rearrange examples
Browse files- utils/examples.json +5 -5
utils/examples.json
CHANGED
@@ -4,11 +4,6 @@
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"value": "def print_hello_world():\n \"\"\"Print 'Hello World!'.\"\"\"",
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"length": 8
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},
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{
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"name": "Is_in_list",
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"value": "def is_in_list(L, e):\n \"\"\"Find if list L contains the element e.\"\"\"",
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"length": 32
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},
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{
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"name": "Scikit-Learn",
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"value": "import numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\n\n# create training data\nX = np.random.randn(100, 100)\ny = np.random.randint(0, 1, 100)\n\n# setup train test split",
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@@ -19,6 +14,11 @@
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"value": "def get_file_size(filepath):",
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"length": 64
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},
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{
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"name": "Python to Numpy",
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"value": "# native Python:\ndef mean(a):\n return sum(a)/len(a)\n\n# with numpy:\nimport numpy as np\n\ndef mean(a):",
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"value": "def print_hello_world():\n \"\"\"Print 'Hello World!'.\"\"\"",
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"length": 8
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},
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{
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"name": "Scikit-Learn",
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"value": "import numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\n\n# create training data\nX = np.random.randn(100, 100)\ny = np.random.randint(0, 1, 100)\n\n# setup train test split",
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"value": "def get_file_size(filepath):",
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"length": 64
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},
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{
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"name": "Is_in_list",
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"value": "def is_in_list(L, e):\n \"\"\"Find if list L contains the element e.\"\"\"",
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"length": 32
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},
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{
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"name": "Python to Numpy",
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"value": "# native Python:\ndef mean(a):\n return sum(a)/len(a)\n\n# with numpy:\nimport numpy as np\n\ndef mean(a):",
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