如何在for循環中填充熊貓數據框列

Question

我正在嘗試在for循環中填充pandas dataframe列。 列名是參數性的，並由循環值分配。 這是我的代碼：

for k in range (-1, -4, -1):
    df_orj = pd.read_csv('something.csv', sep= '\t') 

    df_train = df_orj.head(11900)   
    df_test = df_orj.tail(720) 

    SHIFT = k

    df_train.trend = df_train.trend.shift(SHIFT)
    df_train = df_train.dropna()
    df_test.trend = df_test.trend.shift(SHIFT)
    df_test = df_test.dropna()

    drop_list = some_list

    df_out = df_test[['date','price']]
    df_out.index = np.arange(0, len(df_out)) # start index from 0
    df_out["pred-1"] = np.nan
    df_out["pred-2"] = np.nan
    df_out["pred-3"] = np.nan

    df_train.drop(drop_list, 1, inplace = True )
    df_test.drop(drop_list, 1, inplace = True )

    # some processes here

    rf = RandomForestClassifier(n_estimators = 10)
    rf.fit(X_train,y_train)
    y_pred = rf.predict(X_test)
    print("accuracy score: " , rf.score(X_test, y_test))


    X_test2 = sc.transform(df_test.drop('trend', axis=1))
    y_test2 = df_test['trend'].values

    y_pred2  = rf.predict(X_test2)
    print("accuracy score: ",rf.score(X_test2, y_test2))


    name = "pred{0}".format(k)
    for i in range (0, y_test2.size):
        df_out[name][i] = y_pred2[i]

df_out.head(20)

這是我的輸出：

                time_period_start  price_open  pred-1  pred-2  pred-3
697  2018-10-02T02:00:00.0000000Z       86.80     NaN     NaN     1.0
698  2018-10-02T03:00:00.0000000Z       86.65     NaN     NaN     1.0
699  2018-10-02T04:00:00.0000000Z       86.32     NaN     NaN     1.0

如您所見，僅pred-3被填充。 如何填充所有3個預定義列？

Answer 1

如果我正確理解的話，那么您的問題是，您只能在其他兩個都是nan的情況下填充pred-3。 這是因為您的df_out處於循環中，並且您正在獲得循環的上一次迭代的結果。 您應該在循環之外定義它，以便您的信息不會丟失給其他兩個。

Answer 2

您在每個循環中將這三列設置為空，因此在迭代時會丟失這些值。 可以將那些初始化列移至循環之前，也可以使用以下變量進行初始化：

變出

df_out["pred-1"] = np.nan
df_out["pred-2"] = np.nan
df_out["pred-3"] = np.nan

在循環時僅初始化單個列

name = "pred{0}".format(k)
df_out[name] = np.nan

如此完整的代碼：

for k in range (-1, -4, -1):
    df_orj = pd.read_csv('something.csv', sep= '\t') 

    df_train = df_orj.head(11900)   
    df_test = df_orj.tail(720) 

    SHIFT = k

    df_train.trend = df_train.trend.shift(SHIFT)
    df_train = df_train.dropna()
    df_test.trend = df_test.trend.shift(SHIFT)
    df_test = df_test.dropna()

    drop_list = some_list

    df_out = df_test[['date','price']]
    df_out.index = np.arange(0, len(df_out)) # start index from 0

    name = "pred{0}".format(k)
    df_out[name] = np.nan

    df_train.drop(drop_list, 1, inplace = True )
    df_test.drop(drop_list, 1, inplace = True )

    # some processes here

    rf = RandomForestClassifier(n_estimators = 10)
    rf.fit(X_train,y_train)
    y_pred = rf.predict(X_test)
    print("accuracy score: " , rf.score(X_test, y_test))


    X_test2 = sc.transform(df_test.drop('trend', axis=1))
    y_test2 = df_test['trend'].values

    y_pred2  = rf.predict(X_test2)
    print("accuracy score: ",rf.score(X_test2, y_test2))



    for i in range (0, y_test2.size):
        df_out[name][i] = y_pred2[i]

df_out.head(20)