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Python Articles
Page 365 of 855
How to label bubble chart/scatter plot with column from Pandas dataframe?
To label bubble charts or scatter plots with data from a Pandas DataFrame column, we use the annotate() method to add text labels at each data point position. Creating a Labeled Scatter Plot Here's how to create a scatter plot with labels from a DataFrame column ? import pandas as pd import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Create a dataframe df = pd.DataFrame({ 'x': [1, 3, 2, 4, 5], 'y': [0, 3, 1, 2, 5], ...
Read MoreHow to plot multi-color line if X-axis is datetime index of Pandas?
To plot a multi-color line where the X-axis is a datetime index in Pandas, you need to use LineCollection from matplotlib with a colormap. This creates segments between consecutive points, each colored based on the x-value position. Creating Sample Data First, let's create a datetime-indexed Pandas Series with random walk data ? import pandas as pd import numpy as np from matplotlib import pyplot as plt, dates as mdates, collections as mcoll # Create datetime range and random walk data dates = pd.date_range("2021-01-01", "2021-06-01", freq="7D") values = np.cumsum(np.random.normal(size=len(dates))) series = pd.Series(values, index=dates) print("Sample data:") ...
Read MoreFind Rolling Mean – Python Pandas
To find the rolling mean in Pandas, we use the rolling() method combined with mean(). This calculates the average of values within a sliding window. Let's explore different approaches to compute rolling means. Basic Setup First, import pandas and create a sample DataFrame ? import pandas as pd # Create DataFrame dataFrame = pd.DataFrame({ "Car": ['Tesla', 'Mercedes', 'Tesla', 'Mustang', 'Mercedes', 'Mustang'], "Reg_Price": [5000, 1500, 6500, 8000, 9000, 6000] }) print("DataFrame:") print(dataFrame) DataFrame: Car Reg_Price ...
Read MoreHow to get coordinates from the contour in matplotlib?
To get coordinates from the contour in matplotlib, you can extract the vertices from the contour paths. This is useful for analyzing contour lines or exporting contour data for further processing. Basic Contour Coordinate Extraction Here's how to create a contour plot and extract its coordinates ? import matplotlib.pyplot as plt import numpy as np # Set figure size plt.rcParams["figure.figsize"] = [8, 6] plt.rcParams["figure.autolayout"] = True # Create sample data x = [1, 2, 3, 4] y = [1, 2, 3, 4] z = [[15, 14, 13, 12], ...
Read MoreHow to remove random unwanted space in LaTeX-style maths in matplotlib plot?
LaTeX ignores the spaces you type and uses spacing the way it's done in mathematics texts. When working with matplotlib's LaTeX rendering, you might encounter unwanted spacing that can be controlled using specific commands. LaTeX Spacing Commands You can use the following four commands to control spacing in mathematical expressions ? \; − thick space \: − medium space \, − thin space \! − negative thin space (reduces spacing) Removing Unwanted Space To remove random unwanted space in LaTeX-style maths in matplotlib plots, use \! which creates a negative thin space, effectively ...
Read MoreHow to get pixel coordinates for Matplotlib-generated scatterplot?
When working with matplotlib scatterplots, you might need to convert data coordinates to pixel coordinates for UI interactions or precise positioning. This can be achieved using matplotlib's coordinate transformation system. Understanding Coordinate Systems Matplotlib uses different coordinate systems − Data coordinates − The actual x, y values of your data points Pixel coordinates − Screen/display coordinates in pixels Transform objects − Convert between coordinate systems Getting Pixel Coordinates from Scatterplot Here's how to extract pixel coordinates from a matplotlib scatterplot ? import numpy as np import matplotlib.pyplot as plt # ...
Read MoreHow to animate text in Matplotlib?
Text animation in Matplotlib allows you to create dynamic visual effects by changing text properties over time. This is useful for presentations, data storytelling, and creating engaging visualizations. Basic Text Animation Setup To animate text in matplotlib, we need to import the animation module and set up a figure with an axes object. The animation works by repeatedly calling a function that modifies text properties. Example Here's how to create an animated text that changes color and size over time − from matplotlib import animation import matplotlib.pyplot as plt # Set figure size ...
Read MoreHow to make a grouped boxplot graph in matplotlib?
A grouped boxplot displays the distribution of a continuous variable across different categories, with each group subdivided by another categorical variable. In matplotlib, we can create grouped boxplots using Seaborn, which provides a high-level interface for statistical visualizations. Basic Grouped Boxplot Here's how to create a grouped boxplot using the tips dataset ? import seaborn as sns import matplotlib.pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Load the tips dataset data = sns.load_dataset('tips') # Create a grouped boxplot sns.boxplot(x='day', y='total_bill', hue='sex', data=data) # Add ...
Read MoreHow to plot a rainbow cricle in matplotlib?
To plot a rainbow circle in Matplotlib, we can create concentric circles with different colors representing the colors of a rainbow. This creates a beautiful visual effect with nested circles in rainbow colors. Steps to Create a Rainbow Circle Set the figure size and adjust the padding between and around the subplots Create a figure and a set of subplots Set the X and Y axes to equal scale for perfect circles Define a list of rainbow colors Create concentric circles with decreasing radius Add each circle to the axes with different colors Display the figure using ...
Read MoreHow to plot thousands of circles quickly in Matplotlib?
When plotting thousands of circles in Matplotlib, using individual Circle patches becomes very slow. The efficient approach is to use CircleCollection from matplotlib.collections, which renders all circles in a single operation. Why Use CircleCollection? CircleCollection is optimized for rendering many similar shapes at once. Instead of creating thousands of individual patches, it handles all circles as a single collection, dramatically improving performance. Basic Implementation import numpy as np import matplotlib.pyplot as plt import matplotlib.collections as mc # Generate random data for 1000 circles num_circles = 1000 sizes = 50 * np.random.random(num_circles) positions = 10 ...
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