sandbox.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["%load_ext autoreload\n","\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["import pandas as pd\n","\n","test_df = pd.DataFrame(\n","  columns = ['a','b'],\n","  data = [\n","    [1, -42], # row 0\n","    [1, None], # row 1\n","    [1, None], # row 2\n","    [1, 3.14], # row 3\n","  ],\n","  dtype=object # prevent None from being converted to np.nan - ref: https://stackoverflow.com/a/48453225\n",")\n"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>3.14</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"     a     b\n0  NaN   NaN\n1  NaN   NaN\n2  NaN   NaN\n3  NaN  3.14"},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["test_df.where(test_df > 1)"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"   a    b\n0  1  NaN\n1  1  NaN\n2  1  NaN\n3  1  NaN"},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["test_df.where(test_df == 1)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>42.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>-3.14</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"   a      b\n0  1  42.00\n1  1    NaN\n2  1    NaN\n3  1  -3.14"},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["test_df = pd.DataFrame(\n","  columns = ['a','b'],\n","  data = [\n","    [1, -42], # row 0\n","    [1, None], # row 1\n","    [1, None], # row 2\n","    [1, 3.14], # row 3\n","  ]\n",")\n","\n","test_df.where(test_df==1, -test_df)"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"      a      b\n0  True  False\n1  True  False\n2  True  False\n3  True  False"},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["test_df == 1"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/plain":"[2, 2, 2, 2]"},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["[ x + x for x in test_df['a']]"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/plain":"True"},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["all((test_df == 1)['a'])"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"warn: sum of column \"a\" must be 0\n"}],"source":["import validium as V\n","\n","sum_eq_zero = V.Validator(\n","    lambda df: df['a'].sum() == 0, \n","    'sum of column \"a\" must be 0'\n",")\n","\n","if not sum_eq_zero.predicate(test_df): # should except on assert\n","    print('warn:', sum_eq_zero.fail_msg) \n","\n","sum_eq = lambda n: V.Validator(\n","    lambda df: df['a'].sum() == n, \n","    'sum of column \"a\" must be {}'.format(n)\n",")\n","\n","sum_eq(4).validate(test_df)\n"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["sum_eq = lambda n: V.Validator(\n","    lambda df: df['a'].sum() == n, \n","    'sum of column \"a\" must be {}'.format(n)\n",")\n","\n","sum_eq(4).validate(test_df)"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>-42.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>3.14</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"   a      b\n0  1 -42.00\n1  1    NaN\n2  1    NaN\n3  1   3.14"},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["test_df[['a','b']]"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>3.14</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"   a     b\n2  1   NaN\n3  1  3.14"},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["test_df.loc[2:3,'a':'b']"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"a     2.00\nb   -38.86\ndtype: float64\n-------\n-36.86\n-------\n-36.86\n"}],"source":["nums_df = test_df.loc[[0,3],['a','b']]\n","print(nums_df.sum())\n","print('-------')\n","print(nums_df.sum().sum())\n","print('-------')\n","print(nums_df.values.sum())\n"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"data":{"text/plain":"array([[  1.  , -42.  ],\n       [  1.  ,   3.14]])"},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["nums_df.values"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["v = V.Validator(\n","    lambda df: df.where(isinstance(df, int)).sum().sum() == 4,\n","    'its just 4'\n",")"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"data":{"text/plain":"a    False\nb    False\ndtype: bool"},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["test_df.apply(lambda x : isinstance(x,int))\n","\n","\n"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"[2, 3, 4, 5, 6]\n"}],"source":["def foo(bar):\n","    a = list(range(10))\n","    print(a[bar[0]:bar[1]])\n","\n","foo([2,7])"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"data":{"text/plain":"4"},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["test_df[test_df == 1].count().sum()\n","\n"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[{"ename":"KeyError","evalue":"\"None of [Int64Index([1], dtype='int64')] are in the [index]\"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-29-2bd9df897720>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m )\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_pass\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;31m# turns out its way worse when you have to work directly with the dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m~/GitProjects/json2d/validframe/validframe/core.py\u001b[0m in \u001b[0;36mvalidate\u001b[0;34m(self, df)\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;31m# cells = CellsValidator.iter_cells(df)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m     \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcells\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m~/GitProjects/json2d/validframe/.venv/lib/python3.7/site-packages/validium/core.py\u001b[0m in \u001b[0;36mvalidate\u001b[0;34m(self, target)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfail_msg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m<ipython-input-29-2bd9df897720>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(xs)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# theres brevity in the logic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m cv = vf.CellsValidator(\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0;32mlambda\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;34m'all must equal 1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mcols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-29-2bd9df897720>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# theres brevity in the logic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m cv = vf.CellsValidator(\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0;32mlambda\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;34m'all must equal 1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mcols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m~/GitProjects/json2d/validframe/validframe/core.py\u001b[0m in \u001b[0;36miter_cells\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mrow_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrows_slice\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcols_slice\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0;32myield\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mRowsValidator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSliceValidator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m~/GitProjects/json2d/validframe/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1759\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1760\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1761\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1762\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1763\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m~/GitProjects/json2d/validframe/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1278\u001b[0m         \u001b[0;31m# ugly hack for GH #836\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_multi_take_opportunity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1280\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_multi_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1282\u001b[0m         \u001b[0;31m# no shortcut needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m~/GitProjects/json2d/validframe/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_multi_take\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1337\u001b[0m         d = {\n\u001b[1;32m   1338\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1339\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AXIS_ORDERS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1340\u001b[0m         }\n\u001b[1;32m   1341\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_with_indexers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m~/GitProjects/json2d/validframe/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1337\u001b[0m         d = {\n\u001b[1;32m   1338\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1339\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_AXIS_ORDERS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1340\u001b[0m         }\n\u001b[1;32m   1341\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_with_indexers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m~/GitProjects/json2d/validframe/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1551\u001b[0m         self._validate_read_indexer(\n\u001b[0;32m-> 1552\u001b[0;31m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1553\u001b[0m         )\n\u001b[1;32m   1554\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m~/GitProjects/json2d/validframe/.venv/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1637\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmissing\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1638\u001b[0m                 \u001b[0maxis_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1639\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1641\u001b[0m             \u001b[0;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: \"None of [Int64Index([1], dtype='int64')] are in the [index]\""]}],"source":["# lets try to do a simple one:\n","import validframe as vf\n","\n","df_pass = pd.DataFrame(\n","      columns = ['a','b','c'],\n","      data = [\n","        [1, -42, 1], # row 0\n","        [1, None, 1], # row 1\n","        [1, None, 1], # row 2\n","        [1, 3.14, 1], # row 3\n","      ]\n","    )\n","\n","# this is how it comes out working with the list of cells rather than the dataframe\n","# theres brevity in the logic\n","cv = vf.CellsValidator(\n","    lambda xs: all([x == 1 for x in xs]), \n","    'all must equal 1',\n","    cols=['a', 'c'], rows=[0, 3]\n",")\n","\n","cv.validate(df_pass)\n","\n","# turns out its way worse when you have to work directly with the dataframe\n","# its unintuitive and unreadable unless you really really know the DataFrame api\n","fv = vf.FrameValidator(\n","    lambda df: df.loc[[0,3],['a','c']][df == 1].count().sum() == df.loc[[0,3],['a','c']].count().sum(),\n","    'all must equal 1'\n",")\n","\n","fv.validate(df_pass)\n","\n","# abstracting away the slicing helps but still pretty unintuitive and unreadable\n","fv2 = vf.FrameValidator(\n","    lambda df: df.where(df == 1).count().sum() == df.count().sum(),\n","    'all must equal 1',\n","    cols=['a', 'c'], rows=[0, 3]\n",")\n","\n","fv2.validate(df_pass)\n","\n","# FINAL THOUGHTS: \n","# when working with the dataframe directly you end up having to write a predicate function that does more mathy manipulations (eg. reshaping, pivoting, melting, reducing etc) of the dataframe because that's just how the DataFrame api was designed - while for the right developer this might sound like a fun and challenging way to write prediate functions for validators, it really does make for hacky and unfriendly code\n"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[],"source":["# below are the dataframe manipulations step by step to illustrate the point above:"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[],"source":["df = pd.DataFrame(\n","    columns = ['a','b','c'],\n","    data = [\n","        [1, -42, 1], # row 0\n","        [1, None, 1], # row 1\n","        [1, None, 1], # row 2\n","        [1, 3.14, 1], # row 3\n","    ]\n",")"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"data":{"text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>c</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>","text/plain":"   a  c\n0  1  1\n3  1  1"},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["df.loc[[0,3],['a','c']]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df.loc[[0,3],['a','c']][df == 1]\n","df.loc[[0,3],['a','c']].where(df == 1) # same as prev\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df.loc[[0,3],['a','c']][df == 1].count().sum()"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[{"data":{"text/plain":"1"},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["df3 = pd.DataFrame(\n","      columns = ['a','b','c'],\n","      data = [\n","        [1, -42, 1], # row 0\n","        [1, None, 1], # row 1\n","        [1, None, 1], # row 2\n","        [1, 3.14, 1], # row 3\n","      ]\n","    )\n","\n","df3[['a','c']]\n","df3.loc[[0]]\n","df3.loc[[0],['a']]\n","df3.loc[0,'a']\n","df3.rows"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"data":{"text/plain":"[0, 1, 2, 3]"},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["[i for i, row in df3.iterrows()]"]}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"name":"python","codemirror_mode":{"name":"ipython","version":3},"version":"3.7.1-final"},"orig_nbformat":2,"file_extension":".py","mimetype":"text/x-python","name":"python","npconvert_exporter":"python","pygments_lexer":"ipython3","version":3,"kernelspec":{"name":"python37164bitvenvvenv6af9338a92fb452b8eb45a21992693d1","display_name":"Python 3.7.1 64-bit ('.venv': venv)"}}}