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Mizuno notes and other items, such as scripts.

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Norm Rasmussen
2022-12-05 15:07:58 -05:00
parent ca2b56b6ed
commit c5dad714d9
21 changed files with 4903 additions and 357 deletions
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63
Scripts/Skuid_LPs/find_completed.py
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@ -1,10 +1,10 @@
from collections import Counter
import pandas as pd
import pandas as pd
basecsv = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/skuid_05lp.csv"
lpcsv = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/skuidlps.csv"
basecsv = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/Skuid_MCA125.csv"
lpcsv = "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/skuidlps2.csv"
'''
"""
Example multivalue dictionary
dict = {key1: [value1, value2, value3, value4],
@ -13,39 +13,48 @@ dict = {key1: [value1, value2, value3, value4],
So this could be used for each learning path. In other words:
learning_paths = {'01:Skuid Ethos' : ["Congratulations", "Create", "Skuid Resources"]} etc etc
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]},
{'03:Design System Studio' : ["Get Started with Design Systems", etc etc]},
'Level_2': [{'10 - Data' : ["Tips to Optimize", "Smarter Conditions"]},
{'11-Components': ["Battle", "Engage"]},
]
{'11-Components': ["Battle", "Engage"]},
]
}
How to create this by automation?
'''
"""
def lpLevels(lpcsv):
levels = pd.read_csv(
lpcsv,
index_col=False,
)
print(levels.Level.unique()) # Print only unique values from the Level column
lpcsv,
index_col=None,
header=None,
)
# print(levels.Level.unique()) # Print only unique values from the Level column
def mainFunc(basecsv):
readData = pd.read_csv(
basecsv,
index_col=False,
)
readData.drop_duplicates(subset='Course Name', keep="first")
emailGroups = readData.groupby("Email")["Course Name"].nunique()
print(emailGroups)
emailGroups = emailGroups.to_csv('/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/outtest.csv')
#if readData.loc[readData['Course Name'].isin([
basecsv,
)
# lp01 = readData[readData["Course Name"] ==
# print(lp01)
# people = lp01.groupby(["Email", "Learner Full Name"])["Course Name"].nunique()
# print(people)
# readData.drop_duplicates(subset="Course Name", keep="first")
lvl1 = readData.loc["Course Name"].any()
if lvl1.str.contains('Level 1'):
emailGroups = readData.groupby(["Email", "Learner Full Name"])["Course Name"].nunique()
print(emailGroups)
# emailGroups = people.to_csv(
# "/Users/normrasmussen/Documents/Northpass/Scripts/Skuid_LPs/outtest2.csv"
# )
# if readData.loc[readData['Course Name'].isin([
# 'Get Started with Models - Level 1',
# 'Configure Model Fields - Level 1',
# 'Configure Model Conditions - Level 1',
@ -55,11 +64,11 @@ def mainFunc(basecsv):
# extractedList = readData.loc[readData['Email'].tolist()]
# fourOccs = Counter(extractedList)
# finalNames = []
# for name, occurences in fourOccs.items():
# if occurences == 6:
# for name, occurrences in fourOccs.items():
# if occurrences == 6:
# finalNames.append(name)
if __name__ == "__main__":
#mainFunc(basecsv)
lpLevels(lpcsv)
if __name__ == "__main__":
mainFunc(basecsv)
# lpLevels(lpcsv)