Plotting data in Python with Matplotlib

Description

Useful commands for programming within Jupyter Notebook.

Data Preparation

Useful imports

Data sorting, visualisation, basic statistics and line fitting.

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import seaborn as sns

Plotting Data

Subplots

fig, ax = plt.subplots(1, 1, figsize=(8,8)) # Single column
fig, (ax, ax1) = plt.subplots(1, 2, figsize=(8, 8)) # Single column multiple plots
fig, [[ax, ax1], [ax2, ax3]] = plt.suplots(2, 2) # Multi-row multi-column

Basic Settings

ax.set_ylabel("LABEL")
ax.set_xlabel("LABEL")
ax.grid(color="lightgrey")
ax.set_ylim(lower, upper)
ax.set_xlim(lower, upper)
ax.legend(loc="upper right")

Dates

ax.set_xlim([datetime.date(YYYY, MM, DD), datetime.date(YYYY, MM, DD)])
ax.xaxis.set_major_locator(mdates.DayLocator(interval=n))
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=n))
plt.setp(ax.get_xticklabels(), rotation=30, ha="right, rotation_mode="anchor")

Text and Arrows

ax.text(0.05, 0.95, "TEXT", transform=ax.transAxes, fontsize=16, va='top')
ax.annotate('TEXT', xy=(0, 0), xytext=(0, 0),
            arrowprops=dict(facecolor='black', shrink=0.05))

Exporting and Displaying

plt.tight_layout() # Use this or bbox_inches
plt.savefig("name.png", dpi=300, bbox_inches='tight')
plt.show()

notes