Changing type of entire dataframe using Lambda Function

Question

I'm trying to convert an entire dataframe into integer, i.e. all Variables to type(int), the data has NaNs present, so was going for pd.to_numeric and coercing the errors to NaN, where I can handle them later.

But pd.to_numeric takes a list, tuple, 1-d array, or Series.

I can go on step by step by giving each column name and changing the type. But is there any way to do that optimally in few lines?

I tried this, but getting error.

change = lambda x: pd.to_numeric(df_copy[x], errors='coerce')
df_copy = pd.DataFrame(map(change, x) for x in df_copy.columns)

Answer 1

You can simply do this df_copy.apply(lambda x : pd.to_numeric(x, errors='coerce'))

[convert entire pandas dataframe to integers in pandas (0.17.0)