Review tweaks

Author: craigsdennis

s2n1-exploration-methods.ipynb

@@ -440,7 +440,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you can see, most of the data types of these columns where inferred, or assumed, correctly.  See how automatically **`email_verified`** is `bool`, **`referral_count`** is an integer, and **`balance`** a float. This happened when we used `pd.read_csv`. \n",
+    "As you can see, most of the data types of these columns were inferred, or assumed, correctly.  See how automatically **`email_verified`** is `bool`, **`referral_count`** is an integer, and **`balance`** a float. This happened when we used `pd.read_csv`. \n",
     "\n",
     "One thing to note though that the **`signup_date`** field is an `object` and not a `datetime`. You can convert these druing or after import if you need to, and we'll do some of that later in this course."
    ]
@@ -450,7 +450,7 @@
    "metadata": {},
    "source": [
     "#### Describe your data\n",
-    "The `DataFrame.describe` method is a great way to get a vibe for all numeric data in your data frame.  You'll see lot's of different aggregations."
+    "The `DataFrame.describe` method is a great way to get a vibe for all numeric data in your `DataFrame`. You'll see lot's of different aggregations."
    ]
   },
   {
@@ -662,7 +662,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Since columns are `Series` you can quickly access their counts of different values using the [`value_counts`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) method."
+    "Since columns are in reality a `Series` you can quickly access their counts of different values using the [`value_counts`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) method."
    ]
   },
   {