[英]How to fill pandas dataframe columns in for loop
我正在尝试在for循环中填充pandas
dataframe列。 列名是参数性的,并由循环值分配。 这是我的代码:
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)
这是我的输出:
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
如您所见,仅pred-3
被填充。 如何填充所有3个预定义列?
如果我正确理解的话,那么您的问题是,您只能在其他两个都是nan的情况下填充pred-3。 这是因为您的df_out处于循环中,并且您正在获得循环的上一次迭代的结果。 您应该在循环之外定义它,以便您的信息不会丢失给其他两个。
您在每个循环中将这三列设置为空,因此在迭代时会丢失这些值。 可以将那些初始化列移至循环之前,也可以使用以下变量进行初始化:
变出
df_out["pred-1"] = np.nan
df_out["pred-2"] = np.nan
df_out["pred-3"] = np.nan
在循环时仅初始化单个列
name = "pred{0}".format(k)
df_out[name] = np.nan
如此完整的代码:
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)
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