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Builds database structures and querying methods using SQLite, SQLAlchemy, and Flask. It also writes and execute Python code in a Jupyter notebook to create graphs

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luispsalazar/Surfs_up

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Surfs_up, Module 9 Challenge

The purpose of this challenge is to import information from a database using sqlalchemy and then arranging the data into tuples, lists or dataframes, as needed for analysis.

Deliverable 1

Query to retrieve June temperatures from the Measurement table.

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The temperatures are added to a list.

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​The list of temperatures is converted to a Pandas DataFrame.

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Summary statistics are generated for the DataFrame.

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Deliverable 2

Query to retrieve December temperatures from the Measurement table.

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The temperatures are added to a list.

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​The list of temperatures is converted to a Pandas DataFrame.

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Summary statistics are generated for the DataFrame.

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Deliverable 3: Analysis

Overview of the statistical analysis

Based on data collected by different weather stations, the temperature for June and December were filtered for analysis, 1,574 and 1,405 values respectively.

Additionally, the precipitation was filtered for the same periods for further evaluation.

Results

Hereafter are the key differences in temperature, between the months of June and December.

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Additional Information

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The precipitation data for June and December was also gathered, in the above image (32.png) it can be seen that December has about 50% more rain compared to June.

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Builds database structures and querying methods using SQLite, SQLAlchemy, and Flask. It also writes and execute Python code in a Jupyter notebook to create graphs

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