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--- round_vs._format [2023/02/14 20:45] – raju
+++ print_hundredths [2023/02/14 20:45] – removed - external edit (Unknown date) 127.0.0.1
@@ Line 1: / Line 1: @@
-===== 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 |
-| ::: | <code>
-In [1]:
-a = 10.30467
-In [2]:
-type(round(a,2))
-Out[2]:
-float
-In [3]:
-type('{:.2f}'.format(a))
-Out[3]:
-str
-</code> ||
-| number of digits after the decimal point | varies | always two |
-| ::: | <code>
-In [4]:
-a = 10.30467
-In [5]:
-round(a,2)
-Out[5]:
-.3
-In [6]:
-'{:.2f}'.format(a)
-Out[6]:
-'10.30'
-</code> ||
-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
-<code>
-$ 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
-      A  8.222   6.601
-      B  7.007   7.241
-      C  3.971  -9.341
-      D  9.801  48.001
-</code>
-If it is rounded and dumped into a csv file
-<code>
-In [2]:
-df.round({'price':2, 'change': 2}).to_csv('x/foo1.csv', index=False, lineterminator='\n')
-</code>
-The result does not align
-<code>
-$ 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
-</code>
-and can't easily be aligned using other command line tools
-<code>
-$ 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
-</code>
-However, if format expression is used
-<code>
-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')
-</code>
-the result still does not align
-<code>
-$ 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
-</code>
-but can be using command line tools
-<code>
-$ 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
-</code>
-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
-<code>
-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
-</code>
-For example, consider
-<code>
-$ 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
-      A  8.222   6.601
-      B  7.007   7.241
-      C  3.971  -9.341
-      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
-</code>
-round the data and dump it
-<code>
-In [3]:
-df.round({'price':2, 'change': 2}).to_fwf('x/foo3.txt')
-</code>
-the result is aligned and space separated
-<code>
-$ 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
-</code>
-You can also do it using format expression
-<code>
-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')
-</code>
-which gives the same result
-<code>
-$ 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
-</code>
-Ref :- https://stackoverflow.com/a/35974742 - initial version of the function is from here.