diff --git a/clampe1.ipynb b/clampe1.ipynb
new file mode 100644
index 0000000..198cfcd
--- /dev/null
+++ b/clampe1.ipynb
@@ -0,0 +1,295 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPython version:      %6.6s 7.17.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import IPython\n",
+    "import json\n",
+    "# Numpy is a library for working with Arrays\n",
+    "import numpy as np\n",
+    "# SciPy implements many different numerical algorithms\n",
+    "import scipy as sp\n",
+    "# Pandas is good with data tables\n",
+    "import pandas as pd\n",
+    "# Module for plotting\n",
+    "import matplotlib\n",
+    "#BeautifulSoup parses HTML documents (once you get them via requests)\n",
+    "import bs4\n",
+    "# Nltk helps with some natural language tasks, like stemming\n",
+    "import nltk\n",
+    "# Bson is a binary format of json to be stored in databases\n",
+    "import bson\n",
+    "# Mongo is one of common nosql databases \n",
+    "# it stores/searches json documents natively\n",
+    "import pymongo\n",
+    "print (\"IPython version:      %6.6s\", IPython.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Make a 2 row x 3 column array of random numbers\n",
+      "[[0.56424049 0.32501362 0.33332727]\n",
+      " [0.06728136 0.31634034 0.96429088]]\n",
+      "Add 5 to every element\n",
+      "[[5.56424049 5.32501362 5.33332727]\n",
+      " [5.06728136 5.31634034 5.96429088]]\n",
+      "Get the first row\n",
+      "[5.56424049 5.32501362 5.33332727]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Here is what numpy can do\\n\",\n",
+    "print (\"Make a 2 row x 3 column array of random numbers\")\n",
+    "x = np.random.random((2, 3))\n",
+    "print (x)\n",
+    "\n",
+    "#array operation (as in R)\n",
+    "print (\"Add 5 to every element\")\n",
+    "x = x + 5\n",
+    "print (x)\n",
+    "\n",
+    "# get a slice (first row) (as in R)\n",
+    "print (\"Get the first row\")\n",
+    "print (x[0, :])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function ndarray.any>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# IPython is quite modern: just press <TAB> at the end of the unfinished statement to see the documentation\n",
+    "#       on possible completions.\n",
+    "#       In the code cell below, type x.<TAB>, to find built-in operations for x\n",
+    "x.any"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline \n",
+    "import matplotlib.pyplot as plt\n",
+    "heads = np.random.binomial(500, .5, size=500)\n",
+    "histogram = plt.hist(heads, bins=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Task 1\n",
+    "## write a program to produce Fibonacci numbers up to 1000000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(x):\n",
+    "    if(x <= 1):\n",
+    "        return x\n",
+    "    return fibonacci(x-1)+ fibonacci(x-2)\n",
+    "\n",
+    "x = int(input())\n",
+    "if(x >= 0 or x <= 1000000): \n",
+    "    y= fibonacci(x)\n",
+    "    print(y)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Task 2\n",
+    "## write a program to simulate 1000 tosses of a fair coin (use np.random.binomial)\n",
+    "## Calculate the mean and standard deviation of that sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0) = Tails (1) = Heads\n",
+      "Mean:  0.504\n",
+      "Standard Drviation:  0.49998399974399177 \n",
+      "\n",
+      "[1 0 1 1 1 0 0 1 1 0 1 0 0 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 1\n",
+      " 1 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 0\n",
+      " 0 1 1 1 0 0 1 1 1 1 0 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0\n",
+      " 1 1 0 1 0 1 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 1 0 1 1 0 1\n",
+      " 1 0 1 0 1 1 0 0 0 0 1 1 1 1 1 0 1 1 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 1 0 0 1\n",
+      " 1 0 0 0 1 0 0 1 1 0 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 1 1 0 0\n",
+      " 0 0 1 0 1 0 0 0 1 0 1 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 1 1 1 0 0 0 0 0 1\n",
+      " 0 1 0 1 0 1 1 0 1 0 0 0 1 1 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 1 0 1 1 1\n",
+      " 0 0 0 0 1 1 0 0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 0 0\n",
+      " 0 0 1 1 0 1 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 1 1\n",
+      " 0 0 1 1 0 0 0 0 1 1 1 1 0 1 1 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 0 0 1 0 1 0\n",
+      " 0 1 1 1 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1 1 0 1 1 1 1 1 1 1 0 0 0\n",
+      " 1 1 1 0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 1 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 1 0 1\n",
+      " 1 1 0 0 1 1 1 0 1 0 1 0 1 0 0 1 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 1 1 1 1 0 1\n",
+      " 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 1 0 1 1 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 1 1\n",
+      " 1 0 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 0 1 0 1 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 0 1 1\n",
+      " 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 0 1 0 1 0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 0 1 1\n",
+      " 0 1 1 1 1 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 1\n",
+      " 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 0 1 0 0 1 1 1 0 1 1\n",
+      " 1 0 1 1 0 1 0 0 1 0 1 0 0 0 1 0 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 1\n",
+      " 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 1 0 1 0 1 1\n",
+      " 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0\n",
+      " 1 1 1 1 1 0 0 0 0 1 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 1 0\n",
+      " 0 1 1 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 0 0 1 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 0\n",
+      " 0 1 0 1 0 0 1 1 0 1 1 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0\n",
+      " 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 1 1 1 0 1 1 1\n",
+      " 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "x = np.random.binomial(1, .5,1000)\n",
+    "print( \"(0) = Tails (1) = Heads\")\n",
+    "print(\"Mean: \",np.mean(x))\n",
+    "print (\"Standard Deviation: \", np.std(x),\"\\n\")\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Task 3\n",
+    "## Produce a scatterplot of y = 0.5*x+e where x has gaussian (0, 5) and e has gaussian (0, 1) distributions \n",
+    "### use numpy.random.normal to generate gaussian distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fae81fd8ee0>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "x = np.random.normal(0, 5, 5)\n",
+    "e = np.random.normal(0, 1, 5)\n",
+    "y = .5*x+e\n",
+    "plt.plot(y, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}