Pandas – Minimum rate and corresponding supplier name

Question

[I am currently analyzing some price data and would like to know if it is possible to get the lowest rate per lane id and the corresponding name of the supplier who offered the cheapest rate in pandas dataframe]

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Below is my expected output. df[[‘quoted_rates’]].idxmin() but it is not giving all the information that I would like to have. Please help me. Appreciate the support, thank you

Asked By: kofi nkrumah

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Source

Answer 1

data = [['Supplier 1', 'Lane 1', 'NL', 'DE', 200],
        ['Supplier 2', 'Lane 1', 'NL', 'DE', 150],
        ['Supplier 3', 'Lane 1', 'NL', 'DE', 300],
        ['Supplier 1', 'Lane 2', 'NL', 'DE', 200],
        ['Supplier 2', 'Lane 2', 'NL', 'DE', 105],
        ['Supplier 3', 'Lane 2', 'NL', 'DE', 100]]

columns = ['supplier_name', 'lane_id', 'origin', 'destination', 'quoted_rates']

df = pd.DataFrame(data, columns=columns)

df.sort_values(by='quoted_rates').groupby('lane_id',as_index=False).first()

Output:

    lane_id     supplier_name   origin  destination     quoted_rates
0   Lane 1      Supplier 2      NL      DE              150
1   Lane 2      Supplier 3      NL      DE              100

In Addition:

new = df.sort_values(by='quoted_rates').groupby('lane_id',as_index=False).first()

field = 'supplier_name'
df['min_supplier'] = df.apply(lambda x:new[new.lane_id==x.lane_id][field].iloc[0],axis=1)

    supplier_name   lane_id  origin  destination    quoted_rates    min_supplier
0   Supplier 1      Lane 1   NL      DE             200             Supplier 2
1   Supplier 2      Lane 1   NL      DE             150             Supplier 2
2   Supplier 3      Lane 1   NL      DE             300             Supplier 2
3   Supplier 1      Lane 2   NL      DE             200             Supplier 3
4   Supplier 2      Lane 2   NL      DE             105             Supplier 3
5   Supplier 3      Lane 2   NL      DE             100             Supplier 3

Answered By: Mazhar

Pandas – Minimum rate and corresponding supplier name

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