Module stikpetP.visualisations.vis_butterfly_bin

Functions

def vi_butterfly_bin(catField, scaleField, categories=None, bins=None)

Butterfly Chart of Binned Data (Pyramid chart)

This function creates a simple butterfly chart by binning scaled data. This is sometimes also referred to as a Pyramid chart. The visualisation is also described at PeterStatistics.com

Parameters

catField : list or dataframe

the categories

scaleField : list or dataframe

the scores

categories : list or dictionary, optional

the two categories to use from bin_field. If not set the first two found will be used

bins : list, optional

list of tuples to use as bins.

Returns

butterfly chart

 

Alternatives

To display the results of a binary and scale variable, alternative visualisations include: overlaid histogram, back-to-back histogram, back-to-back stem-and-leaf display, split histogram, split box-plot

Next

After visualizing the data, you might want to run a test: Student t, Welch t, Trimmed means, Yuen-Welch, Z test

Author

Made by P. Stikker

Companion website: https://PeterStatistics.com
YouTube channel: https://www.youtube.com/stikpet
Donations: https://www.patreon.com/bePatron?u=19398076

Examples

>>> import pandas as pd
>>> file1 = "https://peterstatistics.com/Packages/ExampleData/StudentStatistics.csv"
>>> df = pd.read_csv(file1, sep=',', low_memory=False, storage_options={'User-Agent': 'Mozilla/5.0'})
>>> vi_butterfly_bin(df['Gen_Gender'], df['Over_Grade'], bins=[(0, 25), (25, 50), (50, 75), (75, 100)])

Expand source code

def vi_butterfly_bin(catField, scaleField, categories=None, bins=None):
    '''
    Butterfly Chart of Binned Data (Pyramid chart)
    ---------------------------------------

    This function creates a simple butterfly chart by binning scaled data. This is sometimes also referred to as a Pyramid chart. 
    The visualisation is also described at [PeterStatistics.com](https://peterstatistics.com/Terms/Visualisations/PyramidChart.html)

    Parameters
    ----------
    catField : list or dataframe 
        the categories
    scaleField : list or dataframe 
        the scores
    categories : list or dictionary, optional
        the two categories to use from bin_field. If not set the first two found will be used
    bins : list, optional
        list of tuples to use as bins.
        
    Returns
    -------
    butterfly chart

    Alternatives
    ------------
    To display the results of a binary and scale variable, alternative visualisations include: [overlaid histogram](../visualisations/vis_histogram_overlay.html), [back-to-back histogram](../visualisations/vis_histogram_b2b.html), [back-to-back stem-and-leaf display](../visualisations/vis_stem_and_leaf_b2b.html), [split histogram](../visualisations/vis_histogram_split.html), [split box-plot](../visualisations/vis_boxplot_split.html)

    Next
    ----
    After visualizing the data, you might want to run a test: [Student t](../tests/test_student_t_is.html), [Welch t](../tests/test_welch_t_is.html), [Trimmed means](../tests/test_trimmed_mean_is.html), [Yuen-Welch](../tests/test_trimmed_mean_is.html), [Z test](../tests/test_z_is.html)
    
    Author
    ------
    Made by P. Stikker
    
    Companion website: https://PeterStatistics.com  
    YouTube channel: https://www.youtube.com/stikpet  
    Donations: https://www.patreon.com/bePatron?u=19398076

    Examples
    --------
    >>> import pandas as pd
    >>> file1 = "https://peterstatistics.com/Packages/ExampleData/StudentStatistics.csv"
    >>> df = pd.read_csv(file1, sep=',', low_memory=False, storage_options={'User-Agent': 'Mozilla/5.0'})
    >>> vi_butterfly_bin(df['Gen_Gender'], df['Over_Grade'], bins=[(0, 25), (25, 50), (50, 75), (75, 100)])
    '''
    #convert to pandas series if needed
    if type(catField) is list:
        catField = pd.Series(catField)
    
    if type(scaleField) is list:
        scaleField = pd.Series(scaleField)
    
    #combine as one dataframe
    df = pd.concat([catField, scaleField], axis=1)
    df = df.dropna()
    
    #the two categories
    if categories is not None:
        cat1 = categories[0]
        cat2 = categories[1]
    else:
        cat1 = df.iloc[:,0].value_counts().index[0]
        cat2 = df.iloc[:,0].value_counts().index[1]

    var_name = df.iloc[:,1].name
    
    #seperate the scores for each category
    X = list(df.iloc[:,1][df.iloc[:,0] == cat1])
    Y = list(df.iloc[:,1][df.iloc[:,0] == cat2])
    
    #make sure they are floats
    X = [float(x) for x in X]
    Y = [float(y) for y in Y]

    if bins is None:
        # determine the bins from all scores
        scores_table = tab_frequency_bins(X + Y)
        bins = [(scores_table.iloc[i,0], scores_table.iloc[i,1]) for i in range(len(scores_table))]

    # determine counts for each bin for each category
    X_table = tab_frequency_bins(X, bins=bins)
    Y_table = tab_frequency_bins(Y, bins=bins)
    
    # create list of bins for each category
    X_ord = []
    Y_ord = []
    for i in range(len(bins)):
        X_ord = X_ord + [str(bins[i])]*int(X_table.iloc[i, 2])
        Y_ord = Y_ord + [str(bins[i])]*int(Y_table.iloc[i, 2])
    
    # create list of categories
    cats = [cat1]*len(X) + [cat2]*len(Y)
    
    # use the butterfly function for binary-nominal
    vi_butterfly_chart(X_ord + Y_ord, cats, variation='butterfly')

    return