Merge pull request #5 from piidus/pandas

Pandas
piidus · Jan 25, 2024 · 5fa6ea0 · 5fa6ea0
2 parents d649c3c + 72c5203
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diff --git a/02-03-23_matplotlib.ipynb b/02-03-23_matplotlib.ipynb
diff --git a/23-02-23_pandas.ipynb b/23-02-23_pandas.ipynb
diff --git a/24-02-23_panda2.ipynb b/24-02-23_panda2.ipynb
diff --git a/25-02-23_panda.ipynb b/25-02-23_panda.ipynb
diff --git a/26-2-23_numpy.ipynb b/26-2-23_numpy.ipynb
@@ -0,0 +1,360 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "82beff3b-d13e-4f35-9a4c-78d4f0e4eaa4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "list_ = [ '1' , '2' , '3' , '4' , '5' ]\n",
+    "array_list = np.array(object = list_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a48dca8-631c-4d18-bee2-724cc9b3bc57",
+   "metadata": {},
+   "source": [
+    "### Q1. Is there any difference in the data type of variables list_ and array_list? If there is then write a code to print the data types of both the variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b98a16f6-40a0-4cd9-88b7-a578c545b270",
+   "metadata": {},
+   "source": [
+    "> List_ is a python list object and\n",
+    "\n",
+    "> array_list is a numpy array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d0e93297-1c9a-4905-b96b-149170ff8244",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'list'>\n",
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(list_))\n",
+    "print(type(array_list))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89d9edb9-46e6-4167-9a7f-00ab91d02a1b",
+   "metadata": {},
+   "source": [
+    "-------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aaabc819-c1f3-4604-86b3-70053fe406a9",
+   "metadata": {},
+   "source": [
+    "### Q2. Write a code to print the data type of each and every element of both the variables list_ and arra_list. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ccb3047e-316c-44a2-8be4-ced0485ef8e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[str, str, str, str, str]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For List\n",
+    "l = [(type(i)) for i in list_]\n",
+    "l"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9e553caf-e260-4b2d-b5b2-50800cf6d0ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[dtype('<U1'), dtype('<U1'), dtype('<U1'), dtype('<U1'), dtype('<U1')]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For array_list\n",
+    "a = [ i.dtype for i in np.nditer(array_list)]\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81a653e9-ed4b-4613-8e41-07035b16c3cf",
+   "metadata": {},
+   "source": [
+    "--------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de20dd89-236c-4bd7-8855-4875cdede0a5",
+   "metadata": {},
+   "source": [
+    "### Q3. Considering the following changes in the variable, array_list:\n",
+    "\n",
+    "     array_list = np.array(object = list_, dtype = int)\n",
+    "Will there be any difference in the data type of the elements present in both the variables, list_ and\n",
+    "arra_list? If so then print the data types of each and every element present in both the variables, list_\n",
+    "and arra_list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c152f979-d66f-4726-8143-187b9117de39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array_list = np.array(object = list_, dtype = int)\n",
+    "array_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fa0b900-56cd-4491-af1b-443700b644c7",
+   "metadata": {},
+   "source": [
+    ">> array_list is numpy object which values are converted in Integer Format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "09d903c1-40a4-4f78-ba15-6498d7a42f81",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "list Type : [<class 'str'>, <class 'str'>, <class 'str'>, <class 'str'>, <class 'str'>] \n",
+      "array_list  : [dtype('int64'), dtype('int64'), dtype('int64'), dtype('int64'), dtype('int64')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# For List\n",
+    "l = [(type(i)) for i in list_]\n",
+    "al = [ i.dtype for i in np.nditer(array_list)]\n",
+    "print('list Type :', l, '\\n' 'array_list  :', al)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d648e86-3c58-41d9-9648-de9f5050203e",
+   "metadata": {},
+   "source": [
+    "---------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e518bbd7-d11a-48a7-bfed-ece49db526d3",
+   "metadata": {},
+   "source": [
+    "### Q4. Write a code to find the following characteristics of variable, num_array:\n",
+    "    -(i) shape\n",
+    "    -(ii) size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "634bb1b0-4cfb-425f-96cd-dba54de965fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "num_list = [ [ 1 , 2 , 3 ] , [ 4 , 5 , 6 ] ]\n",
+    "num_array = np.array(object = num_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "9f99f94a-91fe-4f28-8fa5-fdd55f02d1cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "for Shape : (2, 3) \n",
+      "for Size : 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('for Shape :', num_array.shape, '\\n''for Size :', num_array.size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55530fb1-a44b-47bd-a36c-fe0371cc5f8b",
+   "metadata": {},
+   "source": [
+    "_______"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f33c8d6-92f7-4da1-b25a-6e1aec76497c",
+   "metadata": {},
+   "source": [
+    "### Q5. Write a code to create numpy array of 3*3 matrix containing zeros only, using a numpy array creation function.\n",
+    "[Hint: The size of the array will be 9 and the shape will be (3,3).]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d90e395a-dbcb-4030-bc2b-56dce122edd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "matrix([[0., 0., 0.],\n",
+       "        [0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy.matlib as nm\n",
+    "nm.zeros((2,3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87c05630-7bfb-4678-99ea-a85109945632",
+   "metadata": {},
+   "source": [
+    "_______"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce5f26f6-21ed-4093-aba6-4f13cad2979a",
+   "metadata": {},
+   "source": [
+    "### Q6. Create an identity matrix of shape (5,5) using numpy functions?\n",
+    "[Hint: An identity matrix is a matrix containing 1 diagonally and other elements will be 0.]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "34bc61d8-3157-4be7-b7a6-095c2936857e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ar = nm.eye(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "5927ab62-b13f-4568-91a5-682471afa83e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "matrix([[1., 0., 0., 0., 0.],\n",
+       "        [0., 1., 0., 0., 0.],\n",
+       "        [0., 0., 1., 0., 0.],\n",
+       "        [0., 0., 0., 1., 0.],\n",
+       "        [0., 0., 0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93a0779c-9959-44a5-a50f-e425b332107c",
+   "metadata": {},
+   "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": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}