Python 在数据框列中拆分值

我有一个数据帧名称df，我想删除fuel列中的这个“|”

id  car       fuel
1   Mercedes  petrol|diesel|gas
2   Audi      gas|petrol   

所以我的数据看起来像这样

id  car        fuel
1   Mercedes   petrol
1   Mercedes   diesel
1   Mercedes   gas
2   Audi       gas
2   Audi       petrol

这是我试过的代码

df_1=hb.copy（）
df_2=hb.copy（）
df_3=hb.copy（）
df_1['fuel']=df_1['fuel'].应用（λx:x.split（'|'）[0]）
df_2['fuel']=df_2['fuel'].应用（λx:x.split（“|”）[1]）
df_3['fuel']=df_3['fuel'].应用（λx:x.split（“|”）[2]）

---

这将使Indexer:list索引超出范围

您可以尝试以下操作：

#Create the dataframe
df = pd.DataFrame({
        "id":[1,2],
        "car":["Mercedes","Audi"],
        "fuel":["petrol|diesel|gas","gas|petrol"]
        })

#Create a new dataframe from the series, with car as the index
new_df = pd.DataFrame(df.fuel.str.split('|').tolist(), index=df.car).stack()

#Get rid of the secondary index
new_df = new_df.reset_index([0, 'car'])

#Add the 'id' back to the dataframe
#Note: There is probably a much more elegant way of doing this
new_df.loc[:,'id'] = new_df.car.apply(lambda x: df[df.loc[:,'car'] == x].id.values[0])

#Rename the columns
new_df.columns = ["car","fuel","id"]

---

您可以尝试以下方法：

#Create the dataframe
df = pd.DataFrame({
        "id":[1,2],
        "car":["Mercedes","Audi"],
        "fuel":["petrol|diesel|gas","gas|petrol"]
        })

#Create a new dataframe from the series, with car as the index
new_df = pd.DataFrame(df.fuel.str.split('|').tolist(), index=df.car).stack()

#Get rid of the secondary index
new_df = new_df.reset_index([0, 'car'])

#Add the 'id' back to the dataframe
#Note: There is probably a much more elegant way of doing this
new_df.loc[:,'id'] = new_df.car.apply(lambda x: df[df.loc[:,'car'] == x].id.values[0])

#Rename the columns
new_df.columns = ["car","fuel","id"]

试试这个：

    df=pd.DataFrame({'car':['Mercedes','Audi'],'fuel':['petrol|diesel|gas','gas|petrol']}) #your dataframe
    df2=pd.DataFrame()                                       #new black dataframe
    for i in range(0,len(df)):                               #iterating over df
        list1=df.iloc[i,1].split('|')                        #split each value of 'fuel' and store it in a list
        for j in range(0,len(list1)):                        #iterating over list1
            list2={'car':df.iloc[i,0],'fuel':list1[j]}       #make a dict of each combination of 'car' and elements of list1-'fuel'
            df2=df2.append(list2,ignore_index=True)          #append each value to the blank df

试试这个：

    df=pd.DataFrame({'car':['Mercedes','Audi'],'fuel':['petrol|diesel|gas','gas|petrol']}) #your dataframe
    df2=pd.DataFrame()                                       #new black dataframe
    for i in range(0,len(df)):                               #iterating over df
        list1=df.iloc[i,1].split('|')                        #split each value of 'fuel' and store it in a list
        for j in range(0,len(list1)):                        #iterating over list1
            list2={'car':df.iloc[i,0],'fuel':list1[j]}       #make a dict of each combination of 'car' and elements of list1-'fuel'
            df2=df2.append(list2,ignore_index=True)          #append each value to the blank df

这是一种方法

Ex:

df = pd.DataFrame({
        "id":[1,2],
        "car":["Mercedes","Audi"],
        "fuel":["petrol|diesel|gas","gas|petrol"]
        })
df["fuel"] = df["fuel"].str.split("|")
#Ref https://stackoverflow.com/a/48532692/532312
lst_col = 'fuel'
df = pd.DataFrame({
      col:np.repeat(df[col].values, df[lst_col].str.len())
      for col in df.columns.drop(lst_col)}
    ).assign(**{lst_col:np.concatenate(df[lst_col].values)})[df.columns]
print(df)

        car    fuel  id
0  Mercedes  petrol   1
1  Mercedes  diesel   1
2  Mercedes     gas   1
3      Audi     gas   2
4      Audi  petrol   2

输出：

df = pd.DataFrame({
        "id":[1,2],
        "car":["Mercedes","Audi"],
        "fuel":["petrol|diesel|gas","gas|petrol"]
        })
df["fuel"] = df["fuel"].str.split("|")
#Ref https://stackoverflow.com/a/48532692/532312
lst_col = 'fuel'
df = pd.DataFrame({
      col:np.repeat(df[col].values, df[lst_col].str.len())
      for col in df.columns.drop(lst_col)}
    ).assign(**{lst_col:np.concatenate(df[lst_col].values)})[df.columns]
print(df)

        car    fuel  id
0  Mercedes  petrol   1
1  Mercedes  diesel   1
2  Mercedes     gas   1
3      Audi     gas   2
4      Audi  petrol   2

这是一种方法

Ex:

df = pd.DataFrame({
        "id":[1,2],
        "car":["Mercedes","Audi"],
        "fuel":["petrol|diesel|gas","gas|petrol"]
        })
df["fuel"] = df["fuel"].str.split("|")
#Ref https://stackoverflow.com/a/48532692/532312
lst_col = 'fuel'
df = pd.DataFrame({
      col:np.repeat(df[col].values, df[lst_col].str.len())
      for col in df.columns.drop(lst_col)}
    ).assign(**{lst_col:np.concatenate(df[lst_col].values)})[df.columns]
print(df)

        car    fuel  id
0  Mercedes  petrol   1
1  Mercedes  diesel   1
2  Mercedes     gas   1
3      Audi     gas   2
4      Audi  petrol   2

输出：

df = pd.DataFrame({
        "id":[1,2],
        "car":["Mercedes","Audi"],
        "fuel":["petrol|diesel|gas","gas|petrol"]
        })
df["fuel"] = df["fuel"].str.split("|")
#Ref https://stackoverflow.com/a/48532692/532312
lst_col = 'fuel'
df = pd.DataFrame({
      col:np.repeat(df[col].values, df[lst_col].str.len())
      for col in df.columns.drop(lst_col)}
    ).assign(**{lst_col:np.concatenate(df[lst_col].values)})[df.columns]
print(df)

        car    fuel  id
0  Mercedes  petrol   1
1  Mercedes  diesel   1
2  Mercedes     gas   1
3      Audi     gas   2
4      Audi  petrol   2

---

可能的重复是如此令人困惑可能的重复是如此令人困惑谢谢你的回答，但这不是我为大数据集找到的正确答案。问题是什么？太慢了吗？谢谢你的回答，但这不是我为大数据集找到的正确答案。问题是什么？是不是太慢了？