CODEWITHSUNDEEP
pythonpandas
getaglow
getaglow

Reputation: 343

Getting average of rows in dataframe greater than or equal to zero

I would like to get the average value of a row in a dataframe where I only use values greater than or equal to zero.

For example: if my dataframe looked like:

df = pd.DataFrame([[3,4,5], [4,5,6],[4,-10,6]])
    3   4   5
    4   5   6
    4   -10 6

currently if I get the average of the row I write :

df['mean'] = df.mean(axis = 1)

and get:

3   4   5   4
4   5   6   5
4   -10 6   0

I would like to get a dataframe that only used values greater than zero to computer the average. I would like a dataframe that looked like:

3   4   5   4
4   5   6   5
4   -10 6   5

In the above example -10 is excluded in the average. Is there a command that excludes the -10?

Upvotes: 5

Views: 5496

Answers (2)

piRSquared
piRSquared

Reputation: 294576

Not nearly as succinct as @Psidom. But if you wanted to use numpy and get some added quickness.

v0 = df.values
v1 = np.where(v0 > 0, v0, np.nan)
v2 = np.nanmean(v1, axis=1)
df.assign(Mean=v2)

   0   1  2  Mean
0  3   4  5   4.0
1  4   5  6   5.0
2  4 -10  6   5.0

Timing
small data 

%timeit df.assign(Mean=df[df > 0].mean(1))
1000 loops, best of 3: 1.71 ms per loop

%%timeit
v0 = df.values
v1 = np.where(v0 > 0, v0, np.nan)
v2 = np.nanmean(v1, axis=1)
df.assign(Mean=v2)
1000 loops, best of 3: 407 µs per loop

Upvotes: 4

akuiper
akuiper

Reputation: 215137

You can use df[df > 0] to query the data frame before calculating the average; df[df > 0] returns a data frame where cells smaller or equal to zero will be replaced with NaN and get ignored when calculating the mean:

df[df > 0].mean(1)

#0    4.0
#1    5.0
#2    5.0
#dtype: float64

Upvotes: 7

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