update examples

utils/examples.json

@@ -1,43 +1,38 @@
 [
-    {
-        "name": "Hello World!",
-        "value": "def print_hello_world():\n    \"\"\"Print 'Hello World!'.\"\"\"",
-        "length": 8
-    },
     {
         "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": "Filesize",
-        "value": "def get_file_size(filepath):",
-        "length": 64
+        "name": "Transformers",
+        "value": "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n\n# build a BERT classifier",
+        "length": 48
+    }
+    {
+        "name": "Count words",
+        "value": "def count_words(filename):\n    \"\"\"Count the number of occurrences of each word in the file\"\"\""
+        "length": 60
     },
     {
-        "name": "Is_in_list",
+        "name": "Is e in L",
         "value": "def is_in_list(L, e):\n    \"\"\"Find if list L contains the element e.\"\"\"",
         "length": 32
     },
-    {
-        "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": "Pizza Problem",
+        "value": "def exercise():\n    \"\"\"Marie ordered one chicken meal that costs 12 dollars, and some boxes of pizza. Marie paid a total of 44 dollars. How many boxes of pizza did Marie order if each box costs 8 dollars?\"\"\",
+        "length": 56
+    },
     {
         "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
-    }
 ]