diff --git a/Python/Module5_OddsAndEnds/WorkingWithFiles.md b/Python/Module5_OddsAndEnds/WorkingWithFiles.md
index e8fce932..28496b63 100644
--- a/Python/Module5_OddsAndEnds/WorkingWithFiles.md
+++ b/Python/Module5_OddsAndEnds/WorkingWithFiles.md
@@ -249,6 +249,119 @@ with open("a_poem.txt", mode="r") as my_open_file:
```
<!-- #endregion -->
+<!-- #region -->
+## Working with Comma Seperated Value Files
+
+Comma Seperated Value (CSV) files are commonly used to store data that you might typically find in a table.
+These files can be formatted in many ways, but the typical format is to have each of the column values in the table be separated by commas while having a newline separate each row.
+Suppose we have the following table of test scores:
+
+| | Exam 1 (%) | Exam 2 (%) |
+| ------------- |:-------------:| -----:|
+| Ashley | $93$ | $95$ |
+| Brad | $84$ | $100$ |
+| Cassie | $99$ | $87$ |
+
+This table depicts the test scores of three students across 2 exams.
+Here is what the corresponding CSV file might look like:
+
+```python
+name,exam one score,exam two score
+Ashley,93,95
+Brad,84,100
+Cassie,99,87
+```
+In addition to the fact that the first line typically contains a header, you are also allowed to have spaces within each of columns as well.
+
+<div class="alert alert-warning">
+
+**Note**:
+
+It is not guaranteed that all CSV files are actually comma separated.
+Non-standard CSV files will typically come with instructions on how the data is organized.
+In general, it is a good practice to open up the CSV file and look at the first few lines to get a sense of how it is organized (unless the file is too large).
+</div>
+
+### How to parse CSVs with NumPy
+
+We will first look into parsing and storing CSV data using our favorite package: `numpy`!
+
+To demonstrate how importing a CSV works, we will try to import [a coastal waves dataset](https://www.kaggle.com/jolasa/waves-measuring-buoys-data-mooloolaba/data) from Kaggle.
+After you extract the *.csv* from the *.zip*, rename it to *coastal_dataset.csv*.
+```python
+from numpy import genfromtxt # genfromtxt() allows for easy parsing of CSVs
+my_data = genfromtxt(r"./Downloads/coastal_dataset.csv", delimiter=',')
+```
+`genfromtxt()` takes in a CSV file path and delimiter (the character used to split the data, typically comma for CSV).
+Let's check out some properties of the data:
+
+```python
+>>> type(my_data)
+numpy.ndarray
+
+>>> my_data.shape
+(43729, 7)
+
+#Let's look at the actual data
+>>> my_data
+array([[ nan, nan, nan, ..., nan, nan, nan],
+ [ nan, -99.9 , -99.9 , ..., -99.9 , -99.9 , -99.9 ],
+ [ nan, 0.875, 1.39 , ..., 4.506, -99.9 , -99.9 ],
+ ...,
+ [ nan, 2.157, 3.43 , ..., 12.89 , 97. , 21.95 ],
+ [ nan, 2.087, 2.84 , ..., 10.963, 92. , 21.95 ],
+ [ nan, 1.926, 2.98 , ..., 12.228, 84. , 21.95 ]])
+```
+You may notice that there are some `nan` values present when we look at this perticular set of data.
+Typically, if there are non-numerical values in the file, such as headers and dates, importing it into a NumPy array will turn those values into `nan`.
+
+### How to parse CSVs with Pandas
+
+A really popular library for parsing CSVs is the [Pandas](https://pandas.pydata.org/pandas-docs/stable/index.html "Pandas Documentation") library. Here is a quick way to parse a CSV using Pandas:
+```Python
+import pandas as pd
+my_data = pd.read_csv(r"./Downloads/coastal_dataset.csv", sep=',', header=None)
+```
+That's it!
+The method `read_csv()` loads the contents of the CSV into the variable `my_data`.
+This method has similar input parameters to `genfromtxt()` and many extra optional parameters as well.
+Look at the docstring for more information.
+
+ Let's parse the same [ocean waves csv](https://www.kaggle.com/jolasa/waves-measuring-buoys-data-mooloolaba/data) from before but with Pandas instead of NumPy:
+
+```Python
+>>> type(my_data)
+pandas.core.frame.DataFrame # Notice that this is a custom type
+
+>>> my_data.shape
+(43729, 7)
+
+>>> my_data.values # This is how we access the values as an array
+array([['Date/Time', 'Hs', 'Hmax', ..., 'Tp', 'Peak Direction', 'SST'],
+ ['01/01/2017 00:00', '-99.9', '-99.9', ..., '-99.9', '-99.9',
+ '-99.9'],
+ ['01/01/2017 00:30', '0.875', '1.39', ..., '4.506', '-99.9',
+ '-99.9'],
+ ...,
+ ['30/06/2019 22:30', '2.157', '3.43', ..., '12.89', '97', '21.95'],
+ ['30/06/2019 23:00', '2.087', '2.84', ..., '10.963', '92',
+ '21.95'],
+ ['30/06/2019 23:30', '1.926', '2.98', ..., '12.228', '84',
+ '21.95']], dtype=object)
+```
+One of the coolest features of Pandas is how it nicely organizes the parsed CSV data for visualization.
+Here is how `my_data` is displayed in a Jupyter Notebook:
+
+```Python
+my_data[0:21] # Prints out first 20 values in nice format
+```
+
+
+One of the main advantages of Pandas is that it **treats all the data as strings**, while NumPy only deals with numerical values.
+This allows Pandas to store information such as headers and dates, while NumPy cannot.
+Read the [Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/index.html "Documentation Link") for more information.
+<!-- #endregion -->
+
<!-- #region -->
## Globbing for Files
@@ -457,4 +570,3 @@ Write a glob pattern for each of the following prompts
- Any file with an odd number in its name (answer: `*[13579]*`)
- All txt files that have the letters 'q' or 'z' in them (answer: `*[qz]*.txt`)
-
diff --git a/Python/Module5_OddsAndEnds/pics/Pandas_CSV.jpg b/Python/Module5_OddsAndEnds/pics/Pandas_CSV.jpg
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