UniversityRankingsPythonScript (#397)

Adding University Rankings Python project to repository. This program ranks universities in its respective order using data from .csv files and incorporates location attributes such as continent, country, and capital.

README.md

@@ -141,6 +141,7 @@ More information on contributing and the general code of conduct for discussion
 | Turtle Graphics                          | [Turtle Graphics](https://github.com/DhanushNehru/Python-Scripts/tree/master/Turtle%20Graphics)                                                          | Code using turtle graphics.                                                                                                                                       |
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+| University Rankings                      | [University Rankings](https://github.com/DhanushNehru/Python-Scripts/tree/master/University%20Rankings)                                                  | Ranks Universities across the globes using the Country, Continent, and Captial                                                                                    |
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University Rankings/README.md

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+# UniversityRankings
+  A program that I constructed for ranking universities based on two files provided by the user. One would contain the list of universities around the world ranked in order, and another file contains the various countries and its corresponding capital. In this instance, I used two files called capitals.csv and TopUni.csv. I chose to create this program by using one function, however, this program can be altered and constructed by using multiple functions. Since the files that we are using are comma separated values (csv), I chose to open them by importing and using pandas as pd. The output was displayed on an output.txt file to record my output in that file. Below are the following calculations that were made in the program.
+
+ 1. The first part of the program details the total number of universities in the TopUni.csv file. 
+ 2. The second part of the program displays all the available countries
+ 3. The third part of the program illustrates all the available continents
+ 4. The next part of the program identifies the international and national rankings that are associated with the selected country
+ 5. The following portion of the code calculates the average score, relative score, 
+ 6. The next part determines the capital of the selected country
+ 7. The last part of the code outputs the universities name(s) that contain the capital

University Rankings/output.txt

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+

University Rankings/pyvenv.cfg

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+home = /Library/Frameworks/Python.framework/Versions/3.10
+implementation = CPython
+version_info = 3.10.0.final.0
+virtualenv = 20.13.0
+include-system-site-packages = false
+base-prefix = /Library/Frameworks/Python.framework/Versions/3.10
+base-exec-prefix = /Library/Frameworks/Python.framework/Versions/3.10
+base-executable = /usr/local/bin/python3.10

University Rankings/univRanking.py

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+import pandas as pd
+
+def getInformation(selectedCountry, rankingFileName, capitalsFileName):
+    # add selected country
+    # Total number of universities
+    # show countries
+    # show continents
+    # calculate average score (sum of all uni scores within the selected country) / (number of universities within the selected country)
+    # display the average score
+    # calculate the relative score (average score) / (The highest score within the continent where the university is selected)
+
+    available_countries = 0
+    counter = 0
+    countries = ""
+    continents = ""
+    countries_lst = []
+    continents_lst = []
+    TopUni = pd.read_csv(rankingFileName)
+    Capitals = pd.read_csv(capitalsFileName)
+    country_counter = 0
+    country_counter_rank = 1
+
+    file = open("output.txt", "w")  # open output.txt file where output is stored
+
+    for line in range(len(TopUni)):
+        counter += 1
+
+    file.write("Total number of Universities => {}\n" .format(counter))  # PART 1 TOTAL NUMBER OF UNIVERSITIES
+
+    # LISTING ALL AVAILABLE COUNTRIES WITHIN THE FILE
+    for country in Capitals["Country Name"]:
+        if country not in countries_lst:
+            countries_lst.append(country)
+        available_countries += 1
+
+    for country in countries_lst:
+        if countries == "":
+            countries = countries + country
+        else:
+            countries = countries + ", " + country
+
+    file.write("Available countries => {}\n" .format(countries))  # PART 2 AVAILABLE COUNTRIES
+
+    # FINDING ALL AVAILABLE CONTINENTS WITHIN THE FILE
+    for continent in Capitals["Continent"]:
+        if continent not in continents_lst:
+            continents_lst.append(continent)
+
+    for continent in continents_lst:
+        if continents == "":
+            continents = continents + continent
+        else:
+            continents = continents + ", " + continent
+
+    file.write("Available Continents => {}\n" .format(continents))  # PART 3 AVAILABLE CONTINENTS
+
+    # FINDING THE INTERNATIONAL RANK OF COUNTRIES ASSOCIATED WITH THE SELECTED COUNTRY
+    for country in TopUni["Country"]:
+        if country == selectedCountry:
+            file.write("At international rank => {} the university name is => {}\n" .format(country_counter_rank, TopUni["Institution name"][country_counter]))  # PART 4 INTERNATIONAL RANK
+        country_counter += 1
+        country_counter_rank += 1
+
+    country_counter = 0
+    country_national_counter_rank = 1
+
+    for country in TopUni["Country"]:
+        if country == selectedCountry:
+            file.write("At national rank => {} the university name is => {}\n" .format(country_national_counter_rank, TopUni["Institution name"][country_counter]))  # PART 5 NATIONAL RANK
+            country_national_counter_rank += 1
+        country_counter += 1
+
+    number_of_universities = 0
+    university_score = 0
+    TopUni = pd.read_csv(rankingFileName)
+    counter = 0
+
+    for country in TopUni["Country"]:
+        if selectedCountry == country:
+            university_score += TopUni["Score"][counter]
+            number_of_universities += 1
+        counter += 1
+
+    # THE AVERAGE SCORE CALCULATIONS
+    averageScore = university_score / number_of_universities
+    file.write("The average score => {}%\n" .format(round(averageScore, 1)))  # PART 6 AVERAGE SCORE                                                                                                         # PART 6
+
+    number_of_universities = 0
+    university_score = 0
+    TopUni = pd.read_csv(rankingFileName)
+    Capitals = pd.read_csv(capitalsFileName)
+    highestScore1 = 0
+    highestScore2 = 0
+    highestScore3 = 0
+    highestScore4 = 0
+    highestScore5 = 0
+    counter1 = 0
+    counter2 = 0
+    counter3 = 0
+    continent = ""
+
+    # CALCULATING THE RELATIVE SCORE
+    for country in TopUni["Country"]:
+        if selectedCountry == country:
+            university_score += TopUni["Score"][counter1]
+            number_of_universities += 1
+        counter1 += 1
+
+    averageScore = university_score / number_of_universities
+
+    for country in Capitals["Country Name"]:
+        if selectedCountry == country:
+            continent = Capitals["Continent"][counter2]
+        counter2 += 1
+
+    for continentScore in TopUni["Score"]:
+        if TopUni["Country"][counter3] in ["Jordan", "Palestine", "China", "Israel", "Japan", "Singapore", "South Korea", "Taiwan"]:
+            if continentScore > highestScore1:
+                highestScore1 = continentScore
+        elif TopUni["Country"][counter3] in "Australia":
+            if continentScore > highestScore2:
+                highestScore2 = continentScore
+        elif TopUni["Country"][counter3] in ["Canada", "USA"]:
+            if continentScore > highestScore3:
+                highestScore3 = continentScore
+        elif TopUni["Country"][counter3] in ["Denmark", "France", "Germany", "Netherlands", "Norway", "Sweden", "Switzerland", "United Kingdom"]:
+            if continentScore > highestScore4:
+                highestScore4 = continentScore
+        elif TopUni["Country"][counter3] in ["Egypt"]:
+            if continentScore > highestScore5:
+                highestScore5 = continentScore
+
+        counter3 += 1
+
+    # PART 7 RELATIVE SCORE
+    if selectedCountry in ["Jordan", "Palestine", "China", "Israel", "Japan", "Singapore", "South Korea", "Taiwan"]:
+        relativeScore = (averageScore / highestScore1) * 100
+        file.write("The relative score to the top university in {} is => ({} / {}) x 100% = {}%\n" .format(continent, averageScore, highestScore1, round(relativeScore, 1)))
+    elif selectedCountry in "Australia":
+        relativeScore = (averageScore / highestScore2) * 100
+        file.write("The relative score to the top university in {} is => ({} / {}) x 100% = {}%\n" .format(continent, averageScore, highestScore2, round(relativeScore, 1)))
+    elif selectedCountry in ["Canada", "USA"]:
+        relativeScore = (averageScore / highestScore3) * 100
+        file.write("The relative score to the top university in {} is => ({} / {}) x 100% = {}%\n" .format(continent, averageScore, highestScore3, round(relativeScore, 1)))
+    elif selectedCountry in ["Denmark", "France", "Germany", "Netherlands", "Norway", "Sweden", "Switzerland", "United Kingdom"]:
+        relativeScore = (averageScore / highestScore4) * 100
+        file.write("The relative score to the top university in {} is => ({} / {}) x 100% = {}%\n" .format(continent, averageScore, highestScore4, round(relativeScore, 1)))
+    elif selectedCountry in ["Egypt"]:
+        relativeScore = (averageScore / highestScore5) * 100
+        file.write("The relative score to the top university in {} is => ({} / {}) x 100% = {}%\n" .format(continent, averageScore, highestScore5, round(relativeScore, 1)))
+
+    # FINDING THE CAPITAL OF THE SELECTED COUNTRY
+    Capitals = pd.read_csv(capitalsFileName)
+    capital = ""
+    counter = 0
+
+    for cap in Capitals["Country Name"]:
+        if cap == selectedCountry:
+            capital = Capitals["Capital"][counter]
+        counter += 1
+
+    file.write("The capital is => {}\n" .format(capital))  # PART 8 CAPITAL OF SELECTED COUNTRY
+
+    # FINDING THE UNIVERSITIES THAT HAVE THE NAME OF THE CAPITAL WITHIN IT
+    TopUni = pd.read_csv(rankingFileName)
+    Capitals = pd.read_csv(capitalsFileName)
+    capital = ""
+    counter1 = 0
+    counter2 = 0
+    number_counter = 1
+    for cap in Capitals["Country Name"]:
+        if cap == selectedCountry:
+            capital = Capitals["Capital"][counter1]
+        counter1 += 1
+
+    file.write("The universities that contain the capital name => \n")  # PART 9 CAPITAL NAME IN UNIVERSITY NAME
+
+    for uni in TopUni["Country"]:
+        if (selectedCountry == uni) and (capital in TopUni["Institution name"][counter2]):
+            file.write("#" + str(number_counter) + " " + TopUni["Institution name"][counter2] + "\n")
+            number_counter += 1
+        counter2 += 1
+
+
+def __main__():
+    country = input("input the country you want to look at: ")
+    file1 = "TopUni.csv"
+    file2 = "capitals.csv"
+    getInformation(country, file1, file2)
+
+
+__main__()