03-ws_breakout_session1.ipynb

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   "source": [
    "## Intro to Python for Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Break out session 1"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "In this breakout session, you will explore some loan data from the Consumer Financial Protection Bureau (www.consumerfinance.gov). This dataset is based on a query that contains \n",
    "loan data from 2012 for the state of Vermont."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine the data"
   ]
  },
  {
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   "source": [
    "Import libraries."
   ]
  },
  {
   "cell_type": "code",
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    "Read in the loans csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "View the first few rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "markdown",
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   "source": [
    "How many rows and columns are there in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "markdown",
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   "source": [
    "Get the first five rows of the applicant income column by slicing."
   ]
  },
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   "cell_type": "code",
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  {
   "cell_type": "code",
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   "source": [
    "Get the first five rows of the agency_abbr column using positional indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Sort the data by loan amount in descending order and return the first five rows of the resulting dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the value counts of action_taken_name column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
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   "source": [
    "Return a boolean value for the preapproval_name column that returns True if preapproval was requested. Display the first four rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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  {
   "cell_type": "markdown",
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   "source": [
    "Get a subset of the data where applicant income is greater than 0 and less than 1000 (use regular subsetting)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "markdown",
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   "source": [
    "Subset the dataframe created in the previous cell to only display applicant income and agency code and display first three rows."
   ]
  },
  {
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   "execution_count": null,
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  {
   "cell_type": "markdown",
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   "source": [
    "Get a subset of the data where applicant income is greater than 0 and less than 1000. Use the query method. Display the applicant income and loan\n",
    "amount columns and just the first three rows of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Groupby and filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Groupby applicant race and loan purpose name and return the mean values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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  {
   "cell_type": "markdown",
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   "source": [
    "Filter the dataset to return only rows containing NCUA in the agency_abbr column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group by purchaser type name and get the median values using the aggregate method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a simple bar plot with loan purpose on the x-axis and loan amount on the y-axis with Seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Create a simple boxplot with loan amount on the x-axis and agency name on the y-axis with Seaborn. Make the loan amount log scale in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a violin plot with loan amount on the x-axis and loan purpose name on the y-axis with Seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display all column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show unique values for action taken name column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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