It looks to me like a bug in pandas.Series.

```
a = pd.Series([1,2,3,4])
b = a.reshape(2,2)
b
```

b has type Series but can not be displayed, the last statement gives exception, very lengthy, the last line is "TypeError: %d format: a number is required, not numpy.ndarray". b.shape returns (2,2), which contradicts its type Series. I am guessing perhaps pandas.Series does not implement reshape function and I am calling the version from np.array? Anyone see this error as well? I am at pandas 0.9.1.

You can call `reshape`

on the *values* array of the Series:

```
In [4]: a.values.reshape(2,2)
Out[4]:
array([[1, 2],
[3, 4]], dtype=int64)
```

I actually think it won't always make sense to apply `reshape`

to a Series (do you ignore the index?), and that you're correct in thinking it's just numpy's reshape:

`a.reshape?`

`Docstring: See numpy.ndarray.reshape`

*that said, I agree the fact that it let's you try to do this looks like a bug.*

The reshape function takes the new shape as a tuple rather than as multiple arguments:

```
In [4]: a.reshape?
Type: function
String Form:<function reshape at 0x1023d2578>
File: /Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/numpy/core/fromnumeric.py
Definition: numpy.reshape(a, newshape, order='C')
Docstring:
Gives a new shape to an array without changing its data.
Parameters
----------
a : array_like
Array to be reshaped.
newshape : int or tuple of ints
The new shape should be compatible with the original shape. If
an integer, then the result will be a 1-D array of that length.
One shape dimension can be -1. In this case, the value is inferred
from the length of the array and remaining dimensions.
```

Reshape is actually implemented in Series and will return an ndarray:

```
In [11]: a
Out[11]:
0 1
1 2
2 3
3 4
In [12]: a.reshape((2, 2))
Out[12]:
array([[1, 2],
[3, 4]])
```

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