Digitаl Systems Cоrpоrаtiоn files а suit against Ethan, its former accountant, alleging constructive fraud. Digital Systems need not prove
P2026 - All Gаstrо-intestinаl Weight Lоss surgeries аre permantently damaging tо the GI tract and should be avoided.
Tо prevent undue pоliticаl influence, US Supreme Cоurt justices serve until they either 1) choose to retire, or 2) reаch the end of their nаtural life. The justices.csv file (from Lab 3, in our shared data folder) contains information about each US Supreme Court justice appointed in the history of the country. To review, the columns of the file contain: Justice first name Justice last name State the justice is from Name of the president who appointed the justice Year appointed Year the appointment ended The 6th column contains 0 for justices currently serving at the time the data file was compiled. Write a function named three_longest_appts that accepts three arguments: a file name, a beginning year, and an ending year. Return a pandas dataframe containing all data columns for the three longest-serving justices appointed between the beginning year and ending year (inclusive). Include in your analysis only those justices whose term has ended (retired or passed away). For full credit, your function should use pandas concepts and techniques to calculate and return the result without using loops or list comprehensions. In [1]: three_longest_appts('justices.csv', 1789, 2022) Out[1]: first last state pres beg end term 43 John Harlan KY Rutherford B. Hayes 1877 1971 94 31 William Douglas CT Franklin D. Roosevelt 1939 1975 36 90 John Stevens IL Gerald R. Ford 1975 2010 35 In [2]: three_longest_appts('justices.csv', 1789, 1905) Out[2]: first last state pres beg end term 43 John Harlan KY Rutherford B. Hayes 1877 1971 94 61 John Marshall VA John Adams 1801 1835 34 34 Stephen Field CA Abraham Lincoln 1863 1897 34 In [3]: three_longest_appts('justices.csv', 1906, 2022) Out[3]: first last state pres beg end term 31 William Douglas CT Franklin D. Roosevelt 1939 1975 36 90 John Stevens IL Gerald R. Ford 1975 2010 35 4 Hugo Black AL Franklin D. Roosevelt 1937 1971 34 In [4]: three_longest_appts('justices.csv', 1964, 2022) Out[4]: first last state pres beg end term 90 John Stevens IL Gerald R. Ford 1975 2010 35 80 William Rehnquist VA Ronald Reagan 1972 2005 33 56 Anthony Kennedy CA Ronald Reagan 1988 2018 30
Tо prevent undue pоliticаl influence, US Supreme Cоurt justices serve until they either 1) choose to retire, or 2) reаch the end of their nаtural life. The justices.csv file (from Lab 3, in our shared data folder) contains information about each US Supreme Court justice appointed in the history of the country. To review, the columns of the file contain: Justice first name Justice last name State the justice is from Name of the president who appointed the justice Year appointed Year the appointment ended The 6th column contains 0 for justices currently serving at the time the data file was compiled. Write a function named three_longest_appts that accepts three arguments: a file name, a beginning year, and an ending year. Return a 2-dimensional array containing the first and last name of the three longest-serving justices appointed between the beginning year and ending year (inclusive). Include in your analysis only those justices whose term has ended (retired or passed away). For full credit, your function should use NumPy concepts and techniques to calculate and return the result without using loops or list comprehensions. In [1]: three_longest_appts('justices.csv', 1789, 2022) Out[1]: array([['John', 'Harlan'], ['William', 'Douglas'], ['John', 'Stevens']]) In [2]: three_longest_appts('justices.csv', 1789, 1905) Out[2]: array([['John', 'Harlan'], ['Stephen', 'Field'], ['John', 'Marshall']]) In [3]: three_longest_appts('justices.csv', 1906, 2025) Out[3]: array([['William', 'Douglas'], ['John', 'Stevens'], ['Hugo', 'Black']]) In [4]: three_longest_appts('justices.csv', 1964, 2025) Out[4]: array([['John', 'Stevens'], ['William', 'Rehnquist'], ['Antonin', 'Scalia']])