Data Visualization

import matplotlib.pyplot as plt
import numpy as np

# Line plot
x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x), label="sin(x)")
plt.plot(x, np.cos(x), label="cos(x)")
plt.xlabel("x")
plt.ylabel("y")
plt.title("Trigonometric Functions")
plt.legend()
plt.show()

# Scatter plot
plt.scatter(df["age"], df["salary"], c="blue", alpha=0.6)
plt.xlabel("Age")
plt.ylabel("Salary")
plt.show()

# Bar chart
categories = ["A", "B", "C", "D"]
values = [23, 45, 12, 67]
plt.bar(categories, values, color="steelblue")
plt.show()

# Histogram
plt.hist(df["age"], bins=20, edgecolor="black")
plt.show()

# Subplots
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
axes[0].plot(x, np.sin(x))
axes[0].set_title("Sine")
axes[1].plot(x, np.cos(x))
axes[1].set_title("Cosine")
plt.tight_layout()
plt.show()

import seaborn as sns

sns.set_theme(style="whitegrid")

# Heatmap (correlation)
sns.heatmap(df.corr(), annot=True, cmap="coolwarm", fmt=".2f")

# Pair plot (all pairwise relationships)
sns.pairplot(df, hue="species")

# Box plot (distribution by category)
sns.boxplot(x="department", y="salary", data=df)

# Violin plot (distribution shape)
sns.violinplot(x="department", y="salary", data=df)

# Count plot
sns.countplot(x="department", data=df, order=df["department"].value_counts().index)

import plotly.express as px

# Interactive scatter plot
fig = px.scatter(df, x="age", y="salary", color="department",
                 hover_data=["name"], title="Salary vs Age")
fig.show()

# Interactive bar chart
fig = px.bar(df.groupby("department")["salary"].mean().reset_index(),
             x="department", y="salary", color="department")
fig.show()

# Interactive line chart with animation
fig = px.line(df, x="date", y="revenue", color="product")
fig.show()

# Matplotlib
plt.savefig("chart.png", dpi=300, bbox_inches="tight")
plt.savefig("chart.svg")  # Vector format

# Plotly
fig.write_html("chart.html")
fig.write_image("chart.png")