Pandas .corr() returning "__" - pandas

Question

It was working great until it wasn't, and no idea what I'm doing wrong. I've reduced it to a very simple datsaset t:
1 2 3 4 5 6 7 8
0 3 16 3 2 17 2 3 2
1 3 16 3 2 19 4 3 2
2 3 16 3 2 9 2 3 2
3 3 16 3 2 19 1 3 2
4 3 16 3 2 17 2 3 1
5 3 16 3 2 17 1 17 1
6 3 16 3 2 19 1 17 2
7 3 16 3 2 19 4 3 1
8 3 16 3 2 19 1 3 2
9 3 16 3 2 7 2 17 1
corr = t.corr()
corr
returns "__"
and
sns.heatmap(corr)
throws the following error "zero-size array to reduction operation minimum which has no identity"
I have no idea what's wrong? I've tried it with more rows etc, and double checked that I don't have nay missing values...what's going on? I had such a pretty heatmap earlier, I've been trying to

Answer 1

As mentioned above, change type to float. Simply,
corr = t.astype('float64').corr()

Answer 2

The problem here is not the dataframe itself but the origin of it. I found same problem by using drop or iloc in a dataframe. The key is the global type the dataframe has.
Let's say we have the following dataframe:
list_ex = [[1.1,2.1,3.1,4,5,6,7,8],[1.2,2.2,3.3,4.1,5.5,6,7,8],
[1.3,2.3,3,4,5,6.2,7,8],[1.4,2.4,3,4,5,6.2,7.3,8.1]]
list_ex_new=pd.DataFrame(list_ex)
you can calculate the list_ex_new.corr() with no problem. If you check the arguments of the dataframe by vars(list_ex_new), you'll obtain:
{'_is_copy': None, '_data': BlockManager
Items: RangeIndex(start=0, stop=8, step=1)
Axis 1: RangeIndex(start=0, stop=4, step=1)
FloatBlock: slice(0, 8, 1), 8 x 4, dtype: float64, '_item_cache': {}}
where dtype is float64.
A new dataframe can be defined by list_new_new = list_ex_new.iloc[1:,:] and the correlations can be evaluated, successfully. A check of the dataframe's attributes shows:
{'_is_copy': ,
'_data': BlockManager
Items: RangeIndex(start=0, stop=8, step=1)
Axis 1: RangeIndex(start=1, stop=4, step=1)
FloatBlock: slice(0, 8, 1), 8 x 3, dtype: float64,
'_item_cache': {}}
where dtype is still float64.
A third dataframe can be defined:
list_ex_w = [['a','a','a','a','a','a','a','a'],[1.1,2.1,3.1,4,5,6,7,8],
[1.2,2.2,3.3,4.1,5.5,6,7,8],[1.3,2.3,3,4,5,6.2,7,8],
[1.4,2.4,3,4,5,6.2,7.3,8.1]]
list_ex_new_w=pd.DataFrame(list_ex_w)
A evaluation of dataframe's correlation will result in a empty dataframe, since list_ex_w attributes look like:
{'_is_copy': None, '_data': BlockManager
Items: RangeIndex(start=0, stop=8, step=1)
Axis 1: Index(['a', 1, 2, 3, 4], dtype='object')
ObjectBlock: slice(0, 8, 1), 8 x 5, dtype: object, '_item_cache': {}}
Where now dtype is 'object', since the dataframe is not consistent in its types. there are strings and floats together. Finally, a fourth dataframe can be generated:
list_new_new_w = list_ex_new_w.iloc[1:,:]
this will generate as a result same notebook but with no 'a's, apparently a perfectly correct dataframe to calculate the correlations. However this will return again an empty dataframe. A final check of the dataframe attributes shows:
vars(list_new_new_w)
{'_is_copy': None, '_data': BlockManager
Items: Index([1, 2, 3, 4], dtype='object')
Axis 1: RangeIndex(start=0, stop=8, step=1)
ObjectBlock: slice(0, 4, 1), 4 x 8, dtype: object, '_item_cache': {}}
where dtype is still object, thus the method corr returns an empty dataframe.
This problem can be solved by using astype(float)
list_new_new_w.astype(float).corr()
In summary, it seems pandas at the time corr or cov among others methods are called generate a new dataframe with same attibutes ignoring the case the new dataframe has a consistent global type. I've been checking out the pandas source code and I understand this is the correct interpretation of pandas' implementation.