熊貓創建從列表字典的字典的df

Question

我有一個動態填充的數據結構，所以鍵和子鍵的數量是未知的。我想將它轉換成熊貓df。該結構看起來像這樣

datastore = { 
    "user1":{ 
     "time1":[1,2,3,4], 
     "time2":[5,6,7,8], 
     "time3":[1,2,3,4] }, 
    "user2":{ 
     "time1":[1,2,3,4], 
     "time2":[5,6,7,8] } 
} 

類型的字典用字典列出了價值

我想把它轉換成熊貓DF這樣

index users times x y z k 
0  user1 time1 1 2 3 4 
1  user1 time2 5 6 7 8 
2  user1 time3 1 2 3 4 
3  user2 time1 1 2 3 4 
4  user2 time2 5 6 7 8 
.... 

我試過pd.DataFrame（字典），from_dict方法，但無法使其工作。任何幫助，將不勝感激。

編輯：很抱歉的語法錯誤，固定

Answer 1

這裏有一個方法

datastore = { 
"user1":{ 
    "time1":[1,2,3,4], 
    "time2":[5,6,7,8], 
    "time3":[1,2,3,4] }, 
"user2":{ 
    "time1":[1,2,3,4], 
    "time2":[5,6,7,8]} 
} 

我們可以使用pd.DataFrame（）與字典則棧（）它，然後reset_index（）它

df = pd.DataFrame(datastore).stack().reset_index() 
print(df) 
    level_0 level_1    0 
0 time1 user1 [1, 2, 3, 4] 
1 time1 user2 [1, 2, 3, 4] 
2 time2 user1 [5, 6, 7, 8] 
3 time2 user2 [5, 6, 7, 8] 
4 time3 user1 [1, 2, 3, 4] 

現在我們將列表中的列表分割爲0，應用pd.Series，然後將它們加入到level_1和level_2。一些列重命名，我們就大功告成了

df = df[['level_1', 'level_0']].join(df[0].apply(pd.Series)) 
df.columns = ['users', 'times', 'x', 'y', 'z', 'k'] 
print(df) 
    users times x y z k 
0 user1 time1 1 2 3 4 
1 user2 time1 1 2 3 4 
2 user1 time2 5 6 7 8 
3 user2 time2 5 6 7 8 
4 user1 time3 1 2 3 4 

Answer 2

選項1

pd.DataFrame.from_dict(datastore, 'index').stack() \ 
    .rename_axis(['users', 'times']) \ 
    .apply(pd.Series, index=list('xyzk')).reset_index() 

    users times x y z k 
0 user1 time1 1 2 3 4 
1 user1 time2 5 6 7 8 
2 user1 time3 1 2 3 4 
3 user2 time1 1 2 3 4 
4 user2 time2 5 6 7 8 

---

選項2

pd.DataFrame(
    [[u, t] + l for u, td in datastore.items() for t, l in td.items()], 
    columns='users times x y z k'.split() 
) 

    users times x y z k 
0 user1 time1 1 2 3 4 
1 user1 time2 5 6 7 8 
2 user1 time3 1 2 3 4 
3 user2 time1 1 2 3 4 
4 user2 time2 5 6 7 8 

---

定時

%timeit pd.DataFrame.from_dict(datastore, 'index').stack().rename_axis(['users', 'times']).apply(pd.Series, index=list('xyzk')).reset_index() 
%timeit pd.DataFrame([[u, t] + l for u, td in datastore.items() for t, l in td.items()], columns='users timets x y z k'.split()) 

100 loops, best of 3: 2.72 ms per loop 
1000 loops, best of 3: 556 µs per loop 

---

DEBUG
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import pandas as pd 

datastore = { 
    "user1":{ 
     "time1":[1,2,3,4], 
     "time2":[5,6,7,8], 
     "time3":[1,2,3,4] }, 
    "user2":{ 
     "time1":[1,2,3,4], 
     "time2":[5,6,7,8]} 
} 

pd.DataFrame.from_dict(datastore, 'index').stack() \ 
    .rename_axis(['users', 'times']) \ 
    .apply(pd.Series, index=list('xyzk')).reset_index()