{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 1\n",
    "Import NumPy under the alias `np`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 2\n",
    "Import pandas under the alias `pd`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 3\n",
    "Given the pandas Series `my_series`, generate a NumPy array that contains only the unique values from `my_series`. Assign this new array to a variable called `my_array`. Print `my_array` to ensure that the operation has been executed successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     1\n",
       "2     2\n",
       "3     2\n",
       "4     3\n",
       "5     3\n",
       "6     4\n",
       "7     4\n",
       "8     5\n",
       "9     5\n",
       "10    6\n",
       "11    6\n",
       "12    7\n",
       "13    7\n",
       "14    8\n",
       "15    8\n",
       "16    9\n",
       "17    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series = pd.Series([1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9])\n",
    "my_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Solution goes here\n",
    "my_array = my_series.unique()\n",
    "my_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 4\n",
    "Given the pandas DataFrame `my_data_frame`, generate a NumPy array that contains only the unique values from the second column. Assign this new array to a variable called `another_array`. Print `another_array` to ensure the operation has been executed successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>0.950120</td>\n",
       "      <td>1.104541</td>\n",
       "      <td>-0.135333</td>\n",
       "      <td>-2.157449</td>\n",
       "      <td>-1.786119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.772171</td>\n",
       "      <td>0.207613</td>\n",
       "      <td>-1.480314</td>\n",
       "      <td>0.191361</td>\n",
       "      <td>-2.296765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.576407</td>\n",
       "      <td>-0.615181</td>\n",
       "      <td>1.233100</td>\n",
       "      <td>0.092227</td>\n",
       "      <td>-1.881353</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.950120  1.104541 -0.135333 -2.157449 -1.786119\n",
       "1 -1.772171  0.207613 -1.480314  0.191361 -2.296765\n",
       "2 -0.576407 -0.615181  1.233100  0.092227 -1.881353"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data_frame = pd.DataFrame(np.random.randn(3,5))\n",
    "my_data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.95011976, -1.7721715 , -0.57640705])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Solution goes here\n",
    "another_array = my_data_frame[0].unique()\n",
    "another_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 5\n",
    "Count the occurence of every element within the `my_series` variable that was created earlier in these practice problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9    2\n",
       "8    2\n",
       "7    2\n",
       "6    2\n",
       "5    2\n",
       "4    2\n",
       "3    2\n",
       "2    2\n",
       "1    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 6\n",
    "Given the function `triple_digit`, apply this to every element within `my_series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def triple_digit(x):\n",
    "    return x + x*10 + x*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     111\n",
       "1     111\n",
       "2     222\n",
       "3     222\n",
       "4     333\n",
       "5     333\n",
       "6     444\n",
       "7     444\n",
       "8     555\n",
       "9     555\n",
       "10    666\n",
       "11    666\n",
       "12    777\n",
       "13    777\n",
       "14    888\n",
       "15    888\n",
       "16    999\n",
       "17    999\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Solution goes here\n",
    "my_series.apply(triple_digit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 7\n",
    "Sort the `my_data_frame` variable that we created earlier based on the contents of its second column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1.772171</td>\n",
       "      <td>0.207613</td>\n",
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       "      <td>0.191361</td>\n",
       "      <td>-2.296765</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.576407</td>\n",
       "      <td>-0.615181</td>\n",
       "      <td>1.233100</td>\n",
       "      <td>0.092227</td>\n",
       "      <td>-1.881353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.950120</td>\n",
       "      <td>1.104541</td>\n",
       "      <td>-0.135333</td>\n",
       "      <td>-2.157449</td>\n",
       "      <td>-1.786119</td>\n",
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      "text/plain": [
       "          0         1         2         3         4\n",
       "1 -1.772171  0.207613 -1.480314  0.191361 -2.296765\n",
       "2 -0.576407 -0.615181  1.233100  0.092227 -1.881353\n",
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   ],
   "source": [
    "my_data_frame.sort_values(0)"
   ]
  }
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