如何根据两个条件迭代 dataframe？

Question

I have a sample of companies with financial figures which I would like to compare. My data looks like this:

    Cusip9      Issuer                       IPO Year   Total Assets    Long-Term Debt  Sales      SIC-Code
1   783755101   Ryerson Tull Inc                1996    9322000.0        2632000.0      633000.0   3661
2   826170102   Siebel Sys Inc                  1996    995010.0         0.0            50250.0    2456
3   894363100   Travis Boats & Motors Inc       1996    313500.0         43340.0        23830.0    3661
4   159186105   Channell Commercial Corp        1996    426580.0         3380.0         111100.0   7483
5   742580103   Printware Inc                   1996    145750.0         0.0            23830.0    8473

For every company I want to calculate a "similarity Score". This score should indicate the comparability with other companies. Therefore I want to compare them in different financial figures. The comparability should be expressed as the euclidean distance, the square root of the sum of the squared differences between the financial figures, to the "closest company". So I need to calculate the distance to every company, that fits these conditions, but only need the closest score. Assets of Company 1 minus Assets of Company 2 plus Debt Company 1 minus Debt Comapny 2....

√((x_1-y_1 )^2+(x_2-y_2 )^2) 

This should only be computed for companies with the same SIC-Code and the IPO Year of the comparable companies should be smaller then for the company for which the "Similarity score" is computed. I only want to compare these companies with already listed companies.

Hopefully, my point get's clear. Has someone any idea where I can start? I am just starting with programming and completely lost with this.

Thanks in advance.

Answer 1

I would first create different dataframes according to the SIC-code, so every new dataframe only contains companies with the same SIC-code. Then for every of those dataframes, just double loop over the companies and compute the scores, and store them in a matrix. (So you'll end up with a symmetrical matrix of scores.)

Answer 2

try this, Here I have taken Compare the company with IPO Year Equal to or Smaller then since You didn't give any company record with smaller IPO year) You can change it to only Smaller than (<) in statement Group=df[...]

def closestCompany(companyRecord):
    Group = df[(df['SIC-Code']==companyRecord['SIC-Code']) & (df['IPO Year'] <= companyRecord['IPO Year']) & (df['Issuer'] != companyRecord['Issuer'])]
    return (((Group['Total Assets']-companyRecord['Total Assets'])**2 + (Group['Long-Term Debt'] - companyRecord['Long-Term Debt'])**2)**0.5).min()

df['Closest Company Similarity Score']=df.apply(closestCompany, axis=1)
df