Finding the smallest number not smaller than x for a rolling data of a dataframe in python

Question

Let suppose i have data in rows for a column(O): 1,2,3,4,5,6,7,8,9,10. Its average is 5.5. I need to find the smallest number which is larger than the average 5.5:- ie '6'

Here is what I have tried soo far.

method 1:

df["test1"] = df["O"].shift().rolling(min_periods=1, window=10).apply(lambda x: pd.Series(x).nlargest(5).iloc[-1])

Discarded as that number may not always be the 6th number.

method 2:

great = []
df['test1'] = ''
df["avg"] = df["O"].shift().rolling(min_periods=1, window=10).mean()
for i in range(1, len(df)):
    for j in range(0, 10):
        if(df.loc[i-j, 'O'] > df.loc[i, 'avg']):
            great.append(df.loc[i-j, 'O'])
    df.loc[i, 'test1'] = min(great)

This throws an error:

KeyError: -1

Please help to find the small error in the code as soon as possible.

Thanks, D.

Answer 1

Mask the Series when it is greater than the mean, then sort, then take the first row.

import pandas as pd
df = pd.DataFrame([1,2,3,4,5,6,7,8,9,10], columns=("vals",))
df[df.vals > df.vals.mean()].sort_values("vals").head(1)
# >     vals
#    5  6

Answer 2

Try with

n = 10
output = df.vals.rolling(10).apply(lambda x : x[x>x.mean()].min())