Data_Scientist_with_Python/02_Intermediate_Python/02-Dictionaries_and_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## `Section 02: Dictionaries & Pandas` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 01- Motivation for dictionaries\n",
    "* Use the `index()` method on `countries` to find the index of `'germany'`. Store this index as `ind_ger`.\n",
    "* Use `ind_ger` to access the capital of Germany from the `capitals` list. Print it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "berlin\n"
     ]
    }
   ],
   "source": [
    "# Definition of countries and capital\n",
    "countries = ['spain', 'france', 'germany', 'norway']\n",
    "capitals = ['madrid', 'paris', 'berlin', 'oslo']\n",
    "\n",
    "# Get index of 'germany': ind_ger\n",
    "ind_ger = countries.index('germany')\n",
    "\n",
    "# Use ind_ger to print out capital of Germany\n",
    "print(capitals[ind_ger])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 02- Create dictionary\n",
    "\n",
    "* With the strings in `countries` and `capitals`, create a dictionary called `europe` with 4 key:value pairs. Beware of capitalization! Make sure you use lowercase characters everywhere.\n",
    "* Print out `europe` to see if the result is what you expected.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'spain': 'madrid', 'france': 'paris', 'germany': 'berlin', 'norway': 'oslo'}\n"
     ]
    }
   ],
   "source": [
    "# Definition of countries and capital\n",
    "countries = ['spain', 'france', 'germany', 'norway']\n",
    "capitals = ['madrid', 'paris', 'berlin', 'oslo']\n",
    "\n",
    "# From string in countries and capitals, create dictionary europe\n",
    "europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo'}\n",
    "\n",
    "# Print europe\n",
    "print(europe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 03- Access dictionary\n",
    "\n",
    "\n",
    "* Check out which keys are in europe by calling the keys() method on europe. Print out the result.\n",
    "* Print out the value that belongs to the key 'norway'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['spain', 'france', 'germany', 'norway'])\n",
      "oslo\n"
     ]
    }
   ],
   "source": [
    "# Definition of dictionary\n",
    "europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }\n",
    "\n",
    "# Print out the keys in europe\n",
    "print(europe.keys())\n",
    "\n",
    "# Print out value that belongs to key 'norway'\n",
    "print(europe['norway'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 04- Dictionary Manipulation (1)\n",
    "\n",
    "* Add the key `'italy'` with the value `'rome'` to `europe`.\n",
    "* To assert that `'italy'` is now a key in `europe`, print out `'italy' in europe`.\n",
    "* Add another key:value pair to `europe`: `'poland'` is the key, `'warsaw'` is the corresponding value.\n",
    "* Print out `europe`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "{'spain': 'madrid', 'france': 'paris', 'germany': 'berlin', 'norway': 'oslo', 'italy': 'rome', 'poland': 'warsaw'}\n"
     ]
    }
   ],
   "source": [
    "# Definition of dictionary\n",
    "europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }\n",
    "\n",
    "# Add italy to europe\n",
    "europe['italy'] = 'rome'\n",
    "\n",
    "# Print out italy in europe\n",
    "print('italy' in europe)\n",
    "\n",
    "# Add poland to europe\n",
    "europe['poland'] = 'warsaw'\n",
    "\n",
    "# Print europe\n",
    "print(europe)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 05- Dictionary Manipulation (2)\n",
    "\n",
    "* The capital of Germany is not `'bonn'`; it's `'berlin'`. Update its value.\n",
    "* Australia is not in Europe, Austria is! Remove the key `'australia'` from `europe`.\n",
    "* Print out `europe` to see if your cleaning work paid off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'spain': 'madrid', 'france': 'paris', 'germany': 'berlin', 'norway': 'oslo', 'italy': 'rome', 'poland': 'warsaw'}\n"
     ]
    }
   ],
   "source": [
    "# Definition of dictionary\n",
    "europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn',\n",
    "          'norway':'oslo', 'italy':'rome', 'poland':'warsaw',\n",
    "          'australia':'vienna' }\n",
    "\n",
    "# Update capital of germany\n",
    "europe['germany'] = 'berlin'\n",
    "\n",
    "# Remove australia\n",
    "del(europe['australia'])\n",
    "\n",
    "# Print europe\n",
    "print(europe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 06- Dictionariception\n",
    "\n",
    "* Use chained square brackets to select and print out the capital of France.\n",
    "* Create a dictionary, named `data`, with the keys `'capital'` and `'population'`. Set them to `'rome'` and `59.83`, respectively.\n",
    "* Add a new key-value pair to `europe`; the key is `'italy'` and the value is `data`, the dictionary you just built."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "paris\n",
      "{'spain': {'capital': 'madrid', 'population': 46.77}, 'france': {'capital': 'paris', 'population': 66.03}, 'germany': {'capital': 'berlin', 'population': 80.62}, 'norway': {'capital': 'oslo', 'population': 5.084}, 'italy': {'capital': 'rome', 'population': 59.83}}\n"
     ]
    }
   ],
   "source": [
    "# Dictionary of dictionaries\n",
    "europe = { 'spain': { 'capital':'madrid', 'population':46.77 },\n",
    "           'france': { 'capital':'paris', 'population':66.03 },\n",
    "           'germany': { 'capital':'berlin', 'population':80.62 },\n",
    "           'norway': { 'capital':'oslo', 'population':5.084 } }\n",
    "\n",
    "\n",
    "# Print out the capital of France\n",
    "print(europe['france']['capital'])\n",
    "\n",
    "# Create sub-dictionary data\n",
    "data = {'capital': 'rome', 'population': 59.83}\n",
    "\n",
    "# Add data to europe under key 'italy'\n",
    "europe['italy'] = data\n",
    "\n",
    "# Print europe\n",
    "print(europe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 07- Dictionary to DataFrame (1)\n",
    "* Import pandas as pd.\n",
    "* Use the pre-defined lists to create a dictionary called `my_dict`. There should be three key value pairs:\n",
    "    * key `'country'` and value `names`.\n",
    "    * key `'drives_right'` and value `dr`.\n",
    "    * key `'cars_per_cap'` and value `cpc`.\n",
    "* Use `pd.DataFrame()` to turn your dict into a DataFrame called cars.\n",
    "* Print out cars and see how beautiful it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country  drives_right  cars_per_cap\n",
      "0  United States          True           809\n",
      "1      Australia         False           731\n",
      "2          Japan         False           588\n",
      "3          India         False            18\n",
      "4         Russia          True           200\n",
      "5        Morocco          True            70\n",
      "6          Egypt          True            45\n"
     ]
    }
   ],
   "source": [
    "# Pre-defined lists\n",
    "names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']\n",
    "dr =  [True, False, False, False, True, True, True]\n",
    "cpc = [809, 731, 588, 18, 200, 70, 45]\n",
    "\n",
    "# Import pandas as pd\n",
    "import pandas as pd \n",
    "\n",
    "# Create dictionary my_dict with three key:value pairs: my_dict\n",
    "my_dict = {'country':names, 'drives_right':dr, 'cars_per_cap':cpc}\n",
    "\n",
    "# Build a DataFrame cars from my_dict: cars\n",
    "cars = pd.DataFrame(my_dict)\n",
    "\n",
    "# Print cars\n",
    "print(cars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 08- Dictionary to DataFrame (2)\n",
    "* Hit Run Code to see that, indeed, the row labels are not correctly set.\n",
    "* Specify the row labels by setting `cars.index` equal to `row_labels`.\n",
    "* Print out `cars` again and check if the row labels are correct this time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country  drives_right  cars_per_cap\n",
      "0  United States          True           809\n",
      "1      Australia         False           731\n",
      "2          Japan         False           588\n",
      "3          India         False            18\n",
      "4         Russia          True           200\n",
      "5        Morocco          True            70\n",
      "6          Egypt          True            45\n",
      "============================================================\n",
      "           country  drives_right  cars_per_cap\n",
      "US   United States          True           809\n",
      "AUS      Australia         False           731\n",
      "JPN          Japan         False           588\n",
      "IN           India         False            18\n",
      "RU          Russia          True           200\n",
      "MOR        Morocco          True            70\n",
      "EG           Egypt          True            45\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Build cars DataFrame\n",
    "names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']\n",
    "dr =  [True, False, False, False, True, True, True]\n",
    "cpc = [809, 731, 588, 18, 200, 70, 45]\n",
    "cars_dict = { 'country':names, 'drives_right':dr, 'cars_per_cap':cpc }\n",
    "cars = pd.DataFrame(cars_dict)\n",
    "print(cars)\n",
    "print(10*'======')\n",
    "\n",
    "# Definition of row_labels\n",
    "row_labels = ['US', 'AUS', 'JPN', 'IN', 'RU', 'MOR', 'EG']\n",
    "\n",
    "# Specify row labels of cars\n",
    "cars.index = row_labels\n",
    "cars.to_csv('data/cars.csv')\n",
    "\n",
    "# Print cars again\n",
    "print(cars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 09- CSV to DataFrame (1)\n",
    "\n",
    "* To import CSV files you still need the `pandas` package: import it as `pd`.\n",
    "* Use `pd.read_csv()` to import `cars.csv` data as a DataFrame. Store this DataFrame as `cars`.\n",
    "* Print out `cars`. Does everything look OK?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Unnamed: 0        country  drives_right  cars_per_cap\n",
      "0         US  United States          True           809\n",
      "1        AUS      Australia         False           731\n",
      "2        JPN          Japan         False           588\n",
      "3         IN          India         False            18\n",
      "4         RU         Russia          True           200\n",
      "5        MOR        Morocco          True            70\n",
      "6         EG          Egypt          True            45\n"
     ]
    }
   ],
   "source": [
    "# Import pandas as pd\n",
    "import pandas as pd \n",
    "\n",
    "# Import the cars.csv data: cars\n",
    "cars = pd.read_csv('data/cars.csv')\n",
    "\n",
    "# Print out cars\n",
    "print(cars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10- CSV to DataFrame (2)\n",
    "* Run the code with Run Code and assert that the first column should actually be used as row labels.\n",
    "* Specify the `index_col` argument inside `pd.read_csv()`: set it to `0`, so that the first column is used as row labels.\n",
    "* Has the printout of `cars` improved now?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           country  drives_right  cars_per_cap\n",
      "US   United States          True           809\n",
      "AUS      Australia         False           731\n",
      "JPN          Japan         False           588\n",
      "IN           India         False            18\n",
      "RU          Russia          True           200\n",
      "MOR        Morocco          True            70\n",
      "EG           Egypt          True            45\n"
     ]
    }
   ],
   "source": [
    "# Import pandas as pd\n",
    "import pandas as pd\n",
    "\n",
    "# Fix import by including index_col\n",
    "cars = pd.read_csv('data/cars.csv', index_col=0)\n",
    "# Print out cars\n",
    "print(cars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11- Square Brackets (1)\n",
    "\n",
    "* Use single square brackets to print out the `country` column of `cars` as a Pandas Series.\n",
    "* Use double square brackets to print out the `country` column of `cars` as a Pandas DataFrame.\n",
    "* Use double square brackets to print out a DataFrame with both the `country` and `drives_right` columns of `cars`, in this order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "US     United States\n",
      "AUS        Australia\n",
      "JPN            Japan\n",
      "IN             India\n",
      "RU            Russia\n",
      "MOR          Morocco\n",
      "EG             Egypt\n",
      "Name: country, dtype: object\n",
      "--------------------------------------------------\n",
      "           country\n",
      "US   United States\n",
      "AUS      Australia\n",
      "JPN          Japan\n",
      "IN           India\n",
      "RU          Russia\n",
      "MOR        Morocco\n",
      "EG           Egypt\n",
      "--------------------------------------------------\n",
      "           country  drives_right\n",
      "US   United States          True\n",
      "AUS      Australia         False\n",
      "JPN          Japan         False\n",
      "IN           India         False\n",
      "RU          Russia          True\n",
      "MOR        Morocco          True\n",
      "EG           Egypt          True\n"
     ]
    }
   ],
   "source": [
    "# Import cars data\n",
    "import pandas as pd\n",
    "cars = pd.read_csv('data/cars.csv', index_col = 0)\n",
    "\n",
    "# Print out country column as Pandas Series\n",
    "print(cars['country'])\n",
    "print(10 * \"-----\")\n",
    "# Print out country column as Pandas DataFrame\n",
    "print(cars[['country']])\n",
    "print(10 * \"-----\")\n",
    "    \n",
    "# Print out DataFrame with country and drives_right columns\n",
    "print(cars[['country', 'drives_right']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12- Square Brackets (2)\n",
    "* Select the first 3 observations from `cars` and print them out.\n",
    "* Select the fourth, fifth and sixth observation, corresponding to row indexes 3, 4 and 5, and print them out.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import cars data\n",
    "import pandas as pd\n",
    "cars = pd.read_csv('cars.csv', index_col = 0)\n",
    "\n",
    "# Print out first 3 observations\n",
    "print(cars[0:3])\n",
    "\n",
    "# Print out fourth, fifth and sixth observation\n",
    "print(cars[3:6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13- loc and iloc (1)\n",
    "* Use `loc` or `iloc` to select the observation corresponding to Japan as a Series. The label of this row is `JPN`, the index is `2`. Make sure to print the resulting Series.\n",
    "* Use `loc` or `iloc` to select the observations for Australia and Egypt as a DataFrame. You can find out about the labels/indexes of these rows by inspecting `cars` in the IPython Shell. Make sure to print the resulting DataFrame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country         Japan\n",
      "drives_right    False\n",
      "cars_per_cap      588\n",
      "Name: JPN, dtype: object\n",
      "       country  drives_right  cars_per_cap\n",
      "AUS  Australia         False           731\n",
      "EG       Egypt          True            45\n"
     ]
    }
   ],
   "source": [
    "# Import cars data\n",
    "import pandas as pd\n",
    "cars = pd.read_csv('data/cars.csv', index_col = 0)\n",
    "\n",
    "# Print out observation for Japan\n",
    "print(cars.loc['JPN'])\n",
    "\n",
    "# Print out observations for Australia and Egypt\n",
    "print(cars.loc[['AUS','EG']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14- loc and iloc (2)\n",
    "\n",
    "* Print out the `drives_right` value of the row corresponding to Morocco (its row label is `MOR`)\n",
    "* Print out a sub-DataFrame, containing the observations for Russia and Morocco and the columns `country` and `drives_right`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "     country  drives_right\n",
      "RU    Russia          True\n",
      "MOR  Morocco          True\n"
     ]
    }
   ],
   "source": [
    "# Import cars data\n",
    "import pandas as pd\n",
    "cars = pd.read_csv('data/cars.csv', index_col = 0)\n",
    "\n",
    "# Print out drives_right value of Morocco\n",
    "print(cars.loc['MOR', 'drives_right'])\n",
    "print(10 * \"-----\")\n",
    "\n",
    "# Print sub-DataFrame\n",
    "print(cars.loc[['RU', 'MOR'],['country', 'drives_right']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15- loc and iloc (3)\n",
    "* Print out the `drives_right` column as a Series using `loc` or `iloc`.\n",
    "* Print out the `drives_right` column as a DataFrame using `loc` or `iloc`.\n",
    "* Print out both the `cars_per_cap` and `drives_right` column as a DataFrame using `loc` or `iloc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "US      True\n",
      "AUS    False\n",
      "JPN    False\n",
      "IN     False\n",
      "RU      True\n",
      "MOR     True\n",
      "EG      True\n",
      "Name: drives_right, dtype: bool\n",
      "--------------------------------------------------\n",
      "     drives_right\n",
      "US           True\n",
      "AUS         False\n",
      "JPN         False\n",
      "IN          False\n",
      "RU           True\n",
      "MOR          True\n",
      "EG           True\n",
      "--------------------------------------------------\n",
      "           country  cars_per_cap\n",
      "US   United States           809\n",
      "AUS      Australia           731\n",
      "JPN          Japan           588\n",
      "IN           India            18\n",
      "RU          Russia           200\n",
      "MOR        Morocco            70\n",
      "EG           Egypt            45\n"
     ]
    }
   ],
   "source": [
    "# Import cars data\n",
    "import pandas as pd\n",
    "cars = pd.read_csv('data/cars.csv', index_col = 0)\n",
    "\n",
    "# Print out drives_right column as Series\n",
    "print(cars.loc[:,'drives_right'])\n",
    "print(10 * \"-----\")\n",
    "\n",
    "# Print out drives_right column as DataFrame\n",
    "print(cars.loc[:,['drives_right']])\n",
    "print(10 * \"-----\")\n",
    "\n",
    "# Print out cars_per_cap and drives_right as DataFrame\n",
    "print(cars.iloc[:,[0,2]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "==================================\n",
    "### `The End`\n",
    "=================================="
   ]
  }
 ],
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