Chocolate Bars Data Analysis
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    Finding the best chocolate bars

    Now let's now move on to the competition and challenge.

    📖 Background

    You work at a specialty foods import company that wants to expand into gourmet chocolate bars. Your boss needs your team to research this market to inform your initial approach to potential suppliers.

    After finding valuable chocolate bar ratings online, you need to explore if the chocolate bars with the highest ratings share any characteristics that could help you narrow your search for suppliers (e.g., cacao percentage, bean country of origin, etc.)

    💾 The data

    Your team created a file with the following information (source):
    • "id" - id number of the review
    • "manufacturer" - Name of the bar manufacturer
    • "company_location" - Location of the manufacturer
    • "year_reviewed" - From 2006 to 2021
    • "bean_origin" - Country of origin of the cacao beans
    • "bar_name" - Name of the chocolate bar
    • "cocoa_percent" - Cocoa content of the bar (%)
    • "num_ingredients" - Number of ingredients
    • "ingredients" - B (Beans), S (Sugar), S* (Sweetener other than sugar or beet sugar), C (Cocoa Butter), (V) Vanilla, (L) Lecithin, (Sa) Salt
    • "review" - Summary of most memorable characteristics of the chocolate bar
    • "rating" - 1.0-1.9 Unpleasant, 2.0-2.9 Disappointing, 3.0-3.49 Recommended, 3.5-3.9 Highly Recommended, 4.0-5.0 Oustanding

    Acknowledgments: Brady Brelinski, Manhattan Chocolate Society

    💪 Challenge

    Create a report to summarize your research. Include:

    1. What is the average rating by country of origin?
    2. How many bars were reviewed for each of those countries?
    3. Create plots to visualize findings for questions 1 and 2.
    4. Is the cacao bean's origin an indicator of quality?
    5. [Optional] How does cocoa content relate to rating? What is the average cocoa content for bars with higher ratings (above 3.5)?
    6. [Optional 2] Your research indicates that some consumers want to avoid bars with lecithin. Compare the average rating of bars with and without lecithin (L in the ingredients).
    7. Summarize your findings.

    Importing modules

    import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
plt.style.use('ggplot')

    Reading the chocolate bars data

    choc_bars = pd.read_csv('data/chocolate_bars.csv')
choc_bars.head()

    Data Understanding

    1. Checking the shape of the dataframe
    2. Generate an overview of the dataframe
    3. Checking the data for null values
    4. Check if the rows are duplicated in the dataframe
    choc_bars.shape
    choc_bars.info()
    choc_bars.isnull().sum()

    Observation

    1. "num_ingredients" and "ingredients" contains null values.
    choc_bars.duplicated().sum()

    Explore the data

    1. Generate a pairwise scatterplot to explore the data
    2. Compute the correlation coefficient for all column pairs in choc_bars dataframe
    sns.pairplot(choc_bars)
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