For this assignment you will again be using the pandas libraries. This time to produce some sophisticated graphs with the pandas plot function. You will find help in this weeks videos, as well as the previous week’s (Module 12) videos.

Objectives

This assignment will:

Searching pandas documentation for methods you may not have encountered before

Plotting data using the groupby functions in pandas dataframes

Beginning with desired outcome (graphs/charts), discover how to manipulate libraries to tame a large data set

Using a lambda function to clean your data

Assignment Instructions

To complete this assignment follow these steps:

Start your Jupyter Notebooks server

Open Jupyter Notebooks in a browser

Download information about data positions and associated salaries for several countries positions (Data_Position_Salary_Survey_Responses.csv) Download Data_Position_Salary_Survey_Responses.csv)and upload the file to your VM.

Clean the SalaryUSD data

Create a method function called cleanData that takes in a salary value and removes all punctuation (commas, dollar signs, spaces etc.) and converts the value to an integer

Use a lambda expression and df.apply to clean the ‘SalaryUSD’ column of your dataframe.

Using the groupby, and plot methods on the pandas Dataframe libraries, to create graphs below. Ensure that all labels, titles, and numeric values match those given below.

Note: You can use the pyplot libraries in Matplotlib but should NOT use the following methods from the Matplotlib libraries (plot, scatter, bar, pie). Instead you should use the ‘kind’ parameter available in the pandas plot method (e.g., df.plot(kind=’pie’) )

Note: There are many ways to complete this assignment. You may want to research how to use some of the following pandas methods to parse and organize the data.

sort_values

tail, head

loc, iloc

isin

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