i10brook/denver_international_airport_climate_data

The attached data set is climate data from Denver International Airport for the first half of February, 2019. Drop any columns you deem unnecessary. Set the date column as the index of the DataFrame. Create an "Elapsed Time" column that shows the amount of time since the first observation. Format the "Elapsed Time" column into some easily-readable form. For example, after two hours, the column should NOT read 7200. Do all the things we've already been doing -- format the headings, deal with missing values, etc. Perform analysis with the tools we've looked at so far. Keep in mind that the data may have to be grouped to be meaningful (average temp per day may be more useful than average for the whole two weeks, for example). Justify your analysis choices. Deliverable is your Jupyter notebook. Remember, just attach the notebook. Don't change the file extension and don't zip it."

Denver_International_Airport_climate_data

The attached data set is climate data from Denver International Airport for the first half of February, 2019. Drop any columns you deem unnecessary. Set the date column as the index of the DataFrame. Create an "Elapsed Time" column that shows the amount of time since the first observation. Format the "Elapsed Time" column into some easily-readable form. For example, after two hours, the column should NOT read 7200. Do all the things we've already been doing -- format the headings, deal with missing values, etc. Perform analysis with the tools we've looked at so far. Keep in mind that the data may have to be grouped to be meaningful (average temp per day may be more useful than average for the whole two weeks, for example). Justify your analysis choices. Deliverable is your Jupyter notebook. Remember, just attach the notebook. Don't change the file extension and don't zip it."

Summary

We were succesfully able to load the dataset and use the correct functions to maipulate the dataset by dropping unnecessary columns, setting DATE column as index, create 'Elapsed time' column with proper format to represent time from first observation, deal with missing or NaN values, and perfrom analysis with meaningful groupby choices. We maipulated the dataset to transform it into a cleaner version to be able to allow new perspective by adding new columns and cleaning it up with formatting and other ways to handle the dataset to be able to run analysis. We succesfully we able to have meaningful groupby functions that could be used for further analysis if desired.

Reference:¶

Chen, D. Y. (2019). Python Data Analysis. Pearson Addison Wesley Data & Analytics Series. In Pandas for Everyone.