(d) Let's assume an equal distance between each label so that we can translate individual scores...

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(d) Let's assume an equal distance between each label so that we can translate individual scores to a percentage. This results in the following scores and add a column called score to you dataframe and place the computed score for each answer: n [34]: equiv = {7001:1, 8001:2, 9001:3} df = pd.DataFrame({"A": [7001, 8001, 9001]} ) df["B"] = df["A"].map(equiv) print(df) TypeError Input In [34], in () 1 equiv = {7001:1, 8001:2, 9001:3} Traceback (most recent call last) 2 df = pd.DataFrame({"answer": [7001, 8001, 9001]} ) ----> 3 df["score"] = equiv(df["answer"]) 4 print(df) TypeError: 'dict' object is not callable Label Description Score 1 strongly disagree 0.20 2 disagree 0.40 3 neutral 0.60 4 agree 0.80 5 strongly disagree 1.00 (d) Let's assume an equal distance between each label so that we can translate individual scores to a percentage. This results in the following scores and add a column called score to you dataframe and place the computed score for each answer: n [34]: equiv = {7001:1, 8001:2, 9001:3} df = pd.DataFrame({"A": [7001, 8001, 9001]} ) df["B"] = df["A"].map(equiv) print(df) TypeError Input In [34], in () 1 equiv = {7001:1, 8001:2, 9001:3} Traceback (most recent call last) 2 df = pd.DataFrame({"answer": [7001, 8001, 9001]} ) ----> 3 df["score"] = equiv(df["answer"]) 4 print(df) TypeError: 'dict' object is not callable Label Description Score 1 strongly disagree 0.20 2 disagree 0.40 3 neutral 0.60 4 agree 0.80 5 strongly disagree 1.00