Plastic Users Analysis with Python

Data Analysis Project for Beginners on Plastic Users Analysis with Python.

Worldwide users of plastic are leaving a negative impact on the oceans and wildlife all around the world. In this article, I will take you through plastic users analysis with Python which is a very simple task of data analysis for beginners.

Plastic Users Analysis

As this is a beginner level task based on data analysis with Python, so I will only use 3 Python libraries here. Pandas for data handling, NumPy for numerical calculations and Matplotlib for data visualization.

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Now let’s import all these libraries and get started with the task to analyse the plastic users with Python:

I will analyse the plastics users based on how much a country uses plastic for the packaging of its food products. So the data I am using is based on food facts which can be easily downloaded from here

Now let’s import the data and move further with the task of Plastic users analysis with Python:

Now before moving further let’s have a quick look at how our data looks like so that we could know what we are going to work with. I will use the pandas.head() method to look at the first 20 rows in the dataset:

manufacturing_places packaging_tags
137                                              Canada        plastic
397                                               china        plastic
478                                                  uk        plastic
629                                                 USA        plastic
735             Crèmerie Soignon,Poitou-Charente,France        plastic
771                                         Royaume-Uni      plastique
964                                       United States        plastic
967                                          États-Unis      plastique
992                                       United States        plastic
1001                                      United States        plastic
1254                                         États-Unis      plastique
1403                                             Mexico        plastic
1438  Ravens Oak Dairy,Nantwich,Cheshire,England,Uni...        plastic
1440                                           Thailand        plastic
1519                                            Ireland      plastique
1806                               Moray,United Kingdom        plastic
1897                                             Canada        plastic
2075                                        Switzerland        plastic
2079                                             Canada        plastic
2223                                        Switzerland        plastic

Hopefully, this dataset does not contain any missing values. So we can move further to analyze the plastic users without any data preparation and cleaning. Now to analyze the worldwide plastics users let’s sort the countries according to their usage of plastic to pack food products:

Australia           153
France              146
Allemagne            24
Belgique             22
Germany              14
Italie               12
Italy                10
United Kingdom        9
Union Européenne      9
United States         9
Name: manufacturing_places, dtype: int64

The output shows that Australia is the biggest user of plastic. To get more detailed insights, let’s prepare the data and visualize it in the form of a bar plot by using the matplotlib library in Python. I will also annotate the graph that will highlight the biggest user of plastic by using an arrow.

Let’s see how to visualize the bar plot with a text annotation:

biggest user of plastic

So this is how to analyze such data with a text annotation by using the Python programming language. I hope you liked this article on Plastic users analysis with Python. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

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