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Mixing indexes and columns

It's time to look more into player safety! But first, you and the team want to settle a bet on the correlation between (1) the draft round in which a player was chosen and (2) current salary. You were able to find relevant player data from a fantasy football web site through it's API. As is common with APIs, the data returned to you is in JSON format.

The first file has already been loaded to a data frame called current. You will be loading a second file, draft.json. As the filename implies, the data file is in JSON format.

This exercise is part of the course

Pandas Joins for Spreadsheet Users

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Exercise instructions

  • View the first 5 rows of current.
  • Load draft.json using pd.read_json() and view the first 5 rows.
  • Merge the two data frames on player_id, noting whether it's the index or a data column.
  • Create a scatter plot of 'draft_round' vs. 'salary_millions'.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# View first 5 rows of 'current'
print(____.head())

# Load `draft.json` and view first 5 rows
draft = pd.____(____, orient='split')
print(draft.____())

# Merge data frames
players = current.merge(____, how='outer', left_index=____,  right_on=____)

# Create scatter plot
players.plot.scatter(____, 'salary_millions')
plt.show()
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