<jupyter_start><jupyter_text>Step 1 - Data EngineeringThe climate data for Hawaii is provided through two CSV files. Start by using Python and Pandas to inspect the content of these files and clean the data.*  Create a Jupyter Notebook file called data_engineering.ipynb and use this to complete all of your Data Engineering tasks.*  Use Pandas to read in the measurement and station CSV files as DataFrames.*  Inspect the data for NaNs and missing values. You must decide what to do with this data.*  Save your cleaned CSV files with the prefix clean_.  Dependencies<jupyter_code>import pandas as pd
import numpy as np<jupyter_output><empty_output><jupyter_text>Read .csv files into Pandas DataFrames<jupyter_code>measurements = pd.read_csv("Resources/hawaii_measurements.csv")
stations = pd.read_csv("Resources/hawaii_stations.csv")<jupyter_output><empty_output><jupyter_text>measurements is long, I want the header<jupyter_code>measurements.head(1)<jupyter_output><empty_output><jupyter_text>stations is short, it's okay to print it all out.<jupyter_code>stations<jupyter_output><empty_output><jupyter_text>Within measurements are some null or missing values, mostly under prcp.  Let's fill those with 0 measurements and write it to a .csv<jupyter_code>measurements.fillna(0).to_csv("Resources/clean_hawaii_measurements.csv", index=None)
stations.fillna(0).to_csv("Resources/clean_hawaii_stations.csv", index=None)<jupyter_output><empty_output>
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