<jupyter_start><jupyter_text>Cast Ballot Weight Comparison TableA vote cast in Alaska has 1.61 times more power than one cast in California before the results are known<jupyter_code>voteWeightsOrdered = [list(i / j for j in weights) for i in weights]
table = pd.DataFrame(voteWeightsOrdered, columns=list(abbrevs), index=list(states))
table
abbrevs = df["Abbreviation"]
states = df["State"]
weights = df["Final Ballot Weight"]<jupyter_output><empty_output><jupyter_text>Final Ballot Weight Comparison TableA vote cast in California for the winning candidate had 1.46 times more power than one cast for the winning candidate in Alabama. We know this after the results of the election are known<jupyter_code>voteWeightsOrdered = [list(i / j for j in weights) for i in weights]
table = pd.DataFrame(voteWeightsOrdered, columns=list(abbrevs), index=list(states))
table
abbrevs = df["Abbreviation"]
states = df["State"]
weights = df["Necessary Ballot Weight"]<jupyter_output><empty_output><jupyter_text>Final Ballot Weight Comparison Table (Necessary Votes)A vote cast in California for the winning candidate had 1.46 times more power than one cast for the winning candidate in Alabama. We know this after the results of the election are known<jupyter_code>voteWeightsOrdered = [list(i / j for j in weights) for i in weights]
table = pd.DataFrame(voteWeightsOrdered, columns=list(abbrevs), index=list(states))
table<jupyter_output><empty_output>
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