I've read that numpy arrays are hashable which means it is immutable but I'm able to change it's values so what does it exactly mean by being hashable?
c=pd.Series('a',index=range(6))
c
Out[276]:
0 a
1 a
2 a
3 a
4 a
5 a
dtype: object
This doesn't give me error then why it gives error if I try to do the same with numpy array.
d=pd.Series(np.array(['a']),index=range(6))
Contrary to what you have read, array are not hashable. You can test this with
import numpy as np,collections
isinstance(np.array(1), collections.Hashable)
or
{np.array(1):1}
This has nothing to do with the error you are getting:
d=pd.Series(np.array('a'),index=range(6))
ValueError: Wrong number of dimensions
the error is specific, and has nothing to do with hashes. The data frame is expecting at least something with 1 dimension, whereas the above has 0 dimensions. This is due to the fact it is getting an array - so it checks the dimension (as opposed to passing the string directly, where Pandas developers have chosen to implement as you have shown. TBH they could have chosen the same for a 0 dimension array).
So you could try:
d=pd.Series(np.array(('a',)),index=range(6))
ValueError: Wrong number of items passed 1, placement implies 6
The index value expects there to be a 6 in one dimension, so it fails. Finally
pd.Series(np.array(['a']*6),index=range(6))
0 a
1 a
2 a
3 a
4 a
5 a
dtype: object
works. So the DataFrame has no problem being initiated from an array, and this has nothing to do with hashability.
cfills all elements with the scalar string value. What's the error ford? That has significant information.