It shines at handling arrays of data of uniform types - like individual columns of a table. NumPy is a performant array library for numeric data. NumPy - Efficient, Interoperable Arrays for Numeric Data At this point, you can consider more efficient alternatives as listed below. In particular when the dataset is large, say, hundreds of megabytes or more, you may notice that pandas takes too much memory or too much time to perform desired operations. It comes with a rich set of functions for filtering, selecting, and grouping data, which makes it a versatile tool for various data science tasks.Ī key tradeoff of pandas is that it is not particularly efficient when storing and processing data. It adds an index over the columns and rows, making it easy to access particular elements by their name. pandas is a dataframe, meaning that it can handle mixed column types such as the table presented above. Pandas is the most common library for handling tabular data in Python. Important Python libraries for dealing with tabular data In the following, we will go through five common choices as illustrated by the figure below: However, some thinking may be required to choose the right library and data structure for the task at hand, as Python comes with a rich ecosystem of tools with varying tradeoffs.

Don’t underestimate the capacity of a single large server! This is a boon to data scientists, as they don’t need to learn new systems or programming languages to be able to process even massive data sets. Thanks to increasing amounts of memory and CPU power available in a single computer - in the cloud in particular - as well as the advent of highly optimized libraries for data science and machine learning, today it is possible to process even hundreds of millions of rows of tabular data in Python efficiently. In the past, it wasn’t considered wise to try to process large amounts of data in Python. Often, structured or relational data like this is stored in a database or a data warehouse where it can be queried using SQL. The metadata about column names and types are called the schema. In this example, the Name column contains strings, Credit Score integers, Last login timestamps, and Balance floating point numbers. Note how each column has a uniform data type. Many data science applications deal with tabular data that looks something like this: Name

Python object and data structure basics