added license/contrib, fixed typos re issues #7 and #8

Author: cutterbuck

CONTRIBUTING.md

@@ -0,0 +1,37 @@
+# Contributing to Learn.co Curriculum
+
+We're really exited that you're about to contribute to the [open curriculum](https://learn.co/content-license) on [Learn.co](https://learn.co). If this is your first time contributing, please continue reading to learn how to make the most meaningful and useful impact possible.
+
+## Raising an Issue to Encourage a Contribution
+
+If you notice a problem with the curriculum that you believe needs improvement
+but you're unable to make the change yourself, you should raise a Github issue
+containing a clear description of the problem. Include relevant snippets of
+the content and/or screenshots if applicable. Curriculum owners regularly review
+issue lists and your issue will be prioritized and addressed as appropriate.
+
+## Submitting a Pull Request to Suggest an Improvement
+
+If you see an opportunity for improvement and can make the change yourself go
+ahead and use a typical git workflow to make it happen:
+
+* Fork this curriculum repository
+* Make the change on your fork, with descriptive commits in the standard format
+* Open a Pull Request against this repo
+
+A curriculum owner will review your change and approve or comment on it in due
+course.
+
+# Why Contribute?
+
+Curriculum on Learn is publicly and freely available under Learn's
+[Educational Content License](https://learn.co/content-license). By
+embracing an open-source contribution model, our goal is for the curriculum
+on Learn to become, in time, the best educational content the world has
+ever seen.
+
+We need help from the community of Learners to maintain and improve the
+educational content. Everything from fixing typos, to correcting
+out-dated information, to improving exposition, to adding better examples,
+to fixing tests—all contributions to making the curriculum more effective are
+welcome.

LICENSE.md

@@ -0,0 +1,23 @@
+# Learn.co Educational Content License
+
+Copyright (c) 2018 Flatiron School, Inc
+
+The Flatiron School, Inc. owns this Educational Content. However, the Flatiron
+School supports the development and availability of educational materials in
+the public domain. Therefore, the Flatiron School grants Users of the Flatiron
+Educational Content set forth in this repository certain rights to reuse, build
+upon and share such Educational Content subject to the terms of the Educational
+Content License set forth [here](http://learn.co/content-license)
+(http://learn.co/content-license). You must read carefully the terms and
+conditions contained in the Educational Content License as such terms govern
+access to and use of the Educational Content.
+
+Flatiron School is willing to allow you access to and use of the Educational
+Content only on the condition that you accept all of the terms and conditions
+contained in the Educational Content License set forth
+[here](http://learn.co/content-license) (http://learn.co/content-license).  By
+accessing and/or using the Educational Content, you are agreeing to all of the
+terms and conditions contained in the Educational Content License.  If you do
+not agree to any or all of the terms of the Educational Content License, you
+are prohibited from accessing, reviewing or using in any way the Educational
+Content.

README.md

@@ -1,5 +1,5 @@
 
-# Going further with dictionaries 
+# Going further with dictionaries
 
 ### Introduction
 
@@ -23,7 +23,7 @@ Now if we download this spreadsheet in the form of an .xlsx file we can start to
 
 We've already placed that file into this lesson, and you can see it [here](https://github.com/learn-co-curriculum/excel-to-python), where the contents of this lesson are also located.
 
-### From Local File to Python 
+### From Local File to Python
 
 Now that we have this file in the folder we are working with, we can get this data into Python code in a few lines.
 
@@ -48,17 +48,17 @@ cities[0]
 
 The code above relies on using an outside library called `pandas`, as it's good at reading excel files.  A library is just a set of reusable functions.  The `pandas` library is available for free online.  We tell our current Jupyter notebook that we are about to use it with the line `import pandas`.  
 
-And that gives us an object, like a dictionary, which has a method on it called `read_excel`.  Similar so how we can call `{'foo': 'bar'}.keys()`.  That's the benefit of a library, we can get methods that do not come out of the box with Python.  So we use the `read_excel` data to read our excel file, by providing the name of the file, `cities.xlsx`, and the preceding `./` just indicates that the file is found in the current folder.  Finally with the line `travel_df.to_dict('records')` we return a list of our dictionaries representing our data.  You can see that when we access the first element of this list, we it returns our first dictionary.  
+And that gives us an object, like a dictionary, which has a method on it called `read_excel`.  Similar so how we can call `{'foo': 'bar'}.keys()`.  That's the benefit of a library, we can get methods that do not come out of the box with Python.  So we use the `read_excel` data to read our excel file, by providing the name of the file, `cities.xlsx`, and the preceding `./` just indicates that the file is found in the current folder.  Finally with the line `travel_df.to_dict('records')` we return a list of our dictionaries representing our data.  You can see that when we access the first element of this list, it returns our first dictionary.  
 
 Here is the code again, with some comments, if you are interested.
 
 
 ```python
-# Here we use a library, which is some code not part of standard Python, to this process easier 
+# Here we use a library, which is some code not part of standard Python, to make this process easier
 import pandas
-# If we use the `import pandas` we have access to the pandas library 
+# If we use the `import pandas` we have access to the pandas library
 travel_df = pandas.read_excel('./cities.xlsx')
-# We call the pandas.read_excel method and pass through the string './cities.xlsx' as the file is called cities.xlsx.  By saying './' we are saying 
+# We call the pandas.read_excel method and pass through the string './cities.xlsx' as the file is called cities.xlsx.  By saying './' we are saying
 # go to the current folder, lists-lab, and find the 'cities.xlsx' file there
 cities = travel_df.to_dict('records')
 ```
@@ -199,7 +199,7 @@ Once again, we call the method, and then coerce it into a list, by using the `li
 
 ### Creating Dictionaries
 
-So far, we have seen one way of creating dictionaries: 
+So far, we have seen one way of creating dictionaries:
 
 
 ```python

index.ipynb

@@ -138,7 +138,7 @@
    "source": [
     "The code above relies on using an outside library called `pandas`, as it's good at reading excel files.  A library is just a set of reusable functions.  The `pandas` library is available for free online.  We tell our current Jupyter notebook that we are about to use it with the line `import pandas`.  \n",
     "\n",
-    "And that gives us an object, like a dictionary, which has a method on it called `read_excel`.  Similar so how we can call `{'foo': 'bar'}.keys()`.  That's the benefit of a library, we can get methods that do not come out of the box with Python.  So we use the `read_excel` data to read our excel file, by providing the name of the file, `cities.xlsx`, and the preceding `./` just indicates that the file is found in the current folder.  Finally with the line `travel_df.to_dict('records')` we return a list of our dictionaries representing our data.  You can see that when we access the first element of this list, we it returns our first dictionary.  "
+    "And that gives us an object, like a dictionary, which has a method on it called `read_excel`.  Similar so how we can call `{'foo': 'bar'}.keys()`.  That's the benefit of a library, we can get methods that do not come out of the box with Python.  So we use the `read_excel` data to read our excel file, by providing the name of the file, `cities.xlsx`, and the preceding `./` just indicates that the file is found in the current folder.  Finally with the line `travel_df.to_dict('records')` we return a list of our dictionaries representing our data.  You can see that when we access the first element of this list, it returns our first dictionary.  "
    ]
   },
   {
@@ -150,13 +150,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Here we use a library, which is some code not part of standard Python, to this process easier \n",
+    "# Here we use a library, which is some code not part of standard Python, to make this process easier \n",
     "import pandas\n",
     "# If we use the `import pandas` we have access to the pandas library \n",
     "travel_df = pandas.read_excel('./cities.xlsx')\n",
@@ -523,7 +521,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,