Started the reorganization of Scripts for github. Some notes changes.

Scripts/API_Tests/find_completed.py

@@ -0,0 +1,76 @@
+from collections import Counter
+import pandas as pd
+
+basecsv = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/Skuid_MCA125.csv"
+lpcsv = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/skuidlps.csv"
+
+"""
+Example multivalue dictionary
+
+dict = {key1: [value1, value2, value3, value4],
+        key2: [value5, value6, value 7],
+        }
+
+So this could be used for each learning path. In other words:
+
+learning_paths = {'01:Skuid Ethos' : ["Congratulations", "Create", "Skuid Resources"]} etc etc
+
+Ideally, we will add Alexa's "levels" in this dictionary as well. Could we do:
+
+learning_paths = {'Level_1': [{'01:Skuid Ethos' : ["Congratulations", "Create", "Skuid Resources"]},
+                              {'02:Composer' : ["Overview", "Get Started with Composer", "Manage Pages"}]
+                              {'03:Design System Studio' : ["Get Started with Design Systems", etc etc]},
+                  'Level_2': [{'10 - Data' : ["Tips to Optimize", "Smarter Conditions"]},
+                              {'11-Components': ["Battle", "Engage"]},
+                  ]
+                }
+
+How to create this by automation?
+"""
+
+
+def lpLevels(basecsv, lpcsv):
+    levels = pd.read_csv(
+        lpcsv,
+        index_col=1,
+    )
+    newDf = levels.groupby("Learning Path")
+    newDf2 = newDf.apply(lambda x: x["Course Name"].unique())
+    learningpaths = newDf2.apply(pd.Series)
+    learningpaths.rename_axis(index=0)
+    mainFunc(basecsv, learningpaths)
+    # print(levels.Level.unique())  # Print only unique values from the Level column
+
+
+def mainFunc(basecsv, learningpaths):
+    # Part 1
+    readData = pd.read_csv(
+        basecsv,
+    )
+    group = readData.groupby("Learner Full Name")
+    df2 = group.apply(lambda x: x["Course Name"].unique())
+    df2 = df2.apply(pd.Series, dtype="string")
+    # print(df2)
+    # This prints a dataframe with the learner's name as the index column and the courses as adjacent columns
+
+    # Part 2
+    courses = learningpaths.set_index(0)
+    # print(courses)
+    # lp_dict = learningpaths.to_dict("index")
+    # courses = lp_dict.values()
+    # Part 3
+    print(df2.isin(df2))
+    # This produces a bunch of T/F in the dataframe. Is the solution to do:
+    # for courses in lp_dict, for row(person) in readData
+    # if number of True == length/# of values in courses
+    # Add to "Finished List"
+
+    # df3 = df2.columns
+    # print(df3)
+    # for name in df3.items():
+    #    print(f"name: {name}")
+
+
+if __name__ == "__main__":
+    # mainFunc(basecsv)
+    lpLevels(basecsv, lpcsv)