EDS 217 Cheatsheet

Step 1: Import Libraries

First, you need to import the necessary libraries. We’ll use matplotlib.pyplot for plotting.

import matplotlib.pyplot as plt

Step 2: Prepare Your Data

Create lists or arrays for the categories (x-axis) and their corresponding values (y-axis). Here’s a simple example:

categories = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
values = [-22.89, -20.7, -20.69, -11.76, -0.8, 8.59, 11.22, 7.23, -0.11, -10.54, -18.34, -21.44]

Step 3: Create the Bar Plot

Use the bar() function from pyplot to create the bar plot. Pass the categories and values as arguments.

plt.bar(categories, values)

Step 4: Add Labels and Title

You can enhance your plot by adding a title and labels for the x-axis and y-axis.

plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Simple Bar Plot')

Step 5: Display the Plot

Finally, use plt.show() to display the plot.

plt.show()

Complete Code Example

Here’s the complete code to create a simple bar plot:

import matplotlib.pyplot as plt

# Data for the plot
categories = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
values = [-22.89, -20.7, -20.69, -11.76, -0.8, 8.59, 11.22, 7.23, -0.11, -10.54, -18.34, -21.44]

# Create the bar plot
plt.bar(categories, values)

# Add labels and title
plt.xlabel('Months')
plt.ylabel('Average Temperature, deg-C')
plt.title('Toolik Lake LTER Average Temperatures, 2008-2019')

# Display the plot
plt.show()

Customizing the Bar Plot

You can further customize your bar plot with additional options:

Color: Set the color of the bars using the color parameter.
```
plt.bar(categories, values, color='skyblue')
```
Width: Adjust the width of the bars using the width parameter.
```
plt.bar(categories, values, width=0.5)
```
Add Grid: Make the plot easier to read by adding a grid.
```
plt.grid(axis='y', linestyle='--', alpha=0.7)
```
Horizontal Bar Plot: Use barh() for horizontal bar plots.
```
plt.barh(categories, values, color='skyblue')
```

Example with Customizations

import matplotlib.pyplot as plt

# Data for the plot
categories = ['Category A', 'Category B', 'Category C', 'Category D']
values = [23, 17, 35, 29]

# Create the bar plot with customizations
plt.bar(categories, values, color='skyblue', width=0.5)

# Add labels and title
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Simple Bar Plot')

# Add grid
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Display the plot
plt.show()

--- title: EDS 217 Cheatsheet subtitle: Bar Plots (<code>plt.bar()</code>) jupyter: eds217_2024 format: html: toc: true toc-depth: 3 code-fold: show code-tools: true --- ### Step 1: Import Libraries First, you need to import the necessary libraries. We'll use `matplotlib.pyplot` for plotting. ```python import matplotlib.pyplot as plt ``` ### Step 2: Prepare Your Data Create lists or arrays for the categories (x-axis) and their corresponding values (y-axis). Here’s a simple example: ```python categories = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'] values = [-22.89, -20.7, -20.69, -11.76, -0.8, 8.59, 11.22, 7.23, -0.11, -10.54, -18.34, -21.44] ``` ### Step 3: Create the Bar Plot Use the `bar()` function from `pyplot` to create the bar plot. Pass the categories and values as arguments. ```python plt.bar(categories, values) ``` ### Step 4: Add Labels and Title You can enhance your plot by adding a title and labels for the x-axis and y-axis. ```python plt.xlabel('Categories') plt.ylabel('Values') plt.title('Simple Bar Plot') ``` ### Step 5: Display the Plot Finally, use `plt.show()` to display the plot. ```python plt.show() ``` ### Complete Code Example Here's the complete code to create a simple bar plot: ```python import matplotlib.pyplot as plt # Data for the plot categories = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'] values = [-22.89, -20.7, -20.69, -11.76, -0.8, 8.59, 11.22, 7.23, -0.11, -10.54, -18.34, -21.44] # Create the bar plot plt.bar(categories, values) # Add labels and title plt.xlabel('Months') plt.ylabel('Average Temperature, deg-C') plt.title('Toolik Lake LTER Average Temperatures, 2008-2019') # Display the plot plt.show() ``` ### Customizing the Bar Plot You can further customize your bar plot with additional options: - **Color**: Set the color of the bars using the `color` parameter. ```python plt.bar(categories, values, color='skyblue') ``` - **Width**: Adjust the width of the bars using the `width` parameter. ```python plt.bar(categories, values, width=0.5) ``` - **Add Grid**: Make the plot easier to read by adding a grid. ```python plt.grid(axis='y', linestyle='--', alpha=0.7) ``` - **Horizontal Bar Plot**: Use `barh()` for horizontal bar plots. ```python plt.barh(categories, values, color='skyblue') ``` ### Example with Customizations ```python import matplotlib.pyplot as plt # Data for the plot categories = ['Category A', 'Category B', 'Category C', 'Category D'] values = [23, 17, 35, 29] # Create the bar plot with customizations plt.bar(categories, values, color='skyblue', width=0.5) # Add labels and title plt.xlabel('Categories') plt.ylabel('Values') plt.title('Simple Bar Plot') # Add grid plt.grid(axis='y', linestyle='--', alpha=0.7) # Display the plot plt.show() ```