Data-Analysis-Jupyter/medium/visuals.py

# Data science imports
from multiprocessing import Pool
import requests
import re
from bs4 import BeautifulSoup
from itertools import chain
from collections import Counter, defaultdict
from timeit import default_timer as timer
import pandas as pd


from scipy import stats

# Interactive plotting
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import iplot
import cufflinks
cufflinks.go_offline()


def make_update_menu(base_title, article_annotations=None, response_annotations=None):
    """
    Make an updatemenu for interative plot

    :param base_title: string for title of plot

    :return updatemenus: a updatemenus object for adding to a layout
    """
    updatemenus = list([
        dict(
            buttons=list([
                dict(
                    label='both', method='update',
                    args=[dict(visible=[True, True]), dict(title=base_title,
                                                           annotations=[article_annotations, response_annotations])]),
                dict(
                    label='articles',
                    method='update',
                    args=[dict(visible=[True, False]), dict(title='Article ' + base_title,
                                                            annotations=[article_annotations])]),
                dict(
                    label='responses',
                    method='update',
                    args=[dict(visible=[False, True]), dict(title='Response ' + base_title,
                                                            annotations=[response_annotations])]),
            ]))
    ])
    return updatemenus


def make_iplot(data, x, y, base_title, time=False, eq_pos=(0.75, 0.25)):
    """
    Make an interactive plot. Adds a dropdown to separate articles from responses
    if there are responses in the data. If there is only articles (or only responses)
    adds a linear regression line.

    :param data: dataframe of entry data
    :param x: string for xaxis of plot
    :param y: sring for yaxis of plot
    :param base_title: string for title of plot
    :param time: boolean for whether the xaxis is a plot
    :param eq_pos: position of equation for linear regression

    :return figure: an interactive plotly object for display

    """

    # Extract the relevant data
    responses = data[data['response'] == 'response'].copy()
    articles = data[data['response'] == 'article'].copy()

    if not responses.empty:
        # Create scatterplot data, articles must be first for menu selection
        plot_data = [
            go.Scatter(
                x=articles[x],
                y=articles[y],
                mode='markers',
                name='articles',
                text=articles['title'],
                marker=dict(color='blue', size=12)),
            go.Scatter(
                x=responses[x],
                y=responses[y],
                mode='markers',
                name='responses',
                marker=dict(color='green', size=12))
        ]

        if not time:
            annotations = {}
            for df, name in zip([articles, responses],
                                ['articles', 'responses']):

                regression = stats.linregress(x=df[x], y=df[y])
                slope = regression.slope
                intercept = regression.intercept
                rvalue = regression.rvalue

                xi = np.array(range(int(df[x].min()), int(df[x].max())))

                line = xi * slope + intercept
                trace = go.Scatter(
                    x=xi,
                    y=line,
                    mode='lines',
                    marker=dict(color='blue' if name ==
                                'articles' else 'green'),
                    line=dict(width=4, dash='longdash'),
                    name=f'{name} linear fit'
                )

                annotations[name] = dict(
                    x=max(xi) * eq_pos[0],
                    y=df[y].max() * eq_pos[1],
                    showarrow=False,
                    text=f'$R^2 = {rvalue:.2f}; Y = {slope:.2f}X + {intercept:.2f}$',
                    font=dict(size=16, color='blue' if name ==
                              'articles' else 'green')
                )

                plot_data.append(trace)

        # Make a layout with update menus
        layout = go.Layout(annotations=list(annotations.values()),
                           height=600,
                           width=900,
                           title=base_title,
                           xaxis=dict(
            title=x.title(),
            tickfont=dict(size=14),
            titlefont=dict(size=16)),
            yaxis=dict(
                title=y.title(),
                tickfont=dict(size=14),
                titlefont=dict(size=16)),
            updatemenus=make_update_menu(base_title, annotations['articles'], annotations['responses']))

    # If there are only articles
    else:
        plot_data = [
            go.Scatter(
                x=data[x],
                y=data[y],
                mode='markers',
                name='observations',
                text=data['title'],
                marker=dict(color='blue', size=12))
        ]

        regression = stats.linregress(x=data[x], y=data[y])
        slope = regression.slope
        intercept = regression.intercept
        rvalue = regression.rvalue

        xi = np.array(range(int(data[x].min()), int(data[x].max())))
        line = xi * slope + intercept
        trace = go.Scatter(
            x=xi,
            y=line,
            mode='lines',
            marker=dict(color='red'),
            line=dict(width=4, dash='longdash'),
            name='linear fit'
        )

        annotations = [dict(
            x=max(xi) * eq_pos[0],
            y=data[y].max() * eq_pos[1],
            showarrow=False,
            text=f'$R^2 = {rvalue:.2f}; Y = {slope:.2f}X + {intercept:.2f}$',
            font=dict(size=16)
        )]

        plot_data.append(trace)

        layout = go.Layout(annotations=annotations,
                           height=600,
                           width=900,
                           title=base_title,
                           xaxis=dict(
                               title=x.title(),
                               tickfont=dict(size=14),
                               titlefont=dict(size=16)),
                           yaxis=dict(
                               title=y.title(),
                               tickfont=dict(size=14),
                               titlefont=dict(size=16)))

    # Add a rangeselector and rangeslider for a data xaxis
    if time:
        rangeselector = dict(
            buttons=list([
                dict(count=1, label='1m', step='month', stepmode='backward'),
                dict(count=6, label='6m', step='month', stepmode='backward'),
                dict(count=1, label='YTD', step='year', stepmode='todate'),
                dict(count=1, label='1y', step='year', stepmode='backward'),
                dict(step='all')
            ]))
        rangeslider = dict(visible=True)
        layout['xaxis']['rangeselector'] = rangeselector
        layout['xaxis']['rangeslider'] = rangeslider

        figure = go.Figure(data=plot_data, layout=layout)

        return figure

    # Return the figure
    figure = go.Figure(data=plot_data, layout=layout)

    return figure