[英]Transforming Pandas DataFrame into List of DataFrames
I have data that looks like this: 我有看起来像这样的数据:
1.00 1.00 1.00
3.23 4.23 0.33
1.23 0.13 3.44
4.55 12.3 14.1
2.00 2.00 2.00
1.21 1.11 1.11
3.55 5.44 5.22
4.11 1.00 4.00
It comes in chunk of 4. The first line of the chunk is index and the rest are the values. 它以4的块进来。该块的第一行是索引,其余的是值。 The chunk always comes in 4 lines, but number of columns can be more than 3.
块总是排成4行,但列数可以大于3。
For example: 例如:
1.00 1.00 1.00 <- 1st chunk, the index = 1
3.23 4.23 0.33 <- values
1.23 0.13 3.44 <- values
4.55 12.3 14.1 <- values
My example above only contains 2 chunks, but actually it can contain more than that. 我上面的示例仅包含2个块,但实际上可以包含更多块。
What I want to do is to create a dictionary of data frames so I can process them chunk by chunk. 我想要做的是创建一个数据帧字典,以便可以逐块处理它们。 Namely from this:
即从此:
In [1]: import pandas as pd
In [2]: df = pd.read_table("http://dpaste.com/29R0BSS.txt",header=None, sep = " ")
In [3]: df
Out[3]:
0 1 2
0 1.00 1.00 1.00
1 3.23 4.23 0.33
2 1.23 0.13 3.44
3 4.55 12.30 14.10
4 2.00 2.00 2.00
5 1.21 1.11 1.11
6 3.55 5.44 5.22
7 4.11 1.00 4.00
Into list of data frame, such that I can do something like this (I do this by hand): 进入数据帧列表,以便我可以执行以下操作(我可以手动执行此操作):
>> # Let's call new data frame `nd`.
>> nd[1]
>> 0 1 2
0 3.23 4.23 0.33
1 1.23 0.13 3.44
2 4.55 12.30 14.10
There are lots of ways to do this; 有很多方法可以做到这一点。 I tend to use
groupby
, eg something like 我倾向于使用
groupby
,例如
>>> grouped = df.groupby(np.arange(len(df)) // 4)
>>> d = {v.iloc[0][0]: v.iloc[1:].reset_index(drop=True) for k,v in grouped}
>>> for k,v in d.items():
... print(k)
... print(v)
...
1.0
0 1 2
0 3.23 4.23 0.33
1 1.23 0.13 3.44
2 4.55 12.30 14.10
2.0
0 1 2
0 1.21 1.11 1.11
1 3.55 5.44 5.22
2 4.11 1.00 4.00
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