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‎MyPyApr0625.ipynb

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"metadata": {
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"colab": {
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"provenance": [],
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"authorship_tag": "ABX9TyNwl8P3FqcCrTrrQzxhk/v+",
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"authorship_tag": "ABX9TyNO/kEgvMMcn4Tjl4wyiSko",
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"include_colab_link": true
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},
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"kernelspec": {
@@ -28,12 +28,341 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 122
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},
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"id": "T5hlPyVxu1mP",
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"outputId": "3828e00f-1fe8-44ca-9afa-7e6e1b5ccfcc"
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},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"'\\nProblem:\\n\\nYou are given a dataset of transactions from an e-commerce platform in the form of a list of dictionaries. \\nEach dictionary contains information about a single transaction. The dataset is as follows:\\n\\ntransactions = [\\n {\"order_id\": \"1001\", \"customer_id\": \"C001\", \"amount\": 150.0, \"category\": \"Electronics\", \"timestamp\": \"2025-04-01 10:00:00\"},\\n {\"order_id\": \"1002\", \"customer_id\": \"C002\", \"amount\": 200.0, \"category\": \"Clothing\", \"timestamp\": \"2025-04-01 11:00:00\"},\\n {\"order_id\": \"1003\", \"customer_id\": \"C001\", \"amount\": 50.0, \"category\": \"Books\", \"timestamp\": \"2025-04-01 12:00:00\"},\\n {\"order_id\": \"1004\", \"customer_id\": \"C003\", \"amount\": 120.0, \"category\": \"Electronics\", \"timestamp\": \"2025-04-01 13:00:00\"},\\n {\"order_id\": \"1005\", \"customer_id\": \"C001\", \"amount\": 75.0, \"category\": \"Clothing\", \"timestamp\": \"2025-04-01 14:00:00\"},\\n {\"order_id\": \"1006\", \"customer_id\": \"C002\", \"amount\": 50.0, \"category\": \"Books\", \"timestamp\": \"2025-04-01 15:00:00\"}\\n]\\n'"
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],
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"application/vnd.google.colaboratory.intrinsic+json": {
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"type": "string"
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}
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},
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"metadata": {},
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"execution_count": 2
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}
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],
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"source": [
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"'''\n",
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"Problem:\n",
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"\n",
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"You are given a dataset of transactions from an e-commerce platform in the form of a list of dictionaries.\n",
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"Each dictionary contains information about a single transaction. The dataset is as follows:\n",
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"\n",
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"transactions = [\n",
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" {\"order_id\": \"1001\", \"customer_id\": \"C001\", \"amount\": 150.0, \"category\": \"Electronics\", \"timestamp\": \"2025-04-01 10:00:00\"},\n",
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" {\"order_id\": \"1002\", \"customer_id\": \"C002\", \"amount\": 200.0, \"category\": \"Clothing\", \"timestamp\": \"2025-04-01 11:00:00\"},\n",
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" {\"order_id\": \"1003\", \"customer_id\": \"C001\", \"amount\": 50.0, \"category\": \"Books\", \"timestamp\": \"2025-04-01 12:00:00\"},\n",
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" {\"order_id\": \"1004\", \"customer_id\": \"C003\", \"amount\": 120.0, \"category\": \"Electronics\", \"timestamp\": \"2025-04-01 13:00:00\"},\n",
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" {\"order_id\": \"1005\", \"customer_id\": \"C001\", \"amount\": 75.0, \"category\": \"Clothing\", \"timestamp\": \"2025-04-01 14:00:00\"},\n",
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" {\"order_id\": \"1006\", \"customer_id\": \"C002\", \"amount\": 50.0, \"category\": \"Books\", \"timestamp\": \"2025-04-01 15:00:00\"}\n",
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"]\n",
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"\n",
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"Write a Python function to perform the following tasks:\n",
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"\n",
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"Filter and transform the dataset:\n",
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"\n",
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"Filter transactions to only include those that are from the Electronics category.\n",
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"\n",
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"Extract the order_id, customer_id, and amount fields from each transaction in the Electronics category and store them in a new list of dictionaries. The list should look like:\n",
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"\n",
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"python\n",
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"Copy\n",
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"[\n",
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" {\"order_id\": \"1001\", \"customer_id\": \"C001\", \"amount\": 150.0},\n",
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" {\"order_id\": \"1004\", \"customer_id\": \"C003\", \"amount\": 120.0}\n",
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"]\n",
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"Calculate total spending per customer:\n",
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"\n",
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"For each customer, calculate the total amount they have spent across all transactions. Return a dictionary where the keys are customer_ids and the values are the total amount spent. For example:\n",
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"\n",
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"python\n",
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"Copy\n",
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"{\"C001\": 375.0, \"C002\": 250.0, \"C003\": 120.0}\n",
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"Find customers with the highest spending:\n",
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"\n",
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"Using the results from the previous step, identify the customer(s) with the highest total spending.\n",
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"\n",
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"Date format manipulation:\n",
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"\n",
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"Convert the timestamp field of each transaction from the string format \"YYYY-MM-DD HH:MM:SS\" to the format \"DD-MM-YYYY HH:MM:SS\". For example, \"2025-04-01 10:00:00\" should become \"01-04-2025 10:00:00\".\n",
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"\n",
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"Count transactions per category:\n",
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"\n",
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"Count the number of transactions in each category (Electronics, Clothing, Books, etc.) and return a dictionary of category counts.\n",
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"\n",
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"Loop through transactions:\n",
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"\n",
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"Write a for loop to iterate over the dataset, and print the order_id and amount of each transaction in the Clothing category.\n",
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"\n",
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"String manipulation:\n",
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"\n",
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"For each customer_id, print the customer_id with all characters in uppercase.\n",
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"'''"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"#Filter and transform the dataset:\n",
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"\n",
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"\n",
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"transactions = [\n",
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" {\"order_id\": \"1001\", \"customer_id\": \"C001\", \"amount\": 150.0, \"category\": \"Electronics\", \"timestamp\": \"2025-04-01 10:00:00\"},\n",
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" {\"order_id\": \"1002\", \"customer_id\": \"C002\", \"amount\": 200.0, \"category\": \"Clothing\", \"timestamp\": \"2025-04-01 11:00:00\"},\n",
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" {\"order_id\": \"1003\", \"customer_id\": \"C001\", \"amount\": 50.0, \"category\": \"Books\", \"timestamp\": \"2025-04-01 12:00:00\"},\n",
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" {\"order_id\": \"1004\", \"customer_id\": \"C003\", \"amount\": 120.0, \"category\": \"Electronics\", \"timestamp\": \"2025-04-01 13:00:00\"},\n",
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" {\"order_id\": \"1005\", \"customer_id\": \"C001\", \"amount\": 75.0, \"category\": \"Clothing\", \"timestamp\": \"2025-04-01 14:00:00\"},\n",
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" {\"order_id\": \"1006\", \"customer_id\": \"C002\", \"amount\": 50.0, \"category\": \"Books\", \"timestamp\": \"2025-04-01 15:00:00\"}\n",
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"]\n",
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"\n",
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"d = {}\n",
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"\n",
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"for diction in transactions:\n",
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" for key, value in diction.items():\n",
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" if value == 'Electronics':\n",
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" print(diction)\n",
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" else:\n",
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" None\n",
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"\n",
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"print(d)\n",
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"'''\n",
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"for dict in transactions:\n",
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" for key,value in enumerate(dict):\n",
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" d[key] = value\n",
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"\n",
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"\n",
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"print(d)\n",
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"\n",
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"'''\n",
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"\n",
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"#this solution is incorrect. check next for proper solution."
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 88
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},
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"id": "EGEaactVvfkD",
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"outputId": "f61a83ba-e43b-47c9-f3c7-49e22f4c92b4"
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},
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"execution_count": 18,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"{'order_id': '1001', 'customer_id': 'C001', 'amount': 150.0, 'category': 'Electronics', 'timestamp': '2025-04-01 10:00:00'}\n",
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"{'order_id': '1004', 'customer_id': 'C003', 'amount': 120.0, 'category': 'Electronics', 'timestamp': '2025-04-01 13:00:00'}\n",
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"{}\n"
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]
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"'\\nfor dict in transactions:\\n for key,value in enumerate(dict):\\n d[key] = value\\n \\n\\nprint(d)\\n\\n'"
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],
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"application/vnd.google.colaboratory.intrinsic+json": {
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"type": "string"
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}
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},
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"metadata": {},
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"execution_count": 18
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"transactions = [\n",
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" {\"order_id\": \"1001\", \"customer_id\": \"C001\", \"amount\": 150.0, \"category\": \"Electronics\", \"timestamp\": \"2025-04-01 10:00:00\"},\n",
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" {\"order_id\": \"1002\", \"customer_id\": \"C002\", \"amount\": 200.0, \"category\": \"Clothing\", \"timestamp\": \"2025-04-01 11:00:00\"},\n",
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" {\"order_id\": \"1003\", \"customer_id\": \"C001\", \"amount\": 50.0, \"category\": \"Books\", \"timestamp\": \"2025-04-01 12:00:00\"},\n",
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" {\"order_id\": \"1004\", \"customer_id\": \"C003\", \"amount\": 120.0, \"category\": \"Electronics\", \"timestamp\": \"2025-04-01 13:00:00\"},\n",
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" {\"order_id\": \"1005\", \"customer_id\": \"C001\", \"amount\": 75.0, \"category\": \"Clothing\", \"timestamp\": \"2025-04-01 14:00:00\"},\n",
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" {\"order_id\": \"1006\", \"customer_id\": \"C002\", \"amount\": 50.0, \"category\": \"Books\", \"timestamp\": \"2025-04-01 15:00:00\"}\n",
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"]\n",
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"\n",
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"lst = [{\"order_id\" : diction[\"order_id\"],\n",
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" \"customer_id\" : diction[\"customer_id\"],\n",
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" \"amount\" : diction[\"amount\"]}\n",
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" for diction in transactions if diction[\"category\"] == 'Electronics']\n",
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"print(lst)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "G4tRYWXsv7nm",
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"outputId": "c354232e-1c7e-4498-e111-0ed5439cbbeb"
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},
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"execution_count": 22,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[{'order_id': '1001', 'customer_id': 'C001', 'amount': 150.0}, {'order_id': '1004', 'customer_id': 'C003', 'amount': 120.0}]\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"##Calculate total spending per customer:\n",
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"\n",
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"#For each customer, calculate the total amount they have spent across all transactions. Return a dictionary where the keys are customer_ids and the values are the total amount spent. For example:\n",
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"\n",
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"transactions = [\n",
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" {\"order_id\": \"1001\", \"customer_id\": \"C001\", \"amount\": 150.0, \"category\": \"Electronics\", \"timestamp\": \"2025-04-01 10:00:00\"},\n",
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" {\"order_id\": \"1002\", \"customer_id\": \"C002\", \"amount\": 200.0, \"category\": \"Clothing\", \"timestamp\": \"2025-04-01 11:00:00\"},\n",
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" {\"order_id\": \"1003\", \"customer_id\": \"C001\", \"amount\": 50.0, \"category\": \"Books\", \"timestamp\": \"2025-04-01 12:00:00\"},\n",
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" {\"order_id\": \"1004\", \"customer_id\": \"C003\", \"amount\": 120.0, \"category\": \"Electronics\", \"timestamp\": \"2025-04-01 13:00:00\"},\n",
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" {\"order_id\": \"1005\", \"customer_id\": \"C001\", \"amount\": 75.0, \"category\": \"Clothing\", \"timestamp\": \"2025-04-01 14:00:00\"},\n",
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" {\"order_id\": \"1006\", \"customer_id\": \"C002\", \"amount\": 50.0, \"category\": \"Books\", \"timestamp\": \"2025-04-01 15:00:00\"}\n",
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"]\n",
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"'''\n",
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"lst = [{\"customer_id\": d1[\"customer_id\"], \"amount\": d1[\"amount\"]} for d1 in transactions]\n",
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"print(lst)\n",
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"\n",
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"dout = {}\n",
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"for d2 in lst:\n",
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" for key, value in enumerate(d2):\n",
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" dout[key] = value\n",
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"\n",
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"print(dout)\n",
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"\n",
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"\n",
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"'''\n",
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"\n",
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"#Working solution:\n",
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"\n",
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"d1 = {}\n",
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"\n",
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"for transaction in transactions:\n",
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" customerid = transaction[\"customer_id\"]\n",
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" amount = transaction[\"amount\"]\n",
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"\n",
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" if customerid in d1:\n",
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" d1[customerid] += amount\n",
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" else:\n",
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" d1[customerid] = amount\n",
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"\n",
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"\n",
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"print(d1)\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "3wLil4fL1Al3",
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"outputId": "35f78b3b-8395-413f-ace8-e2f525cf28ac"
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},
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"execution_count": 49,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"{'C001': 275.0, 'C002': 250.0, 'C003': 120.0}\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"#Using the results from the previous step, identify the customer(s) with the highest total spending.\n",
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"\n",
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"d1 = {}\n",
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"\n",
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"for transaction in transactions:\n",
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" customerid = transaction[\"customer_id\"]\n",
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" amount = transaction[\"amount\"]\n",
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"\n",
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" if customerid in d1:\n",
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" d1[customerid] += amount\n",
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" else:\n",
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" d1[customerid] = amount\n",
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"\n",
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"\n",
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"print(d1)\n",
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"\n",
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"maxvalue = max(d1.values())\n",
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"print(maxvalue)\n",
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"\n",
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"lst = [key for key,value in d1.items() if value == maxvalue ]\n",
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"print(lst)\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Lo09NOhp4Jcf",
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"outputId": "0c1472cd-bcc3-41ec-fa71-93ec16807035"
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},
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"execution_count": 65,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"{'C001': 275.0, 'C002': 250.0, 'C003': 120.0}\n",
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"275.0\n",
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"['C001']\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"\n",
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"\n"
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],
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"metadata": {
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"id": "T5hlPyVxu1mP"
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 176
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},
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"id": "NC5CVTUpDbmq",
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"outputId": "e0204491-51b9-4777-a099-6e845571092f"
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},
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"outputs": [],
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"source": []
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"execution_count": 82,
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"outputs": [
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{
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"output_type": "error",
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"ename": "TypeError",
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"evalue": "'str' object is not callable",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-82-b04f8843d3c4>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter a STring \"\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 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mTypeError\u001b[0m: 'str' object is not callable"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "SEI1mSN4ED9L"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}

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