データの整理について

a = 3
b = 2

print(a + b) #足し算
print(a - b) #引き算
print(a * b) #掛け算
print(a / b) #割り算
print(a ** b) #べき乗

import math
import numpy as np

print(math.sqrt(2))
print(np.sqrt(2))

lst = [2, 7, 5, 3]

print(len(lst))

lst = [2, 7, 5, 3]
max_value = max(lst)
min_value = min(lst)

print("最大値:", max_value)
print("最小値:", min_value)

lst = [5, 7, 3, 4, 8]
sum(lst)

lst = [2, 7, 5, 3]

print(sum(lst)/len(lst))

import statistics as st

data = [20, 10, 40, 20, 40, 50, 30]

print(st.mean(data))

import numpy as np

data = [20, 10, 40, 20, 40, 50, 30]

print(np.var(data, ddof=0))
print(np.var(data, ddof=1))

pip install japanize-matplotlib

import matplotlib.pyplot as plt
import japanize_matplotlib

# データ
categories = ["家電", "食品", "衣料品", "書籍", "その他"]
percentages = [35, 25, 20, 10, 10]

# 円グラフ描画
plt.pie(percentages, labels=categories, autopct="%1.1f%%", startangle=90)
plt.title("製品カテゴリ別売上割合")
plt.axis("equal")  # 円を真円に
plt.show()

import matplotlib.pyplot as plt
import japanize_matplotlib

# データ
stores = ["東京店", "大阪店", "名古屋店", "福岡店", "札幌店"]
sales = [520, 480, 350, 300, 270]

# 棒グラフ描画
plt.bar(stores, sales, color="skyblue")
plt.title("店舗別売上（万円）")
plt.xlabel("店舗")
plt.ylabel("売上（万円）")
plt.show()

pip install japanize-matplotlib

import matplotlib.pyplot as plt
import japanize_matplotlib

# データ
months = ["1月", "2月", "3月", "4月", "5月", "6月",
          "7月", "8月", "9月", "10月", "11月", "12月"]
sales = [120, 150, 130, 170, 160, 180, 200, 190, 210, 230, 220, 250]

# 折れ線グラフ描画
plt.plot(months, sales, marker="o", color="skyblue", label="売上")
plt.title("月別売上")
plt.xlabel("月")
plt.ylabel("売上（万円）")
plt.grid(True)
plt.legend()
plt.show()

pip install japanize-matplotlib

import matplotlib.pyplot as plt
import japanize_matplotlib

# データ
categories = ["家電", "食品", "衣料品", "書籍", "その他"]
percentages = [70, 50, 40, 20, 20]

# 円グラフ描画
plt.pie(percentages, labels=categories, autopct="%1.1f%%", startangle=90)
plt.title("製品カテゴリ別売上割合")
plt.axis("equal")  # 真円に
plt.show()

pip install japanize-matplotlib

import matplotlib.pyplot as plt
import numpy as np
import japanize_matplotlib

# データ
regions = ["東京", "大阪", "名古屋", "福岡"]
teen = [15, 10, 12, 18]
twenty = [25, 30, 28, 22]
thirty = [30, 25, 27, 25]
forty = [20, 25, 23, 20]
fifty_plus = [10, 10, 10, 15]

# 帯グラフの設定・描画
y = np.arange(len(regions))
plt.barh(y, teen, color="#FF9999", label="10代")
plt.barh(y, twenty, left=teen, color="#FFCC99", label="20代")
plt.barh(y, thirty, left=np.array(teen)+np.array(twenty), color="#99CC99", label="30代")
plt.barh(y, forty, left=np.array(teen)+np.array(twenty)+np.array(thirty), color="#66CCCC", label="40代")
plt.barh(y, fifty_plus, left=np.array(teen)+np.array(twenty)+np.array(thirty)+np.array(forty), color="#9999CC", label="50代以上")

plt.yticks(y, regions)
plt.xlabel("割合（％）")
plt.title("地域別 年代構成（帯グラフ）")
plt.legend()
plt.show()

pip install japanize-matplotlib

import matplotlib.pyplot as plt
import numpy as np
import japanize_matplotlib

labels = ['デザイン', '使いやすさ', '性能', '価格', 'サポート']
product_A = [8, 7, 9, 6, 7]
product_B = [6, 8, 7, 9, 6]
product_C = [7, 6, 8, 7, 8]

angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist()
angles += angles[:1]

fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))

def plot_radar(values, label, color):
    values += values[:1]
    ax.plot(angles, values, label=label, color=color)
    ax.fill(angles, values, alpha=0.25, color=color)

plot_radar(product_A, '製品A', 'r')
plot_radar(product_B, '製品B', 'g')
plot_radar(product_C, '製品C', 'b')

ax.set_xticks(angles[:-1])
ax.set_xticklabels(labels)
ax.set_ylim(0, 10)
plt.title('製品評価レーダーチャート')
plt.legend(loc='upper right')
plt.show()

pip install japanize-matplotlib

import numpy as np

data = [
    87, 74, 79, 92, 85, 67, 67, 93, 73, 70,
    69, 85, 72, 70, 79, 65, 65, 56, 73, 60,
    81, 68, 69, 85, 77, 64, 73, 72, 88, 64,
    76, 80, 79, 78, 58, 78, 69, 76, 67, 88,
    65, 90, 66, 83, 89, 67, 84, 82, 79, 71
]

# 階級の上限
bins = [51, 61, 71, 81, 91, 101] 

hist, edges = np.histogram(data, bins=bins)

for i in range(len(hist)):
    print(f"{edges[i]}-{edges[i+1]-1}: {hist[i]}")

import matplotlib.pyplot as plt
import japanize_matplotlib

# データ
scores = [87, 74, 79, 92, 88, 60, 79, 68, 68, 74, 71, 84,
          77, 71, 74, 73, 84, 67, 73, 61, 44, 76, 78, 62,
          92, 55, 70, 68, 85, 84, 71, 73, 61, 50, 66, 71,
          82, 82, 66, 66, 59, 55, 52, 89, 64, 65, 57, 77,
          53, 67]

# ヒストグラムの描画
plt.hist(scores, bins=10, color="skyblue", edgecolor="black")
plt.title("テスト点数の分布")
plt.xlabel("点数")
plt.ylabel("人数")
plt.grid(axis='y')
plt.show()

pip install japanize-matplotlib

import matplotlib.pyplot as plt
import japanize_matplotlib

# データ
class_A = [78, 82, 85, 90, 87, 92, 88, 84, 79, 81, 86, 91, 83, 85, 89, 87, 90, 88, 84, 82, 86, 88, 90, 92, 85, 87, 89, 91, 83, 84]
class_B = [65, 68, 70, 72, 74, 71, 69, 66, 75, 73, 72, 70, 68, 71, 69, 74, 73, 75, 72, 68, 70, 71, 69, 67, 74, 72, 73, 75, 68, 70]
class_C = [55, 60, 65, 50, 70, 75, 40, 85, 60, 55, 50, 80, 75, 45, 65, 70, 55, 60, 85, 40, 75, 70, 50, 55, 60, 65, 45, 80, 75, 50]

# 箱ひげ図
plt.boxplot(
    [class_A, class_B, class_C], labels=["クラスA", "クラスB", "クラスC"],
    vert=False,
    showmeans=True,  # 平均値を表示
    meanprops={"marker":"x", "markerfacecolor":"black", "markeredgecolor":"black", "markersize":8}
)
plt.title("数学テスト点数の分布")
plt.xlabel("点数") # plt.ylabel("点数")
plt.grid(True)
plt.show()

import matplotlib.pyplot as plt
import japanize_matplotlib

# データ
class_A = [78, 82, 85, 90, 87, 92, 88, 84, 79, 81, 86, 91, 83, 85, 89, 87, 90, 88, 84, 82, 86, 88, 90, 92, 85, 87, 89, 91, 83, 84]
class_B = [65, 68, 70, 72, 74, 71, 69, 66, 75, 73, 72, 70, 68, 71, 69, 74, 73, 75, 72, 68, 70, 71, 69, 67, 74, 72, 73, 75, 68, 70]
class_C = [55, 60, 65, 50, 70, 75, 40, 85, 60, 55, 50, 80, 75, 45, 65, 70, 55, 60, 85, 40, 75, 70, 50, 55, 60, 65, 45, 80, 75, 50]

# 箱ひげ図
plt.boxplot(
    [class_A, class_B, class_C], labels=["クラスA", "クラスB", "クラスC"],
    showmeans=True,  # 平均値を表示
    meanprops={"marker":"x", "markerfacecolor":"black", "markeredgecolor":"black", "markersize":8}
)
plt.title("数学テスト点数の分布")
plt.ylabel("点数")
plt.grid(True)
plt.show()

pip install japanize-matplotlib

import matplotlib.pyplot as plt
import japanize_matplotlib

# データ
height = [150, 152, 155, 157, 158, 160, 161, 162, 163, 164,
          165, 166, 167, 168, 169, 170, 171, 172, 173, 174,
          175, 176, 177, 178, 179, 180, 181, 182, 183, 185]
weight = [45, 47, 48, 50, 51, 53, 54, 55, 55, 56,
          57, 58, 59, 60, 61, 62, 63, 64, 64, 65,
          66, 67, 68, 69, 70, 71, 72, 73, 74, 75]

# 散布図の描画
plt.scatter(height, weight, color="skyblue")
plt.title("身長と体重の関係")
plt.xlabel("身長 (cm)")
plt.ylabel("体重 (kg)")
plt.grid(True)
plt.show()

pip install japanize-matplotlib