title: remove typos from a dictionary of dataframes
body: <p>I am trying to remove specific typos from a dictionary of dataframes, which looks like this:</p>
  <pre><code>import pandas as pd
  
  data = {'dataframe_1':pd.DataFrame({'col1': ['John', 'Ashley'], 'col2': ['+10', '-1']}), 'dataframe_2':pd.DataFrame({'col3': ['Italy', 'Brazil', 'Japan'], 'col4': ['Milan', 'Rio do Jaineiro', 'Tokio'], 'percentage':['+95%', '≤0%', '80%+']})}
  
  </code></pre>
  <p>The function <code>remove_typos()</code> is used to remove specific typos, however when applied it returns a corrupted dataframe.</p>
  <pre><code>def remove_typos(string):
  
      # remove '+' and '≤'
      string=string.replace('+', '')
      string=string.replace('≤', '')
      
      return string
  
  # store remove_typos() output in a dictionary of dataframes 
  cleaned_df = pd.concat(data.values()).pipe(remove_typos)
  </code></pre>
  <p>Console Output:</p>
  <pre><code>#   col1    col2    col3    col4    percentage
  #0  John    +10 NaN NaN NaN
  #1  Ashley  -1  NaN NaN NaN
  #0  NaN NaN Italy   Milan   +95%
  #1  NaN NaN Brazil  Rio do Jaineiro ≤0%
  #2  NaN NaN Japan   Tokio   80%+
  </code></pre>
  <p>The idea is that the function returns a cleaned df where each dataframe is represented by a  dictionary key:</p>
  <pre><code>data['dataframe_1']
  
  #   col1    col2
  #0  John    10
  #1  Ashley  -1
  
  </code></pre>
  <p>Is there any other way to apply this function over a dict of df's?</p>