Juanjo Carrasco has successfully completed this project.

Visualizations with Matplotlib

easy

4.56

Understanding Amount plots

Finished

September 9, 2024 11:22 AM

Elapsed time (min)

Completed activities

Resolution

Activities

Bar plot¶

In [1]:

import pandas as pd
import matplotlib.pyplot as plt

Introduction to bar plot¶

In [2]:

# Sample data
categories = ['Category A', 'Category B', 'Category C', 'Category D']
sales = [120, 210, 180, 250]

In [3]:

# Initialize the figure and axes
fig, ax = plt.subplots(figsize=(8, 6))

# Create a basic vertical bar plot
ax.bar(categories, sales, color='skyblue')

# Customize the plot
ax.set_xlabel('Product Categories')
ax.set_ylabel('Number of Products Sold')
ax.set_title('Product Sales by Category')
ax.set_xticklabels(categories, rotation=45)

# Display the plot
plt.show()

/tmp/ipykernel_17/1216360248.py:11: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  ax.set_xticklabels(categories, rotation=45)

No description has been provided for this image

Example¶

In [4]:

df=pd.read_csv('tips.csv')
df.head()

Out[4]:

	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
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Visualize the average tip amount by gender (Male vs. Female)¶

In [5]:

# Calculate the average tip amount by gender
avg_tip_by_gender = df.groupby("sex")["tip"].mean().reset_index()

In [6]:

# Create a figure and a set of subplots
fig, ax = plt.subplots(figsize=(8, 6))

# Create a bar plot to compare average tips by gender using Matplotlib on ax
ax.bar(avg_tip_by_gender["sex"], avg_tip_by_gender["tip"], color=["skyblue", "salmon"])
ax.set_xlabel("Gender")
ax.set_ylabel("Average Tip Amount")
ax.set_title("Average Tips by Gender")

Out[6]:

Text(0.5, 1.0, 'Average Tips by Gender')

Compare the total bill amounts for different days of the week¶

In [7]:

# Prepare the data
days = df["day"].unique()
total_bills_by_day = df.groupby("day")["total_bill"].sum()

In [8]:

# Create a figure and axis object
fig, ax = plt.subplots(figsize=(10, 6))

# Plot the data
ax.bar(days, total_bills_by_day, color="skyblue")

# Set labels and title
ax.set_xlabel("Day of the Week")
ax.set_ylabel("Total Bill Amount")
ax.set_title("Total Bill Amounts by Day of the Week")

Out[8]:

Text(0.5, 1.0, 'Total Bill Amounts by Day of the Week')

Explore the distribution of party sizes¶

In [9]:

# Create a figure and an axes object
fig, ax = plt.subplots(figsize=(8, 6))

# Generate the bar plot on the axes object
party_counts = df["size"].value_counts()
ax.bar(party_counts.index, party_counts, color="skyblue")

# Set the labels and title of the plot
ax.set_xlabel("Party Size")
ax.set_ylabel("Count")
ax.set_title("Distribution of Party Sizes")

Out[9]:

Text(0.5, 1.0, 'Distribution of Party Sizes')

Investigate the preference for mealtime (Lunch vs. Dinner) in terms of tips:¶

In [10]:

# Prepare data for a bar plot to compare average tips for lunch and dinner
fig, ax = plt.subplots(figsize=(8, 6))
meal_times = df["time"].unique()
average_tips = df.groupby("time")["tip"].mean()
colors = ["skyblue", "salmon"]

# Create bar plot
ax.bar(meal_times, average_tips, color=colors)
ax.set_xlabel("Mealtime")
ax.set_ylabel("Average Tip Amount")
ax.set_title("Average Tips by Mealtime (Lunch vs. Dinner)")

Out[10]:

Text(0.5, 1.0, 'Average Tips by Mealtime (Lunch vs. Dinner)')

Stacked bar plots¶

Here's the code to generate and introduce a stacked bar plot for the "Tips" dataset:

In [12]:

df.groupby(["day", "time"])["total_bill"].sum().unstack()

Out[12]:

time	Dinner	Lunch
day
Fri	235.96	89.92
Sat	1778.40	NaN
Sun	1627.16	NaN
Thur	18.78	1077.55

In [13]:

# Calculate the total bill amount by day and time (Lunch vs. Dinner)
total_bill_by_day_time = df.groupby(["day", "time"])["total_bill"].sum().unstack().reset_index()

# Initialize the figure and axes for a more flexible plotting
fig, ax = plt.subplots(figsize=(10, 6))

# Extract the days and total bill amounts for Lunch and Dinner
days = total_bill_by_day_time["day"]
lunch_bills = total_bill_by_day_time["Lunch"]
dinner_bills = total_bill_by_day_time["Dinner"]

# Create the stacked bars for Lunch and Dinner
ax.bar(days, lunch_bills, label="Lunch", color="skyblue")
ax.bar(days, dinner_bills, bottom=lunch_bills, label="Dinner", color="salmon")

ax.set_xlabel("Day of the Week")
ax.set_ylabel("Total Bill Amount")
ax.set_title("Composition of Total Bills by Day and Time")
ax.legend(title="Mealtime", loc="upper right")

Out[13]:

<matplotlib.legend.Legend at 0x7c5f68e7b4d0>

Activity 1. Draw four horizontal bars with different lengths, labels, and colors using Matplotlib.¶

In [14]:

bar_lengths = [3, 4, 5, 6]  # Adjust the lengths as needed
bar_labels = ['Bar 1', 'Bar 2', 'Bar 3', 'Bar 4']
bar_colors = ['skyblue', 'salmon', 'lightgreen', 'lightcoral']

In [19]:

# Create a horizontal bar plot
fig, ax = plt.subplots(figsize=(8, 6))

# Create the horizontal bars
ax.barh(bar_labels, bar_lengths, color =bar_colors)

# Add labels and title
ax.set_title('Horizontal Bar Plot')
ax.set_xlabel('Length')
ax.set_ylabel('Bars')

Out[19]:

Text(0, 0.5, 'Bars')

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