diff --git a/Sarath_200882/Anaconda_install.md b/Sarath_200882/Anaconda_install.md new file mode 100644 index 0000000..f6a650a --- /dev/null +++ b/Sarath_200882/Anaconda_install.md @@ -0,0 +1,34 @@ +# How to install Anaconda on Ubuntu 20.4 +~ Sarath Kamal | Basis of learning, Task-1 + +**1. Download the anaconda package for linux through-** [anaconda](https://www.anaconda.com/download/#linux) + +**2. This step is optional-** +- [Verify data integrity with sha-256](https://docs.anaconda.com/anaconda/install/hashes/) - on this website look for the hash code for the specific anaconda package you just downloaded. In my case, I searched for 'Anaconda3-2022.05-Linux-x86_64.sh' +- Now go to your ubuntu terminal and type +```sh +sha256sum /path/filename +``` +- this will generate hash code for your package. +- in my case, it was- +```sh +sha256sum ~/Downloads/Anaconda3-2022.05-Linux-x86_64.sh +``` +- Now match the hash code generated with that on the website you searched on. It should be the same + +**3. Install Ananconda-** +```sh +bash /path/filename +``` +- in my case it was- +```sh +bash ~/Downloads/Anaconda3-2022.05-Linux-x86_64.sh +``` +- Nicee! you are almost done- just press enter to view the license and type yes for all the propmts coming up during the installation. Going with defaults is recommended. +- close the terminal and open it again + +**4. Open anaconda navigator to play with it!** +```sh +anaconda-navigator +``` + diff --git a/Sarath_200882/Matplotlib.ipynb b/Sarath_200882/Matplotlib.ipynb new file mode 100644 index 0000000..11fecb6 --- /dev/null +++ b/Sarath_200882/Matplotlib.ipynb @@ -0,0 +1,508 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "662b2352", + "metadata": {}, + "source": [ + "# Matplotlib stuff" + ] + }, + { + "cell_type": "markdown", + "id": "74e5aa0e", + "metadata": {}, + "source": [ + "- this is Python’s main plotting library. A number of other libraries such as seaborn rely on this" + ] + }, + { + "cell_type": "markdown", + "id": "d5936770", + "metadata": {}, + "source": [ + "- Have a look at its [Documentation](https://matplotlib.org/)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "04e88a9e", + "metadata": {}, + "outputs": [], + "source": [ + "# import the libraries\n", + "import numpy as np \n", + "import pandas as pd \n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "1fd11590", + "metadata": {}, + "source": [ + "- Create two axes from a dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "ea64934f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SPICPIMotivationBTRoomie CPI
sem13.7786422.5438924.5074713.2814984.911882
sem21.2535054.7001963.1763522.3526562.297437
sem34.0912361.2747204.3127382.5795470.551924
sem43.7079322.9976224.7642024.4981061.435395
sem54.5196201.2250162.4228914.2445970.754915
sem63.5055884.0274083.9798591.1155462.635396
\n", + "
" + ], + "text/plain": [ + " SPI CPI Motivation BT Roomie CPI\n", + "sem1 3.778642 2.543892 4.507471 3.281498 4.911882\n", + "sem2 1.253505 4.700196 3.176352 2.352656 2.297437\n", + "sem3 4.091236 1.274720 4.312738 2.579547 0.551924\n", + "sem4 3.707932 2.997622 4.764202 4.498106 1.435395\n", + "sem5 4.519620 1.225016 2.422891 4.244597 0.754915\n", + "sem6 3.505588 4.027408 3.979859 1.115546 2.635396" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M1 = np.random.uniform(0,5, size=(6,5)) # refer to numpy, pandas for dataframes initiation\n", + "df = pd.DataFrame(M1)\n", + "df.columns = [\"SPI\",\"CPI\",\"Motivation\",\"BT\",\"Roomie CPI\"]\n", + "df.index = [\"sem1\",\"sem2\",\"sem3\",\"sem4\",\"sem5\",\"sem6\"]\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "fd4aa479", + "metadata": {}, + "source": [ + "### - Scatter plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7754a7ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'SPI')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = df.Motivation # define the axes\n", + "y = df.SPI\n", + "\n", + "plt.figure(figsize=(5,5)) #Figure defines the size of canvas for plotting\n", + "\n", + "plt.scatter(x,y, color=\"green\", marker=\"o\") #scatter plot with color and shape of marker\n", + "\n", + "plt.title(\"SPI vs Motivation\")\n", + "plt.xlabel(\"Motivation\")\n", + "plt.ylabel(\"SPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "caae3ebe", + "metadata": {}, + "source": [ + "- use ```Plot``` to show in a linear way" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b86d5ab9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'CPI')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y = df.CPI\n", + "x = df.index\n", + "plt.figure(figsize=(10,5))\n", + "plt.plot(x,y, color=\"red\", marker=\"o\")\n", + "plt.title(\"CPI over semesters\")\n", + "plt.xlabel(\"Semesters\")\n", + "plt.ylabel(\"CPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "26f79057", + "metadata": {}, + "source": [ + "- compare my CPI with my Roomie" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5518a4b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'CPI')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y1 = df.CPI\n", + "y2 = df[\"Roomie CPI\"]\n", + "x = df.index\n", + "plt.figure(figsize=(10,5))\n", + "plt.plot(x,y1, color=\"red\", marker=\"o\")\n", + "plt.plot(x,y2, color=\"blue\", marker=\"o\")\n", + "plt.title(\"CPI over semesters\")\n", + "plt.xlabel(\"Semesters\")\n", + "plt.ylabel(\"CPI\")" + ] + }, + { + "cell_type": "markdown", + "id": "08fbae95", + "metadata": {}, + "source": [ + "- Use the bar plots instead:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "1f76a08a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SPICPIMotivationBTRoomie CPIIndexsem
sem13.7786422.5438924.5074713.2814984.911882sem1sem1
sem21.2535054.7001963.1763522.3526562.297437sem2sem2
sem34.0912361.2747204.3127382.5795470.551924sem3sem3
sem43.7079322.9976224.7642024.4981061.435395sem4sem4
sem54.5196201.2250162.4228914.2445970.754915sem5sem5
sem63.5055884.0274083.9798591.1155462.635396sem6sem6
\n", + "
" + ], + "text/plain": [ + " SPI CPI Motivation BT Roomie CPI Index sem\n", + "sem1 3.778642 2.543892 4.507471 3.281498 4.911882 sem1 sem1\n", + "sem2 1.253505 4.700196 3.176352 2.352656 2.297437 sem2 sem2\n", + "sem3 4.091236 1.274720 4.312738 2.579547 0.551924 sem3 sem3\n", + "sem4 3.707932 2.997622 4.764202 4.498106 1.435395 sem4 sem4\n", + "sem5 4.519620 1.225016 2.422891 4.244597 0.754915 sem5 sem5\n", + "sem6 3.505588 4.027408 3.979859 1.115546 2.635396 sem6 sem6" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.reset_index()\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "7ffd6511", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unhashable type: 'DataFrame'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [39]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m x \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39msem\n\u001b[1;32m 3\u001b[0m y \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mCPI\n\u001b[0;32m----> 4\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbar\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/plotting/_core.py:1131\u001b[0m, in \u001b[0;36mPlotAccessor.bar\u001b[0;34m(self, x, y, **kwargs)\u001b[0m\n\u001b[1;32m 1043\u001b[0m \u001b[38;5;129m@Appender\u001b[39m(\n\u001b[1;32m 1044\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1045\u001b[0m \u001b[38;5;124;03m See Also\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1120\u001b[0m \u001b[38;5;129m@Appender\u001b[39m(_bar_or_line_doc)\n\u001b[1;32m 1121\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbar\u001b[39m(\u001b[38;5;28mself\u001b[39m, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1122\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1123\u001b[0m \u001b[38;5;124;03m Vertical bar plot.\u001b[39;00m\n\u001b[1;32m 1124\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;124;03m other axis represents a measured value.\u001b[39;00m\n\u001b[1;32m 1130\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1131\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbar\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/plotting/_core.py:937\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 935\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(x) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mholds_integer():\n\u001b[1;32m 936\u001b[0m x \u001b[38;5;241m=\u001b[39m data_cols[x]\n\u001b[0;32m--> 937\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m]\u001b[49m, ABCSeries):\n\u001b[1;32m 938\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx must be a label or position\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 939\u001b[0m data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mset_index(x)\n", + "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:3466\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3464\u001b[0m check_deprecated_indexers(key)\n\u001b[1;32m 3465\u001b[0m key \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mitem_from_zerodim(key)\n\u001b[0;32m-> 3466\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[43mcom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_if_callable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3468\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_hashable(key) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_iterator(key):\n\u001b[1;32m 3469\u001b[0m \u001b[38;5;66;03m# is_iterator to exclude generator e.g. test_getitem_listlike\u001b[39;00m\n\u001b[1;32m 3470\u001b[0m \u001b[38;5;66;03m# shortcut if the key is in columns\u001b[39;00m\n\u001b[1;32m 3471\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mis_unique \u001b[38;5;129;01mand\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m 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\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m maybe_callable\n", + "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:10940\u001b[0m, in \u001b[0;36mNDFrame._add_numeric_operations..sem\u001b[0;34m(self, axis, skipna, level, ddof, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 10920\u001b[0m \u001b[38;5;129m@doc\u001b[39m(\n\u001b[1;32m 10921\u001b[0m _num_ddof_doc,\n\u001b[1;32m 10922\u001b[0m desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturn unbiased standard error of the mean over requested \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10938\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 10939\u001b[0m ):\n\u001b[0;32m> 10940\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msem\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mddof\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:10564\u001b[0m, in \u001b[0;36mNDFrame.sem\u001b[0;34m(self, axis, skipna, level, ddof, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 10555\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msem\u001b[39m(\n\u001b[1;32m 10556\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10557\u001b[0m axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10562\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 10563\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m> 10564\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function_ddof\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 10565\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msem\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnansem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mddof\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 10566\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:10551\u001b[0m, in \u001b[0;36mNDFrame._stat_function_ddof\u001b[0;34m(self, name, func, axis, skipna, level, ddof, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 10541\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 10542\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing the level keyword in DataFrame and Series aggregations is \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 10543\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdeprecated and will be removed in a future version. Use groupby \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10546\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 10547\u001b[0m )\n\u001b[1;32m 10548\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_by_level(\n\u001b[1;32m 10549\u001b[0m name, axis\u001b[38;5;241m=\u001b[39maxis, level\u001b[38;5;241m=\u001b[39mlevel, skipna\u001b[38;5;241m=\u001b[39mskipna, ddof\u001b[38;5;241m=\u001b[39mddof\n\u001b[1;32m 10550\u001b[0m )\n\u001b[0;32m> 10551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 10552\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mddof\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mddof\u001b[49m\n\u001b[1;32m 10553\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:9970\u001b[0m, in \u001b[0;36mDataFrame._reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m 9967\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)(mgr)\n\u001b[1;32m 9969\u001b[0m \u001b[38;5;66;03m# TODO: Make other agg func handle axis=None properly GH#21597\u001b[39;00m\n\u001b[0;32m-> 9970\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_axis_number\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9971\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_agg_axis(axis)\n\u001b[1;32m 9972\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m]\n", + "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:550\u001b[0m, in \u001b[0;36mNDFrame._get_axis_number\u001b[0;34m(cls, axis)\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m 547\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 548\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_axis_number\u001b[39m(\u001b[38;5;28mcls\u001b[39m, axis: Axis) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 550\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_AXIS_TO_AXIS_NUMBER\u001b[49m\u001b[43m[\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 551\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 552\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo axis named \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for object type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'DataFrame'" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,5))\n", + "x = df.sem\n", + "y = df.CPI\n", + "df.plot.bar(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e16e889", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Sarath_200882/Numpy.ipynb b/Sarath_200882/Numpy.ipynb new file mode 100644 index 0000000..7030799 --- /dev/null +++ b/Sarath_200882/Numpy.ipynb @@ -0,0 +1,391 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d50824dd", + "metadata": {}, + "source": [ + "# Numpy stuff" + ] + }, + { + "cell_type": "markdown", + "id": "ddaf4ff3", + "metadata": {}, + "source": [ + "- Numpy is one of the major libraries used in machine learning and data science. It is used for a variety of mathematical computations by helping us with large n-dimensional arrays." + ] + }, + { + "cell_type": "markdown", + "id": "ed3b7891", + "metadata": {}, + "source": [ + "- So it comes pre-installed with your Anaconda package." + ] + }, + { + "cell_type": "markdown", + "id": "371fc77a", + "metadata": {}, + "source": [ + "- Have a look at its [documentation](https://numpy.org/doc/)" + ] + }, + { + "cell_type": "markdown", + "id": "92f1e203", + "metadata": {}, + "source": [ + "- You just need to import the libraries like:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "230e1770", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "c1f6e05d", + "metadata": {}, + "source": [ + "- We can create a numpy array with the ```np.array()``` constructor with a regular Python list as its argument" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8df338ad", + "metadata": {}, + "outputs": [], + "source": [ + "a1 = np.array([1,2,3,4])" + ] + }, + { + "cell_type": "markdown", + "id": "5b3cc03d", + "metadata": {}, + "source": [ + "- Then you could implement functions like ```a1.shape``` to get the shape of an array" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dbe0e0a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4,)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a1.shape" + ] + }, + { + "cell_type": "markdown", + "id": "a656371c", + "metadata": {}, + "source": [ + "- In this case, the array above is uni-dimensional, also called a flat-array. We can also use a list of list to obtain a more clear matrix-like shape:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b80c9fad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 4)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a2 = np.array([[1,2,3,4]])\n", + "a2.shape" + ] + }, + { + "cell_type": "markdown", + "id": "8f18f2e9", + "metadata": {}, + "source": [ + "- Now similarly initiate a column matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0ac4e5c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 1)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a3 = np.array([[1],[2],[3],[4]])\n", + "a3.shape" + ] + }, + { + "cell_type": "markdown", + "id": "616ef45f", + "metadata": {}, + "source": [ + "- Or maybe a full matrice the same way" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "63c9b271", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M1 = [[ 1 2 3]\n", + " [ 4 5 6]\n", + " [ 7 8 9]\n", + " [10 11 12]]\n" + ] + }, + { + "data": { + "text/plain": [ + "(4, 3)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M1 = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])\n", + "print(\"M1 = \", M1)\n", + "M1.shape" + ] + }, + { + "cell_type": "markdown", + "id": "989283c8", + "metadata": {}, + "source": [ + "### Some more functions" + ] + }, + { + "cell_type": "markdown", + "id": "52f89757", + "metadata": {}, + "source": [ + " 1. np.reshape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e7bfa2fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M2 = [[ 1 2 3 4 5 6 7 8 9 10 11 12]]\n" + ] + } + ], + "source": [ + "M2 = np.reshape(M1, (1,12)) #reshaping matrix M1 to a row Matrix M2\n", + "print(\"M2 = \", M2)" + ] + }, + { + "cell_type": "markdown", + "id": "c1a25c27", + "metadata": {}, + "source": [ + "- As you can see this works only when the number of elements dont change" + ] + }, + { + "cell_type": "markdown", + "id": "6a3ac796", + "metadata": {}, + "source": [ + " 2. np.dot" + ] + }, + { + "cell_type": "markdown", + "id": "0d4f5e4e", + "metadata": {}, + "source": [ + "- You may be familiar with the dot product and its rules. Here we find 'a3.a2'" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f78ee637", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dot product = [[ 1 2 3 4]\n", + " [ 2 4 6 8]\n", + " [ 3 6 9 12]\n", + " [ 4 8 12 16]]\n" + ] + }, + { + "data": { + "text/plain": [ + "(4, 4)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Dot_res1 = np.dot(a3,a2)\n", + "print(\"Dot product = \", Dot_res1)\n", + "Dot_res1.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "241b29e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dot product = [[30]]\n" + ] + }, + { + "data": { + "text/plain": [ + "(1, 1)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Dot_res2 = np.dot(a2,a3)\n", + "print(\"Dot product = \", Dot_res2)\n", + "Dot_res2.shape" + ] + }, + { + "cell_type": "markdown", + "id": "f08f9b75", + "metadata": {}, + "source": [ + " 3. np.random" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "351a143c", + "metadata": {}, + "outputs": [], + "source": [ + "M3 = np.random.randn(3,4) #Generates a random matrix of dimension (3,4)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b75934af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M3 = [[-1.63637147 0.55883515 -0.7049998 0.6285767 ]\n", + " [ 0.46119871 0.03205836 -0.80597492 0.65375346]\n", + " [-0.55514165 -0.14671001 0.12533635 0.1352804 ]]\n" + ] + } + ], + "source": [ + "print(\"M3 = \", M3)" + ] + }, + { + "cell_type": "markdown", + "id": "22ae095a", + "metadata": {}, + "source": [ + "- You can try and explore more such functions" + ] + } + ], + "metadata": { + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Sarath_200882/Pandas.ipynb b/Sarath_200882/Pandas.ipynb new file mode 100644 index 0000000..69420b7 --- /dev/null +++ b/Sarath_200882/Pandas.ipynb @@ -0,0 +1,596 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a0d5bbf3", + "metadata": {}, + "source": [ + "# Pandas stuff" + ] + }, + { + "cell_type": "markdown", + "id": "a77f3d73", + "metadata": {}, + "source": [ + "- Pandas is a library for data manipulation. It is highly compatible with Numpy" + ] + }, + { + "cell_type": "markdown", + "id": "bbec6ec2", + "metadata": {}, + "source": [ + "- Have a look at its [documentation](https://pandas.pydata.org/docs/)" + ] + }, + { + "cell_type": "markdown", + "id": "542c3d90", + "metadata": {}, + "source": [ + "- Import the libraries in a similar way" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d7046398", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "ea0a1ad3", + "metadata": {}, + "source": [ + "- We first try to generate a random matrix using numpy and then convert it to dataframe using pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b765fef6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.18958499, 2.08857389, -0.81712981],\n", + " [-0.44601169, 0.91602901, -1.14629838],\n", + " [-1.5102345 , -2.98845593, 1.01898122],\n", + " [-1.2425057 , -0.29850487, -0.48765869]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M1 = np.random.randn(4,3)\n", + "df = pd.DataFrame(M1)\n", + "M1" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e4c972b9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SPICPIMotivation
sem10.1895852.088574-0.817130
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" + ], + "text/plain": [ + " SPI CPI Motivation\n", + "sem1 0.189585 2.088574 -0.817130\n", + "sem2 -0.446012 0.916029 -1.146298\n", + "sem3 -1.510235 -2.988456 1.018981\n", + "sem4 -1.242506 -0.298505 -0.487659" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns = [\"SPI\",\"CPI\",\"Motivation\"] #Giving a meaning to the random table\n", + "df.index = [\"sem1\", \"sem2\", \"sem3\", \"sem4\"]\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "d1213451", + "metadata": {}, + "source": [ + "- Access individual index and columns in this way:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "33702f33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sem1 -0.817130\n", + "sem2 -1.146298\n", + "sem3 1.018981\n", + "sem4 -0.487659\n", + "Name: Motivation, dtype: float64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Motivation #Have a look at My Motivation across semesters" + ] + }, + { + "cell_type": "markdown", + "id": "0de6875d", + "metadata": {}, + "source": [ + "- Use ```df[conditions]``` to filter out the data " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3c2906a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SPICPIMotivation
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" + ], + "text/plain": [ + " SPI CPI Motivation\n", + "sem1 0.189585 2.088574 -0.817130\n", + "sem2 -0.446012 0.916029 -1.146298\n", + "sem4 -1.242506 -0.298505 -0.487659" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "condition = df[df.Motivation < 0] #Semesters Where I had Less than ZERO Motivation\n", + "condition" + ] + }, + { + "cell_type": "markdown", + "id": "3dc4844b", + "metadata": {}, + "source": [ + "- ```df.apply``` is used for applying a specific function to a column" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b74db295", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sem1 -1.0\n", + "sem2 -1.0\n", + "sem3 1.0\n", + "sem4 -0.0\n", + "Name: Motivation, dtype: float64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Motivation\"].apply(np.round) #rounds off the Motivation column" + ] + }, + { + "cell_type": "markdown", + "id": "3643be9b", + "metadata": {}, + "source": [ + "- ```df.loc[condition, 'column'] = new_value``` is used to change all rows of a specific column based on a condition" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "8d444ba3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SPICPIMotivation
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" + ], + "text/plain": [ + " SPI CPI Motivation\n", + "sem1 0.189585 2.088574 1.000000\n", + "sem2 -0.446012 0.916029 -1.146298\n", + "sem3 -1.510235 -2.988456 1.018981\n", + "sem4 -1.242506 -0.298505 -0.487659" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[df.CPI > 1, \"Motivation\"] = 1 #changes motivation to 1 where cpi > 1\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "3d57c826", + "metadata": {}, + "source": [ + "- ```df.sort_values``` is used to sort columns" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "ba5e25ef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SPICPIMotivation
sem10.1895852.0885741.000000
sem2-0.4460120.916029-1.146298
sem4-1.242506-0.298505-0.487659
sem3-1.510235-2.9884561.018981
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" + ], + "text/plain": [ + " SPI CPI Motivation\n", + "sem1 0.189585 2.088574 1.000000\n", + "sem2 -0.446012 0.916029 -1.146298\n", + "sem4 -1.242506 -0.298505 -0.487659\n", + "sem3 -1.510235 -2.988456 1.018981" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = df.sort_values(by=[\"SPI\"], ascending=False) #Sorts highest to lowest SPI\n", + "df1" + ] + }, + { + "cell_type": "markdown", + "id": "44181d8a", + "metadata": {}, + "source": [ + "- You can explore more useful functions like these, and try it out on your notebooks!" + ] + } + ], + "metadata": { + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Sarath_200882/jupyter_notebook1.ipynb b/Sarath_200882/jupyter_notebook1.ipynb new file mode 100644 index 0000000..e1557dd --- /dev/null +++ b/Sarath_200882/jupyter_notebook1.ipynb @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "851890be", + "metadata": {}, + "source": [ + "# Jupyter notebook stuff" + ] + }, + { + "cell_type": "markdown", + "id": "66784dbc", + "metadata": {}, + "source": [ + "Basically, Jupyter notebooks are used to create and share documents that contain live code, equations and visualizations. It is used for data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more." + ] + }, + { + "cell_type": "markdown", + "id": "ca253531", + "metadata": {}, + "source": [ + "### Getting Started" + ] + }, + { + "cell_type": "markdown", + "id": "206a23b0", + "metadata": {}, + "source": [ + "- Jupyter notebook comes installed with the Anaconda packages." + ] + }, + { + "cell_type": "markdown", + "id": "494a200e", + "metadata": {}, + "source": [ + "- After installing Anaconda, just launch the Jupyter notebook from the Anaconda navigator. Just type in the terminal:" + ] + }, + { + "cell_type": "markdown", + "id": "74d946db", + "metadata": {}, + "source": [ + "```sh\n", + "anaconda-navigator\n", + "```\n", + " or \n", + "```sh\n", + "jupyter notebook\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "06a1c26e", + "metadata": {}, + "source": [ + "- You would see an interface like this" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "310faa17", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "id": "896363da", + "metadata": {}, + "source": [ + "- It shows you the directories. Click on 'New' and create a python3 jupter notebook and open it to get started." + ] + }, + { + "cell_type": "markdown", + "id": "246602fa", + "metadata": {}, + "source": [ + "- it should look like this" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "d0b422a9", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "id": "c8ab3380", + "metadata": {}, + "source": [ + "- Try experimenting with functions while writing bits of code and text" + ] + }, + { + "cell_type": "markdown", + "id": "b5b6324f", + "metadata": {}, + "source": [ + "- You can seperately run each cell." + ] + }, + { + "cell_type": "markdown", + "id": "92aba010", + "metadata": {}, + "source": [ + "- Output can be not just text, but tables, figures, graphs depending on the code and libraries imported." + ] + }, + { + "cell_type": "markdown", + "id": "75e4ea1f", + "metadata": {}, + "source": [ + "- You can import a ton of libraries and read about each of their documentations and features. eg.-" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2b667d50", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas\n", + "import matplotlib" + ] + }, + { + "cell_type": "markdown", + "id": "09888809", + "metadata": {}, + "source": [ + "- Using shortcuts could help a lot! - visit [here](https://www.edureka.co/blog/cheatsheets/jupyter-notebook-cheat-sheet)" + ] + }, + { + "cell_type": "markdown", + "id": "b1f07602", + "metadata": {}, + "source": [ + "- ig thats enough for a noob guide :)" + ] + } + ], + "metadata": { + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}