Before diving into predictions or advanced analytics, we have to tackle Exploratory Data Analysis (EDA)—a crucial process that turns raw data into true meaningful insights. Whether the data comes from sensors, drones, or lab results, EDA helps us understand the data before moving forward. While visualizations are a key part of EDA, we’ll focus on the groundwork—cleaning, structuring, and finding patterns—leaving the visuals for our next article.
EDA is a structured process, and that’s where the What, Why, and How framework might be of help. Let’s break it down:
What: This asks, what type of data are we dealing with? Sensor readings, field data, or satellite imagery? Before we dive in, we must define the data's source, its variables, and if it includes important details like When and Where the data was collected.
Why: Why are we analyzing this data? Are we optimizing resources, assessing crop variables, or evaluating input performance? The Why drives our goals for analysis and defines what we’re hoping to achieve.
How: How do we go about cleaning and preparing the data? Are we using Python, R, or a specialized software platform? The How determines the tools and techniques we’ll use to ensure the data is ready for deeper analysis.
Without these foundational questions, it’s like trying to grow a crop without knowing if it’s the rainy season or the desert, summer or winter, tomatoes or soybeans! And as for the Who, don’t just think about who collected the data. Who’s going to act on these insights? Spoiler alert: It’s probably you, or us at Bison!
Step 1: Importing the Data – Understanding Different File Formats
Before we do anything, we need to load our data into a working environment. This step is crucial because the format of the data dictates how we interact with it. Whether the dataset comes from an IoT sensor, a drone, or manual field logs, we must import it in a way that allows us to explore, clean, and analyze it effectively.
Each file format is optimized for different purposes, and understanding which one to use (or receive) affects performance, flexibility, and ease of use.
Common Data Formats
CSV (Comma Separated Values)
CSV files are simple, plain-text files where each line is a row of data, and commas separate the columns. It’s one of the most common formats for data exchange because it’s lightweight and supported by nearly every data analysis tool.
Use Case: Great for small to medium datasets that don’t require complex structures like relationships between tables.
Excel (.xlsx)
Excel files can store multiple sheets, formulas, and structured tables. They’re ideal when the dataset is stored in a multi-tab structure or when you want to maintain a user-friendly interface for data entry.
Use Case: Best for structured, multi-sheet datasets or when working with users who need to manually input data into tables.
JSON (JavaScript Object Notation)
JSON is commonly used for web data, APIs, and hierarchical data structures. It stores data in a readable format similar to a Python dictionary, making it flexible for complex, nested data relationships.
Use Case: Ideal when pulling data from APIs, especially in real-time systems such as remote sensing or IoT devices.
SQL Databases
SQL databases allow you to store and query relational data using structured queries. Unlike CSVs, SQL databases are designed for handling larger datasets and maintaining relationships between data tables.
Use Case: When data grows in size or complexity, SQL databases are perfect for storing and querying large datasets from agricultural systems, farms, or weather stations.
Parquet
Parquet is a columnar storage format, making it highly efficient for large-scale data operations like big data analytics. It’s often used in distributed systems like Hadoop or cloud services.
Use Case: Parquet is optimized for performance, especially with large datasets in big data environments or cloud storage.
HDF5 (Hierarchical Data Format)
HDF5 is used to store large amounts of data in a hierarchical structure. It allows for more complex datasets to be stored in one file, which is perfect for scientific computing or when dealing with multi-dimensional data.
Use Case: Useful for large-scale, structured scientific data in research environments like soil experiments, environmental data, or weather simulations.
Correctly importing the data ensures that we maintain the integrity of the dataset and load it in a way that works for further analysis. For example, importing an incorrectly formatted file could lead to missing data or type mismatches that compromise later steps in our analysis process. Ensuring the right file type is crucial for performance, especially when dealing with large datasets common in agriculture.
Step 2: Understanding the Data – Building the Foundation for Deeper Analysis
Once we’ve successfully imported the dataset, the next critical step is to understand what we’re working with. This stage is essential because it reveals how the data is structured, flags potential issues, and prepares us for further steps like cleaning and modeling. Without this foundational knowledge, we risk working with flawed data, which could lead to misleading results.
Data Types
In one of our previous posts, Why Data is the New Fertilizer, we explored the different types of data in agriculture—quantitative, qualitative, time-series, and spatial data. While these categories are essential for understanding the data from an agricultural perspective, when working in an environment, these data types translate into specific data types that coding languages recognize.
Quantitative Data: this data becomes integers for discrete counts (e.g., number of plants) or floats for continuous data (e.g., temperature, growth rate). Ensuring numerical data is correctly classified is crucial for calculations and statistical analysis.
Qualitative Data: This data is often represented as strings or categorical types. These fields need to be encoded correctly when working with models, as algorithms cannot interpret text directly.
Boolean Data: Although not mentioned explicitly in our earlier post, boolean data (e.g., True/False) is commonly used in programming and is crucial for conditional logic, such as whether a field was treated or not.
Date/Time Data: Time-series data, such as monitoring growth over weeks or months, is vital in agriculture. Date columns must be converted into datetime objects to enable time-based calculations and analysis.
Handling these data types correctly ensures we’re setting ourselves up for a successful analysis. Misclassifying numerical data as text, for instance, would prevent us from performing even the simplest calculations.
Descriptive Statistics
Once we’ve reviewed the data types, the next step is to dive into descriptive statistics, which offer a snapshot of the behavior of numerical variables. Metrics like the mean, median, range, and standard deviation provide insights into the central tendencies and variability within our dataset.
For example, if we spot that a value for a variable seems unusually high, this might indicate a data entry error or inconsistency. Understanding these basic statistics allows us to identify outliers or potential data quality issues early on as well as the overall structure of the data.
In future posts, we’ll take a deeper dive into the foundations of statistics. We’ll explain these terms more thoroughly—what the mean tells us, how standard deviation reveals the spread of the data, and why understanding the different data distributions matters.
Identifying Missing Values
It’s rare to find a real-world dataset without missing values, especially in agriculture. Whether due to sensor failures, human error, or external factors, missing data can introduce biases if not handled properly. The first step is to identify where the missing values are concentrated. Is it in a specific column, or are they scattered across different rows?
Unique Values and Duplicates
In this phase, identifying unique values and managing duplicates are key to ensuring data quality and consistency.
Unique Values: Unique values refer to the distinct categories or entries within a variable. We can identify variations such as checking for spelling errors or inconsistent formatting (e.g., "Corn" vs. "corn"), and understand how categories are distributed.
Duplicates: Handling duplicates goes beyond identifying repeated values in a single column. It requires looking at entire rows and considering their context. Duplicates can distort analysis by over-representing specific records.
Step 3: Cleaning the Data – Eliminating Errors for a Clear Analysis
Data cleaning is the foundation of any reliable analysis. In agricultural datasets, errors can stem from sensor malfunctions, data entry mistakes, or environmental anomalies (Navigating the complexities of data). Cleaning the data ensures that we minimize inaccuracies and maintain data integrity, so the insights drawn from it are both valid and actionable.
There are several common challenges to address during data cleaning, and each requires a specific technique to ensure the dataset is free from errors or inconsistencies.
Handling Missing Data
How we handle these missing values can significantly affect the outcomes of our analysis. Two primary approaches are commonly used:
Imputation: Imputation is the process of filling in missing data points. It can be done in several ways, ranging from simple to more advanced techniques:
Mean/Median Imputation: This replaces missing values with the average (mean) or middle value (median) of the available data. It’s a quick fix, but it may oversimplify the dataset.
Forward/Backward Fill: In time-series data, such as crop growth data over weeks, you can fill in missing values with the previous or next available value, assuming a stable pattern in the data.
K-Nearest Neighbors (KNN): This more sophisticated method replaces missing values based on the nearest data points. It’s useful when there’s a relationship between variables that can inform missing values.
Regression Imputation: In this approach, missing values are predicted based on relationships with other variables using regression models.
Removing Rows/Columns: If a dataset contains too much missing data, or if the missing values are spread randomly and cannot be reliably imputed, it may be better to remove rows or columns entirely. This approach ensures that the remaining data is robust, but it risks losing valuable information. The key is to make sure the dataset remains representative after removal.
Dealing with Outliers
Outliers—data points that are significantly different from others—are very common in agricultural datasets. First we need to identify it, outliers can be spotted using visualization techniques (e.g., box plots, scatter plots - we will explore those next week) or through statistical methods like the Interquartile Range (IQR) or Z-scores.
IQR Method: This approach calculates the spread of the middle 50% of the data (the interquartile range) and flags data points that fall outside 1.5 times the IQR as potential outliers.
Z-Score: This method measures how many standard deviations a data point is from the mean. Points with a Z-score greater than a certain threshold (commonly 3) can be considered outliers.
After identification we can handle outliers by removal, transformation or flagging
Remove: If the outliers are clear errors, such as a faulty sensor reading, it’s best to remove them to avoid skewing the results.
Cap or Transform: In cases where outliers are extreme but still valuable (e.g., rare events like droughts), we might choose to cap them at a reasonable threshold or apply a transformation (e.g., logarithmic) to reduce their influence without losing the data entirely.
Flag and Analyze Separately: Sometimes, outliers carry meaningful information. For example, extreme weather events can provide important insights into crop resilience. Instead of removing them, flag these cases for separate analysis.
Standardizing Data Formats
Data collected from different sources often comes in varying formats, making direct comparisons impossible without standardization. This issue can occur with units of measurement (e.g., Celsius vs. Fahrenheit) or inconsistent date formats.
Unit Conversion:
Convert all measurements to the same unit. For example, temperatures should be standardized to Celsius or Fahrenheit throughout the dataset. Similarly, ensure that distances are in the same unit (meters, kilometers, etc.).
Date and Time Standardization:
In agricultural datasets, time is a critical variable, whether you’re measuring crop growth or weather changes. Convert date columns into a consistent format, ideally using ISO 8601 (YYYY-MM-DD), and ensure all times are standardized (e.g., using the same timezone or UTC). You can also extract useful time features such as "Day of the Week" or "Season" for further analysis.
Duplicates
Duplicates can skew our analysis by over-representing certain data points, especially in agricultural systems where data might be collected automatically at multiple intervals.
Step 4: Transforming the Data – Structuring for Better Insights
Once the data is cleaned and free from errors, the next step is transformation, where we restructure the dataset to enhance its interpretability and usefulness. In agricultural datasets, transformations help make sense of diverse data types, especially when working with time-series, spatial data, or different measurement units. Transforming the data allows for smoother analysis, comparisons, and, ultimately, better decision-making.
Let’s walk through some common transformation techniques that are essential in agricultural data analysis.
Normalization and Scaling
When a dataset contains variables with different units or scales (e.g., soil pH vs. crop height), normalization ensures that each variable contributes equally to the analysis. Without normalization, variables with larger scales can dominate and skew results, particularly in algorithms sensitive to scale, such as machine learning models.
Min-Max Scaling: This method scales all variables between 0 and 1, preserving relationships between values.
Standardization (Z-score Scaling): This method rescales the data based on its mean and standard deviation, centering the data around zero.
One-Hot Encoding for Categorical Data
Categorical data, such as crop types, cannot be directly used in most models. One-Hot Encoding transforms categorical variables into numerical. This is critical in agriculture when analyzing variables like soil types, crop varieties, or pest categories, which are represented as labels but need to be converted into a numerical format.
Aggregating Data
Agricultural data is often collected at different intervals—hourly, daily, or even per season. To make sense of this granular data, aggregation is a powerful tool. Aggregating data by averaging or summing values over a time period or spatial boundary simplifies the dataset while preserving important trends.
Temporal Aggregation: If hourly temperature data is available, aggregating it into daily averages can make it more manageable and help highlight broader trends without overloading the analysis with excessive detail.
Spatial Aggregation: For data collected across different fields or regions, aggregation can involve calculating averages, medians, or sums of variables like soil moisture or crop yield per farm or region.
With our data cleaned, structured, and explored, we’ve laid the groundwork for meaningful analysis. Exploratory Data Analysis (EDA) is the phase where we diagnose the data, ensuring it’s free of inconsistencies, identifying key patterns, and uncovering relationships that may not be obvious at first glance. It’s a vital step in preparing data for any advanced analysis.
Over the week, we’ll be releasing mini-tutorials to help you dive deeper into the key EDA techniques discussed in this article. These guides will cover practical tips for handling missing data, detecting outliers, and working with data distributions—all crucial to mastering EDA. Each tutorial is designed to give you immediate, actionable insights that you can apply directly to your own datasets, ensuring your data is fully prepped and reliable for the next stages of analysis.
However, the journey doesn’t end here. Data insights are only as powerful as our ability to communicate them effectively. This is where data visualization becomes a key next stop, and the subject of our upcoming blog.
At Bison Data Labs, we don’t just analyze data—we transform it into a strategic advantage. We work alongside agronomists, research teams, and agricultural leaders to craft ag data-driven solutions that directly address the unique challenges of their operations. We create tailored tools and strategies that make sense for your data, your team, and your goals.
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