function.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9fdb31bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the Pandas dependency.\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3f60faa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the function \"say_hello\" so it prints \"Hello!\" when called.\n",
    "def say_hello():\n",
    "    print(\"Hello!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "082cc9eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello!\n"
     ]
    }
   ],
   "source": [
    "# Call the function.\n",
    "say_hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e34e38c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the function \"say_something\" so it prints whatever is passed as the variable when called.\n",
    "def say_something(something):\n",
    "    print(something)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b42e81c9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "# Call the function.\n",
    "say_something(\"Hello World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07a219da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hi, my name is bishop. I'm learning Python!\n"
     ]
    }
   ],
   "source": [
    "B_says = \"Hi, my name is bishop. I'm learning Python!\"\n",
    "say_something(B_says)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61945ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Define a function that calculates the percentage of students that passed both \n",
    "# math and reading and returns the passing percentage when the function is called.\n",
    "\n",
    "def passing_math_percent(pass_math_count, student_count):\n",
    "    return pass_math_count / float(student_count) * 100\n",
    "\n",
    "passing_math_count = 29370\n",
    "total_student_count = 39170"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e90722d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.9808526933878"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call the function.\n",
    "passing_math_percent(passing_math_count, total_student_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b6f877e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A list of my grades.\n",
    "my_grades = ['B', 'C', 'B' , 'D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bccd67c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    B\n",
       "1    C\n",
       "2    B\n",
       "3    D\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import pandas.\n",
    "import pandas as pd\n",
    "# Convert the my_grades to a Series\n",
    "my_grades = pd.Series(my_grades)\n",
    "my_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "47f46d03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A\n",
       "1    B\n",
       "2    A\n",
       "3    C\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the grades by one letter grade.\n",
    "my_grades.map({'B': 'A', 'C': 'B', 'D': 'C'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "81e3b543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92\n",
      "85\n",
      "87\n",
      "98\n"
     ]
    }
   ],
   "source": [
    "# Using the format() function.\n",
    "my_grades = [92.34, 84.56, 86.78, 98.32]\n",
    "\n",
    "for grade in my_grades:\n",
    "    print(\"{:.0f}\".format(grade))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eb75b15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    92.34\n",
       "1    84.56\n",
       "2    86.78\n",
       "3    78.32\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the numerical grades to a Series.\n",
    "my_grades = pd.Series([92.34, 84.56, 86.78, 78.32])\n",
    "my_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7e02359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    92\n",
       "1    85\n",
       "2    87\n",
       "3    78\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format the grades to the nearest whole number percent.\n",
    "my_grades.map(\"{:.0f}\".format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74bfe0e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Cat:\n",
    "    def __init__(self, name):\n",
    "        self.name = name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "784f155c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Felix\n"
     ]
    }
   ],
   "source": [
    "first_cat = Cat('Felix')\n",
    "print(first_cat.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fea079e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dog:\n",
    "    def __init__(self, name, color, sound):\n",
    "        self.name = name\n",
    "        self.color = color\n",
    "        self.sound = sound\n",
    "\n",
    "    def bark(self):\n",
    "        return self.sound + ' ' + self.sound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dbdde72a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fido\n",
      "brown\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'woof! woof!'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_dog = Dog('Fido', 'brown', 'woof!')\n",
    "print(    first_dog.name)\n",
    "print(first_dog.color)\n",
    "first_dog.bark()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23ac331c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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