from collections import deque

def shortest_path(graph, s, t):
    n = len(graph)
    visited = [False] * n
    distance = [0] * n
    queue = deque([s])
    visited[s] = True

    while queue:
        current = queue.popleft()
        for neighbor in range(n):
            if graph[current][neighbor] != -1 and not visited[neighbor]:
                queue.append(neighbor)
                visited[neighbor] = True
                distance[neighbor] = distance[current] + graph[current][neighbor]
                if neighbor == t:
                    return distance[t]

    return -1  # 如果无法到达 t，则返回 -1

Please write a Python function `shortest_path(graph, s, t) -> int` to solve this problem:
Given an adjacency matrix of an undirected graph, compute the shortest path from source s to target t in the undirected graph. In the adjacency matrix, if there is no direct edge between vertices i and j, then graph[i][j]=-1.
>>> shortest_path([[0, 1, 2, -1], [1, 0, 3, -1], [2, 3, 0, 1], [-1, -1, 1, 0]], 0, 3)
3
In this problem, the shortest path in the graph can be found by the Breadth First Search algorithm. The main steps are as follows:
1. **Initialize Array**: An array is used to record whether each node has been visited and the shortest distance from the source node to each node.
2. **Search Nodes**: Search starts from the root node, and a node is dequeued from the left side of the queue for each iteration. All neighbors of the node are traversed. If there is an edge between the neighbor node and the current node and the neighbor node has not been visited, add the neighbor node to the right side of queue and mark it as visited.
3. **Return Shortest Path**: Update the shortest distance to be the shortest distance of the node plus the weight of the edge from the node to the neighbor node. If the neighbor node is the target node, return the shortest path.
4. **Return Result**: If no shortest path is found after the loop ends, return -1, indicating that the target node cannot be reached.


from collections import deque

def solve(graph):
    rows, cols = len(graph), len(graph[0])
    directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]  # 可能的移动方向：右、下、左、上
    visited = [[False for _ in range(cols)] for _ in range(rows)]

    # 找到起点 'a'
    start = None
    for i in range(rows):
        for j in range(cols):
            if graph[i][j] == 'a':
                start = (i, j)
                break
        if start:
            break

    # 广度优先搜索
    queue = deque([start])
    while queue:
        x, y = queue.popleft()
        if graph[x][y] == 'b':  # 找到终点
            return True
        for dx, dy in directions:
            nx, ny = x + dx, y + dy
            if 0 <= nx < rows and 0 <= ny < cols and not visited[nx][ny] and graph[nx][ny] != '#':
                visited[nx][ny] = True
                queue.append((nx, ny))

    return False  # 没有找到路径

Please write a Python function `solve(graph) -> bool` to solve this problem:
The graph is a maze made up of characters, where '#' is a wall that can't be crossed, '.' is a path through which it can travel, 'a' is the starting point of the target car 1, and 'b' is the end point of the target car 1. Please calculate and return whether the car can travel from 'a' to 'b'.
>>> solve(['####','#a##','####','##b#'])
False
>>> solve(['####','#a.#','##.#','##b#'])
True

Question: Write a piece of python code to achieve the following function.
Find the maximum perfect square number that is less than the nth Fibonacci number.
The function's name is max_square_number, and the input is an integer n.

Answer:
```python
def max_square_number(n):
    if n <= 2:
        return 0  # For the first two numbers in the Fibonacci sequence, no complete square numbers can be used.

    fibs = [0, 1]  # Store the first two numbers of the Fibonacci sequence
    squares = []  # Store the list of perfect square numbers

    max_square = 0
    i = 2

    while i <= n:
        fibs.append(fibs[i-1] + fibs[i-2])  # Calculate the next Fibonacci number
        i += 1

    fib = fibs[-1]
    perfect_squares = [i**2 for i in range(1, int(math.sqrt(fib)) + 1)]
    max_square = max(filter(lambda x: x < fib, perfect_squares))
    return max_square
```

----------------


def n_queens(n):
    def is_safe(row, col):
        for i in range(row):
            if board[i] == col or \
               i - board[i] == row - col or \
               i + board[i] == row + col:
                return False
        return True

    def solve(row):
        nonlocal count
        if row == n:
            count += 1
            return

        for col in range(n):
            if is_safe(row, col):
                board[row] = col
                solve(row + 1)
                board[row] = -1

    count = 0
    board = [-1] * n
    solve(0)
    return count

Please write a Python function `n_queens(n)` to solve this problem:
For a given n, calculate and return how many ways to place n queens from international chess on an nxn chessboard, ensuring that the queens cannot attack each other.
>>> n_queens(4)
2
"In this problem, you can use recursive backtracking to solve the problem. The main steps are as follows:

1. **Define a function to check whether it is safe to place the queen on the given row and column**: Use specific conditions to judge whether it is safe to place the queen at the given position. If the current position is safe, it returns `True`; otherwise, it returns `False`.

2. **Create a recursive function to solve the N queens problem**: Check whether `N` queens have been placed on the current row. If so, increase the value of the global variable. Try to place a queen in each column of the current row. If it is safe to place it in the current column, try to place the next queen recursively.
After returning the recursion, set the queen's position in the current row to -1.

3. **Define a variable to record the number of valid placement methods found**: Initially set it to 0 to track the number of solutions found.

4. **Define a list**: Used to represent the position of the queen on each row.

5. **Result**: Call the function to start solving the N queens problem. Return the defined variable, which represents the number of solutions found."

#<INSERT>

def check():
    assert n_queens(1) == 1
    assert n_queens(4) == 2
    assert n_queens(8) == 92
    assert n_queens(9) == 352
check()


def reverse(llist):
    prev = None
    current = llist.head
    while current:
        next_node = current.next
        current.next = prev
        prev = current
        current = next_node
    llist.head = prev
    return llist.display()

In Python, we have a linked list node class defined as follows.
class Node:
    def __init__(self, data=None):
        self.data = data
        self.next = None
Using this class, we have implemented a singly linked list class, defined as follows.
class LinkedList:

    def append(self, data):
        if not self.head:
            self.head = Node(data)
        else:
            cur = self.head
            while cur.next:
                cur = cur.next
            cur.next = Node(data)

    def __init__(self, nums):
        self.head = None
        for num in nums:
            self.append(num)

    def display(self):
        elements = []
        cur_node = self.head
        while cur_node:
            elements.append(cur_node.data)
            cur_node = cur_node.next
        return elements

Please write a Python function `reverse(llist)` to solve this problem:
For the given singly linked list llist, reverse this list and return the numbers displayed from the head of the reversed list.
>>> reverse(LinkedList([1,2,3,4]))
[4, 3, 2, 1]

class Node:
    def __init__(self, data=None):
        self.data = data
        self.next = None

class LinkedList:
    def __init__(self, nums):
        self.head = None
        for num in nums:
            self.append(num)

    def append(self, data):
        if not self.head:
            self.head = Node(data)
        else:
            cur = self.head
            while cur.next:
                cur = cur.next
            cur.next = Node(data)

    def display(self):
        elements = []
        cur_node = self.head
        while cur_node:
            elements.append(cur_node.data)
            cur_node = cur_node.next
        return elements

#<INSERT>

def check(reverse):
    assert reverse(LinkedList([1,2,3,4]))==[4,3,2,1]
    assert reverse(LinkedList([4]))==[4]
    assert reverse(LinkedList([4, 4]))==[4,4]

check(reverse)


def find_kth_largest(nums, k):
    # 转换k为索引
    k = len(nums) - k

    def quick_select(l, r):
        pivot, p = nums[r], l
        for i in range(l, r):
            if nums[i] <= pivot:
                nums[p], nums[i] = nums[i], nums[p]
                p += 1
        nums[p], nums[r] = nums[r], nums[p]

        if p > k:
            return quick_select(l, p - 1)
        elif p < k:
            return quick_select(p + 1, r)
        else:
            return nums[p]

    return quick_select(0, len(nums) - 1)

Please implement a function using Python to find the kth largest element in an array. The function is defined as def find_kth_largest(nums, k), where nums is a list of integers and k is an integer. The return of the function is the kth largest element in nums. You are not allowed to use built-in functions or sort functions.
In this problem, the kth largest element in an unsorted array can be found using the Quickselect algorithm, with the main steps as follows:

1. **Select a "pivot"**: Randomly select an element from the array as the pivot.
2. **Partition**: Split the array into two parts, one containing all elements less than the pivot, and one containing all elements greater than the pivot. Ensure that all elements less than the pivot are to its left, and all elements greater than the pivot are to its right.
3. **Recursion or Iteration**:
   - If the position (index) of the pivot is equal to k-1 (array index starts from 0), then the pivot is the kth largest element. Return it.
   - If the position of the pivot is less than k-1, then the kth largest element is on the right side of the pivot. Recursively or iteratively perform Quickselect on the subarray on the right side.
   - If the position of the pivot is greater than k-1, then the kth largest element is on the left side of the pivot. Recursively or iteratively perform Quickselect on the subarray on the left side.
4. **Handle Edge Cases**: If the array is empty or contains only one element, directly return that element (if k is 1) or `None` (if k is more than 1).

#<INSERT>

nums = [3, 2, 1, 5, 6, 4]
k = 2
assert find_kth_largest(nums, k) == 5, "Find kth largest element failed."


def multiply(num1: str, num2: str) -> str:
    if num1 == "0" or num2 == "0":
        return "0"

    # 初始化结果数组，最大长度为两数长度之和
    result = [0] * (len(num1) + len(num2))

    # 逐位相乘
    for i in range(len(num1) - 1, -1, -1):
        for j in range(len(num2) - 1, -1, -1):
            mul = (ord(num1[i]) - ord('0')) * (ord(num2[j]) - ord('0'))
            p1, p2 = i + j, i + j + 1
            sum_ = mul + result[p2]

            # 更新结果数组
            result[p1] += sum_ // 10
            result[p2] = sum_ % 10

    # 将结果数组转换为字符串
    result_str = ''.join(map(str, result))

    # 移除结果前面的零
    return result_str.lstrip('0')

Please write a Python function `multiply(num1: str, num2: str) -> str` to solve this problem:
Given two strings num1 and num2 that only contain numbers, which respectively represent two high-precision numbers, compute and return their product. Please do not use any library functions or directly convert the input into integers to simplify this process.
>>> multiply('123', '456')
'56088'
In this problem, the method of simulating manual multiplication can be used to solve it. The main steps are as follows:
1. **Check Input**: Check if the input is 0, if so, return 0 directly.
2. **Array Initialization**: Initialize an array of a length equal to the sum of the lengths of the two input strings, and initialize all its elements to 0.
3. **Simulate the process of manual multiplication**: Traverse the two input strings from back to front, use the `ord()` function to convert the characters in the string into corresponding ASCII values, subtract the ASCII value of '0' to get the corresponding number, multiply each digit, and then update the values in the array.
4. **Return Result**: Convert the array into a string, and remove zeros in front of the result.

#<INSERT>

# Override to disable `int` function
def int(s):
    raise TypeError('int function is forbidden')

def check():
    assert multiply('123', '456') == '56088'
    assert multiply('123', '0') == '0'
    assert multiply('9999', '8888') == '88871112'
check()


def islands(map):
    if not map:
        return 0

    def dfs(i, j):
        if i < 0 or i >= len(map) or j < 0 or j >= len(map[0]) or map[i][j] == '0':
            return
        map[i][j] = '0'  # 将访问过的陆地标记为 '0'
        dfs(i + 1, j)    # 访问下方单元格
        dfs(i - 1, j)    # 访问上方单元格
        dfs(i, j + 1)    # 访问右侧单元格
        dfs(i, j - 1)    # 访问左侧单元格

    count = 0
    for i in range(len(map)):
        for j in range(len(map[0])):
            if map[i][j] == '1':
                dfs(i, j)
                count += 1

    return count

Please write a Python function `islands(map): int` to solve this problem:
You are given a two-dimensional grid map composed of '1' (land) and '0' (water). Calculate and return the number of islands in the grid. Islands are always surrounded by water and each island can only be formed by lands that are adjacent horizontally and/or vertically. Moreover, you can assume that all four edges of the grid are surrounded by water.
>>> islands([
  ['1','1','0','0','0'],
  ['1','1','0','0','0'],
  ['0','0','1','0','0'],
  ['0','0','0','1','1']
])
3
To achieve this function, we can write the code following these steps:

1. **Create Variables**: Create a variable to record the number of islands.

2. **Traverse the Position**: Traverse each position in the two-dimensional grid, if the current position is land, then perform depth-first search recursively visiting the adjacent positions. If the adjacent position is land, continue to perform depth-first search.

3. **Depth-First Search**: Each time performing a depth-first search, an island will be visited, and the variable value will be incremented by 1.

4. **Get Result**: Finally, the result which represents the number of islands is returned.


def level_order_traversal(root):
    if not root:
        return []  # 如果根节点为空，返回空列表

    queue = [root]  # 使用一个队列，初始时包含根节点
    result = []  # 用来存储层序遍历的结果

    while queue:
        current_node = queue.pop(0)  # 从队列中移除当前节点
        result.append(current_node.val)  # 将当前节点的值添加到结果列表中

        # 如果左子节点存在，加入队列
        if current_node.left:
            queue.append(current_node.left)

        # 如果右子节点存在，加入队列
        if current_node.right:
            queue.append(current_node.right)

    return result

Please implement the level order traversal algorithm for binary trees using Python. The function is defined as def level_order_traversal(root), where root is an instance of the Node class. The Node class has three class variables: left, right, and val, where val is an integer variable, and left and right are pointers to the left and right nodes, respectively. The return value of the function is a list (List), with the elements being the val values contained in each Node corresponding to the level order traversal.
The Node class has already been implemented and does not need to be implemented again.
To implement the level order traversal algorithm for binary trees, we can write the code according to the following steps:
1. **Check the node**: First, check whether the root node is empty. If it is empty, it indicates that the binary tree is an empty tree, and a direct return of an empty list as a result.
2. **Create a queue**: Create a queue and add the root node to it initially.
3. **Create a list**: Create an empty list for storing the result of the level order traversal.
4. **Loop through**: Enter the loop, and when the queue is not empty, pop the first node in the queue and assign it to a variable. Add the val value of the variable to the result list. If there is a left child node, add it to the queue. If there is a right child node, add it to the queue.
5. **Get the result**: After the loop ends, return the result list as the result of the level order traversal.

#<INSERT>

class Node:
    def __init__(self, val=0):
        self.val = val
        self.left = None
        self.right = None

def insert_level_order(arr, root, i, n):
    if i < n:
        temp = Node(arr[i])
        root = temp

        root.left = insert_level_order(arr, root.left, 2 * i + 1, n)
        root.right = insert_level_order(arr, root.right, 2 * i + 2, n)
    return root

arr = [i for i in range(1, 8)] # Creating a tree with values 1 to 7
n = len(arr)
root = None
root = insert_level_order(arr, root, 0, n)

# Test case
expected_output = [1, 2, 3, 4, 5, 6, 7]
assert level_order_traversal(root) == expected_output, "Level Order traversal does not match expected output."


def diameter(edges):
    # 构建图
    graph = {}
    for u, v, w in edges:
        if u not in graph:
            graph[u] = []
        if v not in graph:
            graph[v] = []
        graph[u].append((v, w))
        graph[v].append((u, w))

    # DFS 来找到最远的顶点和距离
    def dfs(node, parent):
        max_distance, farthest_node = 0, node
        for neighbor, weight in graph[node]:
            if neighbor != parent:
                distance, next_node = dfs(neighbor, node)
                distance += weight
                if distance > max_distance:
                    max_distance, farthest_node = distance, next_node
        return max_distance, farthest_node

    # 从任意点（比如 1）开始第一次 DFS
    _, farthest = dfs(1, None)
    # 从最远点开始第二次 DFS
    diameter, _ = dfs(farthest, None)

    return diameter

Please write a Python function `diameter(edges) -> int` to solve this problem:
Given an edge table of a tree `edges`, where each element [i,j,w] represents an undirected edge from vertex i to vertex j with a weight of w. Please calculate and return the diameter of the tree.
>>> diameter([[1,3,1],[1,2,3],[2,4,2],[2,5,6]])
10


def del_dup(head):
    if head is None:
        return head

    current = head

    while current.next is not None:
        if current.next.val == current.val:
            current.next = current.next.next
        else:
            current = current.next

    return head

Given a singly linked list, each node is a ListNode object with 2 properties:
- val: the value of this node
- next: the reference to the next node

Given a sorted list, remove all duplicate elements, so that each element appears only once.
Implement this function in python, define the function del_dup, input the head of the sorted list, and return the head of the list after deleting duplicate elements.
In this problem, to delete duplicate elements in a sorted list, you can use an iterative approach to traverse the list, with the following main steps:

1. **Initialization**: Set the `current` pointer to point to the head node of the list. Set the `next_node` pointer to point to the next node of `head`.
2. **Traverse the list**: Use a `while` loop to iterate over the list when `next_node` is not `None`.
3. **Compare and delete duplicate elements**: In the loop, compare the values of `current` and `next_node`. If the values are the same, update `current.next` to `next_node.next` to skip the duplicate node, then update `next_node` to `current.next` and prepare for the next comparison. If the values are different, directly move `current` to `next_node` and then update `next_node`.
4. **Delete duplicate elements**: If duplicate elements are found, skip the current duplicate node. The duplicate nodes in the list will be deleted.
5. **Return the result**: When the next node is `None`, the loop ends, and the pointer points to the last node of the list at this time. Return `head` as the head node of the new list.

#<INSERT>

import random
class ListNode():
    def __init__(self, val):
        self.val = val
        self.next = None

head = ListNode(0)
cur = head
for i in range(10):
    n = random.randint(2, 5)
    for j in range(n):
        node = ListNode(i)
        cur.next = node
        cur = cur.next

head2 = del_dup(head)
cur = head2
arr = []
while cur != None:
    arr.append(cur.val)
    cur = cur.next

assert('.'.join(map(str, arr)) == '0.1.2.3.4.5.6.7.8.9')


def max_coins(nums):
    size = len(nums)
    arr = [0 for _ in range(size + 2)]
    arr[0] = arr[size + 1] = 1
    for i in range(1, size + 1):
        arr[i] = nums[i - 1]

    dp = [[0 for _ in range(size + 2)] for _ in range(size + 2)]

    for l in range(3, size + 3):
        for i in range(0, size + 2):
            j = i + l - 1
            if j >= size + 2:
                break
            for k in range(i + 1, j):
                dp[i][j] = max(dp[i][j], dp[i][k] + dp[k][j] + arr[i] * arr[j] * arr[k])

    return dp[0][size + 1]

There are n balloons, numbered from 0 to n - 1, each balloon has a number on it, and these numbers are stored in an array, nums. Now you start popping the balloons.

You're required to pop all the balloons. Every time you pop a balloon i, where i denotes the ith balloon, you can get nums[i - 1] * nums[i] * nums[i + 1] coins, where i - 1 and i + 1 represent the numbers of the two balloons adjacent to balloon i. Please note that after popping the balloon i, balloon i - 1 and balloon i + 1 become adjacent. If i - 1 or i + 1 goes beyond the boundary of the array, then treat it like a balloon with number 1.
The task is to determine the maximum number of coins that can be obtained.
Write python code, define the function max_coins, which takes in the nums array as an input and returns the maximum number of coins.


def moving_average(nums, window_size):
    if window_size > len(nums) or window_size <= 0:
        return []

    averages = []
    sum_window = sum(nums[:window_size])
    averages.append(sum_window / window_size)

    for i in range(1, len(nums) - window_size + 1):
        sum_window = sum_window - nums[i - 1] + nums[i + window_size - 1]
        averages.append(sum_window / window_size)

    return averages

Please write a Python function `moving_average(nums, window_size) -> List` to solve this problem:
For a given sequence of numbers nums, calculate its moving average with a window length of window_size and return this sequence. Please note, if window_size is smaller than the length of nums itself, in this case, return an empty sequence [].
>>> moving_average([0, 1, 2, -1], 2)
[0.5, 1.5, 0.5]
To implement this function, we can write the code following the steps below:

1. **Check**: Check if the window size is larger than the length of the data sequence or less than or equal to 0, if so, return an empty list directly.

2. **Create and Calculate**: Create an empty list to store the moving averages. Use a function that can calculate the total of the numbers within the window size to calculate the initial average, and add it to the list.

3. **Add to List**: Through looping, calculate the total data in the current window. The total divided by the window size gives the current moving average added to the list.

4. **Return List**: The function returns a list containing all the moving averages.

#<INSERT>

def check(moving_average):
    assert moving_average([0, 1, 2, -1], 2) == [0.5, 1.5, 0.5]
    assert moving_average([0, 1, 2, -1], 10) == []
    assert moving_average([0, 1, 2, -1], 1) == [0, 1, 2, -1]
    assert moving_average([10, 20, 30, 40, 50], 3) == [20, 30, 40]

check(moving_average)


from decimal import Decimal, getcontext

def solve(R, n):
    """
    Calculate R to the power of n with high precision using the decimal module.

    :param R: A real number (0.0 < R < 99.999)
    :param n: An integer (0 < n <= 25)
    :return: R raised to the power of n
    """
    # Set the precision high enough to handle the calculation
    getcontext().prec = 50

    # Use Decimal for high precision calculation
    result = Decimal(R) ** n

    return result

Performing high-precision calculations on large and high-precision numbers is a very common problem. For example, calculating national debt falls into this category.

Please write a Python function `solve(R, n):Float` to solve this problem:
Calculate the nth power of a real number R (0.0 < R < 99.999) precisely. Here n is an integer and 0 < n <= 25.

>>> solve(95.123,12)
548815620517731830194541.899025343415715973535967221869852721
The problem can be solved with the following approach:
Use `getcontext()` from the decimal module to set the precision, use `Decimal` class from the decimal module to calculate the nth power of `R`.
In this way, we have resolved the precision issue in scenarios that require high precision like calculation of national debts.


def dfs(graph, start):
    visited = []  # 存储已访问的节点
    stack = [start]  # 使用栈来存储待访问的节点，初始时栈中只有起始节点

    while stack:
        node = stack.pop()  # 取出栈顶元素
        if node not in visited:
            visited.append(node)  # 访问该节点
            # 将邻接的未访问节点加入栈中，注意按字典序倒序添加，以保证字典序小的节点先被访问
            stack.extend(sorted(graph[node], reverse=True))

    return visited

Please implement the Depth-First Search (DFS) algorithm for a graph in Python. The function is defined as def dfs(graph, start), where 'graph' is a dictionary representing the adjacency list of the graph, and 'start' is a key representing the starting node. The return value of the function is a List, the elements of which are the nodes traversed in the order of DFS.
For the convenience of checking the results, we assume that the values of nodes in the graph are uppercase English letters from 'A' to 'Z'. When a node has multiple adjacent nodes, we prefer to visit the one with the smaller lexicographic order.
In this problem, the Depth-First Search (DFS) Algorithm is used to traverse or search a tree or graph. The stack is used to simulate the recursive process of DFS in solving this problem. The main steps are as follows:
1. **Initialization**: Create a stack called `stack` and add the starting node `start` as the initial element to the stack.
2. **Depth-First Search**: When the stack is not empty, execute a loop, pop the top element of the stack as the current node. If the current node has not been visited, add it to the list. Get all adjacent nodes of the current node and sort them to ensure they are visited lexicographically. Add the sorted adjacent nodes to the stack in reverse (or forward) order to prioritize visiting nodes with smaller lexicographic order.
3. **Return result**: When the stack is empty, it indicates that all reachable nodes have been visited.


Please write a Python function `shortest_path(graph, s, t) -> int` to solve this problem:
Given an adjacency matrix graph of an undirected graph, calculate the shortest path in the undirected graph from the starting point s to the endpoint t. In the adjacency matrix, if there is no direct connection between two vertices i and j, then graph[i][j]=-1.
>>> shortest_path([[0, 1, 2, -1], [1, 0, 3, -1], [2, 3, 0, 1], [-1, -1, 1, 0]], 0, 3)
3


def add(num1: str, num2: str) -> str:
    # 确保 num1 是较长的那个数字字符串
    if len(num1) < len(num2):
        num1, num2 = num2, num1

    num1, num2 = num1[::-1], num2[::-1]  # 反转字符串以方便从最低位开始计算
    carry = 0  # 进位
    result = []

    # 对两个数字的每一位进行相加
    for i in range(len(num1)):
        digit1 = int(num1[i])  # 第一个数的当前位
        digit2 = int(num2[i]) if i < len(num2) else 0  # 第二个数的当前位（如果存在）
        total = digit1 + digit2 + carry
        carry = total // 10  # 计算进位
        result.append(str(total % 10))  # 将当前位的结果添加到结果列表中

    # 如果最后还有进位，需要添加到结果的最高位
    if carry:
        result.append(str(carry))

    # 将结果反转回正常的顺序并合并为一个字符串
    return ''.join(result[::-1])

Please write a Python function `add(num1: str, num2: str) -> str` to solve this problem:
Given two strings num1 and num2 consisting only of digits, representing two high-precision numbers, please calculate and return their sum. Do not use any library functions, and do not directly convert the input into integers to simplify this process.
>>> add ('123', '456')
'579'


def merge_sort(arr):
    # 基线条件：如果数组长度小于或等于1，不需要排序
    if len(arr) <= 1:
        return arr

    # 将数组分为两部分，并递归地进行排序
    mid = len(arr) // 2
    left = merge_sort(arr[:mid])
    right = merge_sort(arr[mid:])

    # 合并两个已排序的数组
    return merge(left, right)

def merge(left, right):
    # 合并两个已排序的数组
    result = []
    i = j = 0

    # 遍历两个数组，将较小的元素依次添加到结果数组中
    while i < len(left) and j < len(right):
        if left[i] < right[j]:
            result.append(left[i])
            i += 1
        else:
            result.append(right[j])
            j += 1

    # 如果左侧数组还有剩余元素，将它们添加到结果数组
    result.extend(left[i:])

    # 如果右侧数组还有剩余元素，将它们添加到结果数组
    result.extend(right[j:])

    return result

Please implement the merge sort algorithm in Python. The function is defined as def merge_sort(arr), where arr is a list of integers. The return value of the function should be a sorted list, with the elements being the sorted integers.

#<INSERT>

test_arr = [12, 11, 13, 5, 6, 7]
assert merge_sort(test_arr) == sorted(test_arr), "Merge Sort did not sort the array correctly."


def merge(a, b):
    i, j = 0, 0
    result = []

    while i < len(a) and j < len(b):
        if a[i] < b[j]:
            result.append(a[i])
            i += 1
        else:
            result.append(b[j])
            j += 1

    # 添加剩余元素
    while i < len(a):
        result.append(a[i])
        i += 1

    while j < len(b):
        result.append(b[j])
        j += 1

    return result

Please write a Python function `merge(a: List[int], b: List[int]) -> List[int]` to solve the following problem:
Given two monotone non-decreasing sequences a and b, merge them into one monotone non-decreasing sequence and return it.
>>> merge([1,4,5], [2,3,7])
[1,2,3,4,5,7]
>>> merge([1], [0])
[0,1]
The function can be implemented via the following steps:
1. **Initialize the pointers and the result sequence**: Create two pointers, each pointing at the beginning of the sequence, and an empty result sequence to store the merged sequence.
2. **Compare and merge elements**: Enter a loop with the condition that both pointers have not exceeded the length of their respective sequences. During each iteration of the loop, compare the elements in each sequence.
3. **Process the remaining elements**: If there are remaining elements in one sequence, add them to the result sequence.
4. **Return the result sequence**: After the loop ends, return the result sequence, which contains the merged monotone non-decreasing sequence.


def partition(s):
    def is_palindrome(sub):
        return sub == sub[::-1]

    def backtrack(start, path):
        if start >= len(s):
            result.append(path[:])
            return
        for end in range(start + 1, len(s) + 1):
            if is_palindrome(s[start:end]):
                backtrack(end, path + [s[start:end]])

    result = []
    backtrack(0, [])
    return result

Please write a Python function `partition(s)` to solve this problem:
Given a string s, partition s such that every substring of the partition is a palindrome. Return all possible partitioning of s.
>>> partition('aab')
[['a', 'a', 'b'], ['aa', 'b']]
In this problem, all possible partitioning ways are explored through backtracking, ensuring each substring is a palindrome. The main steps are as follows:

1. **Define a palindrome check**: Create a helper function to verify whether the string is a palindrome.

2. **Recursive Partitioning**: Use a recursive function to explore all possible partitioning ways, ensuring each partitioned substring is a palindrome.

3. **Collect Results**: During the recursion, when a valid partitioning scheme is found, add it to the result list.

4. **Execution and Return**: Start the recursion from the beginning of the string and finally return all valid partitioning scheme list.


def solve(lis):
    lis.sort(key=lambda x:x[1])
    ans=1
    cnt=lis[0][1]
    bj=1
    while bj<len(lis):
        if lis[bj][0]>=cnt:
            ans+=1
            cnt=lis[bj][1]
        bj+=1
    return ans

There is a set of n activities E = {1, 2, 3, ..., n}, where each activity requires the use of the same resource, and only one activity can use this resource at the same time. Each activity i has a start time s_i and an end time f_i for using the resource. If activity i is selected, it occupies the resource in the time interval [s_i, f_i). If for activity i and activity j, if the intervals [s_i, f_i) and [s_j, f_j) do not overlap, then we say that the two activities are compatible. Select the largest set of mutually compatible activities.

In Python, define the function solve(arr) to solve this problem, where arr is an activity list of length n, each element is a tuple (s, f), representing the start time and end time respectively. Your function should return the size of the largest compatible set.
To implement this function, you can follow these steps:

1.**Initialization of a counter**: Initialize a variable to the end time of the first activity in the sorted list.

2.**Check all activities**: Inside the loop, check whether the start time of the current activity is greater than or equal to the end time of the first activity in the sorted list.

3.**Traverse**: You can use a loop until you have traversed the entire activity list.

4.**Return the result**: Finally, return the maximum number of non-overlapping activities that can be arranged.


def edit_distance(w1, w2):
    if w1 == '': return len(w2)
    if w2 == '': return len(w1)

    m, n = len(w1), len(w2)
    if m < n:
        w1, w2, m, n = w2, w1, n, m

    dp = [int(w1[0] != w2[0])] * n
    dp_new = [0] * n

    for j in range(1, n):
        dp[j] = dp[j - 1] + int(w2[j] != w1[0] or dp[j - 1] != j)

    for i in range(1, m):
        dp_new[0] = dp[0] + int(w2[0] != w1[i] or dp[0] != i)

        for j in range(1, n):
            dp_new[j] = min(dp[j - 1] + int(w2[j] != w1[i]), dp[j] + 1, dp_new[j - 1] + 1)

        dp, dp_new = dp_new, dp

    return dp[-1]

You are given two words A and B, please calculate the minimum number of operations used to convert A into B. One can perform the following three operations on a word: Insert a character, delete a character, or replace a character.
Implement this in python, with the function name 'edit_distance'. The function takes two strings as input and returns the minimum operation count.

#<INSERT>

assert(edit_distance('aaaa', 'aaab') == 1)
assert(edit_distance('horse', 'ros') == 3)


def longest_common_subsequence(text1, text2):
    m, n = len(text1), len(text2)

    # 创建一个二维DP数组，行数和列数分别为两个字符串的长度加1
    dp = [[0] * (n + 1) for _ in range(m + 1)]

    # 填充DP数组
    for i in range(1, m + 1):
        for j in range(1, n + 1):
            # 如果字符相等，则在两个字符串的上一个字符的LCS长度基础上加1
            if text1[i - 1] == text2[j - 1]:
                dp[i][j] = dp[i - 1][j - 1] + 1
            else:
                # 如果字符不等，则取两个字符串去掉一个字符后的LCS长度的较大值
                dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])

    # 返回DP数组最后一个元素，即两个字符串的最长公共子序列长度
    return dp[m][n]

Please implement dynamic programming to solve the longest common subsequence problem in Python. The function is defined as def longest_common_subsequence(text1, text2), where text1 and text2 are two strings. The return value of the function is an integer, representing the length of the longest common subsequence of the two strings.

#<INSERT>

text1 = "AGGTAB"
text2 = "GXTXAYB"
assert longest_common_subsequence(text1, text2) == 4, "Incorrect length of the longest common subsequence."


def sort(nums):
    def quicksort(arr, low, high):
        if low < high:
            pi = partition(arr, low, high)
            quicksort(arr, low, pi - 1)
            quicksort(arr, pi + 1, high)

    def partition(arr, low, high):
        pivot = arr[high]
        i = low - 1
        for j in range(low, high):
            if arr[j] <= pivot:
                i += 1
                arr[i], arr[j] = arr[j], arr[i]
        arr[i + 1], arr[high] = arr[high], arr[i + 1]
        return i + 1

    quicksort(nums, 0, len(nums) - 1)
    return nums

Please write a Python function `sort(nums)` to solve this problem:
Given an array nums, use the Quick Sort algorithm to sort it into a non-decreasing array and return it.
>>> sort([54, 24, 77, 89, 24, 3])
[3, 24, 24, 54, 77, 89]


def first_missing_positive_num(nums):
    n = len(nums)
    for i in range(n):
        while 1 <= nums[i] <= n and nums[nums[i] - 1] != nums[i]:
            nums[nums[i] - 1], nums[i] = nums[i], nums[nums[i] - 1]

    for i in range(n):
        if nums[i] != i + 1:
            return i + 1

    return n + 1

Please write a Python function `first_missing_positive_num(nums): int` to solve this problem: Given an array nums, compute and return the smallest positive integer that is not present in it. Please implement a solution with time complexity of O(n) and only constant additional space.
>>> first_missing_positive_num([1,2,0])
3
To implement this feature, we can code according to the following steps:

1. **Get**: Get the length of the list.

2. **Loop through**: Through a loop, traverse each element in the list, using a nested loop to check whether the current element meets the conditions. If it does, swap the positions of these two elements.

3. **Search**: Use another loop to go through each element in the list to find the missing positive number.

4. **Return**: At the end of the entire loop, if no missing positive integer is found, return the length of the list.

#<INSERT>

def check():
    assert first_missing_positive_num([1,2,0]) == 3
    assert first_missing_positive_num([3,4,-1,1]) == 2
    assert first_missing_positive_num([7,8,9,11,12]) == 1
    assert first_missing_positive_num([0,0,0,1,2,2,3,5]) == 4
check()


def jump(n):
    if n == 0:
        return 1

    f1, f2, f3 = 0, 1, 1
    for i in range(2, n + 1):
        f1, f2 = f2, f3
        f3 = f1 + f2
    return f3

A frog can jump up 1 step at a time, or it can jump up 2 steps. Calculate how many ways the frog can jump up a n-steps stair. Write Python code to answer this question. The function name is jump, the input is n, the return value is the number of ways to jump.
In this problem, each jump can choose to jump one grid or two grids. This problem can be solved by dynamic programming. The main steps are as follows:

1. **Analysis**: Determine whether the stair n is 0, if it is, then return 1 directly, because there is only one way to jump, that is, do not jump.
2. **Derive the recurrence formula**: Assume that there are f(n) ways to jump on the n-level stairs. When n is 0, there is only one way to jump, that is, don't jump. When n is 1, there is one way to jump, that is, jump one level. When n is 2, there are two ways to jump, that is, jump two levels at once or jump one level twice. When n is greater than 2, jumping up to the n-level stairs can be jumped up from the n-1-level stairs or jumped up two levels from the n-2-level stairs. So f(n) = f(n-1) + f(n-2).
3. **Initialization**: There is 1 way to jump 0 steps (do not jump), 1 way to jump 1 step (jump 1 level), and 2 ways to jump 2 steps (jump twice 1 level or jump 2 levels at once).
4. **Result**: After the loop ends, return f(n), which is the number of ways to jump on the n-level stairs.


def lowest_common_ancestor(root, p, q):
    if root is None:
        return None

    if root == p or root == q:
        return root

    left = lowest_common_ancestor(root.left, p, q)
    right = lowest_common_ancestor(root.right, p, q)

    if left is not None and right is not None:
        return root
    elif left is not None:
        return left
    elif right is not None:
        return right
    else:
        return None

There is a binary tree, each node of which is contained in a TreeNode object. The TreeNode object has 3 attributes:
- val: the value of this node
- left: the reference to the TreeNode of the root node of the left subtree
- right: the reference to the TreeNode of the root node of the right subtree

Given a binary tree, find the lowest common ancestor of two specified nodes in the tree.
Implement this in Python, with the function name lowest_common_ancestor. The function should take as input the TreeNode of the root, as well as two specified nodes, and return the TreeNode of the lowest common ancestor.

#<INSERT>

class TreeNode():
    def __init__(self, val):
        self.val = val
        self.left = None
        self.right = None

nodes = [TreeNode(i) for i in range(10)]
nodes[0].left = nodes[1]
nodes[0].right = nodes[2]
nodes[1].left = nodes[3]
nodes[1].right = nodes[4]
nodes[2].left = nodes[5]
nodes[3].left = nodes[6]
nodes[4].left = nodes[7]
nodes[4].right = nodes[8]
nodes[5].left = nodes[9]

assert(lowest_common_ancestor(nodes[0], nodes[6], nodes[8]).val == 1)
assert(lowest_common_ancestor(nodes[0], nodes[6], nodes[5]).val == 0)
assert(lowest_common_ancestor(nodes[0], nodes[4], nodes[5]).val == 0)
assert(lowest_common_ancestor(nodes[0], nodes[0], nodes[9]).val == 0)


def max_ice_cream(costs, coins):
    costs.sort()
    ans = 0
    for cost in costs:
        if coins >= cost:
            ans += 1
            coins -= cost
    return ans

Given an array costs indicating the price of different ice creams, where costs[i] represents the price of the ith ice cream. Also given an integer coins which represents the total amount of cash that Tony has.
Calculate and return the maximum number of ice creams that Tony can buy with coins, bearing in mind that he can only buy one of the same kind of ice cream.
Define a function in Python called max_ice_cream. The input are costs and coins, and it returns the maximum number of ice creams that Tony can buy.
In this problem, a greedy algorithm can be used to solve the problem, and the main steps are as follows:

1. **Sort the costs array**: Sort the costs array in ascending order by price, so that purchases can start with the lowest-priced ice cream.
2. **Initialize variables ans and coins**: ans is used to record the amount of ice cream purchases, and coins are used to record the remaining coin quantity.
3. **Traverse the sorted costs array**: For each ice cream price cost, if coins are greater than or equal to cost, then buy this ice cream, reduce the coin quantity, and increase the ans quantity at the same time.
4. **Return ans**: after the traversal ends, ans would be the maximum number of ice creams that Tony can buy.


def find_kth_largest(nums, k):
    # 转换k为索引
    k = len(nums) - k

    def quick_select(l, r):
        pivot, p = nums[r], l
        for i in range(l, r):
            if nums[i] <= pivot:
                nums[p], nums[i] = nums[i], nums[p]
                p += 1
        nums[p], nums[r] = nums[r], nums[p]

        if p > k:
            return quick_select(l, p - 1)
        elif p < k:
            return quick_select(p + 1, r)
        else:
            return nums[p]

    return quick_select(0, len(nums) - 1)

Please implement a function in Python to find the kth largest element in an array. The function is defined as def find_kth_largest(nums, k), where nums is a list of integers and k is an integer. The return value of the function is the kth largest element in nums. Built-in functions or functions like sort are not allowed to use.

#<INSERT>

#Test
nums = [3, 2, 1, 5, 6, 4]
k = 2
assert find_kth_largest(nums, k) == 5, "Find kth largest element failed."


Write a Python function `moving_average(nums, window_size) -> List` to solve this problem:
Given a sequence of numbers nums, calculate the moving average with a window length of window_size, and return this sequence. Note that if window_size may be smaller than the length of nums, in this case return an empty sequence [].
>>> moving_average([0, 1, 2, -1], 2)
[0.5, 1.5, 0.5]


def count(a, b):
    def is_prime(n):
        if n <= 1:
            return False
        for i in range(2, int(n**0.5) + 1):
            if n % i == 0:
                return False
        return True

    return sum(1 for i in range(a, b) if is_prime(i))

Please write a Python function `count(a:int, b:int) -> int` to solve this problem:
Given the interval [a,b), which includes a but not b, calculate and return how many prime numbers are within this interval.
>>> count(100, 200)
21
This problem can be solved by writing a function. The main idea is as follows:

1. **Analysis**: The function checks whether a number is a prime number first, and then traverses the given interval, counting the number of prime numbers in the interval.

2. **Traverse the given interval**: The function checks all the integers from 2 to the square root of `n` (including the square root) to see if any number can divide `n`. If not, then `n` is a prime number and the function returns `True`.

3. **Generator expression**: The function uses a generator expression to generate the count of all prime numbers in the interval.

4. **Return result**: Add up these counts to get the total number of prime numbers in the interval.

#<INSERT>

def check(count):
    assert count(100, 200)==21
    assert count(1, 2)==0
    assert count(1, 100)==25

check(count)


There is a string of length 15, composed of characters A and B. It is known that the substring "AA" appears 5 times, and the substrings "AB", "BA", and "BB" each appear 3 times. Write a Python function solve() to calculate how many strings satisfy this condition.
In this problem, the bit counting method can be used to solve it, and the main steps are as follows:

1. **Calculate Remaining Characters**: Firstly, subtract the number of characters contributed by the "AA" substring from the total length of 15 to get the number of remaining characters.
2. **Allocate Substrings**: Allocate "AB", "BA", and "BB" substrings.
3. **Check Remaining Characters**: If the remaining 'A' or 'B' is negative, it indicates that a string satisfying the condition cannot be constructed.
4. **Calculate Combination Number**: Use the formula to calculate the arrangement of the remaining characters.
5. **Return Result**: If all conditions are met, calculate the combination number and return the result.


Please implement the depth-first search algorithm for a graph in Python. The function is defined as def dfs(graph, start), where 'graph' is a dictionary representing the adjacency list of the graph and 'start' is a key representing the starting node. The return value of the function is a list (List) with elements being the nodes traversed in DFS order.
For ease of result checking, we assume that the values of nodes in the graph are uppercase English letters 'A' to 'Z'. When a node has multiple adjacent nodes, we prioritize visiting the node with smaller lexicographic order.


def preorder_traversal(root):
    preorder = []
    if root is None:
        return preorder

    s = [root]
    while len(s) > 0:
        node = s.pop()
        preorder.append(node.val)
        if node.right is not None:
            s.append(node.right)
        if node.left is not None:
            s.append(node.left)

    return preorder

I have a binary tree, where each node is saved in a TreeNode object. The TreeNode object has 3 attributes:
- val: the value of the node
- left: reference to the root node TreeNode of the left subtree
- right: reference to the root node TreeNode of the right subtree

This demonstrates the implementation of a binary tree's preorder traversal in python. The function name is preorder_traversal, the input is root TreeNode. The nodes' 'val' encountered in sequence during the traversal is stored in an array and returned.

#<INSERT>

class TreeNode():
    def __init__(self, val):
        self.val = val
        self.left = None
        self.right = None

nodes = [TreeNode(i) for i in range(10)]
nodes[0].left = nodes[1]
nodes[0].right = nodes[2]
nodes[1].left = nodes[3]
nodes[1].right = nodes[4]
nodes[2].left = nodes[5]
nodes[3].left = nodes[6]
nodes[4].left = nodes[7]
nodes[4].right = nodes[8]
nodes[5].left = nodes[9]

assert(','.join([str(x) for x in preorder_traversal(nodes[0])]) == '0,1,3,6,4,7,8,2,5,9')


def inorder_traversal(root):
    s, inorder = [], []
    node = root
    while len(s) > 0 or node is not None:
        if node is not None:
            s.append(node)
            node = node.left
        else:
            node = s.pop()
            inorder.append(node.val)
            node = node.right
    return inorder

I have a binary tree, and each of its nodes is represented as a TreeNode object. TreeNode object has 3 attributes:
- val: the value of the node
- left: a reference to the root TreeNode of the left subtree
- right: a reference to the root TreeNode of the right subtree

I implemented the inorder traversal of a binary tree using Python. The function name is inorder_traversal. It takes a TreeNode root as input and stores the values (val) of the traversed nodes in an array in the order of traversal, and then returns the array.

#<INSERT>

class TreeNode():
    def __init__(self, val):
        self.val = val
        self.left = None
        self.right = None

nodes = [TreeNode(i) for i in range(10)]
nodes[0].left = nodes[1]
nodes[0].right = nodes[2]
nodes[1].left = nodes[3]
nodes[1].right = nodes[4]
nodes[2].left = nodes[5]
nodes[3].left = nodes[6]
nodes[4].left = nodes[7]
nodes[4].right = nodes[8]
nodes[5].left = nodes[9]

assert(','.join([str(x) for x in inorder_traversal(nodes[0])]) == '6,3,1,7,4,8,0,9,5,2')


def quick_sort(arr):
    # 基线条件：如果数组为空或只有一个元素，无需排序，直接返回
    if len(arr) <= 1:
        return arr

    # 选择一个基准值（pivot），这里选择列表的第一个元素
    pivot = arr[0]

    # 对数组进行分区，小于pivot的放在左边，大于pivot的放在右边
    left = [x for x in arr[1:] if x < pivot]
    right = [x for x in arr[1:] if x >= pivot]

    # 递归地对左右两个子列表进行快速排序，然后合并结果
    return quick_sort(left) + [pivot] + quick_sort(right)

Please implement the quick sort algorithm using Python. The function is defined as def quick_sort(arr), where arr is a list of integers. The return value of the function is a sorted list, with the elements being the sorted integers.

#<INSERT>

test_arr = [3, 6, 8, 10, 1, 2, 1]
assert quick_sort(test_arr) == sorted(test_arr), "Quick Sort did not sort the array correctly."


import pandas as pd

def proc_data():
    file_path = 'U.S. Census Data.csv'
    # Load the CSV file into a DataFrame
    data = pd.read_csv(file_path)

    # Handle missing values in the "Fact" column by filling them with an empty string
    data['Fact'] = data['Fact'].fillna('')

    # Extract the relevant population data for Colorado based on the given dates again
    population_2010 = int(data[data['Fact'].str.contains('April 1, 2010')]['Colorado'].values[0].replace(',', ''))
    population_2016 = int(data[data['Fact'].str.contains('July 1, 2016')]['Colorado'].values[0].replace(',', ''))

    # Calculate the percentage change
    percentage_change_colorado = ((population_2016 - population_2010) / population_2010) * 100

    return percentage_change_colorado

The dataset is named 'U.S. Census Data.csv'. The first two lines of the dataset are as follows:

| Fact                                               |   Fact Note | Alabama   | Alaska   | Arizona   | Arkansas   | California   | Colorado   | Connecticut   | Delaware   | Florida    | Georgia    | Hawaii    | Idaho     | Illinois   | Indiana   | Iowa      | Kansas    | Kentucky   | Louisiana   | Maine     | Maryland   | Massachusetts   | Michigan   | Minnesota   | Mississippi   | Missouri   | Montana   | Nebraska   | Nevada    | New Hampshire   | New Jersey   |   New Mexico |   New York |   North Carolina |   North Dakota |     Ohio |   Oklahoma |   Oregon |   Pennsylvania |   Rhode Island |   South Carolina |   South Dakota |   Tennessee | Texas      | Utah      | Vermont   | Virginia   | Washington   | West Virginia   | Wisconsin   | Wyoming   |
|:---------------------------------------------------|------------:|:----------|:---------|:----------|:-----------|:-------------|:-----------|:--------------|:-----------|:-----------|:-----------|:----------|:----------|:-----------|:----------|:----------|:----------|:-----------|:------------|:----------|:-----------|:----------------|:-----------|:------------|:--------------|:-----------|:----------|:-----------|:----------|:----------------|:-------------|-------------:|-----------:|-----------------:|---------------:|---------:|-----------:|---------:|---------------:|---------------:|-----------------:|---------------:|------------:|:-----------|:----------|:----------|:-----------|:-------------|:----------------|:------------|:----------|
| Population estimates, July 1, 2016,  (V2016)       |         nan | 4,863,300 | 741,894  | 6,931,071 | 2,988,248  | 39,250,017   | 5,540,545  | 3,576,452     | 952,065    | 20,612,439 | 10,310,371 | 1,428,557 | 1,683,140 | 12,801,539 | 6,633,053 | 3,134,693 | 2,907,289 | 4,436,974  | 4,681,666   | 1,331,479 | 6,016,447  | 6,811,779       | 9,928,300  | 5,519,952   | 2,988,726     | 6,093,000  | 1,042,520 | 1,907,116  | 2,940,058 | 1,334,795       | 8,944,469    |      2081015 |   19745289 |         10146788 |         757952 | 11614373 |    3923561 |  4093465 |       12784227 |        1056426 |          4961119 |         865454 |     6651194 | 27,862,596 | 3,051,217 | 624,594   | 8,411,808  | 7,288,000    | 1,831,102       | 5,778,708   | 585,501   |
| Population estimates base, April 1, 2010,  (V2016) |         nan | 4,780,131 | 710,249  | 6,392,301 | 2,916,025  | 37,254,522   | 5,029,324  | 3,574,114     | 897,936    | 18,804,592 | 9,688,680  | 1,360,301 | 1,567,650 | 12,831,574 | 6,484,136 | 3,046,869 | 2,853,129 | 4,339,344  | 4,533,479   | 1,328,364 | 5,773,786  | 6,547,813       | 9,884,129  | 5,303,924   | 2,968,103     | 5,988,928  | 989,414   | 1,826,334  | 2,700,691 | 1,316,461       | 8,791,953    |      2059198 |   19378110 |          9535688 |         672591 | 11536727 |

Question: There is a dataset named 'person.csv', the data schema is as follows:

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| id          | int     |
| email       | varchar |
+-------------+---------+

Where id is the primary key column (column with unique values) of this table. Each row of the table contains an email. The emails will not contain uppercase letters.

Write a solution using the python pandas library to remove all duplicate emails, keeping only one unique email with the smallest id.

The answer needs to meet the following requirements:
1. The data file is 'person.csv', please strictly refer to the data header and answer the question with python code.
2. Use 'pandas.read_csv' to read the data, it is assumed that the data is in the same path as the code.
3. Write the analysis code into a function called 'proc_data()', this function does not need any input parameters, and it should return the processed dataframe.

Answer:
```python
import pandas as pd

def proc_data():
    # read CSV file and create DataFrame
    df = pd.read_csv('person.csv')

    # Convert the email column to lower case
    df['email'] = df['email'].str.lower()

    # Sort by the email column, then sort by the id column
    df.sort_values(['email', 'id'], inplace=True)

    # Remove duplicate emails, keep only the unique email with the smallest id
    df.drop_duplicates(subset='email', keep='first', inplace=True)

    return df
```

----------------

Question: There is a dataset named 'animals.csv', the data schema is as follows:

+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| name        | object |
| species     | object |
| age         | int    |
| weight      | int    |
+-------------+--------+

Write a solution to list the names of animals that strictly weigh more than 100 kilograms. Return the animal names in descending order of weight.

The answer needs to meet the following requirements:
1. The data file is 'animals.csv', please strictly refer to the data header and answer the question with python code.
2. Use 'pandas.read_csv' to read the data, it is assumed that the data is in the same path as the code.
3. Write the analysis code into a function called 'proc_data()', this function does not need any input parameters, and it should return a python list.

Answer:
```python
import pandas as pd

def proc_data():
    # read CSV file and create DataFrame
    df = pd.read_csv('animals.csv')

    # filter animals that strictly weigh more than 100 kilograms
    filtered_df = df[df['weight'] > 100]

    # sort by weight in descending order
    sorted_df = filtered_df.sort_values('weight', ascending=False)

    # get the list of animal names
    animal_names = sorted_df['name'].tolist()

    return animal_names
```

----------------

#<INSERT>

answer = proc_data()
assert round(answer, 3) == 10.165


import pandas as pd

def proc_data():
    # Load the provided CSV file
    file_path = '0020300292.csv'
    df = pd.read_csv(file_path)

    # Filter the dataframe for rebound events
    # Rebounds are typically represented by the presence of the word "REBOUND" in the description columns
    home_rebounds_df = df[df['HOMEDESCRIPTION'].str.contains('REBOUND', na=False)]
    visitor_rebounds_df = df[df['VISITORDESCRIPTION'].str.contains('REBOUND', na=False)]

    # Calculate the number of rebounds for home and visitor teams
    home_rebounds_count = len(home_rebounds_df)
    visitor_rebounds_count = len(visitor_rebounds_df)

    if home_rebounds_count > visitor_rebounds_count:
        return 1
    elif home_rebounds_count < visitor_rebounds_count:
        return -1
    return 0

The dataset is named '0020300292.csv', and the first 5 rows of the data are as follows:
|    |   GAME_ID |   EVENTNUM |   EVENTMSGTYPE |   EVENTMSGACTIONTYPE |   PERIOD | WCTIMESTRING   | PCTIMESTRING   | HOMEDESCRIPTION                           |   NEUTRALDESCRIPTION | VISITORDESCRIPTION                               | SCORE   |   SCOREMARGIN |
|---:|----------:|-----------:|---------------:|---------------------:|---------:|:---------------|:---------------|:------------------------------------------|---------------------:|:-------------------------------------------------|:--------|--------------:|
|  0 |  20300292 |          0 |             12 |                    0 |        1 | 7:44 PM        | 12:00          | nan                                       |                  nan | nan                                              | nan     |           nan |
|  1 |  20300292 |          1 |             10 |                    0 |        1 | 7:44 PM        | 12:00          | Jump Ball Grant vs. Williams: Tip to Wade |                  nan | nan                                              | nan     |           nan |
|  2 |  20300292 |          2 |              2 |                   42 |        1 | 7:45 PM        | 11:42          | MISS Odom  Driving Layup                  |                  nan | nan                                              | nan     |           nan |
|  3 |  20300292 |          3 |              4 |                    0 |        1 | 7:45 PM        | 11:40          | nan                                       |                  nan | Marion REBOUND (Off:0 Def:1)                     | nan     |           nan |
|  4 |  20300292 |          4 |              1 |                    1 |        1 | 7:45 PM        | 11:24          | nan                                       |                  nan | Gugliotta 21' Jump Shot (2 PTS) (Hardaway 1 AST) | 2 - 0   |            -2 |

Question:
Comparing the performance of the home team and the visiting team, which team has the advantage in terms of rebounds?

The answer needs to meet the following requirements:
1. The data file is '0020300292.csv', please strictly refer to the data's header, and use python code to answer the question.
2. Use 'pandas.read_csv' to read the data, it is assumed that the data is in the same path as the code.
3. Write the analysis code as a function called 'proc_data()', this function does not require any input parameters. If the home team has the advantage, return 1; if the visiting team has the advantage, return -1; return 0 otherwise.

#<INSERT>

answer = proc_data()
assert answer == 1


import pandas as pd

def proc_data():
    file_path = '20150117-POR.csv'
    # Load the CSV file into a DataFrame
    data = pd.read_csv(file_path)
    # Find the player with the highest defensive rebound percentage (DRB_perc)
    max_drb_player = data[data['DRB_perc'] == data['DRB_perc'].max()]

    player_name = max_drb_player['Name'].values[0]
    drb_perc = max_drb_player['DRB_perc'].values[0]

    return (player_name, drb_perc)

There is a dataset named '20150117-POR.csv', the first two lines of the data are as follows:

| Name            |     Date | Team   |   Home | W   |   W_PTS | L   |   L_PTS |    MP |   FG |   FGA |   FG_perc |   3P |   3PA |   3P_perc |   FT |   FTA |   FT_perc |   ORB |   DRB |   TRB |   AST |   STL |   BLK |   TOV |   PF |   PTS |   +- |   TS_perc |   eFG_perc |   3PAr |   FTr |   ORB_perc |   DRB_perc |   TRB_perc |   AST_perc |   STL_perc |   BLK_perc |   TOV_perc |   USG_perc |   ORtg |   DRtg |
|:----------------|---------:|:-------|-------:|:----|--------:|:----|--------:|------:|-----:|------:|----------:|-----:|------:|----------:|-----:|------:|----------:|------:|------:|------:|------:|------:|------:|------:|-----:|------:|-----:|----------:|-----------:|-------:|------:|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|-------:|-------:|
| Damian Lillard  | 20150117 | POR    |      0 | MEM |     102 | POR |      98 | 40.98 |    9 |    26 |     0.346 |    3 |    10 |       0.3 |    2 |     3 |     0.667 |     3 |     3 |     6 |     6 |     2 |     0 |     2 |    2 |    23 |   -4 |     0.421 |      0.404 |  0.385 | 0.115 |        6.5 |        9.8 |        7.8 |       27.6 |        2.6 |          0 |        6.8 |       32.2 |     98 |    115 |
| Wesley Matthews | 20150117 | POR    |      0 | MEM |     102 | POR |      98 | 39.08 |    9 |    18 |     0.5   |    7 |    14 |       0.5 |    0 |     0 |   nan     |     0 |     4 |     4 |     1 |     0 |     0 |     0 |    2 |    25 |   -1 |     0.694 |      0.694 |  0.778 | 0     |        0   |       13.6 |        5.5 |        4.9 |        0   |          0 |        0   |       20.7 |    141 |    120 |

Question:
Please analyze which player is the strongest in obtaining defensive rebounds in the game, and output the player's name and the percentage of defensive rebounds (DRB_perc).

The answer needs to meet the following requirements:
1. The data file is '20150117-POR.csv', please strictly refer to the data header, and use python code to answer the questions.
2. Use 'pandas.read_csv' to read the data, the data is by default in the same path as the code.
3. The analysis code is written as a function called 'proc_data()', and this function does not require any input parameters, use the player's name and the defensive rebound percentage in the question as the return value. The return type is tuple.

#<INSERT>

answer = proc_data()
assert answer[0] == "Meyers Leonard"
assert round(answer[1], 3) == 29.4


import pandas as pd
import numpy as np

def proc_data():
    file_path = '20160210-UTA.csv'
    dataset = pd.read_csv(file_path)

    # 计算USG_perc列的平均值
    usg_perc_avg = np.mean(dataset['USG_perc'])
    return usg_perc_avg

There is a dataset named '20160210-UTA.csv', the first two lines of the data are as follows:
| Name           |     Date | Team   |   Home | W   |   W_PTS | L   |   L_PTS |    MP |   FG |   FGA |   FG_perc |   3P |   3PA |   3P_perc |   FT |   FTA |   FT_perc |   ORB |   DRB |   TRB |   AST |   STL |   BLK |   TOV |   PF |   PTS |   +- |   TS_perc |   eFG_perc |   3PAr |   FTr |   ORB_perc |   DRB_perc |   TRB_perc |   AST_perc |   STL_perc |   BLK_perc |   TOV_perc |   USG_perc |   ORtg |   DRtg |
|:---------------|---------:|:-------|-------:|:----|--------:|:----|--------:|------:|-----:|------:|----------:|-----:|------:|----------:|-----:|------:|----------:|------:|------:|------:|------:|------:|------:|------:|-----:|------:|-----:|----------:|-----------:|-------:|------:|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|-------:|-------:|
| Gordon Hayward | 20160210 | UTA    |      0 | NOP |     100 | UTA |      96 | 39.63 |    7 |    18 |     0.389 |    2 |     9 |     0.222 |    5 |     7 |     0.714 |     0 |     4 |     4 |     4 |     2 |     0 |     3 |    1 |    21 |    2 |     0.498 |      0.444 |    0.5 | 0.389 |        0   |       10.8 |        5.8 |       19.8 |        2.7 |          0 |       12.5 |       29.1 |     96 |    110 |
| Derrick Favors | 20160210 | UTA    |      0 | NOP |     100 | UTA |      96 | 39.48 |   11 |    15 |     0.733 |    0 |     0 |   nan     |    7 |    10 |     0.7   |     1 |     5 |     6 |     2 |     1 |     0 |     5 |    3 |    29 |   -3 |     0.747 |      0.733 |    0   | 0.667 |        3.1 |       13.5 |        8.7 |       12.4 |        1.3 |          0 |       20.5 |       29.6 |    117 |    112 |

Question:
Please calculate the average of all players' USG_perc based on the "USG_perc" column data of this dataset using the NumPy library of Python.

The answer needs to meet the following requirements:
1. The data file is '20160210-UTA.csv', please strictly refer to the data header and use python code to answer questions.
2. Use 'pandas.read_csv' to read data, the default is the data is in the same path as the code.
3. Write the analysis code into a function called 'proc_data()', and this function does not need any input parameters, the return must be a NumPy data type.

#<INSERT>

answer = proc_data()
assert isinstance(answer, np.number)
assert round(answer, 3) == 17.517


import pandas as pd

def proc_data():
    data = pd.read_csv('20151031-SAC.csv')

    # 计算助攻百分比和盖帽百分比之间的相关系数
    correlation = data['AST_perc'].corr(data['BLK_perc'])

    if correlation >= -0.5 and correlation <= 0.5:
        return 0
    return 1

Here is a dataset named '20151031-SAC.csv', the first two lines of the data are as follows:
| Name        |     Date | Team   |   Home | W   |   W_PTS | L   |   L_PTS |    MP |   FG |   FGA |   FG_perc |   3P |   3PA |   3P_perc |   FT |   FTA |   FT_perc |   ORB |   DRB |   TRB |   AST |   STL |   BLK |   TOV |   PF |   PTS |   +- |   TS_perc |   eFG_perc |   3PAr |   FTr |   ORB_perc |   DRB_perc |   TRB_perc |   AST_perc |   STL_perc |   BLK_perc |   TOV_perc |   USG_perc |   ORtg |   DRtg |
|:------------|---------:|:-------|-------:|:----|--------:|:----|--------:|------:|-----:|------:|----------:|-----:|------:|----------:|-----:|------:|----------:|------:|------:|------:|------:|------:|------:|------:|-----:|------:|-----:|----------:|-----------:|-------:|------:|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|-------:|-------:|
| Rajon Rondo | 20151031 | SAC    |      0 | LAC |     114 | SAC |     109 | 33    |    9 |    17 |     0.529 |    1 |     4 |      0.25 |    2 |     6 |     0.333 |     1 |     4 |     5 |     8 |     2 |     0 |     1 |    5 |    21 |   -3 |     0.535 |      0.559 |  0.235 | 0.353 |        2.7 |       16.2 |        8.1 |       38.9 |        2.8 |          0 |        4.8 |       24   |    123 |    108 |
| Rudy Gay    | 20151031 | SAC    |      0 | LAC |     114 | SAC |     109 | 31.05 |    8 |    23 |     0.348 |    2 |     5 |      0.4  |    2 |     2 |     1     |     5 |     4 |     9 |     2 |     2 |     0 |     3 |    4 |    20 |   -4 |     0.419 |      0.391 |  0.217 | 0.087 |       14.3 |       17.2 |       15.5 |       10.1 |        3   |          0 |       11.2 |       33.2 |     94 |    108 |

Question:
Analyze the relationship between the assist percentage and the block percentage of each player.

The answer needs to meet the following requirements:
1. The data file is '20151031-SAC.csv', please strictly refer to the header of the data and use Python code to answer the question.
2. Use 'pandas.read_csv' to read the data, the data is by default in the same path as the code.
3. The analysis code is written as a function called 'proc_data()', which does not require any input parameters, and if the absolute value of the correlation is in the range [-0.5, 0.5], return 0, otherwise return 1.

#<INSERT>

answer = proc_data()
assert answer == 0


import pandas as pd

def proc_data():
    file_path = '主页列表数据类型统计_0.csv'
    # Load the CSV file into a DataFrame
    data_df = pd.read_csv(file_path)

    # Filter the data based on the given criteria
    filtered_data = data_df[(data_df['统计日期'] == '2021-01-01') & (data_df['城市'] == '北京') & (data_df['实验组id'] == 'vid1')]

    # Extract the device id
    device_id = filtered_data['设备id'].values[0] if not filtered_data.empty else None
    return device_id

There is a dataset named '主页列表数据类型统计_0.csv', the first two lines of the data are as follows:
|   Unnamed: 0 | 设备id   | 实验组id   | 统计日期   | 城市   |   新老用户 |   城市等级 | 操作系统   | 一级标签   | 头腰部作者标签   | 内容体裁   |   首页列表页内容曝光量 |   首页列表页内容消费量 |   停留时长 |   阅读进度 |   阅读pv |   完播量 |   本地内容曝光量 |   优质内容曝光量 |   7日内发布量 |   7日内消费量 |   30日内发布量 |   30日内消费量 |
|-------------:|:---------|:-----------|:-----------|:-------|-----------:|-----------:|:-----------|:-----------|:-----------------|:-----------|-----------------------:|-----------------------:|-----------:|-----------:|---------:|---------:|-----------------:|-----------------:|--------------:|--------------:|---------------:|---------------:|
|            0 | dev1     | vid1       | 2021-01-01 | 北京   |          0 |          1 | iOS        | 标签1      | 标签A            | 类型1      |                    500 |                    200 |        300 |        0.1 |      100 |       50 |              300 |              200 |           100 |            50 |            400 |            200 |
|            1 | dev2     | vid2       | 2021-01-02 | 上海   |          1 |          2 | Android    | 标签2      | 标签B            | 类型2      |                   1000 |                    400 |        600 |        0.5 |      200 |      100 |              600 |              400 |           200 |           100 |            800 |            400 |

Question:
What is 设备id for 实验组id vid1 in 北京 on 2021-01-01?

The answer must meet the following requirements:
1. The data file is '主页列表数据类型统计_0.csv', please strictly refer to the data header with Python code to answer the question.
2. Use 'pandas.read_csv' to read the data. The default is that the data is in the same path as the code.
3. Write the analysis code as a function called 'proc_data()'. This function does not require any input parameters and returns the 设备id mentioned in the question.

#<INSERT>

answer = proc_data()
assert answer == 'dev1'


import pandas as pd

def proc_data():
    # Load the data from the provided CSV file
    data = pd.read_csv('inf_CF_Kontrol.csv')

    # Calculate the Pearson correlation coefficient between S9 and S15
    correlation_coefficient = data['S9'].corr(data['S15'])

    return correlation_coefficient

There is a dataset named 'inf_CF_Kontrol.csv', the first two rows of the data are as follows:
|        S1 |        S2 |        S3 |        S4 |        S7 |        S8 |        S9 |       S10 |       S11 |       S12 |       S13 |       S14 |       S15 |       S16 |       S17 |       S18 |       S19 |       S20 |       S21 |       S22 |       S24 |       S25 |       S26 |       S27 |       S28 |       S29 |       S30 |       S32 | Group   |
|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|:--------|
| 0.0078806 | 0.0052639 | 0.0068071 | 0.0044529 | 0.0018847 | 0.0075123 | 0.0082612 | 0.0041946 | 0.0041948 | 0.0027566 | 0.0019007 | 0.0014818 | 0.0046195 | 0.0030285 | 0.0047127 | 0.0082379 | 0.0017516 | 0.0137974 | 0.0018191 | 0.0029625 | 0.0011339 | 0.0024707 | 0.0130168 | 0.0014845 | 0.0138157 | 0.0082406 | 0.0032925 | 0.0017471 | CF      |
| 0.0081436 | 0.0052761 | 0.0072547 | 0.0042673 | 0.0018654 | 0.0062887 | 0.0083103 | 0.0040309 | 0.0040678 | 0.0028948 | 0.0018558 | 0.0013105 | 0.0047497 | 0.0029697 | 0.0044442 | 0.0080638 | 0.0019563 | 0.0128456 | 0.0018337 | 0.0028977 | 0.0009367 | 0.0025862 | 0.011638  | 0.0018369 | 0.0129395 | 0.0080735 | 0.0032608 | 0.0018968 | CF      |

Question:
Please calculate the Pearson correlation coefficient between S9 and S15, and analyze their linear relationship.

The answer needs to meet the following requirements:
1. The data file is 'inf_CF_Kontrol.csv', please strictly refer to the data header, use python code to answer the question.
2. Use 'pandas.read_csv' to read data, by default, the data is in the same path as the code.
3. Write the analysis code into a function called 'proc_data()', and this function does not need any input parameters, use the correlation coefficient in the question as the return value.

#<INSERT>

answer = proc_data()
assert round(answer, 3) == 0.926


import pandas as pd

def proc_data():
    # Load the CSV file
    file_path = '413.csv'

    # Let's read the CSV file again with the correct delimiter
    data = pd.read_csv(file_path, delimiter=';\t', engine='python')

    # Find the timestamp where the CPU usage is maximum
    max_cpu_usage_timestamp = data.loc[data['CPU usage [%]'].idxmax(), 'Timestamp [ms]']

    return max_cpu_usage_timestamp

There is a dataset named '413.csv'. The first two lines of data are as follows:
|    |   Timestamp [ms] |   CPU cores |   CPU capacity provisioned [MHZ] |   CPU usage [MHZ] |   CPU usage [%] |   Memory capacity provisioned [KB] |   Memory usage [KB] |   Disk read throughput [KB/s] |   Disk write throughput [KB/s] |   Network received throughput [KB/s] |   Network transmitted throughput [KB/s] |
|---:|-----------------:|------------:|---------------------------------:|------------------:|----------------:|-----------------------------------:|--------------------:|------------------------------:|-------------------------------:|-------------------------------------:|----------------------------------------:|
|  0 |      1.37531e+09 |           2 |                             4800 |            1089.6 |         22.7    |                        1.67772e+07 |         1.45402e+06 |                             0 |                         60.4   |                              45.7333 |                                 8.06667 |
|  1 |      1.37531e+09 |           2 |                             4800 |            1155.2 |         24.0667 |                        1.67772e+07 |         1.43165e+06 |                             0 |                        102.467 |                              12.8    |                                 6.4     |

Question:
Please find out which timestamp [ms] has the highest CPU usage [%]?

The answer must satisfy the following requirements:
1. The data file is '413.csv'. Please strictly refer to the header of the data and use Python code to answer the question.
2. Use 'pandas.read_csv' to read the data. By default, the data is in the same path as the code. Be aware that the delimiter is ';\t'.
3. The analysis code should be written as a function called 'proc_data()' and this function does not require any input parameters. The function should return the timestamp from the question.

#<INSERT>

answer = proc_data()
assert answer == 1376060126


import pandas as pd

def proc_data():
    file_path = '0020100882.csv'
    # Load the CSV file into a DataFrame
    data = pd.read_csv(file_path)

    # Check if the column "EVENTMSGTYPE" exists in the dataframe
    if "EVENTMSGTYPE" in data.columns:
        # Get the value counts of different event types
        event_counts = data['EVENTMSGTYPE'].value_counts(normalize=True)

        # Extract the most common event type and its proportion
        most_common_event_type = event_counts.idxmax()
        proportion = event_counts.max()
    else:
        most_common_event_type, proportion = None, None

    return (most_common_event_type, proportion)

There is a dataset named '0020100882.csv', the first two rows of the data are as follows:
|   GAME_ID |   EVENTNUM |   EVENTMSGTYPE |   EVENTMSGACTIONTYPE |   PERIOD | WCTIMESTRING   | PCTIMESTRING   | HOMEDESCRIPTION                              |   NEUTRALDESCRIPTION |   VISITORDESCRIPTION |   SCORE |   SCOREMARGIN |
|----------:|-----------:|---------------:|---------------------:|---------:|:---------------|:---------------|:---------------------------------------------|---------------------:|---------------------:|--------:|--------------:|
|  20100882 |          0 |             12 |                    0 |        1 | 13:10 PM       | 12:00          | nan                                          |                  nan |                  nan |     nan |           nan |
|  20100882 |          1 |             10 |                    0 |        1 | 13:10 PM       | 12:00          | Jump Ball Collins vs. Thomas: Tip to Jackson |                  nan |                  nan |     nan |           nan |

Question:
Among all event types (EVENTMSGTYPE), which event is the most common, and what is its proportion of all events?

The answer needs to meet the following requirements:
1. The data file is '0020100882.csv', please strictly follow the data header to answer with python code.
2. Use 'pandas.read_csv' to read data, the default is that the data is in the same path as the code.
3. The analysis code is written as a function called 'proc_data()', which doesn't require any input parameters, using event in the question and the proportion of all events as return values. The return value type is tuple.

#<INSERT>

answer = proc_data()
assert answer[0] == 4
assert round(answer[1], 3) == 0.224


import pandas as pd

def proc_data():
    file_path = 'data.adult.csv'
    # Load the CSV file into a DataFrame
    data = pd.read_csv(file_path)
    # Filter the dataset to include only individuals who are 'Never-married' and work in the 'Private' sector
    filtered_data = data[(data['marital-status'] == 'Never-married') & (data['workclass'] == 'Private')]

    # Calculate the proportion of such individuals in the entire dataset
    proportion_never_married_private_sector = len(filtered_data) / len(data)
    return proportion_never_married_private_sector

There is a dataset named 'data.adult.csv', the first two lines of the data are as follows:

|   age | workclass   |   fnlwgt | education    |   education-num | marital-status   | occupation      | relationship   | race   | sex   |   capital-gain |   capital-loss |   hours-per-week | >50K,<=50K   |
|------:|:------------|---------:|:-------------|----------------:|:-----------------|:----------------|:---------------|:-------|:------|---------------:|---------------:|-----------------:|:-------------|
|    34 | Local-gov   |   284843 | HS-grad      |               9 | Never-married    | Farming-fishing | Not-in-family  | Black  | Male  |            594 |              0 |               60 | <=50K        |
|    40 | Private     |   190290 | Some-college |              10 | Divorced         | Sales           | Not-in-family  | White  | Male  |              0 |              0 |               40 | <=50K        |

Question:
What is the proportion of individuals who are 'Never-married' and work in the 'Private' sector in this dataset?

The answer needs to meet the following requirements:
1. The data file is 'data.adult.csv', please strictly refer to the header of the data and use python code to answer the question.
2. Use 'pandas.read_csv' to read the data, by default, the data is in the same path as the code.
3. Write the analysis code into a function called 'proc_data()'. This function does not need any input parameters, use the proportion in the question as the return value.

#<INSERT>

answer = proc_data()
assert round(answer, 3) == 0.248


import pandas as pd

def proc_data():
    # Load the CSV file into a DataFrame
    df = pd.read_csv('0021501127.csv')

    # Count non-empty entries in the columns 'HOMEDESCRIPTION' and 'VISITORDESCRIPTION'
    home_count = df['HOMEDESCRIPTION'].notna().sum()
    visitor_count = df['VISITORDESCRIPTION'].notna().sum()

    if home_count > visitor_count:
        return 1
    elif home_count < visitor_count:
        return -1
    else:
        return 0

There is a dataset named '0021501127.csv', the first two rows of the data are as follows:
|   GAME_ID |   EVENTNUM |   EVENTMSGTYPE |   EVENTMSGACTIONTYPE |   PERIOD | WCTIMESTRING   | PCTIMESTRING   | HOMEDESCRIPTION                                |   NEUTRALDESCRIPTION |   VISITORDESCRIPTION |   SCORE |   SCOREMARGIN |
|----------:|-----------:|---------------:|---------------------:|---------:|:---------------|:---------------|:-----------------------------------------------|---------------------:|---------------------:|--------:|--------------:|
|  21501127 |          0 |             12 |                    0 |        1 | 10:12 PM       | 12:00          | nan                                            |                  nan |                  nan |     nan |           nan |
|  21501127 |          1 |             10 |                    0 |        1 | 10:12 PM       | 12:00          | Jump Ball Plumlee vs. Johnson: Tip to Harkless |                  nan |                  nan |     nan |           nan |

Question:
Comparing HOMEDESCRIPTION and VISITORDESCRIPTION, which appears more often in the dataset?

The answer needs to meet the following requirements:
1. The data file is '0021501127.csv'. Please strictly refer to the header of the data and use Python code to answer questions.
2. Use 'pandas.read_csv' to read data, which by default is in the same path as the code.
3. The analysis code is written as a function called 'proc_data()', and this function does not require any input parameters. If HOMEDESCRIPTION appears more often, return 1, if VISITORDESCRIPTION appears more often, return -1, otherwise return 0.

#<INSERT>

answer = proc_data()
assert answer == -1


import pandas as pd

def proc_data():
    # Load the CSV file
    data = pd.read_csv('0021500854.csv')

    # Count the occurrences of "Jump Shot" in the 'HOMEDESCRIPTION' and 'VISITORDESCRIPTION' columns
    home_jump_shots = data['HOMEDESCRIPTION'].str.contains('Jump Shot').sum()
    visitor_jump_shots = data['VISITORDESCRIPTION'].str.contains('Jump Shot').sum()

    return [home_jump_shots, visitor_jump_shots]

There is a dataset named '0021500854.csv', the first two rows of data are as follows:
|   GAME_ID |   EVENTNUM |   EVENTMSGTYPE |   EVENTMSGACTIONTYPE |   PERIOD | WCTIMESTRING   | PCTIMESTRING   | HOMEDESCRIPTION                              |   NEUTRALDESCRIPTION |   VISITORDESCRIPTION |   SCORE |   SCOREMARGIN |
|----------:|-----------:|---------------:|---------------------:|---------:|:---------------|:---------------|:---------------------------------------------|---------------------:|---------------------:|--------:|--------------:|
|  21500854 |          0 |             12 |                    0 |        1 | 10:41 PM       | 12:00          | nan                                          |                  nan |                  nan |     nan |           nan |
|  21500854 |          1 |             10 |                    0 |        1 | 10:41 PM       | 12:00          | Jump Ball Cousins vs. Duncan: Tip to Leonard |                  nan |                  nan |     nan |           nan |

Problem:
Calculate the number of times "Jump Shot" is mentioned in HOMEDESCRIPTION and VISITORDESCRIPTION respectively.

The answer needs to meet the following requirements:
1. The data file is '0021500854.csv', please strictly refer to the header of the data, and answer the question with Python code.
2. Use 'pandas.read_csv' to read the data, assuming that the data is in the same path as the code; note that the data contains "nan".
3. Write the analysis code into a function called 'proc_data()', which does not require any input parameters, and generate a list composed of the number of Jump Shots for the home team and the visiting team as the return value.

#<INSERT>

answer = proc_data()
assert len(answer) == 2
assert answer[0] == 57
assert answer[1] == 63


import pandas as pd

def proc_data():
    file_path = '0020501208.csv'
    # Load the CSV file into a DataFrame
    df = pd.read_csv(file_path)
    # Calculate the percentage of missing values in the 'VISITORDESCRIPTION' column
    total_rows = df.shape[0]
    missing_values = df['VISITORDESCRIPTION'].isna().sum()
    missing_percentage = (missing_values / total_rows) * 100

    return missing_percentage

There is a dataset named '0020501208.csv', the first two rows of the data are as follows:
|   GAME_ID |   EVENTNUM |   EVENTMSGTYPE |   EVENTMSGACTIONTYPE |   PERIOD | WCTIMESTRING   | PCTIMESTRING   | HOMEDESCRIPTION                            |   NEUTRALDESCRIPTION |   VISITORDESCRIPTION |   SCORE |   SCOREMARGIN |
|----------:|-----------:|---------------:|---------------------:|---------:|:---------------|:---------------|:-------------------------------------------|---------------------:|---------------------:|--------:|--------------:|
|  20501208 |          0 |             12 |                    0 |        1 | 8:44 PM        | 12:00          | nan                                        |                  nan |                  nan |     nan |           nan |
|  20501208 |          1 |             10 |                    0 |        1 | 8:44 PM        | 12:00          | Jump Ball Mohammed vs. Okur: Tip to Duncan |                  nan |                  nan |     nan |           nan |

Question:
Calculate the percentage of blank values in the VISITORDESCRIPTION field.

The answer needs to meet the following requirements:
1. The data file is '0020501208.csv', please strictly refer to the data header and use python code to answer the question.
2. Use 'pandas.read_csv' to read data, the default data is in the same path as the code.
3. Write the analysis code as a function named 'proc_data()', and this function does not require any input parameters, use the percentage in the question as the return value.

#<INSERT>

answer = proc_data()
assert round(answer, 3) == 52.513 or round(answer, 3) == 0.525

#comment：百分比常用是*100，但如果不乘应该也可以算对。


import pandas as pd

def proc_data():
    file_path = '应用查询单词显示设备实验统计_0.csv'
    # Load the CSV file into a DataFrame
    data = pd.read_csv(file_path)
    # 根据实验ID和启动方式筛选数据
    filtered_data = data[(data['实验ID'] == 1001) & (data['启动方式'] == 'auto')]

    # 进一步筛选定位在北京和上海的设备
    filtered_data = filtered_data[filtered_data['定位城市名称'].isin(['北京', '上海'])]

    # 计算展现页面浏览量与点击页面浏览量之和
    total_views = filtered_data['展现页面浏览量'].sum() + filtered_data['点击页面浏览量'].sum()
    return total_views

There is a dataset named '应用查询单词显示设备实验统计_0.csv'. The first two rows of the data are as follows:
| Unnamed: 0 | Device ID | Experiment ID | New User Indicator | City Name | Startup Mode | Channel Type | Search Word Display Entry Scene | Selected City Name | Exposure Device ID | Click Device ID | Page Display Count | Page Click Count |
|----------------:|:----------|------------------:|------------------------:|:------------|:----------------|:-------------|:------------------------------|:------------------|:--------------------|:-----------------|------------------------:|------------------:|
| 0 | device123 | 1001 | 0 | Beijing | auto | App Store | search | Chengdu | device789 | device654 | 10000 | 5000 |
| 1 | device456 | 1002 | 1 | Shanghai | manual | Google Play | discover | Chongqing | device987 | device321 | 20000 | 6000 |

Question:
Please find all the device IDs where the experiment ID is 1001 and the start-up mode is auto, filter devices in Beijing and Shanghai, and calculate the sum of the page display count and the page click count for these devices.

The answer needs to meet the following requirements:
1. The data file is '应用查询单词显示设备实验统计_0.csv'. Please strictly refer to the headers of the data and use Python code to answer the questions.
2. Use 'pandas.read_csv' to read the data, assuming the data is in the same directory as the code.
3. The analysis code should be written in a function named 'proc_data()', and this function does not need any input parameters, and the sum of the page view counts should be used as the return value.

#<INSERT>

answer = proc_data()
assert answer == 15000


import pandas as pd

def proc_data():
    # Load the CSV file
    file_path = '0029700946.csv'
    data = pd.read_csv(file_path)

    # Count the number of occurrences of 'Shot' in the 'VISITORDESCRIPTION' column
    shot_count = data['VISITORDESCRIPTION'].str.contains('Shot', na=False).sum()

    return shot_count

There is a dataset named '0029700946.csv', the first two rows of which are as follows:
|   GAME_ID |   EVENTNUM |   EVENTMSGTYPE |   EVENTMSGACTIONTYPE |   PERIOD | WCTIMESTRING   | PCTIMESTRING   | HOMEDESCRIPTION                           |   NEUTRALDESCRIPTION |   VISITORDESCRIPTION |   SCORE |   SCOREMARGIN |
|----------:|-----------:|---------------:|---------------------:|---------:|:---------------|:---------------|:------------------------------------------|---------------------:|---------------------:|--------:|--------------:|
|  29700946 |          0 |             12 |                    0 |        1 | 11:41 AM       | 12:00          | nan                                       |                  nan |                  nan |     nan |           nan |
|  29700946 |          1 |             10 |                    0 |        1 | 11:42 AM       | 12:00          | Jump Ball Dele vs. Ratliff: Tip to Hunter |                  nan |                  nan |     nan |           nan |

Question:
Count the events in the 'VISITORDESCRIPTION' field that contain the word 'Shot'.

The answer needs to meet the following requirements:
1. The data file is '0029700946.csv'. Please strictly refer to the header of the data and use python code to answer the question.
2. Use 'pandas.read_csv' to read the data, assuming that the data is in the same path as the code; note that the data may contain "nan".
3. Write the analysis code into a function called 'proc_data()', and this function does not need any input parameters, the count from the question should be the return value.

#<INSERT>

answer = proc_data()
assert round(answer, 3) == 67.000


import pandas as pd

def proc_data():
    # Load the data from the uploaded CSV file
    hitters_data = pd.read_csv('Hitters_X_train.csv')

    # Calculate the Pearson correlation coefficient between "Assists" and "Errors"
    correlation = hitters_data["Assists"].corr(hitters_data["Errors"])

    return correlation

There is a dataset named 'Hitters_X_train.csv', the first two rows of the data are as follows:
| Unnamed: 0         |   AtBat |   Hits |   HmRun |   Runs |   RBI |   Walks |   Years |   CAtBat |   CHits |   CHmRun |   CRuns |   CRBI |   CWalks |   LeagueN |   DivisionW |   PutOuts |   Assists |   Errors |   NewLeagueN |
|:-------------------|--------:|-------:|--------:|-------:|------:|--------:|--------:|---------:|--------:|---------:|--------:|-------:|---------:|----------:|------------:|----------:|----------:|---------:|-------------:|
| -Darryl Strawberry |     475 |    123 |      27 |     76 |    93 |      72 |       4 |     1810 |     471 |      108 |     292 |    343 |      267 |         1 |           0 |       226 |        10 |        6 |            1 |
| -Glenn Wilson      |     584 |    158 |      15 |     70 |    84 |      42 |       5 |     2358 |     636 |       58 |     265 |    316 |      134 |         1 |           0 |       331 |        20 |        4 |            1 |

Question:
Conduct correlation analysis on the player's assists (Assists) and errors (Errors).

The reply needs to meet the following requirements:
1. The data file is 'Hitters_X_train.csv', please strictly refer to the header of the data and use python code to answer the question.
2. Use 'pandas.read_csv' to read the data, the default data is in the same path as the code.
3. Write the analysis code into a function named 'proc_data()', and this function does not need any input parameters, use the correlation coefficient in the question as the return value.

#<INSERT>

answer = proc_data()
assert round(answer, 3) == 0.642


import pandas as pd

def proc_data():
    file_path = 'train.csv'
    # Load the CSV file into a DataFrame
    titanic_data = pd.read_csv(file_path)

    # Filter passengers with family members onboard (either SibSp or Parch is not 0)
    with_family = titanic_data[(titanic_data['SibSp'] != 0) | (titanic_data['Parch'] != 0)]

    # Calculate the survival rate for passengers with family members onboard
    survival_rate_with_family = with_family['Survived'].mean()

    return survival_rate_with_family

There is a dataset named 'train.csv', the first two lines of the data are as follows:
|   PassengerId |   Survived |   Pclass | Name                                                | Sex    |   Age |   SibSp |   Parch | Ticket    |    Fare | Cabin   | Embarked   |
|--------------:|-----------:|---------:|:----------------------------------------------------|:-------|------:|--------:|--------:|:----------|--------:|:--------|:-----------|
|             1 |          0 |        3 | Braund, Mr. Owen Harris                             | male   |    22 |       1 |       0 | A/5 21171 |  7.25   | nan     | S          |
|             2 |          1 |        1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female |    38 |       1 |       0 | PC 17599  | 71.2833 | C85     | C          |

Question:
What is the survival rate for passengers on board with family members (SibSp and Parch not equal to 0)?

The answer needs to meet the following requirements:
1. The data file is 'train.csv', please strictly refer to the data header and use Python code to answer the question.
2. Use 'pandas.read_csv' to read the data, by default the data is in the same path as the code.
3. Write the analysis code into a function called 'proc_data()', and this function does not need any input parameters, use the survival rate in the question as the return value.

#<INSERT>

answer = proc_data()
assert round(answer, 3) == 0.506


import pandas as pd

def proc_data():
    # Load the CSV file
    data = pd.read_csv('DailyRenewablesWatch_2015.csv')

    # Convert the 'timestamp' column to datetime format
    data['timestamp'] = pd.to_datetime(data['timestamp'])

    # Filter the data for January 2015 and sum the 'biogas' column to get the total production
    biogas_january_2015 = data[(data['timestamp'].dt.month == 1) & (data['timestamp'].dt.year == 2015)]['biogas'].sum()

    return biogas_january_2015

There's a dataset called 'DailyRenewablesWatch_2015.csv', the first two lines of the data are as follows:
| timestamp                 |   biogas |   biomass |   geo |   hydro |   imports |   nuclear |   smhydro |   solarpv |   solarth |   thermal |   wind |   carbon |
|:--------------------------|---------:|----------:|------:|--------:|----------:|----------:|----------:|----------:|----------:|----------:|-------:|---------:|
| 2015-01-01 08:00:00+00:00 |      165 |       290 |  1071 |     496 |      7181 |      1085 |       195 |         0 |         0 |     12461 |    585 |  9238.07 |
| 2015-01-01 09:00:00+00:00 |      165 |       278 |  1071 |     548 |      7435 |      1084 |       194 |         0 |         0 |     11571 |    360 |  8929.18 |

Question:
Please analyze the total production of biogas in January 2015.

The answer needs to meet the following requirements:
1. The data file is 'DailyRenewablesWatch_2015.csv', please strictly refer to the data header, and use python code to answer the question.
2. Use 'pandas.read_csv' to read the data. By default, the data is in the same path as the code.
3. Write the analysis code into a function called 'proc_data()', and this function does not require any input parameters. Use the total production in the question as the return value.

#<INSERT>

answer = proc_data()
assert round(answer, 3) == 123866.000


import pandas as pd

def proc_data():
    file_path = 'Comparison_of_database_tools-4.csv'
    # Load the CSV file into a DataFrame
    df = pd.read_csv(file_path)

    # Calculate the percentage of unknown data (?) in the "ER diagram groupboxes" column
    unknown_percentage = (df["ER diagram groupboxes"] == "?").mean() * 100
    return unknown_percentage

Here is a dataset named 'Comparison_of_database_tools-4.csv', the first two rows of data are as follows:

|   Unnamed: 0 | Unnamed: 0.1       | Visual query builder   | Visual schema/model/E-R diagram design   | Reverse engineering   | Forward engineering   | ER diagram groupboxes   |
|-------------:|:-------------------|:-----------------------|:-----------------------------------------|:----------------------|:----------------------|:------------------------|
|            0 | Adminer            | Yes                    | Yes                                      | Yes                   | No                    | No                      |
|            1 | Altova DatabaseSpy | Yes                    | Yes                                      | Yes                   | Yes                   | ?                       |

Question:
Please analyze the percentage of unknown data (?) in the column "ER diagram groupboxes".

The answer needs to meet the following requirements:
1. The data file is 'Comparison_of_database_tools-4.csv'. Please strictly refer to the header of the data and use python code to answer the question.
2. Use 'pandas.read_csv' to read the data, assuming the data is in the same path as the code.
3. The analysis code should be written into a function named 'proc_data()', and this function doesn't need any input parameters, and uses the percentage of the question as the return value.

#<INSERT>

answer = proc_data()
assert round(answer, 3) == 35.000