循环只占用最后一个值

Question

I have a dataFrame with country-specific population for each year and a pandas Series with the world population for each year. This is the Series I am using:

pop_tot = df3.groupby('Year')['population'].sum()
Year     
1990    4.575442e+09
1991    4.659075e+09
1992    4.699921e+09
1993    4.795129e+09
1994    4.862547e+09
1995    4.949902e+09
...     ...
2017    6.837429e+09

and this is the DataFrame I am using

        Country      Year   HDI     population
0       Afghanistan 1990    NaN     1.22491e+07
1       Albania     1990    0.645   3.28654e+06
2       Algeria     1990    0.577   2.59124e+07
3       Andorra     1990    NaN     54509
4       Angola      1990    NaN     1.21714e+07
...     ...         ...     ...     ...
4096    Uzbekistan  2017    0.71    3.23872e+07 
4097    Vanuatu     2017    0.603   276244  
4098    Zambia      2017    0.588   1.70941e+07 
4099    Zimbabwe    2017    0.535   1.65299e+07 

I want to calculate the proportion of the world's population that the population of that country represents for each year, so I loop over the Series and the DataFrame as follows:

j = 0
for i in range(len(df3)):
    if df3.iloc[i,1]==pop_tot.index[j]:
        df3['pop_tot']=pop_tot[j] #Sanity check
        df3['weighted']=df3['population']/pop_tot[j]
        *df3.iloc[i,2]
    else:
        j=j+1 

However, the DataFrame that I get in return is not the expected one. I end up dividing all the values by the total population of 2017, thus giving me proportions which are not the correct ones for that year (ie for this first rows, pop_tot should be 4.575442e+09 as it corresponds to 1990 according to the Series above and not 6.837429e+09 which corresponds to 2017).

     Country   Year HDI   population  pop_tot      weighted
  0  Albania   1990 0.645 3.28654e+06 6.837429e+09 0.000257158
  1  Algeria   1990 0.577 2.59124e+07 6.837429e+09 0.00202753
  2  Argentina 1990 0.704 3.27297e+07 6.837429e+09 0.00256096

I can't see however what's the mistake in the loop. Thanks in advance.

Answer 1

You don't need loop, you can use groupby.transform to create the column pop_tot in df3 directly. then for the column weighted just do column operation, such as:

df3['pop_tot'] = df3.groupby('Year')['population'].transform(sum)
df3['weighted'] = df3['population']/df3['pop_tot']

As @roganjosh pointed out, the problem with your method is that you replace the whole columns pop_tot and weighted everytime your condition if is met, so at the last iteration where this condition is met, the year being probably 2017, you define the value of the column pop_tot being the one of 2017 and calculate the weithed with this value as well.

Answer 2

You dont have to loop, its slower and can make things really complex quite fast. Use pandas and numpys vectorized solutions like this for example:

df['pop_tot'] = df.population.sum()
df['weighted'] =  df.population / df.population.sum()

print(df)
       Country  Year    HDI  population     pop_tot  weighted
0  Afghanistan  1990    NaN  12249100.0  53673949.0  0.228213
1      Albania  1990  0.645   3286540.0  53673949.0  0.061232
2      Algeria  1990  0.577  25912400.0  53673949.0  0.482774
3      Andorra  1990    NaN     54509.0  53673949.0  0.001016
4       Angola  1990    NaN  12171400.0  53673949.0  0.226766

Edit after OP's comment

df['pop_tot'] = df.groupby('Year').population.transform('sum')

df['weighted'] =  df.population / df['pop_tot']

print(df)
       Country  Year    HDI  population     pop_tot  weighted
0  Afghanistan  1990    NaN  12249100.0  53673949.0  0.228213
1      Albania  1990  0.645   3286540.0  53673949.0  0.061232
2      Algeria  1990  0.577  25912400.0  53673949.0  0.482774
3      Andorra  1990    NaN     54509.0  53673949.0  0.001016
4       Angola  1990    NaN  12171400.0  53673949.0  0.226766

note
I used the small dataset you gave as example:

    Country     Year    HDI     population
0   Afghanistan 1990    NaN     12249100.0
1   Albania     1990    0.645   3286540.0
2   Algeria     1990    0.577   25912400.0
3   Andorra     1990    NaN     54509.0
4   Angola      1990    NaN     12171400.0