pip install matplotlib seaborn

import matplotlib.pyplot as plt

# 創(chuàng)建數(shù)據(jù)
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# 繪制折線圖
plt.plot(x, y, label='Line Chart')

# 添加標題和標簽
plt.title('Simple Line Chart')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')

# 顯示圖例
plt.legend()

# 顯示圖表
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt

# 創(chuàng)建數(shù)據(jù)
data = [1, 2, 2, 3, 3, 3, 4, 4, 5]

# 使用Seaborn創(chuàng)建直方圖
sns.histplot(data, bins=5, kde=True, color='skyblue')

# 添加標題和標簽
plt.title('Histogram with Seaborn')
plt.xlabel('Values')
plt.ylabel('Frequency')

# 顯示圖表
plt.show()

import matplotlib.pyplot as plt
import numpy as np

# 創(chuàng)建數(shù)據(jù)
x = np.linspace(0, 2 * np.pi, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# 創(chuàng)建子圖
plt.subplot(2, 1, 1)  # 兩行一列，當前選中第一個子圖
plt.plot(x, y1, label='Sin')
plt.title('Sin Function')
plt.legend()

plt.subplot(2, 1, 2)  # 兩行一列，當前選中第二個子圖
plt.plot(x, y2, label='Cos')
plt.title('Cos Function')
plt.legend()

plt.tight_layout()  # 調(diào)整子圖布局，防止重疊
plt.show()

plt.plot(x, y, color='red', linestyle='--', marker='o', label='Data Points')

import seaborn as sns
import matplotlib.pyplot as plt

# 使用Seaborn創(chuàng)建Pair Plot
iris = sns.load_dataset('iris')
sns.pairplot(iris, hue='species', markers=['o', 's', 'D'])

plt.show()

plt.savefig('my_plot.png')

plt.plot(x, y, marker='.', markersize=1)

import plotly.express as px

# 創(chuàng)建數(shù)據(jù)
df = px.data.iris()

# 使用Plotly創(chuàng)建交互性散點圖
fig = px.scatter(df, x='sepal_width', y='sepal_length', color='species', size='petal_length', hover_data=['petal_width'])

# 顯示圖表
fig.show()

from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource

# 創(chuàng)建數(shù)據(jù)
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# 創(chuàng)建Bokeh圖表
source = ColumnDataSource(data=dict(x=x, y=y))
p = figure(title='Interactive Line Chart', x_axis_label='X-axis', y_axis_label='Y-axis')

# 添加線條
p.line('x', 'y', source=source, line_width=2)

# 顯示圖表
show(p)

import matplotlib.pyplot as plt
from mplcursors import cursor

# 創(chuàng)建數(shù)據(jù)
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# 繪制散點圖
plt.scatter(x, y, label='Data Points')

# 添加標題和標簽
plt.title('Interactive Scatter Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')

# 使用mplcursors添加懸停信息
cursor(hover=True)

# 顯示圖例
plt.legend()

# 顯示圖表
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# 創(chuàng)建時間序列數(shù)據(jù)
date_rng = pd.date_range(start='2024-01-01', end='2024-01-10', freq='D')
data = {'value': [1, 3, 7, 2, 5, 8, 4, 6, 9, 10]}
df = pd.DataFrame(data, index=date_rng)

# 繪制時間序列折線圖
plt.plot(df.index, df['value'], marker='o', linestyle='-', color='b')

# 添加標題和標簽
plt.title('Time Series Plot')
plt.xlabel('Date')
plt.ylabel('Value')

# 日期標簽自動格式化
plt.gcf().autofmt_xdate()

# 顯示圖表
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# 創(chuàng)建數(shù)據(jù)
x = np.linspace(0, 2 * np.pi, 100)
y1 = np.sin(x)
y2 = np.cos(x)

# 創(chuàng)建Figure和Axes對象
fig, ax = plt.subplots()

# 在Axes對象上繪制折線圖
line1, = ax.plot(x, y1, label='Sin')
line2, = ax.plot(x, y2, label='Cos')

# 添加標題和標簽
ax.set_title('Sine and Cosine Functions')
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')

# 顯示圖例
ax.legend()

# 顯示圖表
plt.show()

from bokeh.plotting import figure, curdoc
from bokeh.models import ColumnDataSource
from bokeh.driving import count

# 創(chuàng)建數(shù)據(jù)源
source = ColumnDataSource(data={'x': [], 'y': []})

# 創(chuàng)建Bokeh圖表
p = figure(title='Dynamic Plot', width=800, height=400)
p.circle(x='x', y='y', size=10, color='navy', alpha=0.5, source=source)

# 定義動態(tài)更新函數(shù)
@count()
def update(i):
    new_data = {'x': [i], 'y': [i % 10]}  # 更新數(shù)據(jù)
    source.stream(new_data, rollover=20)  # 更新數(shù)據(jù)源

# 添加定時器，每100毫秒觸發(fā)一次更新
curdoc().add_periodic_callback(update, 100)

# 顯示圖表
curdoc().title = 'Dynamic Plot'
curdoc().add_root(p)

import plotly.express as px
import pandas as pd

# 創(chuàng)建數(shù)據(jù)
df = pd.DataFrame({'x': range(10), 'y': [i % 10 for i in range(10)]})

# 創(chuàng)建動畫散點圖
fig = px.scatter(df, x='x', y='y', animation_frame=df.index, size_max=50, range_x=[0, 10], range_y=[0, 10])

# 顯示圖表
fig.show()

# 保存Matplotlib圖表
plt.savefig('my_plot.png')

# 保存Bokeh圖表
from bokeh.io import output_file, save
output_file('my_bokeh_plot.html')
save(p)

# 保存Plotly圖表
fig.write_html('my_plotly_plot.html')

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from bokeh.plotting import figure, show
import plotly.express as px

# 創(chuàng)建示例數(shù)據(jù)集
data = {'Date': pd.date_range(start='2024-01-01', end='2024-01-10'),
        'Sentiment': [0.2, -0.1, 0.5, -0.3, 0.6, -0.2, 0.1, 0.4, -0.5, 0.3],
        'News_Count': [10, 8, 12, 6, 15, 9, 11, 14, 7, 13]}

df = pd.DataFrame(data)

# Matplotlib折線圖
plt.figure(figsize=(10, 5))
plt.plot(df['Date'], df['Sentiment'], label='Sentiment Score', marker='o')
plt.plot(df['Date'], df['News_Count'], label='News Count', marker='o')
plt.title('Sentiment Analysis Over Time')
plt.xlabel('Date')
plt.ylabel('Score/Count')
plt.legend()
plt.show()

# Seaborn折線圖
plt.figure(figsize=(10, 5))
sns.lineplot(x='Date', y='Sentiment', data=df, label='Sentiment Score', marker='o')
sns.lineplot(x='Date', y='News_Count', data=df, label='News Count', marker='o')
plt.title('Sentiment Analysis Over Time (Seaborn)')
plt.xlabel('Date')
plt.ylabel('Score/Count')
plt.legend()
plt.show()

# Bokeh交互性折線圖
p = figure(title='Sentiment Analysis Over Time', x_axis_label='Date', y_axis_label='Score/Count', width=800, height=400)
p.line(df['Date'], df['Sentiment'], legend_label='Sentiment Score', line_width=2, line_color='blue')
p.circle(df['Date'], df['Sentiment'], size=8, color='blue')
p.line(df['Date'], df['News_Count'], legend_label='News Count', line_width=2, line_color='green')
p.square(df['Date'], df['News_Count'], size=8, color='green')
p.legend.location = 'top_left'
show(p)

# Plotly交互性折線圖
fig = px.line(df, x='Date', y=['Sentiment', 'News_Count'], labels={'value': 'Score/Count'},
              title='Sentiment Analysis Over Time (Plotly)', markers=True)
fig.show()