Basic Charts

Beginner

Learning Objectives

After completing this recipe, you will be able to:

Compare categories with Bar Charts
Visualize time series changes with Line Charts
Identify relationships between two variables with Scatter Plots
Check composition ratios with Pie Charts
Examine data distribution with Histograms

0. Setup


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
 
# Font settings (adjust based on environment, proceeding with English here)
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['axes.unicode_minus'] = False
 
# Generate data
np.random.seed(42)
df = pd.DataFrame({
    'category': ['A', 'B', 'C', 'D', 'E'],
    'value': [23, 45, 12, 67, 34],
    'value2': [20, 40, 15, 60, 30]
})
 
# Time series data
dates = pd.date_range(start='2023-01-01', periods=100)
ts_df = pd.DataFrame({
    'date': dates,
    'sales': np.random.randn(100).cumsum() + 100,
    'visitors': np.random.randn(100).cumsum() + 50
})

1. Bar Chart

Used to compare the size of categorical data.


plt.figure(figsize=(10, 6))
sns.barplot(x='category', y='value', data=df)
plt.title('Category Values')
plt.show()

Bar Chart

Horizontal Bar Chart

Useful when labels are long or for expressing rankings.


plt.figure(figsize=(10, 6))
sns.barplot(x='value', y='category', data=df, orient='h')
plt.title('Horizontal Bar Chart')
plt.show()

Horizontal Bar Chart

2. Line Chart

Used to show trends over time.


plt.figure(figsize=(12, 6))
sns.lineplot(x='date', y='sales', data=ts_df, label='Sales')
sns.lineplot(x='date', y='visitors', data=ts_df, label='Visitors')
plt.title('Sales & Visitors Trend')
plt.legend()
plt.show()

Line Chart

3. Scatter Plot

Shows the correlation between two continuous variables.


# Generate data for scatter plot
scatter_df = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100)
})
scatter_df['y'] = scatter_df['x'] * 2 + np.random.randn(100) * 0.5  # Create correlation
 
plt.figure(figsize=(8, 8))
sns.scatterplot(x='x', y='y', data=scatter_df)
plt.title('Scatter Plot')
plt.show()

Scatter Plot

4. Histogram

Shows the frequency distribution of data.


plt.figure(figsize=(10, 6))
sns.histplot(scatter_df['y'], kde=True)  # kde=True: add density curve
plt.title('Distribution')
plt.show()

Histogram

5. Pie Chart

Shows the proportion relative to the whole. (Seaborn doesn’t support pie charts, so matplotlib is used)


plt.figure(figsize=(8, 8))
plt.pie(df['value'], labels=df['category'], autopct='%1.1f%%', startangle=90)
plt.title('Category Composition')
plt.show()

실행 결과

[Graph Saved: generated_plot_c84ab52daf_0.png]

Graph