<jupyter_start><jupyter_text>Reading XLS with pandasOne of the most useful features of python is its rich library environment. A fairly recent contribution to the pantheon of polular libraries for analysis is `pandas`. In this notebook we will explore opening and simple manipulations on a `.xls`file using `pandas`. A core functionality of `pandas`is its ability to read popular data-storage filetypes. We begin by loading a prepared spreadsheet into the notebook.<jupyter_code>import pandas as pd

df = pd.read_excel("../sources/example.xls")<jupyter_output><empty_output><jupyter_text>One of the very neat features of the notebook is the ability to display features of of the dataframe in-line:<jupyter_code>df.head()<jupyter_output><empty_output><jupyter_text>As we see the data is some time-stamped measure of consumption with some entires missing.    Ex 1. Visualization  Ex1.a Visualize the three individual columns in the data-frame as a time-series plot.<jupyter_code>#implement here<jupyter_output><empty_output><jupyter_text>Ex 2. Missing data  Ex2.aFind the proportion of missing data in each column.<jupyter_code>#implement here<jupyter_output><empty_output><jupyter_text>Ex2.bFill in the missing data in the dataframe<jupyter_code>#implement here<jupyter_output><empty_output>
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