如何合并具有不同标题但条件相同的数据的csv文件

采用：

print (merged_data)    id    user  product  price[78]  price[79]  Source0  105  dummya      egg         22       28.0  sheet11  119  dummy1     soya         67        NaN  sheet12  567  dummya  spinach         22       28.0  sheet23  897  dummy1     rose         67       99.0  sheet24  345  dummya      egg         87       98.0  sheet35  121  dummy1   potato         98       99.0  sheet3print (Condition)    Sheet   ID  price1_col1  price1_col2         price1_out    sheet1  yes78          NaN        price1_col1   1  sheet2  yes78         79.0       price1_col1+price1_col2   2  sheet3  yes78         79.0  max(price1_col1,price1_col2)   price2_col1  price2_col2         price2_out sheetname  078          NaN        price2_col1       yes  178         79.0       price2_col1+price2_col2       yes  279         78.0  min(price2_col1,price2_col2)        no

#merge data together by left join    df = merged_data.merge(Condition.rename(columns={'Sheet':'Source'}), on='Source', how='left')#replace columns to empty strings, remove sheetname and ID columnsdf['Source'] = np.where(df.pop('sheetname') == 'yes', df['Source'], '')df['id'] = np.where(df.pop('ID') == 'yes', df['id'], '')#filter integers between [] to ned Dataframe df1 = df.filter(regex='[d+]').copy()#filter all columns with price, exclude df1 df2 = df[df.filter(regex='price').columns.difference(df1.columns)].copy()#convert column to integersdf1.columns = df1.columns.str.extract('[(d+)]', expand=False).astype(int)#helper column for match missing valuesdf1['a'] = np.nan#filter columns without/with _outmask = df2.columns.str.endswith(('_col1','_col2'))final_cols = df2.columns[ ~mask]removed_cols = df2.columns[mask]#replace columns by match values from df2for c in removed_cols:    df2[c] = df1.lookup(df1.index, df2[c].fillna('a'))

print (df2)   price1_col1  price1_col2         price1_out  price2_col1  22          NaN        price1_col1         22.0   167          NaN        price1_col1         67.0   222         28.0       price1_col1+price1_col2         22.0   367         99.0       price1_col1+price1_col2         67.0   487         98.0  max(price1_col1,price1_col2)         98.0   598         99.0  max(price1_col1,price1_col2)         99.0   price2_col2         price2_out  0          NaN        price2_col1  1          NaN        price2_col1  2         28.0       price2_col1+price2_col2  3         99.0       price2_col1+price2_col2  4         87.0  min(price2_col1,price2_col2)  5         98.0  min(price2_col1,price2_col2)

#create MultiIndex for separate eah price groupsdf2.columns = df2.columns.str.split('_', expand=True)def f(x):    #remove first level    x.columns = x.columns.droplevel(0)    out = []    #loop each row    for v in x.itertuples(index=False):        #remove prefix        t = v.out.replace(x.name+'_', '')        #loop each namedtuple and replace values        for k1, v1 in v._asdict().items(): t = t.replace(k1, str(v1))        #pd.eval cannot working with min, max, so handled different        if t.startswith('min'): out.append(min(pd.eval(t[3:])))        elif t.startswith('max'): out.append(max(pd.eval(t[3:])))        #handled +-*/        else: out.append(pd.eval(t))    #return back    return pd.Series(out)#overwrite original columnsdf[final_cols] = df2.groupby(level=0, axis=1).apply(f).add_suffix('_out')#if necessary remove helpersdf = df.drop(removed_cols, axis=1)

print (df)    id    user  product  price[78]  price[79]  Source  price1_out  price2_out0  105  dummya      egg         22       28.0  sheet1        22.0        22.01  119  dummy1     soya         67        NaN  sheet1        67.0        67.02  567  dummya  spinach         22       28.0  sheet2        50.0        50.03  897  dummy1     rose         67       99.0  sheet2       166.0       166.04  345  dummya      egg         87       98.0     98.0        87.05  121  dummy1   potato         98       99.0     99.0        98.0