Basic visualization concepts in Matplotlib.ipynb

Kernel: Python 3

Draft Contents

Making a chart Saving to file

Changing the characteristics of the line - color - width - pattern

Creating different chart types

Adjusting the axes

• Changing the limits

• Labeling the axes

• Changing tick frequency

Changing the background

Annotating the chart points

Labeling the data and the chart

Changing spacing

Bucketing

Loading the data

%matplotlib inline
import matplotlib.pyplot as plt
# only necessary for notebook mode
from csv import DictReader
from os import makedirs
from os.path import join

DATA_FILENAME = join('data', 'climate', 'extracted', 'nasa-ghgases-co2-mean.csv')
IMAGES_DIR = join('images', 'climate')
makedirs(IMAGES_DIR, exist_ok=True)

# loading the data
with open(DATA_FILENAME, 'r') as f:
    data = list(DictReader(f))

Here's what the data looks like; because the CSV text format does not indicate whether '285.2' is intended to be a float or a string literal, the data deserializes as a bunch of strings:

data[0:5]

[{'co2_ppm_mean': '285.2', 'year': '1850'},
 {'co2_ppm_mean': '285.1', 'year': '1851'},
 {'co2_ppm_mean': '285.0', 'year': '1852'},
 {'co2_ppm_mean': '285.0', 'year': '1853'},
 {'co2_ppm_mean': '284.9', 'year': '1854'}]

As it turns out, MPL's plotting handles the numerical-values-as-strings without a hitch (for now):

years = [d['year'] for d in data]
co2ppm = [d['co2_ppm_mean'] for d in data]
plt.plot(years, co2ppm);

Axes limits

plt.plot(years, co2ppm);
plt.xlim(1860, 1900) # bucket it

(1860, 1900)

plt.ylim(ymin=0, ymax=max([float(i) for i in co2ppm]))
plt.xlim(1900, 2000)

(1900, 2000)

# TKTKTK