Lecture Notes 10

What is the _ variable?

In an interactive context _ refers to the value of the previous expression, e.g.

>>> 8 * 8
64
>>> _
64
>>> _ * 8
512
>>> _
512
>>> _ * 2
1024

something that can be used to gradually build up a value as in a pocket calculator. This has been inherited in the notebook as well - value of previous cell. E.g.

'hello'

'hello'

_ + ' world'

'hello world'

Note that the print function does not have a return value although things may look the same on the screen, it does not modify the _ variable

print('hello')

hello

_ + ' world'

'hello world world'

What is a list comprehension?

We generate a new list from the members of a nother list (or sequence) with a compact notation, like turning a for loop inside out

[i**2 for i in range(10)]

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

[word.upper() for word in ['hello', 'world']]

['HELLO', 'WORLD']

Cell magic

Functions specific for interactive Python and Jupyter with leading percent (%) signs

• Line commands with leading single %

• Cell commands with leading double %%

E.g. to write the content of a cell to a file story.txt

%%file story.txt
Mr and Mrs Dursley, of number four,
Privet Drive, were proud to say that
they were perfectly normal, thank
you very much.

Overwriting story.txt

Stock example continued

Here we use the csv module to write the collected data to a single file total_results.csv. To start with we organize conceived lines in the resulting file as a list of dictionary with common keys. The keys will become the headers in the file and the values the data lines in the files

"""
Goal:
collect the data in the files into a table



         Total shares      Final value
amazon:  62.2256792377505  6350.13
apple:   63.04476565302408 10691.76
starbucks: 41.58404967698385 4763.87
"""

import csv
import sys
from rich import print
from pathlib import Path


data = []
for result_file in sys.argv[1:]:
    result_file = Path(result_file)
    with open(result_file) as f:
        line1 = f.readline()
        line2 = f.readline()

    total_shares = float(line1.split()[-1])
    final_value = float(line2.split()[-1])

    data.append(
        {
            # 'Company': result_file.replace('.results', ''),
            # 'Company': result_file.split('.')[0],
            'Company': result_file.stem.title(),
            'Total shares': total_shares,
            'Final Value': final_value,
        }
    )
print(data)

with open('total_results.csv', mode='w') as f:
    writer = csv.DictWriter(f, data[0].keys())
    writer.writeheader()
    writer.writerows(data)

In the final rewrite we do a so called refactoring. To rewrite the structure without changing the behaviour We have separate functions for collecting data and writing data and a main function which controls every thing

"""
Goal:
collect the data in the files into a table



         Total shares      Final value
amazon:  62.2256792377505  6350.13
apple:   63.04476565302408 10691.76
starbucks: 41.58404967698385 4763.87
"""

import csv
import sys
from rich import print
from pathlib import Path



def get_data():
    data = []
    for result_file in sys.argv[1:]:
        result_file = Path(result_file)
        with open(result_file) as f:
            line1 = f.readline()
            line2 = f.readline()

        total_shares = float(line1.split()[-1])
        final_value = float(line2.split()[-1])

        data.append(
            {
                # 'Company': result_file.replace('.results', ''),
                # 'Company': result_file.split('.')[0],
                'Company': result_file.stem.title(),
                'Total shares': total_shares,
                'Final Value': final_value,
            }
        )
    return data


def save_data(data):
    with open('total_results.csv', mode='w') as f:
        writer = csv.DictWriter(f, data[0].keys())
        writer.writeheader()
        writer.writerows(data)


def main():
    data = get_data()
    print(data)
    save_data(data)


if __name__ == "__main__":
    main()

Running the code with the downloaded examples

$ python *.results
[
    {'Company': 'Amazon', 'Total shares': 62.2256792377505, 'Final Value': 6350.13},
    {'Company': 'Apple', 'Total shares': 63.04476565302408, 'Final Value': 10691.76},
    {'Company': 'Meta', 'Total shares': 21.859148044183414, 'Final Value': 5229.58},
    {'Company': 'Starbucks', 'Total shares': 41.58404967698385, 'Final Value': 4763.87},
    {'Company': 'Tesla', 'Total shares': 116.52728787723488, 'Final Value': 18680.49}
]