title: Creating a new dataframe that only pulls out rows contains a specific word
body: <p>I am working on a project that identifies the differences between 2 Excel workbooks. Where there is a difference in the cell value the code populates the cell with the string &quot;old value Changed to &quot;new value&quot;</p>
  <p><code>df_orig</code> in the first line of code below contains the comparison with all changes highlighted. I then began creating dataframes to pull out the rows in the <code>df_orig</code> that contains the value &quot;Change&quot; as you will see below.</p>
  <p>What I want to do is check every column in the <code>df_orig</code> and if the value &quot;Change&quot; appears in any row to pull out that row and create a new dataframe that contains all rows with that &quot;Change&quot; value and output it to a new Excel workbook. In my <code>df_orig</code> there are 187 columns so my method below of creating dataframe after dataframe is not going to work.</p>
  <pre><code>df_orig = pd.read_excel ('Horizon/Output/Full_LeaseOutput.xlsx')
  
  df1 = df[df['Lease'].str.contains('Changed', na=False)]
  df2 = df[df['Lease Description'].str.contains('Changed', na=False)]
  df3 = df[df['Type'].str.contains('Changed', na=False)]
  df4 = df[df['Tenant Ref'].str.contains('Changed', na=False)]
  df5 = df[df['Tenant Name'].str.contains('Changed', na=False)]
  df6 = df[df['Property Ref'].str.contains('Changed', na=False)]
  
  df7 = pd.concat([df1,df2,df3,df4,df5,df6])
  df7.to_excel('Horizon/Output/Differences.xlsx', index=True,header=True)
  </code></pre>