generated from user_client2024/78
System save at 23/05/2025 20:39 by user_client2024
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@ -94,7 +94,7 @@
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"cell_type": "code",
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"execution_count": 11,
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"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
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"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
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"metadata": {
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"execution_count": 12,
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"id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897",
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"id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897",
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"execution_count": 13,
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"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
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"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
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"metadata": {
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"execution_count": 4,
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"execution_count": 19,
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"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
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"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
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"metadata": {
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"execution_count": 11,
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"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
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"id": "69d6771d-be1c-4ae1-802a-3ba7b2e8c5fb",
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"execution_count": 12,
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"execution_count": 16,
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"id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897",
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"id": "82c2152f-513c-4fde-a4a9-6ee3a01ef897",
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"metadata": {
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 17,
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"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
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"id": "b6c85de2-6a47-4109-8885-c138c289ec25",
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"metadata": {
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 19,
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"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
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"id": "1f20337b-8116-47e5-8743-1ba41e2df819",
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"metadata": {
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"metadata": {
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"tags": []
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8
main.py
8
main.py
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# return df
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# return df
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# In[11]:
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# In[15]:
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import pandas as pd
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import pandas as pd
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@ -139,7 +139,7 @@ query = """
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"""
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"""
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# In[12]:
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# In[16]:
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def trx_count_sum_groupwise(data_filt_partywise):
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def trx_count_sum_groupwise(data_filt_partywise):
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@ -197,7 +197,7 @@ def scenario9_data(data1):
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# In[13]:
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# In[17]:
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from tms_data_interface import SQLQueryInterface
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from tms_data_interface import SQLQueryInterface
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@ -237,7 +237,7 @@ class Scenario:
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return scenario_data
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return scenario_data
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# In[4]:
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# In[19]:
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# sen = Scenario()
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# sen = Scenario()
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