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I have a dataframe. It has data about suppliers. If the name of the supplier and group are same, number of units should ideally be the same. However, sometimes that is not the case. I am writing code that imports data from SQL into Python then compares for these numbers.

This is for Python 3. Importing the data into Python was easy. I am a Python rookie. To make things easier for myself, I created individual dataframes for each supplier to compare numbers instead of looking at the whole dataframe at once.

supp = data['Supplier']

supplier = 

for s in supp:

    if s not in Supplier:

        supplier.append(s)



su = "Authentic Brands Group LLC"

deal = defaultdict(list)

blist = 

glist = 

columns = ['Supplier','ID','Units','Grp']

df3 = pd.DataFrame(columns=columns)

def add_row(df3, row):

    df3.loc[-1] = row

    df3.index = df3.index + 1  

    return df3.sort_index()



for row in data.itertuples():

    for x in supplier:

        s1 = row.Supplier

        if  s1 == su:

            if row.Supplier_Group not in glist:

                glist.append(row.Supplier_Group)

            for g in range(len(glist)):

                if glist[g]==row.Supplier_Group:

                    supp = x

                    blist=

                    blist.append(row.ID)

                    blist.append(row.Units)

                    blist.append(glist[g])

                    add_row(df3,[b1,row.ID,row.Units,glist[g]])   

                    break

            break                     

        break

for i in range(1,len(df3)):

    if df3.Supplier.loc[i] == df3.Supplier.loc[i-1] and df3.Grp.loc[i] == df3.Grp.loc[i-1]:

        print(df3.Supplier,df3.Grp)  

This gives me a small subset that looks like this:

Now I want to look at the supplier name and Grp, if they are same as others in dataframe, Units should be same. In this case, row 2 is incorrect. Units should be 100. I want to add another column to this dataframe that says 'False' if the number of Units is correct. This is the tricky part for me. I can iterrate over the rows, but I'm unsure how to compare them and add column.

I'm stuck at this point.
Any help is highly appreciated. Thank you.

If you have all of your data in a single dataframe, df, you can do the following:

grp_by_cols = ['Supplier', 'ID', 'Grp']

all_cols = grp_by_cols + ['Unit']

res_df = df.assign(first_unit=lambda df: df.loc[:, all_cols]

                                           .groupby(grp_by_cols)

                                           .transform('first'))

           .assign(incorrect=lambda df: df['Unit'] == df['first_unit'])

           .assign(incorrect=lambda df: df.loc[:, grp_by_cols + ['incorrect']])

                                          .groupby(grp_by_cols)

                                          .transform(np.any))

The first call to assign adds a single new column (called 'first_unit') that is the first value of "Unit" for each group of Supplier/ID/Grp (see grp_by_cols).

The second call to assign adds a column called 'incorrect' that is True when 'Unit' doesn't equal 'first_unit'. The third and final assign call overwrites that column to be True if any rows in that group are True. You can remove that if that's not what you want.

Then, if you want to look at the results for a single supplier, you can do something like:

res_df.query('Supplier = "Authentic Brands Group"')