Transforming Pandas DataFrame into List of DataFrames

Question

I have data that looks like this:

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. The chunk always comes in 4 lines, but number of columns can be more than 3.

For example:

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.

What I want to do is to create a dictionary of data frames so I can process them chunk by chunk. Namely from this:

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):

>> # 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

Answer 1

There are lots of ways to do this; I tend to use groupby , eg something like

>>> 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