mean calculation in pandas excluding zeros

Question

Is there a direct way to calculate the mean of a dataframe column in pandas but not taking into account data that has zero as a value? Like a parameter inside the .mean() function? Was currently doing it like this:

x = df[df[A]!=0] x.mean() 

Answer 1

It also depends on the meaning of 0 in your data.

• If these are indeed '0' values, then your approach is good

• If '0' is a placeholder for a value that was not measured (i.e. 'NaN'), then it might make more sense to replace all '0' occurrences with 'NaN' first. Calculation of the mean then by default exclude NaN values.

  df = pd.DataFrame([1, 0, 2, 3, 0], columns=['a']) df = df.replace(0, np.NaN) df.mean() 

Answer 2

df[df["Column_name"] != 0]["Column_name"].mean() 

or if your column name does not contain space char

df[df.Column_Name != 0].Column_Name.mean() 

hopefully it can be included as a parameter in the next "mean" object version

.mean(exclude=0)  #wondering in next versions