In [11]:

    

# Import a library called Pandas and assigns it to the alias "pd".  This is a standard convention.
import pandas as pd


    

In [27]:

    

# Call a built-in Pandas function called .read_csv() to create a new spreadsheet-like object (i.e., a dataframe).
df = pd.read_csv('resources/sample_data.csv')


    

In [28]:

    

# Print the spreadsheet for easy viewing.
df


    

    
Out[28]:






  

    

      

      
Name
      
Date
      
Item
      
Product Category
      
Time (hrs)
      
Amt Picked (lbs)
      
Sale Value ($/lb)
    
  
  

    

      
0
      
Lucas
      
1/5/17
      
Green Apple
      
Apples
      
2
      
5
      
2.5
    
    

      
1
      
Valter
      
1/5/17
      
European Pear
      
Pears
      
4
      
10
      
2.0
    
    

      
2
      
Erik
      
1/5/17
      
Red Apple
      
Apples
      
2
      
3
      
2.1
    
    

      
3
      
Georg
      
1/5/17
      
Asian Pear
      
Pears
      
8
      
15
      
3.8
    
    

      
4
      
Lucas
      
1/5/17
      
Red Apple
      
Apples
      
6
      
6
      
3.6
    
  

In [29]:

    

# Create a dictionary called item_name_changes and populate it with key/value pairs.
item_name_changes = {'European Pear':'Continental Fruit', 'Red Apple':'Pomme Rouge'}


    

In [30]:

    

# Call the dataframe's built-in .replace() function on the Item column to change the item names
df['Item'].replace(item_name_changes, inplace=True)


    

In [25]:

    

df['Amt Picked (g)'] = [row*2 for row in df['Amt Picked (lbs)']]


    

In [61]:

    

def convert_lb_to_gram(x):
    result = x*453.592
    return '{:.2f}'.format(result)


    

In [62]:

    

[convert_lb_to_gram(row) for row in df['Amt Picked (lbs)']]


    

    
Out[62]:





['2267.96', '4535.92', '1360.78', '6803.88', '2721.55']

In [60]:

    

['{:.2f}'.format(convert_lb_to_gram(row)) for row in df['Amt Picked (lbs)']]


    

    
Out[60]:





['2267.96', '4535.92', '1360.78', '6803.88', '2721.55']

In [ ]: