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![Load the tips dataset from Seaborn
⚫ The Seaborn "Tips" dataset contains information about restaurant bills, tips, and customer demographics.
⚫ Here are the column descriptions of the Tips dataset.
total bill: Meal cost.
tip: Tip amount.
⚫ sex: Payer gender.
smoker: Smoker (yes/no).
⚫day: Day of week.
⚫time: Lunch/dinner.
• size: Party size.
[1] import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
tips sns. load dataset ("tips")
=
display(tips.head(3))
print(tips.info())
print("\nDataset Description:")
print(tips.describe())
ན་
total bill tip sex smoker day time size
0
16.99 1.01 Female
No Sun Dinner
2
1
10.34 1.66 Male
No Sun Dinner
3
2
21.01 3.50 Male
No Sun Dinner
3
<class 'pandas.core.frame.DataFrame">
RangeIndex: 244 entries, 8 to 243
Data columns (total 7 columns):
Non-Null Count Dtype
"
Column
total bill 244 non-null
float64
1
tip
244 non-null
float64
2
sex
244 non-null
category
3
smoker
244 non-null
category
4
day
244 non-null
category
5
time
244 non-null
category
6 size
244 non-null
int64
dtypes: category(4), float64(2), int64(1)
nemory usage: 7.4 KB
None
Dataset Description:
total_bill
tip
size
nean
std
count 244.000000 244.000000 244.000000
19.785943 2.998279
2.569672
8.902412 1.383638 0.951100
min
3.070000
1.000000 1.000000
25%
13.347500
2.000000 2.000000
sex
17.795000
2.900000
2.000000
75%
24.127500
3.562500
3.000000
nax
50.810000 10.000000
6.000000](https://content.bartleby.com/qna-images/question/5038e9fe-a67b-4654-bcd7-3337a3eb6f7a/6965e5f4-f9e2-4e93-aeff-5b92ac30518a/cvagg13_processed.jpeg)
Transcribed Image Text:Load the tips dataset from Seaborn
⚫ The Seaborn "Tips" dataset contains information about restaurant bills, tips, and customer demographics.
⚫ Here are the column descriptions of the Tips dataset.
total bill: Meal cost.
tip: Tip amount.
⚫ sex: Payer gender.
smoker: Smoker (yes/no).
⚫day: Day of week.
⚫time: Lunch/dinner.
• size: Party size.
[1] import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
tips sns. load dataset ("tips")
=
display(tips.head(3))
print(tips.info())
print("\nDataset Description:")
print(tips.describe())
ན་
total bill tip sex smoker day time size
0
16.99 1.01 Female
No Sun Dinner
2
1
10.34 1.66 Male
No Sun Dinner
3
2
21.01 3.50 Male
No Sun Dinner
3
<class 'pandas.core.frame.DataFrame">
RangeIndex: 244 entries, 8 to 243
Data columns (total 7 columns):
Non-Null Count Dtype
"
Column
total bill 244 non-null
float64
1
tip
244 non-null
float64
2
sex
244 non-null
category
3
smoker
244 non-null
category
4
day
244 non-null
category
5
time
244 non-null
category
6 size
244 non-null
int64
dtypes: category(4), float64(2), int64(1)
nemory usage: 7.4 KB
None
Dataset Description:
total_bill
tip
size
nean
std
count 244.000000 244.000000 244.000000
19.785943 2.998279
2.569672
8.902412 1.383638 0.951100
min
3.070000
1.000000 1.000000
25%
13.347500
2.000000 2.000000
sex
17.795000
2.900000
2.000000
75%
24.127500
3.562500
3.000000
nax
50.810000 10.000000
6.000000
![Question 3: Scatter Plot
Create a scatter plot to visualize the relationship between total bill and tip.
• Interpret the plot: Do we see any correlation?
[] #Code goes here
I
ㄛˇ
Scatter Plot: Total Bill vs. Tip
10
6.
4
total_bill
Question 4: Pair Plots
• Create pair plots to visualize relationships between: (total bill, tip, size).
⚫ Interpret the plot.
[] Code goes here
H
total bill
50
40
di
10
10
1-
.
40
2
total bil
bp
sze](https://content.bartleby.com/qna-images/question/5038e9fe-a67b-4654-bcd7-3337a3eb6f7a/6965e5f4-f9e2-4e93-aeff-5b92ac30518a/s6jhvgp_processed.jpeg)
Transcribed Image Text:Question 3: Scatter Plot
Create a scatter plot to visualize the relationship between total bill and tip.
• Interpret the plot: Do we see any correlation?
[] #Code goes here
I
ㄛˇ
Scatter Plot: Total Bill vs. Tip
10
6.
4
total_bill
Question 4: Pair Plots
• Create pair plots to visualize relationships between: (total bill, tip, size).
⚫ Interpret the plot.
[] Code goes here
H
total bill
50
40
di
10
10
1-
.
40
2
total bil
bp
sze
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