Data visualization: matplotlib & seaborn¶
Sources:
Parameter¶
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Adjust default figure size
fig_w, fig_h = plt.rcParams.get('figure.figsize')
plt.rcParams['figure.figsize'] = (fig_w, fig_h * .5)
# inline plot (for jupyter)
%matplotlib inline
Set style
print(plt.style.available)
plt.style.use('seaborn-v0_8-whitegrid')
['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']
Basic plots¶
plt.figure(figsize=(9, 3))
x = np.linspace(0, 10, 50)
sinus = np.sin(x)
plt.plot(x, sinus)
plt.show()
plt.figure(figsize=(9, 3))
plt.plot(x, sinus, "o")
plt.show()
# use plt.plot to get color / marker abbreviations
# Rapid multiplot
plt.figure(figsize=(9, 3))
cosinus = np.cos(x)
plt.plot(x, sinus, "-b", x, sinus, "ob", x, cosinus, "-r", x, cosinus, "or")
plt.xlabel('this is x!')
plt.ylabel('this is y!')
plt.title('My First Plot')
plt.show()
# Step by step
plt.figure(figsize=(9, 3))
plt.plot(x, sinus, label='sinus', color='blue', linestyle='--', linewidth=2)
plt.plot(x, cosinus, label='cosinus', color='red', linestyle='-', linewidth=2)
plt.legend()
plt.show()
Scatter (2D) plots¶
Load dataset
import pandas as pd
try:
salary = pd.read_csv("../datasets/salary_table.csv")
except:
url = 'https://github.com/duchesnay/pystatsml/raw/master/datasets/salary_table.csv'
salary = pd.read_csv(url)
df = salary
print(df.head())
salary experience education management
0 13876 1 Bachelor Y
1 11608 1 Ph.D N
2 18701 1 Ph.D Y
3 11283 1 Master N
4 11767 1 Ph.D N
Simple scatter with colors¶
plt.figure(figsize=(3, 3), dpi=100)
_ = sns.scatterplot(x="experience", y="salary", hue="education", data=salary)
Legend outside
ax = sns.relplot(x="experience", y="salary", hue="education", data=salary)
Linear model¶
ax = sns.lmplot(x="experience", y="salary", hue="education", data=salary)
Scatter plot with colors and symbols¶
ax = sns.relplot(x="experience", y="salary", hue="education", style='management', data=salary)
Saving Figures¶
### bitmap format
plt.plot(x, sinus)
plt.savefig("sinus.png")
plt.close()
# Prefer vectorial format (SVG: Scalable Vector Graphics) can be edited with
# Inkscape, Adobe Illustrator, Blender, etc.
plt.plot(x, sinus)
plt.savefig("sinus.svg")
plt.close()
# Or pdf
plt.plot(x, sinus)
plt.savefig("sinus.pdf")
plt.close()
Boxplot and violin plot: one factor¶
Box plots are non-parametric: they display variation in samples of a statistical population without making any assumptions of the underlying statistical distribution.
ax = sns.boxplot(x="management", y="salary", data=salary)
ax = sns.stripplot(x="management", y="salary", data=salary, jitter=True, color="black")
ax = sns.violinplot(x="management", y="salary", data=salary)
ax = sns.stripplot(x="management", y="salary", data=salary, jitter=True, color="white")
Boxplot and violin plot: two factors¶
ax = sns.boxplot(x="management", y="salary", hue="education", data=salary)
ax = sns.stripplot(x="management", y="salary", hue="education", data=salary, jitter=True, dodge=True, linewidth=1)
ax = sns.violinplot(x="management", y="salary", hue="education", data=salary)
ax = sns.stripplot(x="management", y="salary", hue="education", data=salary, jitter=True, dodge=True, linewidth=1)
Distributions and density plot¶
Histogram as probability density function estimator¶
numpy.histogram
can be used to probability density function at the each histogram bin,
setting density=True parameter.
Warning, histogram doesn’t sum to 1. Histogram as PDF estimator should be multiplied by dx’s to sum to 1.
Sum(Hist)= 5.06334585386319 Sum(Hist * dx)= 1.0
ax = sns.displot(x="salary", hue="management", kind="kde", data=salary, fill=True)
Multiple axis¶
fig, axes = plt.subplots(3, 1, figsize=(9, 9), sharex=True)
i = 0
for edu, d in salary.groupby(['education']):
sns.kdeplot(x="salary", hue="management", data=d, fill=True, ax=axes[i], palette="muted")
axes[i].set_title(edu)
i += 1
Pairwise scatter plots¶
ax = sns.pairplot(salary, hue="management")
Time series¶
import seaborn as sns
sns.set(style="darkgrid")
# Load an example dataset with long-form data
fmri = sns.load_dataset("fmri")
# Plot the responses for different events and regions
ax = sns.pointplot(x="timepoint", y="signal",
hue="region", data=fmri)
