A group of adults and children spent a total of $420 for tickets and food at the circus. Tickets to the circus cost $20 per adult and $10 per child. If the group spent an average of $21 per person for tickets and food, and there were the same number of adults as children in the group, what was the total amount that the group spent on food at the circus?
时间: 2023-03-13 08:30:34 浏览: 193
组合成人和儿童共计花费420美元购买马戏团的门票和食物,成人每人20美元,儿童每人10美元。如果成人和儿童的人数相同,同一组人平均花费21美元,那么该组人在马戏团购买食物的共计花费是多少?答案是:该组人在马戏团购买食物的共计花费是210美元。
相关问题
Please write an article based on the following:The best thing and the worst thing about college
College is often considered to be one of the most exciting and transformative periods in a person's life. It is a time when young adults are able to explore new interests, make lifelong connections, and gain valuable knowledge and skills that can help them succeed in their chosen career paths. However, like any major life transition, college comes with its own set of challenges and drawbacks. In this article, we will explore both the best and worst aspects of college life.
The best thing about college is undoubtedly the opportunity to learn and grow as an individual. For many students, college is the first time they are truly able to pursue their passions and interests in a structured and supportive environment. Whether it's through attending lectures, participating in research projects, or engaging in extracurricular activities, college students are constantly exposed to new ideas and perspectives that can help them develop into well-rounded individuals.
Another great thing about college is the social aspect. College is a time when students are able to make lifelong connections with peers who share their interests and passions. Through clubs, organizations, and dorm life, students are able to form close bonds with others and develop a sense of community that can last long after graduation.
However, college is not without its challenges. One of the biggest drawbacks of college is the cost. With tuition rates rising every year, many students are left with significant debt upon graduation. This can be a major burden that can impact their financial stability for years to come.
Another downside to college is the pressure to succeed. With such high expectations placed on students to excel academically, socially, and professionally, it can be easy to become overwhelmed and stressed out. This pressure can lead to mental health issues such as anxiety and depression, which can have long-term effects on a person's well-being.
In conclusion, college is a complex experience that comes with both advantages and disadvantages. While it provides students with an unparalleled opportunity to learn and grow, it can also be a source of stress and financial burden. Ultimately, the best way to navigate these challenges is to stay focused on your goals, seek out support when needed, and maintain a positive attitude towards your experiences. With the right mindset and resources, college can be a truly transformative and rewarding experience.
预测性别(得分可能低) 读取adults.txt文件,并使用逻辑斯蒂回归算法训练模型,根据种族race、职业occupation、工作时长hours_per_week来预测一个人的性别
首先,需要将adults.txt文件读取为pandas的DataFrame对象。代码如下:
```python
import pandas as pd
df = pd.read_csv('adults.txt', header=None, names=['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income'])
```
接下来,需要对数据进行预处理,将分类变量进行编码,并将性别作为标签列。代码如下:
```python
from sklearn.preprocessing import LabelEncoder
# 将分类变量进行编码
df['workclass'] = LabelEncoder().fit_transform(df['workclass'])
df['education'] = LabelEncoder().fit_transform(df['education'])
df['marital-status'] = LabelEncoder().fit_transform(df['marital-status'])
df['occupation'] = LabelEncoder().fit_transform(df['occupation'])
df['relationship'] = LabelEncoder().fit_transform(df['relationship'])
df['race'] = LabelEncoder().fit_transform(df['race'])
df['native-country'] = LabelEncoder().fit_transform(df['native-country'])
# 将性别作为标签列
df['sex'] = df['sex'].map({'Male': 0, 'Female': 1})
```
接下来,需要将数据集划分为训练集和测试集,并使用逻辑斯蒂回归算法来训练模型。代码如下:
```python
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# 划分训练集和测试集
X = df[['race', 'occupation', 'hours-per-week']]
y = df['sex']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练模型
lr = LogisticRegression()
lr.fit(X_train, y_train)
# 预测
y_pred = lr.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy: ', accuracy)
```
最后,我们可以使用训练好的模型对新数据进行预测,并输出预测结果。代码如下:
```python
import numpy as np
new_data = np.array([[0, 1, 40], [1, 4, 50], [2, 7, 60]])
# 进行预测
y_pred = lr.predict(new_data)
# 输出预测结果
for i in range(len(new_data)):
print('Race:', new_data[i][0], 'Occupation:', new_data[i][1], 'Hours per week:', new_data[i][2], 'Prediction:', 'Female' if y_pred[i] else 'Male')
```
注意,这里我们使用了accuracy_score函数来计算模型的准确率,使用了predict函数来进行预测。
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