python - How to save a pandas DataFrame table as a png

Question

I constructed a pandas dataframe of results. This data frame acts as a table. There are MultiIndexed columns and each row represents a name, ie index=['name1','name2',...] when creating the DataFrame. I would like to display this table and save it as a png (or any graphic format really). At the moment, the closest I can get is converting it to html, but I would like a png. It looks like similar questions have been asked such as How to save the Pandas dataframe/series data as a figure?

However, the marked solution converts the dataframe into a line plot (not a table) and the other solution relies on PySide which I would like to stay away simply because I cannot pip install it on linux. I would like this code to be easily portable. I really was expecting table creation to png to be easy with python. All help is appreciated.

Answer 1

Pandas allows you to plot tables using matplotlib (details here). Usually this plots the table directly onto a plot (with axes and everything) which is not what you want. However, these can be removed first:

import matplotlib.pyplot as plt
import pandas as pd
from pandas.table.plotting import table # EDIT: see deprecation warnings below

ax = plt.subplot(111, frame_on=False) # no visible frame
ax.xaxis.set_visible(False)  # hide the x axis
ax.yaxis.set_visible(False)  # hide the y axis

table(ax, df)  # where df is your data frame

plt.savefig('mytable.png')

The output might not be the prettiest but you can find additional arguments for the table() function here. Also thanks to this post for info on how to remove axes in matplotlib.

---

EDIT:

Here is a (admittedly quite hacky) way of simulating multi-indexes when plotting using the method above. If you have a multi-index data frame called df that looks like:

first  second
bar    one       1.991802
       two       0.403415
baz    one      -1.024986
       two      -0.522366
foo    one       0.350297
       two      -0.444106
qux    one      -0.472536
       two       0.999393
dtype: float64

First reset the indexes so they become normal columns

df = df.reset_index() 
df
    first second       0
0   bar    one  1.991802
1   bar    two  0.403415
2   baz    one -1.024986
3   baz    two -0.522366
4   foo    one  0.350297
5   foo    two -0.444106
6   qux    one -0.472536
7   qux    two  0.999393

Remove all duplicates from the higher order multi-index columns by setting them to an empty string (in my example I only have duplicate indexes in "first"):

df.ix[df.duplicated('first') , 'first'] = '' # see deprecation warnings below
df
  first second         0
0   bar    one  1.991802
1          two  0.403415
2   baz    one -1.024986
3          two -0.522366
4   foo    one  0.350297
5          two -0.444106
6   qux    one -0.472536
7          two  0.999393

Change the column names over your "indexes" to the empty string

new_cols = df.columns.values
new_cols[:2] = '',''  # since my index columns are the two left-most on the table
df.columns = new_cols 

Now call the table function but set all the row labels in the table to the empty string (this makes sure the actual indexes of your plot are not displayed):

table(ax, df, rowLabels=['']*df.shape[0], loc='center')

et voila:

Your not-so-pretty but totally functional multi-indexed table.

EDIT: DEPRECATION WARNINGS

As pointed out in the comments, the import statement for table:

from pandas.tools.plotting import table

is now deprecated in newer versions of pandas in favour of:

from pandas.plotting import table 

EDIT: DEPRECATION WARNINGS 2

The ix indexer has now been fully deprecated so we should use the loc indexer instead. Replace:

df.ix[df.duplicated('first') , 'first'] = ''

with

df.loc[df.duplicated('first') , 'first'] = ''