text: import pandas as pd
  
  
  # TODO: Load up the table, and extract the dataset
  # out of it. If you're having issues with this, look
  # carefully at the sample code provided in the reading
  #
  df = pd.read_html('http://www.espn.com/nhl/statistics/player/_/stat/points/sort/points/year/2015/seasontype/2', header=1)[0]
  df.GP = pd.to_numeric(df.GP, errors='coerce')
  df.G = pd.to_numeric(df.G, errors='coerce')
  df.A = pd.to_numeric(df.A, errors='coerce')
  df.PTS = pd.to_numeric(df.PTS, errors='coerce')
  df['+/-'] = pd.to_numeric(df['+/-'], errors='coerce')
  df.PIM = pd.to_numeric(df.PIM, errors='coerce')
  df['PTS/G'] = pd.to_numeric(df['PTS/G'], errors='coerce')
  df.SOG = pd.to_numeric(df.SOG, errors='coerce')
  df.PCT = pd.to_numeric(df.PCT, errors='coerce')
  df.GWG = pd.to_numeric(df.GWG, errors='coerce')
  df['G.1'] = pd.to_numeric(df['G.1'], errors='coerce')
  df['A.1'] = pd.to_numeric(df['A.1'], errors='coerce')
  df['G.2'] = pd.to_numeric(df['G.2'], errors='coerce')
  df['A.2'] = pd.to_numeric(df['A.2'], errors='coerce')
  
  # TODO: Rename the columns so that they are similar to the
  # column definitions provided to you on the website.
  # Be careful and don't accidentially use any names twice.
  #
  # .. your code here ..
  
  
  # TODO: Get rid of any row that has at least 4 NANs in it,
  # e.g. that do not contain player points statistics
  #
  df = df.dropna(axis=0, thresh=4)
  
  
  # TODO: At this point, look through your dataset by printing
  # it. There probably still are some erroneous rows in there.
  # What indexing command(s) can you use to select all rows
  # EXCEPT those rows?
  #
  # .. your code here ..
  print(df.dropna(axis=0))
  
  
  # TODO: Get rid of the 'RK' column
  #
  df = df.drop(labels=['RK'], axis=1)
  
  
  # TODO: Ensure there are no holes in your index by resetting
  # it. By the way, don't store the original index
  #
  # .. your code here ..
  df = df.reset_index(drop=True)
  
  
  # TODO: Check the data type of all columns, and ensure those
  # that should be numeric are numeric
  #
  # .. your code here ..
  df.dtypes
  
  
  
  # TODO: Your dataframe is now ready! Use the appropriate 
  # commands to answer the questions on the course lab page.
  #
  # .. your code here ..
  df.loc[15, 'GP'] + df.loc[16, 'GP']
  
  