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How to install pandas using Anaconda?
Anaconda is a distribution of packages built for data science. as we know that pandas is a python package that is the best tool for data science operations. Anaconda is a python and R distribution, and it includes 100 plus python packages by default. It is also flexible to use in Windows machines as well as Linux machines.
When you download Anaconda it will automatically come with conda(package manager), Python, and over 150 python scientific packages. It also has some default applications like Jupyter Notebook, Spyder, RStudio, Visual Studio Code, and some more.
To install Anaconda, we need to download the anaconda installer initially into our system from anacondas official website.
Based on our machine configuration we need to download the required installer from the above link. After successfully downloading the installer then we can able to install the anaconda navigator on our machine.
Anaconda Navigator is a graphical user interface (GUI) provided by Anaconda distribution, that allows us to easily manage conda packages and launching applications.
In an anaconda navigator, we can see the applications like jupyter, spyder, and some more. From that list of applications we need to launch the jupyter notebook, it will automatically open a browser because the jupyter notebook is a web-based application suitable for developing, documenting, and executing code. We can create a new jupyter notebook file in our working directory.
After creating the jupyter notebook file we can import the pandas package.
The above line of code will import the python pandas into our working environment, here we no need to install the pandas package additionally. Because anaconda distribution will provide you all these packages by default.
import pandas as pd
In our code block, we renamed the pandas package to pd, which is nothing but giving a nickname. By doing this we can reduce the length of our code for calling a pandas function in further process.
Anaconda will take 300GB of our system space for storage and 600GB for air-gapped deployments, because it comes with the most common data science packages in Python like Numpy, pandas, and a lot more.
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