df['min'] = df.data[(df.data.shift(1) > df.data) & (df.data.shift(-1) > df.data)]
df['max'] = df.data[(df.data.shift(1) < df.data) & (df.data.shift(-1) < df.data)]

n = 3
# Store all peaks in a series
min_vals = df.data[(df.data.shift(1) > df.data) & (df.data.shift(-1) > df.data)]
max_vals = df.data[(df.data.shift(1) < df.data) & (df.data.shift(-1) < df.data)]

# Iterate over all values in my dataframe
for idx, row in df.iterrows():

    # get all peaks that appeared before the current row (avoid look ahead)
    tmp_min = min_vals.loc[(idx >= min_vals.index)]
    tmp_max = max_vals.loc[(idx >= max_vals.index)]

    # Test if at least n mins and max peaks already appeared
    if len(tmp_min) >= n and len(tmp_max) >= n:

         #create counter for min values (needed to create column name)
         min_ctr = 0

         # iterate over last n entries in tmp_min by using tail function
         for x in tmp_min.tail(n):
             df.loc[idx, 'min_' + str(min_ctr)] = row.data
             min_ctr += 1

         max_ctr = 0
         for x in tmp_min.tail(n):
             df.loc[idx, 'max_' + str(max_ctr)] = row.data
             max_ctr += 1