title: Pandas: Filter in rows that have a Null/None/NaN value in any of several specific columns
body: <p>I have a csv file which has a lot of strings called <code>"NULL"</code> in it, in several columns.</p>
  
  <p>I would like to select (filter in) rows that have a <code>"NULL"</code> value in <em>any</em> of <em>several specific</em> columns.</p>
  
  <p>Example:</p>
  
  <blockquote>
  <pre><code>["Firstname"]   ["Lastname"]    ["Profession"]
  "Jeff"          "Goldblum"      "NULL"
  "NULL"          "Coltrane"      "Musician"
  "Richard"       "NULL"          "Physicist"
  </code></pre>
  </blockquote>
  
  <p>Here, I would like to filter in (select) rows in <code>df</code> that have the value <code>"NULL"</code> in the column <code>"Firstname"</code> <em>or</em> <code>"Lastname"</code> – but not if the value is <code>"NULL"</code> in <code>"Profession"</code>.</p>
  
  <p>This manages to filter in strings (not <code>None</code>) in one column:</p>
  
  <pre><code>df = df[df["Firstname"].str.contains("NULL", case=False)]
  </code></pre>
  
  <p><br>
  I have however attempted to convert the <code>"NULL"</code> strings to <code>None</code> via:</p>
  
  <pre><code>df = df.where((pd.notnull(df)), None)
  df.columns = df.columns.str.lower()
  </code></pre>
  
  <p>Given the above <code>str.contains</code> filtering, perhaps it's easier to filter in <code>"NULL"</code> strings before converting to <code>None</code>?</p>