Python for Data Analysis: When and How to Use It

What Is Data Analysis?

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Data analysis involves collecting, standardizing, transforming, and interpreting data to surface actionable insights that drive business value. Additionally, data analysis is part of data science and is therefore the foundation of many AI and ML workflows. Python is one of the most powerful and widely used tools for data analysis.

How Is Python Used in Data Analysis?

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Python’s versatility and powerful libraries make it an excellent fit for data analysis tasks, from simple statistical tests to complex machine learning models and big data processing. Python is excellent for data analysis because it:

• Is easy to learn. 
• Is the most popular programming language. 
• Can handle large datasets efficiently. 
• Provides libraries with pre-built functions and tools that simplify data analysis tasks. 
• Is widely supported by open-source libraries and a large user community.

Python is well-suited for the following data analysis tasks:

• Data cleaning and preprocessing: Python’s data manipulation libraries provide essential tools for preparing datasets for analysis. When working with a customer database that contains duplicate entries, missing values, and inconsistent date formats, these libraries can quickly identify and remove duplicates, fill in missing data using intelligent methods, and standardize date formats across the entire dataset. This process, which might take hours manually, can often be accomplished in minutes with just a few lines of code. 
• Statistical analysis: Libraries focused on statistical operations are ideal for assessing relationships between variables. For example, they can help determine whether an observed correlation indicates a meaningful trend or is merely coincidental.
• Data visualization: Visualization libraries make it easy to create insightful graphics that represent data effectively. For instance, a trend forecaster in real estate could use these tools to build an interactive dashboard illustrating changes in housing prices, complete with moving averages. Such visual representations help analysts uncover patterns and trends that might not be evident from raw numbers alone. 
• Machine learning and predictive modeling: Libraries designed for machine learning streamline the process of building predictive models. For example, a healthcare provider could leverage these tools to create a statistical model forecasting the likelihood of patient readmission based on various factors like age, medical history, and current symptoms.  
• Big data processing: Python’s compatibility with big data technologies enables efficient analysis of large datasets. A data engineer in an e-commerce company could utilize these capabilities to analyze billions of customer transactions, identify purchasing patterns, and generate product recommendations. This type of analysis, which would be impractical with traditional tools, becomes manageable with Python’s big data functionalities.

Data Analysis Process in Python

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Data analysis with Python (or any programming language) includes the following steps.

Defining the Question

Before approaching the data, clearly define the question you want to answer. For example, “What factors have had the biggest impact on our customer retention during the past 12 months?” Having a clear question in mind at the beginning of your process helps to guide and sharpen your analysis.

Collecting Data

Python has various methods for data collection. Web scraping is a popular technique for gathering data from websites; libraries such as Beautiful Soup or Scrapy make this process straightforward. You can use SQL with libraries (such as SQLAlchemy) to query and retrieve data. The Requests HTTP library is helpful for making HTTP API requests and handling responses.

Reading and Preparing Data

Before you can transform and analyze data, you need to make sure it’s “clean” — meaning it is free from missing or duplicate values, improper formatting, or other irregularities. The pandas library is proficient at reading data and can consume various file formats, including CSV, Excel, JSON. With pandas, commonly used functions for reading data include `read_csv()` and `read_excel()`. The `read_json()` function is widely used to create DataFrames (two-dimensional labeled data structures).

During data preparation, you can detect missing values using pandas functions such as `isnull()` to identify missing data and `dropna()` or `fillna()` to handle them. Removing duplicate data points is another common task, easily completed with the `drop_duplicates()` function.

Analyzing Data

Different Python libraries serve different functions of data analysis. You can find ample information and examples online about analyzing data with Python libraries (like this certification course on Python fundamentals, or this lesson on using pandas for data analysis).

The following Python libraries are most widely used for data analysis.

• pandas includes a variety of built-in statistical analysis functions. You can use `describe()` to get a quick overview of your data, including count, mean, standard deviation, and quartiles. For group comparisons, the `groupby()` function allows you to split your data into groups and perform aggregate functions on each group.
• NumPy integrates with pandas by providing additional mathematical functions and the ability to work with multidimensional arrays. It’s also useful for more complex mathematical operations and when you need to optimize for speed.
• SciPy offers several statistical tests and functions for advanced statistical analysis. These tests can help you identify significant differences between groups and meaningful relationships between variables.
• Scikit-learn is helpful for including machine learning in your data analysis. It offers tools for regression, classification, clustering, and dimensionality reduction. 
• statsmodels offers specialized tools for working with time series data (such as trend analysis, seasonal decomposition, and forecasting). It’s useful for financial and economic data analysis.

Interpreting and Presenting Data

Imagine that you’ve completed the complex process of analyzing your data. Now, you want to devote equally careful attention to interpreting and presenting your data.

Data visualization is the “presentation layer” for your data analysis. Using visual representations of data — charts, graphs, and plots — you can present your conclusions in ways that resonate with your audience. You can use visual techniques to highlight patterns, relationships, and other changes that aren’t immediately clear when looking at raw numbers or tables. Different data visualization techniques work best with different types of data. For example, line charts and line graphs make it easy to observe behavior or conditions that change over time, while scatter plots are best for determining whether two variables are related or interdependent.

Effective data visualization can help you improve:

• Pattern recognition: Visualizations can reveal patterns, trends, and relationships within data that may not be apparent in raw numbers or statistics alone.
• Communication: Well-designed visualizations make it easier to convey complicated ideas to technical and non-technical audiences. 
• Data quality: Visualizations can highlight problems with data quality or errors in the analysis process, such as missing values or unusual patterns.
• Decision consensus: Data visualizations help teams understand the implications of a data set and make decisions based on data analysis findings.

Essential Python Tools and Libraries for Data Analysis

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The main Python libraries used by data analysts are pandas, NumPy, Matplotlib, Seaborn, Plotly, SciPy, and Scikit-learn. Other popular Panda tools include Anaconda and Jupyter Notebook.

pandas

pandas is a popular library for data manipulation and analysis. It provides data structures like DataFrames, which work well with structured data. pandas excels at handling tabular data and offers powerful data cleaning, transformation, and analysis functions.

NumPy

NumPy supports large, multidimensional arrays and matrices, and the mathematical functions that operate on them. It’s an essential tool for scientific computing and forms the basis for many other data analysis libraries.

Matplotlib

Matplotlib is used to build static, animated, and interactive visualizations. It’s highly customizable and can produce publication-quality figures in various formats.

Seaborn

Built on Matplotlib, Seaborn enables users to create sophisticated and informative statistical graphics. It simplifies the process of building complex visualizations and includes built-in plotting themes.

Plotly

Plotly is a library for creating high-quality interactive graphs. It works well for creating web-based visualizations and dashboards.

Scikit-learn

Scikit-learn is a machine learning library that provides simple data analysis and modeling tools. It includes algorithms for classification, regression, clustering, and dimensionality reduction.

Anaconda

Anaconda is a distribution of Python that includes many of the libraries mentioned above, along with other valuable tools for data science. It simplifies the work of setting up your Python environment and managing packages.

Jupyter Notebook

Jupyter Notebook is an application that enables users to build and share documents containing code, equations, visualizations, and narrative text

Getting Started With Data Analysis

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We’ve discussed the versatility of Python and the unique characteristics that make it a preferred tool for data analysis. However, first-time Python users may encounter challenges early on, including:

• Technical setup: New learners may struggle when setting up a Python environment for the first time. They may need to become more familiar with the steps for installing the required Python libraries that are used in data analysis.
• Learning curve: For many beginners, Python can be a daunting language to learn. New users are often unsure where to begin and need recommendations on the best resources to support their learning.
• Lack of practical examples: New learners understand that Python is used for data analysis and want to get started, but may need practical examples with code snippets to guide them along.

A simple and effective way to get started with Python for data analysis is to use a full Python distribution (a bundle of programs, tools, and libraries). A distribution delivers components that simplify the process of installing software and its associated libraries. A Python distribution such as Anaconda makes it easier to start learning and using Python for data science. Anaconda also provides a wide range of resources to support new data analysts learning Python.

Get the Most Out of Your Data With Anaconda 

Python’s data visualization libraries can help you transform data from numbers in a database into visual illustrations that convey the findings of your analysis. The Anaconda Cloud Suite is a versatile solution for streamlining common data science tasks. It delivers tools and guidance to get you working on your data analysis projects quickly. Anaconda’s user community can be another reliable resource as you continue to learn more about the field. Request a demo to see firsthand how Anaconda can elevate your data analysis initiatives.