diff --git a/Day 1 homework - Xuanyi Wang (Sloetjes).md b/Day 1 homework - Xuanyi Wang (Sloetjes).md
index 87868fb..28122c2 100644
--- a/Day 1 homework - Xuanyi Wang (Sloetjes).md	
+++ b/Day 1 homework - Xuanyi Wang (Sloetjes).md	
@@ -1,13 +1,13 @@
 # Xuanyi Wang\_Day-1\_Homework\_03/30/23
 
 
-### 1. What is the name of the international standard for floating-point representation?
+#### 1. What is the name of the international standard for floating-point representation?
 
 **IEEE 754**
 
 
 
-### 2. How many bits are the floating point numbers defined by that standard allowed to be? 
+#### 2. How many bits are the floating point numbers defined by that standard allowed to be? 
 
 
 ####Single-precision format (32-bits)
@@ -23,7 +23,7 @@ Double-precision floating-point numbers are represented using 64 bits, with 1 bi
 * However, these formats are less commonly used than the single-precision and double-precision formats.
 
 
-### 3. Does your computer support all those formats? Can you think of an easy way to check with only one line of Python?
+#### 3. Does your computer support all those formats? Can you think of an easy way to check with only one line of Python?
 
 
 
@@ -33,7 +33,7 @@ import platform
 platform.architecture()[0]
 ```
 
-### 4. Why doesn't this code give the same answers to the two multiplications?
+#### 4. Why doesn't this code give the same answers to the two multiplications?
 ```
 mport pandas as pd
 import numpy as np
@@ -60,7 +60,7 @@ 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?
+#### 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?
 
 **The size of a Python floating-point number does not change as it gets larger.** 
 
@@ -71,14 +71,14 @@ 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?
+#### 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?
 
 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?
+#### 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?
 
 ** 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.
 
@@ -116,7 +116,7 @@ Size of s2: 104
 ```
 
 
-### 4. Given this sample CSV file (tomato_sales.csv):
+#### 4. Given this sample CSV file (tomato_sales.csv):
 ```
 Store,Region,Tomato_Sales
 Store1,East,300