### Site Tools

print_hundredths

Let's define hundredths as numbers with two decimal digits. This can be money amounts in dollars and cents.

tags | pennies, dollar-cent amounts, print two digits after decimal

### write single numbers

There are two possible ways - round, format expression. I prefer the format expression as it always gives the same number of digits after the decimal.

round format
output type float string
In [1]:
a = 10.30467

In [2]:
type(round(a,2))
Out[2]:
float

In [3]:
type('{:.2f}'.format(a))
Out[3]:
str
number of digits after the decimal point varies always two
In [4]:
a = 10.30467

In [5]:
round(a,2)
Out[5]:
10.3

In [6]:
'{:.2f}'.format(a)
Out[6]:
'10.30'

Tested with | Python 3.10.9, ipython 8.8.0

tags | round vs. format

### write dataframe to csv files

If you round and dump the data into a csv file, it does not align around the decimal point. The result is also difficult to align using command line tools.

On the other hand, if the data is formatted using the format expression, it will still not align but can be aligned using command line tools.

For example, consider

ipython Python 3.10.9 | packaged by conda-forge | (main, Jan 11 2023, 15:15:40) [MSC v.1916 64 bit (AMD64)] Type 'copyright', 'credits' or 'license' for more information IPython 8.8.0 -- An enhanced Interactive Python. Type '?' for help. In [1]: import pandas as pd df = pd.DataFrame({ 'symbol': ['A', 'B', 'C', 'D'], 'price': [8.222, 7.007, 3.971, 9.801], 'change': [6.601, 7.241, -9.341, 48.001]}) df Out[1]: symbol price change 0 A 8.222 6.601 1 B 7.007 7.241 2 C 3.971 -9.341 3 D 9.801 48.001 If it is rounded and dumped into a csv file In [2]: df.round({'price':2, 'change': 2}).to_csv('x/foo1.csv', index=False, lineterminator='\n') The result does not align  cat ~/x/foo1.csv
symbol,price,change
A,8.22,6.6
B,7.01,7.24
C,3.97,-9.34
D,9.8,48.0

and can't easily be aligned using other command line tools

cat ~/x/foo1.csv | column -t -s, -R 2,3 symbol price change A 8.22 6.6 B 7.01 7.24 C 3.97 -9.34 D 9.8 48.0 However, if format expression is used In [3]: formats = {'price': '{:.2f}', 'change': '{:.2f}'} df2 = df.copy() for col, f in formats.items(): df2[col] = df2[col].apply(lambda x: f.format(x)) df2.to_csv('x/foo2.csv', index=False, lineterminator='\n') the result still does not align  cat ~/x/foo2.csv
symbol,price,change
A,8.22,6.60
B,7.01,7.24
C,3.97,-9.34
D,9.80,48.00

but can be using command line tools

cat ~/x/foo2.csv | column -t -s, -R 2,3 symbol price change A 8.22 6.60 B 7.01 7.24 C 3.97 -9.34 D 9.80 48.00 Ref:- https://stackoverflow.com/questions/20003290/output-different-precision-by-column-with-pandas-dataframe-to-csv - shows how to format different columns with different precision. tags | round vs. format, float_format by column ### align hundredths column with spaces Use import pandas as pd from tabulate import tabulate def to_fwf(df, fname): content = tabulate(df.values.tolist(), list(df.columns), tablefmt="plain") with open(fname, "w") as FileObj: FileObj.write(content) pd.DataFrame.to_fwf = to_fwf For example, consider  ipython
Python 3.10.9 | packaged by conda-forge | (main, Jan 11 2023, 15:15:40) [MSC v.1916 64 bit (AMD64)]
IPython 8.8.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]:
import pandas as pd
df = pd.DataFrame({
'symbol': ['A', 'B', 'C', 'D'],
'price': [8.222, 7.007, 3.971, 9.801],
'change': [6.601, 7.241, -9.341, 48.001]})
df
Out[1]:
symbol  price  change
0      A  8.222   6.601
1      B  7.007   7.241
2      C  3.971  -9.341
3      D  9.801  48.001

In [2]:
import pandas as pd
from tabulate import tabulate

def to_fwf(df, fname):
content = tabulate(df.values.tolist(), list(df.columns), tablefmt="plain")
with open(fname, "w") as FileObj:
FileObj.write(content)

pd.DataFrame.to_fwf = to_fwf

round the data and dump it

In [3]:
df.round({'price':2, 'change': 2}).to_fwf('x/foo3.txt')

the result is aligned and space separated

$cat ~/x/foo3.txt symbol price change A 8.22 6.6 B 7.01 7.24 C 3.97 -9.34 D 9.8 48 You can also do it using format expression In [4]: formats = {'price': '{:.2f}', 'change': '{:.2f}'} df2 = df.copy() for col, f in formats.items(): df2[col] = df2[col].apply(lambda x: f.format(x)) df2.to_fwf('x/foo4.txt') which gives the same result $ cat ~/x/foo4.txt
symbol      price    change
A            8.22      6.6
B            7.01      7.24
C            3.97     -9.34
D            9.8      48