![]() ![]() □ Itamar Turner-Trauring - Speed Python Master (Must ✅). ![]() (□ If you like it.)Ĭomment below the tricks that you used to load your data faster I will add them to the list. Thanks for reaching until the end, I hope you learned something new. Their’s is an another way, You can rent a VM in the cloud, with 64 cores and 432GB RAM, for ~$3/hour or even a better price with some googling.Ĭaveat: you need to spend the next week configuring it. Libraries to try out: Paratext, Datatable. □ Pro Tip: If you want to find the time taken by a jupyter cell to run just add %%time magic function at the start of the cell sample = pd.read_csv("train.csv", nrows=100) # Load Sample data dtypes = sample.dtypes # Get the dtypes cols = lumns # Get the columns dtype_dictionary = ,assume_missing=True) pute() All of this overhead can be reduced using nrows as shown below. □ Pro-Tip: An Effective use of nrows is when you have more than 100’s of columns to check and define proper dtypes for each and every column. df = pd.read_csv('train.csv', skiprows=) # It might remove headings ![]() Skiprows Line numbers to skip (0-indexed) or the number of lines to skip (int) at the start of the file. > Import pandas as pd > df = pd.read_csv("train.csv", nrows=1000) >len(df) 1000 Nrows The number of rows to read from the file. In most of the cases for testing purpose, you don’t need to load all the data when a sample can do just fine. Even before loading all the data into your RAM, it is always a good practice to test your functions and workflows using a small dataset and pandas have made it easier to choose precisely the number of rows (you can even skip the rows that you do not need.) ![]()
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