Added content

2 files changed, 85 insertions, 8 deletions

diff --git a/tutorials/module_4/1_importing_scientific_data.md b/tutorials/module_4/1_importing_scientific_data.md
index b51558b..f609b39 100644
--- a/tutorials/module_4/1_importing_scientific_data.md
+++ b/tutorials/module_4/1_importing_scientific_data.md
@@ -12,12 +12,10 @@ import numpy as np
 import pandas as pd
 ```
 
-
+---
 DataFrame
 
-A `DataFrame` in pandas is a two-dimensional data structure like table with rows and columns.
-
-
+A `DataFrame` in pandas is a two-dimensional data structure like table with rows and columns. We can either load some data in a [python dictionary:](https://www.w3schools.com/python/python_dictionaries.asp)
 
 ```python
 # Tensile test data
@@ -28,13 +26,70 @@ data = {
 }
 ```
 
+Or we can load a spreadsheet in the form of a CSV file with the `read_csv()` function. In order to do this we need 
+
+```python
+# Read CSV file into a DataFrame
+pd.read_csv("data.csv")
+```
+
+---
+Alternatively, the `read_excel()` function allows you to import xlsx files. For this you need to specify the sheet name.
 
+```python
+df.read_excel("foo.xlsx", sheet_name="Sheet1", index_col=None, na_values=["NA"])
+```
+---
 
 Object Creation
 
+Up to this point, the data has been read into memory but not assigned to a variable. By convention, we will assign the resulting DataFrame to the variable `df`, where the name `df` serves as a common shorthand for “data frame.”
+
 ```python
 # Create DataFrame with row labels
 df = pd.DataFrame(data, index=["Test1", "Test2", "Test3", "Test4"])
 
 print(df)
-```
\ No newline at end of file
+```
+
+Creating the object allows us to re-call the data or specific data points if needed.
+
+---
+
+Calling Data
+
+Data can be called using the square brackets. We can get an entire column or row using it's label as follows:
+
+Single point:
+```python
+df[3,2]
+```
+
+Ranges:
+```python
+df[2:4,2:3]
+```
+
+Data series by label
+```python
+df["Test1"]
+```
+
+---
+
+
+Assignment 1:
+
+
+```python
+
+```
+
+---
+
+Assignment 2:
+Load a the data from the csv file and calculate the mean point of the array.
+
+```python
+
+```
diff --git a/tutorials/module_4/2_data_processing.md b/tutorials/module_4/2_data_processing.md
index f969b2d..d75c35a 100644
--- a/tutorials/module_4/2_data_processing.md
+++ b/tutorials/module_4/2_data_processing.md
@@ -3,15 +3,37 @@
 ^7b1480
 
 
-## Data Cleaning and Filtering
+## Signal Processing - Filtering
 
+### Low-Pass
 
+### High-Pass
 
+### Band-Pass
 
+## Data Filtering
 
 
 
----
-## Reading & Writing Files
+## Data Cleaning
 
 
+### Empty Cells
+
+Remove data point - `df.dropna()`
+Replace data point - `fillna(130, inplace = True)`
+We can use this to replace each data point with mean, median or mode - 
+```python
+x = df["Calories"].mean()
+df.fillna({"Calories": x}, inplace=True)
+```
+
+
+
+### 
+
+
+
+Example
+https://www.w3schools.com/python/pandas/pandas_cleaning.asp
+