Python, matplotlib)
We have a list of Bitcoin prices recorded at the end of each week (Sunday) in 2018 and 2019. Create a visualization (either line graph or heatmap, justify your choice) that allows you to answer the question: Which year, 2018 or 2019, tended to provide better returns for Bitcoin holders?
you can assume that the data consists of all weeks of 2018 followed by all weeks of 2019.
prices = [14292.2, 12858.9, 11467.5, 9241.1, 8559.6, 11073.5, 9704.3, 11402.3,
8762.0, 7874.9, 8547.4, 6938.2, 6905.7, 8004.4, 8923.1, 9352.4,
9853.5, 8459.5, 8245.1, 7361.3, 7646.6, 7515.8, 6505.8, 6167.3,
6398.9, 6765.5, 6254.8, 7408.7, 8234.1, 7014.3, 6231.6, 6379.1,
6734.8, 7189.6, 6184.3, 6519.0, 6729.6, 6603.9, 6596.3, 6321.7,
6572.2, 6494.2, 6386.2, 6427.1, 5621.8, 3920.4, 4196.2, 3430.4,
3228.7, 3964.4, 3706.8, 3785.4, 3597.2, 3677.8, 3570.9, 3502.5,
3661.4, 3616.8, 4120.4, 3823.1, 3944.3, 4006.4, 4002.5, 4111.8,
5046.2, 5051.8, 5290.2, 5265.9, 5830.9, 7190.3, 7262.6, 8027.4,
8545.7, 7901.4, 8812.5, 10721.7, 11906.5, 11268.0, 11364.9, 10826.7,
9492.1, 10815.7, 11314.5, 10218.1, 10131.0, 9594.4, 10461.1, 10337.3,
9993.0, 8208.5, 8127.3, 8304.4, 7957.3, 9230.6, 9300.6, 8804.5,
8497.3, 7324.1, 7546.6, 7510.9, 7080.8, 7156.2, 7321.5, 7376.8]
# Your Solution Goes Here
Answers
In order to determine whether 2018 or 2019 provided better returns for Bitcoin holders, we can visualize the Bitcoin price data using Python's matplotlib library.
Since we have a list of Bitcoin prices recorded at the end of each week in 2018 and 2019, a line graph would be the best visualization to represent this data.
Here's the solution code: import matplotlib.pyplot as plt # import the matplotlib library prices = [14292.2, 12858.9, 11467.5, 9241.1, 8559.6, 11073.5, 9704.3, 11402.3,
8762.0, 7874.9, 8547.4, 6938.2, 6905.7, 8004.4, 8923.1, 9352.4,
9853.5, 8459.5, 8245.1, 7361.3, 7646.6, 7515.8, 6505.8, 6167.3,
6398.9, 6765.5, 6254.8, 7408.7, 8234.1, 7014.3, 6231.6, 6379.1,
6734.8, 7189.6, 6184.3, 6519.0, 6729.6, 6603.9, 6596.3, 6321.7,
6572.2, 6494.2, 6386.2, 6427.1, 5621.8, 3920.4, 4196.2, 3430.4,
3228.7, 3964.4, 3706.8, 3785.4, 3597.2, 3677.8, 3570.9, 3502.5,
3661.4, 3616.8, 4120.4, 3823.1, 3944.3, 4006.4, 4002.5, 4111.8,
5046.2, 5051.8, 5290.2, 5265.9, 5830.9, 7190.3, 7262.6, 8027.4,
8545.7, 7901.4, 8812.5, 10721.7, 11906.5, 11268.0, 11364.9, 10826.7,
9492.1, 10815.7, 11314.5, 10218.1, 10131.0, 9594.4, 10461.1, 10337.3,
9993.0, 8208.5, 8127.3, 8304.4, 7957.3, 9230.6, 9300.6, 8804.5,
8497.3, 7324.1, 7546.6, 7510.9, 7080.8, 7156.2, 7321.5, 7376.8]# separate the data into two different years in order to plot the data as two different linesyears = ["2018", "2019"]
data = [prices[:52], prices[52:]]# set the x axis to show weeks from 1 to 52plt.xticks(range(1, 53))# plot the data as two separate linesplt.plot(range(1, 53), data[0], label=years[0])
plt.plot(range(1, 53), data[1], label=years[1])# add chart labels and legendplt.title("Bitcoin Prices by Week")
plt.xlabel("Week")
plt.ylabel("Price (USD)")
plt.legend()# display the plotplt.show()
The output of the program should be a line graph that shows the Bitcoin prices by week for the years 2018 and 2019. By looking at this graph, we can see that Bitcoin prices were generally higher in 2019 than in 2018. Therefore, 2019 tended to provide better returns for Bitcoin holders.
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d) all the given options
Answers
All the other options are true and highlight the benefits of continuous integration in software development.The statement that is false regarding continuous integration (CI) is option d) all the given options.
Continuous integration (CI) is a development practice that involves continuously integrating code changes into a shared repository and frequently running automated builds and tests to ensure that the software is always functional and ready to be released. The process of continuous integration helps to identify integration defects early in the development process, making it easier and cheaper to fix them before they become more serious problems later on.
Option a) is true because code needs to be frequently checked in to ensure that changes are integrated into the codebase in a timely manner. This practice helps to prevent code conflicts and reduces the time and effort required for merging changes from multiple developers.
Option b) is also true because automated builds facilitate CI by automating the process of building and testing the software, allowing developers to focus on coding new features and fixing bugs rather than spending time on manual build and testing tasks.
Option c) is true because CI helps to identify integration defects in the early stages of the project. By running automated tests and builds frequently, developers can quickly identify and fix integration defects before they become more serious problems.
In summary, the false statement regarding continuous integration (CI) is option d) all the given options. All the other options are true and highlight the benefits of continuous integration in software development.
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If a program is designed to find the perfect solution to a problem but would take 150 years to run, we would say that this particular solution is computationally infeasible. This means that the computational resources required to find the solution within a reasonable time frame are not currently available.
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