Three Fundamental Steps in Python Visualization

Data visualziation in Python typically follows three key stages:

1. Determine the problem and choose the right plot type
2. Prepare and transform data
3. Customize parameters for clarity

Commonly Used Visualization Libraries

• Matplotlib: The foundational plotting library in Python, ideal for basic visualizations.
• Seaborn: Built on top of Matplotlib, it simplifies creating complex visualizations for data mining and machine learning tasks.
• Bokeh: Enables interactive visualizations in web browsers.
• Mapbox: Useful for advanced geospatial visualizations.

Step 1: Define the Problem and Choose the Right Visualization

Clarify the message you want to convey with your data. Different relationships in data require different visual representations:

• Points: Scatter plots for 2D data relationships
• Lines: Line plots for time-series data
• Bars: Bar charts for categorical comparisons
• Colors: Heatmaps for showing a third dimension

Step 2: Data Transformation and Function Application

Data preparation is crucial for visualization. Common data transformation techniques include:

• Merging data: merge, concat
• Reshaping data: pivot, reshape
• Removing duplicates: drop_duplicates
• Mapping values: map
• Filling missing values: fillna
• Renaming indices: rename

Step 3: Customize Parameters for Better Readability

Enhance your plots by adjusting visual elements:

• Color: color
• Line style: linestyle
• Markers: marker
• Title: set_title
• Axis labels: set_xlabel, set_ylabel
• Legend: legend

Basic Plotting with Matplotlib

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

Creating Figures and Subplots

fig = plt.figure(figsize=(10, 6))
fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.2, hspace=0.3)

Styling Lines and Markers

plt.plot(np.random.randn(30), color='g', linestyle='--', marker='o')

Customizing Axes

fig, ax = plt.subplots()
ax.plot(np.random.randn(1000).cumsum())
ax.set_xticks([0, 250, 500, 750, 1000])
ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'])
ax.set_title('My First Plot')
ax.set_xlabel('Stage')

Adding Legends and Annotations

fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(np.random.randn(1000).cumsum(), 'k', label='one')
ax.plot(np.random.randn(1000).cumsum(), 'k--', label='two')
ax.plot(np.random.randn(1000).cumsum(), 'k.', label='three')
ax.legend(loc='best')

Adding Text Annotations

plt.plot(np.random.randn(1000).cumsum())
plt.text(600, 10, 'test', family='monospace', fontsize=10)

Saving Plots to Files

plt.savefig('./plot.jpg', dpi=300, bbox_inches='tight')

Plotting with Pandas

Pandas provides high-level wrappers around Matplotlib functions, simplifying the plotting process.

Line Plots

s = pd.Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))
s.plot()

df = pd.DataFrame(np.random.randn(10, 4).cumsum(0), columns=['A', 'B', 'C', 'D'])
df.plot()

Bar Charts

fig, axes = plt.subplots(2, 1)
data = pd.Series(np.random.rand(10), index=list('abcdefghij'))
data.plot(kind='bar', ax=axes[0], rot=0, alpha=0.3)
data.plot(kind='barh', ax=axes[1], grid=True)