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%.
This question is related to
python
pandas
formatting
ipython-notebook
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}')
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.
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
DataFramedepending on the data within, by using theDataFrame.styleproperty. This is a property that returns apandas.Stylerobject, which has useful methods for formatting and displayingDataFrames.
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,
})
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
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:
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
df.style.format({'var1': "{:.2f}",'var2': "{:.2f}",'var3': "{:.2%}"})
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%
If display command is not found try following:
from IPython.display import display
df_style = df.style.format({'var1': "{:.2f}",'var2': "{:.2f}",'var3': "{:.2%}"})
display(df_style)
display command, you need to have installed Ipython in your machine.display command does not work in online python interpreter which do not have IPyton installed such as https://repl.it/languages/python3You 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)
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,
})
Source: Stackoverflow.com