Date string manipulations with Python.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "69783gzunQEp"
   },
   "source": [
    "# Annotated follow-along guide: Date string manipulations with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains the code used in the following instructional video: [Date string manipulations with Python](https://www.coursera.org/learn/go-beyond-the-numbers-translate-data-into-insight/lecture/0GjvK/date-string-manipulations-with-python)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PMRbG2k_FJFj"
   },
   "source": [
    "Throughout this notebook, we will practice manipulating date strings in Python. Before getting started, watch the associated instructional video and complete the in-video question. All of the code we will be implementing and related instructions are contained in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "In this notebook, we will work with 2016–2018 lightning strike data from the National Oceanic and Atmospheric Association (NOAA) to calculate weekly sums of lightning strikes and plot them on a bar graph. Then, we will calculate quarterly lightning strike totals and plot them on bar graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages and libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JAh01LamFJFt"
   },
   "source": [
    "Before getting started, we will need to import all the required libraries and extensions. Throughout the course, we will be using pandas for operations, and matplotlib and seaborn for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "TiZxf5dvGcXB"
   },
   "outputs": [],
   "source": [
    "# Import statements\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Bsgc4dX4BWOA"
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>number_of_strikes</th>\n",
       "      <th>center_point_geom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>2016-08-05</td>\n",
       "      <td>16</td>\n",
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       "      <th>3</th>\n",
       "      <td>2016-08-05</td>\n",
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       "      <th>4</th>\n",
       "      <td>2016-08-05</td>\n",
       "      <td>16</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "         date  number_of_strikes   center_point_geom\n",
       "0  2016-08-05                 16  POINT(-101.5 24.7)\n",
       "1  2016-08-05                 16     POINT(-85 34.3)\n",
       "2  2016-08-05                 16     POINT(-89 41.4)\n",
       "3  2016-08-05                 16   POINT(-89.8 30.7)\n",
       "4  2016-08-05                 16   POINT(-86.2 37.9)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the data.\n",
    "df = pd.read_csv('eda_manipulate_date_strings_with_python.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iiVSolnEm6A5"
   },
   "source": [
    "## Create new time columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hKWRq_huIvEE"
   },
   "source": [
    "As with similar datasets you have worked with, the `date` column data type is a string object, which limits what you can do with the data in this column. Converting the column to datetime will enable you to work with this data much more easily.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "3Mkui6ErGNdw"
   },
   "outputs": [],
   "source": [
    "# Convert the `date` column to datetime.\n",
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's create four new columns: `week`, `month`, `quarter`, and `year`. We can do this by using the `datetime.strftime()` method of the datetime object. `strftime` is short for \"string format time.\" We will use this method on the datetime data in the `week` column, and it will extract the information we specify, formatted as a string. \n",
    "\n",
    "To specify the information to extract, we will use strftime format codes. You can find a full list of available codes to use in the [strftime format codes documentation](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior). In this case, we will use `%Y` for year, `%V` for week number, `%q` for quarter.\n",
    "\n",
    "**NOTE:** The following process might take a minute or two to complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "zdJrDykiBlt2"
   },
   "outputs": [],
   "source": [
    "# Create four new columns.\n",
    "df['week'] = df['date'].dt.strftime('%Y-W%V')\n",
    "df['month'] = df['date'].dt.strftime('%Y-%m')\n",
    "df['quarter'] = df['date'].dt.to_period('Q').dt.strftime('%Y-Q%q')\n",
    "df['year'] = df['date'].dt.strftime('%Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i6zkE-8mFJF0"
   },
   "source": [
    "Let's use `head()` to check that the columns were created as intended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>number_of_strikes</th>\n",
       "      <th>center_point_geom</th>\n",
       "      <th>week</th>\n",
       "      <th>month</th>\n",
       "      <th>quarter</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-08-05</td>\n",
       "      <td>16</td>\n",
       "      <td>POINT(-101.5 24.7)</td>\n",
       "      <td>2016-W31</td>\n",
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       "      <td>2016-Q3</td>\n",
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       "      <th>1</th>\n",
       "      <td>2016-08-05</td>\n",
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       "      <td>2016-W31</td>\n",
       "      <td>2016-08</td>\n",
       "      <td>2016-Q3</td>\n",
       "      <td>2016</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-05</td>\n",
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       "      <th>4</th>\n",
       "      <td>2016-08-05</td>\n",
       "      <td>16</td>\n",
       "      <td>POINT(-86.2 37.9)</td>\n",
       "      <td>2016-W31</td>\n",
       "      <td>2016-08</td>\n",
       "      <td>2016-Q3</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-08-05</td>\n",
       "      <td>16</td>\n",
       "      <td>POINT(-97.8 38.9)</td>\n",
       "      <td>2016-W31</td>\n",
       "      <td>2016-08</td>\n",
       "      <td>2016-Q3</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-08-05</td>\n",
       "      <td>16</td>\n",
       "      <td>POINT(-81.9 36)</td>\n",
       "      <td>2016-W31</td>\n",
       "      <td>2016-08</td>\n",
       "      <td>2016-Q3</td>\n",
       "      <td>2016</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-08-05</td>\n",
       "      <td>16</td>\n",
       "      <td>POINT(-90.9 36.7)</td>\n",
       "      <td>2016-W31</td>\n",
       "      <td>2016-08</td>\n",
       "      <td>2016-Q3</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-08-05</td>\n",
       "      <td>16</td>\n",
       "      <td>POINT(-106.6 26.1)</td>\n",
       "      <td>2016-W31</td>\n",
       "      <td>2016-08</td>\n",
       "      <td>2016-Q3</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-08-05</td>\n",
       "      <td>16</td>\n",
       "      <td>POINT(-108 31.6)</td>\n",
       "      <td>2016-W31</td>\n",
       "      <td>2016-08</td>\n",
       "      <td>2016-Q3</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        date  number_of_strikes   center_point_geom      week    month  \\\n",
       "0 2016-08-05                 16  POINT(-101.5 24.7)  2016-W31  2016-08   \n",
       "1 2016-08-05                 16     POINT(-85 34.3)  2016-W31  2016-08   \n",
       "2 2016-08-05                 16     POINT(-89 41.4)  2016-W31  2016-08   \n",
       "3 2016-08-05                 16   POINT(-89.8 30.7)  2016-W31  2016-08   \n",
       "4 2016-08-05                 16   POINT(-86.2 37.9)  2016-W31  2016-08   \n",
       "5 2016-08-05                 16   POINT(-97.8 38.9)  2016-W31  2016-08   \n",
       "6 2016-08-05                 16     POINT(-81.9 36)  2016-W31  2016-08   \n",
       "7 2016-08-05                 16   POINT(-90.9 36.7)  2016-W31  2016-08   \n",
       "8 2016-08-05                 16  POINT(-106.6 26.1)  2016-W31  2016-08   \n",
       "9 2016-08-05                 16    POINT(-108 31.6)  2016-W31  2016-08   \n",
       "\n",
       "   quarter  year  \n",
       "0  2016-Q3  2016  \n",
       "1  2016-Q3  2016  \n",
       "2  2016-Q3  2016  \n",
       "3  2016-Q3  2016  \n",
       "4  2016-Q3  2016  \n",
       "5  2016-Q3  2016  \n",
       "6  2016-Q3  2016  \n",
       "7  2016-Q3  2016  \n",
       "8  2016-Q3  2016  \n",
       "9  2016-Q3  2016  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6I5Qur7bnIXd"
   },
   "source": [
    "## Plot the number of weekly lightning strikes in 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "slxEJjRRIxi5"
   },
   "source": [
    "Next, we will plot the number of weekly lightning strikes. Let's start by filtering the original dataset to 2018. We will use the `groupby()` and `sum()` functions to get the number of strikes per week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-W01</td>\n",
       "      <td>34843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-W02</td>\n",
       "      <td>353425</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-W03</td>\n",
       "      <td>37132</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-W04</td>\n",
       "      <td>412772</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-W05</td>\n",
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      "text/plain": [
       "       week  number_of_strikes\n",
       "0  2018-W01              34843\n",
       "1  2018-W02             353425\n",
       "2  2018-W03              37132\n",
       "3  2018-W04             412772\n",
       "4  2018-W05              34972"
      ]
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "# Create a new dataframe view of just 2018 data, summed by week.\n",
    "df_by_week_2018 = df[df['year'] == '2018'].groupby(['week']).sum().reset_index()\n",
    "df_by_week_2018.head()\n",
    "\n",
    "# NOTE: In pandas v.2.X+ you must set 'numeric_only=True' in the sum() function or else it will throw an error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Dxh2L5AFJF4"
   },
   "source": [
    "Now we have a table of exactly what we need to plot the weekly lightning strike totals of 2018. We will use the `plt.bar()` function to plot the bar graph. Within the argument field, let's input the x-axis (the `week` column), then input the y-axis (or height) as the `number_of_strikes` column. \n",
    "\n",
    "We will use `plt.xlabel()`, `plt.ylabel()`, and `plt.title()` to add appropriate text to the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "6JJAlQorFJF5"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a bar graph of weekly strike totals in 2018.\n",
    "plt.bar(x = df_by_week_2018['week'], height = df_by_week_2018['number_of_strikes'])\n",
    "plt.plot()\n",
    "plt.xlabel(\"Week number\")\n",
    "plt.ylabel(\"Number of lightning strikes\")\n",
    "plt.title(\"Number of lightning strikes per week (2018)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjust the graph size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fusGHYwHFJF5"
   },
   "source": [
    "We now have a graph, but we can't read the x-axis labels. To fix this problem, first we will make it bigger by using `plt.figure(figsize=(20, 5))`. This will change the size to 20 inches wide by 5 inches tall.\n",
    "\n",
    "Next, we will use the `plt.xticks()` function to access the tick labels on the x-axis. Using the `rotation` and `fontsize` keyword arguments, rotate the labels and make the font smaller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "aX3V8u2WFJF6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 5)) # Increase output size.\n",
    "plt.bar(x = df_by_week_2018['week'], height = df_by_week_2018['number_of_strikes'])\n",
    "plt.plot()\n",
    "plt.xlabel(\"Week number\")\n",
    "plt.ylabel(\"Number of lightning strikes\")\n",
    "plt.title(\"Number of lightning strikes per week (2018)\")\n",
    "plt.xticks(rotation = 45, fontsize = 8) # Rotate x-axis labels and decrease font size.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the number of quarterly lightning strikes from 2016–2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w4DYsd7HFJF6"
   },
   "source": [
    "Next, let's plot lightning strikes by quarter for the full date range of available data. For a visualization, it will be easiest to work with numbers in millions, such as 25.2 million. As an example, the following code will divide the `number_of_strikes` column by one million."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TczH3W_DsVF9",
    "outputId": "e0e4e8e5-d419-42e1-a31b-c5bb0f5a3966"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.000016\n",
       "1    0.000016\n",
       "2    0.000016\n",
       "3    0.000016\n",
       "4    0.000016\n",
       "Name: number_of_strikes, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_quarter = df['number_of_strikes'].div(1000000)\n",
    "df_by_quarter.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format the numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This alone does not help much, because it just moved the decimal to the left. We will need to format the numbers as well.  Let's begin by grouping the data by quarter and summing.\n",
    "\n",
    "Then, we will create a `number_of_strikes_formatted` column in the resulting dataframe by dividing by one million but also rounding to one digit after the decimal point, converting it to a string, and adding \"M\" to the end to represent millions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "v8yYcby_FJF9"
   },
   "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>quarter</th>\n",
       "      <th>number_of_strikes</th>\n",
       "      <th>number_of_strikes_formatted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-Q1</td>\n",
       "      <td>2683798</td>\n",
       "      <td>2.7M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-Q2</td>\n",
       "      <td>15084857</td>\n",
       "      <td>15.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-Q3</td>\n",
       "      <td>21843820</td>\n",
       "      <td>21.8M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-Q4</td>\n",
       "      <td>1969754</td>\n",
       "      <td>2.0M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-Q1</td>\n",
       "      <td>2444279</td>\n",
       "      <td>2.4M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quarter  number_of_strikes number_of_strikes_formatted\n",
       "0  2016-Q1            2683798                        2.7M\n",
       "1  2016-Q2           15084857                       15.1M\n",
       "2  2016-Q3           21843820                       21.8M\n",
       "3  2016-Q4            1969754                        2.0M\n",
       "4  2017-Q1            2444279                        2.4M"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group 2016-2018 data by quarter and sum.\n",
    "df_by_quarter = df.groupby(['quarter']).sum().reset_index()\n",
    "\n",
    "# Format as text, in millions.\n",
    "df_by_quarter['number_of_strikes_formatted'] = df_by_quarter['number_of_strikes'].div(1000000).round(1).astype(str) + 'M'\n",
    "\n",
    "df_by_quarter.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B1rrBgeAFJF8"
   },
   "source": [
    "Before we start plotting, let's write a function that will help label each bar in the plot with its corresponding number_of_strikes_formatted text. The function uses `plt.text()`, which is a pyplot function whose positional arguments are `x`, `y`, and `s`. `x` represents the x-axis coordinates, `y` represents the y-axis coordinates, and `s` represents the text that we want to appear at these coordinates. \n",
    "\n",
    "**Reference:** [pyplot documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.text.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "zm3CnH9sFJF8"
   },
   "outputs": [],
   "source": [
    "def addlabels(x, y, labels):\n",
    "    '''\n",
    "    Iterates over data and plots text labels above each bar of bar graph.\n",
    "    '''\n",
    "    for i in range(len(x)):\n",
    "        plt.text(i, y[i], labels[i], ha = 'center', va = 'bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the bar graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kzr3D8O7FJF-"
   },
   "source": [
    "Next, let's plot the bar graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "PEnfe0iCFJF-",
    "outputId": "9d96ed7c-fe51-40a1-f0e5-4971d4188458"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 5))\n",
    "plt.bar(x = df_by_quarter['quarter'], height = df_by_quarter['number_of_strikes'])\n",
    "addlabels(df_by_quarter['quarter'], df_by_quarter['number_of_strikes'], df_by_quarter['number_of_strikes_formatted'])\n",
    "plt.plot()\n",
    "plt.xlabel('Quarter')\n",
    "plt.ylabel('Number of lightning strikes')\n",
    "plt.title('Number of lightning strikes per quarter (2016-2018)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a grouped bar chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-oPhwEBUUJVm",
    "outputId": "deeef78d-718f-47cb-a45b-74d1de19f43f"
   },
   "source": [
    "Now, we will create a grouped bar chart to better compare year-over-year changes each quarter. We can do this by creating two new columns that break out the quarter and year from the `quarter` column. We will use the `quarter` column and take the last two characters to get `quarter_number`, and take the first four characters to get `year`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "M_jVUZIbFJF_"
   },
   "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>quarter</th>\n",
       "      <th>number_of_strikes</th>\n",
       "      <th>number_of_strikes_formatted</th>\n",
       "      <th>quarter_number</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-Q1</td>\n",
       "      <td>2683798</td>\n",
       "      <td>2.7M</td>\n",
       "      <td>Q1</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-Q2</td>\n",
       "      <td>15084857</td>\n",
       "      <td>15.1M</td>\n",
       "      <td>Q2</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-Q3</td>\n",
       "      <td>21843820</td>\n",
       "      <td>21.8M</td>\n",
       "      <td>Q3</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-Q4</td>\n",
       "      <td>1969754</td>\n",
       "      <td>2.0M</td>\n",
       "      <td>Q4</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-Q1</td>\n",
       "      <td>2444279</td>\n",
       "      <td>2.4M</td>\n",
       "      <td>Q1</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   quarter  number_of_strikes number_of_strikes_formatted quarter_number  year\n",
       "0  2016-Q1            2683798                        2.7M             Q1  2016\n",
       "1  2016-Q2           15084857                       15.1M             Q2  2016\n",
       "2  2016-Q3           21843820                       21.8M             Q3  2016\n",
       "3  2016-Q4            1969754                        2.0M             Q4  2016\n",
       "4  2017-Q1            2444279                        2.4M             Q1  2017"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create two new columns.\n",
    "df_by_quarter['quarter_number'] = df_by_quarter['quarter'].str[-2:]\n",
    "df_by_quarter['year'] = df_by_quarter['quarter'].str[:4]\n",
    "df_by_quarter.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill in the chart parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AplCgkQgFJF_"
   },
   "source": [
    "Next, fill in the bar chart parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "EVL75ZRpFJF_",
    "outputId": "deeef78d-718f-47cb-a45b-74d1de19f43f",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 5))\n",
    "p = sns.barplot(\n",
    "    data = df_by_quarter,\n",
    "    x = 'quarter_number',\n",
    "    y = 'number_of_strikes',\n",
    "    hue = 'year')\n",
    "for b in p.patches:\n",
    "    p.annotate(str(round(b.get_height()/1000000, 1))+'M', \n",
    "                   (b.get_x() + b.get_width() / 2., b.get_height() + 1.2e6), \n",
    "                   ha = 'center', va = 'bottom', \n",
    "                   xytext = (0, -12), \n",
    "                   textcoords = 'offset points')\n",
    "plt.xlabel(\"Quarter\")\n",
    "plt.ylabel(\"Number of lightning strikes\")\n",
    "plt.title(\"Number of lightning strikes per quarter (2016-2018)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LGSryzOLFJF_"
   },
   "source": [
    "**Congratulations!** You've completed this lab. You now understand how to manipulate date strings in Python and should be able to start using it on your own datasets.\n",
    "\n",
    "You may not notice a green check mark next to this item on Coursera's platform. Please continue your progress regardless of the check mark. Just click on the \"save\" icon at the top of this notebook to ensure your work has been logged."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "provenance": [
    {
     "file_id": "19IGrrvzEh7nvoXs9nBFIrNwEvWd_KyUa",
     "timestamp": 1662732384657
    }
   ]
  },
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}