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Collection of useful helper methods for interactive data science work in python. Usually on jupyter notebooks, using the basic python scientific stack.

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python-ds-util

PyPI version

Installation

The library is available on pip:

pip install dsutil

Examples

For full usage examples see notebooks under the examples directory.

Plotting

Full examples for plotting here

  • add_grid(): reasonable default grid settings, with weak grey lines, light alpha, etc.

    import numpy as np
    import matplotlib.pyplot as plt
    from dsutil.plotting import add_grid
    
    x = np.linspace(0.0,100,10)
    y = np.random.uniform(low=0,high=10,size=10)
    
    plt.bar(x,y)
    
    add_grid()

  • add_value_labels() annotates barplots, line plots and scatter plots with values for the coordinates

    import numpy as np
    import matplotlib.pyplot as plt
    from dsutil.plotting import add_value_labels
    
    x = np.linspace(0.0,100,10)
    y = np.random.uniform(low=0,high=10,size=10)
    
    plt.bar(x,y)
    
    add_value_labels()

  • format_yaxis_percentage(): turns values between 0 and 1 in y-axis into percentages

    import numpy as np
    import matplotlib.pyplot as plt
    from dsutil.plotting import format_yaxis_percentage
    
    x = np.linspace(0.0,100,10)
    y = np.random.uniform(low=0,high=1,size=10)
      
    plt.bar(x,y)
    plt.yticks(np.arange(0,1.01,0.1))
    
    format_yaxis_percentage()

  • format_yaxis_thousands(): uses commas as thousands separator in the y-axis labels

    import numpy as np
    import matplotlib.pyplot as plt
    from dsutil.plotting import format_yaxis_thousands
    
    x = np.linspace(0.0,100,10)
    y = np.random.uniform(low=10000,high=100000,size=10)
    
    plt.bar(x,y)
    plt.yticks(np.arange(0,100001,10000))
    
    format_yaxis_thousands()

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Collection of useful helper methods for interactive data science work in python. Usually on jupyter notebooks, using the basic python scientific stack.

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