title: understanding lambda functions in pandas
body: <p>I'm trying to solve a problem for a course in Python and found someone has implemented solutions for the same problem in github. I'm just trying to understand the solution given in github. </p>
  
  <p>I have a pandas dataframe called Top15 with 15 countries and one of the columns in the dataframe is 'HighRenew'. This column stores the % of renewable energy used in each country. My task is to convert the column values in 'HighRenew' column into boolean datatype.</p>
  
  <p>If the value for a particular country is higher than the median renewable energy percentage in all the 15 countries then I should encode it as 1 otherwise it should a 0. The 'HighRenew' column is sliced out as a Series from the dataframe, which is copied below. </p>
  
  <pre><code>Country
  China                  True
  United States         False
  Japan                 False
  United Kingdom        False
  Russian Federation     True
  Canada                 True
  Germany                True
  India                 False
  France                 True
  South Korea           False
  Italy                  True
  Spain                  True
  Iran                  False
  Australia             False
  Brazil                 True
  Name: HighRenew, dtype: bool
  </code></pre>
  
  <p>The github solution is implemented in 3 steps, of which I understand the first 2 but not the last one where <strong>lambda</strong> function is used. Can someone explain how this lambda function works? </p>
  
  <pre><code>median_value = Top15['% Renewable'].median()
  Top15['HighRenew'] = Top15['% Renewable']&gt;=median_value
  Top15['HighRenew'] = Top15['HighRenew'].apply(lambda x:1 if x else 0)
  </code></pre>