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()

Asked By: Gabriel

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Source

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()
```

Answered By: tibi3000

Answer 2

Very late to the discussion, but you can also do:

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

Answered By: SimonM68

Answer 3

You can convert df to numpy array and use numpy.nanmean()

import numpy as np

df = pd.DataFrame(data=np.array([[1, 2], 
                                 [3, 4], 
                                 [6, 7], 
                                 [8, np.nan], 
                                 [np.nan, 11]]), 
                  columns=['A', 'B'])

df_col_means = numpy.nanmean(df.values)  # by columns
df_row_means = numpy.nanmean(df.values, axis=1)  # by rows
col_A_mean = numpy.nanmean(df['A'].values)  # particular column mean

Answered By: Александр Ласкорунский

Answer 4

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

Answered By: nuri

mean calculation in pandas excluding zeros

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