strings.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["Man, how does this work? Hi there! 6\n"],
                }
            ],
            "source": [
                'boo = "Hi there!"\n',
                "x = 1\n",
                "y = 5\n",
                "z = x + y\n",
                'print(f"Man, how does this work? {boo} {z}")',
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["'two one'"]},
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["'{1} {0}'.format('one', 'two')"],
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["'one two'"]},
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["'{0} {1}'.format('one', 'two')"],
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["'one two'"]},
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["'{} {}'.format('one', 'two')"],
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["Sammy has 5 balloons.\n"],
                }
            ],
            "source": ['print("Sammy has {} balloons.".format(5))'],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Combining concepts \n",
                "Using Formatters with Multiple Placeholders\n",
                "You can use multiple pairs of curly braces when using formatters. If we’d like to add another variable substitution to the sentence above, we can do so by adding a second pair of curly braces and passing a second value into the method:",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["Sammy loves open-source software.\n"],
                }
            ],
            "source": [
                'new_open_string = "Sammy loves {} {}." #2 {} placeholders\n',
                'print(new_open_string.format("open-source", "software")) #Pass 2 strings into method, separated by a comma',
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["Sammy the pull request made a shark.\n"],
                }
            ],
            "source": [
                'print("Sammy the {pr} {1} a {0}.".format("shark", "made", pr = "pull request"))'
            ],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Specifying Type\n",
                "We can include more parameters within the curly braces of our syntax. We’ll use the format code syntax {field_name:conversion}, where field_name specifies the index number of the argument to the str.format() method that we went through in the reordering section, and conversion refers to the conversion code of the data type that you’re using with the formatter.\n",
                "\n",
                "The conversion type refers to the the single-character type code that Python uses. The codes that we’ll be using here are s for string, d to display decimal integers (10-base), and f which we’ll use to display floats with decimal places. You can read more about the Format-Specification Mini-Language through Python 3’s official documentation.\n",
                "\n",
                "Let’s look at an example where we have an integer passed through the method, but want to display it as a float by adding the f conversion type argument:",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["Sammy ate 75.000000 percent of a pizza!\n"],
                }
            ],
            "source": [
                'print("Sammy ate {0:f} percent of a {1}!".format(75, "pizza"))'
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["Sammy ate 75.77 percent of a pizza!\n"],
                }
            ],
            "source": [
                'print("Sammy ate {0:.2f} percent of a {1}!".format(75.765367, "pizza"))'
            ],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Padding Variable Substitutions\n",
                "Because the placeholders are replacement fields, you can pad or create space around an element by increasing field size through additional parameters. This can be useful when we need to organize a lot of data visually.\n",
                "\n",
                "We can add a number to indicate field size (in terms of characters) after the colon : in the curly braces of our syntax:",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["Sammy has 5.00 red balloons        !\n"],
                }
            ],
            "source": ['print("Sammy has {0:4.2f} red {1:16}!".format(5, "balloons"))'],
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["Sammy has|    5 |red| balloons        !\n"],
                }
            ],
            "source": ['print("Sammy has| {0:4} |red| {1:16}!".format(5, "balloons"))'],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "As we see, by default strings are left-justified within the field, and numbers are right-justified. You can modify this by placing an alignment code just following the colon. < will left-align the text in a field, ^ will center the text in the field, and > will right-align it.\n",
                "\n",
                "Let’s left-align the number and center the string:",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["Sammy has | 5    |red|     balloons    !\n"],
                }
            ],
            "source": [
                'print("Sammy has | {0:<4} |red| {1:^16}!".format(5, "balloons"))'
            ],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "By default, when we make a field larger with formatters, Python will fill the field with whitespace characters. We can modify that to be a different character by specifying the character we want it to be directly following the colon:"
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["*******Sammy********\n"],
                }
            ],
            "source": ['print("{:*^20s}".format("Sammy"))'],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "We can use variables for both the original string and what is passed into the method :"
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": ["Sammy has 8 balloons today!\n"],
                }
            ],
            "source": [
                'sammy = "Sammy has {} balloons today!"\n',
                "nBalloons = 8\n",
                "print(sammy.format(nBalloons))",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 42,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "3 9 27 18\n",
                        "4 16 64 32\n",
                        "5 25 125 50\n",
                        "6 36 216 72\n",
                        "7 49 343 98\n",
                        "8 64 512 128\n",
                        "9 81 729 162\n",
                        "10 100 1000 200\n",
                        "11 121 1331 242\n",
                        "12 144 1728 288\n",
                        "13 169 2197 338\n",
                        "14 196 2744 392\n",
                        "15 225 3375 450\n",
                        "16 256 4096 512\n",
                        "17 289 4913 578\n",
                        "18 324 5832 648\n",
                        "19 361 6859 722\n",
                    ],
                }
            ],
            "source": ["for i in range(3,20):\n", "    print(i, i*i, i*i*i, i*i*2)"],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### we can improve this formatting for readability\n",
                "\n",
                "While the output is organized in a way, the numbers overflow into each other’s columns, making the bottom of the output less readable. If you are working with a bigger data set with many small and big numbers, this can pose a problem.\n",
                "\n",
                "Let’s use formatters to give more space to these numbers:",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 44,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "  3    9    27\n",
                        "  4   16    64\n",
                        "  5   25   125\n",
                        "  6   36   216\n",
                        "  7   49   343\n",
                        "  8   64   512\n",
                        "  9   81   729\n",
                        " 10  100  1000\n",
                        " 11  121  1331\n",
                        " 12  144  1728\n",
                    ],
                }
            ],
            "source": [
                "for i in range(3,13):\n",
                '    print("{:3d} {:4d} {:5d}".format(i, i*i, i*i*i))',
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 45,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "     3      9     27\n",
                        "     4     16     64\n",
                        "     5     25    125\n",
                        "     6     36    216\n",
                        "     7     49    343\n",
                        "     8     64    512\n",
                        "     9     81    729\n",
                        "    10    100   1000\n",
                        "    11    121   1331\n",
                        "    12    144   1728\n",
                    ],
                }
            ],
            "source": [
                "for i in range(3,13):\n",
                '    print("{:6d} {:6d} {:6d}".format(i, i*i, i*i*i))',
            ],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": ["You can also truncate strings"],
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["'xylop'"]},
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["'{:.5}'.format('xylophone')"],
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["'-23'"]},
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["'{: d}'.format((- 23))"],
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["'Hodor Hodor!'"]},
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": [
                "data = {'first': 'Hodor', 'last': 'Hodor!'}\n",
                "'%(first)s %(last)s' % data # old way of doing it",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["'Hodor Hodor!'"]},
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": [
                "data = {'first': 'Hodor', 'last': 'Hodor!'}\n",
                "'{first} {last}'.format(**data) # new way of doing it",
            ],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                ".format() also accepts keyword arguments.\n",
                "This operation is not available with old-style formatting.",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["'Hodor Hodor!'"]},
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ["'{first} {last}'.format(first='Hodor', last='Hodor!')"],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Getitem and Getattr\n",
                "New style formatting allows even greater flexibility in accessing nested data structures.\n",
                "\n",
                "It supports accessing containers that support __getitem__ like for example dictionaries and lists:\n",
                "\n",
                "This operation is not available with old-style formatting.",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["'23 42'"]},
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": [
                "person = {'first': 'Jean-Luc', 'last': 'Picard'}\n",
                "'{p[first]} {p[last]}'.format(p=person)",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {"text/plain": ["'23 42'"]},
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": [
                "data = [4, 8, 15, 16, 23, 42]\n",
                "'{d[4]} {d[5]}'.format(d=data)",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": ["<matplotlib.figure.Figure at 0x7f5964790550>"],
                    },
                    "metadata": {},
                    "output_type": "display_data",
                }
            ],
            "source": [
                "import matplotlib.pyplot as plt\n",
                "import numpy as np\n",
                "\n",
                "x = np.arange(80)\n",
                "y = [x_i + np.random.randn(1) for x_i in x]\n",
                "a, b = np.polyfit(x, y, 1)\n",
                "_ = plt.plot(x, y, 'o', np.arange(80), a*np.arange(80)+b, '-')",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": ["<matplotlib.figure.Figure at 0x7f59646b4278>"],
                    },
                    "metadata": {},
                    "output_type": "display_data",
                }
            ],
            "source": [
                "# Only needs to be run once at the top of the notebook.\n",
                "!pip install -q matplotlib-venn\n",
                "\n",
                "# Now the newly-installed library can be used anywhere else in the notebook.\n",
                "from matplotlib_venn import venn2\n",
                "_ = venn2(subsets = (3,6, 4))",
            ],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                '# What Is AWS X\\-Ray?<a name="aws-xray"></a>\n',
                "\n",
                "AWS X\\-Ray is a service that collects data about requests that your application serves, and provides tools you can use to view, filter, and gain insights into that data to identify issues and opportunities for optimization\\. For any traced request to your application, you can see detailed information not only about the request and response, but also about calls that your application makes to downstream AWS resources, microservices, databases and HTTP web APIs\\.\n",
                "\n",
                "![\\[Image NOT FOUND\\]](http://docs.aws.amazon.com/xray/latest/devguide/images/scorekeep-PUTrules-timeline.png)\n",
                "\n",
                "The X\\-Ray SDK provides: \n",
                "+ **Interceptors** to add to your code to trace incoming HTTP requests\n",
                "+ **Client handlers** to instrument AWS SDK clients that your application uses to call other AWS services\n",
                "+ An **HTTP client** to use to instrument calls to other internal and external HTTP web services\n",
                "\n",
                "The SDK also supports instrumenting calls to SQL databases, automatic AWS SDK client instrumentation, and other features\\.\n",
                "\n",
                "![\\[How the X-Ray SDK works\\]](http://docs.aws.amazon.com/xray/latest/devguide/images/architecture-dataflow.png)\n",
                "\n",
                "Instead of sending trace data directly to X\\-Ray, the SDK sends JSON segment documents to a daemon process listening for UDP traffic\\. The **[X\\-Ray daemon](xray-daemon.md)** buffers segments in a queue and uploads them to X\\-Ray in batches\\. The daemon is available for Linux, Windows, and macOS, and is included on AWS Elastic Beanstalk and AWS Lambda platforms\\.\n",
                "\n",
                "X\\-Ray uses trace data from the AWS resources that power your cloud applications to generate a detailed **service graph**\\. The service graph shows the client, your front\\-end service, and backend services that your front\\-end service calls to process requests and persist data\\. Use the service graph to identify bottlenecks, latency spikes, and other issues to solve to improve the performance of your applications\\.\n",
                "\n",
                "![\\[Service graph shows the client, front-end service, and backend services that your front-end service calls to process requests and persist data\\]](http://docs.aws.amazon.com/xray/latest/devguide/images/scorekeep-gettingstarted-servicemap-after-github.png)\n",
                "\n",
                "See the [getting started tutorial](xray-gettingstarted.md) to start using X\\-Ray in just a few minutes with an instrumented sample application\\. Or [keep reading](xray-usage.md) to learn about the languages, frameworks, and services that work with X\\-Ray\\.",
            ],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "\n",
                '# Practicing muscle memory with Markdown <a name"Home">http://northredoubt.com</a>\n',
                "I am some test text under the main body. Please use my information.\n",
                "\n",
                "![\\[image not found\\]](http://docs.aws.amazon.com/xray/latest/devguide/images/scorekeep-PUTrules-timeline.png)\n",
                "\n",
                "My outline provides:\n",
                "+ **Two asterisks around each bulleted item.** which makes it nice and fancing\n",
                "+ *what does one asterisk do?* well, let's find out!\n",
                "+ what happens when none are used?\n",
                "\n",
                "[how does this link thing work?](http://docs.aws.amazon.com/xray/latest/devguide/images/scorekeep-PUTrules-timeline.png) I'm not sure \n",
                "\n",
                "here is some **more bulleted text**\\. what did that slash thing do? Not sure. [this appears](http://northtredoubt.com) to be a link.",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [],
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "[(0, '1', 'Private flight', '\\\\N', '-', None, None, None, 'Y'), (1, '2', '135 Airways', '\\\\N', None, 'GNL', 'GENERAL', 'United States', 'N'), (2, '3', '1Time Airline', '\\\\N', '1T', 'RNX', 'NEXTIME', 'South Africa', 'Y'), (3, '4', '2 Sqn No 1 Elementary Flying Training School', '\\\\N', None, 'WYT', None, 'United Kingdom', 'N'), (4, '5', '213 Flight Unit', '\\\\N', None, 'TFU', None, 'Russia', 'N')]\n"
                    ],
                }
            ],
            "source": [
                "import sqlite3\n",
                'conn = sqlite3.connect("./flights.db")\n',
                "cur = conn.cursor()\n",
                'cur.execute("select * from airlines limit 5;")\n',
                "results = cur.fetchall()\n",
                "print(results)\n",
                "cur.close()\n",
                "conn.close()",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            '<table border="1" class="dataframe">\n',
                            "  <thead>\n",
                            '    <tr style="text-align: right;">\n',
                            "      <th></th>\n",
                            "      <th>index</th>\n",
                            "      <th>id</th>\n",
                            "      <th>name</th>\n",
                            "      <th>alias</th>\n",
                            "      <th>iata</th>\n",
                            "      <th>icao</th>\n",
                            "      <th>callsign</th>\n",
                            "      <th>country</th>\n",
                            "      <th>active</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>0</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Private flight</td>\n",
                            "      <td>\\N</td>\n",
                            "      <td>-</td>\n",
                            "      <td>None</td>\n",
                            "      <td>None</td>\n",
                            "      <td>None</td>\n",
                            "      <td>Y</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>1</td>\n",
                            "      <td>2</td>\n",
                            "      <td>135 Airways</td>\n",
                            "      <td>\\N</td>\n",
                            "      <td>None</td>\n",
                            "      <td>GNL</td>\n",
                            "      <td>GENERAL</td>\n",
                            "      <td>United States</td>\n",
                            "      <td>N</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>2</td>\n",
                            "      <td>3</td>\n",
                            "      <td>1Time Airline</td>\n",
                            "      <td>\\N</td>\n",
                            "      <td>1T</td>\n",
                            "      <td>RNX</td>\n",
                            "      <td>NEXTIME</td>\n",
                            "      <td>South Africa</td>\n",
                            "      <td>Y</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>3</td>\n",
                            "      <td>4</td>\n",
                            "      <td>2 Sqn No 1 Elementary Flying Training School</td>\n",
                            "      <td>\\N</td>\n",
                            "      <td>None</td>\n",
                            "      <td>WYT</td>\n",
                            "      <td>None</td>\n",
                            "      <td>United Kingdom</td>\n",
                            "      <td>N</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>4</td>\n",
                            "      <td>5</td>\n",
                            "      <td>213 Flight Unit</td>\n",
                            "      <td>\\N</td>\n",
                            "      <td>None</td>\n",
                            "      <td>TFU</td>\n",
                            "      <td>None</td>\n",
                            "      <td>Russia</td>\n",
                            "      <td>N</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5</th>\n",
                            "      <td>5</td>\n",
                            "      <td>6</td>\n",
                            "      <td>223 Flight Unit State Airline</td>\n",
                            "      <td>\\N</td>\n",
                            "      <td>None</td>\n",
                            "      <td>CHD</td>\n",
                            "      <td>CHKALOVSK-AVIA</td>\n",
                            "      <td>Russia</td>\n",
                            "      <td>N</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>6</td>\n",
                            "      <td>7</td>\n",
                            "      <td>224th Flight Unit</td>\n",
                            "      <td>\\N</td>\n",
                            "      <td>None</td>\n",
                            "      <td>TTF</td>\n",
                            "      <td>CARGO UNIT</td>\n",
                            "      <td>Russia</td>\n",
                            "      <td>N</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>7</th>\n",
                            "      <td>7</td>\n",
                            "      <td>8</td>\n",
                            "      <td>247 Jet Ltd</td>\n",
                            "      <td>\\N</td>\n",
                            "      <td>None</td>\n",
                            "      <td>TWF</td>\n",
                            "      <td>CLOUD RUNNER</td>\n",
                            "      <td>United Kingdom</td>\n",
                            "      <td>N</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>8</th>\n",
                            "      <td>8</td>\n",
                            "      <td>9</td>\n",
                            "      <td>3D Aviation</td>\n",
                            "      <td>\\N</td>\n",
                            "      <td>None</td>\n",
                            "      <td>SEC</td>\n",
                            "      <td>SECUREX</td>\n",
                            "      <td>United States</td>\n",
                            "      <td>N</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9</th>\n",
                            "      <td>9</td>\n",
                            "      <td>10</td>\n",
                            "      <td>40-Mile Air</td>\n",
                            "      <td>\\N</td>\n",
                            "      <td>Q5</td>\n",
                            "      <td>MLA</td>\n",
                            "      <td>MILE-AIR</td>\n",
                            "      <td>United States</td>\n",
                            "      <td>Y</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>",
                        ],
                        "text/plain": [
                            "   index  id                                          name alias  iata  icao  \\\n",
                            "0      0   1                                Private flight    \\N     -  None   \n",
                            "1      1   2                                   135 Airways    \\N  None   GNL   \n",
                            "2      2   3                                 1Time Airline    \\N    1T   RNX   \n",
                            "3      3   4  2 Sqn No 1 Elementary Flying Training School    \\N  None   WYT   \n",
                            "4      4   5                               213 Flight Unit    \\N  None   TFU   \n",
                            "5      5   6                 223 Flight Unit State Airline    \\N  None   CHD   \n",
                            "6      6   7                             224th Flight Unit    \\N  None   TTF   \n",
                            "7      7   8                                   247 Jet Ltd    \\N  None   TWF   \n",
                            "8      8   9                                   3D Aviation    \\N  None   SEC   \n",
                            "9      9  10                                   40-Mile Air    \\N    Q5   MLA   \n",
                            "\n",
                            "         callsign         country active  \n",
                            "0            None            None      Y  \n",
                            "1         GENERAL   United States      N  \n",
                            "2         NEXTIME    South Africa      Y  \n",
                            "3            None  United Kingdom      N  \n",
                            "4            None          Russia      N  \n",
                            "5  CHKALOVSK-AVIA          Russia      N  \n",
                            "6      CARGO UNIT          Russia      N  \n",
                            "7    CLOUD RUNNER  United Kingdom      N  \n",
                            "8         SECUREX   United States      N  \n",
                            "9        MILE-AIR   United States      Y  ",
                        ],
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": [
                "import pandas as pd\n",
                "import sqlite3\n",
                "\n",
                'conn = sqlite3.connect("flights.db")\n',
                'df = pd.read_sql_query("select * from airlines limit 10;", conn)\n',
                "df",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "0              None\n",
                            "1     United States\n",
                            "2      South Africa\n",
                            "3    United Kingdom\n",
                            "4            Russia\n",
                            "5            Russia\n",
                            "6            Russia\n",
                            "7    United Kingdom\n",
                            "8     United States\n",
                            "9     United States\n",
                            "Name: country, dtype: object",
                        ]
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result",
                }
            ],
            "source": ['df["country"]'],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Fun with dates and times using pendulumn\n",
                " Pendulum isn't installed by default, so you need to\n",
                " \n",
                " `conda install -c conda-forge pendulum`\n",
                " \n",
                " where conda is the installer, -c tells it to use the conda-forge channel, pendulum is the package name. See this for more information [https://conda.io/docs/user-guide/tasks/manage-pkgs.html](https://conda.io/docs/user-guide/tasks/manage-pkgs.html)",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "What's the time difference?\n",
                        "3\n",
                        "What time is it now, here?\n",
                        "2018-03-30T21:08:48.802377-04:00\n",
                        "What is today?\n",
                        "2018-03-30T00:00:00-04:00\n",
                        "What is tomorrow?\n",
                        "2018-04-01T00:00:00+01:00\n",
                        "What was yesterday?\n",
                        "2018-03-29T00:00:00-04:00\n",
                    ],
                },
                {
                    "data": {
                        "text/plain": [
                            "' dt = Pendulum.now()\\n\\ndt.is_weekday()\\ndt.is_weekend()\\ndt.is_yesterday()\\ndt.is_today()\\ndt.is_tomorrow()\\ndt.is_future()\\ndt.is_past()\\ndt.is_leap_year()\\ndt.is_same_day(Pendulum.now())\\n\\nborn = pendulum.create(1987, 4, 23)\\nnot_birthday = pendulum.create(2014, 9, 26)\\nbirthday = pendulum.create(2014, 2, 23)\\npast_birthday = pendulum.now().subtract(years=50)\\n\\nborn.is_birthday(not_birthday)\\n\\nborn.is_birthday(birthday)\\n\\npast_birthday.is_birthday()\\n# Compares to now by default\\n'"
                        ]
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result",
                },
            ],
            "source": [
                "# The accepted way of installing from within the notebook\n",
                "# !conda install --yes --prefix {sys.prefix} -c conda-forge pendulum\n",
                "\n",
                "import pendulum\n",
                "import locale\n",
                "import datetime\n",
                "\n",
                "dt_toronto = pendulum.create(2012, 1, 1, tz='America/Toronto')\n",
                "dt_vancouver = pendulum.create(2012, 1, 1, tz='America/Vancouver')\n",
                "\n",
                'print("What\'s the time difference?")\n',
                "print(dt_vancouver.diff(dt_toronto).in_hours())\n",
                "\n",
                "\n",
                'print("What time is it now, here?")\n',
                "now = pendulum.now()\n",
                "print(now)\n",
                "\n",
                "\n",
                'print("What is today?")\n',
                "today = pendulum.today()\n",
                "print(today)\n",
                "\n",
                'print("What is tomorrow?")\n',
                "tomorrow = pendulum.tomorrow('Europe/London')\n",
                "print(tomorrow)\n",
                "\n",
                'print("What was yesterday?")\n',
                "yesterday = pendulum.yesterday()\n",
                "print(yesterday)\n",
                "\n",
                '""" dt = Pendulum.now()\n',
                "\n",
                "dt.is_weekday()\n",
                "dt.is_weekend()\n",
                "dt.is_yesterday()\n",
                "dt.is_today()\n",
                "dt.is_tomorrow()\n",
                "dt.is_future()\n",
                "dt.is_past()\n",
                "dt.is_leap_year()\n",
                "dt.is_same_day(Pendulum.now())\n",
                "\n",
                "born = pendulum.create(1987, 4, 23)\n",
                "not_birthday = pendulum.create(2014, 9, 26)\n",
                "birthday = pendulum.create(2014, 2, 23)\n",
                "past_birthday = pendulum.now().subtract(years=50)\n",
                "\n",
                "born.is_birthday(not_birthday)\n",
                "\n",
                "born.is_birthday(birthday)\n",
                "\n",
                "past_birthday.is_birthday()\n",
                "# Compares to now by default\n",
                '"""',
            ],
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# The accepted ways of installing packages from within a notebook. This is working in Jupyter Lab"
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Solving environment: done\n",
                        "\n",
                        "# All requested packages already installed.\n",
                        "\n",
                    ],
                }
            ],
            "source": [
                "# Install a conda package in the current Jupyter kernel\n",
                "import sys\n",
                "!conda install --yes --prefix {sys.prefix} numpy",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Requirement already up-to-date: pip in /home/jcz/anaconda3/lib/python3.6/site-packages\n",
                        "Requirement already satisfied: pendulum in /home/jcz/anaconda3/lib/python3.6/site-packages\n",
                        "Requirement already satisfied: tzlocal<2.0.0.0,>=1.5.0.0 in /home/jcz/anaconda3/lib/python3.6/site-packages (from pendulum)\n",
                        "Requirement already satisfied: pytzdata>=2018.3.0.0 in /home/jcz/anaconda3/lib/python3.6/site-packages (from pendulum)\n",
                        "Requirement already satisfied: python-dateutil<3.0.0.0,>=2.6.0.0 in /home/jcz/anaconda3/lib/python3.6/site-packages (from pendulum)\n",
                        "Requirement already satisfied: pytz in /home/jcz/anaconda3/lib/python3.6/site-packages (from tzlocal<2.0.0.0,>=1.5.0.0->pendulum)\n",
                        "Requirement already satisfied: six>=1.5 in /home/jcz/anaconda3/lib/python3.6/site-packages (from python-dateutil<3.0.0.0,>=2.6.0.0->pendulum)\n",
                        "Requirement already satisfied: numpy in /home/jcz/anaconda3/lib/python3.6/site-packages\n",
                    ],
                }
            ],
            "source": [
                "# Install a pip package in the current Jupyter kernel\n",
                "import sys\n",
                "!{sys.executable} -m pip install --upgrade pip\n",
                "!{sys.executable} -m pip install pendulum\n",
                "!{sys.executable} -m pip install numpy",
            ],
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [],
        },
    ],
    "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": "3.6.4",
        },
    },
    "nbformat": 4,
    "nbformat_minor": 2,
}