add examples

examples.json

@@ -0,0 +1,38 @@
+[
+    {
+        "name": "Hello World!",
+        "value": "def print_hello_world():\n    \"\"\"Print 'Hello World!'.\"\"\"",
+        "length": 8
+    },
+    {
+        "name": "Filesize",
+        "value": "def get_file_size(filepath):",
+        "length": 64
+    },
+    {
+        "name": "Python to Numpy",
+        "value": "# native Python:\ndef mean(a):\n    return sum(a)/len(a)\n\n# with numpy:\nimport numpy as np\n\ndef mean(a):",
+        "length": 16
+    },
+    {
+        "name": "unittest",
+        "value": "def is_even(value):\n    \"\"\"Returns True if value is an even number.\"\"\"\n    return value % 2 == 0\n\n# setup unit tests for is_even\nimport unittest",
+        "length": 64
+
+    },
+    {
+        "name": "Scikit-Learn",
+        "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",
+        "length": 96
+    },
+    {
+        "name": "Pandas",
+        "value": "# load dataframe from csv\ndf = pd.read_csv(filename)\n\n# columns: \"age_group\", \"income\"\n# calculate average income per age group",
+        "length": 16
+    },
+    {
+        "name": "Transformers",
+        "value": "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n\n# build a BERT classifier",
+        "length": 48
+    }
+]