Python is a must for data scientists as it is the most popular programming language used for data analysis and building machine learning models. A data science project can be completed entirely in Python, using packages such as Pandas, Numpy, scikit-learn, and matplotlib. One aspect of Python that makes it popular is its readability. Python code is clear and easy to understand, which makes it ideal for data science projects.
Data analysts can take their skills to the next level using Python. Typically, analysts are power users of business intelligence tools such as Excel, Power BI, and Tableau. These tools do not provide the flexibility and control that Python does. With Python, analysts can accomplish all of the same tasks they can in other tools, with the added benefit of building custom functions, sharing code, and leveraging packages. To further their skills, and their career, learning Python is a great step for data analysts.
We can use Python for every stage in the world of data science.
Python has bespoke packages for the different stages in a data science project that integrate well together.
Anaconda is a distribution of Python designed specifically for data science.
Top Uses of Python for Data Science
Python can be used to implement all of the skills from the world of data science:
Load & clean data
Transform & analyze data
Load and clean data
Python makes it easy to connect to external data and import it into a development environment for transformation or analysis. The Pandas package has helpful functions to connect to data from a variety of sources, including local .csv files, databases, or online sources. A popular aspect of Pandas are DataFrames. DataFrames structure data into tables, similar to Excel, which is familiar to many users and makes it simpler to select, replace, and create new data.
Although it is possible to load and clean data in a similar way using different tools, such as Excel, Python offers greater efficiency and scalability. This is a crucial strength when working with big amounts of data. Python helps eliminate the need for manual entry of data and is often a superior tool, as it optimizes the speed of work.
Transform and analyze data
Python provides the flexibility to structure, clean, and transform the imported data to prepare it for analysis. The Pandas package also provides functions to help with common data analysis, such as single variable statistics and measuring correlation. Another popular package, NumPy, offers a comprehensive set of mathematical functions for further analysis.
In a data science project, two common applications of data transformation and analysis are exploratory data analysis (EDA) and feature engineering.
Exploratory data analysis (EDA)
Exploratory data analysis can reveal patterns and insights in the data and can help guide a data science project to the right direction. This exploration can be done by manipulating data in DataFrames or generating summary statistics of a data set with the Pandas package. Another option is to visualize the dataset, to identify outliers or patterns more easily in the data, with a data visualization package like Seaborn. Exploratory data analysis is quick and scalable in Python: with just a few lines of code entire data sets can be analyzed and visualized quickly, allowing data scientists to focus on generating insights.
Feature engineering is the act of modifying the structure of the data to make it more suitable for analysis, or to help improve the performance of a data science model. The package scikit-learn contains a collection of preprocessing functions, to prepare data for machine learning models. These functions cover common feature engineering scenarios, such as standardization, normalization, encoding categorical features, and imputing missing values.
The scikit-learn package also contains functions to generate and run machine learning models, including regression and classification models. These functions can be integrated into a larger workflow, so the initial loading, cleaning, and preprocessing of data can occur in the same place as modeling. This makes the entire project easier to read, understand, and audit.
Regression is a popular and powerful type of machine learning model that predicts a continuous variable value. Python is one of the best tools for building regression models, as it has superior speed to run code and quickly iterate through different parameters. Compared to popular programs like Excel, Python can run regression models on much larger datasets, as well as possessing the flexibility to modify parameters to optimize the model output.
Classification is another popular type of machine learning model that predicts which category an object belongs to. Python allows the classification of a large amount of data efficiently, with the option to cycle through different classification algorithms to see which provides the best results.
Python provides several different packages to create stunning data visualizations. Visualizing data can assist in the initial exploration and understanding of a data set, as well as help communicate the key insights after data analysis. Popular packages for visualization include matlplotlib and seaborn. Building visuals with code allows for complete control over the appearance, resulting in more customized and meaningful charts. Combining custom Python functions with matplotlib and seaborn, high-quality visualizations can not only be created, but also duplicated, modified, and easily shared. This is not the case in other business intelligence tools, where visuals often need to be re-created or it is more difficult to share standards across teams.
Where Can I Find Python?
To use Python with a focus on data science, Anaconda is a great choice to download, install, and start coding. Anaconda is a distribution of the Python programming languages for scientific computing that aims to simplify package management and deployment. Anaconda is an open source software that is most commonly used in data science and machine learning. It has a wide variety of tools and libraries which can easily help set up an environment to deploy any project. This includes Jupyter Notebooks and some other useful tools and packages. These notebooks allow Python code to be written in an interactive web browser.