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

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

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

For pandas >= 1.0:

<NA> type was introduced for ‘Int64’. You can now do this:

df['your_column'].astype('Int64').astype('str')

And it will properly convert 1.0 to 1.


Alternative:

If you do not want to change the display options of all pandas, @maxymoo solution does, you can use apply:

df['your_column'].apply(lambda x: f'{x:.0f}')

Add astype(int) right before conversion to a string:

print (df.astype(float).sum().astype(int).astype(str))

Generates the desired result.

based on toto_tico’s solution – alternative , minor changes to avoid null case become nan

df['your_column'].apply(lambda x: f'{x:.0f}' if not pd.isnull(x) else '')