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Articles on Trending Technologies
Technical articles with clear explanations and examples
Matplotlib – How to set xticks and yticks with imshow plot?
When working with imshow() plots in Matplotlib, you often need to customize the tick positions and labels on both axes. The set_xticks() and set_yticks() methods allow you to control exactly where ticks appear on your image plot. Basic Example with Custom Tick Positions Here's how to set custom tick positions for an imshow plot − import numpy as np import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Get current axis ax = plt.gca() # Create random dataset data = np.random.rand(6, 6) # Display data ...
Read MoreRemove white border when using subplot and imshow in Python Matplotlib
When using subplot() and imshow() in Matplotlib, white borders often appear around images due to default padding and axes settings. This can be removed by adjusting figure parameters and axes configuration. Understanding the Problem By default, Matplotlib adds padding around subplots and displays axes with ticks and labels, creating unwanted white space around images displayed with imshow(). Method 1: Using Custom Axes Create a custom axes object that fills the entire figure without any padding ? import numpy as np import matplotlib.pyplot as plt # Set figure parameters plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] ...
Read MoreHow to show tick labels on top of a matplotlib plot?
To show tick labels on top of a matplotlib plot, we can use the set_tick_params() method with labeltop=True. This is useful when you want axis labels at the top instead of the default bottom position. Basic Example Here's how to move tick labels to the top of a plot − import matplotlib.pyplot as plt import numpy as np # Create sample data x = np.linspace(0, 10, 50) y = np.sin(x) # Create the plot fig, ax = plt.subplots(figsize=(8, 4)) ax.plot(x, y, 'b-', linewidth=2) # Move tick labels to top ax.xaxis.set_tick_params(labeltop=True) ax.xaxis.set_tick_params(labelbottom=False) # ...
Read MoreHow should I pass a matplotlib object through a function; as Axis, Axes or Figure?
When passing matplotlib objects through functions, you typically work with Axes objects for individual subplots, Figure objects for the entire figure, or iterate through multiple axes. Here's how to properly structure functions that accept matplotlib objects. Understanding Matplotlib Objects The main matplotlib objects you'll pass through functions are: Figure − The entire figure containing all plots Axes − Individual subplot areas where you draw Array of Axes − Multiple subplot objects when using subplots Example: Passing Axes Objects Here's a complete example showing how to pass matplotlib objects through functions ? ...
Read MoreHow 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 # ...
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