Climate Analysis and Data Exploration of Climate Database Using Python (Pandas, Matplotlib), SQLAlchemy (ORM Queries) and Flask
Data Source: SurfsUp_Challenge_starter_code.ipynb named later as SurfsUp_Challenge.ipynb Data File: hawaii.sqlite Software: Matplotli 3.2.2, Python 3.9, Visual Studio Code 1.50.0, Anaconda 4.8.5, Jupyter Notebook 6.1.4, Pandas, Numpy, Sqlalchemy.
The analysis was designed to assess the temperature statistics for June and December to assist in the decision to establish a surf shop assess its’ sustainability year around. We arrive at acquiring out temperature data by running two separate queries, one for June and the other December. Upon completion of our queries, we store the temperatures in a list then convert them to a dataframe. Statistical analysis was accomplished using the .describe() method.
In June we had a total count of 1700, mean of 74.9, min of 64.0 and max of 85.0 Standard deviation is 3.25 in June and 3.75 -- making a .5 difference in the two different seasons
In December we had a total count of 1517, mean of 71.0, min of 56.0 and max of 83.0.
The temperatures’ standard deviation is 3.25 in June and 3.75 in December-- making a .5 difference in the two different seasons. The averages temperatures variation is 3.3 degrees. Overall, the seasons analyzed are very mild. Although we have uncovered a general trend in the weather for the months requested other attribute to the weather such as precipitation, etc., will influence the traffic flow through the shop. A very insightful analysis would include identifying high areas of traffic flow within the city to capitalize on profit the statistics of high traffic and other parameters to maximize your possible investment.