Python: Getting started with pandas

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python

Installation or Setup Installing pandas with Anaconda

Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users.

The simplest way to install not only pandas but Python and the most popular packages that make up the SciPy stack (IPython, NumPy, Matplotlib, …) is with Anaconda, a cross-platform (Linux, Mac OS X, Windows) Python distribution for data analytics and scientific computing.

After running a simple installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software to be compiled.

An additional advantage of installing with Anaconda is that you don’t require admin rights to install it, it will install in the user’s home directory, and this also makes it trivial to delete Anaconda at a later date (just delete that folder).

Installing pandas with Miniconda

The previous section outlined how to get pandas installed as part of the Anaconda distribution. However, this approach means you will install well over one hundred packages and involves downloading the installer which is a few hundred megabytes in size.

If you want to have more control over which packages, or have a limited internet bandwidth, then installing pandas with Miniconda may be a better solution.

Conda is the package manager that the Anaconda distribution is built upon. It is a package manager that is both cross-platform and language agnostic (it can play a similar role to a pip and virtualenv combination).

Miniconda allows you to create a minimal self contained Python installation, and then use the Conda command to install additional packages.

First, you will need Conda to be installed and downloading and running the Miniconda will do this for you.

The next step is to create a new conda environment (these are analogous to a virtualenv but they also allow you to specify precisely which Python version to install also). Run the following commands from a terminal window:

This will create a minimal environment with only Python installed in it. To put your self inside this environment run:

source activate name_of_my_env

On Windows the command is:

activate name_of_my_env

The final step required is to install pandas. This can be done with the following command:

conda install pandas

To install a specific pandas version:

conda install pandas=0.13.1

To install other packages, IPython for example:

conda install ipython

To install the full Anaconda distribution:

conda install anaconda

If you require any packages that are available to pip but not conda, simply install pip, and use pip to install these packages:

Usually, you would install pandas with one of packet managers.

PIP example:

pip install pandas

This will likely require the installation of a number of dependencies, including NumPy, will require a compiler to compile required bits of code, and can take a few minutes to complete.

conda install pip pip install django

Install via anaconda

First download anaconda from the Continuum site. Either via the graphical installer (Windows/OSX) or running a shell script (OSX/Linux). This includes pandas!

If you don’t want the 150 packages conveniently bundled in anaconda, you can install miniconda. Either via the graphical installer (Windows) or shell script (OSX/Linux).

Install pandas on miniconda using:

conda install pandas

To update pandas to the latest version in anaconda or miniconda use:

conda update pandas

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Harshad Patel

7x GCP | 2X Oracle Cloud| 1X Azure Certified | Cloud Data Engineer