From 5d80020ca690b9aad6f8e77a1b676f5f77a6502c Mon Sep 17 00:00:00 2001
From: darinbradley <71664029+darinbradley@users.noreply.github.com>
Date: Wed, 7 Dec 2022 15:42:06 -0600
Subject: [PATCH] Update Mission177Solutions.ipynb

---
 Mission177Solutions.ipynb | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/Mission177Solutions.ipynb b/Mission177Solutions.ipynb
index 3f118ec..d898722 100644
--- a/Mission177Solutions.ipynb
+++ b/Mission177Solutions.ipynb
@@ -28,7 +28,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We chose a dictionary where the keys are the stock symbols and the values are DataFrames with the from the corresponding CSV file.\n",
+    "We chose a dictionary where the keys are the stock symbols and the values are DataFrames from the corresponding CSV file.\n",
     "\n",
     "Let's display the data stored for the `aapl` stock symbol:"
    ]
@@ -146,7 +146,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Computing Average Closing Prices "
+    "## Computing average closing prices "
    ]
   },
   {
@@ -791,21 +791,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It appears the `amzn` and `aapl` have the highest average closing prices, while `blfs`, and `apdn` have the lowest average closing prices."
+    "It appears the `amzn` and `aapl` have the highest average closing prices, while `blfs` and `apdn` have the lowest average closing prices."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Organizing the trades per day"
+    "# Organizing the Trades Per Day"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We are going to calculate a dictionary where the keys are the days and the values are list of pairs `(volume, stock_symbol)` of all trades that occurred on that day."
+    "We are going to calculate a dictionary where the keys are the days and the values are lists of pairs `(volume, stock_symbol)` of all trades that occurred on that day."
    ]
   },
   {
@@ -830,14 +830,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Finding The Most Traded Stock Each Day"
+    "# Finding the Most Traded Stock Each Day"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Calculate a dictionary there the keys are the days and the value of each day is a pair `(volume, stock_symbol)` with the most traded stock symbol on that day."
+    "Calculate a dictionary where the keys are the days and the value of each day is a pair `(volume, stock_symbol)` with the most traded stock symbol on that day."
    ]
   },
   {
@@ -857,7 +857,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Verify a few of the results"
+    "## Verify a Few of the Results"
    ]
   },
   {
@@ -887,7 +887,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Searching For High Volume Days"
+    "# Searching for High Volume Days"
    ]
   },
   {
@@ -998,7 +998,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,