使用函数获取每列中的平均值/中位数/众数/四分位数/分位数

Question

I'm new in jupyter notebook and wondering how to get a quantile of a column inside a function:

DataFrame:

num_likes | num_post | ... | 
464.0     | 142.0    | ... |
364.0     | 125.0    | ... |
487.0     | 106.0    | ... |
258.0     | 123.0    | ... |
125.0     | 103.0    | ... |

myFunction:

def myFunction(x):
    q22 = dataframe["num_likes"].quantile(0.22)
    q45 = dataframe["num_likes"].quantile(0.45)
    qc = q45 - q22
    k = 3

    if x >= q45 + k * qc:
        return q45 + k * qc
    elif x <= q22 - k * qc:
        return q22 - k * qc

Right now, since I don't know how to get it, I ended up running the function for each column that I have. Also, I tried to run it and it seems that it is not working

data["num_likes"].apply(lambda x : myFunction(x))[:5]

Also, the result seems to be wrong as I didn't see the any returns

    num_likes | num_post | ... | 
    NaN       | None     | ... |
    NaN       | None     | ... |
    NaN       | None     | ... |
    NaN       | None     | ... |
    NaN       | None     | ... |

Answer 1

The reason you're getting None is because no paths of your if-elseif block return true so myFunction is returning None . Did you mean if-else ?

Besides that to clean up what you have I would do things a little bit different. First q22, q45, and qc only need to be calculated once (based on logic above) and these can be passed into the function instead calculated each time in the function. Second you do not need to create a lambda in this situation, apply ( docs ) takes a python callable (your function) and one can pass additional arguments like below.

df = pd.DataFrame({
    'num_likes': [464.0, 364.0, 487.0, 258.0, 125.0],
    'num_post': [142.0, 125.0, 106.0, 123.0, 103.0]
})

def myFunction(x, q22, q45, qc):
    k = 3

    if x >= q45 + k * qc:
        return q45 + k * qc
    elif x <= q22 - k * qc:
        return q22 - k * qc
    else:
        return -1

q22 = df["num_likes"].quantile(0.22)
q45 = df["num_likes"].quantile(0.45)
qc = q45 - q22

# pass additional arguments in an tuple, they will be passed to myFunction
df.num_likes.apply(myFunction, args=(q22, q45, qc))

# this will return a series which can be assigned to new column
# 0   -1
# 1   -1
# 2   -1
# 3   -1
# 4   -1
# Name: num_likes, dtype: int64