title: How to count null values for each columns as well as finding percentage in pandas dataframe?
body: <p>So I have a dataframe that I read from a csv file and assigned it to 'ski_data'.</p>
  <p>I am just a little confused on the wording of the instruction. It says:</p>
  <blockquote>
  <p>Count (using .sum()) the number of missing values
  (.isnull()) in each column of ski_data as well
  as the percentages (using .mean() instead of .sum()) and order them using sort_values.</p>
  </blockquote>
  <blockquote>
  <p>Call <code>pd.concat</code> to present these in a single table (DataFrame) with the helpful column names 'count' and '%'</p>
  </blockquote>
  <p>The only way I would go about doing this is by copying ski_data into a new df but the problem now is I have a new dataframe and after transposing it, the new dataframe has the name of each column as index and a number corresponding to the amount of null values it has. Also, I don't know how to compute the mean the way the question is asking because it assumes I can write it all into one line.</p>
  <p>I assume it is largely a syntax issue although I couldn't find any helpful resource online. Was wondering if anyone can help.</p>