Please Help, Appending into list problem!

[mizukage]

I figured out the answer myself and no longer need this post.

[bvdet]

Ok, so lets say I have a .csv file called flags.csv, with the following data

(Column 1)- (Column 2)
Country- Colour
Australia- Red
Australia- Red
Australia- Red
America - Red
UK - Red
Japan- Red
China - Red
Australia- White
America - White
UK - White
Japan - White
Australia -Blue
America - Blue
UK - Blue
China - Yellow


I would like to arrange this data so as to present the countries with flags of at least three colours, and the total number of colours they have. I want the output to end up in this format.
Column1-Column2-Column3-Column4-Column5
--------- Blue - Red - White - Yellow - Total
America-- 1 - 3 - 1 - 0 - 5
Australia- 1 - 1 - 1 - 0 - 3
UK------- 1 - 1 - 1 - 0 - 3



I have no idea what the formulas for manipulating the data so as it forms in this way are. Could you please give me some help?

Thank you.

Compile the data into a dictionary. From there you can extract the data you need and convert to another format if needed.

data = """Country- Colour
Australia- Red
Australia- Red
Australia- Red
America - Red
UK - Red
Japan- Red
China - Red
Australia- White
America - White
UK - White
Japan - White
Australia -Blue
America - Blue
UK - Blue
China - Yellow"""
 
dataList = data.split("\n")[1:]
dd = {}
for item in dataList:
    country, color = [s.strip() for s in item.split("-")]
    dd.setdefault(country, []).append(color)
 
print
print "   Country: Blue  Red  White Yellow Total"
 
for country in dd:
    print "%10s:%5s%5s%5s%5s%5s" % (country,
                                    dd[country].count("Blue"),
                                    dd[country].count("Red"),
                                    dd[country].count("White"),
                                    dd[country].count("Yellow"),
                                    len(dd[country]))

The output:

>>> 
   Country: Blue  Red  White Yellow Total
     Japan:    0    1    1    0    2
 Australia:    1    3    1    0    5
     China:    0    1    0    1    2
   America:    1    1    1    0    3
        UK:    1    1    1    0    3
>>>