![]() ![]() Although similar, a Series has differences from a numpy array.įor example, a series of numbers would look like this: Index It can hold any data type and has a labeled axis, referred to as the index. It is a 1-dimensional object, similar to an array. Think of a Series as a single column in a spreadsheet. Many of the operations in the pandas library-like aggregating, slicing, and transforming data-can be done on both a Series and a DataFrame. When using the Pandas library, most of the functionality revolves around two data structures: Series and DataFrame. Ultimately, the Python pandas library is an essential tool for making sense of your data. I can use these tools to perform complex data analysis tasks and extract valuable insights from my data. In addition to these basic functions, pandas also provides a range of more advanced tools for data analysis, such as time series analysis, statistical modeling, and machine learning. We’ll cover these functions in depth in the following sections. It even has a pivot_table() function to create pivot tables, which are a useful way to summarize data. Once the data is in a DataFrame, it’s possible to group the data and apply aggregate functions such as mean() or sum() to calculate statistics. In pandas, one of the primary data structures is the DataFrame, which makes it easy to work with data structured into rows and columns. Because of these reasons, the pandas library is often the first library you’ll explore when learning data analytics with Python. It is a single library that allows you to import data from various sources, clean and transform the data, and then analyze it and visualize it using a variety of functionality. One of the main benefits of using pandas is its ability to read in and work with a wide range of data formats, like CSV, Excel, databases, and JSON. The library uses Cython under the hood, so it loads your data into memory efficiently. Pandas is a powerful library that provides easy-to-use data structures and data analysis tools for handling and manipulating numerical tables and time series data. How do data analysts use pandas?īefore getting into the Python pandas tutorial code examples, let’s review how data analysts use the Pandas library. Check out this article if you’re new to Python and want to learn more about it. This article assumes you already have a basic understanding of the Python programming language. Python pandas tutorial: Installing pandas.An introduction to Series and DataFrame.We’ll cover the following topics in this article: In this article we’re going to walk through a Python pandas tutorial so you have a better understanding of how and when to use it. Created by Wes McKinney, the Pandas library has remained open source and has a solid community that is regularly updating the package. Pandas is one of the most popular Python libraries for handling data, and is widely used in analytics, data science, and finance because of its robust functionality and ability to process data quickly. If you have to work with big data containing millions or billions of records, Python is a much better tool for the job. Back when I first started to learn data analytics, the first tool I used was spreadsheets, since I didn’t know how to code.Īlthough tools like Excel and Google Sheets are powerful, spreadsheets become difficult to use when handling large datasets and can quickly feel cumbersome or run out of memory. If you’re learning Python for data analytics, odds are you’ve heard of the pandas library. ![]()
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