountFrequency (%)
M 6191
21.7%
A 6142
21.5%
I 4996
17.5%
G 2612
9.2%
O 2162
 
7.6%
B 1903
 
6.7%
E 882
 
3.1%
D 802
 
2.8%
S 524
 
1.8%
L 408
 
1.4%
Other values (13) 1890
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 6191
21.7%
A 6142
21.5%
I 4996
17.5%
G 2612
9.2%
O 2162
 
7.6%
B 1903
 
6.7%
E 882
 
3.1%
D 802
 
2.8%
S 524
 
1.8%
L 408
 
1.4%
Other values (13) 1890
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 6191
21.7%
A 6142
21.5%
I 4996
17.5%
G 2612
9.2%
O 2162
 
7.6%
B 1903
 
6.7%
E 882
 
3.1%
D 802
 
2.8%
S 524
 
1.8%
L 408
 
1.4%
Other values (13) 1890
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 6191
21.7%
A 6142
21.5%
I 4996
17.5%
G 2612
9.2%
O 2162
 
7.6%
B 1903
 
6.7%
E 882
 
3.1%
D 802
 
2.8%
S 524
 
1.8%
L 408
 
1.4%
Other values (13) 1890
 
6.6%

YEAR
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
2023
4752 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters19008
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 4752
100.0%

Length

2024-09-18T21:50:42.781384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T21:50:42.813672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 4752
100.0%

Most occurring characters

ValueCountFrequency (%)
2 9504
50.0%
0 4752
25.0%
3 4752
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 9504
50.0%
0 4752
25.0%
3 4752
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 9504
50.0%
0 4752
25.0%
3 4752
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 9504
50.0%
0 4752
25.0%
3 4752
25.0%

MONTH
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5595539
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:42.843416image/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 deviation2.3208782
Coefficient of variation (CV)0.50901432
Kurtosis-1.2506251
Mean4.5595539
Median Absolute Deviation (MAD)2
Skewness-0.058454967
Sum21667
Variance5.3864757
MonotonicityNot monotonic
2024-09-18T21:50:42.885867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 655
13.8%
1 649
13.7%
8 616
13.0%
4 612
12.9%
6 591
12.4%
3 570
12.0%
5 561
11.8%
2 498
10.5%
ValueCountFrequency (%)
1 649
13.7%
2 498
10.5%
3 570
12.0%
4 612
12.9%
5 561
11.8%
6 591
12.4%
7 655
13.8%
8 616
13.0%
ValueCountFrequency (%)
8 616
13.0%
7 655
13.8%
6 591
12.4%
5 561
11.8%
4 612
12.9%
3 570
12.0%
2 498
10.5%
1 649
13.7%

RTK_FIS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2731
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44433.509
Minimum0
Maximum408576.49
Zeros1423
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:42.940212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median289.476
Q350545.725
95-th percentile298529.44
Maximum408576.49
Range408576.49
Interquartile range (IQR)50545.725

Descriptive statistics

Standard deviation87694.214
Coefficient of variation (CV)1.9736054
Kurtosis6.013824
Mean44433.509
Median Absolute Deviation (MAD)289.476
Skewness2.578237
Sum2.1114803 × 108
Variance7.6902752 × 109
MonotonicityNot monotonic
2024-09-18T21:50:43.000733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1423
29.9%
245.85 19
 
0.4%
2.436 16
 
0.3%
1.639 15
 
0.3%
3.278 13
 
0.3%
262.24 12
 
0.3%
4.872 11
 
0.2%
17.052 10
 
0.2%
19.488 9
 
0.2%
12.18 9
 
0.2%
Other values (2721) 3215
67.7%
ValueCountFrequency (%)
0 1423
29.9%
1.639 15
 
0.3%
1.651 2
 
< 0.1%
1.757 2
 
< 0.1%
2.244 6
 
0.1%
2.26548 1
 
< 0.1%
2.436 16
 
0.3%
3.278 13
 
0.3%
3.302 3
 
0.1%
3.514 1
 
< 0.1%
ValueCountFrequency (%)
408576.4899 1
< 0.1%
408161.478 1
< 0.1%
404081.145 1
< 0.1%
400926.542 1
< 0.1%
400568.904 1
< 0.1%
396914.4266 1
< 0.1%
395044.596 1
< 0.1%
394491.2795 1
< 0.1%
393962.204 1
< 0.1%
393716.5295 1
< 0.1%

RTK_VOL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2825
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51198.463
Minimum0
Maximum496073.78
Zeros1478
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:43.060049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median295.614
Q358304.118
95-th percentile361266.91
Maximum496073.78
Range496073.78
Interquartile range (IQR)58304.118

Descriptive statistics

Standard deviation101215.02
Coefficient of variation (CV)1.9769152
Kurtosis5.8793818
Mean51198.463
Median Absolute Deviation (MAD)295.614
Skewness2.5606237
Sum2.432951 × 108
Variance1.024448 × 1010
MonotonicityNot monotonic
2024-09-18T21:50:43.118948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1478
31.1%
1.639 14
 
0.3%
2.436 14
 
0.3%
3.278 12
 
0.3%
12.18 10
 
0.2%
4.872 10
 
0.2%
286.825 9
 
0.2%
19.488 8
 
0.2%
77.952 7
 
0.1%
7.308 7
 
0.1%
Other values (2815) 3183
67.0%
ValueCountFrequency (%)
0 1478
31.1%
1.639 14
 
0.3%
1.651 2
 
< 0.1%
2.244 2
 
< 0.1%
2.436 14
 
0.3%
2.6355 1
 
< 0.1%
3.278 12
 
0.3%
3.302 3
 
0.1%
3.654 1
 
< 0.1%
4.488 4
 
0.1%
ValueCountFrequency (%)
496073.7835 1
< 0.1%
474838.9615 1
< 0.1%
472821.1216 1
< 0.1%
469704.7205 1
< 0.1%
461334.635 1
< 0.1%
461042.024 1
< 0.1%
460816.1586 1
< 0.1%
458172.4219 1
< 0.1%
456926.086 1
< 0.1%
454797.7971 1
< 0.1%

ATK_FIS
Real number (ℝ)

HIGH CORRELATION 

Distinct3815
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83772.648
Minimum0
Maximum432835
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:43.175165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3334.1537
Q19650.7346
median44621.013
Q3127908
95-th percentile357366.13
Maximum432835
Range432835
Interquartile range (IQR)118257.27

Descriptive statistics

Standard deviation99613.137
Coefficient of variation (CV)1.189089
Kurtosis2.4012451
Mean83772.648
Median Absolute Deviation (MAD)39962.49
Skewness1.6649225
Sum3.9808762 × 108
Variance9.9227771 × 109
MonotonicityNot monotonic
2024-09-18T21:50:43.276849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99876 165
 
3.5%
107184 115
 
2.4%
98736 64
 
1.3%
92004 60
 
1.3%
70395 50
 
1.1%
138852 47
 
1.0%
243600 41
 
0.9%
158340 41
 
0.9%
133980 36
 
0.8%
71839 34
 
0.7%
Other values (3805) 4099
86.3%
ValueCountFrequency (%)
0 5
0.1%
1.639 1
 
< 0.1%
31.668 1
 
< 0.1%
170.52 1
 
< 0.1%
401.9004 1
 
< 0.1%
404.361 1
 
< 0.1%
405.594 1
 
< 0.1%
466.5276 1
 
< 0.1%
554.6772 1
 
< 0.1%
632.8179 1
 
< 0.1%
ValueCountFrequency (%)
432835 1
 
< 0.1%
432720 5
0.1%
425010.7365 1
 
< 0.1%
419528.7944 1
 
< 0.1%
419202.8729 1
 
< 0.1%
417175.2042 1
 
< 0.1%
415778.2331 1
 
< 0.1%
414563.6843 1
 
< 0.1%
414450.6028 1
 
< 0.1%
414340.1561 1
 
< 0.1%

ATK_VOL
Real number (ℝ)

HIGH CORRELATION 

Distinct1465
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92162.698
Minimum0
Maximum496073.78
Zeros26
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:43.331780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1789.9524
Q15297.5962
median30511.51
Q3143724
95-th percentile427299.6
Maximum496073.78
Range496073.78
Interquartile range (IQR)138426.4

Descriptive statistics

Standard deviation120003.11
Coefficient of variation (CV)1.3020789
Kurtosis2.5540267
Mean92162.698
Median Absolute Deviation (MAD)29409.097
Skewness1.7139664
Sum4.3795714 × 108
Variance1.4400746 × 1010
MonotonicityNot monotonic
2024-09-18T21:50:43.388711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164673.6 229
 
4.8%
109620 163
 
3.4%
195431.6 131
 
2.8%
114569.952 113
 
2.4%
151694.4 105
 
2.2%
143724 83
 
1.7%
125295.9233 76
 
1.6%
210168.4 71
 
1.5%
105539.808 64
 
1.3%
427299.6 62
 
1.3%
Other values (1455) 3655
76.9%
ValueCountFrequency (%)
0 26
0.5%
1.639 2
 
< 0.1%
3.278 1
 
< 0.1%
13.112 1
 
< 0.1%
13.9315 1
 
< 0.1%
35.2385 1
 
< 0.1%
44.253 1
 
< 0.1%
86.0475 1
 
< 0.1%
94.2425 1
 
< 0.1%
95.062 1
 
< 0.1%
ValueCountFrequency (%)
496073.7835 1
 
< 0.1%
487531.2 20
 
0.4%
487122.7303 1
 
< 0.1%
483551.7987 1
 
< 0.1%
483310.6474 1
 
< 0.1%
482839.2531 1
 
< 0.1%
481379.6 53
1.1%
480496.0619 1
 
< 0.1%
478721.8321 1
 
< 0.1%
477590.4496 1
 
< 0.1%

UNDERLOAD
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3413
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14761.268
Minimum-44531.165
Maximum104000
Zeros82
Zeros (%)1.7%
Negative1
Negative (%)< 0.1%
Memory size37.2 KiB
2024-09-18T21:50:43.441929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-44531.165
5-th percentile530
Q12127.86
median3979.798
Q321718.459
95-th percentile55168.28
Maximum104000
Range148531.17
Interquartile range (IQR)19590.599

Descriptive statistics

Standard deviation19297.734
Coefficient of variation (CV)1.3073222
Kurtosis3.3547882
Mean14761.268
Median Absolute Deviation (MAD)3240.0105
Skewness1.7883502
Sum70145547
Variance3.7240254 × 108
MonotonicityNot monotonic
2024-09-18T21:50:43.500483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82
 
1.7%
55000 68
 
1.4%
57000 58
 
1.2%
41000 42
 
0.9%
100000 40
 
0.8%
44000 36
 
0.8%
2782 20
 
0.4%
3327 15
 
0.3%
65000 14
 
0.3%
2355 14
 
0.3%
Other values (3403) 4363
91.8%
ValueCountFrequency (%)
-44531.165 1
 
< 0.1%
0 82
1.7%
3 1
 
< 0.1%
6.92 1
 
< 0.1%
22 1
 
< 0.1%
24.22 1
 
< 0.1%
25.085 2
 
< 0.1%
46 2
 
< 0.1%
47.575 1
 
< 0.1%
48.44 1
 
< 0.1%
ValueCountFrequency (%)
104000 10
 
0.2%
100000 40
0.8%
97899 1
 
< 0.1%
81732 1
 
< 0.1%
65000 14
 
0.3%
64847 1
 
< 0.1%
64684 1
 
< 0.1%
64563 1
 
< 0.1%
64241 1
 
< 0.1%
63984 1
 
< 0.1%

DAY
Real number (ℝ)

HIGH CORRELATION 

Distinct241
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.89962
Minimum1
Maximum241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:43.561081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q162
median122
Q3185
95-th percentile230
Maximum241
Range240
Interquartile range (IQR)123

Descriptive statistics

Standard deviation70.57688
Coefficient of variation (CV)0.57426442
Kurtosis-1.2328114
Mean122.89962
Median Absolute Deviation (MAD)62
Skewness-0.039317592
Sum584019
Variance4981.096
MonotonicityNot monotonic
2024-09-18T21:50:43.623902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 29
 
0.6%
119 29
 
0.6%
28 29
 
0.6%
35 28
 
0.6%
122 28
 
0.6%
112 28
 
0.6%
29 27
 
0.6%
33 27
 
0.6%
121 27
 
0.6%
25 26
 
0.5%
Other values (231) 4474
94.1%
ValueCountFrequency (%)
1 18
0.4%
2 16
0.3%
3 18
0.4%
4 18
0.4%
5 22
0.5%
6 19
0.4%
7 22
0.5%
8 19
0.4%
9 18
0.4%
10 14
0.3%
ValueCountFrequency (%)
241 23
0.5%
240 15
0.3%
239 22
0.5%
238 26
0.5%
237 20
0.4%
236 23
0.5%
235 20
0.4%
234 23
0.5%
233 17
0.4%
232 22
0.5%

WEEK
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.019781
Minimum2
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:43.677537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q110
median19
Q328
95-th percentile34
Maximum53
Range51
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.244353
Coefficient of variation (CV)0.53861574
Kurtosis-0.90968946
Mean19.019781
Median Absolute Deviation (MAD)9
Skewness0.066261384
Sum90382
Variance104.94678
MonotonicityNot monotonic
2024-09-18T21:50:43.730510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5 177
 
3.7%
18 175
 
3.7%
19 174
 
3.7%
6 171
 
3.6%
32 153
 
3.2%
17 152
 
3.2%
29 151
 
3.2%
35 151
 
3.2%
31 149
 
3.1%
4 148
 
3.1%
Other values (26) 3151
66.3%
ValueCountFrequency (%)
2 134
2.8%
3 126
2.7%
4 148
3.1%
5 177
3.7%
6 171
3.6%
7 124
2.6%
8 113
2.4%
9 110
2.3%
10 126
2.7%
11 128
2.7%
ValueCountFrequency (%)
53 18
 
0.4%
36 38
 
0.8%
35 151
3.2%
34 147
3.1%
33 146
3.1%
32 153
3.2%
31 149
3.1%
30 146
3.1%
29 151
3.2%
28 147
3.1%

DOW
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1399411
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:43.772821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9751103
Coefficient of variation (CV)0.47708657
Kurtosis-1.2393653
Mean4.1399411
Median Absolute Deviation (MAD)2
Skewness-0.06709273
Sum19673
Variance3.9010607
MonotonicityNot monotonic
2024-09-18T21:50:43.811367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 776
16.3%
7 724
15.2%
4 708
14.9%
3 678
14.3%
2 677
14.2%
5 635
13.4%
1 554
11.7%
ValueCountFrequency (%)
1 554
11.7%
2 677
14.2%
3 678
14.3%
4 708
14.9%
5 635
13.4%
6 776
16.3%
7 724
15.2%
ValueCountFrequency (%)
7 724
15.2%
6 776
16.3%
5 635
13.4%
4 708
14.9%
3 678
14.3%
2 677
14.2%
1 554
11.7%

COUNTRY ORIGIN
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
US
4752 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9504
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
US 4752
100.0%

Length

2024-09-18T21:50:43.855794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T21:50:43.888884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
us 4752
100.0%

Most occurring characters

ValueCountFrequency (%)
U 4752
50.0%
S 4752
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4752
50.0%
S 4752
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4752
50.0%
S 4752
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4752
50.0%
S 4752
50.0%

COUNTRY DESTINATION
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
CO
2828 
NI
650 
EC
 
237
SV
 
186
BR
 
168
Other values (11)
683 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters9504
Distinct characters19
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 rowAR
2nd rowCO
3rd rowDO
4th rowBR
5th rowPY

Common Values

ValueCountFrequency (%)
CO 2828
59.5%
NI 650
 
13.7%
EC 237
 
5.0%
SV 186
 
3.9%
BR 168
 
3.5%
DO 145
 
3.1%
PY 141
 
3.0%
GT 89
 
1.9%
CR 86
 
1.8%
PA 78
 
1.6%
Other values (6) 144
 
3.0%

Length

2024-09-18T21:50:43.925128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
co 2828
59.5%
ni 650
 
13.7%
ec 237
 
5.0%
sv 186
 
3.9%
br 168
 
3.5%
do 145
 
3.1%
py 141
 
3.0%
gt 89
 
1.9%
cr 86
 
1.8%
pa 78
 
1.6%
Other values (6) 144
 
3.0%

Most occurring characters

ValueCountFrequency (%)
C 3152
33.2%
O 2973
31.3%
N 650
 
6.8%
I 650
 
6.8%
R 328
 
3.5%
E 245
 
2.6%
P 227
 
2.4%
S 190
 
2.0%
V 186
 
2.0%
Y 179
 
1.9%
Other values (9) 724
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 3152
33.2%
O 2973
31.3%
N 650
 
6.8%
I 650
 
6.8%
R 328
 
3.5%
E 245
 
2.6%
P 227
 
2.4%
S 190
 
2.0%
V 186
 
2.0%
Y 179
 
1.9%
Other values (9) 724
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 3152
33.2%
O 2973
31.3%
N 650
 
6.8%
I 650
 
6.8%
R 328
 
3.5%
E 245
 
2.6%
P 227
 
2.4%
S 190
 
2.0%
V 186
 
2.0%
Y 179
 
1.9%
Other values (9) 724
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 3152
33.2%
O 2973
31.3%
N 650
 
6.8%
I 650
 
6.8%
R 328
 
3.5%
E 245
 
2.6%
P 227
 
2.4%
S 190
 
2.0%
V 186
 
2.0%
Y 179
 
1.9%
Other values (9) 724
 
7.6%

REGION ORG
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
NAM
4752 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14256
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 rowNAM
2nd rowNAM
3rd rowNAM
4th rowNAM
5th rowNAM

Common Values

ValueCountFrequency (%)
NAM 4752
100.0%

Length

2024-09-18T21:50:43.966750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T21:50:44.000422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
nam 4752
100.0%

Most occurring characters

ValueCountFrequency (%)
N 4752
33.3%
A 4752
33.3%
M 4752
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 4752
33.3%
A 4752
33.3%
M 4752
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 4752
33.3%
A 4752
33.3%
M 4752
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 4752
33.3%
A 4752
33.3%
M 4752
33.3%

REGION DST
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
SAM
3495 
CAM
1089 
CAR
 
145
MEX
 
19
NAM
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14256
Distinct characters8
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 rowSAM
2nd rowSAM
3rd rowCAR
4th rowSAM
5th rowSAM

Common Values

ValueCountFrequency (%)
SAM 3495
73.5%
CAM 1089
 
22.9%
CAR 145
 
3.1%
MEX 19
 
0.4%
NAM 4
 
0.1%

Length

2024-09-18T21:50:44.035809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T21:50:44.073695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sam 3495
73.5%
cam 1089
 
22.9%
car 145
 
3.1%
mex 19
 
0.4%
nam 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 4733
33.2%
M 4607
32.3%
S 3495
24.5%
C 1234
 
8.7%
R 145
 
1.0%
E 19
 
0.1%
X 19
 
0.1%
N 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4733
33.2%
M 4607
32.3%
S 3495
24.5%
C 1234
 
8.7%
R 145
 
1.0%
E 19
 
0.1%
X 19
 
0.1%
N 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4733
33.2%
M 4607
32.3%
S 3495
24.5%
C 1234
 
8.7%
R 145
 
1.0%
E 19
 
0.1%
X 19
 
0.1%
N 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4733
33.2%
M 4607
32.3%
S 3495
24.5%
C 1234
 
8.7%
R 145
 
1.0%
E 19
 
0.1%
X 19
 
0.1%
N 4
 
< 0.1%

REGION LEG
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
NAMSAM
3495 
NAMCAM
1089 
NAMCAR
 
145
NAMMEX
 
19
NAMNAM
 
4

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters28512
Distinct characters8
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 rowNAMSAM
2nd rowNAMSAM
3rd rowNAMCAR
4th rowNAMSAM
5th rowNAMSAM

Common Values

ValueCountFrequency (%)
NAMSAM 3495
73.5%
NAMCAM 1089
 
22.9%
NAMCAR 145
 
3.1%
NAMMEX 19
 
0.4%
NAMNAM 4
 
0.1%

Length

2024-09-18T21:50:44.116890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-18T21:50:44.157473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
namsam 3495
73.5%
namcam 1089
 
22.9%
namcar 145
 
3.1%
nammex 19
 
0.4%
namnam 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 9485
33.3%
M 9359
32.8%
N 4756
16.7%
S 3495
 
12.3%
C 1234
 
4.3%
R 145
 
0.5%
E 19
 
0.1%
X 19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 9485
33.3%
M 9359
32.8%
N 4756
16.7%
S 3495
 
12.3%
C 1234
 
4.3%
R 145
 
0.5%
E 19
 
0.1%
X 19
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 9485
33.3%
M 9359
32.8%
N 4756
16.7%
S 3495
 
12.3%
C 1234
 
4.3%
R 145
 
0.5%
E 19
 
0.1%
X 19
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 9485
33.3%
M 9359
32.8%
N 4756
16.7%
S 3495
 
12.3%
C 1234
 
4.3%
R 145
 
0.5%
E 19
 
0.1%
X 19
 
0.1%

PAX OB
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct210
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.678872
Minimum0
Maximum251
Zeros2265
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:44.206786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median64.5
Q3143
95-th percentile179
Maximum251
Range251
Interquartile range (IQR)143

Descriptive statistics

Standard deviation74.627009
Coefficient of variation (CV)1.0411298
Kurtosis-1.3988705
Mean71.678872
Median Absolute Deviation (MAD)64.5
Skewness0.3476668
Sum340618
Variance5569.1905
MonotonicityNot monotonic
2024-09-18T21:50:44.303393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2265
47.7%
180 76
 
1.6%
176 33
 
0.7%
174 33
 
0.7%
172 32
 
0.7%
159 32
 
0.7%
149 31
 
0.7%
179 31
 
0.7%
165 30
 
0.6%
150 30
 
0.6%
Other values (200) 2159
45.4%
ValueCountFrequency (%)
0 2265
47.7%
8 1
 
< 0.1%
12 2
 
< 0.1%
27 2
 
< 0.1%
28 1
 
< 0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
34 2
 
< 0.1%
36 4
 
0.1%
38 3
 
0.1%
ValueCountFrequency (%)
251 1
 
< 0.1%
250 10
0.2%
249 9
0.2%
248 2
 
< 0.1%
247 4
 
0.1%
246 1
 
< 0.1%
245 8
0.2%
244 1
 
< 0.1%
242 1
 
< 0.1%
241 5
0.1%

BAGWGT OB
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1712
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1214.2759
Minimum0
Maximum5721
Zeros2269
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:44.355639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median980
Q32293.25
95-th percentile3484.35
Maximum5721
Range5721
Interquartile range (IQR)2293.25

Descriptive statistics

Standard deviation1310.5775
Coefficient of variation (CV)1.0793078
Kurtosis-0.95633595
Mean1214.2759
Median Absolute Deviation (MAD)980
Skewness0.54477869
Sum5770239
Variance1717613.3
MonotonicityNot monotonic
2024-09-18T21:50:44.409670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2269
47.7%
2504 7
 
0.1%
2328 7
 
0.1%
1832 6
 
0.1%
2028 5
 
0.1%
2078 5
 
0.1%
2023 5
 
0.1%
2303 5
 
0.1%
2539 5
 
0.1%
2544 5
 
0.1%
Other values (1702) 2433
51.2%
ValueCountFrequency (%)
0 2269
47.7%
30 1
 
< 0.1%
110 1
 
< 0.1%
117 1
 
< 0.1%
161 1
 
< 0.1%
180 1
 
< 0.1%
263 1
 
< 0.1%
276 1
 
< 0.1%
333 1
 
< 0.1%
352 1
 
< 0.1%
ValueCountFrequency (%)
5721 1
< 0.1%
5605 1
< 0.1%
5334 1
< 0.1%
5266 1
< 0.1%
5198 1
< 0.1%
5180 1
< 0.1%
5146 1
< 0.1%
5119 1
< 0.1%
5092 1
< 0.1%
4989 1
< 0.1%

ACTUALPAYLOAD
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2358
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14061.531
Minimum0
Maximum67930
Zeros1405
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:44.462789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median157.5
Q328472.75
95-th percentile55925.7
Maximum67930
Range67930
Interquartile range (IQR)28472.75

Descriptive statistics

Standard deviation19946.916
Coefficient of variation (CV)1.418545
Kurtosis-0.27508254
Mean14061.531
Median Absolute Deviation (MAD)157.5
Skewness1.0984131
Sum66820398
Variance3.9787945 × 108
MonotonicityNot monotonic
2024-09-18T21:50:44.519337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1405
29.6%
1 41
 
0.9%
2 32
 
0.7%
150 25
 
0.5%
7 22
 
0.5%
8 19
 
0.4%
3 19
 
0.4%
4 18
 
0.4%
20 15
 
0.3%
14 15
 
0.3%
Other values (2348) 3141
66.1%
ValueCountFrequency (%)
0 1405
29.6%
0.93 1
 
< 0.1%
1 41
 
0.9%
2 32
 
0.7%
3 19
 
0.4%
4 18
 
0.4%
5 13
 
0.3%
6 11
 
0.2%
7 22
 
0.5%
8 19
 
0.4%
ValueCountFrequency (%)
67930 1
< 0.1%
67308 1
< 0.1%
65966 1
< 0.1%
65734 1
< 0.1%
65688 1
< 0.1%
65238 1
< 0.1%
65002 1
< 0.1%
64932 1
< 0.1%
64912 1
< 0.1%
64898 1
< 0.1%

MAXALLOWEDPAYLOAD
Real number (ℝ)

HIGH CORRELATION 

Distinct3613
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30486.796
Minimum0
Maximum104000
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:44.576866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1694.295
Q14659.8085
median19540.35
Q359135.183
95-th percentile65711.58
Maximum104000
Range104000
Interquartile range (IQR)54475.374

Descriptive statistics

Standard deviation26843.272
Coefficient of variation (CV)0.88048845
Kurtosis-1.4039141
Mean30486.796
Median Absolute Deviation (MAD)18342.9
Skewness0.32259339
Sum1.4487325 × 108
Variance7.2056123 × 108
MonotonicityNot monotonic
2024-09-18T21:50:44.633698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41000 252
 
5.3%
44000 179
 
3.8%
65000 120
 
2.5%
55000 85
 
1.8%
39000 78
 
1.6%
57000 70
 
1.5%
39800 50
 
1.1%
100000 42
 
0.9%
40500 19
 
0.4%
58000 11
 
0.2%
Other values (3603) 3846
80.9%
ValueCountFrequency (%)
0 5
0.1%
1 1
 
< 0.1%
13 1
 
< 0.1%
70 1
 
< 0.1%
161.1 1
 
< 0.1%
166.5 1
 
< 0.1%
179.1 1
 
< 0.1%
207.9 1
 
< 0.1%
227.7 1
 
< 0.1%
330.3 1
 
< 0.1%
ValueCountFrequency (%)
104000 10
 
0.2%
100000 42
0.9%
70820.864 1
 
< 0.1%
68341.4141 1
 
< 0.1%
68319.7156 1
 
< 0.1%
68191.0848 1
 
< 0.1%
68180.0074 1
 
< 0.1%
68120.0992 1
 
< 0.1%
68118.8897 1
 
< 0.1%
68091.7179 1
 
< 0.1%

VOLUME
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1956
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.532765
Minimum0
Maximum417.35881
Zeros1483
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:44.686897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q3159.72052
95-th percentile301.605
Maximum417.35881
Range417.35881
Interquartile range (IQR)159.72052

Descriptive statistics

Standard deviation109.09027
Coefficient of variation (CV)1.4442775
Kurtosis-0.29406435
Mean75.532765
Median Absolute Deviation (MAD)1
Skewness1.1083392
Sum358931.7
Variance11900.688
MonotonicityDecreasing
2024-09-18T21:50:44.740208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1483
31.2%
1 975
20.5%
2 228
 
4.8%
3 44
 
0.9%
4 16
 
0.3%
50 8
 
0.2%
51 7
 
0.1%
11 6
 
0.1%
9 6
 
0.1%
5 5
 
0.1%
Other values (1946) 1974
41.5%
ValueCountFrequency (%)
0 1483
31.2%
0.005 1
 
< 0.1%
0.017 1
 
< 0.1%
0.031 1
 
< 0.1%
0.06 1
 
< 0.1%
0.082 1
 
< 0.1%
0.156 1
 
< 0.1%
0.221 1
 
< 0.1%
0.28 1
 
< 0.1%
0.282 1
 
< 0.1%
ValueCountFrequency (%)
417.3588108 1
< 0.1%
396.9730926 1
< 0.1%
382.469 1
< 0.1%
374.077 1
< 0.1%
371.844 1
< 0.1%
369.6013174 1
< 0.1%
367.617 1
< 0.1%
365.6877373 1
< 0.1%
361.718 1
< 0.1%
360.425 1
< 0.1%

ATD_L
Date

Distinct4724
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
Minimum2023-01-01 06:59:00
Maximum2023-08-29 21:08:00
2024-09-18T21:50:44.791325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:44.843821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ATA_L
Date

Distinct4704
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
Minimum2023-01-01 09:28:00
Maximum2023-08-30 00:06:00
2024-09-18T21:50:44.894169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:44.946444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ATD_Z
Date

Distinct4724
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
Minimum2023-01-01 11:59:00
Maximum2023-08-30 01:08:00
2024-09-18T21:50:44.996814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:45.091668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ATA_Z
Date

Distinct4710
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size37.2 KiB
Minimum2023-01-01 14:28:00
Maximum2023-08-30 05:06:00
2024-09-18T21:50:45.142244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:45.195352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BAGQTY OB
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct252
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.760311
Minimum0
Maximum301
Zeros2269
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:45.249257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median53
Q3133
95-th percentile205
Maximum301
Range301
Interquartile range (IQR)133

Descriptive statistics

Standard deviation75.856204
Coefficient of variation (CV)1.0873834
Kurtosis-1.0215394
Mean69.760311
Median Absolute Deviation (MAD)53
Skewness0.55420339
Sum331501
Variance5754.1638
MonotonicityNot monotonic
2024-09-18T21:50:45.304102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2269
47.7%
118 36
 
0.8%
95 27
 
0.6%
138 27
 
0.6%
122 27
 
0.6%
123 27
 
0.6%
126 27
 
0.6%
136 25
 
0.5%
114 25
 
0.5%
105 25
 
0.5%
Other values (242) 2237
47.1%
ValueCountFrequency (%)
0 2269
47.7%
2 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
17 1
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
301 1
< 0.1%
300 1
< 0.1%
288 1
< 0.1%
283 1
< 0.1%
278 2
< 0.1%
274 1
< 0.1%
273 1
< 0.1%
271 1
< 0.1%
270 1
< 0.1%
269 1
< 0.1%

PAYLOAD IN CABIN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2163
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5462.8361
Minimum0
Maximum20374
Zeros2267
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:45.358561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4914.5
Q310856.75
95-th percentile13801
Maximum20374
Range20374
Interquartile range (IQR)10856.75

Descriptive statistics

Standard deviation5690.9223
Coefficient of variation (CV)1.0417523
Kurtosis-1.3760378
Mean5462.8361
Median Absolute Deviation (MAD)4914.5
Skewness0.35405781
Sum25959397
Variance32386596
MonotonicityNot monotonic
2024-09-18T21:50:45.416811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2267
47.7%
12524 4
 
0.1%
12554 4
 
0.1%
8444 4
 
0.1%
10508 3
 
0.1%
7114 3
 
0.1%
9717 3
 
0.1%
8494 3
 
0.1%
11992 3
 
0.1%
11010 3
 
0.1%
Other values (2153) 2455
51.7%
ValueCountFrequency (%)
0 2267
47.7%
614 1
 
< 0.1%
966 1
 
< 0.1%
2082 1
 
< 0.1%
2101 1
 
< 0.1%
2270 1
 
< 0.1%
2372 1
 
< 0.1%
2464 1
 
< 0.1%
2620 1
 
< 0.1%
2652 1
 
< 0.1%
ValueCountFrequency (%)
20374 1
< 0.1%
20146 1
< 0.1%
19895 1
< 0.1%
19758 1
< 0.1%
19757 1
< 0.1%
19704 1
< 0.1%
19703 1
< 0.1%
19578 1
< 0.1%
19474 1
< 0.1%
19472 1
< 0.1%

AVAILABLE SEATS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.494739
Minimum0
Maximum252
Zeros2263
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:45.469011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median144
Q3180
95-th percentile182
Maximum252
Range252
Interquartile range (IQR)180

Descriptive statistics

Standard deviation92.784866
Coefficient of variation (CV)0.9716228
Kurtosis-1.8174354
Mean95.494739
Median Absolute Deviation (MAD)106
Skewness0.022828755
Sum453791
Variance8609.0313
MonotonicityNot monotonic
2024-09-18T21:50:45.516617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 2263
47.6%
180 2015
42.4%
250 136
 
2.9%
120 95
 
2.0%
144 57
 
1.2%
251 37
 
0.8%
150 35
 
0.7%
182 32
 
0.7%
249 20
 
0.4%
188 8
 
0.2%
Other values (19) 54
 
1.1%
ValueCountFrequency (%)
0 2263
47.6%
118 1
 
< 0.1%
119 3
 
0.1%
120 95
 
2.0%
144 57
 
1.2%
147 1
 
< 0.1%
149 6
 
0.1%
150 35
 
0.7%
158 1
 
< 0.1%
169 1
 
< 0.1%
ValueCountFrequency (%)
252 2
 
< 0.1%
251 37
 
0.8%
250 136
2.9%
249 20
 
0.4%
248 4
 
0.1%
247 3
 
0.1%
246 5
 
0.1%
245 1
 
< 0.1%
244 5
 
0.1%
241 2
 
< 0.1%

VOL WGT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2316
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15411.107
Minimum0
Maximum116521.02
Zeros1489
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:45.565974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median128
Q332385.284
95-th percentile59965.49
Maximum116521.02
Range116521.02
Interquartile range (IQR)32385.284

Descriptive statistics

Standard deviation22501.586
Coefficient of variation (CV)1.4600889
Kurtosis0.067033729
Mean15411.107
Median Absolute Deviation (MAD)128
Skewness1.166449
Sum73233579
Variance5.0632137 × 108
MonotonicityNot monotonic
2024-09-18T21:50:45.623082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1489
31.3%
1 33
 
0.7%
2 30
 
0.6%
4 23
 
0.5%
3 22
 
0.5%
7 21
 
0.4%
14 20
 
0.4%
5 19
 
0.4%
8 17
 
0.4%
9 16
 
0.3%
Other values (2306) 3062
64.4%
ValueCountFrequency (%)
0 1489
31.3%
1 33
 
0.7%
2 30
 
0.6%
3 22
 
0.5%
4 23
 
0.5%
5 19
 
0.4%
6 7
 
0.1%
7 21
 
0.4%
8 17
 
0.4%
9 16
 
0.3%
ValueCountFrequency (%)
116521.02 1
< 0.1%
107804.8316 1
< 0.1%
107358.11 1
< 0.1%
106603.4026 1
< 0.1%
103728.56 1
< 0.1%
103358.9944 1
< 0.1%
102780.18 1
< 0.1%
102503.4202 1
< 0.1%
101527.8897 1
< 0.1%
99688.82 1
< 0.1%

KG CHG OPERATIONAL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2316
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15411.092
Minimum0
Maximum116521.02
Zeros1489
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-09-18T21:50:45.677922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median128
Q332385.284
95-th percentile59965.49
Maximum116521.02
Range116521.02
Interquartile range (IQR)32385.284

Descriptive statistics

Standard deviation22501.596
Coefficient of variation (CV)1.4600909
Kurtosis0.067032255
Mean15411.092
Median Absolute Deviation (MAD)128
Skewness1.1664486
Sum73233511
Variance5.063218 × 108
MonotonicityNot monotonic
2024-09-18T21:50:45.734820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1489
31.3%
1 33
 
0.7%
2 30
 
0.6%
4 23
 
0.5%
3 22
 
0.5%
7 21
 
0.4%
14 20
 
0.4%
5 19
 
0.4%
8 17
 
0.4%
9 16
 
0.3%
Other values (2306) 3062
64.4%
ValueCountFrequency (%)
0 1489
31.3%
1 33
 
0.7%
2 30
 
0.6%
3 22
 
0.5%
4 23
 
0.5%
5 19
 
0.4%
6 7
 
0.1%
7 21
 
0.4%
8 17
 
0.4%
9 16
 
0.3%
ValueCountFrequency (%)
116521.02 1
< 0.1%
107804.8316 1
< 0.1%
107358.11 1
< 0.1%
106603.4026 1
< 0.1%
103728.56 1
< 0.1%
103358.9944 1
< 0.1%
102780.18 1
< 0.1%
102503.4202 1
< 0.1%
101527.8897 1
< 0.1%
99688.82 1
< 0.1%

Interactions

2024-09-18T21:50:38.983107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:05.180200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:06.338946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:07.586851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:08.840229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:10.238890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:11.546243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:12.853435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:14.049498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:15.260548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:16.363036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:17.481772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:18.629217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:19.913264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:21.034726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:22.167607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:23.345292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:24.629567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:25.814221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:26.984045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:28.155917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:29.315417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:30.506432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:31.757119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:32.932847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:34.099083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:35.308645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:36.556666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:37.734442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:39.024322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:05.221368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:06.378414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:07.629370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:08.888006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:10.281633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:11.590583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:12.898378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:14.090503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:15.298752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:16.400272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:17.522219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:18.713911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:19.952466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:21.117055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:22.207617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:23.388675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:24.671071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:25.855737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:27.024359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:28.194898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:29.355128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:30.547535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:31.797774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:32.973849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:34.144782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:35.351398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:36.596276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:37.848486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:39.063379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:05.259958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:06.414547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:07.670677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:08.930930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:10.322978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:11.631937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:12.941630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:14.129677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:15.334443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:16.435947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-18T21:50:06.104501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:07.275656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:08.584342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:09.913127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:11.271280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:12.593016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:13.814837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:15.011465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:16.140407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:17.215987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:18.387814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:19.619599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:20.799176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:21.940112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:23.106183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:24.368901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:25.571260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:26.752365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:27.920358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:29.083792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:30.266252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:31.510514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:32.653616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:33.829375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:35.016018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:36.263415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:37.487472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:38.739069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:39.981195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:06.141449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:07.350938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:08.622676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:09.957824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:11.313117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:12.632471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:13.851041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:15.048755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:16.174406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:17.250640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:18.425380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:19.658517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:20.835544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:21.974501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:23.143176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:24.408463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:25.608873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:26.787277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:27.956132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:29.118916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:30.303181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:31.547922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:32.731015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:33.863264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:35.051853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:36.302156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:37.524459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:38.775326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:40.022579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:06.180768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:07.409895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:08.663359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:10.008787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:11.357799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:12.675151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:13.889461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:15.089789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:16.211401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:17.286966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:18.464994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:19.699667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:20.874233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:22.012722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:23.182125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:24.453584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:25.648622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:26.826727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:27.995580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:29.157463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:30.343676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:31.589790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:32.770283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:33.901852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:35.091851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:36.344298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:37.565452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:38.815473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:40.067096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:06.223362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:07.459901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:08.709379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:10.060341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:11.413227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:12.720683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:13.932890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:15.135780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:16.251415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:17.327984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:18.508933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:19.745052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:20.917128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:22.053796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:23.224861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:24.499679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:25.694310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:26.868338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:28.038779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:29.199796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:30.386660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:31.634846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:32.812566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:33.942997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:35.134827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:36.388942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:37.610719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:38.858533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:40.106522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:06.260600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:07.503980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:08.750443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:10.106341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:11.457491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:12.761615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:13.970131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:15.177962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:16.287677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:17.363755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:18.547824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:19.786014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:20.954759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:22.091438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:23.263288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:24.541838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:25.733865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:26.905539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:28.076771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:29.236802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:30.425674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:31.674243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:32.850818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:34.021898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:35.173902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:36.472063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:37.649870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:38.900001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:40.146198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:06.299994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:07.545637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:08.795340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:10.150591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:11.502831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:12.806020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:14.010491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:15.219100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:16.324936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:17.401732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:18.588380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:19.871253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:20.995174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:22.129460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:23.303458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:24.585084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:25.774477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:26.943961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:28.115502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:29.275512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:30.465426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:31.716232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:32.889863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:34.059455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:35.213723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:36.513448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:37.690967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-18T21:50:38.941884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-18T21:50:45.842231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AC CATEGORYAC TYPEACTUALPAYLOADATKATK_FISATK_VOLAVAILABLE SEATSBAGQTY OBBAGWGT OBCAP FIS (Kg)CAP VOL (Kg)CARRIERCOUNTRY DESTINATIONDAYDOWHBHVKG CHGKG CHG OPERATIONALKG GROSSKMLEGLEG DESTINATIONMAXALLOWEDPAYLOADMONTHOPERATING FLIGHT NUMBERPAX OBPAYLOAD IN CABINREGION DSTREGION LEGROUTE OWNERRTKRTK_FISRTK_VOLUNDERLOADVOL WGTVOLUMEWEEK
AC CATEGORY1.0000.9980.5760.7030.7490.7041.0000.7440.8080.8880.9790.7190.4600.1190.0540.2990.2680.5630.5400.5650.3220.5180.5180.8880.1120.7080.8890.8960.2090.2090.7210.4560.4750.4650.4990.5400.5540.114
AC TYPE0.9981.0000.3180.4630.4760.4520.7880.3590.3890.7890.7680.7700.2590.2440.0470.2030.2010.3060.2990.3070.2680.2330.2330.7680.2740.4840.4400.4410.2690.2690.7810.2530.2610.2580.4740.2990.3030.279
ACTUALPAYLOAD0.5760.3181.0000.6610.6610.669-0.648-0.616-0.6130.6670.6720.5740.346-0.0240.0200.2310.2340.9750.9690.9720.2810.3810.3810.665-0.0230.541-0.613-0.6110.1960.1960.5740.9690.9660.9690.2880.9690.965-0.029
ATK0.7030.4630.6611.0000.9790.993-0.783-0.875-0.8650.8960.9510.7030.546-0.1490.0290.5120.5260.6200.6080.6430.5840.6080.6080.917-0.1510.673-0.830-0.8250.3510.3510.7030.6400.6640.6390.7200.6080.615-0.148
ATK_FIS0.7490.4760.6610.9791.0000.968-0.795-0.866-0.8540.9240.9360.6770.536-0.1950.0290.4730.4880.6220.6100.6450.5450.6070.6070.945-0.1960.689-0.857-0.8530.3040.3040.6770.6400.6640.6410.7040.6100.618-0.193
ATK_VOL0.7040.4520.6690.9930.9681.000-0.781-0.877-0.8670.8890.9570.7040.620-0.1310.0270.5180.5330.6280.6160.6510.5920.7160.7160.906-0.1340.667-0.823-0.8190.3850.3850.7040.6470.6710.6470.7100.6160.622-0.131
AVAILABLE SEATS1.0000.788-0.648-0.783-0.795-0.7811.0000.9110.917-0.803-0.7870.7190.2890.132-0.061-0.245-0.242-0.604-0.604-0.634-0.2540.3320.332-0.7990.135-0.8470.9280.9270.1480.1480.722-0.603-0.633-0.603-0.625-0.604-0.6080.136
BAGQTY OB0.7440.359-0.616-0.875-0.866-0.8770.9111.0000.997-0.870-0.8910.7240.2190.138-0.018-0.272-0.276-0.573-0.573-0.603-0.2960.2560.256-0.8660.140-0.7810.9610.9600.1610.1610.724-0.574-0.604-0.574-0.744-0.573-0.5770.135
BAGWGT OB0.8080.389-0.613-0.865-0.854-0.8670.9170.9971.000-0.858-0.8800.7220.2180.123-0.023-0.269-0.273-0.571-0.570-0.600-0.2900.2590.259-0.8540.125-0.7860.9570.9560.1620.1620.724-0.571-0.601-0.571-0.731-0.570-0.5740.121
CAP FIS (Kg)0.8880.7890.6670.8960.9240.889-0.803-0.870-0.8581.0000.9450.7120.436-0.1880.0010.2080.2270.6150.6120.6440.2840.4920.4920.976-0.1900.716-0.873-0.8700.2510.2510.7130.6090.6380.6100.7650.6120.612-0.186
CAP VOL (Kg)0.9790.7680.6720.9510.9360.957-0.787-0.891-0.8800.9451.0000.7160.340-0.1520.0150.3160.3320.6260.6190.6490.3910.3710.3710.959-0.1540.691-0.847-0.8440.1910.1910.7180.6280.6510.6280.7710.6190.621-0.150
CARRIER0.7190.7700.5740.7030.6770.7040.7190.7240.7220.7120.7161.0000.8010.1300.0710.4960.4810.5530.5400.5610.5820.8160.8160.7120.1250.7090.7220.7290.5410.5410.9570.4560.4750.4650.5150.5400.5460.125
COUNTRY DESTINATION0.4600.2590.3460.5460.5360.6200.2890.2190.2180.4360.3400.8011.0000.0150.1750.5940.6480.3350.3220.3500.7730.9990.9990.4000.0280.3570.2160.2210.9990.9990.7840.4320.4300.4530.2060.3220.3350.028
DAY0.1190.244-0.024-0.149-0.195-0.1310.1320.1380.123-0.188-0.1520.1300.0151.000-0.005-0.013-0.025-0.0080.004-0.010-0.0590.0790.079-0.2080.992-0.1010.1650.1610.0240.0240.132-0.014-0.016-0.013-0.2080.004-0.0020.977
DOW0.0540.0470.0200.0290.0290.027-0.061-0.018-0.0230.0010.0150.0710.175-0.0051.0000.0290.0080.0330.0270.0370.0350.2250.2250.014-0.0070.062-0.024-0.0270.0670.0670.0540.0330.0370.032-0.0420.0270.028-0.014
HB0.2990.2030.2310.5120.4730.518-0.245-0.272-0.2690.2080.3160.4960.594-0.0130.0291.0000.9380.2190.2010.2290.9030.6690.6690.259-0.0160.087-0.200-0.1930.3950.3950.5270.2720.2850.2720.1300.2010.212-0.016
HV0.2680.2010.2340.5260.4880.533-0.242-0.276-0.2730.2270.3320.4810.648-0.0250.0080.9381.0000.2150.1960.2240.9390.7540.7540.279-0.0270.076-0.203-0.1970.4840.4840.4840.2690.2810.2690.1360.1960.210-0.025
KG CHG0.5630.3060.9750.6200.6220.628-0.604-0.573-0.5710.6150.6260.5530.335-0.0080.0330.2190.2151.0000.9920.9780.2650.3580.3580.613-0.0060.509-0.571-0.5690.2030.2030.5530.9950.9740.9950.2390.9920.991-0.014
KG CHG OPERATIONAL0.5400.2990.9690.6080.6100.616-0.604-0.573-0.5700.6120.6190.5400.3220.0040.0270.2010.1960.9921.0000.9720.2430.3380.3380.6070.0050.509-0.571-0.5690.1790.1790.5400.9850.9650.9850.2391.0000.988-0.012
KG GROSS0.5650.3070.9720.6430.6450.651-0.634-0.603-0.6000.6440.6490.5610.350-0.0100.0370.2290.2240.9780.9721.0000.2720.3880.3880.642-0.0110.536-0.600-0.5980.1930.1930.5610.9730.9940.9730.2690.9720.969-0.017
KM0.3220.2680.2810.5840.5450.592-0.254-0.296-0.2900.2840.3910.5820.773-0.0590.0350.9030.9390.2650.2430.2721.0000.9980.9980.336-0.0600.087-0.218-0.2130.5800.5800.6270.3200.3310.3200.1750.2430.257-0.061
LEG0.5180.2330.3810.6080.6070.7160.3320.2560.2590.4920.3710.8160.9990.0790.2250.6690.7540.3580.3380.3880.9981.0001.0000.4240.0970.3790.2550.2600.9970.9970.8000.5020.5070.5290.2480.3380.3550.094
LEG DESTINATION0.5180.2330.3810.6080.6070.7160.3320.2560.2590.4920.3710.8160.9990.0790.2250.6690.7540.3580.3380.3880.9981.0001.0000.4240.0970.3790.2550.2600.9970.9970.8000.5020.5070.5290.2480.3380.3550.094
MAXALLOWEDPAYLOAD0.8880.7680.6650.9170.9450.906-0.799-0.866-0.8540.9760.9590.7120.400-0.2080.0140.2590.2790.6130.6070.6420.3360.4240.4241.000-0.2090.712-0.869-0.8660.2390.2390.7130.6130.6410.6120.7640.6070.609-0.207
MONTH0.1120.274-0.023-0.151-0.196-0.1340.1350.1400.125-0.190-0.1540.1250.0280.992-0.007-0.016-0.027-0.0060.005-0.011-0.0600.0970.097-0.2091.000-0.1060.1670.1620.0280.0280.127-0.013-0.017-0.011-0.2120.005-0.0000.972
OPERATING FLIGHT NUMBER0.7080.4840.5410.6730.6890.667-0.847-0.781-0.7860.7160.6910.7090.357-0.1010.0620.0870.0760.5090.5090.5360.0870.3790.3790.712-0.1061.000-0.830-0.8320.1700.1700.7090.5050.5330.5050.6150.5090.512-0.107
PAX OB0.8890.440-0.613-0.830-0.857-0.8230.9280.9610.957-0.873-0.8470.7220.2160.165-0.024-0.200-0.203-0.571-0.571-0.600-0.2180.2550.255-0.8690.167-0.8301.0000.9960.1520.1520.724-0.569-0.598-0.570-0.703-0.571-0.5760.163
PAYLOAD IN CABIN0.8960.441-0.611-0.825-0.853-0.8190.9270.9600.956-0.870-0.8440.7290.2210.161-0.027-0.193-0.197-0.569-0.569-0.598-0.2130.2600.260-0.8660.162-0.8320.9961.0000.1620.1620.731-0.567-0.596-0.567-0.700-0.569-0.5740.158
REGION DST0.2090.2690.1960.3510.3040.3850.1480.1610.1620.2510.1910.5410.9990.0240.0670.3950.4840.2030.1790.1930.5800.9970.9970.2390.0280.1700.1520.1621.0001.0000.5880.1250.1820.1420.1900.1790.1990.023
REGION LEG0.2090.2690.1960.3510.3040.3850.1480.1610.1620.2510.1910.5410.9990.0240.0670.3950.4840.2030.1790.1930.5800.9970.9970.2390.0280.1700.1520.1621.0001.0000.5880.1250.1820.1420.1900.1790.1990.023
ROUTE OWNER0.7210.7810.5740.7030.6770.7040.7220.7240.7240.7130.7180.9570.7840.1320.0540.5270.4840.5530.5400.5610.6270.8000.8000.7130.1270.7090.7240.7310.5880.5881.0000.4560.4750.4650.5130.5400.5460.128
RTK0.4560.2530.9690.6400.6400.647-0.603-0.574-0.5710.6090.6280.4560.432-0.0140.0330.2720.2690.9950.9850.9730.3200.5020.5020.613-0.0130.505-0.569-0.5670.1250.1250.4561.0000.9791.0000.2500.9850.985-0.020
RTK_FIS0.4750.2610.9660.6640.6640.671-0.633-0.604-0.6010.6380.6510.4750.430-0.0160.0370.2850.2810.9740.9650.9940.3310.5070.5070.641-0.0170.533-0.598-0.5960.1820.1820.4750.9791.0000.9790.2800.9650.964-0.022
RTK_VOL0.4650.2580.9690.6390.6410.647-0.603-0.574-0.5710.6100.6280.4650.453-0.0130.0320.2720.2690.9950.9850.9730.3200.5290.5290.612-0.0110.505-0.570-0.5670.1420.1420.4651.0000.9791.0000.2490.9850.985-0.019
UNDERLOAD0.4990.4740.2880.7200.7040.710-0.625-0.744-0.7310.7650.7710.5150.206-0.208-0.0420.1300.1360.2390.2390.2690.1750.2480.2480.764-0.2120.615-0.703-0.7000.1900.1900.5130.2500.2800.2491.0000.2390.242-0.206
VOL WGT0.5400.2990.9690.6080.6100.616-0.604-0.573-0.5700.6120.6190.5400.3220.0040.0270.2010.1960.9921.0000.9720.2430.3380.3380.6070.0050.509-0.571-0.5690.1790.1790.5400.9850.9650.9850.2391.0000.988-0.012
VOLUME0.5540.3030.9650.6150.6180.622-0.608-0.577-0.5740.6120.6210.5460.335-0.0020.0280.2120.2100.9910.9880.9690.2570.3550.3550.609-0.0000.512-0.576-0.5740.1990.1990.5460.9850.9640.9850.2420.9881.000-0.009
WEEK0.1140.279-0.029-0.148-0.193-0.1310.1360.1350.121-0.186-0.1500.1250.0280.977-0.014-0.016-0.025-0.014-0.012-0.017-0.0610.0940.094-0.2070.972-0.1070.1630.1580.0230.0230.128-0.020-0.022-0.019-0.206-0.012-0.0091.000

Missing values

2024-09-18T21:50:40.284236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-18T21:50:40.526512image/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.

Sample

CARRIERROUTE OWNERTAILOPERATING FLIGHT NUMBERLEG ORIGINLEG DESTINATIONDATECAP VOL (Kg)CAP FIS (Kg)KG CHGKG GROSSATKRTKKMAC CATEGORYAC TYPEDEPSHBHVLEGYEARMONTHRTK_FISRTK_VOLATK_FISATK_VOLUNDERLOADDAYWEEKDOWCOUNTRY ORIGINCOUNTRY DESTINATIONREGION ORGREGION DSTREGION LEGPAX OBBAGWGT OBACTUALPAYLOADMAXALLOWEDPAYLOADVOLUMEATD_LATA_LATD_ZATA_ZBAGQTY OBPAYLOAD IN CABINAVAILABLE SEATSVOL WGTKG CHG OPERATIONAL
0QTQTN332QT4099MIAEZE2023-01-0167600.057000.0057137.93020345236.382481379.600000406879.2009737121.000000FREIGHTERSA330F18.5833338.216667MIAEZE20231322128.276222406879.200973405897.000000481379.60000011763.61801537USARNAMSAMNAMSAM0045236.38257000.0000417.3588112023-01-01 12:18:002023-01-01 22:53:002023-01-01 17:18:002023-01-02 01:53:000000.000.00
1QTQTN330QT4009MIABOG2023-01-0167600.065000.0042420.50000036192.000164673.600000103336.3380002436.000000FREIGHTERSA330F13.3666673.000000MIABOG2023188163.712000103336.338000148758.631258164673.60000024874.76161537USCONAMSAMNAMSAM0041084.00065958.7616396.9730932023-01-01 13:48:002023-01-01 17:10:002023-01-01 18:48:002023-01-01 22:10:000000.000.00
2QTQTN330QT4085MIASDQ2023-05-0870642.565000.0070642.50000051482.00096497.65500096497.6550001366.000000FREIGHTERSA330F12.3833331.750000MIASDQ2023570324.41200096497.65500072122.30909996497.6550001316.1765128201USDONAMCARNAMCAR0055342.00056658.1765382.4690002023-05-08 11:24:002023-05-08 13:47:002023-05-08 15:24:002023-05-08 17:47:0000070409.5770409.57
3QTQTN336QT4055MIACWB2023-05-0767600.061000.0065569.50000050418.500445525.919922432143.6657746590.620117FREIGHTERSA330F18.1333337.716667MIACWB20235332289.180378432143.665774402027.827148445525.91992210581.5000127197USBRNAMSAMNAMSAM0050418.50061000.0000374.0770002023-05-07 11:14:002023-05-07 20:22:002023-05-07 15:14:002023-05-07 23:22:0000065476.9365476.93
4QTQTN334QT4047MIAAGT2023-07-0967600.058526.0063770.50000058526.000371785.449105369942.8460006321.000000FREIGHTERSA330F17.9833337.616667MIAAGT20237369942.846000403093.330500371785.449105427299.600000291.5050190287USPYNAMSAMNAMSAM0062234.00062525.5050371.8440002023-07-09 02:02:002023-07-09 10:01:002023-07-09 06:02:002023-07-09 14:01:0000063698.1463698.14
5QTQTN331QT4047MIAAGT2023-04-2967600.058154.0065400.00000058154.000371861.680365367591.4340006321.000000FREIGHTERSA330F18.1166677.616667MIAAGT20234367591.434000413393.400000371861.680365427299.600000675.5650119186USPYNAMSAMNAMSAM0062538.00063213.5650369.6013172023-04-29 23:24:002023-04-30 07:31:002023-04-30 03:24:002023-04-30 11:31:0000065386.8665386.86
6QTQTN332QT4047MIAAGT2023-04-2267600.060547.0063710.00000060547.000383023.776240382717.5870006321.000000FREIGHTERSA330F18.0166677.483333MIAAGT20234382717.587000402710.910000383023.776240427299.60000048.4400112176USPYNAMSAMNAMSAM0065002.00065050.4400367.6170002023-04-22 21:55:002023-04-23 05:56:002023-04-23 01:55:002023-04-23 09:56:0000063692.8263692.82
7QTQTN331QT4047MIAAGT2023-07-0267600.058997.2964893.00000058997.290373074.964710372921.8700906321.000000FREIGHTERSA330F17.8500007.483333MIAAGT20237372921.870090410188.653000373074.964710427299.60000024.2200183277USPYNAMSAMNAMSAM0063682.00063706.2200365.6877372023-07-02 01:08:002023-07-02 08:59:002023-07-02 05:08:002023-07-02 12:59:0000064868.0664868.06
8QTQTN332QT4071MIAPTY2023-08-0567600.065000.0061660.00000055290.000125295.923340114286.1927981853.489990FREIGHTERSA330F13.1000002.583333MIAPTY20238102479.461560114286.192798114066.115328125295.9233406251.2632217326USPANAMCAMNAMCAM0059868.00066119.2632361.7180002023-08-05 20:19:002023-08-05 22:25:002023-08-06 00:19:002023-08-06 03:25:0000061619.5961619.59
9QTQTN332QT4075MIAPTY2023-08-1267600.065000.0060861.50000052621.000125295.923340112806.1810411853.489990FREIGHTERSA330F13.6333332.383333MIAPTY2023897532.496776112806.181041114639.958794125295.9233409229.8648224336USPANAMCAMNAMCAM0055900.00065129.8648360.4250002023-08-12 21:28:002023-08-13 00:06:002023-08-13 01:28:002023-08-13 05:06:0000060848.5760848.57
CARRIERROUTE OWNERTAILOPERATING FLIGHT NUMBERLEG ORIGINLEG DESTINATIONDATECAP VOL (Kg)CAP FIS (Kg)KG CHGKG GROSSATKRTKKMAC CATEGORYAC TYPEDEPSHBHVLEGYEARMONTHRTK_FISRTK_VOLATK_FISATK_VOLUNDERLOADDAYWEEKDOWCOUNTRY ORIGINCOUNTRY DESTINATIONREGION ORGREGION DSTREGION LEGPAX OBBAGWGT OBACTUALPAYLOADMAXALLOWEDPAYLOADVOLUMEATD_LATA_LATD_ZATA_ZBAGQTY OBPAYLOAD IN CABINAVAILABLE SEATSVOL WGTKG CHG OPERATIONAL
4742AVAVN411AV9MIABOG2023-02-121028.1205622653.20.00.02504.5016890.02436.0NB32013.6833333.100000MIABOG202320.00.06463.19522504.5016891028.04377USCONAMSAMNAMSAM16831520.02653.20.02023-02-12 10:21:002023-02-12 14:02:002023-02-12 15:21:002023-02-12 19:02:00192129801800.00.0
4743TATAN980AV451MIAMGA2023-02-123658.87317610588.50.00.05996.8931350.01639.0NB32012.6333332.150000MIAMGA202320.00.017354.55155996.8931353659.04377USNINAMCAMNAMCAM7616750.010588.50.02023-02-12 15:16:002023-02-12 16:54:002023-02-12 20:16:002023-02-12 22:54:008160161800.00.0
4744TATAN686TA393MIAMGA2023-02-123469.2693849113.40.00.05686.1325200.01639.0NB32012.5000002.166667MIAMGA202320.00.014936.86265686.1325203469.04377USNINAMCAMNAMCAM9018140.09113.40.02023-02-12 16:11:002023-02-12 17:41:002023-02-12 21:11:002023-02-12 23:41:008970321800.00.0
4745TATAN522TA451MIAMGA2023-02-082593.5518709334.80.00.04250.8315150.01639.0NB31912.5333332.166667MIAMGA202320.00.015299.73724250.8315152594.03973USNINAMCAMNAMCAM5812960.09334.80.02023-02-08 15:15:002023-02-08 16:47:002023-02-08 20:15:002023-02-08 22:47:006544261200.00.0
4746TATAN426AV393MIAMGA2023-02-084109.18218211998.80.00.06734.9495960.01639.0NB32012.5666672.250000MIAMGA202320.00.019666.03326734.9495964109.03973USNINAMCAMNAMCAM6411200.011998.80.02023-02-08 16:18:002023-02-08 17:52:002023-02-08 21:18:002023-02-08 23:52:006249961800.00.0
4747TATAN821AV397MIAMGA2023-02-122829.3565868946.00.00.04637.3154440.01639.0NB32012.4666672.166667MIAMGA202320.00.014662.49404637.3154442829.04377USNINAMCAMNAMCAM9623450.08946.00.02023-02-12 13:56:002023-02-12 15:24:002023-02-12 18:56:002023-02-12 21:24:0011673261800.00.0
4748AVAVN536AV39MIACLO2023-02-084109.18218211774.70.00.010314.0472770.02510.0NB32013.4666673.116667MIACLO202320.00.029554.497010314.0472774109.03973USCONAMSAMNAMSAM6813220.011774.70.02023-02-08 15:09:002023-02-08 18:37:002023-02-08 20:09:002023-02-08 23:37:006252971800.00.0
4749AVAVN920CG127MIABOG2023-02-123042.6608525626.80.00.07411.9218350.02436.0NB2NA13.4333333.050000MIABOG202320.00.013706.88487411.9218353043.04377USCONAMSAMNAMSAM13821770.05626.80.02023-02-12 18:31:002023-02-12 21:57:002023-02-12 23:31:002023-02-13 02:57:00107108761800.00.0
4750AVAVN956AV5MIABOG2023-02-123350.7670147708.50.00.08162.4684460.02436.0NB20B13.6000003.133333MIABOG202320.00.018777.90608162.4684463351.04377USCONAMSAMNAMSAM10918530.07708.50.02023-02-12 04:50:002023-02-12 08:26:002023-02-12 09:50:002023-02-12 13:26:009484941800.00.0
4751AVAVN401AV39MIACLO2023-01-264772.7954547551.00.00.011979.7165900.02510.0NB32013.6833333.283333MIACLO202310.00.018953.010011979.7165904773.02654USCONAMSAMNAMSAM466580.07551.00.02023-01-26 16:35:002023-01-26 20:16:002023-01-26 21:35:002023-01-27 01:16:003434351800.00.0
CARRIERROUTE OWNERTAILOPERATING FLIGHT NUMBERLEG ORIGINLEG DESTINATIONDATECAP VOL (Kg)CAP FIS (Kg)KG CHGKG GROSSATKRTKKMAC CATEGORYAC TYPEDEPSHBHVLEGYEARMONTHRTK_FISRTK_VOLATK_FISATK_VOLUNDERLOADDAYWEEKDOWCOUNTRY ORIGINCOUNTRY DESTINATIONREGION ORGREGION DSTREGION LEGPAX OBBAGWGT OBACTUALPAYLOADMAXALLOWEDPAYLOADVOLUMEATD_LATA_LATD_ZATA_ZBAGQTY OBPAYLOAD IN CABINAVAILABLE SEATSVOL WGTKG CHG OPERATIONAL
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