iterate dataframe fast way in python

When it comes to iterating through a pandas DataFrame, there are several ways to do so. However, some methods are faster than others. Here are a few options to consider:

1. Using iterrows(): This method iterates through the rows of the DataFrame, returning each row as a tuple of (index, series) pairs. While this method works, it can be slow since it involves creating a new Series object for each row.

main.py
for index, row in df.iterrows():
    # do something with `row`
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2. Using apply(): This method applies a given function to each row or column of the DataFrame. By default, it operates on columns, so you'll need to specify axis=1 to apply the function to each row instead.

main.py
def my_func(row):
    # do something with `row`
    
df.apply(my_func, axis=1)
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3. Vectorization: This involves performing operations on entire columns or rows at once, without the need for iteration. This is often the fastest approach since it takes advantage of optimized library functions.

main.py
# example of vectorized function
df['new_col'] = df['col1'] + df['col2']
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Overall, the choice of method will depend on the specific use case and the size of the DataFrame. The above solutions can help you iterate through your DataFrame in a faster way.