If I have a pandas dataframe called df, I can find the average reading ability for each age by using the code
df.groupby('Age').apply(lambda x: x['ReadingAbility'].mean())But what if I want to find the average reading ability for all ages except one, say, age=k?
One way to do this is:
mu_other_ages = {}
for age in df['Age'].unique():
mu_other_ages[age] = df[df['Age'] != age]['ReadingAbility'].mean()
This approach seems like the opposite of using groupby + apply. Is there a more efficient shortcut to achieve the same result?
Consider the example below:
In [52]: d = pd.DataFrame([[1,10], [2,4],[1, 9], [2,3]], columns=['Age', 'ReadingAbility'])
In [53]:
In [53]: d
Out[53]:
Age ReadingAbility
0 1 10
1 2 4
2 1 9
3 2 3
In [54]: d.groupby('Age').apply(lambda x: x['ReadingAbility'].mean())
Out[54]:
Age
1 9.5
2 3.5
dtype: float64
In the case where there are only 2 different age values, the results should be inverted as follows: 2=9.5 and 1=3.5. For more classes, the value for age=k would be calculated as:
df[df['Age'] != k]['ReadingAbility'].mean()To clarify, the expected result for this example is: 2=9.5 and 1=3.5