Update Mission257Solutions.ipynb

Mission257Solutions.ipynb

@@ -11,7 +11,6 @@
    "cell_type": "code",
    "execution_count": 1,
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@@ -45,14 +44,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll begin by getting a sense of what the data looks like."
+    "We'll begin by exploring the data."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
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@@ -175,15 +173,15 @@
    "source": [
     "Here are the descriptions for some of the columns:\n",
     "\n",
-    "* `name` - The name of the country.\n",
-    "* `area` - The total land and sea area of the country.\n",
-    "* `population` - The country's population.\n",
-    "* `population_growth`- The country's population growth as a percentage.\n",
-    "* `birth_rate` - The country's birth rate, or the number of births a year per 1,000 people.\n",
-    "* `death_rate` - The country's death rate, or the number of death a year per 1,000 people.\n",
-    "* `area`- The country's total area (both land and water).\n",
-    "* `area_land` - The country's land area in [square kilometers](https://www.cia.gov/library/publications/the-world-factbook/rankorder/2147rank.html).\n",
-    "* `area_water` - The country's waterarea in square kilometers.\n",
+    "* `name` — the name of the country.\n",
+    "* `area` — the total land and sea area of the country.\n",
+    "* `population` — the country's population.\n",
+    "* `population_growth`— the country's population growth as a percentage.\n",
+    "* `birth_rate` — the country's birth rate, or the number of births a year per 1,000 people.\n",
+    "* `death_rate` — the country's death rate, or the number of death a year per 1,000 people.\n",
+    "* `area`— the country's total area (both land and water).\n",
+    "* `area_land` — the country's land area in [square kilometers](https://www.cia.gov/library/publications/the-world-factbook/rankorder/2147rank.html).\n",
+    "* `area_water` — the country's water area in square kilometers.\n",
     "\n",
     "Let's start by calculating some summary statistics and see what they tell us."
    ]
@@ -199,7 +197,6 @@
    "cell_type": "code",
    "execution_count": 3,
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@@ -252,12 +249,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A few things stick out from the summary statistics in the last screen:\n",
+    "A few things are interesting in the summary statistics on the previous screen:\n",
     "\n",
-    "- There's a country with a population of `0`\n",
-    "- There's a country with a population of `7256490011` (or more than 7.2 billion people) \n",
+    "- There's a country with a population of `0`.\n",
+    "- There's a country with a population of `7256490011` (or more than 7.2 billion people).\n",
     "\n",
-    "Let's use subqueries to zoom in on just these countries _without_ using the specific values."
+    "Let's use subqueries to concentrate on these countries _without_ using the specific values."
    ]
   },
   {
@@ -271,7 +268,6 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
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@@ -347,7 +343,6 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
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@@ -429,9 +424,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -497,14 +490,13 @@
    "source": [
     "Let's explore density. Density depends on the population and the country's area. Let's look at the average values for these two columns.\n",
     "\n",
-    "We should take care of discarding the row for the whole planet."
+    "We should discard the row for the whole planet."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false,
     "jupyter": {
      "outputs_hidden": false
     }
@@ -565,17 +557,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To finish, we'll build on the query above to find countries that are densely populated.  We'll identify countries that have:\n",
+    "To finish, we'll build on the query above to find countries that are densely populated. We'll identify countries that have the following:\n",
     "\n",
-    "- Above average values for population.\n",
-    "- Below average values for area."
+    "- Above-average values for population.\n",
+    "- Below-average values for area."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
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     "jupyter": {
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@@ -849,7 +840,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.8.5"
   }
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  "nbformat": 4,