Book Image

Interactive Data Visualization with Python - Second Edition

By : Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar
Book Image

Interactive Data Visualization with Python - Second Edition

By: Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar

Overview of this book

With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories.
Table of Contents (9 chapters)

7. Avoiding Common Pitfalls to Create Interactive Visualizations

Activity 7: Determining Which Features to Visualize on a Scatter Plot

Solution

  1. Navigate to the folder where you have stored the .csv files and initiate a Jupyter Notebook.
  2. Import pandas, numpy, and plotly.express:
    import pandas as pd
    import numpy as np
    import plotly.express as px
  3. Create the same DataFrame, but instead of including only the gdp column from the gm DataFrame, include the population, fertility, and life columns as well:
    co2 = pd.read_csv('co2.csv')
    gm = pd.read_csv('gapminder.csv')
    df_gm = gm[['Country', 'region']].drop_duplicates()
    df_w_regions = pd.merge(co2, df_gm, left_on='country', right_on='Country', how='inner')
    df_w_regions = df_w_regions.drop('Country', axis='columns')
    new_co2 = pd.melt(df_w_regions, id_vars=['country', 'region'])
    columns = ['country', 'region...