Don’t use loc/iloc with Loops In Python, Instead, Use This!

Run your loops at a 60X faster speed

Pic Credit: Unsplash

Recently, I was experimenting with loops in Python and realized that using ‘iloc’/ ‘loc’ within the loops takes a lot of time to execute. The immediate next question was why is ‘loc’ taking too much time and what is the alternative to ‘loc’?

In this blog, we will answer these questions by looking at some practical examples.

What is loc — if you don’t know already!

The loc[] function is a pandas function that is used to access the values within a DataFrame using the row index and column name. It is used when you know which row and column you want to access.

Let’s understand loc using an example. We have the following pandas DataFrame named df(shown below) and we want to access the value corresponding to the 2nd row in the column ‘a’ i.e. 10.

DataFrame df (Image by Author)

We can access the value using the following code:

##df.loc[index, column_name]

df.loc[1,'a']

### Output: 10

Similarly, iloc is used to access the value using index and column numbers.

##df.loc[index, column_number]

df.iloc[1,0]

### Output: 10

So, the loc function is used to access columns using column names while the iloc function is used to access columns using column indexes.

What happens if you use loc/iloc with loops in Python?

Imagine, we want to add a new column ‘c’, which is equal to the sum of values of column ‘a’ and column ‘b’, to our DataFrame df.

Using the ‘for’ loop, we can iterate through our DataFrame and add a new column ‘c’ using the loc function as shown below:

import timestart = time.time()

# Iterating through the DataFrame df
for index, row in df.iterrows():
        df.loc[index,'c'] = row.a + row.b

end = time.time()
print(end - start)

### Time taken: 2414 seconds