Workshop Week 2 Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal this week is to pratice using Markdown to write descriptive text in notebooks and then look at Python data structures including the pandas module that supports data structures designed for handling the kind of data we'll be working with.  \n",
    "\n",
    "There is probably too much work to complete here in the workshop session, but please use this notebook after the workshop to practice your Python.  Remember to commit your changes to git as you go and push back to Github when you are done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "student_name = \"Your Name Here\"\n",
    "student_id = \"Your ID Here\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markdown Practice\n",
    "\n",
    "Complete this section as per the instructions in the iLearn practical page.  Add the required cells below this one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We are doing Markdown practice here. \n",
    "\n",
    "You can double click this cell. Once you double click this cell, the first heading will turn \"Blue\". You will also notice that in the Menu option, the option selected is \"Markdown\" which means that this cell will be executed as markdown.\n",
    "\n",
    "You can learn more about **Markdown Syntax** from [Markdown Guide](https://www.markdownguide.org/)\n",
    "\n",
    "1. This is the first item \n",
    "2. This is the second item\n",
    "3. This is the third item\n",
    "    - This is the first sub-item in the third item\n",
    "    - This is the second sub-item in the third item\n",
    "    - This is the third sub-item in the third item\n",
    "    \n",
    "You can make text \"bold\" like this **This text is bold**.\n",
    "\n",
    "You can make text \"italic\" like this *This text is in italics*\n",
    "\n",
    "You can also write mathematical expressions using Markdown syntax.\n",
    "\n",
    "Here is the first mathematical expression: $y = f(x)$\n",
    "\n",
    "Here is the second mathematical expression: $ f(x) = 4x^3 + 3x^2 - 2x + 9$\n",
    "\n",
    "Here is the third mathematical expression: $\\sqrt{3x-1} + 4x + 1$\n",
    "\n",
    "You can also put code using markdown syntax:\n",
    "\n",
    "```python\n",
    "def countWords(string):\n",
    "    \"\"\"This function will return number of words for the input string\"\"\"\n",
    "    words = string.split()\n",
    "    n = len(words)\n",
    "    return n\n",
    "```\n",
    "    \n",
    "\n",
    "You can also embed (or add) images in Jupyter notebooks using Markdown:\n",
    "![Github Logo](Git-Logo-2Color.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lists and Dictionaries\n",
    "\n",
    "First we look at some built in Python data structures: lists and dictionaries. \n",
    "\n",
    "A list is a sequence of things, unlike strongly typed languages (Java, C#) a list can contain a mixture of different types - there is no type for a list of integers or a list of lists.   Here are some lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = [12, 99, 51, 3, 55]\n",
    "names = ['steve', 'jim', 'mary', 'carrie', 'zin']\n",
    "stuff = [12, 'eighteen', 6, ['another', 'list']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. write code to print the first and third elements of each list\n",
    "2. write code to select and print everything except the first element of each list\n",
    "3. write a for loop that prints each element of the 'names' list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First item:  12 ; Third element:  51\n",
      "First item:  steve ; Third element:  mary\n",
      "First item:  12 ; Third element:  6\n"
     ]
    }
   ],
   "source": [
    "def firstNThird(myList):\n",
    "    \"\"\"Function to print first and third element for the given list\"\"\"\n",
    "    print(\"First item: \", myList[0], \";\", \"Third element: \", myList[2])\n",
    "    \n",
    "firstNThird(ages)\n",
    "firstNThird(names)\n",
    "firstNThird(stuff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[99, 51, 3, 55]\n",
      "['jim', 'mary', 'carrie', 'zin']\n",
      "['eighteen', 6, ['another', 'list']]\n"
     ]
    }
   ],
   "source": [
    "def exceptFirstElement(myList):\n",
    "    \"\"\"Function to print all elements except the first element for the given list\"\"\"\n",
    "    print(myList[1:])\n",
    "    \n",
    "exceptFirstElement(ages)\n",
    "exceptFirstElement(names)\n",
    "exceptFirstElement(stuff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steve\n",
      "jim\n",
      "mary\n",
      "carrie\n",
      "zin\n"
     ]
    }
   ],
   "source": [
    "for name in names:\n",
    "    print(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dictionary is an associative array - it associates a value (any Python data type) with a key. The key is usually a string but can be any immutable type (string, number, tuple).  Here's some code that counts the occurence of words in a string.  It stores the count for each word in a dictionary using the word as a key. If the word is already stored in the dictionary, it adds one to the count, if not, it initialises the count to one.  \n",
    "\n",
    "The second for loop iterates over the keys in the dictionary and prints one line per entry.\n",
    "\n",
    "Modify this example to be a bit smarter:\n",
    "- make sure that punctuation characters are not included as parts of a word, be careful with hyphens - should they be included or not?\n",
    "- make the count use the lowercase version of a word, so that 'The' and 'the' are counted as the same word\n",
    "- **Challenge**: find the first and second most frequent words in the text\n",
    "- **Challenge**: take your code and write it as a function that takes a string and returns a list of words with their counts in order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This 1\n",
      "unit 3\n",
      "introduces 1\n",
      "students 1\n",
      "to 4\n",
      "the 5\n",
      "fundamental 1\n",
      "techniques 1\n",
      "and 3\n",
      "tools 2\n",
      "of 3\n",
      "data 5\n",
      "science, 1\n",
      "such 1\n",
      "as 1\n",
      "graphical 1\n",
      "display 1\n",
      "data, 1\n",
      "predictive 1\n",
      "models, 1\n",
      "evaluation 1\n",
      "methodologies, 1\n",
      "regression, 1\n",
      "classification 1\n",
      "clustering. 1\n",
      "The 1\n",
      "provides 1\n",
      "practical 1\n",
      "experience 1\n",
      "applying 1\n",
      "these 2\n",
      "methods 3\n",
      "using 1\n",
      "industry-standard 1\n",
      "software 1\n",
      "real-world 2\n",
      "sets. 1\n",
      "Students 1\n",
      "who 1\n",
      "have 2\n",
      "completed 1\n",
      "this 1\n",
      "will 1\n",
      "be 1\n",
      "able 1\n",
      "identify 1\n",
      "which 1\n",
      "science 1\n",
      "are 1\n",
      "most 1\n",
      "appropriate 1\n",
      "for 1\n",
      "a 1\n",
      "set, 2\n",
      "apply 1\n",
      "interpret 1\n",
      "results 1\n",
      "analysis 1\n",
      "they 1\n",
      "performed. 1\n"
     ]
    }
   ],
   "source": [
    "description = \"\"\"This unit introduces students to the fundamental techniques and \n",
    "tools of data science, such as the graphical display of data, \n",
    "predictive models, evaluation methodologies, regression, \n",
    "classification and clustering. The unit provides practical \n",
    "experience applying these methods using industry-standard \n",
    "software tools to real-world data sets. Students who have \n",
    "completed this unit will be able to identify which data \n",
    "science methods are most appropriate for a real-world data \n",
    "set, apply these methods to the data set, and interpret the \n",
    "results of the analysis they have performed. \"\"\"\n",
    "\n",
    "count = dict()\n",
    "for word in description.split():\n",
    "    if word in count:\n",
    "        count[word] += 1\n",
    "    else:\n",
    "        count[word] = 1\n",
    "        \n",
    "for word in count:\n",
    "    print(word, count[word])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this 2\n",
      "unit 3\n",
      "introduces 1\n",
      "students 2\n",
      "to 4\n",
      "the 6\n",
      "fundamental 1\n",
      "techniques 1\n",
      "and 3\n",
      "tools 2\n",
      "of 3\n",
      "data 6\n",
      "science 2\n",
      "such 1\n",
      "as 1\n",
      "graphical 1\n",
      "display 1\n",
      "predictive 1\n",
      "models 1\n",
      "evaluation 1\n",
      "methodologies 1\n",
      "regression 1\n",
      "classification 1\n",
      "clustering 1\n",
      "provides 1\n",
      "practical 1\n",
      "experience 1\n",
      "applying 1\n",
      "these 2\n",
      "methods 3\n",
      "using 1\n",
      "industry-standard 1\n",
      "software 1\n",
      "real-world 2\n",
      "sets 1\n",
      "who 1\n",
      "have 2\n",
      "completed 1\n",
      "will 1\n",
      "be 1\n",
      "able 1\n",
      "identify 1\n",
      "which 1\n",
      "are 1\n",
      "most 1\n",
      "appropriate 1\n",
      "for 1\n",
      "a 1\n",
      "set 2\n",
      "apply 1\n",
      "interpret 1\n",
      "results 1\n",
      "analysis 1\n",
      "they 1\n",
      "performed 1\n"
     ]
    }
   ],
   "source": [
    "desc = description.replace(',', '').replace('.','')\n",
    "desc = desc.lower()\n",
    "\n",
    "count = dict()\n",
    "for word in desc.split():\n",
    "    if word in count:\n",
    "        count[word] += 1\n",
    "    else:\n",
    "        count[word] = 1\n",
    "        \n",
    "for word in count:\n",
    "    print(word, count[word])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'the': 6, 'data': 6, 'to': 4, 'unit': 3, 'and': 3, 'of': 3, 'methods': 3, 'this': 2, 'students': 2, 'tools': 2, 'science': 2, 'these': 2, 'real-world': 2, 'have': 2, 'set': 2, 'introduces': 1, 'fundamental': 1, 'techniques': 1, 'such': 1, 'as': 1, 'graphical': 1, 'display': 1, 'predictive': 1, 'models': 1, 'evaluation': 1, 'methodologies': 1, 'regression': 1, 'classification': 1, 'clustering': 1, 'provides': 1, 'practical': 1, 'experience': 1, 'applying': 1, 'using': 1, 'industry-standard': 1, 'software': 1, 'sets': 1, 'who': 1, 'completed': 1, 'will': 1, 'be': 1, 'able': 1, 'identify': 1, 'which': 1, 'are': 1, 'most': 1, 'appropriate': 1, 'for': 1, 'a': 1, 'apply': 1, 'interpret': 1, 'results': 1, 'analysis': 1, 'they': 1, 'performed': 1})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "c = Counter(desc.split())\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('the', 6), ('data', 6), ('to', 4), ('unit', 3), ('and', 3)]\n"
     ]
    }
   ],
   "source": [
    "print(c.most_common(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('the', 6), ('data', 6), ('to', 4)]\n"
     ]
    }
   ],
   "source": [
    "print(c.most_common(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Data Frames\n",
    "\n",
    "[Pandas](https://pandas.pydata.org) is a Python module that provides some important data structures for Data Science work and a large collection of methods for data analysis. \n",
    "\n",
    "The two main data structures are the [Series]() and [DataFrame](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe).  \n",
    "\n",
    "A Series is a one dimensional array of data, but unlike the Python list the data is indexed - the index is like the dictionary key, any immutable value like a number or string.  You can use the label to select elements from the series as well as positional values.  \n",
    "\n",
    "A DataFrame is analogous to a spreadsheet - a two dimensional table of data with indexed rows and named columns. \n",
    "\n",
    "You should read up on these and follow the examples in the text.  Here are a few exercises to complete with data frames."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are given three csv files containing sample data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "ds1 = 'files/ds1.csv'\n",
    "ds2 = 'files/ds2.csv'\n",
    "ds3 = 'files/ds3.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write code below to read one of these data files into a pandas data frame and:\n",
    "- show the first few rows: .head\n",
    "- find the summary data for each column: .describe\n",
    "- select just those rows where the value of x and y is over 50\n",
    "- select the column 'x' and create a series\n",
    "- plot the 'x' series as a line graph\n",
    "- plot the dataframe as a scatterplot\n",
    "\n",
    "Once you have the code for this, you can change the file you use for input of the data (ds2, ds3) and re-run the following cells to see the different output that is generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df1 shape:  (142, 2)\n",
      "df2 shape:  (142, 2)\n",
      "df3 shape:  (142, 2)\n"
     ]
    }
   ],
   "source": [
    "# Reading csv files as dataframes\n",
    "df1 = pd.read_csv(ds1)\n",
    "df2 = pd.read_csv(ds2)\n",
    "df3 = pd.read_csv(ds3)\n",
    "\n",
    "print(\"df1 shape: \", df1.shape)\n",
    "print(\"df2 shape: \", df2.shape)\n",
    "print(\"df3 shape: \", df3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         x        y\n",
      "0  55.3846  97.1795\n",
      "1  51.5385  96.0256\n",
      "2  46.1538  94.4872\n",
      "3  42.8205  91.4103\n",
      "4  40.7692  88.3333\n"
     ]
    }
   ],
   "source": [
    "# getting first five rows of df1\n",
    "print(df1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                x           y\n",
      "count  142.000000  142.000000\n",
      "mean    54.263273   47.832253\n",
      "std     16.765142   26.935403\n",
      "min     22.307700    2.948700\n",
      "25%     44.102600   25.288450\n",
      "50%     53.333300   46.025600\n",
      "75%     64.743600   68.525675\n",
      "max     98.205100   99.487200\n"
     ]
    }
   ],
   "source": [
    "# getting statistics\n",
    "print(df1.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new_df1 shape:  (33, 2)\n",
      "          x        y\n",
      "0   55.3846  97.1795\n",
      "1   51.5385  96.0256\n",
      "22  53.0769  55.6410\n",
      "23  56.6667  56.0256\n",
      "24  59.2308  57.9487\n"
     ]
    }
   ],
   "source": [
    "new_df1 = df1.loc[(df1.x > 50) & (df1.y > 50)]\n",
    "print(\"new_df1 shape: \", new_df1.shape)\n",
    "print(new_df1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting x column\n",
    "xCol = df1['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cd1acec470>]"
      ]
     },
     "execution_count": 17,
     "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": [
    "# plotting x column\n",
    "plt.plot(xCol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1cd1ada8f28>"
      ]
     },
     "execution_count": 18,
     "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": [
    "# scatter plot for df1\n",
    "plt.scatter(x = df1['x'], y = df1['y'], s = 20, marker = '*', color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1cd1ae245c0>"
      ]
     },
     "execution_count": 19,
     "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": [
    "# scatter plot for df3\n",
    "plt.scatter(x = df2['x'], y = df2['y'], s = 20, marker = 'o', color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1cd1ae91278>"
      ]
     },
     "execution_count": 20,
     "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": [
    "# scatter plot for df3\n",
    "plt.scatter(x = df3['x'], y = df3['y'], s = 20, marker = '+', color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checkpoint\n",
    "\n",
    "Congratulations! you have finished the required task for Week 2. Since you got this empty (without your code and output) notebook by downloading from iLearn, place this notebook (Workshop Week 2.ipynb) into your local copy of your Github repository (e.g. practical-workshops-sonitsingh)and commit your work with a suitable commit message and push your changes back to your Github repository. Show your tutor your updated Github repository to get your checkpoint mark."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you finished all the above questions, save the notebook by clicking 'save' button in the toolbar.\n",
    "\n",
    "You need to follow the same instructions to commit your work on your Github repository.\n",
    "\n",
    "Step 1. Change your current directory to `practical-workshops-yourName` by doing `cd` command. You can type:\n",
    "                    `cd practical-workshops-yourName`\n",
    "                    \n",
    "Step 2: Add your Workshop Week 2.ipynb using:\n",
    "                 `git add \"Workshop Week 2.ipynb`\n",
    "                 \n",
    "Step 3: Commit your work:\n",
    "                `git commit -m \"Finished Workshop 2`\n",
    "                \n",
    "Step 4: Push your changes:\n",
    "                `git push origin main`\n",
    "                \n",
    "Step 5: Confirm whether your finished work is now on Github repository by signing into your Github account and clicking on your repository. You can see your added `Workshop Week 2.ipynb` file as well as your `commit message` and `time` of your commit.\n",
    "\n",
    "Step 6: Hurray! Well done. You have now finished your Practical Workshop Week 2. Appreciate yourself. Here is a quote for your reference:\n",
    "\n",
    "As **William James** said:\n",
    "> “The deepest principle in human nature is the craving to be appreciated.”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further Practice\n",
    "\n",
    "If you finish this task you can practice more with pandas data frames by following the examples in the text, section 2.6.  The CSV file that they use in that section is available in the `files` directory of this repository as `educ_figdp_1_Data.csv`.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "edufile = 'files/educ_figdp_1_Data.csv'"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}