How to draw graphs in Python


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  1. To draw graphs in Python, there are several libraries available, each with its own features and use cases. Here are some of the most commonly used ones:

    1. Matplotlib: This is the most popular Python library for creating static, animated, and interactive visualizations. It's highly versatile and can be used to create a wide variety of graphs and plots.

      Example code to create a simple line plot using Matplotlib:

      import matplotlib.pyplot as plt
      # Data
      x = [1, 2, 3, 4, 5]
      y = [2, 3, 5, 7, 11]
      # Create a figure and an axis
      plt.plot(x, y)  # Line plot
      # Add title and labels
      plt.title("Simple Line Plot")
      # Show plot
    2. Seaborn: This is a statistical data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

      Example code to create a simple scatter plot using Seaborn:

      import seaborn as sns
      import matplotlib.pyplot as plt
      # Sample data
      data = sns.load_dataset('iris')
      # Create a scatter plot
      sns.scatterplot(data=data, x="sepal_length", y="sepal_width", hue="species")
      # Show plot
    3. Plotly: This library is used for creating interactive plots and is particularly useful for web-based dashboards and applications.

      Example code to create a simple interactive plot using Plotly:

      import as px
      # Sample data
      df =
      # Create an interactive scatter plot
      fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")
      # Show plot
    4. Pandas Plotting: Pandas, the data manipulation library, also has built-in capabilities for basic plotting that are useful for quick and easy visualizations directly from DataFrames.

      Example code to create a plot using Pandas:

      import pandas as pd
      import numpy as np
      # Sample data
      df = pd.DataFrame({
          'A': np.random.rand(100),
          'B': np.random.rand(100)
      # Simple line plot

    Each of these libraries has its own strengths and is suitable for different types of data visualization tasks. Depending on the complexity and requirements of your graphs, you can choose the one that best fits your needs.

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