Learn Data Analysis with Julia

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Julia is another programming language like Python and R. It combines the speed of low-level languages like C with simplicity like Python. Julia is becoming popular in the data science space, so if you want to expand your portfolio and learn a new language, you have come to the right place.

In this tutorial, we will learn to set up Julia for data science, load the data, perform data analysis, and then visualize it. The tutorial is made so simple that anyone, even a student, can start using Julia to analyze the data in 5 minutes.

1. Setting Up Your Environment

1. Download the Julia and install the package by going to the (julialang.org).
2. We need to set up Julia for Jupyter Notebook now. Launch a terminal (PowerShell), type `julia` to launch the Julia REPL, and then type the following command.

using Pkg  Pkg.add("IJulia")

1. Launch the Jupyter Notebook and start the new notebook with Julia as Kernel.
2. Create the new code cell and type the following command to install the necessary data science packages.

using Pkg  Pkg.add("DataFrames")  Pkg.add("CSV")  Pkg.add("Plots")  Pkg.add("Chain")

2. Loading Data

For this example, we are using the Online Sales Dataset from Kaggle. It contains data on online sales transactions across different product categories.

We will load the CSV file and convert it into DataFrames, which is similar to Pandas DataFrames.

using CSV  using DataFrames    # Load the CSV file into a DataFrame  data = CSV.read("Online Sales Data.csv", DataFrame)

3. Exploring Data

We will use the' first' function instead of `head` to view the top 5 rows of the DataFrame.

first(data, 5)

To generate the data summary, we will use the `describe` function.

describe(data)

Similar to Pandas DataFrame, we can view specific values by providing the row number and column name.

data[3,"Product Name"]

Output:

"Levi's 501 Jeans"

4. Data Manipulation

We will use the `filter` function to filter the data based on certain values. It requires the column name, the condition, the values, and the DataFrame.

filtered_data = filter(row -> row[:"Unit Price"] > 230, data)  last(filtered_data, 5)

We can also create a new column similar to Pandas. It is that simple.

data[!, :"Total Revenue After Tax"] = data[!, :"Total Revenue"] .* 0.9    last(data, 5)

Now, we will calculate the mean values of “Total Revenue After Tax” based on different “Product Category”.

using Statistics    grouped_data = groupby(data, :"Product Category")  aggregated_data = combine(grouped_data, :"Total Revenue After Tax" .=> mean)  last(aggregated_data, 5)

5. Visualization

Visualization is similar to Seaborn. In our case, we are visualizing the bar chart of recently created aggregated data. We will provide the X and Y columns, and then the Title and labels.

using Plots    # Basic plot  bar(aggregated_data[!, :"Product Category"], aggregated_data[!, :"Total Revenue After Tax_mean"], title="Product Analysis", xlabel="Product Category", ylabel="Total Revenue After Tax Mean")

The majority of total mean revenue is generated through electronics. The visualization looks perfect and clear.

To generate histograms, we just have to provide X column and label data. We want to visualize the frequency of items sold.

histogram(data[!, :"Units Sold"], title="Units Sold Analysis", xlabel="Units Sold", ylabel="Frequency")

It seems like the majority of people bought one or two items.

To save the visualization, we will use the `savefig` function.

savefig("hist.png")

6. Creating Data Processing Pipeline

Creating a proper data pipeline is necessary to automate data processing workflows, ensure data consistency, and enable scalable and efficient data analysis.

We will use the `Chain` library to create chains of various functions previously used to calculate total mean revenue based on various product categories.

using Chain  # Example of a simple data processing pipeline  processed_data = @chain data begin         filter(row -> row[:"Unit Price"] > 230, _)         groupby(_, :"Product Category")         combine(_, :"Total Revenue" => mean)  end  first(processed_data, 5)

To save the processed DataFrame as a CSV file, we will use the `CSV.write` function.

CSV.write("output.csv", processed_data)

Conclusion

In my opinion, Julia is simpler and faster than Python. Many of the syntax and functions that I am used to are also available in Julia, like Pandas, Seaborn, and Scikit-Learn. So, why not learn a new language and start doing things better than your colleagues? Also, it will help you get a Job related to research, as most clinical researchers prefer Julia over Python.

In this tutorial, we learned how to set up the Julia environment, load the dataset, perform powerful data analysis and visualization, and build the data pipeline for reproducibility and reliability. If you are interested in learning more about Julia for data science, please let me know so I can write even more simple tutorials for your guys.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

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