<jupyter_start><jupyter_text>Data Science is Software Developer lifehacks for the Jupyter Data Scientist Section 2:  This is my house Environment reproducibility for Python  Question 1Set up a new virtual environment using conda. We strongly recommending naming the environment the same thing as your project root folder so that it is easy to remember.Once the environment is created, activate the environment.When you're finished your command line prompt should look something like:`(water-pumps)machine:~ user$`Use the `conda env list` command to list the environments that are available and confirm that the `water-pumps` environment is marked with a `*` to indicate it is active.  Question 2Now that you have a virtual environment, let's populate it with the packages we need. For convenience, we've listed the _some_ of the contents of your `requirements.txt` file. If you see an import package error later in the tutorial, you'll be expected to update `requirements.txt` with the additional dependency!At this step you need to: - Ensure you have a `requirements.txt` file. It should have been created by the data-science-cookiecutter process. - Update that `requirements.txt` with the packages you need for analysis. Remember, the point is not to pre-load the environment with every possible package you could ever need. Just your minimum reproducible environment. - Run the command that will install the packages listed in that file. Here's a start for what your `requirements.txt` should contain.<jupyter_code>click
Sphinx
coverage
awscli
flake8
watermark
python-dotenv>=0.5.1
pandas>=0.18.1
matplotlib>=1.5.1<jupyter_output><empty_output>
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