Overview

Dataset statistics

Number of variables14
Number of observations10683
Missing cells0
Missing cells (%)0.0%
Duplicate rows197
Duplicate rows (%)1.8%
Total size in memory3.1 MiB
Average record size in memory309.0 B

Variable types

Categorical6
Numeric7
DateTime1

Alerts

Dataset has 197 (1.8%) duplicate rowsDuplicates
Dep_Hour is highly overall correlated with Is_Peak_HourHigh correlation
Destination is highly overall correlated with SourceHigh correlation
Duration is highly overall correlated with Price and 1 other fieldsHigh correlation
Is_Peak_Hour is highly overall correlated with Dep_HourHigh correlation
Price is highly overall correlated with DurationHigh correlation
Source is highly overall correlated with DestinationHigh correlation
Total_Stops is highly overall correlated with DurationHigh correlation
Dep_Min has 2062 (19.3%) zerosZeros
Arrival_Hour has 322 (3.0%) zerosZeros
Arrival_Min has 1447 (13.5%) zerosZeros

Reproduction

Analysis started2026-03-27 16:48:10.401464
Analysis finished2026-03-27 16:48:18.090411
Duration7.69 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Airline
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size613.7 KiB
Jet Airways
3849 
IndiGo
2053 
Air India
1752 
Multiple carriers
1196 
SpiceJet
818 
Other values (7)
1015 

Length

Max length33
Median length23
Mean length9.8099785
Min length5

Characters and Unicode

Total characters104800
Distinct characters28
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 rowIndiGo
2nd rowAir India
3rd rowJet Airways
4th rowIndiGo
5th rowIndiGo

Common Values

ValueCountFrequency (%)
Jet Airways3849
36.0%
IndiGo2053
19.2%
Air India1752
16.4%
Multiple carriers1196
 
11.2%
SpiceJet818
 
7.7%
Vistara479
 
4.5%
Air Asia319
 
3.0%
GoAir194
 
1.8%
Multiple carriers Premium economy13
 
0.1%
Jet Airways Business6
 
0.1%
Other values (2)4
 
< 0.1%

Length

2026-03-27T16:48:18.188411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jet3855
21.6%
airways3855
21.6%
air2071
11.6%
indigo2053
11.5%
india1752
9.8%
multiple1209
 
6.8%
carriers1209
 
6.8%
spicejet818
 
4.6%
vistara482
 
2.7%
asia319
 
1.8%
Other values (5)233
 
1.3%

Most occurring characters

ValueCountFrequency (%)
i13984
13.3%
r10246
 
9.8%
a8099
 
7.7%
e7948
 
7.6%
7173
 
6.8%
A6439
 
6.1%
t6365
 
6.1%
s5883
 
5.6%
J4673
 
4.5%
y3871
 
3.7%
Other values (18)30119
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)104800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i13984
13.3%
r10246
 
9.8%
a8099
 
7.7%
e7948
 
7.6%
7173
 
6.8%
A6439
 
6.1%
t6365
 
6.1%
s5883
 
5.6%
J4673
 
4.5%
y3871
 
3.7%
Other values (18)30119
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)104800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i13984
13.3%
r10246
 
9.8%
a8099
 
7.7%
e7948
 
7.6%
7173
 
6.8%
A6439
 
6.1%
t6365
 
6.1%
s5883
 
5.6%
J4673
 
4.5%
y3871
 
3.7%
Other values (18)30119
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)104800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i13984
13.3%
r10246
 
9.8%
a8099
 
7.7%
e7948
 
7.6%
7173
 
6.8%
A6439
 
6.1%
t6365
 
6.1%
s5883
 
5.6%
J4673
 
4.5%
y3871
 
3.7%
Other values (18)30119
28.7%

Source
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size577.0 KiB
Delhi
4537 
Kolkata
2871 
Banglore
2197 
Mumbai
697 
Chennai
 
381

Length

Max length8
Median length7
Mean length6.2910231
Min length5

Characters and Unicode

Total characters67207
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowBanglore
2nd rowKolkata
3rd rowDelhi
4th rowKolkata
5th rowBanglore

Common Values

ValueCountFrequency (%)
Delhi4537
42.5%
Kolkata2871
26.9%
Banglore2197
20.6%
Mumbai697
 
6.5%
Chennai381
 
3.6%

Length

2026-03-27T16:48:18.289698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-27T16:48:18.377243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
delhi4537
42.5%
kolkata2871
26.9%
banglore2197
20.6%
mumbai697
 
6.5%
chennai381
 
3.6%

Most occurring characters

ValueCountFrequency (%)
l9605
14.3%
a9017
13.4%
e7115
10.6%
i5615
8.4%
o5068
7.5%
h4918
 
7.3%
D4537
 
6.8%
n2959
 
4.4%
K2871
 
4.3%
t2871
 
4.3%
Other values (9)12631
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)67207
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l9605
14.3%
a9017
13.4%
e7115
10.6%
i5615
8.4%
o5068
7.5%
h4918
 
7.3%
D4537
 
6.8%
n2959
 
4.4%
K2871
 
4.3%
t2871
 
4.3%
Other values (9)12631
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)67207
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l9605
14.3%
a9017
13.4%
e7115
10.6%
i5615
8.4%
o5068
7.5%
h4918
 
7.3%
D4537
 
6.8%
n2959
 
4.4%
K2871
 
4.3%
t2871
 
4.3%
Other values (9)12631
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)67207
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l9605
14.3%
a9017
13.4%
e7115
10.6%
i5615
8.4%
o5068
7.5%
h4918
 
7.3%
D4537
 
6.8%
n2959
 
4.4%
K2871
 
4.3%
t2871
 
4.3%
Other values (9)12631
18.8%

Destination
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size583.4 KiB
Cochin
4537 
Banglore
2871 
Delhi
1265 
New Delhi
932 
Hyderabad
697 

Length

Max length9
Median length8
Mean length6.9121969
Min length5

Characters and Unicode

Total characters73843
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Delhi
2nd rowBanglore
3rd rowCochin
4th rowBanglore
5th rowNew Delhi

Common Values

ValueCountFrequency (%)
Cochin4537
42.5%
Banglore2871
26.9%
Delhi1265
 
11.8%
New Delhi932
 
8.7%
Hyderabad697
 
6.5%
Kolkata381
 
3.6%

Length

2026-03-27T16:48:18.495524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-27T16:48:18.581426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cochin4537
39.1%
banglore2871
24.7%
delhi2197
18.9%
new932
 
8.0%
hyderabad697
 
6.0%
kolkata381
 
3.3%

Most occurring characters

ValueCountFrequency (%)
o7789
10.5%
n7408
10.0%
h6734
9.1%
i6734
9.1%
e6697
9.1%
l5449
 
7.4%
a5027
 
6.8%
C4537
 
6.1%
c4537
 
6.1%
r3568
 
4.8%
Other values (13)15363
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)73843
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o7789
10.5%
n7408
10.0%
h6734
9.1%
i6734
9.1%
e6697
9.1%
l5449
 
7.4%
a5027
 
6.8%
C4537
 
6.1%
c4537
 
6.1%
r3568
 
4.8%
Other values (13)15363
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)73843
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o7789
10.5%
n7408
10.0%
h6734
9.1%
i6734
9.1%
e6697
9.1%
l5449
 
7.4%
a5027
 
6.8%
C4537
 
6.1%
c4537
 
6.1%
r3568
 
4.8%
Other values (13)15363
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)73843
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o7789
10.5%
n7408
10.0%
h6734
9.1%
i6734
9.1%
e6697
9.1%
l5449
 
7.4%
a5027
 
6.8%
C4537
 
6.1%
c4537
 
6.1%
r3568
 
4.8%
Other values (13)15363
20.8%

Duration
Real number (ℝ)

High correlation 

Distinct368
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean643.09323
Minimum5
Maximum2860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2026-03-27T16:48:18.702022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile90
Q1170
median520
Q3930
95-th percentile1615
Maximum2860
Range2855
Interquartile range (IQR)760

Descriptive statistics

Standard deviation507.862
Coefficient of variation (CV)0.78971753
Kurtosis-0.16729132
Mean643.09323
Median Absolute Deviation (MAD)350
Skewness0.86107405
Sum6870165
Variance257923.81
MonotonicityNot monotonic
2026-03-27T16:48:18.827995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170550
 
5.1%
90386
 
3.6%
175337
 
3.2%
165337
 
3.2%
155329
 
3.1%
180261
 
2.4%
140238
 
2.2%
150220
 
2.1%
160158
 
1.5%
135135
 
1.3%
Other values (358)7732
72.4%
ValueCountFrequency (%)
51
 
< 0.1%
7524
 
0.2%
8061
 
0.6%
85135
 
1.3%
90386
3.6%
9515
 
0.1%
135135
 
1.3%
140238
2.2%
14598
 
0.9%
150220
2.1%
ValueCountFrequency (%)
28601
 
< 0.1%
28201
 
< 0.1%
25651
 
< 0.1%
25251
 
< 0.1%
24801
 
< 0.1%
24201
 
< 0.1%
23452
 
< 0.1%
23154
 
< 0.1%
23005
< 0.1%
229512
0.1%

Total_Stops
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size521.8 KiB
1
5626 
0
3491 
2
1520 
3
 
45
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10683
Distinct characters5
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 row0
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
15626
52.7%
03491
32.7%
21520
 
14.2%
345
 
0.4%
41
 
< 0.1%

Length

2026-03-27T16:48:18.947258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-27T16:48:19.030860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15626
52.7%
03491
32.7%
21520
 
14.2%
345
 
0.4%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
15626
52.7%
03491
32.7%
21520
 
14.2%
345
 
0.4%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15626
52.7%
03491
32.7%
21520
 
14.2%
345
 
0.4%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15626
52.7%
03491
32.7%
21520
 
14.2%
345
 
0.4%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15626
52.7%
03491
32.7%
21520
 
14.2%
345
 
0.4%
41
 
< 0.1%

Journey_Day
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.508378
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2026-03-27T16:48:19.114097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median12
Q321
95-th percentile27
Maximum27
Range26
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.4792775
Coefficient of variation (CV)0.62770509
Kurtosis-1.2728424
Mean13.508378
Median Absolute Deviation (MAD)6
Skewness0.11835054
Sum144310
Variance71.898146
MonotonicityNot monotonic
2026-03-27T16:48:19.200841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
91406
13.2%
61288
12.1%
271130
10.6%
211111
10.4%
11075
10.1%
241052
9.8%
15984
9.2%
12957
9.0%
3848
7.9%
18832
7.8%
ValueCountFrequency (%)
11075
10.1%
3848
7.9%
61288
12.1%
91406
13.2%
12957
9.0%
15984
9.2%
18832
7.8%
211111
10.4%
241052
9.8%
271130
10.6%
ValueCountFrequency (%)
271130
10.6%
241052
9.8%
211111
10.4%
18832
7.8%
15984
9.2%
12957
9.0%
91406
13.2%
61288
12.1%
3848
7.9%
11075
10.1%

Journey_Month
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size521.8 KiB
5
3466 
6
3414 
3
2724 
4
1079 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10683
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 row3
2nd row5
3rd row6
4th row5
5th row3

Common Values

ValueCountFrequency (%)
53466
32.4%
63414
32.0%
32724
25.5%
41079
 
10.1%

Length

2026-03-27T16:48:19.302306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-27T16:48:19.381745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
53466
32.4%
63414
32.0%
32724
25.5%
41079
 
10.1%

Most occurring characters

ValueCountFrequency (%)
53466
32.4%
63414
32.0%
32724
25.5%
41079
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)10683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
53466
32.4%
63414
32.0%
32724
25.5%
41079
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
53466
32.4%
63414
32.0%
32724
25.5%
41079
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
53466
32.4%
63414
32.0%
32724
25.5%
41079
 
10.1%

Dep_Hour
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.490686
Minimum0
Maximum23
Zeros40
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2026-03-27T16:48:19.476443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7486501
Coefficient of variation (CV)0.46023494
Kurtosis-1.1948465
Mean12.490686
Median Absolute Deviation (MAD)5
Skewness0.11307279
Sum133438
Variance33.046978
MonotonicityNot monotonic
2026-03-27T16:48:19.575333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
9916
 
8.6%
7867
 
8.1%
8697
 
6.5%
17695
 
6.5%
6687
 
6.4%
20651
 
6.1%
5629
 
5.9%
11580
 
5.4%
19567
 
5.3%
10536
 
5.0%
Other values (14)3858
36.1%
ValueCountFrequency (%)
040
 
0.4%
137
 
0.3%
2194
 
1.8%
324
 
0.2%
4170
 
1.6%
5629
5.9%
6687
6.4%
7867
8.1%
8697
6.5%
9916
8.6%
ValueCountFrequency (%)
23161
 
1.5%
22387
3.6%
21492
4.6%
20651
6.1%
19567
5.3%
18444
4.2%
17695
6.5%
16472
4.4%
15319
3.0%
14523
4.9%

Dep_Min
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.411214
Minimum0
Maximum55
Zeros2062
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2026-03-27T16:48:19.658023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median25
Q340
95-th percentile55
Maximum55
Range55
Interquartile range (IQR)35

Descriptive statistics

Standard deviation18.76798
Coefficient of variation (CV)0.76882617
Kurtosis-1.2928236
Mean24.411214
Median Absolute Deviation (MAD)20
Skewness0.16702906
Sum260785
Variance352.23708
MonotonicityNot monotonic
2026-03-27T16:48:19.738357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
02062
19.3%
301215
11.4%
551058
9.9%
10890
8.3%
45876
8.2%
5773
 
7.2%
15692
 
6.5%
25691
 
6.5%
20666
 
6.2%
35665
 
6.2%
Other values (2)1095
10.2%
ValueCountFrequency (%)
02062
19.3%
5773
 
7.2%
10890
8.3%
15692
 
6.5%
20666
 
6.2%
25691
 
6.5%
301215
11.4%
35665
 
6.2%
40504
 
4.7%
45876
8.2%
ValueCountFrequency (%)
551058
9.9%
50591
5.5%
45876
8.2%
40504
4.7%
35665
6.2%
301215
11.4%
25691
6.5%
20666
6.2%
15692
6.5%
10890
8.3%

Arrival_Hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.348778
Minimum0
Maximum23
Zeros322
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2026-03-27T16:48:19.825826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median14
Q319
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.8591252
Coefficient of variation (CV)0.51383917
Kurtosis-1.0752021
Mean13.348778
Median Absolute Deviation (MAD)5
Skewness-0.36998825
Sum142605
Variance47.047599
MonotonicityNot monotonic
2026-03-27T16:48:19.921954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
191626
15.2%
12897
 
8.4%
4838
 
7.8%
21703
 
6.6%
22647
 
6.1%
1529
 
5.0%
18514
 
4.8%
9490
 
4.6%
23485
 
4.5%
10476
 
4.5%
Other values (14)3478
32.6%
ValueCountFrequency (%)
0322
 
3.0%
1529
5.0%
279
 
0.7%
347
 
0.4%
4838
7.8%
569
 
0.6%
652
 
0.5%
7417
3.9%
8471
4.4%
9490
4.6%
ValueCountFrequency (%)
23485
 
4.5%
22647
 
6.1%
21703
6.6%
20377
 
3.5%
191626
15.2%
18514
 
4.8%
17191
 
1.8%
16370
 
3.5%
15182
 
1.7%
14295
 
2.8%

Arrival_Min
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.69063
Minimum0
Maximum55
Zeros1447
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2026-03-27T16:48:20.009017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median25
Q335
95-th percentile50
Maximum55
Range55
Interquartile range (IQR)25

Descriptive statistics

Standard deviation16.506036
Coefficient of variation (CV)0.66851416
Kurtosis-1.0281949
Mean24.69063
Median Absolute Deviation (MAD)10
Skewness0.11094486
Sum263770
Variance272.44922
MonotonicityNot monotonic
2026-03-27T16:48:20.091805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
01447
13.5%
251302
12.2%
151286
12.0%
351111
10.4%
20902
8.4%
30832
7.8%
50750
7.0%
45697
6.5%
5660
6.2%
40629
5.9%
Other values (2)1067
10.0%
ValueCountFrequency (%)
01447
13.5%
5660
6.2%
10577
 
5.4%
151286
12.0%
20902
8.4%
251302
12.2%
30832
7.8%
351111
10.4%
40629
5.9%
45697
6.5%
ValueCountFrequency (%)
55490
 
4.6%
50750
7.0%
45697
6.5%
40629
5.9%
351111
10.4%
30832
7.8%
251302
12.2%
20902
8.4%
151286
12.0%
10577
5.4%
Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size83.6 KiB
Minimum2019-01-03 00:00:00
Maximum2019-12-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-03-27T16:48:20.196672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:20.314660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)

Is_Peak_Hour
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size521.8 KiB
1
6790 
0
3893 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10683
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 row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
16790
63.6%
03893
36.4%

Length

2026-03-27T16:48:20.680512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-03-27T16:48:20.748522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16790
63.6%
03893
36.4%

Most occurring characters

ValueCountFrequency (%)
16790
63.6%
03893
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16790
63.6%
03893
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16790
63.6%
03893
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16790
63.6%
03893
36.4%

Price
Real number (ℝ)

High correlation 

Distinct1870
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9087.0641
Minimum1759
Maximum79512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2026-03-27T16:48:20.839074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1759
5-th percentile3543
Q15277
median8372
Q312373
95-th percentile15764
Maximum79512
Range77753
Interquartile range (IQR)7096

Descriptive statistics

Standard deviation4611.3592
Coefficient of variation (CV)0.50746414
Kurtosis13.30333
Mean9087.0641
Median Absolute Deviation (MAD)3382
Skewness1.8125524
Sum97077106
Variance21264633
MonotonicityNot monotonic
2026-03-27T16:48:20.974505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10262258
 
2.4%
10844212
 
2.0%
7229162
 
1.5%
4804160
 
1.5%
4823131
 
1.2%
14714109
 
1.0%
3943104
 
1.0%
1512993
 
0.9%
384191
 
0.9%
1289886
 
0.8%
Other values (1860)9277
86.8%
ValueCountFrequency (%)
17594
 
< 0.1%
18401
 
< 0.1%
196536
0.3%
201735
0.3%
205010
 
0.1%
20716
 
0.1%
21757
 
0.1%
222740
0.4%
22289
 
0.1%
23856
 
0.1%
ValueCountFrequency (%)
795121
 
< 0.1%
624271
 
< 0.1%
572091
 
< 0.1%
548263
< 0.1%
522851
 
< 0.1%
522291
 
< 0.1%
464901
 
< 0.1%
369831
 
< 0.1%
362352
< 0.1%
351851
 
< 0.1%

Interactions

2026-03-27T16:48:16.551799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:11.453202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:12.211636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:12.921582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:13.796655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:14.560959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:15.530368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:16.715926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:11.558361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:12.321857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:13.213060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:13.898647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:14.704615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:15.675963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:17.194414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:11.674021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:12.417545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:13.313238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:13.997664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:14.841974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:15.813194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:17.322628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:11.773409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:12.512952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:13.405744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:14.090882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:14.975378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:15.945582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:17.443664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:11.875709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:12.615351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:13.501555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:14.203020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:15.108109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:16.080714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:17.545248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:11.973891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:12.712201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:13.593133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:14.298442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:15.242792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:16.218821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:17.652593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:12.088603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:12.811559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:13.690342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:14.394721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:15.381881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-03-27T16:48:16.375354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-03-27T16:48:21.077406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AirlineArrival_HourArrival_MinDep_HourDep_MinDestinationDurationIs_Peak_HourJourney_DayJourney_MonthPriceSourceTotal_Stops
Airline1.0000.2010.1920.1520.1550.2560.2490.1310.0640.1230.3780.2760.334
Arrival_Hour0.2011.000-0.1720.0550.0460.1840.0530.272-0.0030.1060.0400.2050.165
Arrival_Min0.192-0.1721.0000.064-0.0180.199-0.1120.168-0.0170.126-0.1040.2120.179
Dep_Hour0.1520.0550.0641.000-0.0330.152-0.0120.8130.0020.0550.0080.1640.121
Dep_Min0.1550.046-0.018-0.0331.0000.186-0.0370.133-0.0070.083-0.0620.1900.134
Destination0.2560.1840.1990.1520.1861.0000.3340.1400.1290.3840.2271.0000.383
Duration0.2490.053-0.112-0.012-0.0370.3341.0000.172-0.0240.1450.6920.3400.549
Is_Peak_Hour0.1310.2720.1680.8130.1330.1400.1721.0000.0180.0900.0760.1340.022
Journey_Day0.064-0.003-0.0170.002-0.0070.129-0.0240.0181.0000.196-0.1220.1390.060
Journey_Month0.1230.1060.1260.0550.0830.3840.1450.0900.1961.0000.1870.2280.136
Price0.3780.040-0.1040.008-0.0620.2270.6920.076-0.1220.1871.0000.2020.309
Source0.2760.2050.2120.1640.1901.0000.3400.1340.1390.2280.2021.0000.345
Total_Stops0.3340.1650.1790.1210.1340.3830.5490.0220.0600.1360.3090.3451.000

Missing values

2026-03-27T16:48:17.835910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-03-27T16:48:17.996203image/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

AirlineSourceDestinationDurationTotal_StopsJourney_DayJourney_MonthDep_HourDep_MinArrival_HourArrival_MinJourney_DateIs_Peak_HourPrice
0IndiGoBangloreNew Delhi1700243222011024-03-201903897
1Air IndiaKolkataBanglore445215550131501-05-201907662
2Jet AirwaysDelhiCochin114029692542509-06-2019113882
3IndiGoKolkataBanglore3251125185233012-05-201916218
4IndiGoBangloreNew Delhi2851131650213501-03-2019113302
5SpiceJetKolkataBanglore145024690112524-06-201913873
6Jet AirwaysBangloreNew Delhi93011231855102512-03-2019111087
7Jet AirwaysBangloreNew Delhi1265113805501-03-2019022270
8Jet AirwaysBangloreNew Delhi15301123855102512-03-2019011087
9Multiple carriersDelhiCochin47012751125191527-05-201918625
AirlineSourceDestinationDurationTotal_StopsJourney_DayJourney_MonthDep_HourDep_MinArrival_HourArrival_MinJourney_DateIs_Peak_HourPrice
10673Jet AirwaysDelhiCochin9002275132542527-05-2019116704
10674Jet AirwaysBangloreNew Delhi148511232035212012-03-2019111087
10675Air IndiaMumbaiHyderabad8009662074009-06-201903100
10676Multiple carriersDelhiCochin520115102019001-05-201919794
10677SpiceJetBangloreDelhi160021555583521-05-201903257
10678Air AsiaKolkataBanglore1500941955222509-04-201914107
10679Air IndiaKolkataBanglore15502742045232027-04-201914145
10680Jet AirwaysBangloreDelhi1800274820112027-04-201907229
10681VistaraBangloreNew Delhi1600131130141001-03-2019112648
10682Air IndiaDelhiCochin5002951055191509-05-2019111753

Duplicate rows

Most frequently occurring

AirlineSourceDestinationDurationTotal_StopsJourney_DayJourney_MonthDep_HourDep_MinArrival_HourArrival_MinJourney_DateIs_Peak_HourPrice# duplicates
6Air IndiaDelhiCochin1275295220191509-05-20190104413
8Air IndiaDelhiCochin12752155220191515-05-20190112813
10Air IndiaDelhiCochin12752215220191521-05-20190102313
11Air IndiaDelhiCochin12752246220191524-06-2019091813
29Air IndiaDelhiCochin156021851715191518-05-20191123923
38Air IndiaDelhiCochin217026375191506-03-20190115523
49Air IndiaKolkataBanglore1665215100134501-05-20191151643
102Jet AirwaysDelhiCochin119529523519009-05-20190151293
104Jet AirwaysDelhiCochin1195224623519024-06-20190128193
106Jet AirwaysDelhiCochin1195227623519027-06-20190111503