import matplotlib.pyplot as plt
import numpy as np

# 創(chuàng)建數(shù)據(jù)
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 創(chuàng)建圖形
plt.figure()

# 繪制折線圖
plt.plot(x, y, '-')

# 設(shè)置圖表標(biāo)題和坐標(biāo)軸標(biāo)簽
plt.title("Simple Plot of Sine Function")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")

# 顯示圖形
plt.show()

pip install matplotlib

import matplotlib.pyplot as plt

plt.plot(x, y, label='Line 1') # x和y是數(shù)據(jù)點(diǎn)

plt.scatter(x, y) # x和y是數(shù)據(jù)點(diǎn)

plt.bar(x, y) # x是分類變量，y是對(duì)應(yīng)值

plt.hist(data, bins=10) # data是要統(tǒng)計(jì)的數(shù)據(jù)，bins是分組數(shù)量

plt.pie(sizes, labels=labels) # sizes是各部分大小，labels是標(biāo)簽

plt.title('My Plot')        # 添加標(biāo)題
plt.xlabel('X Axis Label')  # 添加X(jué)軸標(biāo)簽
plt.ylabel('Y Axis Label')  # 添加Y軸標(biāo)簽
plt.legend()                # 添加圖例
plt.xlim(0, 10)             # 設(shè)置X軸范圍
plt.ylim(0, 100)            # 設(shè)置Y軸范圍

plt.show()  # 顯示圖形
# 或者
plt.savefig('my_plot.png')  # 保存圖形為PNG文件

fig, axs = plt.subplots(nrows=2, ncols=2)  # 創(chuàng)建一個(gè)2x2的子圖網(wǎng)格
# 然后可以在每個(gè)子圖上進(jìn)行繪制，例如：
axs[0, 0].plot(x1, y1)  # 在第一個(gè)子圖上繪圖
axs[0, 1].scatter(x2, y2)  # 在第二個(gè)子圖上繪圖
# ...以此類推

import matplotlib.pyplot as plt
import numpy as np

# 創(chuàng)建數(shù)據(jù)
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 繪制線圖
plt.plot(x, y)
plt.title('Line Plot')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.grid(True)
plt.show()

# 創(chuàng)建數(shù)據(jù)
x = np.random.rand(50)
y = np.random.rand(50)

# 繪制散點(diǎn)圖
plt.scatter(x, y)
plt.title('Scatter Plot')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.grid(True)
plt.show()

# 創(chuàng)建數(shù)據(jù)
categories = ['Category1', 'Category2', 'Category3', 'Category4']
values = [23, 45, 56, 12]
# 設(shè)置字體為支持中文的字體，比如'SimHei'（黑體），確保你的系統(tǒng)中安裝了該字體
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用于正常顯示中文標(biāo)簽
plt.rcParams['axes.unicode_minus'] = False  # 用來(lái)正常顯示負(fù)號(hào)

# 繪制條形圖
plt.bar(categories, values, label='測(cè)試圖例')
plt.title('Bar Chart')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.legend()
plt.show()

# 創(chuàng)建數(shù)據(jù)（正態(tài)分布的隨機(jī)樣本）
data = np.random.rand(100)
print(data)

data = np.sort(data)
print(data)
# 繪制直方圖
plt.hist(data, bins=20, edgecolor='black', color='skyblue', alpha=0.7)

# 定制圖形
plt.title('Histogram of Data Distribution')
plt.xlabel('Value Range')
plt.ylabel('Frequency')
plt.grid(True)

# 顯示圖形
plt.show()

# 創(chuàng)建數(shù)據(jù)
labels = ['A', 'B', 'C', 'D']
sizes = [15, 30, 45, 10]

# 繪制餅圖
plt.pie(sizes, labels=labels)
plt.title('Pie Chart')
plt.show()

# 創(chuàng)建數(shù)據(jù)
x = np.array([1, 2, 3, 4, 2, 2, 2, 1])
y = np.array([1, 2, 3, 4, 1, 2, 3, 4])

# 繪制直方圖
plt.hist2d(x, y, bins=[20, 20], cmap=plt.cm.jet)
plt.title('Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')
# 添加帶有標(biāo)簽的顏色條
plt.colorbar(label='Counts')
plt.grid(True)
plt.show()

import matplotlib.pyplot as plt  
import numpy as np  
  
# 模擬數(shù)據(jù)  
x = np.arange(1, 6)  
y1 = np.random.randint(1, 5, 5)  
y2 = np.random.randint(1, 5, 5)  
  
# 繪制面積圖  
# 繪制 y1 到 0 之間的區(qū)域
plt.fill_between(x, y1, color='blue', alpha=0.5, label='Area 1')  
# 繪制 y1 到 y2 之間的區(qū)域
plt.fill_between(x, y1, y2, color='red', alpha=0.5, label='Area 2')  
  
plt.legend()  
plt.show()

import matplotlib.pyplot as plt  
import numpy as np  
import seaborn as sns  
  
# 創(chuàng)建一個(gè)數(shù)據(jù)矩陣  
data = np.random.rand(10, 12)  
  
# 繪制熱力圖  
sns.heatmap(data, cmap='coolwarm')  
plt.show()

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# 創(chuàng)建一個(gè)假設(shè)的數(shù)據(jù)集
data = {
    'Product': ['A', 'B', 'C', 'D', 'E'],
    'January': np.random.randint(10, 100, 5),
    'February': np.random.randint(10, 100, 5),
    'March': np.random.randint(10, 100, 5),
    'April': np.random.randint(10, 100, 5),
    'May': np.random.randint(10, 100, 5),
    'June': np.random.randint(10, 100, 5)
}

# 將數(shù)據(jù)轉(zhuǎn)換為Pandas DataFrame
df = pd.DataFrame(data)
df = df.set_index('Product')

# 繪制熱力圖
plt.figure(figsize=(10, 8))
sns.heatmap(df, annot=True, cmap='coolwarm', fmt='d', linewidth=0.5)

# 設(shè)置圖表標(biāo)題和坐標(biāo)軸標(biāo)簽
plt.title('Monthly Sales Heatmap')
plt.xlabel('Month')
plt.ylabel('Product')

# 顯示圖表
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# 創(chuàng)建3D坐標(biāo)軸
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 模擬三維數(shù)據(jù)點(diǎn)
x = np.random.standard_normal(100)
y = np.random.standard_normal(100)
z = np.random.standard_normal(100)

# 繪制三維散點(diǎn)圖
ax.scatter(x, y, z, c='b', marker='o')

plt.show()

import matplotlib.pyplot as plt
import numpy as np

# 模擬數(shù)據(jù)
N = 5
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
ind = np.arange(N)  # the x locations for the groups
width = 0.35  # the width of the bars: can also be len(x) sequence

# 繪制男性數(shù)據(jù)堆疊圖
p1 = plt.bar(ind, menMeans, width, label='Men')

# 繪制女性數(shù)據(jù)堆疊圖，注意這里的bottom參數(shù)，它使得女性數(shù)據(jù)的柱狀圖從男性數(shù)據(jù)的頂部開(kāi)始繪制
p2 = plt.bar(ind, womenMeans, width, label='Women', bottom=menMeans)

plt.ylabel('Scores')
plt.title('Scores by group and gender')
# 設(shè)置X軸刻度
plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
# 設(shè)置y軸刻度
plt.yticks(np.arange(0, 81, 10))
# 顯示圖例
plt.legend()
plt.show()