function.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "say_hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "say_something(\"Hello World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hi, my name is Jane. I'm learning Python!\n"
     ]
    }
   ],
   "source": [
    "Jane_says = \"Hi, my name is Jane. I'm learning Python!\"\n",
    "say_something(Jane_says)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function that calculates the percentage of students that passed both # math and reading and prints the passing percentage to the output when the\n",
    "# function is called.\n",
    "def passing_math_percent(pass_math_count,student_count):\n",
    "    return(pass_math_count)/float(student_count)*100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.9808526933878"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passing_math_count = 29370\n",
    "total_student_count = 39170\n",
    "passing_math_percent(passing_math_count,total_student_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A list of my grades.\n",
    "my_grades = ['B', 'C', 'B' , 'D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    B\n",
       "1    C\n",
       "2    B\n",
       "3    D\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#convert my_grades to series\n",
    "my_grades = pd.Series(my_grades)\n",
    "my_grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A\n",
       "1    B\n",
       "2    A\n",
       "3    C\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#map(\"current_value_1\" : \"new_value_1\",  \"current_value_2\" : \"new_value_2\", etc)\n",
    "# Change the grades by one letter grade.\n",
    "my_grades.map({'B':'A','C':'B','D':'C'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92.34\n",
      "84.56\n",
      "86.78\n",
      "98.32\n"
     ]
    }
   ],
   "source": [
    "# Using the format() function.\n",
    "my_grades = [92.34, 84.56, 86.78, 98.32]\n",
    "for grade in my_grades:\n",
    "    print(\"{:.2f}\".format(grade))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    92\n",
       "1    85\n",
       "2    87\n",
       "3    78\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_grades.map(\"{:.0f}\".format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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