{"cells":[{"metadata":{},"cell_type":"markdown","source":"![Image](https://docs.dask.org/en/latest/_static/images/dask-horizontal-white.svg)"},{"metadata":{},"cell_type":"markdown","source":"To Lean More about Dask [Follow Here](https://dask.org/) "},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-output":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Pandas Dataframe"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"%%time\ntrain= pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv',\n       usecols=[1, 2, 3,4,7,8,9], dtype={'timestamp': 'int64', 'user_id': 'int32' ,'content_id': 'int16','content_type_id': 'int8',\n        'answered_correctly':'int8','prior_question_elapsed_time': 'float32','prior_question_had_explanation': 'boolean'}\n              )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\ntrain.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nresults_u = train[['user_id','answered_correctly']].groupby(['user_id']).agg(['mean', 'sum'])\nresults_u.columns = [\"answered_correctly_user\", 'sum']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"It takes 14.3 seconds to compute the above cell, Lets see how dask is performing on that "},{"metadata":{},"cell_type":"markdown","source":"# Dask Dataframe"},{"metadata":{"trusted":true},"cell_type":"code","source":"import dask.dataframe as dd","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\ntrain_dask = dd.read_csv(\"/kaggle/input/riiid-test-answer-prediction/train.csv\",\n       usecols=[1, 2, 3,4,7,8,9], dtype={'timestamp': 'int64', 'user_id': 'int32' ,'content_id': 'int16','content_type_id': 'int8',\n        'answered_correctly':'int8','prior_question_elapsed_time': 'float32','prior_question_had_explanation': 'boolean'})","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### That was a huge time difference between pandas and DASK"},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\ntrain_dask.head(npartitions=-1)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"> usually dask read the csv files in a chunk of partition so to take up all the partition we must use the *nparameter=-1* argument ."},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\ntrain_dask.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Yes it is faster than previous one, as it is printing from one partition"},{"metadata":{},"cell_type":"markdown","source":"## Let's see how many partitions are there in the dask dataframe\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_dask.npartitions","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### There are toally 92 partitions dask has created on this dataset"},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\ntrain_dask","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Printing the entire dataframe takes less than 10 micro seconds"},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nresults_u = train_dask[['user_id','answered_correctly']].groupby(['user_id']).agg(['mean', 'sum'])\nresults_u.columns = [\"answered_correctly_user\", 'sum']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"voila It takes just 19.7 ms to compute the above cell, Which is actually great to see "}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}