pandas converting floats to strings without decimals

Question

I have a dataframe

df = pd.DataFrame([         ['2', '3', 'nan'],         ['0', '1', '4'],         ['5', 'nan', '7']     ])  print df     0    1    2 0  2    3  nan 1  0    1    4 2  5  nan    7 

I want to convert these strings to numbers and sum the columns and convert back to strings.

Using astype(float) seems to get me to the number part. Then summing is easy with sum(). Then back to strings should be easy too with astype(str)

df.astype(float).sum().astype(str)  0     7.0 1     4.0 2    11.0 dtype: object 

That's almost what I wanted. I wanted the string version of integers. But floats have decimals. How do I get rid of them?

I want this

0     7 1     4 2    11 dtype: object 

Answer 1

Converting to int (i.e. with .astype(int).astype(str)) won't work if your column contains nulls; it's often a better idea to use string formatting to explicitly specify the format of your string column; (you can set this in pd.options):

>>> pd.options.display.float_format = '{:,.0f}'.format >>> df.astype(float).sum() 0     7 1     4 2    11 dtype: float64 

Answer 2

Add a astype(int) in the mix:

df.astype(float).sum().astype(int).astype(str)  0     7 1     4 2    11 dtype: object 

---

Demonstration of example with empty cells. This was not a requirement from the OP but to satisfy the detractors

df = pd.DataFrame([         ['2', '3', 'nan', None],         [None, None, None, None],         ['0', '1', '4', None],         ['5', 'nan', '7', None]     ])  df        0     1     2     3 0     2     3   nan  None 1  None  None  None  None 2     0     1     4  None 3     5   nan     7  None 

Then

df.astype(float).sum().astype(int).astype(str)  0     7 1     4 2    11 3     0 dtype: object 

Because the OP didn't specify what they'd like to happen when a column was all missing, presenting zero is a reasonable option.

However, we could also drop those columns

df.dropna(1, 'all').astype(float).sum().astype(int).astype(str)  0     7 1     4 2    11 dtype: object