diff --git a/.ipynb_checkpoints/main-checkpoint.ipynb b/.ipynb_checkpoints/main-checkpoint.ipynb index f0cf87f..df10f10 100644 --- a/.ipynb_checkpoints/main-checkpoint.ipynb +++ b/.ipynb_checkpoints/main-checkpoint.ipynb @@ -94,8 +94,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb", + "execution_count": 41, + "id": "87cf157c-3725-4d90-bfb6-91cba5028826", "metadata": { "tags": [] }, @@ -103,6 +103,35 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", + "from tms_data_interface import SQLQueryInterface" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "b68368ea-9cbb-4060-b138-d8b7c3d8a193", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "query2 = \"\"\"\n", + " SELECT *\n", + "\n", + " FROM percentile_dist\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", "query = \"\"\"\n", " SELECT \n", " t.transaction_key,\n", @@ -151,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 44, "id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897", "metadata": { "tags": [] @@ -215,15 +244,42 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 45, + "id": "fdefde12-97ab-4545-9612-153f707b7bc9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "seq = SQLQueryInterface(schema=\"transactionschema\")\n", + "data = seq.execute_raw(query2)\n", + "Columns = ['point_of_percentile', 'value', 'total_event',\n", + " 'true_positive', 'false_positive', 'tpsar']\n", + "percent_dist = pd.DataFrame(data,columns = Columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "8cc4b541-75a6-4fa7-978a-12df50f94d32", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# round(int(percent_dist[percent_dist['point_of_percentile']\\\n", + "# == 75]['value'].iloc[0])/100)*100" + ] + }, + { + "cell_type": "code", + "execution_count": 53, "id": "b6c85de2-6a47-4109-8885-c138c289ec25", "metadata": { "tags": [] }, "outputs": [], "source": [ - "\n", - "from tms_data_interface import SQLQueryInterface\n", "\n", "class Scenario:\n", " seq = SQLQueryInterface(schema=\"transactionschema\")\n", @@ -252,7 +308,8 @@ " ]\n", " df = pd.DataFrame(row_list, columns = cols)\n", " df['Segment'] = 'SME'\n", - " df['MIN_LIMIT'] = 50000\n", + " df['MIN_LIMIT'] = round(int(percent_dist[percent_dist['point_of_percentile']\\\n", + " == 75]['value'].iloc[0])/100)*100\n", " df['PCT_RANGE'] = 20\n", " \n", " scenario_data = scenario9_data(df)\n", @@ -262,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "1f20337b-8116-47e5-8743-1ba41e2df819", "metadata": { "tags": [] diff --git a/main.ipynb b/main.ipynb index f0cf87f..df10f10 100644 --- a/main.ipynb +++ b/main.ipynb @@ -94,8 +94,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb", + "execution_count": 41, + "id": "87cf157c-3725-4d90-bfb6-91cba5028826", "metadata": { "tags": [] }, @@ -103,6 +103,35 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", + "from tms_data_interface import SQLQueryInterface" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "b68368ea-9cbb-4060-b138-d8b7c3d8a193", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "query2 = \"\"\"\n", + " SELECT *\n", + "\n", + " FROM percentile_dist\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", "query = \"\"\"\n", " SELECT \n", " t.transaction_key,\n", @@ -151,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 44, "id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897", "metadata": { "tags": [] @@ -215,15 +244,42 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 45, + "id": "fdefde12-97ab-4545-9612-153f707b7bc9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "seq = SQLQueryInterface(schema=\"transactionschema\")\n", + "data = seq.execute_raw(query2)\n", + "Columns = ['point_of_percentile', 'value', 'total_event',\n", + " 'true_positive', 'false_positive', 'tpsar']\n", + "percent_dist = pd.DataFrame(data,columns = Columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "8cc4b541-75a6-4fa7-978a-12df50f94d32", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# round(int(percent_dist[percent_dist['point_of_percentile']\\\n", + "# == 75]['value'].iloc[0])/100)*100" + ] + }, + { + "cell_type": "code", + "execution_count": 53, "id": "b6c85de2-6a47-4109-8885-c138c289ec25", "metadata": { "tags": [] }, "outputs": [], "source": [ - "\n", - "from tms_data_interface import SQLQueryInterface\n", "\n", "class Scenario:\n", " seq = SQLQueryInterface(schema=\"transactionschema\")\n", @@ -252,7 +308,8 @@ " ]\n", " df = pd.DataFrame(row_list, columns = cols)\n", " df['Segment'] = 'SME'\n", - " df['MIN_LIMIT'] = 50000\n", + " df['MIN_LIMIT'] = round(int(percent_dist[percent_dist['point_of_percentile']\\\n", + " == 75]['value'].iloc[0])/100)*100\n", " df['PCT_RANGE'] = 20\n", " \n", " scenario_data = scenario9_data(df)\n", @@ -262,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "1f20337b-8116-47e5-8743-1ba41e2df819", "metadata": { "tags": [] diff --git a/main.py b/main.py index aaee945..776b536 100644 --- a/main.py +++ b/main.py @@ -87,11 +87,27 @@ # return df -# In[1]: +# In[41]: import pandas as pd import numpy as np +from tms_data_interface import SQLQueryInterface + + +# In[42]: + + +query2 = """ + SELECT * + + FROM percentile_dist +""" + + +# In[43]: + + query = """ SELECT t.transaction_key, @@ -138,7 +154,7 @@ query = """ """ -# In[3]: +# In[44]: def trx_count_sum_groupwise(data_filt_partywise): @@ -196,10 +212,25 @@ def scenario9_data(data1): -# In[4]: +# In[45]: -from tms_data_interface import SQLQueryInterface +seq = SQLQueryInterface(schema="transactionschema") +data = seq.execute_raw(query2) +Columns = ['point_of_percentile', 'value', 'total_event', + 'true_positive', 'false_positive', 'tpsar'] +percent_dist = pd.DataFrame(data,columns = Columns) + + +# In[52]: + + +# round(int(percent_dist[percent_dist['point_of_percentile']\ +# == 75]['value'].iloc[0])/100)*100 + + +# In[53]: + class Scenario: seq = SQLQueryInterface(schema="transactionschema") @@ -228,7 +259,8 @@ class Scenario: ] df = pd.DataFrame(row_list, columns = cols) df['Segment'] = 'SME' - df['MIN_LIMIT'] = 50000 + df['MIN_LIMIT'] = round(int(percent_dist[percent_dist['point_of_percentile']\ + == 75]['value'].iloc[0])/100)*100 df['PCT_RANGE'] = 20 scenario_data = scenario9_data(df) @@ -236,7 +268,7 @@ class Scenario: return scenario_data -# In[ ]: +# In[55]: # sen = Scenario()