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License: MIT License
Code and associated files for the AI Programming with Python Nanodegree Program
License: MIT License
Hello, I downloaded Mean Normalization and Data Separation.ipynb from https://github.com/udacity/AIPND/tree/master/NumPy%20Mini-Project, but the following error message appeared when I tried to open the file in a Jupyter Notebook:
"Unreadable Notebook: C:\Users\Christophe\Documents\Informatique\MOOC\Data Scientist Nanodegree - Udacity\Jupyter Notebooks\Mean Normalization and Data Separation.ipynb NotJSONError("Notebook does not appear to be JSON: '\n\n\n\n\n\n\n<html lang...")"
Any suggestions? Thanks.
A file is missing from the Matplotlib / data directory. A dataset is consistently used in the lessons that includes "Alpha","Beta", "Gamma", etc. values.
In order to properly do those exercises, I had to randomly generate Grade data as a substitute:
# Back to auto-generated data for the following steps
student_grades = []
for student in range(30):
student_grades.append(random.choice(['A','B','C','D','F']))
grades = pd.DataFrame({'Grades': pd.Series(student_grades)})
grades.head()
I had to replace all instances of df in his example code with grades, and replaced all instances of cat_var with Grades. His examples in messing with the resulting bar graphs should apply in the same way with grades as he is doing with Alpha, Beta, etc.
This worked properly with examples, such as categorical ordering:
# With grades, or categorical / ordinal data that is ranked by type,
# we may care about an explicit order. Ordering it up!
level_order = ['A', 'B', 'C', 'D', 'F']
ordered_cat = pd.api.types.CategoricalDtype(ordered = True, categories = level_order)
grades['Grades'] = grades['Grades'].astype(ordered_cat)
# Display graph
base_color = sb.color_palette()[0]
sb.countplot(data = grades, x = 'Grades', color = base_color);
I have added more detailed information as an answer to a question in the Udacity Knowledge Base, where someone ran into the same problem, accessible behind an authenticated page: How to practice examples (matplotlib/seaborn) with no .csv file available for download??
in the panda mini project's solution , in the input7 error
The usage of variable 'found' starting from line 148 is not required and can be done without it. Usage of 'found' is creating unboundLocalError.
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