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    "info": {
        "author": "Ruinan(Victor) Zhang\uff0c YinJun(YJ)",
        "author_email": "ruinan.zhang@icloud.com, yjun1989@gmail.com",
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            "Programming Language :: Python :: 3"
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        "description": "This project handles series data\n\n\nSupport Series data Feature Calculation.\n\n\nInstallation\n==============\n\nYou need Python installed on your system to able to use seq2ftr.\n\nThis package contains many feature extraction methods.\n\nSupport different type (continues/class) features calculation.\n\n>>> $ pip install seq2ftr\n\nInstall Requirements\n * numpy\n * pandas\n * sklearn\n\n\nFeature Calculation\n^^^^^^^^^^^^^^^^^^^^^^^^\n Support Function\n\n - mean\n - max\n - min\n - freq_of_max\n - freq_of_min\n - median\n - median_mean_distance\n - percentage_below_mean\n - var\n - std\n - uniqueCount\n - ...\n\nSupport Type\n^^^^^^^^^^^^^^^^^\n - 0 - boolean\n - 1 - numericla\n - 2 - categorical\n\n\nExample\n^^^^^^^^^^^^^\nTo start , we load data to python\n\n>>>\nimport pandas as pd\ndf = pd.DataFrame([[1,200,\"1\"],[1,500,\"2\"],[2,300,\"2\"],[2,600,\"2\"]],columns=['id','stock_price',\"type\"])\ndf = df.set_index(\"id\")\n\n>>>\nfrom seq2ftr import SequenceTransformer\nst_num = SequenceTransformer()\nst_num.transformer(df['stock_price']\uff09\n# output all features",
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        "name": "seq2ftr",
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        "summary": "A Nice and Convenient Feature Engineering Tool on Sequential Data",
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