Overview

Brought to you by YData

Dataset statistics

Number of variables7
Number of observations244
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.4%
Total size in memory7.9 KiB
Average record size in memory33.1 B

Variable types

Numeric3
Categorical4

Alerts

Dataset has 1 (0.4%) duplicate rowsDuplicates
day is highly overall correlated with timeHigh correlation
size is highly overall correlated with total_billHigh correlation
time is highly overall correlated with dayHigh correlation
tip is highly overall correlated with total_billHigh correlation
total_bill is highly overall correlated with size and 1 other fieldsHigh correlation

Reproduction

Analysis started2025-06-25 06:35:31.765578
Analysis finished2025-06-25 06:35:34.381681
Duration2.62 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

total_bill
Real number (ℝ)

High correlation 

Distinct229
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.785943
Minimum3.07
Maximum50.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-25T06:35:34.577381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3.07
5-th percentile9.5575
Q113.3475
median17.795
Q324.1275
95-th percentile38.061
Maximum50.81
Range47.74
Interquartile range (IQR)10.78

Descriptive statistics

Standard deviation8.902412
Coefficient of variation (CV)0.44993621
Kurtosis1.218484
Mean19.785943
Median Absolute Deviation (MAD)5.03
Skewness1.133213
Sum4827.77
Variance79.252939
MonotonicityNot monotonic
2025-06-25T06:35:34.869987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.42 3
 
1.2%
20.69 2
 
0.8%
13 2
 
0.8%
15.98 2
 
0.8%
18.29 2
 
0.8%
10.07 2
 
0.8%
20.29 2
 
0.8%
17.92 2
 
0.8%
15.69 2
 
0.8%
10.33 2
 
0.8%
Other values (219) 223
91.4%
ValueCountFrequency (%)
3.07 1
0.4%
5.75 1
0.4%
7.25 2
0.8%
7.51 1
0.4%
7.56 1
0.4%
7.74 1
0.4%
8.35 1
0.4%
8.51 1
0.4%
8.52 1
0.4%
8.58 1
0.4%
ValueCountFrequency (%)
50.81 1
0.4%
48.33 1
0.4%
48.27 1
0.4%
48.17 1
0.4%
45.35 1
0.4%
44.3 1
0.4%
43.11 1
0.4%
41.19 1
0.4%
40.55 1
0.4%
40.17 1
0.4%

tip
Real number (ℝ)

High correlation 

Distinct123
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9982787
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-25T06:35:35.165177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.44
Q12
median2.9
Q33.5625
95-th percentile5.1955
Maximum10
Range9
Interquartile range (IQR)1.5625

Descriptive statistics

Standard deviation1.3836382
Coefficient of variation (CV)0.46147751
Kurtosis3.6483759
Mean2.9982787
Median Absolute Deviation (MAD)0.9
Skewness1.465451
Sum731.58
Variance1.9144546
MonotonicityNot monotonic
2025-06-25T06:35:35.410111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 33
 
13.5%
3 23
 
9.4%
4 12
 
4.9%
5 10
 
4.1%
2.5 10
 
4.1%
3.5 9
 
3.7%
1.5 9
 
3.7%
1 4
 
1.6%
3.48 3
 
1.2%
1.25 3
 
1.2%
Other values (113) 128
52.5%
ValueCountFrequency (%)
1 4
1.6%
1.01 1
 
0.4%
1.1 1
 
0.4%
1.17 1
 
0.4%
1.25 3
1.2%
1.32 1
 
0.4%
1.36 1
 
0.4%
1.44 2
0.8%
1.45 1
 
0.4%
1.47 1
 
0.4%
ValueCountFrequency (%)
10 1
0.4%
9 1
0.4%
7.58 1
0.4%
6.73 1
0.4%
6.7 1
0.4%
6.5 2
0.8%
6 1
0.4%
5.92 1
0.4%
5.85 1
0.4%
5.65 1
0.4%

sex
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size604.0 B
Male
157 
Female
87 

Length

Max length6
Median length4
Mean length4.7131148
Min length4

Characters and Unicode

Total characters1150
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 157
64.3%
Female 87
35.7%

Length

2025-06-25T06:35:35.615070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-25T06:35:35.797277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
male 157
64.3%
female 87
35.7%

Most occurring characters

ValueCountFrequency (%)
e 331
28.8%
a 244
21.2%
l 244
21.2%
M 157
13.7%
F 87
 
7.6%
m 87
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 331
28.8%
a 244
21.2%
l 244
21.2%
M 157
13.7%
F 87
 
7.6%
m 87
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 331
28.8%
a 244
21.2%
l 244
21.2%
M 157
13.7%
F 87
 
7.6%
m 87
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 331
28.8%
a 244
21.2%
l 244
21.2%
M 157
13.7%
F 87
 
7.6%
m 87
 
7.6%

smoker
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size599.0 B
No
151 
Yes
93 

Length

Max length3
Median length2
Mean length2.3811475
Min length2

Characters and Unicode

Total characters581
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
No 151
61.9%
Yes 93
38.1%

Length

2025-06-25T06:35:35.998087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-25T06:35:36.178056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
no 151
61.9%
yes 93
38.1%

Most occurring characters

ValueCountFrequency (%)
N 151
26.0%
o 151
26.0%
Y 93
16.0%
e 93
16.0%
s 93
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 151
26.0%
o 151
26.0%
Y 93
16.0%
e 93
16.0%
s 93
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 151
26.0%
o 151
26.0%
Y 93
16.0%
e 93
16.0%
s 93
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 151
26.0%
o 151
26.0%
Y 93
16.0%
e 93
16.0%
s 93
16.0%

day
Categorical

High correlation 

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size785.0 B
Sat
87 
Sun
76 
Thur
62 
Fri
19 

Length

Max length4
Median length3
Mean length3.2540984
Min length3

Characters and Unicode

Total characters794
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Sat 87
35.7%
Sun 76
31.1%
Thur 62
25.4%
Fri 19
 
7.8%

Length

2025-06-25T06:35:36.367261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-25T06:35:36.516245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
sat 87
35.7%
sun 76
31.1%
thur 62
25.4%
fri 19
 
7.8%

Most occurring characters

ValueCountFrequency (%)
S 163
20.5%
u 138
17.4%
a 87
11.0%
t 87
11.0%
r 81
10.2%
n 76
9.6%
T 62
 
7.8%
h 62
 
7.8%
F 19
 
2.4%
i 19
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 163
20.5%
u 138
17.4%
a 87
11.0%
t 87
11.0%
r 81
10.2%
n 76
9.6%
T 62
 
7.8%
h 62
 
7.8%
F 19
 
2.4%
i 19
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 163
20.5%
u 138
17.4%
a 87
11.0%
t 87
11.0%
r 81
10.2%
n 76
9.6%
T 62
 
7.8%
h 62
 
7.8%
F 19
 
2.4%
i 19
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 163
20.5%
u 138
17.4%
a 87
11.0%
t 87
11.0%
r 81
10.2%
n 76
9.6%
T 62
 
7.8%
h 62
 
7.8%
F 19
 
2.4%
i 19
 
2.4%

time
Categorical

High correlation 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size605.0 B
Dinner
176 
Lunch
68 

Length

Max length6
Median length6
Mean length5.7213115
Min length5

Characters and Unicode

Total characters1396
Distinct characters9
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 rowDinner
2nd rowDinner
3rd rowDinner
4th rowDinner
5th rowDinner

Common Values

ValueCountFrequency (%)
Dinner 176
72.1%
Lunch 68
 
27.9%

Length

2025-06-25T06:35:36.700656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-25T06:35:36.841664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
dinner 176
72.1%
lunch 68
 
27.9%

Most occurring characters

ValueCountFrequency (%)
n 420
30.1%
D 176
12.6%
i 176
12.6%
e 176
12.6%
r 176
12.6%
L 68
 
4.9%
u 68
 
4.9%
c 68
 
4.9%
h 68
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 420
30.1%
D 176
12.6%
i 176
12.6%
e 176
12.6%
r 176
12.6%
L 68
 
4.9%
u 68
 
4.9%
c 68
 
4.9%
h 68
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 420
30.1%
D 176
12.6%
i 176
12.6%
e 176
12.6%
r 176
12.6%
L 68
 
4.9%
u 68
 
4.9%
c 68
 
4.9%
h 68
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 420
30.1%
D 176
12.6%
i 176
12.6%
e 176
12.6%
r 176
12.6%
L 68
 
4.9%
u 68
 
4.9%
c 68
 
4.9%
h 68
 
4.9%

size
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5696721
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2025-06-25T06:35:36.977431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9510998
Coefficient of variation (CV)0.37012496
Kurtosis1.7317001
Mean2.5696721
Median Absolute Deviation (MAD)0
Skewness1.4478815
Sum627
Variance0.90459084
MonotonicityNot monotonic
2025-06-25T06:35:37.191047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 156
63.9%
3 38
 
15.6%
4 37
 
15.2%
5 5
 
2.0%
1 4
 
1.6%
6 4
 
1.6%
ValueCountFrequency (%)
1 4
 
1.6%
2 156
63.9%
3 38
 
15.6%
4 37
 
15.2%
5 5
 
2.0%
6 4
 
1.6%
ValueCountFrequency (%)
6 4
 
1.6%
5 5
 
2.0%
4 37
 
15.2%
3 38
 
15.6%
2 156
63.9%
1 4
 
1.6%

Interactions

2025-06-25T06:35:33.284327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-06-25T06:35:32.103324image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-06-25T06:35:32.654040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-06-25T06:35:33.468335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-06-25T06:35:32.278006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-06-25T06:35:32.835065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-06-25T06:35:33.652190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-06-25T06:35:32.457016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-06-25T06:35:33.010328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2025-06-25T06:35:37.381956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
daysexsizesmokertimetiptotal_bill
day1.0000.2050.1420.3060.9390.0000.000
sex0.2051.0000.0580.0000.1850.0000.138
size0.1420.0581.0000.0510.2100.4680.605
smoker0.3060.0000.0511.0000.0000.0000.114
time0.9390.1850.2100.0001.0000.0390.139
tip0.0000.0000.4680.0000.0391.0000.679
total_bill0.0000.1380.6050.1140.1390.6791.000

Missing values

2025-06-25T06:35:33.916463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-25T06:35:34.227328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
525.294.71MaleNoSunDinner4
68.772.00MaleNoSunDinner2
726.883.12MaleNoSunDinner4
815.041.96MaleNoSunDinner2
914.783.23MaleNoSunDinner2
total_billtipsexsmokerdaytimesize
23415.533.00MaleYesSatDinner2
23510.071.25MaleNoSatDinner2
23612.601.00MaleYesSatDinner2
23732.831.17MaleYesSatDinner2
23835.834.67FemaleNoSatDinner3
23929.035.92MaleNoSatDinner3
24027.182.00FemaleYesSatDinner2
24122.672.00MaleYesSatDinner2
24217.821.75MaleNoSatDinner2
24318.783.00FemaleNoThurDinner2

Duplicate rows

Most frequently occurring

total_billtipsexsmokerdaytimesize# duplicates
013.02.0FemaleYesThurLunch22