将数据帧分解为子集/组或子集的新数据帧.从其他数据框创建数据子集/组的新数据框 [英] Dismantle dataframe into new dataframes of subsets/groups resp. create new dataframes of data subsets/groups from other dataframe
问题描述
我有一个如下所示的pandas数据框，并通过列id保存数据组:
I have a pandas dataframe that looks like the following and holds groups of data via a column id:
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))
df['id'] = ['W', 'W', 'W', 'Z', 'Z', 'Y', 'Y', 'Y', 'Z', 'Z']
print(df)
A B C D id
0 0.347501 -1.152416 1.441144 -0.144545 w
1 0.775828 -1.176764 0.203049 -0.305332 w
2 1.036246 -0.467927 0.088138 -0.438207 w
3 -0.737092 -0.231706 0.268403 0.464026 x
4 -1.857346 -1.420284 -0.515517 -0.231774 x
5 -0.970731 0.217890 0.193814 -0.078838 y
6 -0.318314 -0.244348 0.162103 1.204386 y
7 0.340199 1.074977 1.201068 -0.431473 y
8 0.202050 0.790434 0.643458 -0.068620 z
9 -0.882865 0.687325 -0.008771 -0.066912 z
现在我想创建新的数据框(命名为df_w，df_x，df_y，df_z)，这些数据框仅保存其原始数据中的数据，并在某些可迭代的示例中进行最佳组合.列表:
Now I want to create new dataframes (named df_w, df_x, df_y, df_z) which only hold their data from the original dataframe and are optimally combined within some iterable e.g. a list:
df_w
A B C D id
0 0.347501 -1.152416 1.441144 -0.144545 w
1 0.775828 -1.176764 0.203049 -0.305332 w
2 1.036246 -0.467927 0.088138 -0.438207 w
df_x
A B C D id
0 -0.737092 -0.231706 0.268403 0.464026 x
1 -1.857346 -1.420284 -0.515517 -0.231774 x
df_y
A B C D id
0 -0.970731 0.217890 0.193814 -0.078838 y
1 -0.318314 -0.244348 0.162103 1.204386 y
2 0.340199 1.074977 1.201068 -0.431473 y
df_z
A B C D id
0 0.202050 0.790434 0.643458 -0.068620 z
1 -0.882865 0.687325 -0.008771 -0.066912 z
使用groupby，apply和/或applymap和函数是否有任何智能(矢量化熊猫)方式来实现这一目标?
Is there any smart (vectorized pandas) way to achieve this using groupby, apply and/or applymap and a function?
我当时正在考虑对数据框进行迭代，但这似乎不是很优雅.
I was thinking about iterating over the dataframe but it doesn't seem to be very elegant..
在此先感谢您的提示！
推荐答案
我们可以创建DF字典:
we can create a dict of DFs:
In [166]: dfs = {k:v for k,v in df.groupby('id')}
In [168]: dfs.keys()
Out[168]: dict_keys(['W', 'Y', 'Z'])
In [169]: dfs['W']
Out[169]:
A B C D id
0 -0.373021 -0.555218 0.022980 -0.512323 W
1 -1.599466 0.637292 0.045059 -0.334030 W
2 0.100659 0.557068 0.142226 -0.186214 W
In [170]: dfs['Y']
Out[170]:
A B C D id
5 0.540107 -0.739077 0.992408 2.010203 Y
6 -0.201376 -0.913222 -0.173284 1.837442 Y
7 -1.367659 0.915360 0.072720 -0.886071 Y
In [171]: dfs['Z']
Out[171]:
A B C D id
3 -0.329087 0.842431 0.839319 -0.597823 Z
4 -0.594375 -0.950486 1.125584 0.116599 Z
8 0.366667 -0.978279 -1.449893 0.192451 Z
9 -0.007439 -0.084612 0.010192 -0.417602 Z
更新:重置索引:
In [177]: {k:v.reset_index(drop=True) for k,v in df.groupby('id')}
Out[177]:
{'W': A B C D id
0 -0.373021 -0.555218 0.022980 -0.512323 W
1 -1.599466 0.637292 0.045059 -0.334030 W
2 0.100659 0.557068 0.142226 -0.186214 W,
'Y': A B C D id
0 0.540107 -0.739077 0.992408 2.010203 Y
1 -0.201376 -0.913222 -0.173284 1.837442 Y
2 -1.367659 0.915360 0.072720 -0.886071 Y,
'Z': A B C D id
0 -0.329087 0.842431 0.839319 -0.597823 Z
1 -0.594375 -0.950486 1.125584 0.116599 Z
2 0.366667 -0.978279 -1.449893 0.192451 Z
3 -0.007439 -0.084612 0.010192 -0.417602 Z}
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