I'm using the tabulate module to print a fixed width file and I have one column that I need formatted in such a way that there are 19 places to the left of the decimal and 2 places to the right of the decimal.
import pandas as pd
from tabulate import tabulate
df = pd.DataFrame.from_dict({'A':['x','y','z'],
'B':[1,1.1,11.21],'C':[34.2334,81.1,11]})
df
Out[4]:
A B C
0 x 1.00 34.2334
1 y 1.10 81.1000
2 z 11.21 11.0000
df['C'] = df['C'].apply(lambda x: format(x,'0>22.2f'))
df
Out[6]:
A B C
0 x 1.00 0000000000000000034.23
1 y 1.10 0000000000000000081.10
2 z 11.21 0000000000000000011.00
print(tabulate(df))
- - ----- -----
0 x 1 34.23
1 y 1.1 81.1
2 z 11.21 11
- - ----- -----
Is there any way I can preserve the formatting in column C without affecting the formatting in column B? I know I could use floatfmt = '0>22.2f' but I don't need column B to look that way just column C.
According to the tabulate documentation strings that look like decimals will be automatically converted to numeric. If I could suppress this then format my table before printing (as in the example above) that would solve it for me as well.
The documentation at GitHub is more up-to-date and it states that with floatfmt "every column may have different number formatting". Here is an example using your data:
import pandas as pd
from tabulate import tabulate
df = pd.DataFrame.from_dict({'A':['x','yy','zzz'],
'B':[1,1.1,11.21],'C':[34.2334,81.1,11]})
print(tabulate(df, floatfmt=(None, None, '.2f', '0>22.2f',)))
The result is:
- --- ----- ----------------------
0 x 1.00 0000000000000000034.23
1 yy 1.10 0000000000000000081.10
2 zzz 11.21 0000000000000000011.00
- --- ----- ----------------------
Additionally, as you suggested, you also have the option disable_numparse which disables the automatic convert from string to numeric. You can then format each field manually but this requires more coding. The option colalign may come handy in such a case, so that you can specify different column alignment for strings and numbers (which you would have converted to formatted strings, too).