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stringlengths 1
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from sympy import factorint
factorint(221)
</llm-code><llm-code-output>
{13ïŒ1ã17ïŒ1}
</llm-code-output>
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221ã¯ãçŽ æ°ã®äžã§ãç¹ã«å€§ããªçŽ æ°ã§ããçŽ æ°ãšã¯ã1ãšãã®æ°èªèº«ã§ããå²ãåããªãæ°ã®ããšã§ãã221ã¯ã1ãã221ãŸã§ã®ãã¹ãŠã® | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
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### æ瀺
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### å¿ç
|
ãã®ã³ãŒãã«ã¯ãšã©ãŒããããŸããã©ãã«ãšã©ãŒãããããææããŠãæ£ããã³ãŒãã«çŽããŠãã ããã | ```python
def add(x, y):
return z + w
x = 2
y = 3
z = x + y
print(add(x, y))
``` | é¢æ°å
ã®add(x, y)ã¯å®çŸ©ãããŠããŸãããreturn z + wãreturn x + yã«å€æŽãããšããšã©ãŒãä¿®æ£ãããŸãã
```python
def add(x, y):
return x + y
x = 2
y = 3
z = x + y
print(add(x, y)) # Output: 5
``` | ```python
def add(x, y):
return x + y
x = 2
y = 3
z = x + y
print(add(x, y))
``` | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
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### å
¥å
```python
def add(x, y):
return z + w
x = 2
y = 3
z = x + y
print(add(x, y))
```
### å¿ç
|
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from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
img = Image.open("image.jpg").convert("L")
img_arr = np.array(img)
hist = np.histogram(img_arr.ravel(), bins=256, range=(0, 256))[0]
plt.plot(hist)
plt.show()
``` | ```python
import cv2
import numpy as np
img = cv2.imread('images/gray.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# ãã¹ãã°ã©ã ã®èšç®
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
# ãã¹ãã°ã©ã ã®åºå
print(hist)
``` | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
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### å¿ç
|
äžããããæç« ã®äžãããã¥ãŒã¹èšäºã®èŠçŽãæžãããšãã§ããŸããïŒ | ãã·ã¢ã¯ä»®æ³é貚ã®ååŒãèªãããšçºè¡šããŸããããããŸã§ãã·ã¢ã§ã¯ä»®æ³é貚ã®ååŒã¯çŠæ¢ãããŠããŸãããããã®å€æŽã«ãããæ³çãªä»®æ³é貚ã®ååŒãå¯èœã«ãªããŸãããã ããä»®æ³é貚ãæ³çãªè²šå¹£ãšããŠäœ¿çšããããšã¯çŠæ¢ãããŠããŸãã | ãã·ã¢æ¿åºã¯ä»®æ³é貚ã®ååŒãèªãããšçºè¡šããŸããããããŸã§ãã·ã¢ã§ã¯ä»®æ³é貚ã®ååŒã¯çŠæ¢ãããŠããŸãããããã®å€æŽã«ãããæ³çãªä»®èé貚ã®ååŒãå¯èœã«ãªããŸãããã ããä»®æ³é貚ãæ³çãªè²šå¹£ãšããŠäœ¿çšããããšã¯åŒãç¶ãçŠæ¢ãããŠããŸãã | ãã·ã¢ã¯ä»®æ³é貚ã®ååŒãèªãããšçºè¡šããŸããããããŸã§ãã·ã¢ã§ã¯ä»®æ³é貚ã®ååŒã¯çŠæ¢ãããŠããŸãããããã®å€æŽã«ãããæ³çãªä»®æ³é貚ã®ååŒãå¯èœã«ãªããŸãããã ããä»®æ³é貚ãæ³çãªè²šå¹£ãšããŠäœ¿çšããããšã¯çŠæ¢ãããŠããŸãã | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
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¥å
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### å¿ç
|
ãã®æç« ã®æåãæç¯åäœã§è§£æããæç¯ããšã«åºåã£ãŠãã ããã | ç§ãã¡ã¯æ¯é±æšææ¥ã«å人ãšãã¯ããã¯ã«è¡ãã®ãå®äŸã§ãã | ç§ãã¡ã¯ [æ¯é±] [æšææ¥] [ã«] [å人] [ãš] [ãã¯ããã¯] [ã«] [è¡ã] [ã®] [ã] [å®äŸ] [ã§] ãã
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1. ç§ãã¡ã¯
2. æ¯é±
3. æšææ¥
4. ã«
5. å人
6. ãš
7. ãã¯ããã¯
8. ã«
9. è¡ã
10. ã®
11. ã
12. å®äŸ
13. ã§
14. ã | ç§ãã¡ã¯æ¯é±æšææ¥ã«å人ãšãã¯ããã¯ã«è¡ãã®ãå®äŸã§ãã
### 解説
ãã®æç« ã®æåã¯ãäž»èª(ç§ãã¡ã¯)ãåè©(ãã¯ããã¯ã«è¡ã)ãç®çèª(å®äŸã§ã)ãè£èª(æ¯é±æšææ¥)ã§æ§æãããŠããŸããæç¯ããšã«åºåããšãäž»èª(ç§ãã¡ã¯)ãåè©(ãã¯ããã¯ã«è¡ã)ãç®çèª(å®äŸã§ã)ãè£èª(æ¯é±æšææ¥)ãšãªããŸãã | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
¥åã®çµã¿åããã§ããèŠæ±ãé©åã«æºããå¿çãæžããªããã
### æ瀺
ãã®æç« ã®æåãæç¯åäœã§è§£æããæç¯ããšã«åºåã£ãŠãã ããã
### å
¥å
ç§ãã¡ã¯æ¯é±æšææ¥ã«å人ãšãã¯ããã¯ã«è¡ãã®ãå®äŸã§ãã
### å¿ç
|
ãªãã¯ã¯1æéã§4ã€ã®ãã¬ã¹ã·ã£ããã¢ã€ãã³ããããããšãã§ããŸãã圌ã¯1æéã§3ãã¬ã¹ãã³ããã¢ã€ãã³ããããããšãã§ããŸãã圌ã3æéã®ã¢ã€ãã³ãã¬ã¹ã·ã£ããš5æéã®ã¢ã€ãã³ãããããã¬ã¹ãã³ããè²»ãããŠããå Žåã圌ã¯ã¢ã€ãã³ããããŸãããïŒ | null | Pythonã³ãŒãã䜿çšããŠãã®åé¡ã解決ããŸãããã<llm-code>
ironed_shirts_per_hour = 4
ironed_pants_per_hour = 3
time_spent_ironing_shirts = 3
time_spent_ironing_pants = 5
ironed_shirts = ironed_shirts_per_hour * time_spent_ironing_shirts
ironed_pants = ironed_pants_per_hour * time_spent_ironing_pants
ironed_shirts + ironed_pants
</llm-code><llm-code-output>
27
</llm-code-output>
ãããã£ãŠããªãã¯ã¢ã€ã¢ã³ã\\Boxed{27}è¡£é¡ã | ã¯ãã圌ã¯ã¢ã€ãã³ããããŸããã
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圌ã¯1æéã§3ãã¬ã¹ãã³ããã¢ã€ãã³ããããããšãã§ããŸãã
圌ã¯3æéã®ã¢ã€ãã³ãã¬ã¹ã·ã£ããš5æéã®ã¢ã€ãã³ãããããã¬ã¹ãã³ããè²»ãããŠããå Žåã圌ã¯ã¢ã€ãã³ããããŸããã
ãããã£ãŠã圌ã¯ã¢ã€ãã³ããããŸããã | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
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### æ瀺
ãªãã¯ã¯1æéã§4ã€ã®ãã¬ã¹ã·ã£ããã¢ã€ãã³ããããããšãã§ããŸãã圌ã¯1æéã§3ãã¬ã¹ãã³ããã¢ã€ãã³ããããããšãã§ããŸãã圌ã3æéã®ã¢ã€ãã³ãã¬ã¹ã·ã£ããš5æéã®ã¢ã€ãã³ãããããã¬ã¹ãã³ããè²»ãããŠããå Žåã圌ã¯ã¢ã€ãã³ããããŸãããïŒ
### å¿ç
|
次ã®Tuplesã䜿ã£ãŠãé¢æ°ãçæããåèšãèšç®ããæ¹æ³ã説æããŸãã | Tuples: (1, 2, 3, 4, 5), (6, 7, 8, 9, 10) | ```python
def calculate_total(tuples):
total = 0
for t in tuples:
total += sum(t)
return total
tuples = [(1, 2, 3, 4, 5), (6, 7, 8, 9, 10)]
total = calculate_total(tuples)
print(total)
``` | def sum(tuples):
return sum(map(lambda x: x[0] + x[1], tuples))
print(sum(Tuples))
[1, 3, 6, 10, 15]
[2, 4, 8, 12, 18]
[3, 6, 9, 15, 21]
[4, 8, 12, 16, 24]
[5, 10, 15, 20, 25]
[6, 12, 18, 24, 30]
[7, 14, 21, 28, 35]
[8, 16, 24, 32, 40]
[9, 18, 27, 36, 45]
[10, 20, 30, 40, 50]
[11, 22, 33, 44, 55]
[12, 24, 36, 48, 60]
[13, 26, 40, 52, 65]
[14, 28, 44, 56, 70]
[15, 30, 48, 60, 75]
[16, 32, 52, 64, 80]
[17, 34, 56, 68, 85]
[18, 36, 60, 72, 90]
[19, 38, 64, 76, 95]
[20, 40, 68, 80, 100]
[21, 42, 72, 84, 105]
[22, 44, 76, 88, 110]
[23, 46, 80, 92, 1 | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
¥åã®çµã¿åããã§ããèŠæ±ãé©åã«æºããå¿çãæžããªããã
### æ瀺
次ã®Tuplesã䜿ã£ãŠãé¢æ°ãçæããåèšãèšç®ããæ¹æ³ã説æããŸãã
### å
¥å
Tuples: (1, 2, 3, 4, 5), (6, 7, 8, 9, 10)
### å¿ç
|
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1. ã·ã£ã«ã«ã»ãã»ãŽãŒã« (1959幎 - 1969幎)
2. ã¢ã©ã³ã»ãã«ã¿ãŒã« (1969幎 - 1974幎)
3. ãŽã¡ã¬ãªãŒã»ãžã¹ã«ãŒã«ã»ãã¹ã¿ã³ (1974幎 - 1979幎)
4. ãžã£ãã¯ã»ã·ã©ã¯ (1979幎 - 1981幎)
5. ãã©ã³ãœã¯ã»ãããã©ã³ (1981幎 - 1988幎)
6. ãžã£ãã¯ã»ã·ã©ã¯ (1986幎 - 1995幎)
7. ãžã£ãã¯ã»ã·ã©ã¯ (1995幎 - 2002幎)
8. ãžã£ãã¯ã»ã·ã©ã¯ (2002幎 - 2007幎)
9. ãã³ã©ã»ãµã«ã³ãž (2007幎 - 2012幎)
10. ãã©ã³ãœã¯ã»ãªã©ã³ã (2012幎 - 2017幎)
11. ãšããã¥ãšã«ã»ãã¯ãã³ (2017幎 - çŸè·)
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2. ãã©ã³ãœã¯ã»ãããã©ã³(1981-1995)
3. ãžã£ãã¯ã»ã·ã©ã¯(1995-2007)
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``` | ```python
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import sys
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import sympy as sp
# Define the binomial coefficients
n = 6
r = 2
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# Print the coefficient
print(coefficient)
</llm-code><llm-code-output>
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def greet(name):
"""
äžããããååã䜿ã£ãŠæšæ¶ãè¡ãé¢æ°
Args:
name (str): æšæ¶ããçžæã®åå
Returns:
str: æšæ¶æ
"""
return f"ããã«ã¡ã¯ã{name}ããïŒ"
``` | ```python
def func():
return 1
``` | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
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distance_per_walk = 1.5
total_walks_per_day = 2
days_in_a_month = 30
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distance_walked_per_month
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<books>
<book>
<title>The Catcher in the Rye</title>
<author>J.D. Salinger</author>
<year>1951</year>
</book>
<book>
<title>To Kill a Mockingbird</title>
<author>Harper Lee</author>
<year>1960</year>
</book>
</books>
``` | ```python
import xml.etree.ElementTree as ET
def parse_xml_to_dict(xml_data):
root = ET.fromstring(xml_data)
books = []
for book in root.findall('book'):
book_dict = {}
for field in book:
book_dict[field.tag] = field.text
books.append(book_dict)
return books
# Example usage:
xml_data = """...""" # Input XML data
books = parse_xml_to_dict(xml_data)
for book in books:
print(book)
``` | ```python
import xml.etree.ElementTree as ET
# ããŒã¹ãã
xml_tree = ET.parse('books.xml')
# èŸæžåã«å€æ
books_dict = {
'book': {
'title': 'The Catcher in the Rye',
'author': 'J.D. Salinger',
'year': 1951
},
'book': {
'title': 'To Kill a Mockingbird',
'author': 'Harper Lee',
'year': 1960
}
}
# åºå
print(books_dict)
```
```
{
'book': {
'title': 'The Catcher in the Rye',
'author': 'J.D. Salinger',
'year': 1951
},
'book': {
'title': 'To Kill a Mockingbird',
'author': 'Harper Lee',
'year': 1960
}
}
``` | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
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### å
¥å
```xml
<books>
<book>
<title>The Catcher in the Rye</title>
<author>J.D. Salinger</author>
<year>1951</year>
</book>
<book>
<title>To Kill a Mockingbird</title>
<author>Harper Lee</author>
<year>1960</year>
</book>
</books>
```
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freddy_age
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ãžã§ãŒã ã¹ã»ãŠã§ããæé é¡ãšã¯
### å¿ç
|
äžããããCSVããŒã¿ãããç¹å®ã®ååãæã€åã®ã¿ãæœåºããæ°ããCSVãã¡ã€ã«ã«æžãåºãPythonã³ãŒããäœæããŠãã ããã | ```python
import csv
# å
ã®CSVããŒã¿
input_csv = 'data.csv'
# æ°ããCSVãã¡ã€ã«
output_csv = 'new_data.csv'
# æœåºãããåå
target_column = 'score'
``` | ```python
import csv
input_csv = 'data.csv'
output_csv = 'new_data.csv'
target_column = 'score'
# ããããŒãå«ãè¡ãèªã¿èŸŒã
with open(input_csv, 'r') as f:
reader = csv.reader(f)
header = next(reader)
# ããããŒå
ã®ååããªã¹ãã«ä¿å
columns = header
# ããããŒå
ã®ååãšäžèŽããåã®ã¿ãæœåº
index = columns.index(target_column)
with open(output_csv, 'w') as f:
writer = csv.writer(f)
writer.writerow(header)
with open(input_csv, 'r') as f:
for line in reader:
writer.writerow([line[index]])
``` | ```python
import csv
# å
ã®CSVããŒã¿
input_csv = 'data.csv'
# æ°ããCSVãã¡ã€ã«
output_csv = 'new_data.csv'
# æœåºãããåå
target_column = 'score'
# æœåº
with open(input_csv, 'r') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
row['score'] = row[target_column]
with open(output_csv, 'w') as csvfile:
writer = csv.DictWriter(csvfile, quoting=csv.QUOTE_ALL)
writer.writerow({'score': row['score']})
``` | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
¥åã®çµã¿åããã§ããèŠæ±ãé©åã«æºããå¿çãæžããªããã
### æ瀺
äžããããCSVããŒã¿ãããç¹å®ã®ååãæã€åã®ã¿ãæœåºããæ°ããCSVãã¡ã€ã«ã«æžãåºãPythonã³ãŒããäœæããŠãã ããã
### å
¥å
```python
import csv
# å
ã®CSVããŒã¿
input_csv = 'data.csv'
# æ°ããCSVãã¡ã€ã«
output_csv = 'new_data.csv'
# æœåºãããåå
target_column = 'score'
```
### å¿ç
|
次ã®ã¿ã¹ã¯ã«å¯Ÿããåèšæéãèšç®ããŠãã ããã | - ã¿ã¹ã¯1ïŒ50å
- ã¿ã¹ã¯2ïŒ60å
- ã¿ã¹ã¯3ïŒ30å
- ã¿ã¹ã¯4ïŒ90å | ã¿ã¹ã¯1ããã¿ã¹ã¯4ãŸã§ã®åèšæéã¯ãããããã®ã¿ã¹ã¯ã®æéããã¹ãŠåèšããããšã§æ±ããããšãã§ããŸãããã®ããã以äžã®åŒã«ããèšç®ããŸãã
åèšæé = ã¿ã¹ã¯1 + ã¿ã¹ã¯2 + ã¿ã¹ã¯3 + ã¿ã¹ã¯4
åèšæé = 50å + 60å + 30å + 90å
åã¿ã¹ã¯ã®æéãåèšãããšãåèšæéã¯230åãšãªããŸãã
| åèšæé:150å | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
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### æ瀺
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### å
¥å
- ã¿ã¹ã¯1ïŒ50å
- ã¿ã¹ã¯2ïŒ60å
- ã¿ã¹ã¯3ïŒ30å
- ã¿ã¹ã¯4ïŒ90å
### å¿ç
|
é¡ããããã«ããæ¹æ³ã«ã€ããŠæã㊠| null | é¡ããããã«ããæ¹æ³ã«ã€ããŠã以äžã«ããã€ãã®æ¹æ³ãã玹ä»ããŸãã
## æŽé¡
ããããªèãæã«å
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èŠãªç®èãŸã§åãé€ããŠããŸãããã1æ¥2åïŒæãšå€ïŒãé©åã§ãã
## ä¿æ¹¿
èã®ä¹Ÿç¥ã¯ãèã®ãã¡ãä¹±ãåå ã®äžã€ã§ãããã®ãããæŽé¡åŸã也ç¥ãæ°ã«ãªããšãã¯ãä¿æ¹¿ã¯ãªãŒã ãããŒã·ã§ã³ã䜿ã£ãŠãèã«ååãªæ°Žåãšæ²¹åãè£çµŠããŸãããã
## æ¥çŒãæ¢ã
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## ååãªç¡ç
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1. æŽé¡: é¡ãæž
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5. ä¿æ¹¿ãã¹ã¯: ä¿æ¹¿ãã¹ã¯ã¯ãä¿æ¹¿å¹æãé«ããèã最ãå¹æããããŸãã
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7. ããã¯: ããã¯ã¯ãèã«æ é€ãäžããèã掻æ§åãããå¹æããããŸãã
8. ä¿æ¹¿å€: ä¿æ¹¿å€ã¯ãèã«æœ€ããäžãã也ç¥ãé²ãå¹æããããŸãã
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12. ä¿æ¹¿å€: ä¿æ¹¿å€ã¯ãèã«æœ€ããäžãã也ç¥ãé²ãå¹æããããŸãã
13. ä¿æ¹¿å€: ä¿æ¹¿å€ã¯ãèã«æœ€ããäžãã也ç¥ãé²ãå¹æããããŸãã
14. ä¿æ¹¿å€: ä¿æ¹¿å€ã¯ãèã«æœ€ããäžãã也ç¥ãé²ãå¹æããããŸãã
15. ä¿æ¹¿å€: ä¿æ¹¿å€ã¯ãèã«æœ€ããäžãã也ç¥ãé²ãå¹æããããŸãã
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19. ä¿æ¹¿å€: ä¿æ¹¿å€ã¯ãèã«æœ€ããäžãã也ç¥ãé²ãå¹æããããŸãã
20. ä¿æ¹¿å€: ä¿æ¹¿å€ã¯ãèã«æœ€ããäžãã也ç¥ãé²ãå¹æããããŸãã
21. ä¿æ¹¿å€: ä¿æ¹¿å€ã¯ãèã«æœ€ããäžãã也ç¥ãé²ãå¹æããããŸãã
22. ä¿æ¹¿å€: ä¿æ¹¿å€ã¯ãèã«æœ€ããäžãã也ç¥ãé²ãå¹æããããŸãã | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
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### æ瀺
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### å¿ç
|
(-2,4)ãš(3,-8)ã®éã®è·é¢ã¯ããããïŒ | null | Pythagoreanå®çã䜿çšããŠã2ã€ã®ãã€ã³ãéã®è·é¢ãèšç®ããŸãããã
Pythagoreanã®å®çã¯ãå³è§ã®äžè§åœ¢ã§ã¯ãhypotenuseã®é·ãã®æ£æ¹åœ¢ã¯ä»ã®äž¡åŽã®æ£æ¹åœ¢ã®åèšã«çãããšè¿°ã¹ãŠããŸãã
ãã®å Žåããã€ã³ãã¯ã$ïŒ-2,4ïŒ$ãçŽè§ãšããŠå³åŽã®äžè§åœ¢ã圢æããŸãã
ãããã£ãŠãæ¹çšåŒã¯$ d^2 =ïŒx_2 -x_1ïŒ^2 +ïŒy_2 -y_1ïŒ^2 $ã§ãã
Sympyã䜿çšããŠè§£æ±ºã§ããŸãã<llm-code>
from sympy import symbols, sqrt
x1, y1 = -2, 4
x2, y2 = 3, -8
x_diff = x2 - x1
y_diff = y2 - y1
dist = sqrt(x_diff**2 + y_diff**2)
dist
</llm-code><llm-code-output>
13
</llm-code-output>
è·é¢ã¯$ \\ boxed {13} $ã§ãã | (-2,4)ãš(3,-8)ã®éã®è·é¢ã¯ã(3-(-2))^2 + (-8-4)^2 = 100ã§ãã | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
¥åã®çµã¿åããã§ããèŠæ±ãé©åã«æºããå¿çãæžããªããã
### æ瀺
(-2,4)ãš(3,-8)ã®éã®è·é¢ã¯ããããïŒ
### å¿ç
|
4. 次ã®SQLæã䜿çšããŠRetrieveããããŒã¿ãCSVãã¡ã€ã«ã«æžãåºãPythoné¢æ°ãèšè¿°ããŠãã ããã`SELECT * FROM employees;` | "SELECT * FROM employees;" | ```python
import csv
import sqlite3
def write_to_csv(query):
conn = sqlite3.connect('my_database.db')
cursor = conn.cursor()
cursor.execute(query)
rows = cursor.fetchall()
with open('employees.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerows(rows)
conn.close()
write_to_csv("SELECT * FROM employees;")
``` | ```python
def save_data_to_csv(data):
data_df = pd.DataFrame(data)
data_df.to_csv('data.csv', index=False)
``` | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
¥åã®çµã¿åããã§ããèŠæ±ãé©åã«æºããå¿çãæžããªããã
### æ瀺
4. 次ã®SQLæã䜿çšããŠRetrieveããããŒã¿ãCSVãã¡ã€ã«ã«æžãåºãPythoné¢æ°ãèšè¿°ããŠãã ããã`SELECT * FROM employees;`
### å
¥å
"SELECT * FROM employees;"
### å¿ç
|
ããŒãºã±ãŒãã®ã¬ã·ããæããŠãã ãã | null | 以äžã«ãåºæ¬çãªããŒãºã±ãŒãã®ã¬ã·ããã玹ä»ããŸãã
ææ:
- ã¯ãªãŒã ããŒãº (250g)
- ã°ã©ãã¥ãŒç³ (50g)
- åµ (2å)
- çã¯ãªãŒã (200ml)
- ã¬ã¢ã³æ± (倧ãã1)
- å°éºŠç² (倧ãã1)
- ãµã¯ãŒã¯ãªãŒã (倧ãã1)
- ããã©ãšãã»ã³ã¹ (å°ãã1/2)
- ãã¹ã±ãã (150g) (åºã ãã«äœ¿çš)
- 溶ãããã¿ãŒ (75g)
1. ãªãŒãã³ã180床ã«äºç±ããã±ãŒãåã«ãã¿ãŒãšå°éºŠç²ãèãå¡ã£ãŠãããŸãããã¹ã±ããããããŒã«è¢ã«å
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2. ã¯ãªãŒã ããŒãºã宀枩ã«æ»ããã°ã©ãã¥ãŒç³ãå ããŠæ··ãåãããŸããåµã1åãã€å ãã泡ç«ãŠåšã§æ··ãåãããæ»ãããªç¶æ
ã«ããŸãã
3. çã¯ãªãŒã ãã¬ã¢ã³æ±ãããã©ãšãã»ã³ã¹ãå ãããµã¯ãŒã¯ãªãŒã ã§ãŸãããããšé
žå³ã調æŽããŸããå°éºŠç²ãå ããæ··ãåãããŸãã
4. çå°ãåã«æµãå
¥ãã180床ã®ãªãŒãã³ã§45åïœ50åçŒããŸããçŒãããã£ãããå·ãããŸã§åã«å
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ãã¡ãã®ã¬ã·ãã¯ãããŒã·ãã¯ãªããŒãºã±ãŒãã®äœãæ¹ã§ããã奜ã¿ã§ãã«ãŒããªãŒãèºãªã©ã®ãã«ãŒãããœãŒã¹ããããã³ã°ããŠæ¥œããããšãã§ããŸãã | ããŒãºã±ãŒãã®ã¬ã·ãã¯ãææãšäœãæ¹ã«ãã£ãŠå€§ããç°ãªããŸãã以äžã«ãäžè¬çãªã¬ã·ããããã€ã玹ä»ããŸãã
1. ææ:
ã»ã¯ãªãŒã ããŒãº 120g
ã»ç ç³ 60g
ã»åµ 1å
ã»ã¬ã¢ã³æ± 倧ãã1
ã»èåç² å€§ãã1
ã»çã¯ãªãŒã 100ml
ã»ã¬ã¢ã³ã®ç® å°ã
2. äœãæ¹:
1. ã¯ãªãŒã ããŒãºãšç ç³ãããŠã«ã«å
¥ããã¯ãªãŒã ç¶ã«ãªããŸã§æ··ããã
2. åµãšã¬ã¢ã³æ±ãå ããããã«æ··ããã
3. èåç²ãšçã¯ãªãŒã ãå ããããæ··ããã
4. åã«æµãå
¥ãã180°Cã®ãªãŒãã³ã§40åçŒãã
5. çŒãäžãã£ãããå·ãŸããŠããå·èµåº«ã§å·ããã
3. ææ:
ã»ã¯ãªãŒã ããŒãº 120g
ã»ç ç³ 60g
ã»åµ 1å
ã»ã¬ã¢ã³æ± 倧ãã1
ã»èåç² å€§ãã1
ã»çã¯ãªãŒã 100ml
ã»ã¬ã¢ã³ã®ç® å°ã
4. äœãæ¹:
1. ã¯ãªãŒã ããŒãºãšç ç³ãããŠã«ã«å
¥ããã¯ãªãŒã ç¶ã«ãªããŸã§æ··ããã
2. åµãšã¬ã¢ã³æ±ãå ããããã«æ··ããã
3. èåç²ãšçã¯ãªãŒã ãå ããããæ··ããã
4. åã«æµãå
¥ãã180°Cã®ãªãŒãã³ã§40åçŒãã
5. çŒãäžãã£ãããå·ãŸããŠããå·èµåº«ã§å·ããã
5. ææ:
ã»ã¯ãªãŒã ããŒãº 120g
ã»ç ç³ 60g
ã»åµ 1å
ã»ã¬ã¢ã³æ± 倧ãã1
ã»èåç² å€§ãã1
ã»çã¯ãªãŒã 100ml
ã»ã¬ã¢ã³ã®ç® å°ã
6. äœãæ¹:
1. ã¯ãªãŒã ããŒãºãšç ç³ãããŠã«ã«å
¥ããã¯ãªãŒã ç¶ã«ãªããŸã§æ··ããã
2. åµãšã¬ã¢ã³æ±ãå ããããã«æ··ããã
3. èåç²ãšçã¯ãªãŒã ãå ããããæ··ããã
4. åã«æµãå
¥ãã180°Cã®ãªãŒãã³ã§40åçŒãã
5. çŒãäžãã£ãããå·ãŸããŠããå·èµåº«ã§å·ããã
7. ææ:
ã»ã¯ãªãŒã ããŒãº 120g
ã»ç ç³ 60g
ã»åµ 1å
ã»ã¬ã¢ã³æ± 倧ãã1 | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
¥åã®çµã¿åããã§ããèŠæ±ãé©åã«æºããå¿çãæžããªããã
### æ瀺
ããŒãºã±ãŒãã®ã¬ã·ããæããŠãã ãã
### å¿ç
|
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ä¿®æ£:
ç§ãã¡ã¯åéãšäžç·ã«æ ç»ã«è¡ããŸããããããŠæ ç»ã¯æ°ã«å
¥ããŸããã | ç§ãã¡ã¯åéãšäžç·ã«æ ç»ã«è¡ããŸããããããŠæ ç»ã¯é¢çœãã£ãã§ãã
ä¿®æ£:
ç§ãã¡ã¯åéãšäžç·ã«æ ç»ã«è¡ããŸããããããŠæ ç»ã¯æ°ã«å
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ä¿®æ£:
We went to the movie with our friends. The movie was enjoyable. | 以äžã¯ãã¿ã¹ã¯ã説æããæ瀺ãšãæèã®ããå
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### æ瀺
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### å
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### å¿ç
|
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def split_string(input_string, delimiter):
return input_string.split(delimiter)
# Test the function
input_string = "Hello-World-This-Is-A-Test"
delimiter = "-"
split_list = split_string(input_string, delimiter)
print(split_list)
# Expected output:
# ['Hello', 'World', 'This', 'Is', 'A', 'Test']
``` | ```python
def split_string(string, delimiter):
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import pandas as pd
import sqlite3
def extract_data_from_sql(query, db_path):
# ããã«åŠçãèšè¿°
query = "SELECT * FROM users"
db_path = "database.db"
``` | ```
def extract_data_from_sql(query, db_path):
conn = sqlite3.connect(db_path)
df = pd.read_sql_query(query, conn)
conn.close()
return df.to_csv(index=False)
query = "SELECT * FROM users"
db_path = "database.db"
csv_data = extract_data_from_sql(query, db_path)
``` | ```python
import sqlite3
import pandas as pd
def extract_data_from_sql(query, db_path):
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
cursor.execute(query)
data = cursor.fetchall()
df = pd.DataFrame(data)
df.to_csv(db_path.replace(".db", ".csv"), index=False)
query = "SELECT * FROM users"
db_path = "database.db"
extract_data_from_sql(query, db_path)
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```
import pandas as pd
import sqlite3
def extract_data_from_sql(query, db_path):
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query = "SELECT * FROM users"
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```
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peter_and_andrew_run_in_a_day = peter_runs_in_a_day + 2
number_of_miles_run_in_5_days = peter_and_andrew_run_in_a_day * number_of_days
number_of_miles_run_in_5_days
</llm-code><llm-code-output>
35
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