Useful Seaborn plots for data exploration

Types of plots:

  1. Multiple features histogram in single chart
  2. Diagonal Correlation Matrix
  3. Missing values Heat Map

Boston Housing prices dataset is used for 1, 2. Titanic Dataset for item 3.

Basic Python module import

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
% matplotlib inline

from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target
df = pd.DataFrame(X, columns= boston.feature_names)

Multiple Histogram plots of numeric features

  • Stack the dataframe with all the features together. May consume significant memory if dataset have large number of features and observations.
numeric_features= df.select_dtypes(exclude=["object","bool"])
numeric_features = numeric_features.stack().reset_index().rename(columns = {"level_1":"Features",0:"Value"})
g = sns.FacetGrid(data =numeric_features, col="Features",  col_wrap=5, sharex=False, sharey=False)
g = g.map(sns.distplot, "Value", color ='blue')
plt.subplots_adjust(top=0.9)
plt.suptitle("Histograms of various features")

multiplehist

Diagonal Heat Map of Correlation Matrix

Reference: seaborn.pydata.org. Utilize the Seaborn heat map with masking of the upper diagonal.

f, ax = plt.subplots(figsize=(12, 12))
corr = df.select_dtypes(exclude=["object","bool"]).corr()

# TO display diagonal matrix instead of full matrix.
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True

# Generate a custom diverging colormap.
cmap = sns.diverging_palette(220, 10, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio.
g = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0, annot=True, fmt='.2f',\
square=True, linewidths=.5, cbar_kws={"shrink": .5})

# plt.subplots_adjust(top=0.99)
plt.title("Diagonal Correlation HeatMap")

heatmap

Missing values Heat Map

Reference: Robin Kiplang’at github

dataset ='https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw/ff414a1bcfcba32481e4d4e8db578e55872a2ca1/titanic.csv'

titanic_df = pd.read_csv(dataset, sep='\t')
sns.heatmap(titanic_df.isnull(), yticklabels=False, cbar = False, cmap = 'viridis')

plt.title("Titanic Dataset Missing Data")

missingdata