Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations89935
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory36.0 B

Variable types

Categorical6
Numeric3
DateTime1
TimeSeries2

Timeseries statistics

Number of series2
Time series length89935
Starting point2019-09-26 00:00:00
Ending point2021-05-22 00:00:00
Period9 minutes and 40.53 seconds
2025-09-05T04:46:17.562591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:18.184477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

month is non stationary Non stationary
day is non stationary Non stationary
month is seasonal Seasonal
day is seasonal Seasonal

Reproduction

Analysis started2025-09-05 04:44:59.808673
Analysis finished2025-09-05 04:46:17.075597
Duration1 minute and 17.27 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

from
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size791.3 KiB
Campo Grande (MS)
11020 
Recife (PE)
10961 
Aracaju (SE)
10929 
Brasilia (DF)
10889 
Florianopolis (SC)
10480 
Other values (4)
35656 

Length

Max length19
Median length14
Mean length13.979318
Min length10

Characters and Unicode

Total characters1257230
Distinct characters34
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 rowRecife (PE)
2nd rowBrasilia (DF)
3rd rowRecife (PE)
4th rowRecife (PE)
5th rowAracaju (SE)

Common Values

ValueCountFrequency (%)
Campo Grande (MS) 11020
12.3%
Recife (PE) 10961
12.2%
Aracaju (SE) 10929
12.2%
Brasilia (DF) 10889
12.1%
Florianopolis (SC) 10480
11.7%
Natal (RN) 10297
11.4%
Sao Paulo (SP) 10217
11.4%
Salvador (BH) 7622
8.5%
Rio de Janeiro (RJ) 7520
8.4%

Length

2025-09-05T04:46:18.455057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-05T04:46:18.593618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
campo 11020
 
5.1%
ms 11020
 
5.1%
grande 11020
 
5.1%
recife 10961
 
5.1%
pe 10961
 
5.1%
aracaju 10929
 
5.1%
se 10929
 
5.1%
brasilia 10889
 
5.0%
df 10889
 
5.0%
florianopolis 10480
 
4.8%
Other values (12) 107049
49.5%

Most occurring characters

ValueCountFrequency (%)
a 139948
 
11.1%
126212
 
10.0%
( 89935
 
7.2%
) 89935
 
7.2%
o 85556
 
6.8%
i 68739
 
5.5%
S 60485
 
4.8%
l 59985
 
4.8%
r 58460
 
4.6%
e 47982
 
3.8%
Other values (24) 429993
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1257230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 139948
 
11.1%
126212
 
10.0%
( 89935
 
7.2%
) 89935
 
7.2%
o 85556
 
6.8%
i 68739
 
5.5%
S 60485
 
4.8%
l 59985
 
4.8%
r 58460
 
4.6%
e 47982
 
3.8%
Other values (24) 429993
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1257230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 139948
 
11.1%
126212
 
10.0%
( 89935
 
7.2%
) 89935
 
7.2%
o 85556
 
6.8%
i 68739
 
5.5%
S 60485
 
4.8%
l 59985
 
4.8%
r 58460
 
4.6%
e 47982
 
3.8%
Other values (24) 429993
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1257230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 139948
 
11.1%
126212
 
10.0%
( 89935
 
7.2%
) 89935
 
7.2%
o 85556
 
6.8%
i 68739
 
5.5%
S 60485
 
4.8%
l 59985
 
4.8%
r 58460
 
4.6%
e 47982
 
3.8%
Other values (24) 429993
34.2%

to
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size791.3 KiB
Florianopolis (SC)
18337 
Aracaju (SE)
13599 
Campo Grande (MS)
12815 
Brasilia (DF)
11173 
Recife (PE)
11059 
Other values (4)
22952 

Length

Max length19
Median length17
Mean length14.258309
Min length10

Characters and Unicode

Total characters1282321
Distinct characters34
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 rowFlorianopolis (SC)
2nd rowSao Paulo (SP)
3rd rowNatal (RN)
4th rowFlorianopolis (SC)
5th rowNatal (RN)

Common Values

ValueCountFrequency (%)
Florianopolis (SC) 18337
20.4%
Aracaju (SE) 13599
15.1%
Campo Grande (MS) 12815
14.2%
Brasilia (DF) 11173
12.4%
Recife (PE) 11059
12.3%
Natal (RN) 7854
8.7%
Sao Paulo (SP) 7852
8.7%
Salvador (BH) 3638
 
4.0%
Rio de Janeiro (RJ) 3608
 
4.0%

Length

2025-09-05T04:46:18.776278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-05T04:46:18.894536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
florianopolis 18337
 
8.8%
sc 18337
 
8.8%
aracaju 13599
 
6.5%
se 13599
 
6.5%
campo 12815
 
6.2%
grande 12815
 
6.2%
ms 12815
 
6.2%
brasilia 11173
 
5.4%
df 11173
 
5.4%
recife 11059
 
5.3%
Other values (12) 72031
34.7%

Most occurring characters

ValueCountFrequency (%)
a 135807
 
10.6%
117818
 
9.2%
o 94384
 
7.4%
) 89935
 
7.0%
( 89935
 
7.0%
i 77295
 
6.0%
l 67191
 
5.2%
S 64093
 
5.0%
r 63170
 
4.9%
e 42149
 
3.3%
Other values (24) 440544
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1282321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 135807
 
10.6%
117818
 
9.2%
o 94384
 
7.4%
) 89935
 
7.0%
( 89935
 
7.0%
i 77295
 
6.0%
l 67191
 
5.2%
S 64093
 
5.0%
r 63170
 
4.9%
e 42149
 
3.3%
Other values (24) 440544
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1282321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 135807
 
10.6%
117818
 
9.2%
o 94384
 
7.4%
) 89935
 
7.0%
( 89935
 
7.0%
i 77295
 
6.0%
l 67191
 
5.2%
S 64093
 
5.0%
r 63170
 
4.9%
e 42149
 
3.3%
Other values (24) 440544
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1282321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 135807
 
10.6%
117818
 
9.2%
o 94384
 
7.4%
) 89935
 
7.0%
( 89935
 
7.0%
i 77295
 
6.0%
l 67191
 
5.2%
S 64093
 
5.0%
r 63170
 
4.9%
e 42149
 
3.3%
Other values (24) 440544
34.4%

flightType
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size790.7 KiB
firstClass
38498 
economic
25770 
premium
25667 

Length

Max length10
Median length8
Mean length8.5707344
Min length7

Characters and Unicode

Total characters770809
Distinct characters15
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 rowfirstClass
2nd rowfirstClass
3rd rowpremium
4th rowpremium
5th rowfirstClass

Common Values

ValueCountFrequency (%)
firstClass 38498
42.8%
economic 25770
28.7%
premium 25667
28.5%

Length

2025-09-05T04:46:19.103644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-05T04:46:19.196388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
firstclass 38498
42.8%
economic 25770
28.7%
premium 25667
28.5%

Most occurring characters

ValueCountFrequency (%)
s 115494
15.0%
i 89935
11.7%
m 77104
10.0%
r 64165
8.3%
c 51540
 
6.7%
o 51540
 
6.7%
e 51437
 
6.7%
f 38498
 
5.0%
t 38498
 
5.0%
C 38498
 
5.0%
Other values (5) 154100
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 770809
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 115494
15.0%
i 89935
11.7%
m 77104
10.0%
r 64165
8.3%
c 51540
 
6.7%
o 51540
 
6.7%
e 51437
 
6.7%
f 38498
 
5.0%
t 38498
 
5.0%
C 38498
 
5.0%
Other values (5) 154100
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 770809
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 115494
15.0%
i 89935
11.7%
m 77104
10.0%
r 64165
8.3%
c 51540
 
6.7%
o 51540
 
6.7%
e 51437
 
6.7%
f 38498
 
5.0%
t 38498
 
5.0%
C 38498
 
5.0%
Other values (5) 154100
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 770809
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 115494
15.0%
i 89935
11.7%
m 77104
10.0%
r 64165
8.3%
c 51540
 
6.7%
o 51540
 
6.7%
e 51437
 
6.7%
f 38498
 
5.0%
t 38498
 
5.0%
C 38498
 
5.0%
Other values (5) 154100
20.0%

time
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3508712
Minimum0.44
Maximum2.4400001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-09-05T04:46:19.322926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.44
5-th percentile0.46000001
Q10.85000002
median1.4400001
Q31.76
95-th percentile2.1600001
Maximum2.4400001
Range2
Interquartile range (IQR)0.90999997

Descriptive statistics

Standard deviation0.54849827
Coefficient of variation (CV)0.406033
Kurtosis-0.94423157
Mean1.3508712
Median Absolute Deviation (MAD)0.39999998
Skewness-0.055488564
Sum121490.6
Variance0.30085036
MonotonicityNot monotonic
2025-09-05T04:46:19.466021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1.75999999 5593
 
6.2%
2.099999905 4424
 
4.9%
1.49000001 4281
 
4.8%
1.659999967 4087
 
4.5%
1.690000057 3690
 
4.1%
1.460000038 3489
 
3.9%
1.840000033 3467
 
3.9%
1.110000014 3442
 
3.8%
1.440000057 3429
 
3.8%
0.7200000286 3332
 
3.7%
Other values (23) 50701
56.4%
ValueCountFrequency (%)
0.4399999976 2837
3.2%
0.4600000083 2915
3.2%
0.4799999893 1541
1.7%
0.5799999833 2530
2.8%
0.6299999952 3093
3.4%
0.6499999762 2824
3.1%
0.6700000167 2455
2.7%
0.7200000286 3332
3.7%
0.8500000238 1928
2.1%
0.8600000143 929
 
1.0%
ValueCountFrequency (%)
2.440000057 2513
2.8%
2.299999952 1518
 
1.7%
2.160000086 1875
 
2.1%
2.099999905 4424
4.9%
2.089999914 1744
 
1.9%
2.049999952 1541
 
1.7%
1.850000024 958
 
1.1%
1.840000033 3467
3.9%
1.75999999 5593
6.2%
1.690000057 3690
4.1%

distance
Real number (ℝ)

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean519.91769
Minimum168.22
Maximum937.77002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-09-05T04:46:19.610737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum168.22
5-th percentile176.33
Q1327.54999
median555.73999
Q3676.53003
95-th percentile830.85999
Maximum937.77002
Range769.55005
Interquartile range (IQR)348.98004

Descriptive statistics

Standard deviation211.18279
Coefficient of variation (CV)0.40618504
Kurtosis-0.94346303
Mean519.91769
Median Absolute Deviation (MAD)153.63
Skewness-0.059036959
Sum46758797
Variance44598.168
MonotonicityNot monotonic
2025-09-05T04:46:19.740183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
808.8499756 4424
 
4.9%
573.8099976 4281
 
4.8%
637.5599976 4087
 
4.5%
676.5300293 4027
 
4.5%
650.0999756 3690
 
4.1%
562.1400146 3489
 
3.9%
709.3699951 3467
 
3.9%
425.980011 3442
 
3.8%
555.7399902 3429
 
3.8%
277.7000122 3332
 
3.7%
Other values (25) 52267
58.1%
ValueCountFrequency (%)
168.2200012 2837
3.2%
176.3300018 2915
3.2%
183.3699951 1541
1.7%
222.6699982 2530
2.8%
242.2100067 3093
3.4%
250.6799927 2824
3.1%
257.8099976 2455
2.7%
277.7000122 3332
3.7%
327.5499878 1928
2.1%
331.8900146 929
 
1.0%
ValueCountFrequency (%)
937.7700195 2513
2.8%
885.5700073 1518
 
1.7%
830.8599854 1875
2.1%
808.8499756 4424
4.9%
806.4799805 1744
 
1.9%
788.5499878 1541
 
1.7%
710.5700073 958
 
1.1%
709.3699951 3467
3.9%
676.5599976 1566
 
1.7%
676.5300293 4027
4.5%

agency
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size790.7 KiB
Rainbow
38557 
CloudFy
38518 
FlyingDrops
12860 

Length

Max length11
Median length7
Mean length7.5719686
Min length7

Characters and Unicode

Total characters680985
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFlyingDrops
2nd rowCloudFy
3rd rowCloudFy
4th rowRainbow
5th rowCloudFy

Common Values

ValueCountFrequency (%)
Rainbow 38557
42.9%
CloudFy 38518
42.8%
FlyingDrops 12860
 
14.3%

Length

2025-09-05T04:46:19.872849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-05T04:46:19.962406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
rainbow 38557
42.9%
cloudfy 38518
42.8%
flyingdrops 12860
 
14.3%

Most occurring characters

ValueCountFrequency (%)
o 89935
13.2%
i 51417
 
7.6%
n 51417
 
7.6%
F 51378
 
7.5%
y 51378
 
7.5%
l 51378
 
7.5%
R 38557
 
5.7%
b 38557
 
5.7%
w 38557
 
5.7%
a 38557
 
5.7%
Other values (8) 179854
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 680985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 89935
13.2%
i 51417
 
7.6%
n 51417
 
7.6%
F 51378
 
7.5%
y 51378
 
7.5%
l 51378
 
7.5%
R 38557
 
5.7%
b 38557
 
5.7%
w 38557
 
5.7%
a 38557
 
5.7%
Other values (8) 179854
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 680985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 89935
13.2%
i 51417
 
7.6%
n 51417
 
7.6%
F 51378
 
7.5%
y 51378
 
7.5%
l 51378
 
7.5%
R 38557
 
5.7%
b 38557
 
5.7%
w 38557
 
5.7%
a 38557
 
5.7%
Other values (8) 179854
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 680985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 89935
13.2%
i 51417
 
7.6%
n 51417
 
7.6%
F 51378
 
7.5%
y 51378
 
7.5%
l 51378
 
7.5%
R 38557
 
5.7%
b 38557
 
5.7%
w 38557
 
5.7%
a 38557
 
5.7%
Other values (8) 179854
26.4%

date
Date

Distinct433
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Minimum2019-09-26 00:00:00
Maximum2021-05-22 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-05T04:46:20.087663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:20.454243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

month
Numeric time series

Non stationary  Seasonal 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.426597
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size790.4 KiB
2025-09-05T04:46:20.810369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7459568
Coefficient of variation (CV)0.58288341
Kurtosis-1.4361494
Mean6.426597
Median Absolute Deviation (MAD)4
Skewness0.065595211
Sum577976
Variance14.032192
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2097979907
2025-09-05T04:46:20.956832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
2025-09-05T04:46:21.892984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps86
min3 days
max3 days
mean3 days
std0 seconds
2025-09-05T04:46:22.370896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
10 9640
10.7%
1 9359
10.4%
12 9255
10.3%
11 9245
10.3%
4 8718
9.7%
3 8655
9.6%
2 8455
9.4%
5 7581
8.4%
9 5198
5.8%
7 4700
5.2%
Other values (2) 9129
10.2%
ValueCountFrequency (%)
1 9359
10.4%
2 8455
9.4%
3 8655
9.6%
4 8718
9.7%
5 7581
8.4%
6 4452
5.0%
7 4700
5.2%
8 4677
5.2%
9 5198
5.8%
10 9640
10.7%
ValueCountFrequency (%)
12 9255
10.3%
11 9245
10.3%
10 9640
10.7%
9 5198
5.8%
8 4677
5.2%
7 4700
5.2%
6 4452
5.0%
5 7581
8.4%
4 8718
9.7%
3 8655
9.6%
2025-09-05T04:46:21.458066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size790.6 KiB
2020
55095 
2021
18687 
2019
16153 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters359740
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 row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2020 55095
61.3%
2021 18687
 
20.8%
2019 16153
 
18.0%

Length

2025-09-05T04:46:22.639455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-05T04:46:22.723468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2020 55095
61.3%
2021 18687
 
20.8%
2019 16153
 
18.0%

Most occurring characters

ValueCountFrequency (%)
2 163717
45.5%
0 145030
40.3%
1 34840
 
9.7%
9 16153
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 359740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 163717
45.5%
0 145030
40.3%
1 34840
 
9.7%
9 16153
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 359740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 163717
45.5%
0 145030
40.3%
1 34840
 
9.7%
9 16153
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 359740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 163717
45.5%
0 145030
40.3%
1 34840
 
9.7%
9 16153
 
4.5%

day
Numeric time series

Non stationary  Seasonal 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.760327
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size790.4 KiB
2025-09-05T04:46:23.025984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.800451
Coefficient of variation (CV)0.55839267
Kurtosis-1.1982163
Mean15.760327
Median Absolute Deviation (MAD)8
Skewness0.0079789854
Sum1417405
Variance77.447938
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.593375305 × 10-11
2025-09-05T04:46:23.213239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
2025-09-05T04:46:24.181134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps86
min3 days
max3 days
mean3 days
std0 seconds
2025-09-05T04:46:24.659375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
26 3274
 
3.6%
6 3156
 
3.5%
20 3126
 
3.5%
14 3113
 
3.5%
7 3106
 
3.5%
28 3101
 
3.4%
21 3094
 
3.4%
13 3091
 
3.4%
2 3011
 
3.3%
16 2972
 
3.3%
Other values (21) 58891
65.5%
ValueCountFrequency (%)
1 2756
3.1%
2 3011
3.3%
3 2958
3.3%
4 2948
3.3%
5 2901
3.2%
6 3156
3.5%
7 3106
3.5%
8 2727
3.0%
9 2943
3.3%
10 2913
3.2%
ValueCountFrequency (%)
31 1733
1.9%
30 2770
3.1%
29 2635
2.9%
28 3101
3.4%
27 2964
3.3%
26 3274
3.6%
25 2930
3.3%
24 2768
3.1%
23 2834
3.2%
22 2713
3.0%
2025-09-05T04:46:23.730439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

day_name
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size790.9 KiB
Thursday
29104 
Saturday
15350 
Sunday
15195 
Friday
15169 
Monday
15117 

Length

Max length8
Median length6
Mean length6.9885806
Min length6

Characters and Unicode

Total characters628518
Distinct characters15
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 rowThursday
2nd rowThursday
3rd rowThursday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Thursday 29104
32.4%
Saturday 15350
17.1%
Sunday 15195
16.9%
Friday 15169
16.9%
Monday 15117
16.8%

Length

2025-09-05T04:46:25.246265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-05T04:46:25.359680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
thursday 29104
32.4%
saturday 15350
17.1%
sunday 15195
16.9%
friday 15169
16.9%
monday 15117
16.8%

Most occurring characters

ValueCountFrequency (%)
a 105285
16.8%
d 89935
14.3%
y 89935
14.3%
u 59649
9.5%
r 59623
9.5%
S 30545
 
4.9%
n 30312
 
4.8%
T 29104
 
4.6%
h 29104
 
4.6%
s 29104
 
4.6%
Other values (5) 75922
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 628518
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 105285
16.8%
d 89935
14.3%
y 89935
14.3%
u 59649
9.5%
r 59623
9.5%
S 30545
 
4.9%
n 30312
 
4.8%
T 29104
 
4.6%
h 29104
 
4.6%
s 29104
 
4.6%
Other values (5) 75922
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 628518
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 105285
16.8%
d 89935
14.3%
y 89935
14.3%
u 59649
9.5%
r 59623
9.5%
S 30545
 
4.9%
n 30312
 
4.8%
T 29104
 
4.6%
h 29104
 
4.6%
s 29104
 
4.6%
Other values (5) 75922
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 628518
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 105285
16.8%
d 89935
14.3%
y 89935
14.3%
u 59649
9.5%
r 59623
9.5%
S 30545
 
4.9%
n 30312
 
4.8%
T 29104
 
4.6%
h 29104
 
4.6%
s 29104
 
4.6%
Other values (5) 75922
12.1%

price
Real number (ℝ)

Distinct490
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean944.78518
Minimum301.51001
Maximum1754.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2025-09-05T04:46:25.513060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum301.51001
5-th percentile427.25
Q1667
median889.07001
Q31209.04
95-th percentile1581.8
Maximum1754.17
Range1452.66
Interquartile range (IQR)542.04004

Descriptive statistics

Standard deviation357.4715
Coefficient of variation (CV)0.37836273
Kurtosis-0.81610227
Mean944.78518
Median Absolute Deviation (MAD)263.57001
Skewness0.35388133
Sum84969255
Variance127785.88
MonotonicityNot monotonic
2025-09-05T04:46:25.835583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1515.790039 368
 
0.4%
1132.810059 368
 
0.4%
1446.339966 366
 
0.4%
869.1300049 366
 
0.4%
1499.930054 364
 
0.4%
1031.589966 364
 
0.4%
764.5300293 361
 
0.4%
852.1799927 359
 
0.4%
1582.410034 357
 
0.4%
1371.829956 357
 
0.4%
Other values (480) 86305
96.0%
ValueCountFrequency (%)
301.5100098 157
0.2%
301.6099854 257
0.3%
313.6199951 86
 
0.1%
317.0799866 87
 
0.1%
332.1000061 158
0.2%
335.1600037 247
0.3%
344.2600098 78
 
0.1%
358.6199951 94
 
0.1%
360.2200012 166
0.2%
361.6799927 249
0.3%
ValueCountFrequency (%)
1754.170044 84
 
0.1%
1747.310059 59
 
0.1%
1744.869995 57
 
0.1%
1724.48999 77
 
0.1%
1723.709961 85
 
0.1%
1718.280029 328
0.4%
1714.75 83
 
0.1%
1706.890015 77
 
0.1%
1702.22998 145
0.2%
1692.640015 87
 
0.1%

Interactions

2025-09-05T04:46:16.564507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:12.052851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:13.558213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:14.544183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:15.623182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:16.786350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:12.314174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:13.751717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:14.766260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:15.882979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:17.004906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:12.915283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:13.931617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:15.031355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:16.018681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:16.007232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:13.138613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:14.066994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:15.227437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:16.169152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:16.341086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:13.361172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:14.271141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:15.430826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-05T04:46:16.370519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Missing values

2025-09-05T04:46:16.543375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-05T04:46:16.840039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fromtoflightTypetimedistanceagencydatemonthyeardayday_nameprice
2019-09-26Recife (PE)Florianopolis (SC)firstClass1.76676.530029FlyingDrops2019-09-269201926Thursday1434.380005
2019-09-26Brasilia (DF)Sao Paulo (SP)firstClass0.67257.809998CloudFy2019-09-269201926Thursday893.650024
2019-09-26Recife (PE)Natal (RN)premium0.58222.669998CloudFy2019-09-269201926Thursday474.600006
2019-09-26Recife (PE)Florianopolis (SC)premium1.76676.530029Rainbow2019-09-269201926Thursday1070.540039
2019-09-26Aracaju (SE)Natal (RN)firstClass0.46176.330002CloudFy2019-09-269201926Thursday598.609985
2019-09-26Brasilia (DF)Campo Grande (MS)firstClass0.72277.700012FlyingDrops2019-09-269201926Thursday1009.710022
2019-09-26Aracaju (SE)Salvador (BH)economic2.16830.859985Rainbow2019-09-269201926Thursday943.359985
2019-09-26Aracaju (SE)Brasilia (DF)firstClass1.11425.980011FlyingDrops2019-09-269201926Thursday898.039978
2019-09-26Aracaju (SE)Sao Paulo (SP)premium1.02392.760010CloudFy2019-09-269201926Thursday630.340027
2019-09-26Aracaju (SE)Sao Paulo (SP)premium1.02392.760010Rainbow2019-09-269201926Thursday588.140015
fromtoflightTypetimedistanceagencydatemonthyeardayday_nameprice
2021-05-22Rio de Janeiro (RJ)Florianopolis (SC)economic1.21466.299988CloudFy2021-05-225202122Saturday533.690002
2021-05-22Sao Paulo (SP)Florianopolis (SC)economic1.46562.140015CloudFy2021-05-225202122Saturday947.950012
2021-05-22Florianopolis (SC)Campo Grande (MS)firstClass1.49573.809998Rainbow2021-05-225202122Saturday857.320007
2021-05-22Natal (RN)Recife (PE)economic0.58222.669998CloudFy2021-05-225202122Saturday429.769989
2021-05-22Rio de Janeiro (RJ)Florianopolis (SC)firstClass1.21466.299988Rainbow2021-05-225202122Saturday941.270020
2021-05-22Natal (RN)Florianopolis (SC)premium1.84709.369995CloudFy2021-05-225202122Saturday1165.989990
2021-05-22Aracaju (SE)Natal (RN)economic0.46176.330002Rainbow2021-05-225202122Saturday380.320007
2021-05-22Aracaju (SE)Florianopolis (SC)firstClass2.10808.849976Rainbow2021-05-225202122Saturday1582.410034
2021-05-22Rio de Janeiro (RJ)Florianopolis (SC)firstClass1.21466.299988CloudFy2021-05-225202122Saturday875.650024
2021-05-22Salvador (BH)Natal (RN)firstClass1.85710.570007FlyingDrops2021-05-225202122Saturday1241.040039