Seaborn Part 2

The Python visualization library Seaborn is based on matplotlib and provides a high-level interface for drawing attractive statistical graphics.

Make use of the following aliases to import the libraries:

from dataidea.packages import plt, sns, np, pd

The basic steps to creating plots with Seaborn are

  1. Prepare some data
  2. Control figure aesthetics
  3. Plot with Seaborn
  4. Further customize your plot
tips = sns.load_dataset('tips')
tips.head()
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4 
sns.set_style("whitegrid")
g = sns.lmplot(x="tip", y="total_bill", data=tips, aspect=2)
g.set_axis_labels("Tip","Total bill(USD)")
plt.show()

Data

Seaborn also offers built-in data sets:

uniform_data = np.random.rand(10, 12)
data = pd.DataFrame({'x':np.arange(1,101),
'y':np.random.normal(0,4,100)})
titanic = sns.load_dataset("titanic")
iris = sns.load_dataset("iris")
fig, ax = plt.subplots()

Plotting with Seaborn

Axis Grids

# Subplot grid for plotting conditional relationships
g = sns.FacetGrid(titanic, col="survived", row="sex")
g = g.map(plt.hist,"age")
#Draw a categorical plot onto a Facetgrid

Subplot grid for plotting pairwise relationships

h = sns.PairGrid(iris)
h = h.map(plt.scatter)

sns.pairplot(iris)
plt.show()

Grid for bivariate plot with marginal univariate plots

i = sns.JointGrid(x="x",
y="y",
data=data)
i = i.plot(sns.regplot,
sns.distplot)
/home/jumashafara/venvs/programming_for_data_science/lib/python3.10/site-packages/seaborn/axisgrid.py:1886: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  func(self.x, **orient_kw_x, **kwargs)
/home/jumashafara/venvs/programming_for_data_science/lib/python3.10/site-packages/seaborn/axisgrid.py:1892: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  func(self.y, **orient_kw_y, **kwargs)

# Plot data and regression model fitsacross a FacetGrid
sns.lmplot(x="sepal_width",
y="sepal_length",
hue="species",
x_ci = 'sd',
data=iris)
plt.show()

Categorical Plots

Scatterplot

Scatterplot with one categorical variable

sns.stripplot(x="species",
y="petal_length",
data=iris, hue='species')
plt.show()

Categorical scatterplot with non-overlapping points

sns.swarmplot(x="species",
y="petal_length",
data=iris, hue='species')
plt.show()
/home/jumashafara/venvs/programming_for_data_science/lib/python3.10/site-packages/seaborn/categorical.py:3399: UserWarning: 12.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

Bar Chart

Show point estimates and confidence intervals with scatterplot glyphs

sns.barplot(x="sex",
y="survived",
hue="class",
data=titanic)
plt.show()

Count Plot

Show count of observations

sns.countplot(
    x="deck", 
    data=titanic, 
    palette="Greens_d", 
    hue='survived')
plt.show()

Point Plot

Show point estimates and confidence intervals as rectangular bars

sns.pointplot(x="class", y="survived", hue="sex", data=titanic,
            palette={"male":"g","female":"m"}, markers=["^","o"], linestyles=["-","--"])
plt.show()

Boxplot

sns.boxplot(x="alive", y="age", hue="adult_male", data=titanic)
plt.show()

sns.boxplot(data=iris,orient="h")
plt.show()

Violin Plot

sns.violinplot(x="age",
y="sex",
hue="survived",
data=titanic)

Distribution Plots

Plot univariate distribution

plot = sns.displot(data.y, kde=False, color="b")

sns.displot(data.y, kde=True, color="b")

Matrix Plots

Heatmap

# Exclude non-numeric columns from correlation calculation
numeric_cols = tips.select_dtypes(include=['float64', 'int64'])
correlation_matrix = numeric_cols.corr()

# Plotting the heatmap
sns.heatmap(correlation_matrix, annot=True)
plt.title('Correlation Heatmap')
plt.show()

plt.show()
plt.savefig("foo.png")

# You can save a transparent figure
plt.savefig("foo.png", transparent=True)
<Figure size 640x480 with 0 Axes>
fig3, ax = plt.subplots()
# Create the regression plot
sns.regplot(x="petal_width", y="petal_length", data=iris, ax=ax)

# Display the plot
plt.show()

Back to top