Table of Contents
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)]
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
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
See also:-
- https://stackoverflow.com/a/35974742 - initial version of the function is from here.
- To see it in action
- https://github.com/KamarajuKusumanchi/market_data_processor/blob/master/src/utils/DataFrameUtils.py - I generalized the original version to print index if needed.
Sample code demonstrates | add a new method to pandas dataframe