Lab2_Tasks-checkpoint.ipynb

{
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  {
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   "source": [
    "# Lab2 Python for Data Science 1\n",
    "----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, convert the list of weights from a list to a Numpy array.  \n",
    "Then, convert all of the weights from kilograms to pounds. Use the scalar conversion of 2.2 lbs per kilogram to make your conversion.  \n",
    "Lastly, print the resulting array of weights in pounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "weight_kg = [81.65, 97.52, 95.25, 92.98, 86.18, 88.45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "# import numpy\n",
    "\n",
    "# Create a numpy array np_weight_kg from weight_kg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create np_weight_lbs from np_weight_kg\n",
    "\n",
    "# Print out np_weight_lbs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a Dataframe from scratch, view the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
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   "outputs": [],
   "source": [
    "# we have a dictionary of data\n",
    "dict = {\"country\": [\"Brazil\", \"Russia\", \"India\", \"China\", \"South Africa\"],\n",
    "       \"capital\": [\"Brasilia\", \"Moscow\", \"New Dehli\", \"Beijing\", \"Pretoria\"],\n",
    "       \"area\": [8.516, 17.10, 3.286, 9.597, 1.221],\n",
    "       \"population\": [200.4, 143.5, 1252, 1357, 52.98] }\n",
    "# import pandas\n",
    "\n",
    "# create a dataframe from the dictionary\n",
    "\n",
    "# print the dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Change the index to two-letter contry code\n",
    "brics.index = [\"BR\", \"RU\", \"IN\", \"CH\", \"SA\"]\n",
    "brics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#show the first and last two rows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# describe the data using describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save the dataframe to a .csv file (e.g. brics.csv)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the csv file to a dataframe, then perform data selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
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   },
   "outputs": [],
   "source": [
    "# Import pandas\n",
    "\n",
    "# read the brics.csv file in data folder to a dataframe, \n",
    "# using read_csv(filename, index_col = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Print out country column as Pandas Series using \"[]\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Print out 3rd - 5th rows using \"[]\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Print out observation for China and Brazil \n",
    "# using Select by Label (.loc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Print out observation for China and Brazil \n",
    "# using Select by location (.iloc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# select the rows of countries with more \n",
    "# than 200m in population using boolean indexing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create two dataframes, join the two data frames based on a key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Import pandas\n",
    "import pandas as pd\n",
    "# create dataframe 1\n",
    "raw_data = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "df_a = pd.DataFrame(raw_data, columns = ['subject_id', 'first_name', 'last_name'])\n",
    "df_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create dataframe 2\n",
    "raw_data = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'test_id': [51, 15, 15, 61, 16]}\n",
    "df_b = pd.DataFrame(raw_data, columns = ['subject_id','test_id'])\n",
    "df_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# join the two dataframes based on subject_id \n",
    "# using merge(df1,df2,on='keyname')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataframe with missing value, then replace the NaN with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import pandas and numpy\n",
    "\n",
    "\n",
    "# Create a dataframe with missing value\n",
    "df = pd.DataFrame(np.random.randn(3, 3), index=['a', 'c', 'e'],columns=['one',\n",
    "'two', 'three'])\n",
    "df = df.reindex(['a', 'b', 'c','d'])\n",
    "print df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# replace NaN with '0' using fillna(value = ?)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# exclude missing value using dropna()\n"
   ]
  }
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