Pandaspretty 1.1.0

Powerful package to prettify DataFrame into table.

What is it ?

Pandaspretty is a python package which provides you feature to convert your DataFrame in a good looking table, just in few steps. It aims to make everything simple.

What's new ?

• More customizable options
• New methods to create

Main features

1. Custom styles
2. Attractive tables
3. Fast
4. Automatically resizable cells

Where to get it

The source code is currently available on github

Installation from sources

To install it using PIP use the following command

pip install Pandaspretty

Usage

here is an example :

Let's suppose you have a DataFrame named df having value

	Name	Class	Roll_no	Section
0	Ayush kumar	12	8	A
1	Prince kumar	12	23	A
2	Khushi singh	12	18	B
3	Prathisha	12	23	B

Code to prettify your DataFrame (df)

import Pandaspretty as pp
[...]
prettyfied = pp.pretty(df)
print(prettyfied)

Output :

+----------------+---------+-----------+-----------+
|           Name |   Class |   Roll_no |   Section |
+----------------+---------+-----------+-----------+
|    Ayush kumar |      12 |         8 |         A |
+----------------+---------+-----------+-----------+
|   Prince kumar |      12 |        23 |         A |
+----------------+---------+-----------+-----------+
|   Khushi singh |      12 |        18 |         B |
+----------------+---------+-----------+-----------+
|      Prathisha |      12 |        23 |         B |
+----------------+---------+-----------+-----------+

More About it

Importing

import Pandaspretty as pp 

Methods :

• pretty(data = df, corner='%', separator=';', joins='=')

• to_sql(data, index = True)

• tabulate(data,index = True ,corner = '+', separator='|', joins='-')

Parameters :

• data : Accepts a dataframe object.

• index : Set index True/False to see the index of dataframe in table (default value is "True").

• corner : Accepts character to be shown on corner points (default value is "+").

• separator : Accepts character to be shown in place to the line separating two values (default value is "|").

• joins : Accepts character to be shown in place to the line joining two rows (default value is "-").

Passing parameter

[...]
prettyfied = pp.pretty(data = df, index = True ,corner='%', separator=';', joins='=')
print(prettyfied)

Output :

%=====%================%=========%===========%===========%
;     ;           Name ;   Class ;   Roll_no ;   Section ;
%=====%================%=========%===========%===========%
;   0 ;    Ayush kumar ;      12 ;         8 ;         A ;
%=====%================%=========%===========%===========%
;   1 ;   Prince kumar ;      12 ;        23 ;         A ;
%=====%================%=========%===========%===========%
;   2 ;   Khushi singh ;      12 ;        18 ;         B ;
%=====%================%=========%===========%===========%
;   3 ;      Prathisha ;      12 ;        23 ;         B ;
%=====%================%=========%===========%===========%

More examples

Example 1 :

Code

[...]
prettyfied = pp.pretty(data = df, corner='#')
print(prettyfied)

Output

#-----#----------------#---------#-----------#-----------#
|     |           Name |   Class |   Roll_no |   Section |
#-----#----------------#---------#-----------#-----------#
|   0 |    Ayush kumar |      12 |         8 |         A |
#-----#----------------#---------#-----------#-----------#
|   1 |   Prince kumar |      12 |        23 |         A |
#-----#----------------#---------#-----------#-----------#
|   2 |   Khushi singh |      12 |        18 |         B |
#-----#----------------#---------#-----------#-----------#
|   3 |      Prathisha |      12 |        23 |         B |
#-----#----------------#---------#-----------#-----------#

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Example 2:

Code

[...]
prettyfied = pp.pretty(data = df, index = False, separator='!')
print(prettyfied)

Output

+----------------+---------+-----------+-----------+
!           Name !   Class !   Roll_no !   Section !
+----------------+---------+-----------+-----------+
!    Ayush kumar !      12 !         8 !         A !
+----------------+---------+-----------+-----------+
!   Prince kumar !      12 !        23 !         A !
+----------------+---------+-----------+-----------+
!   Khushi singh !      12 !        18 !         B !
+----------------+---------+-----------+-----------+
!      Prathisha !      12 !        23 !         B !
+----------------+---------+-----------+-----------+

---

Example 3 :

Code

[...]
prettyfied = pp.to_sql(data = df, index = False)
print(prettyfied)

Output

+----------------+---------+-----------+-----------+
|           Name |   Class |   Roll_no |   Section |
+----------------+---------+-----------+-----------+
|    Ayush kumar |      12 |         8 |         A |
|   Prince kumar |      12 |        23 |         A |
|   Khushi singh |      12 |        18 |         B |
|      Prathisha |      12 |        23 |         B |
+----------------+---------+-----------+-----------+

---

Example 4 :

Code

[...]
prettyfied = pp.to_sql(data = df, index = True)
print(prettyfied)

Output

+-----+----------------+---------+-----------+-----------+
|     |           Name |   Class |   Roll_no |   Section |
+-----+----------------+---------+-----------+-----------+
|   0 |    Ayush kumar |      12 |         8 |         A |
|   1 |   Prince kumar |      12 |        23 |         A |
|   2 |   Khushi singh |      12 |        18 |         B |
|   3 |      Prathisha |      12 |        23 |         B |
+-----+----------------+---------+-----------+-----------+

---

Example 5 :

Code

[...]
prettyfied = pp.tabulate(data = df, separator=':')
print(prettyfied)

Output

+-----+----------------+---------+-----------+-----------+
:     :           Name :   Class :   Roll_no :   Section :
+-----+----------------+---------+-----------+-----------+
:   0 :    Ayush kumar :      12 :         8 :         A :
:   1 :   Prince kumar :      12 :        23 :         A :
:   2 :   Khushi singh :      12 :        18 :         B :
:   3 :      Prathisha :      12 :        23 :         B :
+-----+----------------+---------+-----------+-----------+

---

Example 6 :

Code

[...]
prettyfied = pp.tabulate(data = df, separator=':', index = False, joins = '—', corner='#')
print(prettyfied)

Output

#————————————————#—————————#———————————#———————————#
:           Name :   Class :   Roll_no :   Section :
#————————————————#—————————#———————————#———————————#
:    Ayush kumar :      12 :         8 :         A :
:   Prince kumar :      12 :        23 :         A :
:   Khushi singh :      12 :        18 :         B :
:      Prathisha :      12 :        23 :         B :
#————————————————#—————————#———————————#———————————#

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