That variety presents another problem with issuing requirements for spacecraft.qi请分析上述句子的英语知识点
时间: 2023-06-02 19:08:12 浏览: 78
该句子是一个复杂的句子,包含了多个英语知识点:名词词组、动词短语、形容词词组、介词短语和从句。其中,问题所在的“issuing requirements for spacecraft”是一个动词短语,其中,“issuing”是现在分词充当定语修饰“requirements”,表示发出、发布的意思;“for spacecraft”是介词短语,表示针对航天器。因此,整个句子的意思是:“这种多样性会给航天器发布要求带来另一个问题。”
相关问题
There is a midterm.csv file about the grading result of a course's midterm exam. The first row presents the header. Then, each row presents a student's enrolled name and his or her score. Write a program to read the file and collect all the scores in a list (without header row!) Download the midterm.csv file. Classify those scores according to the criteria given by question 2 in Problem Set 1. Then, draw a histogram with plotly for ranks A, B, C, D, F.
Sure, here's the Python code to read the midterm.csv file and collect the scores in a list:
```python
import csv
scores = []
with open('midterm.csv', 'r') as csvfile:
reader = csv.reader(csvfile)
next(reader) # skip header row
for row in reader:
scores.append(int(row[1]))
print(scores)
```
To classify the scores based on the criteria given in question 2 of Problem Set 1, we can write a function like this:
```python
def classify_scores(scores):
a_scores = []
b_scores = []
c_scores = []
d_scores = []
f_scores = []
for score in scores:
if score >= 90:
a_scores.append(score)
elif score >= 80:
b_scores.append(score)
elif score >= 70:
c_scores.append(score)
elif score >= 60:
d_scores.append(score)
else:
f_scores.append(score)
return (a_scores, b_scores, c_scores, d_scores, f_scores)
```
Finally, we can use Plotly to draw a histogram for the classified scores:
```python
import plotly.graph_objects as go
(a_scores, b_scores, c_scores, d_scores, f_scores) = classify_scores(scores)
fig = go.Figure()
fig.add_trace(go.Histogram(x=a_scores, name='A'))
fig.add_trace(go.Histogram(x=b_scores, name='B'))
fig.add_trace(go.Histogram(x=c_scores, name='C'))
fig.add_trace(go.Histogram(x=d_scores, name='D'))
fig.add_trace(go.Histogram(x=f_scores, name='F'))
fig.update_layout(
title='Histogram of Midterm Exam Scores',
xaxis_title='Score',
yaxis_title='Count',
barmode='stack'
)
fig.show()
```
This should produce a histogram with 5 bars representing the counts of scores in each rank (A, B, C, D, F).
There is a midterm.csv file about the grading result of a course's midterm exam. The first row presents the header. Then, each row presents a student's enrolled name and his or her score. Write a program to read the file and collect all the scores in a list (without header row!) Download the midterm.csv file. Classify those scores according to the criteria given by question 2 in Problem Set 1. Then, draw a histogram with plotly for ranks A, B, C, D, F
Sure! For reading and processing the CSV file, we can use the `csv` module in Python. Here's the code to read and collect all the scores in a list:
```python
import csv
scores = []
with open('midterm.csv', newline='') as csvfile:
reader = csv.reader(csvfile)
next(reader) # skip header row
for row in reader:
scores.append(int(row[1]))
print(scores)
```
This code will create a list `scores` containing all the numeric scores from the CSV file.
For the second part of the question, we need to classify the scores into A, B, C, D, and F categories. According to the criteria given in Problem Set 1, we can use the following classification:
- A: score >= 90
- B: 80 <= score < 90
- C: 70 <= score < 80
- D: 60 <= score < 70
- F: score < 60
Here's the code to classify the scores and count the number of scores in each category:
```python
counts = {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'F': 0}
for score in scores:
if score >= 90:
counts['A'] += 1
elif score >= 80:
counts['B'] += 1
elif score >= 70:
counts['C'] += 1
elif score >= 60:
counts['D'] += 1
else:
counts['F'] += 1
print(counts)
```
This code will create a dictionary `counts` containing the number of scores in each category.
Finally, we can use the `plotly` library to create a histogram of the scores. Here's the complete code:
```python
import csv
import plotly.graph_objs as go
scores = []
with open('midterm.csv', newline='') as csvfile:
reader = csv.reader(csvfile)
next(reader) # skip header row
for row in reader:
scores.append(int(row[1]))
counts = {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'F': 0}
for score in scores:
if score >= 90:
counts['A'] += 1
elif score >= 80:
counts['B'] += 1
elif score >= 70:
counts['C'] += 1
elif score >= 60:
counts['D'] += 1
else:
counts['F'] += 1
labels = ['A', 'B', 'C', 'D', 'F']
values = [counts[label] for label in labels]
fig = go.Figure([go.Bar(x=labels, y=values)])
fig.show()
```
This code will create a histogram of the scores, showing the number of scores in each category.