Data Without Visualization Is Noise
A table of 10,000 rows tells you nothing. A well-designed chart reveals patterns, trends, and anomalies in seconds. Visualization is how data becomes insight.
Matplotlib: The Foundation
import matplotlib.pyplot as plt
import numpy as np
# Line chart
x = np.linspace(0, 10, 100)
plt.figure(figsize=(10, 6))
plt.plot(x, np.sin(x), label="sin(x)", linewidth=2)
plt.plot(x, np.cos(x), label="cos(x)", linewidth=2)
plt.title("Trigonometric Functions")
plt.xlabel("x")
plt.ylabel("y")
plt.legend()
plt.grid(True, alpha=0.3)
plt.savefig("trig.png", dpi=150)
plt.show()Seaborn: Beautiful Statistics
import seaborn as sns
import pandas as pd
df = pd.read_csv("data.csv")
# Distribution plot
sns.histplot(data=df, x="salary", hue="department", kde=True)
plt.title("Salary Distribution by Department")
# Heatmap for correlations
sns.heatmap(df.corr(), annot=True, cmap="coolwarm")Plotly: Interactive Charts
import plotly.express as px
df = px.data.gapminder()
fig = px.scatter(df.query("year==2007"),
x="gdpPercap", y="lifeExp",
size="pop", color="continent",
hover_name="country",
size_max=60, title="World Development 2007")
fig.show()Choosing the Right Chart
| Data Type | Best Chart |
|---|---|
| Trends over time | Line chart |
| Comparisons | Bar chart |
| Parts of whole | Pie / Donut chart |
| Distributions | Histogram / Box plot |
| Relationships | Scatter plot |
| Geographic | Map / Choropleth |