Format certain floating dataframe columns into percentage in pandas

Question

I am trying to write a paper in IPython notebook  but encountered some issues with display format  Say I have following dataframe df  is there any way to format var1 and var2 into 2 digit decimals and var3 into percentages          var1        var2         var3     id                                               0    1 458315    1 500092   -0 005709    1    1 576704    1 608445   -0 005122     2    1 629253    1 652577   -0 004754     3    1 669331    1 685456   -0 003525    4    1 705139    1 712096   -0 003134    5    1 740447    1 741961   -0 001223    6    1 775980    1 770801   -0 001723     7    1 812037    1 799327   -0 002013     8    1 853130    1 822982   -0 001396     9    1 943985    1 868401    0 005732   The numbers inside are not multiplied by 100  e g   -0 0057 -0 57

User · Answer

Just another way of doing it should you require to do it over a larger range of columns

using applymap

df[['var1','var2']] = df[['var1','var2']].applymap("{0:.2f}".format)
df['var3'] = df['var3'].applymap(lambda x: "{0:.2f}%".format(x*100))

applymap is useful if you need to apply the function over multiple columns; it's essentially an abbreviation of the below for this specific example:

df[['var1','var2']].apply(lambda x: map(lambda x:'{:.2f}%'.format(x),x),axis=1)

Great explanation below of apply, map applymap:

Difference between map, applymap and apply methods in Pandas

User · Answer

Often times we are interested in calculating the full significant digits  but for the visual aesthetics  we may want  to see only few decimal point when we display the dataframe  In jupyter-notebook  pandas can utilize the html formatting taking advantage of the method called style  For the case of just seeing two significant digits of some columns  we can use this code snippet  Given dataframe import numpy as np import pandas as pd  df   pd DataFrame   var1    1 458315  1 576704  1 629253  1 6693310000000001  1 705139  1 740447  1 77598  1 812037  1 85313  1 9439849999999999              var2    1 500092  1 6084450000000001  1 652577  1 685456  1 7120959999999998  1 741961  1 7708009999999998  1 7993270000000001  1 8229819999999999  1 8684009999999998              var3    -0 0057090000000000005  -0 005122  -0 0047539999999999995  -0 003525  -0 003134  -0 0012230000000000001  -0 0017230000000000001  -0 002013  -0 001396  0 005732     print df         var1      var2      var3 0  1 458315  1 500092 -0 005709 1  1 576704  1 608445 -0 005122 2  1 629253  1 652577 -0 004754 3  1 669331  1 685456 -0 003525 4  1 705139  1 712096 -0 003134 5  1 740447  1 741961 -0 001223 6  1 775980  1 770801 -0 001723 7  1 812037  1 799327 -0 002013 8  1 853130  1 822982 -0 001396 9  1 943985  1 868401  0 005732  Style to get required format     df style format   var1    quot    2f  quot   var2    quot    2f  quot   var3    quot    2   quot     Gives  var1    var2    var3 id           0   1 46    1 50    -0 57  1   1 58    1 61    -0 51  2   1 63    1 65    -0 48  3   1 67    1 69    -0 35  4   1 71    1 71    -0 31  5   1 74    1 74    -0 12  6   1 78    1 77    -0 17  7   1 81    1 80    -0 20  8   1 85    1 82    -0 14  9   1 94    1 87    0 57   Update If display command is not found try following  from IPython display import display  df style   df style format   var1    quot    2f  quot   var2    quot    2f  quot   var3    quot    2   quot     display df style   Requirements  To use display command  you need to have installed Ipython in your machine  The display command does not work in online python interpreter which do not have IPyton installed such as https   repl it languages python3 The display command works in jupyter-notebook  jupyter-lab  Google-colab  kaggle-kernels  IBM-watson Mode-Analytics and many other platforms out of the box  you do not even have to import display from IPython display

User · Answer

style format is vectorized  so we can simply apply it to the entire df  or just its numerical columns   df num cols  style format      3f

User · Answer

As a similar approach to the accepted answer that might be considered a bit more readable  elegant  and general  YMMV   you can leverage the map method     OP example df  var3   map lambda n       2    format n      also works on a series series example map lambda n       2    format n     Performance-wise  this is pretty close  marginally slower  than the OP solution   As an aside  if you do choose to go the pd options display float format route  consider using a context manager to handle state per this parallel numpy example

User · Answer

You could also set the default format for float   pd options display float format       2    format  Use     2    instead of     2f    - The former converts 0 41 to 41 00   correctly   the latter to 0 41   incorrectly

User · Answer

As suggested by  linqu you should not change your data for presentation  Since pandas 0 17 1   conditional  formatting was made easier  Quoting the documentation      You can apply conditional formatting  the visual styling of a DataFrame depending on the data within  by using the DataFrame style property  This is a property that returns a pandas Styler object  which has useful methods for formatting and displaying DataFrames    For your example  that would be  the usual table will show up in Jupyter    df style format        var1        2f   format       var2        2f   format       var3        2    format

User · Answer

The accepted answer suggests to modify the raw data for presentation purposes  something you generally do not want  Imagine you need to make further analyses with these columns and you need the precision you lost with rounding   You can modify the formatting of individual columns in data frames  in your case   output   df to string formatters        var1        2f   format       var2        2f   format       var3        2    format    print output    For your information      2    format 0 214  yields 21 40   so no need for multiplying by 100   You don t have a nice HTML table anymore but a text representation  If you need to stay with HTML use the to html function instead   from IPython core display import display  HTML output   df to html formatters        var1        2f   format       var2        2f   format       var3        2    format    display HTML output     Update  As of pandas 0 17 1  life got easier and we can get a beautiful html table right away   df style format        var1        2f   format       var2        2f   format       var3        2    format

User · Answer

replace the values using the round function  and format the string representation of the percentage numbers   df  var2     pd Series  round val  2  for val in df  var2     index   df index  df  var3     pd Series    0  2f    format val   100  for val in df  var3     index   df index    The round function rounds a floating point number to the number of decimal places provided as second argument to the function    String formatting allows you to represent the numbers as you wish  You can change the number of decimal places shown by changing the number before the f   p s  I was not sure if your  percentage  numbers had already been multiplied by 100  If they have then clearly you will want to change the number of decimals displayed  and remove the hundred multiplication

[python] Format certain floating dataframe columns into percentage in pandas

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