title: Iterate over dataframe and adding rows to new dataframe
body: <p>I have following table which I have in data frame</p>
  <div class="s-table-container">
  <table class="s-table">
  <thead>
  <tr>
  <th>sku</th>
  <th>Description</th>
  </tr>
  </thead>
  <tbody>
  <tr>
  <td>91567</td>
  <td>Hole</td>
  </tr>
  </tbody>
  </table>
  </div>
  <p>What I need is translate every Description for given SKu into several languages and place them into new dataframe, so that I am able to export it in excel.</p>
  <p>I was able to do it for 1 sku if values are in lists, but with dataframe I am completely lost. Plus it doesnt work for multiple skus and descriptions as decription is always linked to specific sku</p>
  <pre><code>
  import pandas as pd
  from deep_translator import GoogleTranslator
  #from deep_translator import DeepL
  #print(DeepL(source='auto', target='cs').translate(text='happy coding'))
  #import  deep_translator as dt
  
  lang=['cs','fr','nl','de','hu','pl','pt','ro','sk','es']
  
  term=['Hole']
  sku=['91567']
  
  lst = []
  cols = ['sku','original', 'translation','lang']
  for s in sku:
     for t in term:
         for l in lang:
             m= GoogleTranslator(source='auto', target=l).translate(text=t)
             lst.append([s,t,m,l])
  
  df1 = pd.DataFrame(lst, columns=cols)
  
     pd.get_option('display.max_columns')
  pd.set_option('display.max_colwidth', -1) 
  df1.to_csv('mycsvfile.csv',index=False,encoding='utf-8-sig')
  df1
  
  </code></pre>
  <p>My inteded result is to have new dataframe with sku, description original, language to which it was translated, translation.
  That means = sku and original description will be there as many times as many languages we have in the lang list.</p>
  <p>Please, could you help me?</p>