Session6-Plotting graphs with seaborn_31Jan

Different cubehelix palettes:

!pip install seaborn

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline
sns.set(style="dark")
rs = np.random.RandomState(50)

# Set up the matplotlib figure
f, axes = plt.subplots(3, 3, figsize=(9, 9), sharex=True, sharey=True)
# Rotate the starting point around the cubehelix hue circle
for ax, s in zip(axes.flat, np.linspace(0, 3, 10)):

    # Create a cubehelix colormap to use with kdeplot
    cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True)

    # Generate and plot a random bivariate dataset
    x, y = rs.randn(2, 50)
    sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=ax)
    ax.set(xlim=(-3, 3), ylim=(-3, 3))

f.tight_layout()

Discovering structure in heatmap data:

!pip install seaborn

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(font="monospace")

%matplotlib inline
# Load the brain networks example dataset
df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)

# Select a subset of the networks
used_networks = [1, 5, 6, 7, 8, 11, 12, 13, 16, 17]
used_columns = (df.columns.get_level_values("network")
                          .astype(int)
                          .isin(used_networks))
df = df.loc[:, used_columns]

# Create a custom palette to identify the networks
network_pal = sns.cubehelix_palette(len(used_networks),
                                    light=.9, dark=.1, reverse=True,
                                    start=1, rot=-2)
network_lut = dict(zip(map(str, used_networks), network_pal))

# Convert the palette to vectors that will be drawn on the side of the matrix
networks = df.columns.get_level_values("network")
network_colors = pd.Series(networks, index=df.columns).map(network_lut)

# Create a custom colormap for the heatmap values
cmap = sns.diverging_palette(h_neg=210, h_pos=350, s=90, l=30, as_cmap=True)

# Draw the full plot
sns.clustermap(df.corr(), row_colors=network_colors, linewidths=.5,
               col_colors=network_colors, figsize=(13, 13), cmap=cmap)

Multiple linear regression:

import seaborn as sns
sns.set(style="ticks", context="talk")

# Load the example tips dataset
tips = sns.load_dataset("tips")

# Make a custom sequential palette using the cubehelix system
pal = sns.cubehelix_palette(4, 1.5, .75, light=.6, dark=.2)

# Plot tip as a function of toal bill across days
g = sns.lmplot(x="total_bill", y="tip", hue="day", data=tips,
               palette=pal, size=7)

# Use more informative axis labels than are provided by default
g.set_axis_labels("Total bill ($)", "Tip ($)")

Scatterplot Matrix:

import seaborn as sns
sns.set()

df = sns.load_dataset("iris")
sns.pairplot(df, hue="species")

Scatterplot with categorical variables:

import pandas as pd
import seaborn as sns
sns.set(style="whitegrid", palette="muted")

# Load the example iris dataset
iris = sns.load_dataset("iris")

# "Melt" the dataset to "long-form" or "tidy" representation
iris = pd.melt(iris, "species", var_name="measurement")

# Draw a categorical scatterplot to show each observation
sns.swarmplot(x="measurement", y="value", hue="species", data=iris)

Grouped Boxplots:

import seaborn as sns
sns.set(style="ticks")

# Load the example tips dataset
tips = sns.load_dataset("tips")

# Draw a nested boxplot to show bills by day and sex
sns.boxplot(x="day", y="total_bill", hue="sex", data=tips, palette="PRGn")
sns.despine(offset=10, trim=True)