如何加快此 python 代码中的求和过程？

Question

I have df.Ah, column of a dataframe, which has either positive values or zeros. I want to store into one another dataframe the sum of values before a zero and follow that with one another zero.

for example: df.Ah = [ 1,2,3,0,46,0,24,1], gives as out put [6,0,46,0,25].

my attempt was:

lst = df.Ah;
lst1 = pd.DataFrame(index=np.arange(100000000)); #if I replace values of a pre-existing column code #speeds up


summ = 0;
for i , elem in enumerate(lst):
     
    if elem != 0:
        summ = summ + elem;
    else:
        if summ:
            lst1.loc[i,'Ah']=sum;
            
        lst1.loc[i,'Ah']=elem;
        summ = 0;
...

if summ:
    lst1.iloc[i+1, 0] = summ;

#If I print the index i for each loop , it generates 100.000 prints for each minute;
# which means it would take around five hours to complete checking 31 milion values
# of my dataframe and I don't have all that time for this basic operation.

is there a way to speed up this code?

Answer 1

You can use the cumsum method from Pandas to calculate the cumulative sum of the values in df.Ah and then create a new DataFrame using the indices where the cumulative sum is 0 as the index. This will avoid the need to use a loop, which can be slow when working with large DataFrames.

Here's an example of how you could do this:

# Calculate the cumulative sum of values in df.Ah
df['cumulative_sum'] = df.Ah.cumsum()

# Create a new DataFrame using the indices where the cumulative sum is 0 as the index
df1 = pd.DataFrame(df.cumulative_sum.values, index=df.index[df.cumulative_sum == 0], columns=['Ah'])

# Append a 0 to the end of the new DataFrame if necessary
if df1.iloc[-1] != 0:
    df1 = df1.append(pd.DataFrame([0], columns=['Ah']))

This should be much faster than using a loop, since the cumsum method is implemented in C and can be much faster than a Python loop.