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Articles by Rishikesh Kumar Rishi
Page 70 of 102
Putting a newline in Matplotlib label with TeX in Python
When creating plots with Matplotlib, you may need to add newlines to axis labels for better formatting. This is easily achieved using the escape character in your label strings. Basic Example with Newlines in Labels Here's how to add newlines to both X and Y axis labels ? import matplotlib.pyplot as plt # Create simple data x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10] # Plot with newlines in labels plt.plot(x, y, 'b-', linewidth=2) plt.ylabel("Y-axis with newline") plt.xlabel("X-axis with newline") plt.title("Plot with Newlines in Labels") ...
Read MoreGenerating a movie from Python without saving individual frames to files
Creating animated movies in Python using matplotlib's FuncAnimation allows you to generate smooth animations without saving individual frames to disk. This approach is memory-efficient and perfect for real-time particle simulations. Key Concepts The animation works by repeatedly calling an update function that modifies particle positions and returns updated plot elements. FuncAnimation handles the timing and display automatically. Steps to Create the Animation Initialize particles with position, velocity, force, and size properties Create a matplotlib figure with specified dimensions Add axes with appropriate x and y limits Create initial scatter plot for particle positions Define an ...
Read MoreHow to prevent numbers being changed to exponential form in Python Matplotlib?
When plotting large numbers in Matplotlib, the axis labels often switch to scientific notation (exponential form) automatically. You can prevent this by using the ticklabel_format() with style='plain' parameter. Syntax plt.ticklabel_format(style='plain') The style='plain' parameter turns off scientific notation and displays numbers in their regular decimal format. Example Here's how to prevent exponential notation when plotting data ? import matplotlib.pyplot as plt # Plot data that would normally trigger scientific notation plt.plot([1, 2, 3, 4, 5], [11000, 12000, 13000, 14000, 15000]) # Prevent scientific notation on y-axis plt.ticklabel_format(style='plain') plt.title('Numbers in ...
Read MorePlotting dates on the X-axis with Python\'s Matplotlib
Using Pandas, we can create a dataframe and set datetime values as the index. Matplotlib's gcf().autofmt_xdate() automatically formats date labels on the X-axis for better readability. Steps to Plot Dates on X-axis Create a list of date strings and convert them to datetime using pd.to_datetime() Prepare your data values (e.g., [1, 2, 3]) Create a DataFrame and assign the data to a column Set the DataFrame index using the datetime values Plot the DataFrame using plt.plot() Format the X-axis dates using plt.gcf().autofmt_xdate() Display the plot with plt.show() Example import pandas as pd import ...
Read MoreShow only certain items in legend Python Matplotlib
In Python Matplotlib, you can control which items appear in the legend by using the plt.legend() method with a list of labels. This allows you to show only specific plot elements in the legend rather than all plotted data. Basic Syntax The plt.legend() method accepts a list of labels to display ? plt.legend(["label1", "label2"], loc=location, frameon=True/False) Parameters labels − List of strings to show in the legend loc − Location of the legend (0 for best location) frameon − Boolean flag to show/hide legend border Example Here's how to ...
Read MoreMaking matplotlib scatter plots from dataframes in Python's pandas
Creating scatter plots from pandas DataFrames using matplotlib is a powerful way to visualize relationships between variables. We can use the DataFrame structure to organize our data and create colorful scatter plots with proper labeling. Steps to Create a Scatter Plot Import matplotlib and pandas libraries Create lists for your data variables (x-axis, y-axis, and colors) Build a pandas DataFrame from your data Create figure and axes objects using plt.subplots() Add axis labels using plt.xlabel() and plt.ylabel() Generate the scatter plot using ax.scatter() method Display the plot with plt.show() Example Here's how to create ...
Read MoreManually add legend Items Python Matplotlib
In Matplotlib, you can manually add legend items using the plt.legend() method. This allows you to create custom legends with specific labels, positions, and styling options like borders. Basic Syntax plt.legend(labels, loc=location, frameon=True/False) Parameters labels − List of strings for legend labels loc − Location of the legend (0 for best location) frameon − Boolean to show/hide the legend border Example Here's how to create a plot with manually added legend items ? import matplotlib.pyplot as plt # Set axis labels plt.xlabel("X-axis") plt.ylabel("Y-axis") # Plot two ...
Read MoreMake 3D plot interactive in Jupyter Notebook (Python & Matplotlib)
Interactive 3D plots in Jupyter Notebook allow you to rotate, zoom, and pan your visualizations. Matplotlib provides built-in interactivity when using the %matplotlib notebook or %matplotlib widget magic commands. Setting Up Interactive Mode To enable interactivity, use the appropriate magic command at the beginning of your notebook ? %matplotlib notebook # or use %matplotlib widget for newer versions import matplotlib.pyplot as plt import numpy as np Creating an Interactive 3D Sphere Here's how to create an interactive 3D wireframe sphere ? import matplotlib.pyplot as plt import numpy as np # ...
Read MoreHow do you plot a vertical line on a time series plot in Pandas?
When working with time series data in Pandas, you often need to highlight specific dates or events by adding vertical lines to your plots. This can be achieved using matplotlib's axvline() method on the plot axes. Creating a Time Series DataFrame First, let's create a sample time series dataset with dates as the index ? import pandas as pd import matplotlib.pyplot as plt # Create a DataFrame with date range df = pd.DataFrame(index=pd.date_range("2019-07-01", "2019-07-31")) df["value"] = range(1, 32) # Sample values for each day print(df.head()) ...
Read MorePython xticks in subplots
Subplots allow you to display multiple plots in a single figure by dividing it into a grid. When working with subplots, you can customize the x-axis ticks for each subplot independently using plt.xticks(). Understanding Subplot Layout The plt.subplot() function creates subplots using three parameters: nrows, ncols, and index. For example, plt.subplot(121) creates a 1×2 grid and selects the first subplot. Basic Subplot with Different X-ticks Here's how to create two subplots with custom x-tick positions − import matplotlib.pyplot as plt line1 = [21, 14, 81] line2 = [31, 6, 12] # First ...
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