In [7]:

    

from functional import seq
import pandas as pd


    

In [3]:

    

!ls


    

    




PyFunctional-pandas-tutorial.ipynb  chat_logs.jsonl  users.db
camping_purchases.csv		    gear_list.txt    users.json

In [28]:

    

# Data file this notebook works with
cat camping_purchases.csv


    

    




1,tent,300
2,food,100
3,shoes,200
4,sleeping bag, 350
5,stove,100
6,backpack,200
7,sunscreen,15
8,plates,10

In [23]:

    

# Load the initial data using pandas, possibly do some work in pandas
df = pd.read_csv('camping_purchases.csv', header=None)
df


    

    
Out[23]:






  

    

      

      
0
      
1
      
2
    
  
  

    

      
0
      
1
      
tent
      
300
    
    

      
1
      
2
      
food
      
100
    
    

      
2
      
3
      
shoes
      
200
    
    

      
3
      
4
      
sleeping bag
      
350
    
    

      
4
      
5
      
stove
      
100
    
    

      
5
      
6
      
backpack
      
200
    
    

      
6
      
7
      
sunscreen
      
15
    
    

      
7
      
8
      
plates
      
10
    
  

In [24]:

    

# This shows PyFunctional using the dataframe correctly.
# PyFunctional also detects the notebook environment and that the data is table like
# so renders a nice HTML table representation.
seq(df)


    

    
Out[24]:





1
tent        
300
2
food        
100
3
shoes       
200
4
sleeping bag
350
5
stove       
100
6
backpack    
200
7
sunscreen   
 15
8
plates      
 10

In [25]:

    

# Turning this into a list can see that the sequence is a list of rows.
# They are arrays since that is how pandas outputs lists of rows
seq(df).list()


    

    
Out[25]:





[array([1, 'tent', 300], dtype=object),
 array([2, 'food', 100], dtype=object),
 array([3, 'shoes', 200], dtype=object),
 array([4, 'sleeping bag', 350], dtype=object),
 array([5, 'stove', 100], dtype=object),
 array([6, 'backpack', 200], dtype=object),
 array([7, 'sunscreen', 15], dtype=object),
 array([8, 'plates', 10], dtype=object)]

In [26]:

    

# Show representation using PyFunctional's csv parsing
seq.csv('camping_purchases.csv')


    

    
Out[26]:





1
tent        
 300
2
food        
 100
3
shoes       
 200
4
sleeping bag
 350
5
stove       
 100
6
backpack    
 200
7
sunscreen   
  15
8
plates      
  10

In [27]:

    

# PyFunctional doesn't try to parse the columns, perhaps an area for improvement
seq.csv('camping_purchases.csv').list()


    

    
Out[27]:





[['1', 'tent', '300'],
 ['2', 'food', '100'],
 ['3', 'shoes', '200'],
 ['4', 'sleeping bag', ' 350'],
 ['5', 'stove', '100'],
 ['6', 'backpack', '200'],
 ['7', 'sunscreen', '15'],
 ['8', 'plates', '10']]

In [ ]: