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Pre-bundled with the most important packages Data Scientists need, ActiveState Python is pre-compiled so you and your team don’t have to waste time configuring the open source distribution. This is why organizations choose ActiveState Python for their data science, big data processing and statistical analysis needs. While the open source distribution of Python may be satisfactory for an individual, it doesn’t always meet the support, security, or platform requirements of large organizations. Why use ActiveState Python for Data Science # Create a Line2D instance with x and y data in sequences xdata, ydata: # x data:įigure 3. In this example, pyplot is imported as plt, and then used to plot three vertical bar graphs: import matplotlib.pyplot as plt Pie plot generated by Matplotlib: Matplotlib Bar Plot Plt.pie(sizes, labels=labels, colors=colors)įigure 2. Labels = 'Broccoli', 'Chocolate Cake', 'Blueberries', 'Raspberries'Ĭolors = # Data labels, sizes, and colors are defined: In this example, pyplot is imported as plt, and then used to create a chart with four sections that have different labels, sizes and colors: import matplotlib.pyplot as plt Line plot generated by Matplotlib: Matplotlib Pie Plot In this example, pyplot is imported as plt, and then used to plot three numbers in a straight line: import matplotlib.pyplot as pltįigure 1.
How to install matplotlib python 2.7 how to#
This section shows how to create examples of different kinds of plots with matplotlib.
How to install matplotlib python 2.7 code#
The source code for this example is available in the Matplotlib: Plot a Pandas Dataframe section further down in this article. Pandas and numpy are often used together, as shown in the following code snippet: Unlike numpy, pandas is not a required dependency of matplotlib. Pandas provides an in-memory 2D data table object called a Dataframe.
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Pandas is a library used by matplotlib mainly for data manipulation and analysis. The source code for this example is available in the Matplotlib: Plot a Numpy Array section further down in this article. Numpy is a required dependency for matplotlib, which uses numpy functions for numerical data and multi-dimensional arrays as shown in the following code snippet: Numpy is a package for scientific computing. The UI can be used to customize the plot, as well as to pan/zoom and toggle various elements. When matplotlib is used to create a plot, a User Interface (UI) and menu structure are generated. Alternatively, consider using the ActiveState Platform to automatically build matplotlib from source and package it for your OS.
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This can result in a fairly complex installation. Compiling from source will require your local system to have the appropriate compiler for your OS, all dependencies, setup scripts, configuration files, and patches available. Matplotlib is also available as uncompiled source files. Matplotlib and its dependencies can be downloaded as a binary (pre-compiled) package from the Python Package Index (PyPI), and installed with the following command: python -m pip install matplotlib Axes include the X-Axis, Y-Axis, and possibly a Z-Axis, as well.įor more information about the pyplot API and interface, refer to What Is Pyplot In Matplotlib Installing Matplotlib
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As such, it offers a viable open source alternative to MATLAB. Matplotlib is a cross-platform, data visualization and graphical plotting library for Python and its numerical extension NumPy.