Beyond the Basics: Pushing the Limits of Seaborn and Matplotlib#

Installation and Setup#

Before diving in, ensure you have Python 3.x installed. If you haven’t already, you can install Matplotlib and Seaborn using pip:

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pip install matplotlib seaborn

Alternatively, if you prefer conda, you can run:

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conda install matplotlib seaborn

These commands also ensure you have the libraries�?dependencies (like NumPy, SciPy, pandas) installed.

A Quick Tour of the Basic Workflow#

Both Matplotlib and Seaborn rely heavily on arrays or dataframes as input. If you have (x, y) arrays, you can create a simple line plot with:

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import matplotlib.pyplot as plt
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x = [0, 1, 2, 3, 4, 5]
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y = [0, 1, 4, 9, 16, 25]
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plt.plot(x, y)
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plt.show()

For Seaborn, if you have a pandas DataFrame with labeled columns, you can directly plot columns by name:

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import seaborn as sns
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import pandas as pd
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df = pd.DataFrame({
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    'time': [1, 2, 3, 4, 5],
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    'values': [10, 15, 8, 12, 20]
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})
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sns.lineplot(x='time', y='values', data=df)
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plt.show()

In both cases, the easiest path is to do quick, inline plotting in Jupyter or IPython using %matplotlib inline. However, this blog will focus on deeper usage for robust data analysis and dynamic reporting.

Figures and Axes#

Matplotlib organizes plots using a hierarchy:

1. Figure: The top-level container in which everything is drawn.
2. Axes: The area where the data is actually plotted. A figure may contain one or more axes (subplots).
3. Axis: The x-axis or y-axis object, controlling scales and tick marks.

At the simplest level:

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import matplotlib.pyplot as plt
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plt.figure(figsize=(8, 6))  # Create a figure with a certain size
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plt.plot([1, 2, 3, 4], [10, 20, 15, 25])  # Plot a line
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plt.title("Simple Plot")
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plt.xlabel("X-axis Label")
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plt.ylabel("Y-axis Label")
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plt.show()

• plt.figure() instantiates a figure.
• plt.plot() draws onto a set of axes implicitly created or referenced.
• plt.show() displays the figure.

If you prefer a more explicit approach:

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fig, ax = plt.subplots(figsize=(8, 6))
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ax.plot([1, 2, 3, 4], [10, 20, 15, 25], marker='o')
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ax.set_title("Simple Plot (OO Style)")
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ax.set_xlabel("X-axis")
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ax.set_ylabel("Y-axis")
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plt.show()

This object-oriented style offers greater flexibility, especially for multi-plot layouts or advanced customization.

Plotting Lines, Points, and More#

Matplotlib excels at drawing lines, scatter plots, bar charts, histograms—essentially everything. A few examples:

• Line Plot:

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  plt.plot([0, 1, 2, 3],[0, 2, 4, 3], color='blue', linestyle='--', marker='o')
  

• Scatter Plot:

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  plt.scatter([1, 2, 3, 4], [2, 3, 5, 6], s=50, c='red', alpha=0.5)
  

• Bar Plot:

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  categories = ['A', 'B', 'C']
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  values = [5, 7, 3]
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  plt.bar(categories, values, color='green')
  

• Histogram:

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  import numpy as np
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  data = np.random.randn(1000)
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  plt.hist(data, bins=20, edgecolor='black')
  

Basic Customizations#

You can easily customize labels, titles, and legend:

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plt.plot([1, 2, 3, 4], label='Sample Data', color='purple')
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plt.title("Basic Customizations")
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plt.xlabel("X-axis")
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plt.ylabel("Y-axis")
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plt.legend()
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plt.show()

Styling elements such as colors, line widths, and markers can be specified in the plotting functions or set globally via rcParams.

Seaborn’s Plotting Functions#

Seaborn organizes chart types into a few high-level categories:

1. Relational: (e.g., lineplot, scatterplot)
2. Distribution: (e.g., histplot, kdeplot, displot)
3. Categorical: (e.g., catplot, boxplot, violinplot, barplot)
4. Regression: (e.g., regplot, lmplot)

Each function can be used with a DataFrame so that you can specify x and y columns by name.

Distribution, Categorical, and Relational Plots#

A few examples to illustrate Seaborn’s main offerings:

• Distribution Plots:

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  import seaborn as sns
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  data = np.random.randn(500)
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  sns.histplot(data, kde=True)
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  plt.title("Histogram with KDE")
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  plt.show()
  

  histplot will create a histogram, and setting kde=True adds a kernel density estimation line on top.

• Categorical Plots:

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  tips = sns.load_dataset('tips')
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  sns.barplot(x='day', y='total_bill', data=tips)
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  plt.title("Average Bill by Day")
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  plt.show()
  

  Here, Seaborn automatically computes means and confidence intervals for each day, showcasing how Seaborn’s defaults help analyze data quickly.

• Relational Plots:

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  sns.scatterplot(x='total_bill', y='tip', data=tips, hue='day', style='time')
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  plt.title("Scatter Plot of Tips vs Total Bill")
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  plt.show()
  

  By adding hue and style, you separate data by categories (day and time in this case).

Seaborn Themes and Styles#

Seaborn has five built-in themes for your plots: darkgrid, whitegrid, dark, white, and ticks. You can set a style with:

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sns.set_style("whitegrid")

You can also configure color palettes using sns.set_palette("Blues") or choose from many built-in palettes.

Working with Multiple Subplots#

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fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
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# First subplot
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axs[0, 0].plot([1, 2, 3], [2, 4, 1], color='blue')
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axs[0, 0].set_title("Line Plot")
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# Second subplot
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axs[0, 1].scatter([1, 2, 3], [3, 5, 1], color='green')
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axs[0, 1].set_title("Scatter Plot")
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# Third subplot
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axs[1, 0].bar(["A", "B", "C"], [4, 7, 2], color='orange')
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axs[1, 0].set_title("Bar Plot")
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# Fourth subplot
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data = np.random.randn(1000)
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axs[1, 1].hist(data, bins=30, alpha=0.7, color='purple')
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axs[1, 1].set_title("Histogram")
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plt.tight_layout()
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plt.show()

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tips = sns.load_dataset("tips")
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g = sns.relplot(
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    x="total_bill", y="tip", data=tips,
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    col="time", hue="day", style="day", kind="scatter"
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)
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g.fig.suptitle("Relational Plot Facets", y=1.02)

Customizing Legends, Labels, and Ticks#

Fine control over your legend, labels, and axis ticks can significantly improve the clarity of your plots.

• Legend:

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  plt.plot([0, 1, 2], [10, 20, 15], label="Dataset 1")
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  plt.plot([0, 1, 2], [5, 15, 9], label="Dataset 2")
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  plt.legend(title="Legend Title", loc="upper left")
  

• Axis Ticks:

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  ax.set_xticks([0, 1, 2, 3])
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  ax.set_xticklabels(["Zero", "One", "Two", "Three"])
  

• Axis Spines:

  Removing or styling spines can create a cleaner look:

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  sns.set_style("white")
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  for spine in ["top", "right"]:
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      ax.spines[spine].set_visible(False)
  

Color Palettes and Advanced Styling#

Colors can evoke strong perceptual and emotional responses. Understanding how to choose the right palette is crucial.

• Seaborn Color Palettes:

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  # Preset palette
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  sns.set_palette("pastel")
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  # Custom palette
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  custom_palette = sns.color_palette(["#3498db", "#e74c3c", "#2ecc71"])
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  sns.set_palette(custom_palette)
  

• Context Settings:

  Seaborn provides a context function to scale plot elements for different purposes (paper, notebook, talk, poster):

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  sns.set_context("talk")
  

These aspects help your visuals stand out for presentations or publications, ensuring clarity at different scales.

The Matplotlib Object-Oriented Approach#

Though we introduced the OO style earlier, advanced plots or specialized axes (like 3D axes or polar plots) rely heavily on it. A typical pattern is:

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fig = plt.figure(figsize=(10, 6))
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ax1 = fig.add_subplot(121)  # left subplot
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ax2 = fig.add_subplot(122)  # right subplot
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ax1.plot(...)
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ax1.set_title(...)
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ax2.scatter(...)
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ax2.set_title(...)
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plt.tight_layout()
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plt.show()

The OO approach offers more granular control. You can manipulate spines, ticks, or overlays on each Axes separately.

FacetGrid, PairGrid, and JointGrid in Seaborn#

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g = sns.FacetGrid(tips, col="day")
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g.map(sns.histplot, "total_bill", bins=10)
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plt.show()

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iris = sns.load_dataset("iris")
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g = sns.PairGrid(iris, hue="species")
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g.map_diag(sns.histplot)
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g.map_offdiag(sns.scatterplot)
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g.add_legend()
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plt.show()

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g = sns.JointGrid(data=tips, x="total_bill", y="tip")
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g.plot(sns.regplot, sns.histplot)

Annotations, Text, and Fine-Tuning Layouts#

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fig, ax = plt.subplots()
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ax.plot([1, 2, 3, 4], [10, 20, 15, 25])
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ax.annotate(
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    'Important Point',
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    xy=(2, 20),
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    xytext=(3, 22),
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    arrowprops=dict(facecolor='black', shrink=0.05)
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)

Interactive Plots and Backends#

Matplotlib supports interactive backends like TkAgg, Qt5Agg, or web-based backends via mpld3. In a Jupyter environment, you can enable interactive features:

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%matplotlib widget

Then you can interact with the figure zoom, pan, and event handling for dynamic updates.

Seaborn alone does not create interactive plots, but because it’s based on Matplotlib, you can still combine them with interactive backends or specialized libraries like Plotly if you export from Seaborn to Plotly.

Animation and 3D Visualizations#

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import matplotlib.animation as animation
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fig, ax = plt.subplots()
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x_data, y_data = [], []
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line, = ax.plot([], [], lw=2)
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def init():
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    ax.set_xlim(0, 10)
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    ax.set_ylim(0, 10)
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    return line,
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def update(frame):
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    x_data.append(frame)
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    y_data.append(frame**0.5)
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    line.set_data(x_data, y_data)
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    return line,
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ani = animation.FuncAnimation(
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    fig,
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    update,
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    frames=range(11),
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    init_func=init,
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    blit=True
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)
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plt.show()

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from mpl_toolkits.mplot3d import Axes3D
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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X = np.linspace(-5, 5, 50)
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Y = np.linspace(-5, 5, 50)
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X, Y = np.meshgrid(X, Y)
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Z = np.sin(np.sqrt(X**2 + Y**2))
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ax.plot_surface(X, Y, Z, cmap='viridis')
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plt.title("3D Surface Plot")
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plt.show()

Saving and Sharing High-Quality Plots#

Once you have a polished chart, share or publish it in high resolution. Specify dpi (dots per inch) and file formats such as PNG, PDF, or SVG:

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plt.savefig("my_plot.png", dpi=300)
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plt.savefig("my_plot.pdf", bbox_inches='tight')

For ultimate clarity in print or editorial mediums, PDF and SVG formats typically scale better.