import pandas as pd source_url="https://raw.githubusercontent.com/austinlasseter/hosting_some_files/main/pandas_files/Production.Product.csv"

# read the dataset as a DataFrame into a variable named 'prod_df' prod_df = pd.read_csv(source_url, sep = '\t') # if get ParserError: Error tokenizing data. C error: Expected 1 fields in line 211, saw 2 # ... might need to use seperator

# your answer here prod_df.head(4)

# your answer here prod_df.shape[0]

# your answer here print(int(prod_df.shape[1]))

# your answer here prod_df['ProductLine'].nunique()

# your answer here list(prod_df['ProductLine'].unique())

# your answer here # No, because the unique function included NaN's (Not a Number)

# your answer here list(prod_df['ProductLine'].unique()) len(list(prod_df['ProductLine'].unique())[1])

# your answer here #2 but expected 1 # there's a trailing whitespace # arg len(list(prod_df['ProductLine'].unique())[1].strip())

# your answer here prod_df[prod_df['ProductLine']=='R '].shape[0]

# your answer here prod_df[(prod_df['ProductLine']=='M ') & (prod_df['Style']=='W ')].shape[0] #ProductLine - R = Road, M = Mountain, T = Touring, S = Standard #Style - W = Womens, M = Mens, U = Universal

# your answer here # create boolean index for women's and mountain w_m = (prod_df['ProductLine']=='M ') & (prod_df['Style']=='W ') w_m.head()

# create boolean index for silver s = (prod_df['Color']=='Silver') s.head()

# combine those indices combo = w_m | s combo.head()

# create placeholders for column name lists and dictionaries cols = ['Name', 'ListPrice'] new_cols = {'Name': 'NewName'}

# sort values and display # Kathy way prod_df[combo][['Name', 'ListPrice']].sort_values(by='ListPrice', ascending=False).head(3)