A custom Pandas dataframe to_string method

Question

Oftentimes I find myself converting pandas.DataFrame objects to lists of formatted row strings, so I can print the rows into, e.g. a tkinter.Listbox. To do this, I have been utilizing pandas.DataFrame.to_string. There is a lot of nice functionality built into the method, but when the number of dataframe rows/columns gets relatively large, to_string starts to tank.

Below I implement a custom pandas.DataFrame class with a few added methods for returning formatted row lines. I am looking to improve upon the get_lines_fast_struct method.

import pandas class DataFrame2(pandas.DataFrame): def __init__( self, *args, **kwargs ): pandas.DataFrame.__init__(self, *args, **kwargs) def get_lines_standard(self): """standard way to convert pandas dataframe to lines with fomrmatted column spacing""" lines = self.to_string(index=False).split('\n') return lines def get_lines_fast_unstruct(self): """ lighter version of pandas.DataFrame.to_string() with no special spacing format""" df_recs = self.to_records(index=False) col_titles = [' '.join(list(self))] col_data = map(lambda rec:' '.join( map(str,rec) ), df_recs.tolist()) lines = col_titles + col_data return lines def get_lines_fast_struct(self,col_space=1): """ lighter version of pandas.DataFrame.to_string() with special spacing format""" df_recs = self.to_records(index=False) # convert dataframe to array of records str_data = map(lambda rec: map(str,rec), df_recs ) # map each element to string self.space = map(lambda x:len(max(x,key=len))+col_space, # returns the max string length in each column as a list zip(*str_data)) col_titles = [self._format_line(list(self))] col_data = [self._format_line(row) for row in str_data ] lines = col_titles + col_data return lines def _format_line(self, row_vals): """row_vals: list of strings. Adds variable amount of white space to each list entry and returns a single string""" line_val_gen = ( ('{0: >%d}'%self.space[i]).format(entry) for i,entry in enumerate(row_vals) ) # takes dataframe row entries and adds white spaces based on a format line = ''.join(line_val_gen) return line 

Here I make some test data

import random import numpy #SOME TEST DATA df = DataFrame2({'A':numpy.random.randint(0,1000,1000), 'B':numpy.random.random(1000), 'C':[random.choice(['EYE', '<3', 'PANDAS', '0.16']) for _ in range(1000)]}) 

Method outputs

df.get_lines_standard() #[u' A B C', # u' 504 0.924385 <3', # u' 388 0.285854 0.16', # u' 984 0.254156 0.16', # u' 446 0.472621 PANDAS'] # ... df.get_lines_fast_struct() #[' A B C', # ' 504 0.9243853594 <3', # ' 388 0.285854082778 0.16', # ' 984 0.254155910401 0.16', # ' 446 0.472621088021 PANDAS'] # ... df.get_lines_fast_unstruct() #['A B C', # '504 0.9243853594 <3', # '388 0.285854082778 0.16', # '984 0.254155910401 0.16', # '446 0.472621088021 PANDAS'] # ... 

Timing results

In [262]: %timeit df.get_lines_standard() 10 loops, best of 3: 70.3 ms per loop In [263]: %timeit df.get_lines_fast_struct() 100 loops, best of 3: 15.4 ms per loop In [264]: %timeit df.get_lines_fast_unstruct() 100 loops, best of 3: 2.3 ms per loop 

Answer 1

import pandas np = pandas.np 

What you are doing here is using the numpy that pandas imports, which can lead to confusion. There is an agreed standard to import pandas and numpy:

import pandas as pd import numpy as np 

And importing numpy yourself does not load the module twice, as imports are cached. Your import only costs a lookup in sys.modules because numpy already gets imported on the pandas import, but you add a lot of readability.

At the end you use random.choice() but you never imported random.

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In get_lines_standard() you first convert the complete DataFrame to a string, then split it on the line breaks. In your example and then you slice the top 5 off it to display. The way your code works here, there is no way to only show the top 5 rows without rendering the complete DataFrame - which applies to all 3 methods. Just to demonstrate the difference of slicing before and after (using random data generated at the end of your code but with 10k rows instead of 1k):

# both calls have the same output: %timeit df.to_string(index=False).split('\n')[:5] 1 loops, best of 3: 1.51 s per loop %timeit df[:5].to_string(index=False).split('\n') 100 loops, best of 3: 3.38 ms per loop 

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PS: I don't want to pep8ify you, but please don't line up your equal signs.

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/edit:

Ok, let's focus on get_lines_fast_struct(). You are doing things manually for which there actually exist tools:

• Creating a copy of a DataFrame with the same values as strings can be accomplished by str_df = self.astype(str)
• The maximum lengths of cells per column of such a dataframe could be determined by self.spaces= [str_df[c].map(len).max() for c in str_df.columns]
• For col_data you use a list comprehension that just call a method for each element, which is basically just map()
• In _format_line() you fill up the strings with spaces on the left until they have length n+1 with n being the maximum col length by even mixing 2 styles of string formatting (old and new). string.rjust() does the same thing and might be faster.

All those things in mind the code might look like this:

def get_lines_fast_struct2(self, col_space=1): str_df = self.astype(str) self.space = [str_df[c].map(len).max() for c in str_df.columns] col_titles = map(_format_line2, [self.columns]) col_data = map(_format_line2, str_df.to_records(index=False)) return col_titles + col_data def _format_line2(self, row_vals): return "".join(cell.rjust(width) for (cell, width) in zip(row_vals, self.space)) 

Let's compare this with the original in terms of speed and equality:

In [160]: %timeit df.get_lines_fast_struct() 100 loops, best of 3: 11.3 ms per loop In [161]: %timeit df.get_lines_fast_struct2() 100 loops, best of 3: 9.78 ms per loop In [162]: df.get_lines_fast_struct() == df.get_lines_fast_struct2() Out[162]: True 

Maybe there is even a better way with more pandas magic involved, but I am not that experienced with pandas yet.