本文将带您了解关于合并来自原始pandasDataFrame的model.predict的新内容，同时我们还将为您解释结果？的相关知识，另外，我们还将为您提供关于dicttopandaspandasD

本文将带您了解关于合并来自原始pandas DataFrame的model.predict的新内容，同时我们还将为您解释结果？的相关知识，另外，我们还将为您提供关于dict to pandas pandas DataFrame的dict列表、Pandas - 使用一个 Dataframe 列的子字符串比较两个 Dataframe、Pandas Dataframe 在两个不同的键上合并以获取原始数据、Pandas DataFrame.to_sql（）函数是否需要后续的commit（）？的实用信息。

• 合并来自原始pandas DataFrame的model.predict（）结果？（合并dataframe的方法）
• dict to pandas pandas DataFrame的dict列表
• Pandas - 使用一个 Dataframe 列的子字符串比较两个 Dataframe
• Pandas Dataframe 在两个不同的键上合并以获取原始数据
• Pandas DataFrame.to_sql（）函数是否需要后续的commit（）？

合并来自原始pandas DataFrame的model.predict（）结果？（合并dataframe的方法）

我正在尝试将predict方法的结果与pandas.DataFrame对象中的原始数据合并在一起。

from sklearn.datasets import load_irisfrom sklearn.cross_validation import train_test_splitfrom sklearn.tree import DecisionTreeClassifierimport pandas as pdimport numpy as npdata = load_iris()# bear with me for the next few steps... I''m trying to walk you through# how my data object landscape looks... i.e. how I get from raw data # to matrices with the actual data I have, not the iris dataset# put feature matrix into columnar format in dataframedf = pd.DataFrame(data = data.data)# add outcome variabledf[''class''] = data.targetX = np.matrix(df.loc[:, [0, 1, 2, 3]])y = np.array(df[''class''])# finally, split into train-testX_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8)model = DecisionTreeClassifier()model.fit(X_train, y_train)# I''ve got my predictions nowy_hats = model.predict(X_test)

要将这些预测与原始预测合并起来df，我可以尝试以下方法：

df[''y_hats''] = y_hats

但这引起了：

ValueError：值的长度与索引的长度不匹配

我知道可以将分解df为train_df，test_df并且这个问题可以解决，但实际上，我需要按照上述路径创建矩阵，X并且y（我的实际问题是文本分类问题，在分解之前，我要对
整个 特征矩阵进行归一化培训和测试）。df由于y_hats数组的索引为零，并且关于 哪些
行包含在中X_test并y_test丢失了，因此如何将这些预测值与我的适当行对齐？还是将我降级为先将数据帧拆分为训练测试，然后再构建特征矩阵？我想只需填写包括在行train与np.nan
数据框中的值。

答案1

您的y_hats长度仅是测试数据上的长度（20％），因为您在X_test上进行了预测。一旦模型通过验证并且对测试预测满意（通过检查模型在X_test预测上与X_test真实值相比的准确性），您应该在完整数据集（X）上重新运行预测。将这两行添加到底部：

y_hats2 = model.predict(X)df[''y_hats''] = y_hats2

*根据您的评论进行 *编辑 ，这是一个更新的结果，返回带有预测的数据集并附加在测试数据集中

from sklearn.datasets import load_irisfrom sklearn.cross_validation import train_test_splitfrom sklearn.tree import DecisionTreeClassifierimport pandas as pdimport numpy as npdata = load_iris()# bear with me for the next few steps... I''m trying to walk you through# how my data object landscape looks... i.e. how I get from raw data # to matrices with the actual data I have, not the iris dataset# put feature matrix into columnar format in dataframedf = pd.DataFrame(data = data.data)# add outcome variabledf_class = pd.DataFrame(data = data.target)# finally, split into train-testX_train, X_test, y_train, y_test = train_test_split(df,df_class, train_size = 0.8)model = DecisionTreeClassifier()model.fit(X_train, y_train)# I''ve got my predictions nowy_hats = model.predict(X_test)y_test[''preds''] = y_hatsdf_out = pd.merge(df,y_test[[''preds'']],how = ''left'',left_index = True, right_index = True)

dict to pandas pandas DataFrame的dict列表

item1 = {''name'': ''A'',''parameters'': [{''Parameter'': ''P1'',''Value'': 0.0},{''Parameter'': ''P2'',''Value'': 0.4}]}
item2 = {''name'': ''B'',''Value'': 3},''Value'': 7}]}
future_df = [item1,item2]

df = {}
for item in future_df:
    dic = {key[''Parameter'']:key[''Value''] for key in item[''parameters'']}
    df[future_df[''name'']] = dic
    
df = pd.DataFrame(df.values(),index=df.keys())

def ToDF(item):
    x = pd.DataFrame(item[''parameters''])
    x.insert(0,"name",item[''name''])
    return x.pivot_table(columns="Parameter",values="Value",index="name")

df = pd.concat([ToDF(i) for i in future_df])
df

Parameter   P1   P2
name               
A          0.0  0.4
B          3.0  7.0

print (pd.DataFrame({i["name"]: {v["Parameter"]: v["Value"] for v in i["parameters"]}
                     for i in future_df}).T)

    P1   P2
A  0.0  0.4
B  3.0  7.0

df1.merge(df2,left_on = df2.prod_ref.str.extract(''(\d+)'',expand = False),right_on = df1.prod_id.str.extract(''(\d+)'',how = ''left'')

d = {''ID'':[''A1'',''A2'',''A3'',''A4''],''ID2'': [''B1'',''B2'',''B3'',''B4''],''B'':[-1,5,6,7000],''ExtD'':[''CA'',''CB'',''CC'',''CD'']}
df = pd.DataFrame(data=d)
df

d = {''RetID'':[''A1'',''C'':[1.3,5.4,4.5,1.3]}
df2 = pd.DataFrame(data=d)
df2

df2 = df2.merge(df[[''ID'',''ExtD'',]],''left'',left_on=[''RetID''],right_on=[''ID''])
df2 = df2.merge(df[[''ID2'',''ExtD'']],right_on=[''ID2''],suffixes = (''_d1'',''_d2''))
df2.rename({''ExtD_d1'':''ExtD''},axis=1,inplace=True)
df2.loc[df2[''ExtD''].isnull(),''ExtD''] = df2[''ExtD_d2'']
df2.drop({''ID2'',''ExtD_d2''},inplace=True)

res = {''RetID'':[''A1'',1.3],''CD'']}
df2= pd.DataFrame(data=res)
df2

res = {''RetID'':[''A1'',''B1'',''B2''],''CD''],''ID'':[''A1'',''B4'']}

tmp = df.melt(''ExtD'',value_vars=[''ID'',''ID2''],value_name=''RetID'')
df2 = df2.merge(tmp[[''ExtD'',''RetID'']])

>>> df2
  RetID    C ExtD
0    A1  1.3   CA
1    A2  5.4   CB
2    B1  4.5   CA
3    B2  1.3   CB

>>> tmp
  ExtD variable RetID
0   CA       ID    A1
1   CB       ID    A2
2   CC       ID    A3
3   CD       ID    A4
4   CA      ID2    B1
5   CB      ID2    B2
6   CC      ID2    B3
7   CD      ID2    B4

df2 = df2.merge(df[[''ID'',''ID2'',''ExtD'']],on=''ExtD'')

>>> df2
  RetID    C ExtD  ID ID2
0    A1  1.3   CA  A1  B1
1    A2  5.4   CB  A2  B2
2    B3  4.5   CC  A3  B3
3    B4  1.3   CD  A4  B4

conn.executemany(self.insert_statement(), data_list)

def run_transaction(self):    cur = self.con.cursor()    try:        yield cur        self.con.commit()    except:        self.con.rollback()        raise    finally:        cur.close()