如何通过数据框的行循环并计算每一行并继续其他行？答案

【问题标题】：How to make a loop through the rows of data frame and calculate on each row and go on other rows?如何通过数据框的行循环并计算每一行并继续其他行？
【发布时间】：2021-05-24 07:54:08
【问题描述】：

这是我的桌子

School  Year_16 ATAR_16 Year_17 ATAR_17 Year_18 ATAR_18 Year_19 ATAR_19 Year_20 ATAR_20
0   Perth Modern School 2016    95.55   2017    95.90   2018    97.00   2019    96.75   2020    97.55
1   Presbyterian Ladies' College    2016    92.90   2017    89.60   2018    86.90   2019    90.75   2020    89.20
2   Penrhos College 2016    92.65   2017    91.20   2018    88.15   2019    88.30   2020    90.65
3   Christ Church Grammar School    2016    92.50   2017    92.45   2018    91.60   2019    92.50   2020    92.50
4   Santa Maria College 2016    91.85   2017    89.90   2018    90.10   2019    87.45   2020    89.35

这是我的代码：

for i in df.index : 
    X_train = df['Year_16'][i], df['Year_17'][i], df['Year_18'][i], df['Year_19'][i]
    X_train = list(X_train)
    X_train=[[j] for j in X_train]
    X_train = np.array(X_train) 
    X_test = [df['Year_20'][i]]
    y_train = (df['ATAR_16'][i], df['ATAR_17'][i], df['ATAR_18'][i], df['ATAR_19'][i])
    y_train = list(y_train)
    y_train=[[j] for j in y_train]
    y_train = np.array(y_train) 
    y_test = (df['ATAR_20'][i])
    from sklearn.linear_model import LinearRegression
    regressor = LinearRegression()
    reg = regressor.fit(X_train, y_train)
    print(regressor.intercept_)
    print(regressor.coef_)
    X_2020 = np.array(X_test)
    X_2020 = X_2020.reshape(-1, 1)
    y_pred = reg.predict(X_2020)
    print(y_pred)
    y_pred = y_pred[0]
    predict = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})

我想要一张包含每所学校的实际和预测的表格，但这段代码给我的只是一所学校。我该如何解决这个问题？

【问题讨论】：

标签： python pandas scikit-learn

【解决方案1】：

使用predict 作为列表：

predict = []
for i in df.index
    ...
    predict.append(pd.DataFrame(...))
predict = pd.concat(predict)

你的代码是什么样子的：

predict = []
for i in df.index : 
    X_train = df['Year_16'][i], df['Year_17'][i], df['Year_18'][i], df['Year_19'][i]
    X_train = list(X_train)
    X_train=[[j] for j in X_train]
    X_train = np.array(X_train) 
    X_test = [df['Year_20'][i]]
    y_train = (df['ATAR_16'][i], df['ATAR_17'][i], df['ATAR_18'][i], df['ATAR_19'][i])
    y_train = list(y_train)
    y_train=[[j] for j in y_train]
    y_train = np.array(y_train) 
    y_test = (df['ATAR_20'][i])
    from sklearn.linear_model import LinearRegression
    regressor = LinearRegression()
    reg = regressor.fit(X_train, y_train)
    print(regressor.intercept_)
    print(regressor.coef_)
    X_2020 = np.array(X_test)
    X_2020 = X_2020.reshape(-1, 1)
    y_pred = reg.predict(X_2020)
    print(y_pred)
    y_pred = y_pred[0]
    predict.append(pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}, index=[df['School'][i]]))

>>> pd.concat(predict)
                              Actual  Predicted
Perth Modern School            97.55     97.475
Presbyterian Ladies' College   89.20     87.750
Penrhos College                90.65     86.050
Christ Church Grammar School   92.50     92.050
Santa Maria College            89.35     86.575

这是你要找的吗？

【讨论】：