Lab 3: Creating and Analyzing DataFrames with Pandas
This lab with introduce you to methods for building DataFrames in pandas and for computing summary statistics about the data in the DataFrames. You will build a DataFrame from scratch. Then you will load a DataFrame from a CSV file and compute some summary statistics.
Lab 3.a: Building a DataFrame from Lists and Dictionaries
Moving between data in a DataFrame and in a list or dictionary is important. The general method for building a new DataFrame from in the Basics (Links to an external site.) documentation for pandas.
Consider the following DataFrame:
Please deliver some code that encodes this sample stock data into a Pandas DataFrame using lists and dictionaries in Python.
Deliverables:
· Create a DataFrame with the information above
· Use pandas to calculate the average value of each of the price of this stock and its volume
Lab 3.b: Summary Statistics on Real Data Â
Take the Miner Shares and compute the values listed in the deliverable using pandas or using lists and loops. Consult the Descriptive Statistics (Links to an external site.) section of the Pandas User Guide for more information about computing statistics. You should review how to select from Data Frames and apply to DataFrames. Review the references below and attend office hours with questions. You may use lists and loops to solve these problems as well.
Miner Shares file can be found under Files, it is called: "Miner_shares_2020_01_28.csv"
References:
· Descriptive Statistics (Links to an external site.)
· Selection (Links to an external site.)
Deliverables:
· For each deliverable, submit the code you used to solve the problem as well as a single numeric answer:
· How many nonces were scanned by this miner? Miners scan from start_nonce to end_nonce so this is the sum of all the nonces scanned in this file.
· What was the miner's hashrate? The number of nonces scanned over time.Â
· What was the miner's hashrate over each of the days (1/26, 1/27, 1/28)