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Style Pandas Dataframe Like a Master


In addition, the cmap argument allows us to choose a color palette for the gradient. The matplotlib documentation lists all the available options (seaborn has some options as well).

Lightning Fast XGBoost on Multiple GPUs


XGBoost is one of the most used libraries fora data science. At the time XGBoost came into existence, it was lightning fast compared to its nearest rival Python's Scikit-learn GBM. But as the times have progressed, it has been rivaled by some awesome libraries like LightGBM and Catboost, both on speed as well as accuracy. I, for one, use LightGBM for most of the use cases where I have just got CPU for training. But when I have a GPU or multiple GPUs at my disposal, I still love to train with XGBoost.

r/datascience - Dataframe library for python, pandas alternative


Do you have benchmarks showing speed improvements over pandas? Pandas can be dreadfully slow, and a restricted implementation that uses only a subset of the "most useful" pandas features might be significantly faster. For instance, consider this benchmark for row and column access to a pandas DataFrame vs a dict of ndarrays columns. For row access, the fastest pandas way to iterate through rows (iterrows) is x6 slower than the simple dict implementation: 24ms vs 4ms. Furthermore, pandas DataFrame a column-based data structure is a whopping 36x slower than a dict of ndarrays for access to a single column of data.

datas-frame – Modern Pandas (Part 8): Scaling


We can answer questions like "Which employer's employees donated the most?" Or "what is the average amount donated per occupation?" Since Dask is lazy, we haven't actually computed anything.

Oracle python pandas merge DataFrames


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