Project Python Data Analysis

Expectations: Understand the code demonstrated in the video tutorials, slightly modify the code to accomplish tasks below. Remember, you will not be able to accomplish some of the tasks if you do not understand what each line of the code does.

1. Work on LinkedIn Learning video tutorial Python Data Analysis Links to an external site.. Complete the sections: “4. Arrays with NumPy”, and “5. Use Case: Weather Data”. Create a Word file and save it as M7_FL where FL are your First and Last name initials.

2. Save your ipython notebook from working on the Section 5.5 Weather Charts as Weather_Charts and this file should have a .ipynb extension name (5 points). Next, modify the code so that your own name can be included in the last New York plot’s title. Make a screenshot of the modified code (not shown in the sample below) and the plot that you created, save the screenshot in M7_FL. (5 points).

Then write your own code in Weather_Charts to plot a chart for ‘DALLAS FT WORTH AP‘ in year of 2020. Make a screenshot of the your code (not shown in the sample below) and the plot that you created, save the screenshot in M7_FL (5 points). The average temperature in your plot might show a different value because my plot was created on a different day from your plot.

3. The Section 5: Challenge is answered in the tutorial but I want you to think and try to solve the problem before looking at the Solution. Use the solution to help understanding how the problem is solved. Save your ipython notebook from working on the Section 5: Challenge as Weather_Anomalies and this file should have a .ipynb extension name (5 points).

Modify the code to include ‘DALLAS FT WORTH AP’ in the last plot. Make a screenshot of the code (not shown in the sample below) and the plot that you created, save the screenshot in M7_FL (5 point

When you look this chart, you can see much data is missing for cities like Minneapolis before 1950 and could not be displayed. Therefore, you need to add new code to “compute and plot the temperature anomaly time series for any station” between 1950-2020. In this task, the temperature anomaly is defined very similar to the Challenge, but the years average temperature is now in the range of year 1950-2020 (not challenge’s 1880-2020), and the “midcentury average” should be (TMIN + TMAX)/2 averaged over all days of every year between 1980 and 1990 (not challenge’s 1945-1955, because we are looking at range of 1950-2020 and ‘midcentury’ in this range is 1980-1990). Add a title to the plot to include your own name.

Make a screenshot of the code (not shown in the sample below) and the plot that you created for New York, Minneapolis, and Dallas Ft Worth AP. (20 points) Write your analysis of the temperature anomaly observed in this plot in the M7_FL document (5 points).

4. Work on LinkedIn Learning video tutorial Python Data AnalysisLinks to an external site.. Complete the sections: “6. pandas”, and “7. Use Case: Baby Names”. Save your ipython notebook from working on the Section 7.3 “Comparing name popularity” as Name_Popularity and this file should have a .ipynb extension name (5 points). Next, add your own code to compare the popularity of your own name and one of your relatives. Also add a title to the plot as shown the following screenshot. In my example, my name is “Jane”, my relative’s name is “Frances”. Make a screenshot of the modified code (not shown in the sample below) and the plot that you created, save the screenshot in M7_FL. (10 points). Write your analysis of the names’ popularity shown in your plot in the M7_FL document (5 points).

If your name or family member’s name is not in the data set, use “Penny” as a female name and “Ken” as a male name depending on your own case.

5. The Section 7: Challenge is answered in the tutorial but I want you to think and try to solve the problem before looking at the Solution. Use the solution to help understanding how the problem is solved. Save your Jupyter notebook from working on the Section 7: Challenge as Pandas_Names and this file should have a .ipynb extension name (5 points). In the Pandas_Names notebook, write your own code to plot your own name and at least one relative’s name in the male and female name popularity. In the following example, I plot my name “Jane” and a relative’s name “John” in the male and female name popularity. We can see John is used as male name dominantly. Take a screenshot of the code (not shown in the sample below) and the plot your created, save the screenshot in M7_FL. (20 points) Write your analysis of your chosen names’ in male and female name popularity shown in this plot in the M7_FL document (5 points).

If your name or a relative’s name is not in the data set, use “Penny” as a female name and “Ken” as a male name depending on your own case.

7. Submit Weather_Charts.ipynb, Weather_Anomalies.ipynb, Name_Popularity.ipynb, Pandas_Names.ipynb, and M7_FL document as a ZIP file.