This community-built FAQ covers the “Choosing a Merge Method” exercise from the lesson “Merging Datasets”.
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This exercise can be found in the following Codecademy content:
[Beta] Python for Data Science: Working with Data
FAQs on the exercise Choosing a Merge Method
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This is the first question:
- We’ve loaded two DataFrames. The first,
results, contains data on the final score of each game. The second,
worldcupgames, lists every game in each FIFA World Cup.
You’ve been asked to use this data to determine the number of goals scored in each FIFA World Cup. Select the most precise method to merge these two DataFrames into a dataset capable of answering that question.
This is the code that i believe to be correct:
YOUR SOLUTION HERE
merged_df = pd.merge(results,
left_on = ‘date’,
right_on = ‘date’,
how = ‘inner’)
Results uising this code:
My point of view:
I believe that im possibly missing code for the count of goals scored
hey there, i believe you’re missing matches and having some random results because your left_on and right_on statements should contain lists with the date, home_team and away_team to merge or join both of the tables exactly how we need to
I´m being asked to merge this two tables results and worldcupgames,
I need to know how many goals were scored in each world cup, so my code is the following
merged_df = pd.merge(left = worldcupgames, right = results,
left_on = [‘date’, ‘home_team’, ‘away_team’],
right_on = [‘date’, ‘home_team’, ‘away_team’],
how = ‘left’)
since i just need the world cup games to then assign a result for each, i choosed to put the left table as worldcupgames and use a left merge, this should just do the work, keeping me with all the matches from worldcup, but i get this message
Thanks my g, i can’t remember what the answer was, but i got it figured out. Also didn’t realize that there is a ‘solution’ button. Hope your coding journey is going well!!
Like he mentioned there is a solution button if you click on the “get unstuck” button in the upper right hand corner.