How to iterate over two pandas dataframes in chunks

Question

For a machine learning task I need to deal with data sets that are too big to fit in my memory all at once, so I need to break it down into chunk. Fortunately, pandas.read_csv has a parameter chunk_size in which you can specify the amount of data that you want to use for analysis and then loop over the data set in chunks with a for loop, which looks like this: ``` #This example can be found at http://pandas.pydata.org/pandas-docs/dev/io.html In [120]: reader = pd.read_table('tmp.sv', sep='|', chunksize=4) In [121]: reader <pandas.io.parsers.TextFileReader at 0xaa94ad0> In [122]: for chunk in reader: .....: print(chunk) .....: Unnamed: 0 0 1 2 3 0 0 0.469112 -0.282863 -1.509059 -1.135632 1 1 1.212112 -0.173215 0.119209 -1.044236 2 2 -0.861849 -2.104569 -0.494929 1.071804 3 3 0.721555 -0.706771 -1.039575 0.271860 [4 rows x 5 columns] Unnamed: 0 0 1 2 3 0 4 -0.424972 0.567020 0.276232 -1.087401 1 5 -0.673690 0.113648 -1.478427 0.524988 2 6 0.404705 0.577046 -1.715002 -1.039268 3 7 -0.370…

Accepted Answer

In a for loop you have access to the variables in the current scope:

```
In [11]: a = [1, 2, 3]

In [12]: b = 4

In [13]: for L in a:  # no need to "and b"
             print L, b
1 4
2 4
3 4
```

Be careful, this means assigning in a for loop overwrites variables:

```
In [14]: for b in a:
             print b
1
2
3

In [15]: b
Out[15]: 3
```

To iterate through two iterables at the same time use zip:

```
In [21]: c = [4, 5, 6]

In [22]: zip(a, c)
Out[22]: [(1, 4), (2, 5), (3, 6)]
```

In python 2 this is a list, so evaluated in memory (not so in python 3). You can use izip, it's iterator accomplice.

```
In [23]: from itertools import izip  # in python 3, just use zip

In [24]: for La, Lc in izip(a, c):
             print La, Lb
1 4
2 5
3 6
```

How to iterate over two pandas dataframes in chunks

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