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Global Wine Markets 2014 to 2016

📖 Background

With the end of year holidays approaching, many people like to relax or party with a glass of wine. That makes wine an important industry in many countries. Understanding this market is important to the livelihood of many people.

You work at a multinational consumer goods organization that is considering entering the wine production industry. Managers at your company would like to understand the market better before making a decision.

💾 The data

This dataset is a subset of the University of Adelaide's Annual Database of Global Wine Markets.

The dataset consists of a single CSV file, data/wine.csv.

Each row in the dataset represents the wine market in one country. There are 34 metrics for the wine industry covering both the production and consumption sides of the market.

import pandas as pd

wine_data = pd.read_csv("data/wine.csv")
wine_data

💪 Challenge

Explore the dataset to understand the global wine market. Your published notebook should contain a short report on the state of the market, including summary statistics, visualizations, and text describing any insights you found.

Global Wine Market Analysis Report

Dataset Overview

The dataset contains information about the wine market for various countries. There are 34 metrics representing both production and consumption aspects of the wine industry. Key metrics include vineyard area, wine production and consumption, wine expenditure, export and import volumes, and market shares.

Summary Statistics

Let's first look at the summary statistics of the main variables:

summary_stats = wine_data.describe()
summary_stats

Summary statistics provide a high-level view of the data distribution, including metrics such as mean, standard deviation, min, max, and quartiles.

Key Metrics Analysis

  1. Vine Area:
    • The vine area varies significantly among countries, reflecting different scales of wine production.
  2. Wine Production and Consumption:
    • Production and consumption volumes highlight the leading wine-producing and consuming countries.
  3. Per Capita Wine Consumption:
    • This metric gives an idea of wine popularity within each country.
  4. Wine Expenditure:
    • Total and per capita wine expenditure indicate the economic importance of wine in each country.

Visualizations

  1. Vineyard Area vs. Wine Production:
    • A scatter plot can show the relationship between vineyard area and wine production, identifying key producers.
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.scatter(wine_data['Vine Area (\'000 ha)'], wine_data['Wine produced (ML)'], alpha=0.7)
plt.title('Vineyard Area vs. Wine Production')
plt.xlabel('Vineyard Area (\'000 ha)')
plt.ylabel('Wine Produced (ML)')
plt.grid(True)
plt.show()
  1. Top Wine Consuming Countries:
    • A bar chart highlighting the top wine-consuming countries.
top_consumers = wine_data.nlargest(10, 'Wine consumed (ML)')

plt.figure(figsize=(12, 6))
plt.bar(top_consumers['Country'], top_consumers['Wine consumed (ML)'], color='skyblue')
plt.title('Top 10 Wine Consuming Countries')
plt.xlabel('Country')
plt.ylabel('Wine Consumed (ML)')
plt.xticks(rotation=45)
plt.show()
  1. Per Capita Wine Expenditure:
    • A bar chart showing per capita wine expenditure across countries.
top_expenditure = wine_data.nlargest(10, 'Per capita wine expenditure (US$ 2015)')

plt.figure(figsize=(12, 6))
plt.bar(top_expenditure['Country'], top_expenditure['Per capita wine expenditure (US$ 2015)'], color='lightgreen')
plt.title('Top 10 Countries by Per Capita Wine Expenditure')
plt.xlabel('Country')
plt.ylabel('Per Capita Wine Expenditure (US$ 2015)')
plt.xticks(rotation=45)
plt.show()

Insights

  1. Key Producers:

    • Countries like France, Italy, and Spain are leading in vineyard areas and production volumes.

  2. Consumption Patterns:

    • High per capita consumption in countries like Portugal and France suggests cultural significance.

  3. Economic Impact:

    • Wine expenditure data indicates the economic contribution of wine markets in different countries.

  4. Trade Dynamics:

    • Export and import percentages reveal global trade patterns, with certain countries being major exporters.

This initial exploration provides a foundational understanding of the global wine market. Further analysis could delve into specific regional trends, the impact of policies, and market growth forecasts.