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There are several ways to increase the efficiency of iterating through a large dataframe in Pandas:

1. Avoid iterating through the dataframe whenever possible: Instead of iterating through each row of the dataframe, try to vectorize operations using built-in Pandas functions. This can significantly speed up your code.

2. Use the apply() function: The apply() function allows you to apply a function to each row or column of a dataframe. This can be much faster than iterating through the dataframe yourself.

3. Use the iterrows() function: If you need to iterate through each row of the dataframe, use the iterrows() function instead of a for loop. This function returns a tuple containing the row index and the row data, which can be accessed directly.

4. Use chunking: If the dataframe is too large to fit into memory, you can read it in chunks using the readcsv() function or the readsql() function. This allows you to process the data in smaller pieces, which can be more efficient.

5. Use Numpy: Numpy functions perform operations faster than Pandas functions. You can convert your Pandas dataframe to Numpy and then apply Numpy functions on it to increase efficiency.

6. Use parallel processing: If you have a multi-core processor, you can use parallel processing to speed up operations. The joblib or multiprocessing libraries in Python can be used to implement this.