What Is Pandas in Python?

Pandas is an open-source library for data manipulation and analysis in Python. Pandas 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 might use it for your professional tasks, explore the core components of this library, common applications, and advantages and limitations to consider.

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Core components of pandas 

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

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, make sure your data is all the same type. Your stored data will then be stored as one column of information. 

Each element has an associated label, known as the index, which allows you to easily retrieve your information. 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 of data. This includes both rows and columns. Each individual column is a Series object, which will have a label to identify the contents. For example, one column might be “Last Name,” while another column might be “Height.” 

It’s important to create a dictionary of values linked to these identifiers, which allows you to later reference specific columns. The structure of a DataFrame 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 an extensive range of data-related tasks, such as cleaning and preparation, transformation, and analysis. A few areas you might begin by exploring include:

Viewing your data

Ensuring you have a clear idea of your data structure and content is important before further analysis. 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 underlying features of your data, such as using shape to return the shape of your underlying data, size to see the number of elements included, or ndim() to return the dimensions of your data.

Data cleaning

When you work with raw data, you might have missing, inconsistent, or duplicated information. 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 subgroup of a column, you can use additional tools in pandas. You can also rename columns with the rename() function or alter the grouping of data 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 functions for similar operations. For example, if you wanted to aggregate daily data to represent weekly averages, you could use resample() to do so.

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. Taking the time to understand 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 pandas in Python to more easily work 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 professionals who work with financial data.

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

Pros and cons of using pandas

Understanding the benefits and challenges of pandas is important 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 benefits of pandas center around ease of use. For professionals working with data, pandas makes cleaning and manipulation more straightforward 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 have a very 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 available memory, you can sometimes overcome this by scaling your analysis or using more efficient data types. 

How to start learning Python

To effectively use pandas, learning Python fundamentals provides a basis for you to explore 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 library in Python with a variety of data handling and manipulation functions. By learning pandas, along with 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 both theoretical and practical skills in computer programming.

specialization

Python for Everybody

Learn to Program and Analyze Data with Python. Develop programs to gather, clean, analyze, and visualize data.

4.8

(215,365 ratings)

1,763,376 already enrolled

Beginner level

Learn More

Average time: 2 month(s)

Learn at your own pace

Skills you'll build:

Json, Xml, Python Programming, Database (DBMS), Python Syntax And Semantics, Basic Programming Language, Computer Programming, Sqlite, SQL, Data Structure, Tuple, Data Analysis, Data Visualization, Web Scraping

Master of Science in Computer Science

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