Data Science — Data Visualization

Matplotlib

import matplotlib.pyplot as plt

# Line plot
plt.plot([1, 2, 3, 4], [1, 4, 2, 3])
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Line Plot')
plt.show()

# Scatter plot
plt.scatter([1, 2, 3, 4], [1, 4, 2, 3])
plt.show()

# Histogram
plt.hist([1, 2, 2, 3, 3, 3, 4, 4, 4, 4])
plt.show()

Seaborn

import seaborn as sns
import pandas as pd

# Load dataset
tips = sns.load_dataset('tips')

# Distribution plot
sns.histplot(data=tips, x='total_bill')
plt.show()

# Box plot
sns.boxplot(data=tips, x='day', y='total_bill')
plt.show()

# Heatmap
sns.heatmap(tips.corr(), annot=True)
plt.show()

Plotly

import plotly.express as px

# Interactive scatter
fig = px.scatter(tips, x='total_bill', y='tip', color='sex')
fig.show()

# Interactive line
fig = px.line(x=[1,2,3], y=[1,4,2])
fig.show()

Best practices

1. Choose right chart type
2. Label axes clearly
3. Use appropriate colors
4. Tell a story

Mini Practice

1. Create line plots
2. Make scatter plots
3. Build heatmaps
4. Use interactive plots

Up Next

Continue with EDA - Exploratory Data Analysis.