{ "cells": [ { "cell_type": "code", "execution_count": 144, "id": "59e7fd1c-b4a6-40b6-9d14-b471cabcdcc5", "metadata": { "tags": [] }, "outputs": [], "source": [ "import sketch\n", "import pandas as pd\n", "import os\n", "\n", "os.environ['SKETCH_MAX_COLUMNS'] = '40'" ] }, { "cell_type": "code", "execution_count": 155, "id": "ec1df080-81d8-4e93-8fe8-6893026af6df", "metadata": { "tags": [] }, "outputs": [], "source": [ "cust_data = pd.read_csv(\"/Users/normrasmussen/Downloads/churned_analysis.csv\")" ] }, { "cell_type": "code", "execution_count": 156, "id": "3c724cbb-74a9-4727-8826-ebf58afa29ad", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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332999202BrightLine Eating Solutions LLC2023-0310,3627,8907,2728,1778,4687,5248,5095,638581.0NaNNaNNaNNaNNaNNaNNaN
430867752Casio America Inc2023-03NaNNaNNaNNaNNaN1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" ], "text/plain": [ " OrgID Organization Churn Date 2022-01 2022-02 \n", "0 30867753 Aquent 2023-04 58 38 \\\n", "1 33375202 BioLife Solutions, Inc. 2023-04 11 7 \n", "2 30867495 ZyXel Communications Inc 2023-04 1 1 \n", "3 32999202 BrightLine Eating Solutions LLC 2023-03 10,362 7,890 \n", "4 30867752 Casio America Inc 2023-03 NaN NaN \n", "\n", " 2022-03 2022-04 2022-05 2022-06 2022-07 2022-08 2022-09 2022-10 2022-11 \n", "0 50 50 39 36 46 46 43.0 27.0 24.0 \\\n", "1 4 11 4 NaN NaN NaN NaN NaN NaN \n", "2 2 1 2 4 2 2 1.0 NaN NaN \n", "3 7,272 8,177 8,468 7,524 8,509 5,638 581.0 NaN NaN \n", "4 NaN NaN NaN 1 NaN NaN NaN NaN NaN \n", "\n", " 2022-12 2023-01 2023-02 2023-03 2023-04 \n", "0 26 22 8.0 10.0 2.0 \n", "1 NaN NaN NaN NaN NaN \n", "2 NaN NaN 1.0 1.0 NaN \n", "3 NaN NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN NaN " ] }, "execution_count": 156, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cust_data.head(5)" ] }, { "cell_type": "code", "execution_count": 157, "id": "720c2582-f382-4fe7-bfca-768c696302aa", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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\n",
       "cust_data.fillna(0, inplace=True)\n",
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\n", " 
\n",
       "# Remove the OrgID and Churn Date columns\n",
       "cust_data = cust_data.drop(columns=['OrgID', 'Churn Date'])\n",
       "
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0Aquent583850503936464643.027.024.026228.010.02.0
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2ZyXel Communications Inc112124221.00.00.0001.01.00.0
3BrightLine Eating Solutions LLC103627890727281778468752485095638581.00.00.0000.00.00.0
4Casio America Inc000001000.00.00.0000.00.00.0
5Riviera Partners LLC811951262632796835.023.033.0257571.029.04.0
6Viveve000004100.00.00.01930.00.00.0
7connectRN Inc.3863783967329477621203889438.0178.0115.021492165.00.00.0
8Hammer Nutrition011275456.06.00.0000.00.00.0
9National Campus and Community Radio Association631510482304.04.06.0552.00.00.0
10Portnoy, Messinger, Pearl & Associates, Inc.2827791713801.00.01.0120.00.00.0
11Safari Energy23719213824621.035.010.0710.00.00.0
12Starwood Pet Travel0142221700.00.00.0202.00.00.0
13Trax Technology Solutions Pte Ltd38167758367151538018144912.00.01.0110.00.00.0
14Uber London Limited (POOL)721775825606578501497543628.0505.0650.01,2701,957190.00.00.0
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\n", " 
\n",
       "# Convert index row, columns 1-17 value to datetime\n",
       "cust_data.columns = pd.to_datetime(cust_data.columns)\n",
       "
\n", " \n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cust_data.sketch.howto(\"Convert index row, columns 1-17 value to datetime\")" ] }, { "cell_type": "code", "execution_count": 179, "id": "c229b344-94c8-4ab1-aa8d-1e15d6dc12bc", "metadata": { "tags": [] }, "outputs": [ { "ename": "ValueError", "evalue": "Length mismatch: Expected axis has 17 elements, new values have 15 elements", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[179], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Convert index row, columns 1-17 value to datetime\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mcust_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(cust_data\u001b[38;5;241m.\u001b[39mcolumns[\u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m16\u001b[39m])\n", "File \u001b[0;32m~/.pyenv/versions/3.11.1/envs/jupyter/lib/python3.11/site-packages/pandas/core/generic.py:6002\u001b[0m, in \u001b[0;36mNDFrame.__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m 6000\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 6001\u001b[0m \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getattribute__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)\n\u001b[0;32m-> 6002\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__setattr__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6003\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m 6004\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", "File \u001b[0;32m~/.pyenv/versions/3.11.1/envs/jupyter/lib/python3.11/site-packages/pandas/_libs/properties.pyx:69\u001b[0m, in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[0;34m()\u001b[0m\n", "File \u001b[0;32m~/.pyenv/versions/3.11.1/envs/jupyter/lib/python3.11/site-packages/pandas/core/generic.py:730\u001b[0m, in \u001b[0;36mNDFrame._set_axis\u001b[0;34m(self, axis, labels)\u001b[0m\n\u001b[1;32m 725\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 726\u001b[0m \u001b[38;5;124;03mThis is called from the cython code when we set the `index` attribute\u001b[39;00m\n\u001b[1;32m 727\u001b[0m \u001b[38;5;124;03mdirectly, e.g. `series.index = [1, 2, 3]`.\u001b[39;00m\n\u001b[1;32m 728\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 729\u001b[0m labels \u001b[38;5;241m=\u001b[39m ensure_index(labels)\n\u001b[0;32m--> 730\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 731\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clear_item_cache()\n", "File \u001b[0;32m~/.pyenv/versions/3.11.1/envs/jupyter/lib/python3.11/site-packages/pandas/core/internals/managers.py:225\u001b[0m, in \u001b[0;36mBaseBlockManager.set_axis\u001b[0;34m(self, axis, new_labels)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_axis\u001b[39m(\u001b[38;5;28mself\u001b[39m, axis: AxisInt, new_labels: Index) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 224\u001b[0m \u001b[38;5;66;03m# Caller is responsible for ensuring we have an Index object.\u001b[39;00m\n\u001b[0;32m--> 225\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_set_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_labels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis] \u001b[38;5;241m=\u001b[39m new_labels\n", "File \u001b[0;32m~/.pyenv/versions/3.11.1/envs/jupyter/lib/python3.11/site-packages/pandas/core/internals/base.py:70\u001b[0m, in \u001b[0;36mDataManager._validate_set_axis\u001b[0;34m(self, axis, new_labels)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m new_len \u001b[38;5;241m!=\u001b[39m old_len:\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 71\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength mismatch: Expected axis has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mold_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m elements, new \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues have \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnew_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m elements\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 73\u001b[0m )\n", "\u001b[0;31mValueError\u001b[0m: Length mismatch: Expected axis has 17 elements, new values have 15 elements" ] } ], "source": [ "# Convert index row, columns 1-17 value to datetime\n", "cust_data.columns = pd.to_datetime(cust_data.columns[1:16])" ] }, { "cell_type": "code", "execution_count": null, "id": "c574a86b-67a8-4cdd-9cbd-b7461c2f1c8c", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { 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