下記の設問に対する答えとして相応しものを選択肢から選び、次のコードの空欄（##########）を埋めてください．
Q1： “http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data” から csv 形式のワインに関数データを読み込み、DataFrame 形式で変数 df_wine に代入してください．
また、列名は “連番” で読み込んでください．
Q2：wine_df のカラムに、”, ‘Alcohol’, “Malic acid”, “Ash”, “Alcalinity of ash”, “Magnesium”, “Total phenols”, “Flavanoids”, “Nonflavanoid phenol”, “Proanthocyanis”,
“Color intensity”, “Hue”, “OD280/0D315 of diluted wines”, “Proline” を追加してください．
import pandas as pd
# Q1
wine_df = ##########
# Q2
##########
wine_df.head()
[Q1 の選択肢]
1. pd.read_csv(“http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data”, header=True)
2. pd.read_csv(“http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data”, header=None)
3. pd.csv_read(“http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data”, header=True)
4. pd.csv_read(“http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data”, header=None)
[Q2 の選択肢]
1. wine_df.columns = {0 : ”, 1 : ‘Alcohol’, 2 : “Malic acid”, 3 : “Ash”, 4 : “Alcalinity of ash”, 5 : “Magnesium”, 6 : “Total phenols”, 7 : “Flavanoids”, 8 : “Nonflavanoid phenol”, 9 : “Proanthocyanis”,
10 : “Color intensity”, 11 : “Hue”, 12 : “OD280/0D315 of diluted wines”, “Proline”}
2. wine_df.columns = [”, ‘Alcohol’, “Malic acid”, “Ash”, “Alcalinity of ash”, “Magnesium”, “Total phenols”, “Flavanoids”, “Nonflavanoid phenol”, “Proanthocyanis”,
“Color intensity”, “Hue”, “OD280/0D315 of diluted wines”, “Proline”]
3. wine_d[“columns”] = {0 : ”, 1 : ‘Alcohol’, 2 : “Malic acid”, 3 : “Ash”, 4 : “Alcalinity of ash”, 5 : “Magnesium”, 6 : “Total phenols”, 7 : “Flavanoids”, 8 : “Nonflavanoid phenol”, 9 : “Proanthocyanis”,
10 : “Color intensity”, 11 : “Hue”, 12 : “OD280/0D315 of diluted wines”, “Proline”}
4. wine_df[“columns”] = [”, ‘Alcohol’, “Malic acid”, “Ash”, “Alcalinity of ash”, “Magnesium”, “Total phenols”, “Flavanoids”, “Nonflavanoid phenol”, “Proanthocyanis”,
“Color intensity”, “Hue”, “OD280/0D315 of diluted wines”, “Proline”]