What Is Pandas in Python?

Pandas is an open-source library in Python for data analysis and manipulation. It allows you to efficiently work with structured data through flexible data structures and various data-handling capabilities. To build your understanding of pandas and how you could use it for your professional tasks, explore the core components of this library, common applications, and advantages and limitations to consider.

Core components of pandas 

The foundational data structures in pandas are Series and DataFrame. These core structures differ in dimensionality: Series is one-dimensional, while DataFrame is two-dimensional. 

Series

In pandas, Series is a one-dimensional labeled array where you can store data types such as integers, strings, Python objects, and floating-point numbers. When creating a series, ensure your data type is all the same. Your stored data will then be stored as one column of information. 

Each element has an associated label—the index—allowing you to retrieve your information easily. You can use the index to specify values within your data set. For example, the first value has an index of zero, the second one has an index of one, and so on. You can use series independently or as part of a DataFrame.

DataFrame

For more complex data, you can store information in a two-dimensional array. This includes both rows and columns. Each column is a Series object with a label to identify the contents. For example, one column might be “Last Name,” while another might be “Height.” 

Creating a dictionary of values linked to these identifiers is essential, as it allows you to reference specific columns later. A DataFrame's structure is similar to a spreadsheet, and you can perform functions to filter, combine, or analyze your data. 

What is pandas used for? 

You can use Pandas for extensive data-related tasks, such as cleaning and preparation, transformation, and analysis. A few areas you might begin by exploring include:

Viewing your data

Before further analysis, ensure you have a clear idea of your data structure and content. You can begin with head([n]) to preview the first n rows of data, or use index to return all index labels of your Series. You may also use describe() for summary statistics or info() for a summary of a specific Series.

You can analyze your data's underlying features, such as using shape to return the shape of your underlying data, size to see the number of elements included, or dim () to return the dimensions of your data.

Data cleaning

You might have missing, inconsistent, or duplicated information when you work with raw data. You can efficiently identify and handle these types of issues using functions within the pandas library. For example, dropna() removes missing data, and fillna() replaces it with a value of your choice. You can also use duplicated() and drop_duplicates() to identify and remove duplicates. 

Transforming your data

If you need to convert your data into another format, such as reformatting a variable or creating a column subgroup, you can use additional tools in pandas. You can also rename columns with the rename() function or alter the data grouping using groupby(). Tools such as filter() allow you to subset rows or columns by a specified condition, such as a data range. 

If you have operations you want to apply across a column or row, the apply() function allows you to do so. For time data, you can find specific tasks for similar operations. For example, if you wanted to aggregate daily data to represent weekly averages, you could use resample().

Analyzing your data

As you begin your data analysis, pandas has a range of functions to help you provide a quick analytical overview and gain insights. For descriptive statistics, mean(), median(), mode(), and std() provide insight into the distribution of your variables and the underlying variability. 

Another approach is to examine how your variables relate to each other using corr(), which calculates the correlation matrix between two variables. For more complex correlation insights, such as how several variables group together, you can use cov() to create a covariance matrix.

Ultimately, the type of functions you utilize with pandas will depend on your data and analysis. Understanding your research question, the underlying data patterns, and how to appropriately analyze your variables can help you decide the appropriate function to use.

Who uses pandas?

Professionals working with data sets that require cleaning, manipulation, and analysis may use Python's pandas to work more easily with their data. Common groups that benefit from Python and pandas include:

• Data scientists and analysts: Pandas has a range of preprocessing and cleaning tools, so data scientists and analysts may use it to handle large data sets efficiently and prepare data for more complex analyses. 

• Financial analysts: Pandas can help with time series analysis and developing risk metrics, making it applicable to financial data professionals.

• Software engineers: When working with smaller data sets or for exploratory analysis, software engineers may choose pandas for data manipulation and preprocessing.

Pros and cons of using pandas

Understanding the benefits and challenges of pandas is vital for anyone looking to work efficiently with data in Python. While pandas offers a variety of tools that streamline many data-centric tasks, understanding limitations can help you make informed decisions about when and how to use pandas in your workflow.

Benefits

Many of pandas' benefits center around ease of use. Pandas make data cleaning and manipulation more straightforward for professionals working with data than many other applications.

Example benefits of pandas include:

• Missing data handling

• Aligning data

• Grouping and splitting data

• Merging and joining data sets

• Reshaping and pivoting data sets

• Hierarchical labeling

• Time-series functions

Limitations

You might find limitations of pandas that affect whether it’s appropriate to use for your data. For example, if you're working with a large data set, memory limitations with pandas might reduce efficiency. This can cause slower processing times and worse performance. However, if your data set is larger than the available memory, you could overcome this by scaling your analysis or using more efficient data types. 

How to start learning Python

Learning Python fundamentals provides a basis for exploring and effectively using pandas functionalities. To begin, consider the following steps:

1. Learn basic Python concepts, such as keywords and data types.

2. Install Python and, if interested, set up an environment using Jupyter Notebook.

3. Practice basic tutorials, such as creating a simple calculation or printing a word.

4. Explore libraries such as NumPy or pandas.

5. Complete online courses or Guided Projects to explore more complex concepts.

Continue learning Python on Coursera

Pandas is a popular Python library with various data handling and manipulation functions. By learning pandas and other Python tools, you can enhance your professional workflow and improve your data insights. To explore Python fundamentals and begin learning basics, consider taking exciting courses on Coursera.

The Python for Everybody Specialization by the University of Michigan offers a beginner-friendly, five-course series to help you build fundamental programming skills. For a more comprehensive education, consider the Master of Science in Computer Science program from the University of Colorado, where you learn theoretical and practical computer programming skills.