{ "cells": [ { "cell_type": "markdown", "id": "bb0ffed8", "metadata": { "id": "bb0ffed8" }, "source": [ "# Exercise Session 1 – Jupyter Notebook tutorial\n", "### ENV–501 Material and Energy Flow Analysis\n", "\n", "September 12, 2024\n", "\n", "\n", "#### Content of this tutorial:\n", "\n", "1. [Introduction to Jupyter notebook](#notebook)\n", "2. [An informal introduction to Python](#python)\n", "3. [Data handling with Pandas](#pandas)\n", "\n", "#### Sources:\n", "- Jupyter Notebook documentation: https://jupyter-notebook.readthedocs.io/en/stable/notebook.html\n", "- Jupyter Notebook tutorial: https://www.dataquest.io/blog/jupyter-notebook-tutorial/\n", "- Basic Python tutorial: https://docs.python.org/3/tutorial/introduction.html#using-python-as-a-calculator\n", "- Pandas tutorial: https://www.w3schools.com/python/pandas/default.asp\n" ] }, { "cell_type": "markdown", "id": "53340197", "metadata": { "id": "53340197" }, "source": [ "\n", "## Introduction to Jupyter Notebook\n", "\n", "### What is a “notebook”?\n", "\n", "A notebook integrates code and its output into a single document that combines visualizations, narrative text, mathematical equations, and other rich media.\n", "\n", "In other words: it’s a single document where you can run code, display the output, and also add explanations, formulas, charts, and make your work more transparent, understandable, repeatable, and shareable.\n", "\n", "### Kernel and Cell\n", "There are two main concepts that should be clarified:\n", "\n", "- A **kernel** is a “computational engine” that executes the code contained in a notebook document.\n", "- A **cell** is a container for text to be displayed in the notebook or code to be executed by the notebook’s kernel.\n", "\n", "### Types of cells\n", "There are two main cell types that we will use:\n", "\n", "- A **code cell** contains code to be executed in the kernel. When the code is run, the notebook displays the output below the code cell that generated it.\n", "- A **Markdown cell** contains text formatted using Markdown and displays its output in-place when the Markdown cell is run.\n", "\n", "Markdown is a lightweight, easy to learn markup language for formatting plain text.\n", "\n", "### Keyboard shortcuts\n", "\n", "In a Jupyter Notebook, there is always one “active” cell highlighted with a border whose color denotes its current mode:\n", "\n", "- Green outline — cell is in “edit mode” and you can type into the cells like a normal text editor\n", "- Blue outline — cell is in “command mode” and you can edit the notebook as a whole, but not type into individual cells\n", "\n", "You can change between edit and command mode with Enter and Esc, respectively.\n", "Below some shortcuts in command mode:\n", "- Basic navigation: k (move up), j (move down)\n", "- Saving the notebook: s\n", "- Change Cell types: y (code cell), m (markdown cell)\n", "- Cell creation: a (add cell above), b (add cell below)\n", "- Cell editing: x (remove cell), c (copy cell), v (cell paste), z (undo delete cell)\n", "- Kernel operations: 0 (press twice for restart kernel)\n", "\n", "Below shortcuts in edit mode:\n", "- run cell: shift-enter" ] }, { "cell_type": "markdown", "id": "658b967d", "metadata": { "id": "658b967d" }, "source": [ "\n", "## An informal introduction to Python" ] }, { "cell_type": "markdown", "id": "77975990", "metadata": { "id": "77975990" }, "source": [ "### Numbers\n", "\n", "The interpreter acts as a simple calculator: you can type an expression at it and it will write the value." ] }, { "cell_type": "code", "execution_count": 4, "id": "a489c80d", "metadata": { "id": "a489c80d", "outputId": "371b0f38-fd08-4cfc-f0b7-7e6a43527f9f" }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3 + 1" ] }, { "cell_type": "code", "execution_count": 6, "id": "6168f9fe", "metadata": { "id": "6168f9fe", "outputId": "a5bec6cb-e385-4c75-8ab3-4a3b3aa990c6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sum: 9\n", "product: 14\n", "exponential: 49\n", "division: 3.5\n", "floor division: 3\n", "modulus: 1\n" ] } ], "source": [ "# define some variables and perform some operations\n", "a = 7\n", "b = 2\n", "\n", "print('sum:', a + b)\n", "print('product:', a * b)\n", "print('exponential:', a ** b)\n", "print('division:', a / b)\n", "print('floor division:', a // b)\n", "print('modulus:', a % b)" ] }, { "cell_type": "markdown", "id": "fca6dcc2", "metadata": { "id": "fca6dcc2" }, "source": [ "### Strings\n", "\n", "Besides numbers, Python can also manipulate strings, which can be expressed as \"...\" or '...'." ] }, { "cell_type": "code", "execution_count": 8, "id": "64b919da", "metadata": { "id": "64b919da" }, "outputs": [], "source": [ "# define your string\n", "mystring = \"My first string\"" ] }, { "cell_type": "code", "execution_count": 10, "id": "f3353f81", "metadata": { "id": "f3353f81", "outputId": "bf9c2e8c-6a13-4438-f656-bf18236fa76e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "i\n" ] } ], "source": [ "# string indexing\n", "print(mystring[4])" ] }, { "cell_type": "code", "execution_count": 12, "id": "8c2b9bf5", "metadata": { "id": "8c2b9bf5", "outputId": "6c37c600-c074-467c-ed72-7c18792cedb0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "My\n" ] } ], "source": [ "# string slicing\n", "print(mystring[0:2])" ] }, { "cell_type": "markdown", "id": "5a231fb8", "metadata": { "id": "5a231fb8" }, "source": [ "### Lists\n", "\n", "Python knows a number of compound data types, used to group together other values.\n", "\n", "The most versatile is the list, which can be written as a list of comma-separated values (items) between square brackets. Lists might contain items of different types, but usually the items all have the same type.\n", "\n", "Python lists are 0-indexed." ] }, { "cell_type": "code", "execution_count": 15, "id": "d8df34e5", "metadata": { "id": "d8df34e5", "outputId": "f5705393-c06e-44e8-917f-69534f048311" }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 2, 1]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# define your list\n", "mylist = [1,2,3,4,2,1]\n", "mylist" ] }, { "cell_type": "code", "execution_count": 17, "id": "64878219", "metadata": { "id": "64878219", "outputId": "a57fa808-fe51-4499-f706-f26c21f03221" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "# list indexing\n", "print(mylist[2])" ] }, { "cell_type": "code", "execution_count": 19, "id": "081ccfc3", "metadata": { "id": "081ccfc3", "outputId": "3acc8034-3ef8-4cd2-ba45-b9fb09d79658" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3, 4, 2, 1]\n" ] } ], "source": [ "# list slicing\n", "print(mylist[2:])" ] }, { "cell_type": "code", "execution_count": 21, "id": "bafbba00", "metadata": { "id": "bafbba00", "outputId": "ddc14862-03d9-4a3c-ab34-1b09f8dafe7a" }, "outputs": [ { "data": { "text/plain": [ "[1, 0, 3, 4, 2, 1]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# change an element of a list\n", "mylist[1] = 0\n", "mylist" ] }, { "cell_type": "code", "execution_count": 23, "id": "d77432df", "metadata": { "id": "d77432df", "outputId": "5470109e-52be-4299-eebb-c9169a62c529" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 4, 9, 0, 2]\n" ] } ], "source": [ "# join or concatenate two lists\n", "list1 = [1,4,9]\n", "list2 = [0,2]\n", "\n", "list3 = list1 + list2\n", "print(list3)" ] }, { "cell_type": "code", "execution_count": 25, "id": "e7023943", "metadata": { "id": "e7023943", "outputId": "e69f99f3-0d39-41ef-e1b3-3662765e9327" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 4, 9, 18]\n" ] } ], "source": [ "# append an element to a list\n", "list1 = [1,4,9]\n", "\n", "list1.append(18)\n", "print(list1)" ] }, { "cell_type": "markdown", "id": "8410b8b5", "metadata": { "id": "8410b8b5" }, "source": [ "### Sets, tuples and dictionaries\n", "\n", "Together with list there are other 3 built-in data types in Python used to store collections of data.\n", "\n", "- A **sets** is a collection which is unordered, unchangeable, and unindexed.\n", "- A **tuple** is a collection which is ordered and unchangeable.\n", "- A **dictionary** is used to store data values in key:value pairs. It is ordered, changeable and do not allow duplicates.\n", "\n" ] }, { "cell_type": "code", "execution_count": 28, "id": "f4019955", "metadata": { "id": "f4019955", "outputId": "4476b0f1-9e10-4ec7-b99c-a3e627baf08b" }, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# set\n", "myset = {1,2,3}\n", "myset" ] }, { "cell_type": "code", "execution_count": 30, "id": "915f4cc6", "metadata": { "id": "915f4cc6", "outputId": "2572d986-6a76-459a-b7a1-88343a7bdd24" }, "outputs": [ { "data": { "text/plain": [ "(1, 2, 3)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# tuple\n", "mytuple = (1,2,3)\n", "mytuple" ] }, { "cell_type": "code", "execution_count": 32, "id": "cdf53639", "metadata": { "id": "cdf53639", "outputId": "d45d1a60-1eaa-4f6d-a10a-cc47565e05dd" }, "outputs": [ { "data": { "text/plain": [ "{2: 4, 'key2': 3, 'key3': 10}" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# dictionary\n", "mydict = {2:4, 'key2':3, 'key3':10}\n", "mydict" ] }, { "cell_type": "code", "execution_count": 34, "id": "bc432317", "metadata": { "id": "bc432317", "outputId": "67063eba-f04c-463f-b03d-a064348b089a" }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# call an element of a dictionary using the key\n", "mydict['key2']" ] }, { "cell_type": "markdown", "id": "44c1998a", "metadata": { "id": "44c1998a" }, "source": [ "\n", "## Pandas\n", "\n", "Pandas is a Python library used for working with datasets.\n", "\n", "It has functions for analyzing, cleaning, exploring, and manipulating data.\n", "\n", "Source tutorial: https://www.w3schools.com/python/pandas/default.asp\n", "\n", "if you have not installed pandas yet, you should enterthe command “pip install pandas” on the terminal or \"conda install pandas\" if you are using Anaconda Navigator." ] }, { "cell_type": "code", "execution_count": 37, "id": "3efa87df", "metadata": { "id": "3efa87df" }, "outputs": [], "source": [ "# import the library\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 71, "id": "cec6a86a", "metadata": { "id": "cec6a86a", "outputId": "969c7e21-bcff-44bf-feb3-ed543eba02d9" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " A B C D\n", "0 -0.985487 0.314422 -0.619870 -1.810922\n", "1 -0.515624 -1.061167 -0.516456 -0.255987\n", "2 1.688606 -1.568390 0.526393 1.242514\n", "3 -1.011228 0.650628 1.642922 -0.251697\n", "4 1.776371 -0.432107 0.686684 0.418252" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create sample dataset\n", "import pandas as pd\n", "import pandas._testing as tm\n", "import numpy as np\n", "\n", "df = pd.DataFrame(\n", " np.random.randn(5, 4),\n", " columns=list(\"ABCD\")\n", ")\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 73, "id": "2620f682", "metadata": { "id": "2620f682", "outputId": "52d01c58-90e3-48e9-be1a-b32e8a7e7336" }, "outputs": [ { "data": { "text/plain": [ "(5, 4)" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show dataframe shape\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 75, "id": "3bc02896", "metadata": { "id": "3bc02896", "outputId": "98217c3b-4d15-486d-c1a6-fc44d10c3ec9" }, "outputs": [ { "data": { "text/plain": [ "A float64\n", "B float64\n", "C float64\n", "D float64\n", "dtype: object" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# column data types\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": 77, "id": "47b431c1", "metadata": { "id": "47b431c1", "outputId": "4a0c9ac3-a490-49a2-82bd-f807f0d89e37" }, "outputs": [ { "data": { "text/html": [ "
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ABCD
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" ], "text/plain": [ " COL1 COL2 COL3 COL4\n", "I1 -0.985487 0.314422 -0.619870 -1.810922\n", "I2 -0.515624 -1.061167 -0.516456 -0.255987\n", "I3 1.688606 -1.568390 0.526393 1.242514\n", "I4 -1.011228 0.650628 1.642922 -0.251697\n", "I5 1.776371 -0.432107 0.686684 0.418252" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# change name to the index or columns\n", "df.index = ['I1', 'I2', 'I3', 'I4', 'I5']\n", "df.columns = ['COL1', 'COL2', 'COL3', 'COL4']\n", "df" ] }, { "cell_type": "code", "execution_count": 85, "id": "2e5e2c1e", "metadata": { "id": "2e5e2c1e", "outputId": "c24ed0c7-2cf1-450d-dd61-8ac2f9d13a9a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.516455611291013\n", "-1.810921821312494\n" ] } ], "source": [ "# A DataFrame is like a table with rows and columns.\n", "# Pandas use the loc attribute to return one or more specified elements of a dataframe\n", "print(df.loc['I2','COL3'])\n", "\n", "# otherwise use iloc for indexing using the corresponsng row and column numbers\n", "print(df.iloc[0,3])" ] }, { "cell_type": "code", "execution_count": 87, "id": "f4e7c663", "metadata": { "id": "f4e7c663", "outputId": "327c38ee-c11f-42a8-9dff-f26b7b8127d9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I1 -0.985487\n", "I2 -0.515624\n", "I3 1.688606\n", "I4 -1.011228\n", "I5 1.776371\n", "Name: COL1, dtype: float64\n" ] } ], "source": [ "# you can also call an entire column\n", "print(df.loc[:,'COL1'])" ] }, { "cell_type": "code", "execution_count": 89, "id": "133bbb43", "metadata": { "id": "133bbb43", "outputId": "ce8bef44-06f8-4b79-e6dd-84d8c34f2867" }, "outputs": [ { "data": { "text/html": [ "
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3jack18
\n", "
" ], "text/plain": [ " Name Age\n", "0 Tom 20\n", "1 nick 21\n", "2 krish 19\n", "3 jack 18" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# dataframes can be also initialized starting from dictionaries\n", "data = {'Name': ['Tom', 'nick', 'krish', 'jack'],\n", " 'Age': [20, 21, 19, 18],\n", " }\n", "\n", "# create DataFrame from a dictionary\n", "df = pd.DataFrame(data)\n", "\n", "# print the output\n", "df" ] }, { "cell_type": "code", "execution_count": 93, "id": "146de052", "metadata": { "id": "146de052", "outputId": "0f71374a-c4cd-4637-fa93-e3c63089b83f" }, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: 'data_tutorial.xlsx'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[93], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# simple problem: find the color of the box with the largest volume\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# load dataframe from an excel file\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m df_boxes \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_excel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata_tutorial.xlsx\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 5\u001b[0m df_boxes\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:495\u001b[0m, in \u001b[0;36mread_excel\u001b[1;34m(io, sheet_name, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, date_format, thousands, decimal, comment, skipfooter, storage_options, dtype_backend, engine_kwargs)\u001b[0m\n\u001b[0;32m 493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(io, ExcelFile):\n\u001b[0;32m 494\u001b[0m should_close \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 495\u001b[0m io \u001b[38;5;241m=\u001b[39m ExcelFile(\n\u001b[0;32m 496\u001b[0m io,\n\u001b[0;32m 497\u001b[0m storage_options\u001b[38;5;241m=\u001b[39mstorage_options,\n\u001b[0;32m 498\u001b[0m engine\u001b[38;5;241m=\u001b[39mengine,\n\u001b[0;32m 499\u001b[0m engine_kwargs\u001b[38;5;241m=\u001b[39mengine_kwargs,\n\u001b[0;32m 500\u001b[0m )\n\u001b[0;32m 501\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m engine \u001b[38;5;129;01mand\u001b[39;00m engine \u001b[38;5;241m!=\u001b[39m io\u001b[38;5;241m.\u001b[39mengine:\n\u001b[0;32m 502\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 503\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEngine should not be specified when passing \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 504\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124man ExcelFile - ExcelFile already has the engine set\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 505\u001b[0m )\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:1550\u001b[0m, in \u001b[0;36mExcelFile.__init__\u001b[1;34m(self, path_or_buffer, engine, storage_options, engine_kwargs)\u001b[0m\n\u001b[0;32m 1548\u001b[0m ext \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxls\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1549\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1550\u001b[0m ext \u001b[38;5;241m=\u001b[39m inspect_excel_format(\n\u001b[0;32m 1551\u001b[0m content_or_path\u001b[38;5;241m=\u001b[39mpath_or_buffer, storage_options\u001b[38;5;241m=\u001b[39mstorage_options\n\u001b[0;32m 1552\u001b[0m )\n\u001b[0;32m 1553\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ext \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1554\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1555\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExcel file format cannot be determined, you must specify \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1556\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124man engine manually.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1557\u001b[0m )\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:1402\u001b[0m, in \u001b[0;36minspect_excel_format\u001b[1;34m(content_or_path, storage_options)\u001b[0m\n\u001b[0;32m 1399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(content_or_path, \u001b[38;5;28mbytes\u001b[39m):\n\u001b[0;32m 1400\u001b[0m content_or_path \u001b[38;5;241m=\u001b[39m BytesIO(content_or_path)\n\u001b[1;32m-> 1402\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m get_handle(\n\u001b[0;32m 1403\u001b[0m content_or_path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m, storage_options\u001b[38;5;241m=\u001b[39mstorage_options, is_text\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 1404\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m handle:\n\u001b[0;32m 1405\u001b[0m stream \u001b[38;5;241m=\u001b[39m handle\u001b[38;5;241m.\u001b[39mhandle\n\u001b[0;32m 1406\u001b[0m stream\u001b[38;5;241m.\u001b[39mseek(\u001b[38;5;241m0\u001b[39m)\n", "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\io\\common.py:882\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m 874\u001b[0m handle,\n\u001b[0;32m 875\u001b[0m ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 878\u001b[0m newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 879\u001b[0m )\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m--> 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n\u001b[0;32m 883\u001b[0m handles\u001b[38;5;241m.\u001b[39mappend(handle)\n\u001b[0;32m 885\u001b[0m \u001b[38;5;66;03m# Convert BytesIO or file objects passed with an encoding\u001b[39;00m\n", "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data_tutorial.xlsx'" ] } ], "source": [ "# simple problem: find the color of the box with the largest volume\n", "\n", "# load dataframe from an excel file\n", "df_boxes = pd.read_excel('data_tutorial.xlsx')\n", "df_boxes\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9ef521de", "metadata": { "id": "9ef521de", "outputId": "fec0c36f-45a1-4325-d022-b56c35ffd120" }, "outputs": [], "source": [ "# calculate the volume of the boxes using the width, length and height columns\n", "df_boxes.loc[:,'volume'] = df_boxes.loc[:,'width'] * df_boxes.loc[:,'length'] * df_boxes.loc[:,'height']\n", "df_boxes" ] }, { "cell_type": "code", "execution_count": null, "id": "a4923028", "metadata": { "id": "a4923028", "outputId": "88bb831b-aeaa-4c25-d448-90df0fcb72a1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the blue box has the largest volume\n" ] } ], "source": [ "# index of the box with highest volumne\n", "index_max_vol = df_boxes.loc[:,'volume'].idxmax()\n", "\n", "# pick the corresponding color\n", "color = df_boxes.loc[index_max_vol,'color']\n", "print(f'the {color} box has the largest volume')" ] }, { "cell_type": "code", "execution_count": null, "id": "72eb3d0d", "metadata": { "id": "72eb3d0d" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "553b128b", "metadata": { "id": "553b128b" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 }