Beneath the Surface A Python-Based Mining Data Analysis

Have you ever wondered what happens beneath the surface of a mining operation? Just like a detective piecing together clues, I found myself diving into a world of data to uncover the mysteries of a flotation plant. As a data analyst, I was tasked with a project that revolved around June 1, 2017—a day marked for unusual activity. The stakes were high, and I was curious to see what secrets the data would reveal.

Why THIS Project?

The inspiration for this project came from the critical need to understand the operations of the flotation plant on that particular day. The mining company flagged June 1 due to unexpected fluctuations, and they wanted to know whether operational inefficiencies, equipment issues, or other factors might have contributed to this moment. My background in data analysis and passion for solving complex issues made this project not just a job, but a chance to contribute to the mining industry’s efficiency.

What Readers Will Gain

In this article, I’ll share insights from my analysis, discussing the findings and the journey I took to reach them. You’ll learn about the data I used, the analysis process, and the surprising results I encountered.

Key Takeaways

• No anomalies were found on June 1, 2017.
• There’s an inverse relationship between % Iron Concentrate and % Silica Concentrate.
• Ore pulp pH levels were mostly stable, with a high frequency between 9.9 to 10.1.
• Surprisingly, the main variables had no significant correlation, suggesting other influencing factors.

Dataset Details

For my analysis, I utilized a dataset sourced from Kaggle, which contained a staggering 737,454 rows and 24 variables spanning from March to September 2017. This dataset was perfect for the project as it provided minute-by-minute and hourly data, giving me the granularity needed to uncover the nuances of flotation plant operations.

Library Installation

I used Deepnote, a browser-based Integrated Development Environment (IDE), to explore and visualize the data by writing Python code for analysis and visualization. Before starting, I installed and imported three libraries: Pandas for data manipulation, and Seaborn and Matplotlib for creating visualizations.

Next, I connected to the dataset in Python using Pandas' DataFrame structure and reviewed a preview of the data.

Analysis Process

The analysis began with data cleaning, which was crucial for ensuring accuracy. I noticed that some decimals were incorrectly formatted with commas. This required me to replace commas with decimal points.

Additionally, I had to redefine the data types of certain columns to ensure they were in the correct format for analysis.

The date column was imported as a string. To fix that, the below code will redefine the column to a date using the _datetime() function.

Once the data was clean, I created summary statistics that provided key insights into average, median, minimum, and maximum values. The focus was particularly on the first week of June, where I filtered the data to hone in on the variables of interest: date, % Iron Concentrate, % Silica Concentrate, ore pulp pH, and flotation column levels.

Were there any anomalies on June 1, 2017?

Management indicated that something unusual occurred on June 1, 2017, and requested an investigation. To begin, I filtered the data for the first week of June and created a new data frame, df_june.

The focus was particularly on the first week of June, where I filtered the data to hone in on the variables of interest: date, % Iron Concentrate, % Silica Concentrate, ore pulp pH, and flotation column levels.

I then created a new DataFrame, df_june_important, by selecting values from the original df_june. This allowed df_june_important to focus on the five key columns from the first week of June.

Using Python’s Seaborn library, I visualized the data. explore potential relationships between the variables.

It was surprising to see that the relationship between the key variables was weak, indicating that other factors might be influencing the concentrations.

To validate this, I generated a correlation matrix, which showed low correlation values as expected. This indicates weak relationships between the variables, suggesting that other factors might be influencing the data.

Fluctuations in % Iron Concentrate, % Silica Concentrate, Flotation Column 05 Level

Management wanted to understand how % concentration changes throughout the day, as previous insights had raised more questions. I used Seaborn to create a line plot to visualize these daily fluctuations in concentration levels.

The line plot for % Iron Concentrate showed fluctuations, particularly around 11 a.m. This was fascinating to observe, as it prompted questions about what operational changes were occurring at that time.

The line plot for % Silica Concentrate showed multiple fluctuations, particularly around 5 a.m., 11 a.m., and 6 p.m.

Similarly, a pronounced drop in the Flotation Column 05 Level around 3 p.m. raised eyebrows.

Ore Pulp pH Level Histogram

In examining the ore pulp pH levels, I created histograms that showed a high frequency of values between 9.9 and 10.1, which fell within acceptable limits. This consistency was a relief to see, as it indicated a stable process.

Main Takeaways

This project reinforced several key points in my data analysis journey:

• Cleaning data in Python is critical for accurate analysis; simple formatting changes can make a big difference.
• Generating summary statistics quickly provides essential insights that guide further analysis.
• Visualization tools like line plots and correlation matrices are invaluable in identifying trends and relationships, or the lack thereof.
• The unexpected lack of correlation among key variables suggests that factors outside the data set may be influencing the results, prompting further investigation.

Conclusion and Personal Reflections

Reflecting on this project, I learned the importance of thorough data cleaning and the value of visualizing data to uncover trends. While I faced challenges, such as the initial formatting issues, I found solutions through patience and experimentation. This project has shaped my perspective on data analysis, highlighting the complexities of operational data in the mining industry.

I’m excited about the future and how I can apply these insights to improve processes in various industries.

Call To Action

I would love to hear your thoughts on this analysis! Connect with me on LinkedIn, and if you're looking to hire a data analyst or have questions about my project, let’s chat. Leave a comment below with your insights or queries!