Module stikpetP.visualisations.vis_cleveland_dot_plot

Functions

def vi_cleveland_dot_plot(data)

Cleveland Dot Plot

A Cleveland dot plot (Cleveland & McGill, 1984) is a bar chart where instead of bars a dot is placed at the center of the top of the bar (and then the bars removed). It is a dot plot only showing the top dot.This requires less ink.

The function simply uses the scatter() function from the matplotlib pyplot library.

A video on (Cleveland) dot plots is available here.

This function is shown in this YouTube video and the visualisation is also described at PeterStatistics.com

Parameters

data : list or pandas data series

the data

Notes

The function uses the pyplot library plot function.

See Also

Before the visualisation you might first want to get an impression using a frequency table: * [tab_frequency](../other/table_frequency.html#tab_frequency)

After visualisation you might want some descriptive measures: * [me_mode](../measures/meas_mode.html#me_mode) for the mode * [me_qv](../measures/meas_qv.html#me_qv) for Measures of Qualitative Variation

or perform a test: * [ts_pearson_gof](../tests/test_pearson_gof.html#ts_pearson_gof) for Pearson Chi-Square Goodness-of-Fit Test * [ts_freeman_tukey_gof](../tests/test_freeman_tukey_gof.html#ts_freeman_tukey_gof) for Freeman-Tukey Test of Goodness-of-Fit * [ts_freeman_tukey_read](../tests/test_freeman_tukey_read.html#ts_freeman_tukey_read) for Freeman-Tukey-Read Test of Goodness-of-Fit * [ts_g_gof](../tests/test_g_gof.html#ts_g_gof) for G (Likelihood Ratio) Goodness-of-Fit Test * [ts_mod_log_likelihood_gof](../tests/test_mod_log_likelihood_gof.html#ts_mod_log_likelihood_gof) for Mod-Log Likelihood Test of Goodness-of-Fit * [ts_multinomial_gof](../tests/test_multinomial_gof.html#ts_multinomial_gof) for Multinomial Goodness-of-Fit Test * [ts_neyman_gof](../tests/test_neyman_gof.html#ts_neyman_gof) for Neyman Test of Goodness-of-Fit * [ts_powerdivergence_gof](../tests/test_powerdivergence_gof.html#ts_powerdivergence_gof) for Power Divergence GoF Test

Alternatives for this visualisation could be: * [vi_bar_simple](../visualisations/vis_bar_simple.html#vi_bar_simple) for Simple Bar Chart * [vi_dot_plot](../visualisations/vis_dot_plot.html#vi_dot_plot) for Dot Plot * [vi_pareto_chart](../visualisations/vis_pareto_chart.html#vi_pareto_chart) for Pareto Chart * [vi_pie](../visualisations/vis_pie.html#vi_pie) for Pie Chart

References

Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531–554. https://doi.org/10.2307/2288400

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

Example 1: pandas series

>>> df1 = pd.read_csv('https://peterstatistics.com/Packages/ExampleData/GSS2012a.csv', sep=',', low_memory=False, storage_options={'User-Agent': 'Mozilla/5.0'})
>>> ex1 = df1['mar1']
>>> vi_cleveland_dot_plot(ex1);

Example 2: a list

>>> ex2 = ["MARRIED", "DIVORCED", "MARRIED", "SEPARATED", "DIVORCED", "NEVER MARRIED", "DIVORCED", "DIVORCED", "NEVER MARRIED", "MARRIED", "MARRIED", "MARRIED", "SEPARATED", "DIVORCED", "NEVER MARRIED", "NEVER MARRIED", "DIVORCED", "DIVORCED", "MARRIED"]
>>> vi_cleveland_dot_plot(ex2);

Expand source code

def vi_cleveland_dot_plot(data):
    
    '''
    Cleveland Dot Plot
    ----------------
    
    A Cleveland dot plot (Cleveland & McGill, 1984) is a bar chart where instead of bars a dot is placed at the center of the top of the bar (and then the bars removed). It is a dot plot only showing the top dot.This requires less ink. 
    
    The function simply uses the scatter() function from the matplotlib pyplot library.
    
    A video on (Cleveland) dot plots is available [here](https://youtu.be/qs1nh0CMiIY).

    This function is shown in this [YouTube video](https://youtu.be/CqYgnBDXKh0) and the visualisation is also described at [PeterStatistics.com](https://peterstatistics.com/Terms/Visualisations/DotPlot.html)
    
    Parameters
    ----------
    data : list or pandas data series
        the data
    
    Notes
    -----
    The function uses the *pyplot* library *plot* function.

    See Also
    --------
    Before the visualisation you might first want to get an impression using a frequency table:
    * [tab_frequency](../other/table_frequency.html#tab_frequency)

    After visualisation you might want some descriptive measures:
    * [me_mode](../measures/meas_mode.html#me_mode) for the mode
    * [me_qv](../measures/meas_qv.html#me_qv) for Measures of Qualitative Variation

    or perform a test:
    * [ts_pearson_gof](../tests/test_pearson_gof.html#ts_pearson_gof) for Pearson Chi-Square Goodness-of-Fit Test
    * [ts_freeman_tukey_gof](../tests/test_freeman_tukey_gof.html#ts_freeman_tukey_gof) for Freeman-Tukey Test of Goodness-of-Fit
    * [ts_freeman_tukey_read](../tests/test_freeman_tukey_read.html#ts_freeman_tukey_read) for Freeman-Tukey-Read Test of Goodness-of-Fit
    * [ts_g_gof](../tests/test_g_gof.html#ts_g_gof) for G (Likelihood Ratio) Goodness-of-Fit Test
    * [ts_mod_log_likelihood_gof](../tests/test_mod_log_likelihood_gof.html#ts_mod_log_likelihood_gof) for Mod-Log Likelihood Test of Goodness-of-Fit
    * [ts_multinomial_gof](../tests/test_multinomial_gof.html#ts_multinomial_gof) for Multinomial Goodness-of-Fit Test
    * [ts_neyman_gof](../tests/test_neyman_gof.html#ts_neyman_gof) for Neyman Test of Goodness-of-Fit
    * [ts_powerdivergence_gof](../tests/test_powerdivergence_gof.html#ts_powerdivergence_gof) for Power Divergence GoF Test
    
    Alternatives for this visualisation could be:
    * [vi_bar_simple](../visualisations/vis_bar_simple.html#vi_bar_simple) for Simple Bar Chart
    * [vi_dot_plot](../visualisations/vis_dot_plot.html#vi_dot_plot) for Dot Plot
    * [vi_pareto_chart](../visualisations/vis_pareto_chart.html#vi_pareto_chart) for Pareto Chart
    * [vi_pie](../visualisations/vis_pie.html#vi_pie) for Pie Chart
    
    References
    ----------
    Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. *Journal of the American Statistical Association, 79*(387), 531–554. https://doi.org/10.2307/2288400 
    
    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
    --------
    Example 1: pandas series
    >>> df1 = pd.read_csv('https://peterstatistics.com/Packages/ExampleData/GSS2012a.csv', sep=',', low_memory=False, storage_options={'User-Agent': 'Mozilla/5.0'})
    >>> ex1 = df1['mar1']
    >>> vi_cleveland_dot_plot(ex1);
    
    Example 2: a list
    >>> ex2 = ["MARRIED", "DIVORCED", "MARRIED", "SEPARATED", "DIVORCED", "NEVER MARRIED", "DIVORCED", "DIVORCED", "NEVER MARRIED", "MARRIED", "MARRIED", "MARRIED", "SEPARATED", "DIVORCED", "NEVER MARRIED", "NEVER MARRIED", "DIVORCED", "DIVORCED", "MARRIED"]
    >>> vi_cleveland_dot_plot(ex2);
    
    '''
    
    if type(data) == list:
        data = pd.Series(data)
        
    freq = data.value_counts()
    
    plt.scatter(x=freq.keys(), y=freq.values)
    plt.ylabel('Frequency')
    plt.show()
    return