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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 <a href='http
Step1:  If you created the environment last week, make sure to git pull, activate the hvtutorial environment in the notebooks directory and run
Step2:  And you should see the HoloViews logo after running the following cell
Step3:  The next cell tests the key imports needed for this tutorial
Step4:  Lastly, let's make sure the large taxi dataset is available - instructions for acquiring this dataset may be found in the README
Step5:  Recommended configuration
The following configuration options are recommended additions to your '~/.holoviews.rc' file as they improve the tutorial experience and will be the default behaviour in future
Step6:  If you do not have a holoviews.rc already, simply run the following cell to generate one containing the above lines
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Python Code:
from IPython.core import page
with open('../README.rst', 'r') as f:
    page.page(f.read())
Explanation: <a href='http://www.holoviews.org'><img src="assets/hv+bk.png" alt="HV+BK logos" width="40%;" align="left"/></a>
<div style="float:right;"><h2>00. Introduction and Setup</h2></div>
<img src="./assets/tutorial_app.gif"></img>
Welcome to the HoloViews+Bokeh SciPy 2017 tutorial!
This notebook serves as the homepage of the tutorial, including a general overview, instructions to check that everything is installed properly, and a table of contents listing each tutorial section.
What is this all about?
HoloViews is an open-source Python library that makes it simpler to explore your data and communicate the results to others.  Compared to other tools, the most important feature of HoloViews is that:
HoloViews lets you work seamlessly with both the data and its graphical representation.
When using HoloViews, the focus is on bundling your data together with the appropriate metadata to support both analysis and plotting, making your raw data and its visualization equally accessible at all times.  This tutorial will introduce HoloViews and guide you through the process of building rich, deployable visualizations based on Bokeh, Datashader, and (briefly) matplotlib.
Index and Schedule
This four-hour tutorial is broken down into the following sections:
40 min &nbsp;1 - Introduction: Get started by creating a variety of different HoloViews "elements".
20 min &nbsp;2 - Customizing visual appearance: How to change the appearance and output format of elements.
30 min &nbsp;3 - Exploration with containers: Using HoloViews "containers" for quick, easy data exploration.
15 min &nbsp;Break
30 min &nbsp;4 - Working with tabular data: Exploring a tabular (columnar) dataset.
20 min &nbsp;5 - Working with gridded data: Exploring a gridded (n-dimensional) dataset.
30 min &nbsp;6 - Custom interactivity: Using HoloViews "streams" to add interactivity to your visualizations.
&nbsp;&nbsp;5 min &nbsp;Break
20 min &nbsp;7 - Working with large data: Using datasets too large to feed directly to your browser.
30 min &nbsp;8 - Deploying Bokeh Apps: Deploying your visualizations using Bokeh server.
Related links
You will find extensive support material on our website holoviews.org. In particular, you may find these links useful during the tutorial:
Reference gallery: Visual reference of all elements and containers, along with some other components
Getting started guide: Covers some of the same topics as this tutorial, but without exercises
Getting set up
Please consult the tutorial repository README for instructions on setting up your environment. Here is the condensed version of these instructions for unix-based systems (Linux or Mac OS X):
bash
$ conda env create -f environment.yml
$ source activate hvtutorial
$ cd notebooks
If you have any problems with running these instructions, you can conveniently view the full instructions within this notebook by running the following cell:
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import holoviews as hv
hv.__version__
Explanation: If you created the environment last week, make sure to git pull, activate the hvtutorial environment in the notebooks directory and run:
git pull
conda env update -f ../environment.yml
Now you can launch the notebook server:
bash
$ jupyter notebook --NotebookApp.iopub_data_rate_limit=100000000
Once the environment is set up, the following cell should print '1.8.1':
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hv.extension('bokeh', 'matplotlib')
Explanation: And you should see the HoloViews logo after running the following cell:
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import bokeh
import matplotlib
import pandas
import datashader
import dask
import geoviews
Explanation: The next cell tests the key imports needed for this tutorial:
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import os
if not os.path.isfile('../data/nyc_taxi.csv'):
    print('Taxi dataset not found.')
Explanation: Lastly, let's make sure the large taxi dataset is available - instructions for acquiring this dataset may be found in the README:
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lines = ['import holoviews as hv', 'hv.extension.case_sensitive_completion=True',
         "hv.Dataset.datatype = ['dataframe']+hv.Dataset.datatype"]
print('\n'.join(lines))
Explanation: Recommended configuration
The following configuration options are recommended additions to your '~/.holoviews.rc' file as they improve the tutorial experience and will be the default behaviour in future:
End of explanation
rcpath = os.path.join(os.path.expanduser('~'), '.holoviews.rc')
if not os.path.isfile(rcpath):
    with open(rcpath, 'w') as f:
        f.write('\n'.join(lines))
Explanation: If you do not have a holoviews.rc already, simply run the following cell to generate one containing the above lines:
End of explanation
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