Use the following example to create a basic Series. A Series consists of two arrays – the main array that holds the data and the index array that holds the paired labels. They give structure to simple, one-dimensional datasets by pairing each data element with a unique label. Series represent an object within the Pandas library. Users already familiar with relational databases innately understand basic Pandas concepts and commands. These two data structures are the backbone of Pandas’ versatility. Python Pandas uses Series and DataFrames to structure data and prepare it for various analytic actions. It is vital to import the Pandas library each time you start a new Python environment.
Otherwise, it would be necessary to enter the full module name every time. This action allows you to use pd or np when typing commands. It is considered good practice to import pandas as pd and the numpy scientific library as np. Start a Python session and import Pandas using the following commands: import pandas as pd import numpy as np To analyze and work on data, you need to import the Pandas library in your Python environment. The practical examples and commands in this tutorial are presented using Jupyter Notebook. This includes basic Python code editors, commands issued from your terminal’s Python shell, interactive environments such as Spyder, P圜harm, Atom, and many others. Python’s flexibility allows you to use Pandas in a wide variety of frameworks. Keep in mind that packages in Linux repositories often do not contain the latest available version.
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For example, use the following command to install the basic Pandas module on Ubuntu 20.04: sudo apt install python3-pandas -y You can install Pandas on any Linux distribution using the same method as with other modules. Installing a prepackaged solution might not always be the preferred option.