# import code; code.interact(local=locals())
from __future__ import division
import csv
import matplotlib.pyplot as plt


plt.plot([1, 2, 3])
plt.legend(['A simple line'])
plt.show

# print ag_im[2]
# print ag_im[2]['Country Name']
# print ag_im[2]['1975']


        #food and water - which countries are most vulnerable, which are in the
        #best position, and where does the United States stack up
        #take US compare it to the top ten and the bottom ten for each category


        #agricultural raw material imports (% of merchandise imports) metadata
            #Code
            #Long Name
            #Income Group (Low income, Lower middle income, Upper middle income, High income: nonOECD, High income: OECD)
            #Region


        #data cleaning, processing, presentation
        #go over each row and only keep it if there amount of data exceeds threshold
            #if not enough data, then select the row for deletion
        #for the ones that stay, go over each year for each row and only sum the non empty strings and find the average
        #use this average for a baseline
        #find the standard deviation - ones with the highest will be the most interesting perhaps
        #input the average in the missing data cells?
        #select the top ten, lowest ten, and the US and display results, graph it!

2cc03199f2bc3aa1b759b81fbcdfdaaa7120a79c
<mask token>

<mask token>
plt.plot([1, 2, 3])
plt.legend(['A simple line'])
plt.show

from __future__ import division
import csv
import matplotlib.pyplot as plt
plt.plot([1, 2, 3])
plt.legend(['A simple line'])
plt.show

# import code; code.interact(local=locals())
from __future__ import division
import csv
import matplotlib.pyplot as plt


plt.plot([1, 2, 3])
plt.legend(['A simple line'])
plt.show

# print ag_im[2]
# print ag_im[2]['Country Name']
# print ag_im[2]['1975']


        #food and water - which countries are most vulnerable, which are in the
        #best position, and where does the United States stack up
        #take US compare it to the top ten and the bottom ten for each category


        #agricultural raw material imports (% of merchandise imports) metadata
            #Code
            #Long Name
            #Income Group (Low income, Lower middle income, Upper middle income, High income: nonOECD, High income: OECD)
            #Region


        #data cleaning, processing, presentation
        #go over each row and only keep it if there amount of data exceeds threshold
            #if not enough data, then select the row for deletion
        #for the ones that stay, go over each year for each row and only sum the non empty strings and find the average
        #use this average for a baseline
        #find the standard deviation - ones with the highest will be the most interesting perhaps
        #input the average in the missing data cells?
        #select the top ten, lowest ten, and the US and display results, graph it!
