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Sympyã䜿çšããŠãã®åçãèšç®ããæ¹æ³ãèŠãŠã¿ãŸãããã<llm-code>
from sympy import factorint
# factor 42 into primes
# factorint(number) returns a dict with the primes and their multiplicities
factor_dict = factorint(42)
# calculate M and print it
M = len(factor_dict)
print(M)
</llm-code><llm-code-output>
3
</llm-code-output>
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ä¿æ°ãèšç®ããŸãããã<llm-code>
import sympy as sp
# compute the binomial coefficient
sp.binomial(10, 8)
</llm-code><llm-code-output>
45
</llm-code-output>
çãã¯\\ boxed {45}ã§ãã |
ãã®Pythonã³ãŒããJupyter Notebookã®åœ¢åŒã«å€æããŠãã ããã | ```python
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.title('Simple Sine Wave')
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.grid(True)
plt.show()
``` | ```python
# %%
import numpy as np
import matplotlib.pyplot as plt
# %%
x = np.linspace(0, 10, 100)
y = np.sin(x)
# %%
plt.plot(x, y)
plt.title('Simple Sine Wave')
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.grid(True)
plt.show()
``` |
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```python
def find_max(numbers):
max_value = numbers[0]
for num in numbers:
if num > max_value:
max_value = num
return max_value
print(find_max([4, 2, 3, 1]))
``` |
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name,age,city
Alice,30,New York
Bob,25,Chicago
Carol,35,Los Angeles
Dave,40,Miami
Eve,28,Boston
``` | ```python
import csv
data = []
with open("sample.csv", "r") as f:
reader = csv.DictReader(f)
for row in reader:
data.append(row)
age_sum = sum(int(row["age"]) for row in data)
average_age = age_sum / len(data)
print(f"å¹³å幎霢: {average_age}")
``` |
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```csv
name,age
Alice,30
Bob,25
``` | ```python
import pandas as pd
data = pd.read_csv('example.csv')
print(data)
``` |
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ã®å€§åŠã§ãæ¥æ¬èªã®ææ¥ãåããŠããŸããã圌ã¯ãæ¥æ¬ã®åŠçãã¡ãšäžç·ã«ææ¥ãåããæ¥æ¬èªã®å匷ã«å±ãã§ããŸãããŸãã圌ã¯æ¥æ¬ã®æåã«ã€ããŠãã£ãšåŠã³ãããšæããæ¥æ¬ã®äŒçµ±çãªèžè¡ãé³æ¥œã«ã€ããŠã®ææ¥ãåããŠããŸãã |
次㮠XML ãã¡ã€ã«ããããŒã¿ãååŸããŠãããŒã¿ãæ°ããããŒã¿ãã¬ãŒã ã«ä¿åããŠãã ããã | ```xml
<data>
<record>
<name>John</name>
<age>30</age>
<country>USA</country>
</record>
<record>
<name>Mike</name>
<age>25</age>
<country>Canada</country>
</record>
<record>
<name>Sara</name>
<age>20</age>
<country>Australia</country>
</record>
</data>
``` | ```python
import xml.etree.ElementTree as ET
import pandas as pd
xml_data = '''
<data>
<record>
<name>John</name>
<age>30</age>
<country>USA</country>
</record>
<record>
<name>Mike</name>
<age>25</age>
<country>Canada</country>
</record>
<record>
<name>Sara</name>
<age>20</age>
<country>Australia</country>
</record>
</data>
'''
root = ET.fromstring(xml_data)
records = []
for record in root.findall('record'):
name = record.find('name').text
age = record.find('age').text
country = record.find('country').text
records.append({'name': name, 'age': age, 'country': country})
new_data = pd.DataFrame(records)
``` |
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ã£ãŠããããé»åäžè¶³ã®ããåè»ãéè¡ãããŠããªãç¶æ³ãäžæºã«æã£ãŠããŸãããŸããä¹å®¢ãžã®æ
å ±æäŸãã¢ããã€ã¹ããªãããšã«ãäžæºãæã£ãŠããŸãããã®åŸãSW_Helpããé»åäžè¶³ã®ããéè¡ãèŠåãããŠããæšã®ãè©«ã³ãããããã°ããããŠåè»ãéè¡ãåéãããšã®é£çµ¡ããããŸããã |
次ã®Pythonã³ãŒããæžããäžããããCSVãã¡ã€ã«ããããŒã¿ãèªã¿èŸŒã¿ããã®ããŒã¿ã䜿ã£ãŠãç·åœ¢ååž°ã®äºæž¬ã¢ãã«ãäœæããŠãã ããã | ```
<noinput>
``` | ```python
import pandas as pd
from sklearn.linear_model import LinearRegression
def create_linear_regression_model(csv_file, target_column):
"""
This function will create a linear regression model using the given CSV file and target column.
For example:
csv_file: 'path/to/csv/file.csv'
target_column: 'target'
output: The trained linear regression model
"""
data = pd.read_csv(csv_file)
X = data.drop(target_column, axis=1)
y = data[target_column]
model = LinearRegression()
model.fit(X, y)
return model
``` |
äžããããé³å£°ãã¡ã€ã«ã®é³éãåæããé³éãäžå®ã®ã¬ãã«ãè¶
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import librosa
import numpy as np
def detect_volume_peaks(audio_path, threshold):
y, sr = librosa.load(audio_path)
loudness = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max)
peaks = librosa.util.peak_pick(loudness, threshold)
timestamps = librosa.times_like(loudness, sr=sr)[peaks]
return timestamps
``` |
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# Bill is paid $20 per hour for first 40 hours
first_40_hours_pay = 40 * 20
# and double for the remaining 10 hours
remaining_10_hours_pay = 20 * 2 * 10
# adding up the total pay
total_pay = first_40_hours_pay + remaining_10_hours_pay
total_pay
</llm-code><llm-code-output>
1200
</llm-code-output>
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å ±ã¯ãªãŒã¹ãã©ãªã¢ã®æ³åçããªãŒã¹ãã©ãªã¢åŒè·å£«äŒã®ãŠã§ããµã€ããªã©ã§ã確èªãã ããã |
ãããã£ãšåœŒå¥³ã®å人ã§ãããã¬ã¬ã€ã¯ããžã£ã³ãããŒãã䜿çšããŠãžã£ã³ãã³ã³ãã¹ããããŠããŸãããããã£ã¯æåã®ã©ãŠã³ãã§180ã®ãžã£ã³ããè¡ãããã¬ã©ã€ã¯ãããã£ããžã£ã³ãããã®ãšåãããã3/4ã«ãžã£ã³ãããŸãã第2ã©ãŠã³ãã§ã¯ãLoreleiã¯Hattieããã50åã®ãžã£ã³ããè¡ããŸãããããã£ãæåã®ã©ãŠã³ãã§è¡ã£ããžã£ã³ãã®æ°ã®2/3åã«ãžã£ã³ãã§ããå Žåã2ã©ãŠã³ãã§2ã€ã®ãžã£ã³ãã®ç·æ°ãèšç®ããŸãã | null | Pythonã³ãŒãã䜿çšããŠãã®åé¡ã解決ããŸãããã<llm-code>
# Hattie's jumps in the first round
hattie_jumps_first_round = 180
# Lorelei's jumps in the first round
lorelei_jumps_first_round = hattie_jumps_first_round * 3 / 4
# Hattie's jumps in the second round
hattie_jumps_second_round = hattie_jumps_first_round * 2 / 3
# Lorelei's jumps in the second round
lorelei_jumps_second_round = hattie_jumps_second_round + 50
# total number of jumps
hattie_jumps_total = hattie_jumps_first_round + hattie_jumps_second_round
lorelei_jumps_total = lorelei_jumps_first_round + lorelei_jumps_second_round
total_jumps_in_two_rounds = hattie_jumps_total + lorelei_jumps_total
total_jumps_in_two_rounds
</llm-code><llm-code-output>
605.0
</llm-code-output>
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print(max_in_list([1, 2, 3, 4, 5]))
``` | ```python
def max_in_list(lst):
max_value = max(lst)
return max_value
print(max_in_list([1, 2, 3, 4, 5]))
``` |
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2. ãã©ããã»ããã: 俳åªãšããŠæåããŠããŸãããã¹ããŒãé¢é£ã®æ ç»ã«ãåºæŒããŠããŸãã
3. ã±ãã³ã»ãã¥ã©ã³ã: ãã¹ã±ããããŒã«éžæã§ãããæ ç»ãããã¥ã¡ã³ã¿ãªãŒã«ãåºæŒããŠããŸãã
4. 倧谷ç¿å¹³: ããã®éçéžæã§ãããã¹ããŒãããã¥ã¡ã³ã¿ãªãŒããã¬ãã·ãªãŒãºã«ãåºæŒããŠããŸãã
5. ããªãœã³ã»ãã©ãŒã: 俳åªãšããŠæåããŠããŸããããŽã«ãã趣å³ã§ããããŸãã
6. ããŠã§ã€ã³ã»ãžã§ã³ãœã³: ã¬ã¹ãªã³ã°éžæãšããŠæŽ»èºããåŸã俳åªã«è»¢åããŸããã
7. ãžã§ã€ãœã³ã»ã¹ãã€ãµã : ã¹ã€ããŒã§ããã俳åªãšããŠãç¥ãããŠããŸãã
8. ã¹ãã£ãŒãã³ã»ã«ãªãŒ: ãã¹ã±ããããŒã«éžæã§ãããã«ã¡ãªåºæŒãããã¥ã¡ã³ã¿ãªãŒãæ°æ¬ããªããŠããŸãã
9. ããã³ã»ãŠã£ãªã¢ã ãº: 俳åªãšããŠæåããŠããŸãããã¹ããŒãé¢é£ã®æ ç»ã«ãåºæŒããŠããŸãã
10. ã¹ã«ãŒã¬ããã»ãšãã³ãœã³: 俳åªãšããŠæåããŠããŸãããã¹ããŒãé¢é£ã®æ ç»ã«ãåºæŒããŠããŸãã
11. ãã ã»ãã¬ãã£: ã¹ããŒãã¿ã¬ã³ããšããŠã®åŽé¢ã倧ããã§ãããæ ç»ã«ãåºæŒããŠããŸãã
12. ãããªãã¯ã»ãããŒã ãº: ã¹ããŒãé¢é£ã®æ ç»ã«åºæŒããŠããŸããã俳åªãšããŠæåããŠããŸããã |
次ã®ã³ãŒãã§ã'sentence' æååã®äžã®ã'word' æååãå«ãŸããŠãããã©ãããå€å®ããŠãã ããã | ```python
sentence = "I love to play the guitar."
word = "guitar"
_______
``` | ```python
sentence = "I love to play the guitar."
word = "guitar"
if word in sentence:
print("Contains")
else:
print("Does not contain")
``` |
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1. **Cypressãã»ããã¢ãããã**:
- ãããžã§ã¯ãã«Cypressãã€ã³ã¹ããŒã«ããŸãã
```
npm install cypress
```
- Cypressã®èšå®ãã³ãã³ãã®è©³çŽ°ã¯[å
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2. **ãã¹ããã¡ã€ã«ãäœæãã**:
`cypress/integration` ãã£ã¬ã¯ããªé
äžã«ãã¹ããã¡ã€ã«ãäœæããŸãïŒ`sample-test.spec.js`ãšããŸãïŒã
```jsx
describe('Sample Component Test', () => {
it('renders correctly', () => {
// Your test goes here...
});
});
```
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`cy.visit()`ã䜿ã£ãŠãã¹ã察象ã®ããŒãžã蚪åããŸãã
```jsx
cy.visit('/your-component-page');
```
4. **DOMèŠçŽ ãæ€èšŒãã**:
`cy.get()`ã䜿ã£ãŠDOMèŠçŽ ãååŸããæåŸ
ããèŠçŽ ãååšããããã§ãã¯ããŸãã
```jsx
cy.get('h1').should('have.text', 'Expected Title');
```
5. **Reactã³ã³ããŒãã³ãã®ç¶æ
ãæ€èšŒãã**:
`cy.window()`ã䜿ã£ãŠãã©ãŠã¶äžã®Reactã¢ããªã±ãŒã·ã§ã³ã®ç¶æ
ã確èªã§ããŸãã
```jsx
cy.window().its('yourAppState.title').should('equal', 'Expected Title');
```
6. **ã€ãã³ããã·ãã¥ã¬ãŒããã**:
`cy.get()` ã䜿ã£ãåŸã« `.click()`, `.type()`, `.submit()` ãªã©ã®ã¡ãœããã§ãã€ãã³ããã·ãã¥ã¬ãŒãããŸãã
```jsx
cy.get('button').click();
cy.get('form').type('Hello Cypress!').submit();
```
以äžãReact.jsã³ã³ããŒãã³ãã®ãã¹ãã«Cypressã䜿çšããåºæ¬çãªæé ã«ãªããŸãããã ããã®æ¹æ³ã¯å®éã®ãã©ãŠã¶ã§ãã¹ããèµ°ããããããReactã®ãã¹ãã«ç¹åãã`@testing-library/react`ãªã©ã®ã©ã€ãã©ãªãããé«éã§ã¯ãããŸããããããã®ã©ã€ãã©ãªã䜿ãå Žåã¯ãCypressã䜿ãã¡ãªãããèããããšããç解ãã ããã |
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Pythonã䜿çšããŠããã®èšç®ãè¡ãããšãã§ããŸãã<llm-code>
print(7 ** 35 % 10)
</llm-code><llm-code-output>
3
</llm-code-output>
ãããã£ãŠã7^35ã®æ°åã¯\\ boxed {3}ã§ãã |
ããªãŒã¯ãå匷ã«è²»ããå¿
èŠãããæéãèããŠããŸãã圌女ã¯æ°åŠã®å®¿é¡ã«60åãããããšãç¥ã£ãŠããŸãã圌女ã®å°ççãªå®¿é¡ã¯åœŒå¥³ã®æ°åŠã®å®¿é¡ã®ååã®æéããããã圌女ã®ç§åŠã®å®¿é¡ã¯åœŒå¥³ãæ°åŠãšå°çã®å匷ã«è²»ãããå¹³åæéã«çããæéããšããŸããããªãŒã¯å匷ã«äœåã«è²»ãããŸããïŒ | null | Pythonã³ãŒãã䜿çšããŠãã®åé¡ã解決ããŸãããã<llm-code>
time_spent_on_math = 60
time_spent_on_geography = time_spent_on_math / 2
time_spent_on_science = (time_spent_on_math + time_spent_on_geography) / 2
time_spent_on_all_three = time_spent_on_science + time_spent_on_math + time_spent_on_geography
time_spent_on_all_three
</llm-code><llm-code-output>
135.0
</llm-code-output>
ãããã£ãŠãMillyã¯å®¿é¡ã«\\ Boxed {135}åãè²»ãããŸãã |
次ã®ã³ãŒããã³ãŒãã£ã³ã°èŠçŽã«åŸã£ãŠã³ã¡ã³ããè¿œå ããŠãã ããã | ```python
def calculate_average(numbers):
return sum(numbers) / len(numbers)
numbers = [1, 2, 3, 4, 5]
average = calculate_average(numbers)
``` | ```python
# äžãããããªã¹ãã®æ°å€ã®å¹³åå€ãèšç®ããé¢æ°
def calculate_average(numbers):
# ãªã¹ãã®åèšå€ãèšç®
total = sum(numbers)
# ãªã¹ãã®èŠçŽ æ°ãèšç®
length = len(numbers)
# å¹³åå€ãèšç®
return total / length
# èšç®ããæ°å€ã®ãªã¹ã
numbers = [1, 2, 3, 4, 5]
# å¹³åå€ãèšç®
average = calculate_average(numbers)
``` |
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