<jupyter_start><jupyter_text>Pandas, Conda and Jupyter  Chapters:* [Short story of these tools](Short-story-of-these-tools)* [How to use them together](How-to-use-them-together)* [How to use conda to manage your envs](How-to-use-conda-to-manage-your-envs)  Short story of these tools  [Pandas](pandas.pydata.org) Pandas is a Python library, started by <NAME> around 5 years ago.The library was specifically made to for with timeseries, mostly because it was made to work with financial data.Today Pandas is de-facto the standard if you want to deal with Data and Python.If you have some experience with **R**, Pandas tries to bring the *dataframe* experience in Python.  [Conda](https://www.conda.org) Conda is a packet manager develop by Anaconda Inc. (before was Continuum Analitycs).Conda is the company attempt to resolve some of the issues that you have with the packet managers in Python, specifcally with **pip** (do you know what it PIP?).It's not limited only to Python and to Python libraries and also comes with a virtual envs manager, similar to the Python virtual envs.On the Anaconda's website you will find to ways to download `conda`:- Anaconda- MinicondaAnaconda, apart from being also the name of the company right now, is the name of a software distribution from Anaconda Inc.. It contains conda, Python and a huge collection of libraries used with for analytics and research.Miniconda, on the other side, is just conda with a Python interpreter, much more lean.Conda is de-facto the standard when you are working with Python and Data, it makes much easier the painful routine of dealing with dependencies, specifically when they are not just related to Python.  [Jupyter](https://www.jupyter.org) Jupyter is the new name for what once called IPython Notebook.The name has 2 purposes:- underline the strong connection with science and with the first `notebook` wrote by <NAME> in which data and analysis are put together, in the first `notebook` Galileo Galilei was studying Jupiter- The strong connection with 3 languages, Julia, Python and RJupyter today is a big collection of projects, including Jupyter Notebook, JupyterHub, the new JupyterLab and many others.Jupyter notebook brings an enchanced REPL experience inside your browser, basically you can run Python commands from your browser and Jupyter Notebook will take care of execute them.Under the hood this process is done by the Jupyter Notebook, a communication protocol and a kernel (generally IPython)![jupyter architecture](../images/notebook_components.png)  How to use them together Let's reformulate this question a little bit: **Why should you use them?**You can use Python with an editor, install the dependencies using pip, an exe installer or apt.**And Excel is still widely used....so?**<jupyter_code>Well someone wrote something very interesting about the growth of Python:<jupyter_output><empty_output>
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