Manipulate a DataFrame

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import pandas as pd
import numpy as np

# Since we will be working with ratings, we will set the precision of our 
# dataframes to one decimal place.
pd.set_option('precision', 1)

# Create a Pandas DataFrame that contains the ratings some users have given to a
# series of books. The ratings given are in the range from 1 to 5, with 5 being
# the best score. The names of the books, the authors, and the ratings of each user
# are given below:

books = pd.Series(data = ['Great Expectations', 'Of Mice and Men', 'Romeo and Juliet', 'The Time Machine', 'Alice in Wonderland' ])
authors = pd.Series(data = ['Charles Dickens', 'John Steinbeck', 'William Shakespeare', ' H. G. Wells', 'Lewis Carroll' ])

user_1 = pd.Series(data = [3.2, np.nan ,2.5])
user_2 = pd.Series(data = [5., 1.3, 4.0, 3.8])
user_3 = pd.Series(data = [2.0, 2.3, np.nan, 4])
user_4 = pd.Series(data = [4, 3.5, 4, 5, 4.2])

# Users that have np.nan values means that the user has not yet rated that book.
# Use the data above to create a Pandas DataFrame that has the following column
# labels: 'Author', 'Book Title', 'User 1', 'User 2', 'User 3', 'User 4'. Let Pandas
# automatically assign numerical row indices to the DataFrame. 

# Create a dictionary with the data given above
dat = 

# Use the dictionary to create a Pandas DataFrame
book_ratings = 

# If you created the dictionary correctly you should have a Pandas DataFrame
# that has column labels: 'Author', 'Book Title', 'User 1', 'User 2', 'User 3',
# 'User 4' and row indices 0 through 4.

# Now replace all the NaN values in your DataFrame with the average rating in
# each column. Replace the NaN values in place. HINT: you can use the fillna()
# function with the keyword inplace = True, to do this. Write your code below:

import pandas as pd
import numpy as np

pd.set_option('precision', 1)

books = pd.Series(data = ['Great Expectations', 'Of Mice and Men', 'Romeo and Juliet', 'The Time Machine', 'Alice in Wonderland' ])
authors = pd.Series(data = ['Charles Dickens', 'John Steinbeck', 'William Shakespeare', ' H. G. Wells', 'Lewis Carroll' ])
user_1 = pd.Series(data = [3.2, np.nan ,2.5])
user_2 = pd.Series(data = [5., 1.3, 4.0, 3.8])
user_3 = pd.Series(data = [2.0, 2.3, np.nan, 4])
user_4 = pd.Series(data = [4, 3.5, 4, 5, 4.2])

dat = {'Book Title' : books,
       'Author' : authors,
       'User 1' : user_1,
       'User 2' : user_2,
       'User 3' : user_3,
       'User 4' : user_4}

book_ratings = pd.DataFrame(dat)

book_ratings.fillna(book_ratings.mean(), inplace = True)

INSTRUCTOR NOTE:

From the DataFrame above you can now pick all the books that had a rating of 5. You can do this in just one line of code. Try to do it yourself first, you'll find the answer below:

best_rated = book_ratings[(book_ratings == 5).any(axis = 1)]['Book Title'].values

The code above returns a NumPy ndarray that only contains the names of the books that had a rating of 5.

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