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æåã®ãããªã²ãŒã ã¯äœã ã£ãã®ã§ãããã? | null | æåã®ãããªã²ãŒã ã¯ã1950幎代åŸåãã1960幎代åé ã«ãããŠç»å ŽããŸããããã®äžã§ãæãæåãªã®ã¯ã1962幎ã«ããµãã¥ãŒã»ããå·¥ç§å€§åŠã®åŠçã§ãã£ãã¹ãã£ãŒãã³ã»ã©ãã»ã«ã«ãã£ãŠäœæããããSpacewar!ãã§ãã
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ã«ãåºãæ®åãå§ããŸãã1972幎ã«ã¯ãã¢ã¿ãªç€Ÿãåžè²©ã®å®¶åºçšã²ãŒã æ©ãPongããçºå£²ãããããæãåæã®åçšãããªã²ãŒã ãšãããŠããŸãã以éããããªã²ãŒã ã¯æ¥éã«é²åããã²ãŒã ãã¶ã€ã³ãã°ã©ãã£ãã¯ãã¹ããŒãªãŒããªã³ã°ã®åäžãªã©ã仿¥ã®ã²ãŒã ç£æ¥ã®çºå±ã«è³ããŸãã |
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Blender 3.4.1ã§åäœãããé路網ã®ããã«éãªãåãçµåãã200ã®æ²ç·ãäœæããPythonã¹ã¯ãªãããäœæããŸãã | null | Blender 3.4.1ã§åäœãããé路網ã®ããã«éãªãåãçµåãã200ã®æ²ç·ãäœæããPythonã¹ã¯ãªããã以äžã«ç€ºããŸãïŒ
``py
ã€ã³ããŒã bpy
ã€ã³ããŒãã©ã³ãã
# äœæããã«ãŒãã®æ°ãèšå®
num_curves = 200
# é路網ã®ãµã€ãºãèšå®ãã
ãµã€ãº = 20
# éè·¯ãããã¯ãŒã¯ã®é«ããèšå®ãã
é«ã = 0.5
# ã«ãŒããä¿æããããã«ç©ºã®ãªã¹ããäœæãã
ã«ãŒã = [].
# ã«ãŒããäœæãã
for i in range(num_curves)ïŒ
# æ°ããæ²ç·ãäœæãã
curve = bpy.data.curves.new('Curve', 'CURVE')
curve.dimensions = '3D'
ã¹ãã©ã€ã³ = curve.splines.new('BEZIER')
# æ²ç·ã®å§ç¹ãšçµç¹ãèšå®ãã
start = (random.uniform(-size, size), random.uniform(-size, size), height)
end = (random.uniform(-size, size), random.uniform(-size, size), é«ã)
spline.bezier_points.add(2)
spline.bezier_points[0].co = start
spline.bezier_points[1].co = (start[0] + random.uniform(-size/2, size/2), start[1] + random.uniform(-size/2, size/2), height)
spline.bezier_points[2].co = end
# æ²ç·ãã·ãŒã³ã«è¿œå
obj = bpy.data.objects.new('Curve', curve)
bpy.context.scene.collection.objects.link(obj)
curves.append(obj)
# ã«ãŒãã1ã€ã®ãªããžã§ã¯ãã«ãŸãšãã
bpy.ops.object.select_all(action='DESELECT')
for curve in curvesïŒ
curve.select_set(True)
bpy.context.view_layer.objects.active=curves[0]ã
bpy.ops.object.join()
# ã«ãŒããã¡ãã·ã¥ã«å€æãã
bpy.ops.object.convert(target='MESH')
```
ãã®ã¹ã¯ãªããã¯ãã©ã³ãã ãªå§ç¹ãšçµç¹ãæã€200æ¬ã®ã«ãŒããäœæããããããé路網ã®ããã«èŠãã1ã€ã®ãªããžã§ã¯ãã«ãŸãšããŸãããããã¯ãŒã¯ã®å€§ãããšé«ãã¯ãsize倿°ãšheight倿°ã倿Žããããšã§èª¿æŽã§ããŸãã |
ãæ¬§å·é£åãïŒEuropean UnionïŒã¯ããç±³åœãïŒUnited StatesïŒãšåæ§ã«ãé£éŠå¶ãïŒfederal systemïŒãæ¡çšããŠãããããããšããåäžåœå®¶ãïŒunitary stateïŒã§ããïŒ | null | 欧å·é£åïŒEuropean UnionïŒã¯ããåäžåœå®¶ãïŒunitary stateïŒã§ããæ¬§å·é£åã¯ã欧å·è«žåœãå
±åã§éå¶ããåœéçµç¹ã§ããããé£éŠå¶ãïŒfederal systemïŒãæ¡çšããŠããããã§ã¯ãããŸãããæ¬§å·é£åã®ã¡ã³ããŒåœã¯ããããç¬èªã®æ¿åºãšæ³åŸãæã¡ã欧å·é£åã®æ±ºå®ãæ¹éã¯ååœã®æ¿åºã宿œããããšã«ãªããŸãããŸããæ¬§å·é£åã¯äžã€ã®åœå®¶ã§ã¯ãªããæ¿æ²»ã»çµæžã»ç€ŸäŒãå
±åã§éå¶ããåå®ãçµãã åœã
ã®é£åäœã§ãããã®ãããæ¬§å·é£åã¯ãåäžåœå®¶ãïŒunitary stateïŒãšèŠãªãããšãã§ããŸãã |
äžããããCSVãã¡ã€ã«ããã幎霢ã25æ³æªæºã®äººã®ååãååŸããã³ãŒããæžããªããã | ```
name,age
Alice,24
Bob,30
Carol,22
``` | ```python
import csv
names = []
with open('people.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if int(row['age']) < 25:
names.append(row['name'])
print(names)
``` |
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1. ã°ãªã«: ãã©ãŠãããã¬ã€ã«æŽããå¡©ãšã¬ã¢ã³æ±ã§ããªãããŸãããã®åŸãã°ãªã«ãã³ã«äžŠã¹ãŠçŒããŸããäž¡é¢ããã€ãè²ã«ãªããŸã§ãäžç«ã§5-7åçŒããŸãã
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5. çŒãé: ãã©ãŠãããã¬ã€ã«æŽããæ°Žæ°ãåããŸããã°ãªã«ãã³ã«äžŠã¹ãå¡©ãšã¬ã¢ã³æ±ããããŸããäžç«ã§5-7åçŒããŸãã
ãããã®æ¹æ³ã®ããããã䜿çšããŠããã©ãŠãã®çŸå³ãã調çããããšãã§ããŸãã調çæéã¯ãã©ãŠãã®çš®é¡ã倧ããã«ãã£ãŠç°ãªãå Žåããããããæ³šæããŠãã ããã |
ãã£ãµãªã³ã«ã¯ãåæ°ã®éçãšãã³ããããŸããã圌女ã60åã®ãã³ãæã£ãŠããŠã圌女ã®7人ã®å人ã®ããããã«8åã®ãã³ãš6åã®éçãäžããæ®ããèªåã®ããã«ä¿ç®¡ããå Žåã圌女ã¯äœåã®ãã³ãšéçãæ®ããŸãããïŒ | null | Pythonã³ãŒãã䜿çšããŠãã®åé¡ã解決ããŸãããã<llm-code>
pens_per_friend = 8
pencils_per_friend = 6
friends = 7
total_pens = 60
total_pencils = total_pens
pens_left = total_pens - friends * pens_per_friend
pencils_left = total_pencils - friends * pencils_per_friend
total_pens_and_pencils_left = pens_left + pencils_left
total_pens_and_pencils_left
</llm-code><llm-code-output>
22
</llm-code-output>
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ãã®æã®åŠå®åœ¢ãäœæããªããïŒåœŒå¥³ã¯æ¥æ¬èªã話ããŸãã | null | åœŒå¥³ã¯æ¥æ¬èªã話ããŸããã |
ãžã§ã³ã¯ç¶èŠªãã24æ³è¥ãã§ãã圌ãã®å¹Žéœ¢ã®åèšã¯68æ³ã§ãããžã§ã³ã¯äœæ³ã§ããïŒ | null | ãã®åé¡ã解決ããããã«ãæå®ãããã¹ããŒãã¡ã³ããæ¹çšåŒã®ã·ã¹ãã ãšããŠçå®ããSympyã®ãœã«ããŒã䜿çšããŠãœãªã¥ãŒã·ã§ã³ãèŠã€ããŸãã<llm-code>
from sympy import symbols, Eq, solve
# Define the symbols
j, d = symbols('j d')
# Formulate the equations
eq1 = Eq(j, d - 24)
eq2 = Eq(j + d, 68)
# Solve the system of equations
solution = solve((eq1, eq2), (j, d))
# Display the result
solution
</llm-code><llm-code-output>
{DïŒ46ãJïŒ22}
</llm-code-output>
ãããã£ãŠããžã§ã³ã¯$ \\ boxed {22} $霢ã§ãã |
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èšåŒµïŒ1+xïŒ^nã䜿çšããŠãè¡ã®4çªç®ã®æ°å€ãèŠã€ããããšãã§ããŸããããã§ãnã¯è¡çªå·ã§ãã<llm-code>
from sympy import binomial
N = 12
row = []
for i in range(N+1):
binom = binomial(N, i)
row.append(binom)
print(row[3])
</llm-code><llm-code-output>
220
</llm-code-output>
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import pandas as pd
def csv_to_dataframe(file_path):
return pd.read_csv(file_path)
``` |
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initial_small_notebooks = 4
ordered_more_notebooks = 6
lost_notebooks = 2
total_notebooks_now = initial_small_notebooks + ordered_more_notebooks - lost_notebooks
total_notebooks_now
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import pandas as pd
df = pd.read_csv('example.csv')
print(df.mean())
``` |
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ã¿ã°ã®åå ("tag_name") ãäžããããŸãã | ```python
import xml.etree.ElementTree as ET
def extract_tag_content(file_path, tag_name):
tree = ET.parse(file_path)
root = tree.getroot()
return [elem.text for elem in root.iter(tag_name)]
``` |
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Python ã³ãŒããæžããŠé¢æ° y=x**2 ãšãã®åŸ®åã 1 ã€ã®ã°ã©ãã«ãããããã | null | Python ã§é¢æ° `y=x^2` ãšãã®åŸ®åã 1 ã€ã®ã°ã©ãã«ããããããã«ã¯ã`matplotlib` ãš `numpy` ã®ã©ã€ãã©ãªã䜿çšããŸãã以äžã«ãã®æé ã瀺ããŸãã
1. `matplotlib` ãš `numpy` ãã€ã³ããŒãããŸãã
```python
import numpy as np
import matplotlib.pyplot as plt
```
2. x ã®ç¯å²ãå®çŸ©ããŸãã
```python
x_min = -5
x_max = 5
x_range = np.linspace(x_min, x_max, 1000)
```
ããã§ã¯ã`x_range` 㯠-5 ãã 5 ãŸã§ 1000 åã®çééã®æ°å€ã§äœæãããŸãã
3. y ã®å€ãèšç®ããŸãã
```python
y = x_range**2
```
4. 埮åãèšç®ããŸãã
```python
dy_dx = 2 * x_range
```
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5. ã°ã©ããããããããŸãã
```python
plt.figure(figsize=(8, 6))
plt.plot(x_range, y, label='y = x^2')
plt.plot(x_range, dy_dx, '--', label='dy/dx')
plt.legend()
plt.show()
```
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以äžã®æé ãèžãã°ãPython ã³ãŒãã§ `y=x^2` ãšãã®åŸ®åã 1 ã€ã®ã°ã©ãã«ããããããããšãã§ããŸãã |
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æŽæ°ãå°æ°ã§å²ãæ¹æ³ã«ã€ããŠæã㊠| null | æŽæ°ãå°æ°ã§å²ãæ¹æ³ã¯ãéåžžã®å°æ°ã®èšç®ãšåãã§ããæŽæ°ãå°æ°ã§å²ãå Žåããã®æäœã¯å°æ°ã®å²ãç®ã«çžåœããŸãã
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2. å°æ°ç¹ã®åŸãã§å²ãç®ãè¡ããŸããå°æ°ç¹ã®äžã®æ¡ãåãæšãŠãããå°æ°ç¹ã®äžã®æ¡ãåãäžããããããšã«æ³šæããŠãã ããã
äŸãæããŠèª¬æããŸããããæŽæ° 5 ãå°æ° 0.3 ã§å²ãå Žåã以äžã®ããã«ãªããŸãïŒ
```
5 ÷ 0.3
```
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```
5.0 ÷ 0.30
```
2. å°æ°ç¹ã®åŸãã§å²ãç®ãè¡ããŸãïŒ
```
5.0 ÷ 0.30 = 16.666...
```
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import wave
with wave.open('audio.wav', 'rb') as wave_file:
frame_rate = wave_file.getframerate()
num_frames = wave_file.getnframes()
duration = num_frames / frame_rate
print(f'åçæé: {duration}s')
``` |
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numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
``` | ```python
def find_max_and_min(numbers):
max_value = max(numbers)
min_value = min(numbers)
print("Max:", max_value)
print("Min:", min_value)
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
find_max_and_min(numbers)
``` |
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---
å
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é·æ¹åœ¢ã®ã¿ã€ã«ã¯3ã€ã³ãÃ4ã€ã³ãã§ããã2ãã£ãŒãÃ5ãã£ãŒãã®é·æ¹åœ¢ã®é åãå®å
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2 ft = 2 * 12 = 24ã€ã³ãããã³5 ft = 5 * 12 = 60ã€ã³ãã
åºã®é¢ç©ã¯24 * 60 = 1440ã€ã³ã$^2 $ã§ãã
åã¿ã€ã«ã®é¢ç©ã¯3 * 4 = 12ã€ã³ã$^2 $ã§ãã
ã¿ã€ã«ã®æå°æ°ã¯ãåã¿ã€ã«ã®é¢ç©ãŸãã¯1440/12 = 120ã®åºã®é¢ç©ã®æ¯ã§ãã
ãããã£ãŠãçãã¯$ \\ boxed {120} $ã§ãã |
æ¬¡ã®æçš¿ã«å察ããåçãæžããªããïŒ"æè¡ãšã¯ããŸã æ©èœããŠããªããã®ãã¹ãŠã§ããã" | null | ããã¯çå®ã§ã¯ãªãããã¯ãããžãŒã¯å€ãã®å®çšçãªç®çãæã€ãçºå±éäžã®ç¥èã®äžåéã§ãããå®éãçŸåšã§ã¯ãã¯ãããžãŒã«ãã£ãŠã»ãšãã©ã®ãã®ãæ©èœããŠãããšèšããã |
for ã«ãŒãã§ 1000 åã®ãªããžã§ã¯ããå埩åŠçãã Python ã³ãŒããæžããŠããã | null | 以äžã«ãã«ãŒãã§1000åã®ãªããžã§ã¯ããå埩åŠçããPythonã³ãŒãã瀺ããŸãïŒ
```python
class MyObject:
def __init__(self, value):
self.value = value
def process_object(obj):
print(f"Processing object with value {obj.value}")
if __name__ == "__main__":
for i in range(1, 1001):
obj = MyObject(i)
process_object(obj)
```
ãã®ã³ãŒãã¯ã`MyObject` ã¯ã©ã¹ãå®çŸ©ãã`process_object` 颿°ãå®çŸ©ããŠããŸãããããŠãã¡ã€ã³é¢æ°ã§ã¯ã`MyObject` ãªããžã§ã¯ãã1000åçæããããããã `process_object` 颿°ã§åŠçããŸãã |
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COMPUTE $ 2011 \\ CDOT 2012 \\ CDOT 2013 \\ CDOT 2014 $ modulo 5ã | null | ãã®åé¡ã解決ããããã«2ã€ã®æé ã䜿çšããŸãã
1. Pythonã®$ 2011 \\ CDOT 2012 \\ CDOT 2013 \\ CDOT 2014 $ã®Modulo 5ãèšç®ããŸãã
2.ã¢ãžã¥ã©ãŒç®è¡ã®ããããã£$ a \\ equiv b $ïŒmod nïŒ$ \\ã¯ab \\ equiv bc $ïŒmod nïŒãæç€ºããŸããn \\ cdot o \\ cdot p $ãšmodulo 5ãèšç®ããŸãã<llm-code>
from sympy import Integer
# Calculate the modulo 5 of 2011 * 2012 * 2013 * 2014
product = Integer(2011) * Integer(2012) * Integer(2013) * Integer(2014)
modulo_5 = product % Integer(5)
# Calculate the modulo 5 of M * N * O * P
m = Integer(2011) % Integer(5)
n = Integer(2012) % Integer(5)
o = Integer(2013) % Integer(5)
p = Integer(2014) % Integer(5)
modulo_5_2 = m * n * o * p
print("Modulo 5:", modulo_5)
print("Modulo 5 (M * N * O * P):", modulo_5_2)
</llm-code><llm-code-output>
Modulo 5ïŒ4
Modulo 5ïŒm * n * o * pïŒïŒ24
</llm-code-output>
ãããã£ãŠã補åã$ m \\ cdot n \\ cdot o \\ cdot p = 2 \\ cdot 1 \\ cdot 3 \\ cdot 4 = 24 $ã«å€æãã$ 24 \\ equiv 4 $ modulo 5以éã$ 2011 \\ cdot 2012 \\ãçµè«ä»ããããšãã§ããŸããCDOT 2013 \\ cdot 2014 \\ equiv \\ boxed {4} $ modulo 5ã |
ãã ã¯1åããã90èªãå
¥åã§ããŸããããŒãžã¯450èªã§ãã圌ã10ããŒãžãå
¥åããã®ã«ã©ããããæéãããããŸããïŒ | null | ãã ã¯10ããŒãžãšå
¥åã§ããŸãã<llm-code>
tom_can_type_90_words_in_a_minute = 90
number_of_words_in_a_page = 450
number_of_words_in_10_pages = 450 * 10
number_of_minutes_to_type_10_pages = number_of_words_in_10_pages / tom_can_type_90_words_in_a_minute
number_of_minutes_to_type_10_pages
</llm-code><llm-code-output>
50.0
</llm-code-output>
\\ boxed {50}åã |
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ãæ³šãã°ãçŽ æŽãããããŒãããŒãšãªãããšã§ãããã |
次ã®ããã¹ãããŒã¿ãèªã¿èŸŒã¿ãæç« å
ã®åèªæ°ãã«ãŠã³ãããŠãã ããã | ããã¹ãããŒã¿ïŒtext.txtïŒ:
```
Python is a popular programming language.
It was created by Guido van Rossum.
Python is used for web development, data analysis, and many other purposes.
``` | ```python
def count_words_in_text(text_path):
with open(text_path, 'r') as file:
text = file.read()
word_count = len(text.split())
print(f'The text contains {word_count} words.')
count_words_in_text('text.txt')
``` |
æ©æ¢°åŠç¿äºæž¬ã¢ãã«ã®ã³ãŒããæžã | null | 確ãã«ïŒæ©æ¢°åŠç¿ã¯åºå€§ãªããŒãã§ããããšãå¿ã«çããŠãããŠã»ãããããããå§ããã°ããã®äººãããŒããçè§£ããã®ã«åœ¹ç«ã€ãç°¡åãªpythonã®æ±ºå®æšã®äŸãæäŸããããšãã§ããïŒ
python
matplotlib.pyplot ã plt ãšããŠã€ã³ããŒãããã
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.datasets import load_iris
# äŸãšããŠè±ã®è¹åœ©ã®ããŒã¿ã»ãããããŒããã
data = load_iris()
# èšç·Žãšãã¹ãã®å
¥å (X) ãšã©ãã« (y) ãåå²ãã
X_train = train[['sepal_length','sepal_width','petal_length','petal_width']] ãšããã
y_train = train.species
X_test = test[['sepal_length','sepal_width','petal_length','petal_width']] y_test = test.species.
y_test = test.species
# æ±ºå®æšåé¡åšãèšç·Žãã
mod_dt = DecisionTreeClassifier(max_depth = 3, random_state = 1)
mod_dt.fit(X_train,y_train)
# ãã£ããããæ±ºå®æšãå¯èŠåãã
plt.figure(figsize = (10,8))
fn = ["sepal_length", "sepal_width", "petal_length", "petal_width"].
cn = ['setosa', 'versicolor', 'virginica'].
plot_tree(mod_dt, feature_names = fn, class_names = cn, filled = True)
# ãã¹ãã»ããã®äºæž¬å€ãåŸã
prediction=mod_dt.predict(X_test)
```
ãã®ã³ãŒããå®è¡ããã«ã¯ python ã«ãŒãã«ãå¿
èŠã§ãsklearn ãš matplotlib ããã±ãŒãžãã€ã³ã¹ããŒã«ãããŠããå¿
èŠããããŸããDecisionTreeClassifier ã®ãã©ã¡ãŒã¿ max_depth ãš random_state ã倿Žããããšã§ããã£ãããããããªãŒãã©ã®ããã«å€åããããèŠãããšãã§ããŸããäœã詳ãã説æããŠã»ããããšããããŸããããããšãä»ã«äœãèããŠããããšããããŸããïŒ |
ãã£ãã¡ããŒã¯ããŒãã«è¡ãã圌女ãååãªæ¥çŒãæ¢ããæã£ãŠããããšã確èªããããšæã£ãŠããŸãã圌女ã¯ã2æéåŸã«æ¥çŒãæ¢ããåé©çšããå¿
èŠãããããšãç¥ã£ãŠããŸãã圌女ã¯ãŸããåã¢ããªã±ãŒã·ã§ã³ã«3ãªã³ã¹ã®æ¥çŒãæ¢ããå¿
èŠã§ãããããã«ã«ã¯12ãªã³ã¹ãå«ãŸããŠããã3.5ãã«ãããããšãç¥ã£ãŠããŸãã圌女ã16æéããã«ããå Žåãæ¥çŒãæ¢ãã¯ãããã§ããïŒ | null | Pythonã³ãŒãã䜿çšããŠãã®åé¡ã解決ããŸãããã<llm-code>
applications = 16 / 2
ounces_used = applications * 3
ounces_used_per_bottle = 12
bottles_needed = ounces_used / ounces_used_per_bottle
cost_per_bottle = 3.5
total_cost = bottles_needed * cost_per_bottle
total_cost
</llm-code><llm-code-output>
7.0
</llm-code-output>
ãããã£ãŠããã£ãã¡ããŒã¯æ¥çŒãæ¢ãã«\\ Boxed {7}ãã«ã䜿ãå¿
èŠããããŸãã |
以äžã® Python ã³ãŒããæé©åããå¯èªæ§ãé«ããŠãã ããã | ```python
def calculate_total_cost(price, quantity, tax_rate):
pre_tax_cost = price * quantity
tax = pre_tax_cost * tax_rate
total_cost = pre_tax_cost + tax
return total_cost
# Test the function
price = 10
quantity = 5
tax_rate = 0.08
total_cost = calculate_total_cost(price, quantity, tax_rate)
print("åèšéé¡ïŒ", total_cost)
``` | ```python
def calculate_total_cost(price, quantity, tax_rate):
return price * quantity * (1 + tax_rate)
# Test the function
price, quantity, tax_rate = 10, 5, 0.08
total_cost = calculate_total_cost(price, quantity, tax_rate)
print(f"åèšéé¡ïŒ{total_cost:.2f}")
``` |
次ã®JSONããŒã¿ãæäœããPythonã³ãŒããäœæããŠãã ããããscoresãã®å€ã®åèšãè¿ã颿°ãå®è£
ããªããã | ```json
{
"students": [
{
"name": "Alice",
"scores": [70, 80, 90]
},
{
"name": "Bob",
"scores": [65, 75, 85]
}
]
}
``` | ```python
import json
def sum_scores(json_str):
data = json.loads(json_str)
total = 0
for student in data["students"]:
total += sum(student["scores"])
return total
```
åºå:
```
465
``` |
ãžã§ãŒã ãºã¯ãµãã«ãŒããŒã ã«å ãããã¹ã¿ãŒã«ãªããŸãã圌ã¯ã²ãŒã ããšã«4ã€ã®ã¿ããããŠã³ãç²åŸããåã¿ããããŠã³ã¯6ãã€ã³ãã®äŸ¡å€ããããŸããã·ãŒãºã³ã«ã¯15ã²ãŒã ããããŸãã圌ã¯ãŸããã·ãŒãºã³äžã«2ãã€ã³ãã®ã³ã³ããŒãžã§ã³ã6ååŸç¹ããããšãã§ããŸãããå€ãèšé²ã¯ã·ãŒãºã³äžã«300ãã€ã³ãã§ããããžã§ãŒã ãºã¯å€ãèšé²ãäœæåããŸãããïŒ | null | Pythonã³ãŒãã䜿çšããŠãã®åé¡ã解決ããŸãããã<llm-code>
games_per_season = 15
points_per_touchdown = 6
touchdowns_per_game = 4
number_of_two_point_conversions = 6
# formula to calculate total points
points = games_per_season * touchdowns_per_game * points_per_touchdown + number_of_two_point_conversions * 2
points
</llm-code><llm-code-output>
372
</llm-code-output>
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number_of_butterflies_before = 9
number_of_butterflies_flew_away = number_of_butterflies_before / 3
number_of_butterflies_left = number_of_butterflies_before - number_of_butterflies_flew_away
number_of_butterflies_left
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notebook_purchased = 7
book_cost = 7
book_purchased = 2
purchase_money = notebook_cost * notebook_purchased + book_cost * book_purchased
money_spent = purchase_money
remaining_money = 56 - money_spent
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CSV ãã¡ã€ã«å: employees.csv
SQL ã¯ãšãª: SELECT * FROM employees | ```python
import csv
import sqlite3
# Connect to the SQLite database
conn = sqlite3.connect('database.db')
# Create a cursor object
cur = conn.cursor()
# Execute the SQL query
cur.execute("SELECT * FROM employees")
# Fetch all the rows
rows = cur.fetchall()
# Open the CSV file for writing
with open('employees.csv', 'w', newline='') as csvfile:
# Create a CSV writer
csvwriter = csv.writer(csvfile)
# Write the column headers
csvwriter.writerow([i[0] for i in cur.description])
# Write the rows
csvwriter.writerows(rows)
# Close the connection
conn.close()
``` |
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initial_sale = 327
total_sales_in_initial_year = initial_sale + 50
total_sales_in_second_year = total_sales_in_initial_year + 50
total_sales_in_third_year = total_sales_in_second_year + 50
total_sales_in_third_year
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doctor_visit_cost = 250
vaccine_cost = 45
number_of_vaccines = 10
total_doctor_and_vaccine_cost = doctor_visit_cost + vaccine_cost * number_of_vaccines
total_amount_to_pay = total_doctor_and_vaccine_cost * 20 / 100 + 1200
total_amount_to_pay
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èŠã§ããTHCãå«ã倧麻補å€ã¯ãç¹ã«æé·éçšã«ããè¥è
ã®è³ã«æªåœ±é¿ãåãŒãå¯èœæ§ããããæ³šæåãèšæ¶åãåŠç¿èœåã®äœäžãªã©ã®çæç圱é¿ãããããããšãç¥ãããŠããŸããããã«ãé倧ãªç²Ÿç¥çã»èº«äœçå¥åº·åé¡ã瀟äŒçãªåé¡ãåŒãèµ·ããå¯èœæ§ããããŸãããã®ãããããªãã¡ãïŒTHCïŒã°ãã®äœ¿çšã¯ãæ³åŸãèŠå¶ã«åŸã£ãŠè¡ãããšãåŒ·ãæšå¥šãããŸãã |
CSVãã¡ã€ã«ããèªã¿èŸŒãã ããŒã¿ãPythonã§åŠçããã³ãŒããèšè¿°ããŠãã ããã | ```python
# CSVãã¡ã€ã«ã®å
容 (users.csv)
name,age
Alice,30
Bob,25
Carol,35
``` | ```python
import csv
# CSVãã¡ã€ã«ãèªã¿èŸŒã¿
with open('users.csv', 'r') as file:
reader = csv.reader(file)
# æåã®è¡ã¯ããããŒãªã®ã§ã¹ããã
next(reader)
# ããŒã¿ãåŠç
for row in reader:
name = row[0]
age = int(row[1])
print(f'åå: {name}, 幎霢: {age}')
``` |
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