Python Data Analysis: Discovering trends and patterns in your data
Python is one of the most popular programming languages used today, and it’s no wonder why. Many different types of developers use Python in their work, including scientists and engineers.
If you work with large sets of data, or have data from an experiment that you want to analyze, then Python can be an invaluable tool.
This article will take you through some common Python libraries that are used in scientific research and engineering and demonstrate how they can be used to turn your datasets into valuable information. Read on to learn more!
Importing the libraries you need
When it comes to data analysis, there are a few key libraries you’ll need to import in order to get started. Python’s standard library, `sys`, is great for handling input and output.
The `csv` module will help you read and write CSV files. And the `pandas` library will be incredibly useful for data manipulation and analysis. Let’s take a look at how to import each of these libraries.
First, let’s import `sys`. sys provides access to things like environment variables and exit codes (among other things).
Next, we’ll import `csv`. We’re going to use this library when we read in our dataset from csv file. Finally, we’ll import pandas which we’ll use for some data manipulation and plotting tasks!
How to handle missing data
If you’re working with data in Python, there’s a good chance you’ll run into missing values at some point.
Before you can start analyzing your data, you need to figure out how to handle missing values. The most common approach is to remove any rows containing missing values before analysis so that the remaining rows are complete.
However, this can lead to bias in the results of your analysis because it reduces the number of rows that you have available for analysis.
If you have a small number of records with missing values (less than 10%), it may be worth keeping those records and using an imputation method like mean or median instead of removing them.
Another option is to remove all the rows that contain more than one missing value (i.e., all rows containing N/A) before doing any other processing on the data set so that it has only complete cases (i.e., no missing values).
Visualizing patterns over time
When you’re looking at data, it can be helpful to visualize it to see if there are any patterns. This is especially true when you’re looking at time-based data. By plotting your data, you can start to see trends and patterns that you might not have noticed otherwise.
For example, take a look at the plot below of employee height versus weight for 10 different employees over time. There’s a pretty clear pattern here where people’s weights seem to plateau around their thirties and then steadily increase as they get older.
A more complicated visualization like this one of average air temperature by month over the course of 20 years can tell us so much more about how temperature changes through the year.
If we were wondering about global warming, then we could easily see that July is getting warmer each year, which supports global warming theories.
This is just scratching the surface on what’s possible with data visualization! The most important thing is to make sure you are always representing your data accurately because visuals can often lead to false conclusions.
Creating a supervised model with scikit-learn
Supervised learning is a method of machine learning where you train a model on a dataset with known labels.
This allows the model to learn how to predict the labels for new data. In this blog post, we’ll be using the scikit-learn library to create a supervised learning model.
Scikit-learn provides many algorithms for supervised classification including k-nearest neighbors, linear regression, decision trees, and random forests. For this blog post, we will use a decision tree algorithm called random forest. Random forest uses the results from multiple decision trees that are created by sampling at random with replacement.
The number of features used by each tree is determined by a hyperparameter called mtry which defaults to all features (mtry=1).
Using cluster analysis for better insights
Cluster analysis is a powerful tool that can help you discover trends and patterns in your data. By grouping together similar data points, you can better understand the relationships between them.
This can be especially helpful when you have large datasets with many variables. In this post, we’ll explore how to use Python to perform cluster analysis using both manual and automated methods.
First we’ll take a look at K-means clustering which uses an iterative process of assigning different clusters based on the similarity of the mean of each group to one another.
