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