Week 6 Data Visualization

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Week 6: Mapplotlib, Seaborn and Plotly

Introduction to Matplotlib¶

Matplotlib is the most widely used library for creating static, interactive, and animated visualizations in Python. It offers extensive functionality to create a wide variety of plots, from simple line plots to complex visualizations like heatmaps and 3D plots.

Why Use Matplotlib?¶

• Simplicity and Flexibility: Matplotlib provides a simple interface for quickly generating basic plots, while also offering extensive control over the appearance and behavior of plots. You can customize every aspect of your figures, from colors and markers to axes and labels.

• Wide Range of Plot Types: Whether you need basic line plots, bar charts, histograms, scatter plots, or more specialized visualizations like pie charts, Matplotlib can handle it all. It also supports more advanced features such as subplots and multiple axes.

• Integration with Other Libraries: Matplotlib integrates well with other libraries such as Pandas and NumPy, making it easy to plot data directly from these popular data science tools. It’s commonly used alongside Seaborn, a higher-level statistical visualization library that builds on Matplotlib for more aesthetically pleasing plots.

• Publication-Quality Figures: One of the most powerful features of Matplotlib is its ability to generate high-quality, publication-ready figures in various formats (e.g., PNG, PDF, SVG).

Setting up Matplotlib¶

To begin with, let's ensure that we have matplotlib installed and set up:

!pip install matplotlib

In [32]:

!pip install matplotlib

!pip install matplotlib

Defaulting to user installation because normal site-packages is not writeable
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In [2]:

import matplotlib.pyplot as plt
import pandas as pd

plt.rcParams['figure.figsize'] = [10, 5] # Set the size of the plot

import matplotlib.pyplot as plt import pandas as pd plt.rcParams['figure.figsize'] = [10, 5] # Set the size of the plot

Basic Plot and Workflow¶

Every plot in Matplotlib follows a standard structure:

1. Figure: The entire image or plot, which can contain multiple subplots.

2. Axis: Represents the horizontal (x) and vertical (y) scales on the graph, including ticks and labels.

3. Plot Elements: These include lines, markers, bars, etc., that represent data points on the axes.

Example: Basic Line Plot¶

In [38]:

# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Creating a simple line plot
plt.plot(x, y)

# Adding title and labels
plt.title("Temperature")
plt.xlabel("Year")
plt.ylabel("Temp")

# Display the plot
plt.show()

# Sample data x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10] # Creating a simple line plot plt.plot(x, y) # Adding title and labels plt.title("Temperature") plt.xlabel("Year") plt.ylabel("Temp") # Display the plot plt.show()

Out[38]:

Text(0, 0.5, 'Temp')

You can modify line styles and colors by adding parameters to the plt.plot() function.

• Simple linestyles can be defined using the strings "solid", "dotted", "dashed" or "dashdot".

  • linestyles documents

    

• Matplotlib supports multiple categories of markers which are selected using the marker parameter of plot commands:

  • Marker reference

    

In [40]:

plt.plot(x, y, linestyle='solid', color='g', marker='s')
plt.title("Styled Line Plot")
plt.show()

plt.plot(x, y, linestyle='solid', color='g', marker='s') plt.title("Styled Line Plot") plt.show()

Additional parameters include:

• xlabel/ylabel: Defines labels for the axes.

• legend(loc): Displays a legend, with loc specifying the position (e.g., 'upper left').

  • Legend guide

• grid: Adds gridlines to the plot.

In [42]:

# Create a basic line plot
plt.plot(x, y, marker='D', linestyle='dashed', color='r', label="Quadratic")

# Customize the plot
plt.title("Basic Line Plot")  # Set title of the plot
plt.xlabel("X-axis")          # Label for x-axis
plt.ylabel("Y-axis")          # Label for y-axis
plt.legend(loc="upper right")  # Add legend
plt.grid(True)                # Display gridlines
plt.show()

# Create a basic line plot plt.plot(x, y, marker='D', linestyle='dashed', color='r', label="Quadratic") # Customize the plot plt.title("Basic Line Plot") # Set title of the plot plt.xlabel("X-axis") # Label for x-axis plt.ylabel("Y-axis") # Label for y-axis plt.legend(loc="upper right") # Add legend plt.grid(True) # Display gridlines plt.show()

Exmaple: Basic Scatter Plot¶

• s: Sets the size of the markers.

• c: Sets the color of the markers.

  • Colors in Matplotlib

In [44]:

# Sample data
x = [5, 7, 8, 7, 2, 17, 2, 9]
y = [99, 86, 87, 88, 100, 86, 103, 87]
size = [100,100,160,200,60,40,60,80]
colors = [1,2,3,4,5,6,7,8]

# Create a scatter plot
plt.scatter(x, y, c=colors, marker='o', s=size)

# Customize the plot
plt.title("Scatter Plot")      # Set title
plt.xlabel("X-axis")           # Label for x-axis
plt.ylabel("Y-axis")           # Label for y-axis
plt.show()

# Sample data x = [5, 7, 8, 7, 2, 17, 2, 9] y = [99, 86, 87, 88, 100, 86, 103, 87] size = [100,100,160,200,60,40,60,80] colors = [1,2,3,4,5,6,7,8] # Create a scatter plot plt.scatter(x, y, c=colors, marker='o', s=size) # Customize the plot plt.title("Scatter Plot") # Set title plt.xlabel("X-axis") # Label for x-axis plt.ylabel("Y-axis") # Label for y-axis plt.show()

Example: Basic Histogram¶

• bins: Specifies the number of bins for the histogram.

• edgecolor: Defines the color of the bin edges.

In [46]:

# Sample data
data = [1, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 7]

# Create a histogram
plt.hist(data, bins=7, color='g', edgecolor='black')

# Customize the plot
plt.title("Histogram Plot")
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.show()

# Sample data data = [1, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 7] # Create a histogram plt.hist(data, bins=7, color='g', edgecolor='black') # Customize the plot plt.title("Histogram Plot") plt.xlabel("Value") plt.ylabel("Frequency") plt.show()

Example: Basic Bar Plot¶

In [47]:

# Sample data
categories = ['A', 'B', 'C', 'D', 'E']
values = [5, 7, 3, 8, 6]

# Define colors for each category
colors = ['red', 'green', 'blue', 'orange', 'purple']

# Create a bar plot with different colors for each category
plt.bar(categories, values, color=colors)
plt.title("Basic Bar Plot with Custom Colors")
plt.show()

# Sample data categories = ['A', 'B', 'C', 'D', 'E'] values = [5, 7, 3, 8, 6] # Define colors for each category colors = ['red', 'green', 'blue', 'orange', 'purple'] # Create a bar plot with different colors for each category plt.bar(categories, values, color=colors) plt.title("Basic Bar Plot with Custom Colors") plt.show()

Example: Horizontal bar chart¶

• plt.barh(y, width, color):

  • y: Labels for the bars (y-axis).
  • width: The length of each bar (values).
  • color: Specifies the color for each bar.

• sorted(iterable, key, reverse):

  • iterable: The data to be sorted.
  • key: Specifies the function used to extract a comparison key from each element (optional).
  • reverse: Whether to sort in descending order (default is ascending).

• plt.text(x, y, s, va, fontweight):

  • x: The x-coordinate where the text will appear.
  • y: The y-coordinate where the text will appear.
  • s: The string to display (in this case, the value of the bar).
  • va: Vertical alignment of the text.
  • fontweight: Sets the weight of the text (e.g., 'bold').

In [51]:

categories = ['A', 'B', 'C', 'D', 'E']
values = [5, 7, 3, 8, 6]

# Define colors for each category
colors = ['red', 'green', 'blue', 'orange', 'purple']

# Sort the categories and values based on the values
sorted_data = sorted(zip(values, categories, colors))
print(sorted_data)

values_sorted, categories_sorted, colors_sorted = zip(*sorted_data)

print(values_sorted)
print(categories_sorted)
print(colors_sorted)

categories = ['A', 'B', 'C', 'D', 'E'] values = [5, 7, 3, 8, 6] # Define colors for each category colors = ['red', 'green', 'blue', 'orange', 'purple'] # Sort the categories and values based on the values sorted_data = sorted(zip(values, categories, colors)) print(sorted_data) values_sorted, categories_sorted, colors_sorted = zip(*sorted_data) print(values_sorted) print(categories_sorted) print(colors_sorted)

[(3, 'C', 'blue'), (5, 'A', 'red'), (6, 'E', 'purple'), (7, 'B', 'green'), (8, 'D', 'orange')]
(3, 5, 6, 7, 8)
('C', 'A', 'E', 'B', 'D')
('blue', 'red', 'purple', 'green', 'orange')

In [57]:

demo = [3,2,5,6,4,8]
for i, value in enumerate(demo):
    print(str(i) + ' ' + str(value))

demo = [3,2,5,6,4,8] for i, value in enumerate(demo): print(str(i) + ' ' + str(value))

0 3
1 2
2 5
3 6
4 4
5 8

In [62]:

# Sample data
categories = ['A', 'B', 'C', 'D', 'E']
values = [5, 7, 3, 8, 6]

# Define colors for each category
colors = ['red', 'green', 'blue', 'orange', 'purple']

# Sort the categories and values based on the values
sorted_data = sorted(zip(values, categories, colors))

values_sorted, categories_sorted, colors_sorted = zip(*sorted_data)

# Create a horizontal bar plot with sorted data
plt.barh(y = categories_sorted, width = values_sorted, color=colors_sorted)
plt.title("Horizontal Bar Plot (Sorted by Value)")

# Add annotation (text) on top of each bar
for i, value in enumerate(values_sorted):
    plt.text(x = value+0.2, y = i, s = str(value), va='center', fontweight='bold')

plt.show()

# Sample data categories = ['A', 'B', 'C', 'D', 'E'] values = [5, 7, 3, 8, 6] # Define colors for each category colors = ['red', 'green', 'blue', 'orange', 'purple'] # Sort the categories and values based on the values sorted_data = sorted(zip(values, categories, colors)) values_sorted, categories_sorted, colors_sorted = zip(*sorted_data) # Create a horizontal bar plot with sorted data plt.barh(y = categories_sorted, width = values_sorted, color=colors_sorted) plt.title("Horizontal Bar Plot (Sorted by Value)") # Add annotation (text) on top of each bar for i, value in enumerate(values_sorted): plt.text(x = value+0.2, y = i, s = str(value), va='center', fontweight='bold') plt.show()

Example: Basic Pie Chart¶

• labels: Defines the labels for each slice of the pie.

• autopct: Displays the percentage value on each slice.

• startangle: Sets the starting angle for the first slice.

In [65]:

# Sample data
labels = ['A', 'B', 'C', 'D']
sizes = [20, 30, 25, 25]
colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']

# Create a pie chart
plt.pie(x = sizes, labels=labels, colors=colors, autopct='%1.4f%%', startangle=180)

# Customize the plot
plt.title("Pie Chart Example")
plt.axis('equal')  # Ensures the pie is drawn as a circle
plt.show()

# Sample data labels = ['A', 'B', 'C', 'D'] sizes = [20, 30, 25, 25] colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue'] # Create a pie chart plt.pie(x = sizes, labels=labels, colors=colors, autopct='%1.4f%%', startangle=180) # Customize the plot plt.title("Pie Chart Example") plt.axis('equal') # Ensures the pie is drawn as a circle plt.show()

Example: Basic Box Plot¶

• patch_artist: If True, the boxes are drawn with a fill color.

In [67]:

# Sample data
data = [[1, 2, 5, 6, 8], [2, 3, 4, 7, 9], [3, 5, 6, 8, 10]]

# Create a box plot
plt.boxplot(data, patch_artist=True)

# Customize the plot
plt.title("Box Plot")
plt.xlabel("Dataset")
plt.ylabel("Values")
plt.show()

# Sample data data = [[1, 2, 5, 6, 8], [2, 3, 4, 7, 9], [3, 5, 6, 8, 10]] # Create a box plot plt.boxplot(data, patch_artist=True) # Customize the plot plt.title("Box Plot") plt.xlabel("Dataset") plt.ylabel("Values") plt.show()

Example: Basic Subplots¶

Subplots allow multiple plots in one figure for better comparison.

In [68]:

# Sample data
data = [1, 1, 2, 2, 2, 3, 4, 4, 5, 6, 6, 6, 7, 8, 9]

# Create subplots
fig, (ax1, ax2) = plt.subplots(2, 1)

# First subplot - Line plot
ax1.plot(data)
ax1.set_title('Line Plot')

# Second subplot - Bar plot
ax2.hist(data, bins=6)
ax2.set_title('Histogram')

# Show both plots
plt.tight_layout()
plt.show()

# Sample data data = [1, 1, 2, 2, 2, 3, 4, 4, 5, 6, 6, 6, 7, 8, 9] # Create subplots fig, (ax1, ax2) = plt.subplots(2, 1) # First subplot - Line plot ax1.plot(data) ax1.set_title('Line Plot') # Second subplot - Bar plot ax2.hist(data, bins=6) ax2.set_title('Histogram') # Show both plots plt.tight_layout() plt.show()

Saving Your Plots¶

You can save your visualizations in various formats, such as PNG, JPG, PDF, and SVG:

In [12]:

# Sample data
categories = ['A', 'B', 'C', 'D', 'E']
values = [5, 7, 3, 8, 6]

# Define colors for each category
colors = ['red', 'green', 'blue', 'orange', 'purple']

# Create a basic bar plot
plt.bar(categories, values,color=colors)
plt.title("Basic Bar Plot")

plt.savefig('./output/plot.png')

# Sample data categories = ['A', 'B', 'C', 'D', 'E'] values = [5, 7, 3, 8, 6] # Define colors for each category colors = ['red', 'green', 'blue', 'orange', 'purple'] # Create a basic bar plot plt.bar(categories, values,color=colors) plt.title("Basic Bar Plot") plt.savefig('./output/plot.png')

Additional Learning Resources:¶

• Matplotlib Documentation: Official Documentation

• Matplotlib Tutorials: Matplotlib Tutorials on Real Python

Hands-On¶

In this tutorial, we'll use Worcester Police Use of Force Incidents (July 2024) to solve various problems using techiques in matplotlib.

Key Questions and Visualizations:

• What is the time trend of incidents over the month of July?

  • Visualization: Line plot to observe the trend over time.

• What is the percentage distribution of incidents involving injuries?

  • Visualization: Pie chart to analyze the proportion of incidents resulting in injury vs. no injury.

• Which techniques were most frequently used in incidents?

  • Visualization: Bar plot to visualize the frequency of different use-of-force techniques.

• What are the top locations for incidents?

  • Visualization: Horizontal bar plot to analyze location frequency.

Load data¶

In [8]:

data = pd.read_csv('F:/Clark_Universiy/Clark_Teaching/Git_Repo/ssj-302/docs/Lectures/data/Worcester_Police_Use_of_Force_Incidents_(July_2024).csv')
data.head(3)

data = pd.read_csv('F:/Clark_Universiy/Clark_Teaching/Git_Repo/ssj-302/docs/Lectures/data/Worcester_Police_Use_of_Force_Incidents_(July_2024).csv') data.head(3)

Out[8]:

	Incident_No	Date___Time	Location	Officer	Narr	Implement	Injury	Supervisor	Notified	ObjectId
0	2024000078279	7/29/24 1:09 PM	9 LINCOLN SQ Worcester, MA	Daniel Adjei	1	R. Empty Hand Compliance Techniques / Level 3	Visible Injury	Neil F O'Connor	7/29/24 4:27 PM	1
1	2024000078796	7/30/24 4:38 PM	6 SHANNON ST Apt #: 2 Worcester, MA	Rachel E Frisch	3	R. Empty Hand Compliance Techniques / Level 3	_None	Michael A Cappabianca Jr	7/30/24 6:57 PM	2
2	2024000078796	7/30/24 4:38 PM	6 SHANNON ST Apt #: 2 Worcester, MA	Michael Genese	2	R. Empty Hand Compliance Techniques / Level 3	_None	Michael A Cappabianca Jr	7/30/24 6:55 PM	3

Time Trend of Incidents Over the Month of July¶

• Question: What's the time trend of incidents over the month of July 2024?

• We will create a line plot to see how incidents vary over time.

• plt.plot(): The basic function to draw a line plot. Here, we specify the x and y data. The marker adds symbols at each data point (e.g., 'o' for circles), while linestyle defines how the line appears (e.g., solid or dashed).

• plt.title(), plt.xlabel(), plt.ylabel(): These functions are used to set the title and axis labels. The fontsize parameter helps to adjust the font size for readability.

• plt.xticks(): Used to rotate the labels on the x-axis to avoid overlapping when displaying dates.

• plt.grid(): Adds a grid to the plot for better visual understanding of data trends.

In [9]:

# Convert 'Date___Time' column to datetime format
data['Date___Time'] = pd.to_datetime(data['Date___Time'], format='%m/%d/%y %I:%M %p')

# Create a new column for the date
data['Date'] = data['Date___Time'].dt.date

# Group data by date and count incidents per day
incidents_per_day = data.groupby('Date').size()

print(incidents_per_day.index)
print(incidents_per_day.values)

# Convert 'Date___Time' column to datetime format data['Date___Time'] = pd.to_datetime(data['Date___Time'], format='%m/%d/%y %I:%M %p') # Create a new column for the date data['Date'] = data['Date___Time'].dt.date # Group data by date and count incidents per day incidents_per_day = data.groupby('Date').size() print(incidents_per_day.index) print(incidents_per_day.values)

Index([2024-07-01, 2024-07-02, 2024-07-03, 2024-07-04, 2024-07-05, 2024-07-06,
       2024-07-07, 2024-07-10, 2024-07-11, 2024-07-12, 2024-07-13, 2024-07-15,
       2024-07-16, 2024-07-17, 2024-07-20, 2024-07-21, 2024-07-22, 2024-07-24,
       2024-07-26, 2024-07-27, 2024-07-29, 2024-07-30, 2024-07-31],
      dtype='object', name='Date')
[1 5 3 5 1 7 1 1 4 3 4 6 2 3 3 6 2 3 2 1 5 3 3]

In [13]:

# Convert 'Date___Time' column to datetime format
data['Date___Time'] = pd.to_datetime(data['Date___Time'], format='%m/%d/%y %I:%M %p')

# Create a new column for the date
data['Date'] = data['Date___Time'].dt.date

# Group data by date and count incidents per day
incidents_per_day = data.groupby('Date').size()

# Plotting the line plot
plt.figure(figsize=(10, 6))
plt.plot(incidents_per_day.index, incidents_per_day.values, marker='o', linestyle='-')
plt.title("Time Trend of Incidents in July 2024", fontsize=16)
plt.xlabel("Date", fontsize=12)
plt.ylabel("Number of Incidents", fontsize=12)
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

# Convert 'Date___Time' column to datetime format data['Date___Time'] = pd.to_datetime(data['Date___Time'], format='%m/%d/%y %I:%M %p') # Create a new column for the date data['Date'] = data['Date___Time'].dt.date # Group data by date and count incidents per day incidents_per_day = data.groupby('Date').size() # Plotting the line plot plt.figure(figsize=(10, 6)) plt.plot(incidents_per_day.index, incidents_per_day.values, marker='o', linestyle='-') plt.title("Time Trend of Incidents in July 2024", fontsize=16) plt.xlabel("Date", fontsize=12) plt.ylabel("Number of Incidents", fontsize=12) plt.xticks(rotation=45) plt.grid(True) plt.tight_layout() plt.show()

Percentage Distribution of Injury vs. Non-Injury Incidents¶

• Question: What's the percentage distribution of incidents involving injuries?

• We'll use a pie chart to understand the proportion of incidents involving injuries versus no injuries.

• plt.pie(): This function generates a pie chart. Here are the key parameters:

  • labels: Labels for each slice of the pie (e.g., injury vs. non-injury).
  • autopct='%1.1f%%': This formats the percentage display inside the slices.
  • startangle=90: Rotates the start of the pie chart to improve visual orientation.
  • colors: You can provide a list of colors to customize the pie chart.

• plt.axis('equal'): Ensures that the pie chart is drawn as a circle rather than an ellipse.

In [75]:

# Calculate the count of incidents with injury and without injury
injury_counts = data['Injury'].value_counts()

# Create a pie chart
plt.figure(figsize=(7, 7))
plt.pie(injury_counts, labels=injury_counts.index, autopct='%1.1f%%', startangle=90, colors=['#ff9999','#66b3ff'])
plt.title("Percentage Distribution of Injury vs. Non-Injury Incidents", fontsize=16)
plt.axis('equal')  # Equal aspect ratio ensures that pie chart is drawn as a circle.
plt.show()

# Calculate the count of incidents with injury and without injury injury_counts = data['Injury'].value_counts() # Create a pie chart plt.figure(figsize=(7, 7)) plt.pie(injury_counts, labels=injury_counts.index, autopct='%1.1f%%', startangle=90, colors=['#ff9999','#66b3ff']) plt.title("Percentage Distribution of Injury vs. Non-Injury Incidents", fontsize=16) plt.axis('equal') # Equal aspect ratio ensures that pie chart is drawn as a circle. plt.show()

Most Frequently Used Techniques¶

• Question: Which techniques were most frequently used in the incidents?

• We’ll create a bar plot to identify which techniques were most frequently employed.

• plt.bar(): This function creates a vertical bar plot.

  • color='skyblue': Customizes the color of the bars.
  • plt.xticks(): Helps rotate and position the x-axis labels, ensuring they don’t overlap.
  • plt.tight_layout(): Ensures the plot and labels fit well within the figure area, avoiding overlap.

In [17]:

# Count occurrences of each use-of-force technique
technique_counts = data['Implement'].value_counts()

# Create a bar plot
plt.figure(figsize=(10, 6))
plt.bar(technique_counts.index, technique_counts.values, color='skyblue')
plt.title("Most Frequently Used Techniques", fontsize=16)
plt.xlabel("Technique", fontsize=12)
plt.ylabel("Frequency", fontsize=12)
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

# Count occurrences of each use-of-force technique technique_counts = data['Implement'].value_counts() # Create a bar plot plt.figure(figsize=(10, 6)) plt.bar(technique_counts.index, technique_counts.values, color='skyblue') plt.title("Most Frequently Used Techniques", fontsize=16) plt.xlabel("Technique", fontsize=12) plt.ylabel("Frequency", fontsize=12) plt.xticks(rotation=45, ha='right') plt.tight_layout() plt.show()

Top Locations for Incidents¶

• Question: What are the top locations for incidents?

• Using a horizontal bar plot, we will examine which locations had the most incidents.

• plt.barh(): Creates a horizontal bar plot.

  • This is especially useful when your category labels (e.g., locations) are long and might overlap if displayed on the x-axis.

• plt.barh(index, values): The index represents the labels (categories), and values are the frequencies or counts.

• color='lightcoral': Customizes the bar color to improve visual appeal.

In [35]:

# Count occurrences of incidents at each location
location_counts = data['Location'].value_counts()

# Sort the values in descending order and get the top 10 locations
location_counts = location_counts.sort_values(ascending=False).head(10)

# Reverse the order of the data
location_counts = location_counts.iloc[::-1]

# Create a horizontal bar plot
plt.figure(figsize=(10, 6))
plt.barh(location_counts.index, location_counts.values, color='lightcoral')
plt.title("Top 10 Locations for Incidents", fontsize=16)
plt.xlabel("Number of Incidents", fontsize=12)
plt.ylabel("Location", fontsize=12)

# Add annotation (text) on top of each bar
for i, value in enumerate(location_counts.values):
    plt.text(value + 0.1, i, str(value), va='center', fontweight='bold')

plt.tight_layout()
plt.show()

# Count occurrences of incidents at each location location_counts = data['Location'].value_counts() # Sort the values in descending order and get the top 10 locations location_counts = location_counts.sort_values(ascending=False).head(10) # Reverse the order of the data location_counts = location_counts.iloc[::-1] # Create a horizontal bar plot plt.figure(figsize=(10, 6)) plt.barh(location_counts.index, location_counts.values, color='lightcoral') plt.title("Top 10 Locations for Incidents", fontsize=16) plt.xlabel("Number of Incidents", fontsize=12) plt.ylabel("Location", fontsize=12) # Add annotation (text) on top of each bar for i, value in enumerate(location_counts.values): plt.text(value + 0.1, i, str(value), va='center', fontweight='bold') plt.tight_layout() plt.show()

Introduction to Seaborn¶

Seaborn is a Python data visualization library built on top of Matplotlib. It is specifically designed to make it easier to create aesthetically pleasing and informative statistical graphics.

Seaborn comes with high-level interfaces for drawing attractive and informative statistical graphics, making it a great tool for exploratory data analysis.

Key Features of Seaborn:

• Simplified Syntax: Seaborn allows you to create complex visualizations with just a few lines of code.

• Integration with Pandas: It works seamlessly with Pandas dataframes, allowing for easy plotting of complex datasets.

• Built-in Statistical Functions: Seaborn offers built-in functions to handle common statistical plots like boxplots, violin plots, and heatmaps.

• Aesthetic Plotting: Seaborn’s default settings create plots with modern, clean styles that are aesthetically pleasing without requiring additional customization.

Comparison Between Seaborn and Matplotlib¶

Feature	Matplotlib	Seaborn
Customization	Highly customizable but requires more effort	Built-in themes and styles for modern designs
Ease of Use	Requires more code for complex plots	Simplified syntax for complex visualizations
Default Styles	Simple and basic	Aesthetically pleasing by default
Statistical Plotting	No built-in statistical functions	Built-in functions for statistical visualizations
Integration with Pandas	Requires conversion or manual work	Integrates directly with Pandas dataframes
Plot Types	Supports a wide range of plot types	Best suited for statistical plots

In [2]:

import seaborn as sns
import pandas as pd

import seaborn as sns import pandas as pd

Exercise 01 lineplot: Time Trend of Incidents Over the Month of July (Seaborn)¶

• sns.lineplot(): This function simplifies the creation of a line plot with additional styling. The marker='o' adds markers at each data point

• Smoother Design: Seaborn automatically applies a more polished design, including better line widths and colors.

In [ ]:

In [1]:

data = pd.read_csv('F:/Clark_Universiy/Clark_Teaching/Git_Repo/ssj-302/docs/Lectures/data/Worcester_Police_Use_of_Force_Incidents_(July_2024).csv')

# Convert 'Date___Time' column to datetime format
data['Date___Time'] = pd.to_datetime(data['Date___Time'], format='%m/%d/%y %I:%M %p')

# Create a new column for the date
data['Date'] = data['Date___Time'].dt.date

# Group data by date and count incidents per day
incidents_per_day = data.groupby('Date').size()

incidents_per_day

data = pd.read_csv('F:/Clark_Universiy/Clark_Teaching/Git_Repo/ssj-302/docs/Lectures/data/Worcester_Police_Use_of_Force_Incidents_(July_2024).csv') # Convert 'Date___Time' column to datetime format data['Date___Time'] = pd.to_datetime(data['Date___Time'], format='%m/%d/%y %I:%M %p') # Create a new column for the date data['Date'] = data['Date___Time'].dt.date # Group data by date and count incidents per day incidents_per_day = data.groupby('Date').size() incidents_per_day

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[1], line 1
----> 1 data = pd.read_csv('F:/Clark_Universiy/Clark_Teaching/Git_Repo/ssj-302/docs/Lectures/data/Worcester_Police_Use_of_Force_Incidents_(July_2024).csv')
      3 # Convert 'Date___Time' column to datetime format
      4 data['Date___Time'] = pd.to_datetime(data['Date___Time'], format='%m/%d/%y %I:%M %p')

NameError: name 'pd' is not defined

In [26]:

# Create a line plot with Seaborn
plt.figure(figsize=(10, 6))
sns.lineplot(x=incidents_per_day.index, y=incidents_per_day.values,  marker='o', color='royalblue')

# Customize the plot
plt.title("Time Trend of Incidents in July 2024", fontsize=16)
plt.xlabel("Date", fontsize=12)
plt.ylabel("Number of Incidents", fontsize=12)
plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()

# Create a line plot with Seaborn plt.figure(figsize=(10, 6)) sns.lineplot(x=incidents_per_day.index, y=incidents_per_day.values, marker='o', color='royalblue') # Customize the plot plt.title("Time Trend of Incidents in July 2024", fontsize=16) plt.xlabel("Date", fontsize=12) plt.ylabel("Number of Incidents", fontsize=12) plt.xticks(rotation=45) plt.grid(True) plt.tight_layout() plt.show()

Plotly¶

In [1]:

!pip install plotly

!pip install plotly

Collecting plotly
  Downloading plotly-5.18.0-py3-none-any.whl (15.6 MB)
Requirement already satisfied: packaging in g:\anaconda\lib\site-packages (from plotly) (20.1)
Collecting tenacity>=6.2.0
  Downloading tenacity-8.2.3-py3-none-any.whl (24 kB)
Requirement already satisfied: six in g:\anaconda\lib\site-packages (from packaging->plotly) (1.14.0)
Requirement already satisfied: pyparsing>=2.0.2 in g:\anaconda\lib\site-packages (from packaging->plotly) (2.4.6)
Installing collected packages: tenacity, plotly
Successfully installed plotly-5.18.0 tenacity-8.2.3

Plotly Express¶

Plotly Express is a high-level interface for creating interactive visualizations in Python. It simplifies the process of making complex, interactive plots by offering concise syntax.

In [6]:

## Scatter plots 
import plotly.express as px

df = px.data.stocks()
print(df.head(5))
fig = px.line(df, x='date', y="GOOG")
fig.show()

## Scatter plots import plotly.express as px df = px.data.stocks() print(df.head(5)) fig = px.line(df, x='date', y="GOOG") fig.show()

         date      GOOG      AAPL      AMZN        FB      NFLX      MSFT
0  2018-01-01  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000
1  2018-01-08  1.018172  1.011943  1.061881  0.959968  1.053526  1.015988
2  2018-01-15  1.032008  1.019771  1.053240  0.970243  1.049860  1.020524
3  2018-01-22  1.066783  0.980057  1.140676  1.016858  1.307681  1.066561
4  2018-01-29  1.008773  0.917143  1.163374  1.018357  1.273537  1.040708

Exercise 02 Line plot: Time Trend of Incidents Over the Month of July (Plotly)¶

In [14]:

fig = px.line(x=incidents_per_day.index, y=incidents_per_day.values, labels={'x': 'Date', 'y': 'Incidents'},title='Interactive Time Series')
fig.show()

fig = px.line(x=incidents_per_day.index, y=incidents_per_day.values, labels={'x': 'Date', 'y': 'Incidents'},title='Interactive Time Series') fig.show()

Bar charts with Plotly Express¶

In [20]:

data_canada = px.data.gapminder().query("country == 'Canada'")
fig = px.bar(data_canada, x='year', y='pop')
fig.show()

data_canada = px.data.gapminder().query("country == 'Canada'") fig = px.bar(data_canada, x='year', y='pop') fig.show()

Exercise 03 Bar chart: Time Trend of Incidents Over the Month of July (Plotly)¶

In [98]:

fig = px.bar(x=data['Implement'].value_counts().index, y=data['Implement'].value_counts().values, 
             labels={'x': 'Implement', 'y': 'Count'},title='Implement')
fig

fig = px.bar(x=data['Implement'].value_counts().index, y=data['Implement'].value_counts().values, labels={'x': 'Implement', 'y': 'Count'},title='Implement') fig

Spatial Map¶

Data Source

Code Demo

In [105]:

import plotly.graph_objects as go

trees = pd.read_csv('F:\\Clark_Universiy\\Clark_Teaching\\Git_Repo\\ssj-302\\docs\\Lectures\\Week06_visualization\\Street_Trees.csv')

valid_trees = trees[trees['dbh'] > 0]

import plotly.graph_objects as go trees = pd.read_csv('F:\\Clark_Universiy\\Clark_Teaching\\Git_Repo\\ssj-302\\docs\\Lectures\\Week06_visualization\\Street_Trees.csv') valid_trees = trees[trees['dbh'] > 0]

In [106]:

valid_trees['text'] = 'Neighborhoods:' +  valid_trees['neighborhood'] + ' ' + 'Tree:' + valid_trees['spp_bot'] + ' Diamemter:' + valid_trees['dbh'].astype(str)

valid_trees['text'] = 'Neighborhoods:' + valid_trees['neighborhood'] + ' ' + 'Tree:' + valid_trees['spp_bot'] + ' Diamemter:' + valid_trees['dbh'].astype(str)

g:\Anaconda\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

In [111]:

fig = go.Figure(data=go.Scattergeo(
        lon = valid_trees['x_longitude'],
        lat = valid_trees['y_latitude'],
        text = valid_trees['text'],
        mode = 'markers'    ))

fig.update_layout(
        title = 'Treekeeper Street Trees',
        geo = dict(
            scope = 'usa',
            center=dict(lat=42.3601, lon=-71.0589),
            projection_scale=50
        ),
        width=1000,  # Set figure width
        height=600   # Set figure height
    )
fig.show()

fig = go.Figure(data=go.Scattergeo( lon = valid_trees['x_longitude'], lat = valid_trees['y_latitude'], text = valid_trees['text'], mode = 'markers' )) fig.update_layout( title = 'Treekeeper Street Trees', geo = dict( scope = 'usa', center=dict(lat=42.3601, lon=-71.0589), projection_scale=50 ), width=1000, # Set figure width height=600 # Set figure height ) fig.show()