Multiple operations iterating over dataframe columns (apply function?) - pandas

Question

I have a pandas dataframe with thousands of columns and I would like to perform the following operations for each column of the dataframe:
check if the value i-th and i-1-th values are in the range (between x and y);
if #1 is satisfied, then find log(i/i-1) ** 2 of the column;
if #1 is not satisfied, assume 0;
find the total of #2 for each column.
Here is a dataframe with a single column:
d = {'col1': [10, 15, 23, 16, 5, 14, 11, 4]}
df = pd.DataFrame(data = d)
df
x = 10 and y = 20
Here is what I can do for this single column:
df["IsIn"] = "NA"
for i in range(1, len(df.col1)):
if (x < df.col1[i] < y) & (x < df.col1[i - 1] < y):
df.IsIn[i] = 1
else:
df.IsIn[i] = 0
df["rets"] = np.log(df["col1"] / df["col1"].shift(1))
df["var"] = df["IsIn"] * df["rets"]**2
Total = df["var"].sum()
Total
Ideally, I would have a (1 by n-cols) dataframe of Totals for each column. How can I best achieve this? I would also appreciate if you can supplement your answer with detailed explanation.

Answer 1

Yes, this is an instance where apply works. You only need to wrap your logic in a function. Also, consider between and shift on the condition to eliminate the first loop:
def func(s, x=10, y=20):
'''
compute the value given a series
'''
# mask where values are between x and y
valid = s.between(x,y)
# shift `valid` and double check
valid = valid & valid.shift(fill_value=False)
# squared log, mask with `valid`, and sum
return (np.log(s/s.shift())**2 * valid).sum()
# apply `func` on the columns
df.apply(func, x=10, y=20)
Output:
col1 0.222561
dtype: float64