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Articles on Trending Technologies
Technical articles with clear explanations and examples
Creating curved edges with NetworkX in Python3 (Matplotlib)
Creating curved edges in NetworkX graphs enhances visual clarity when multiple edges exist between nodes. The connectionstyle parameter with arc3 creates smooth curved connections instead of straight lines. Steps to Create Curved Edges Import required libraries (NetworkX and Matplotlib) Configure figure settings for optimal display Create a directed graph object Add nodes and edges to the graph Use connectionstyle="arc3, rad=0.4" in the draw function Display the graph with curved edges Example Here's how to create a directed graph with curved edges ? import matplotlib.pyplot as plt import networkx as nx # ...
Read MoreHow to Create a Diverging Stacked Bar Chart in Matplotlib?
A diverging stacked bar chart displays data that can extend both above and below a baseline (usually zero). This visualization is useful for comparing positive and negative values across categories, such as survey responses, profit/loss data, or demographic comparisons. Understanding Diverging Stacked Bar Charts In a diverging stacked bar chart, bars can extend in both directions from a central baseline. The key is using both positive and negative values to create the diverging effect, with stacked segments building upon each other. Creating a Basic Diverging Stacked Bar Chart Here's how to create a diverging stacked bar ...
Read MoreHow to use Matplotlib to plot PySpark SQL results?
To use Matplotlib to plot PySpark SQL results, we need to convert Spark DataFrames to Pandas DataFrames and then use Matplotlib for visualization. This process involves setting up a Spark context, creating a DataFrame, running SQL queries, and converting results for plotting. Setting Up the Environment First, we need to import the required libraries and configure Matplotlib ? from pyspark.sql import SparkSession, Row import matplotlib.pyplot as plt # Configure matplotlib plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create Spark session spark = SparkSession.builder.appName("MatplotlibExample").getOrCreate() Creating Sample Data and DataFrame We'll create ...
Read MoreHow to manipulate figures while a script is running in Python Matplotlib?
Manipulating figures while a script is running allows you to create dynamic, animated plots that update in real-time. This is useful for visualizing data streams, creating interactive demonstrations, or building animated visualizations. Basic Figure Manipulation To manipulate figures during script execution, you need to create a figure, display it, and then update it using canvas.draw() and plt.pause() ? import numpy as np import matplotlib.pyplot as plt # Set figure size and layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create figure and get current axis fig = plt.figure() ax = fig.gca() fig.show() ...
Read MoreHow to plot a kernel density plot of dates in Pandas using Matplotlib?
A kernel density plot visualizes the probability density function of data. When working with dates in Pandas, we need to convert them to numerical values before plotting the density estimate. Steps to Create a Kernel Density Plot Create a DataFrame with date values Convert dates to ordinal numbers for numerical processing Plot the kernel density estimate using plot(kind='kde') Format x-axis ticks back to readable date labels Example Here's how to create a kernel density plot for date data ? import pandas as pd import numpy as np import datetime import matplotlib.pyplot as ...
Read MoreHow can I get the length of a single unit on an axis in Matplotlib?
To get the length of a single unit on an axis in Matplotlib, you need to use the transData transform to convert data coordinates to display coordinates. This helps determine how many pixels represent one unit on each axis. Understanding the Transform Method The transData transform converts data coordinates to display (pixel) coordinates. By transforming unit vectors and comparing them to the origin, we can calculate the pixel length of a single unit on each axis. Example Here's how to calculate the length of a single unit on both axes ? import numpy as ...
Read MoreHow do I redraw an image using Python's Matplotlib?
To redraw an image using Python's Matplotlib, you can dynamically update plots by clearing and redrawing content. This is useful for creating animations or real-time data visualization. Basic Steps for Redrawing The process involves these key steps: Set the figure size and adjust the padding between and around the subplots Create a new figure or activate an existing figure Get the current axis using gca() method Show the current figure Iterate and redraw the plot with new data ...
Read MoreColoring the Intersection of Circles/Patches in Matplotlib
To color the intersection of circles/patches in Matplotlib, we use geometric operations to separate overlapping areas. This technique involves creating circular patches and using set operations to identify and color distinct regions. Required Libraries We need three key libraries for this task ? import shapely.geometry as sg import matplotlib.pyplot as plt import descartes Creating Overlapping Circles First, we create two overlapping circular patches using Shapely's geometry operations ? import shapely.geometry as sg import matplotlib.pyplot as plt import descartes # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ...
Read MoreHow to change the DPI of a Pandas Dataframe Plot in Matplotlib?
To change the DPI (dots per inch) of a Pandas DataFrame plot in Matplotlib, you can use rcParams to set the resolution. Higher DPI values produce sharper, more detailed plots. What is DPI? DPI controls the resolution of your plot. Higher DPI means more pixels per inch, resulting in sharper images. The default DPI is usually 100, but you can increase it for better quality or decrease it for smaller file sizes. Setting DPI Using rcParams The most common way is to set the DPI globally using matplotlib's rcParams ? import pandas as pd ...
Read MoreHow to create a seaborn.heatmap() with frames around the tiles?
To create frames around the tiles in a Seaborn heatmap, we can use the linewidths and linecolor parameters in the heatmap() method. This adds visual separation between cells, making the data more readable. Basic Syntax sns.heatmap(data, linewidths=width, linecolor='color') Parameters linewidths − Width of the lines that will divide each cell (float) linecolor − Color of the lines that will divide each cell (string or RGB) Example with Green Frames Here's how to create a heatmap with green frames around each tile − import seaborn as sns import pandas ...
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