Update page 'Day 1 homework - Xuanyi Wang (Sloetjes)'

Day 1 homework - Xuanyi Wang (Sloetjes).md

@@ -59,7 +59,7 @@ print(df)
 
 **`num1`** and **`num2` **are both 64-bit integers, which means that **` num1 * num2`** is performed using 64-bit integer arithmetic. However,**`df[‘Number1’] `** and **`df[‘Number2’] `** are Pandas Series objects and they are 64-bit floating point numbers so **`df[‘Number1’] * df[‘Number2’] `** is performed using floating-point arithmetic which involves approximations and rounding errors.
 
-#### 1. You can use sys.getsizeof to see how large a Python object is. Does the size of a Python floating-point number change as it gets larger? Why or why not?
+#### 5. You can use sys.getsizeof to see how large a Python object is. Does the size of a Python floating-point number change as it gets larger? Why or why not?
 
 **The size of a Python floating-point number does not change as it gets larger.** 
 
@@ -70,14 +70,14 @@ Regardless of the magnitude of the float, the number of bytes used to represent
 
 However, it's worth noting that as the magnitude of a floating-point number grows larger, the number of significant digits it can represent decreases. This is due to the limited precision of floating-point arithmetic, and can result in rounding errors when performing calculations with very large or very small floating-point numbers.
 
-#### 2. We already know that the precision of floating-point numbers decreases as their magnitude increases. Using Pandas, create a dataframe representing a large amount of US Dollars. What is the smallest number of dollars where adding one cent begins to yield wrong results?
+#### 6. We already know that the precision of floating-point numbers decreases as their magnitude increases. Using Pandas, create a dataframe representing a large amount of US Dollars. What is the smallest number of dollars where adding one cent begins to yield wrong results?
 
 Assuming double-precision floating-point numbers (64 bits), the smallest number of dollars that can be represented is $0.01 divided by 2 to the power of 52 (the number of bits used to represent the fraction part of a double-precision number). This equals approximately **$0.0000000000002384**.
 
 Therefore, any calculation involving adding 0.01 to a dollar amount smaller than **$0.0000000000002384** may yield wrong results due to the limited precision of floating-point arithmetic.
 
 
-#### 3. Name three practical differences between Python strings and bytes. Using sys.getsizeof, see how the sizes may be different. Do emoji change this answer?
+#### 7. Name three practical differences between Python strings and bytes. Using sys.getsizeof, see how the sizes may be different. Do emoji change this answer?
 
 ** Encoding**: Python strings are used to represent text data, while bytes are used to represent binary data. Strings are typically encoded using a character encoding such as UTF-8 or ASCII, which maps each character to a sequence of bytes, while bytes represent raw binary data that may not necessarily correspond to any particular character encoding.
 
@@ -113,7 +113,7 @@ Size of s2: 104
 ```
 
 
-#### 4. Given this sample CSV file (tomato_sales.csv):
+#### 8. Given this sample CSV file (tomato_sales.csv):
 ```
 Store,Region,Tomato_Sales
 Store1,East,300
@@ -141,7 +141,7 @@ describe a hypothetical situation where modifying the CSV file to create a type
 A hypothetical situation where modifying the CSV file would create a type confusion error is that if the initial information that has been written into the CSV is modified through **string slicing**, for example, “Store4South350” sliced into “Store3, South, 350”. Though “350” looks like a numeric input, it remains a string. And this kind of input would cause type error in the read\_tomato\_sales(). 
 
 
-#### 1. Prevent that nuclear war by modifying read_tomato_sales to perform better data validation.
+#### 9. Prevent that nuclear war by modifying read_tomato_sales to perform better data validation.
 
 ```
 def read_tomato_sales(file_name):
@@ -152,7 +152,7 @@ def read_tomato_sales(file_name):
 read_tomato_sales("tomato_sales_error.csv")
 ```
 
-#### 2. Given today's conversation about character encodings, describe why you would want to use bytes instead of strings when processing data containing multiple human languages at the same time. Write a small Python function to demonstrate this.
+#### 10. Given today's conversation about character encodings, describe why you would want to use bytes instead of strings when processing data containing multiple human languages at the same time. Write a small Python function to demonstrate this.
 
 When processing data containing multiple human languages at the same time, it's often better to use bytes instead of strings. The reason for this is that **bytes represent the raw data in its original form, while strings represent a specific encoding of the data.** When working with multiple languages, it's possible that the data may contain characters that are not supported by the encoding used by the string. **This can result in data loss or incorrect character conversion.**