Creating Heatmap from Pandas DataFrame correlation matrix

# import pandas.rpy.common as com
# # load the R package ISLR
# infert = com.importr("ISLR")

# # load the Auto dataset
# auto_df = com.load_data('Auto')

import pandas as pd
import seaborn as sns
%matplotlib inline

data = {
    "x": range(0, 100),
    "y": range(100, 0, -1),
    "z": range(0, 100),
    "a": [4, 5] * 50,
    "b": [4, 5, 6, 7] * 25
}

df = pd.DataFrame(data)
print(df.head())

corr = df.corr()
print(corr)
# plot the heatmap
sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)

OUTPUT IN JUPYTER NOTEBOOK:



Changing color scheme of heatmap:

Code: 
 sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, cmap="YlGnBu")

Output:



Another:

cmap = sns.diverging_palette(5, 250, as_cmap=True)
sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, cmap = cmap)



Viewing heatmap like color scheme in the Pandas textual output itself:

Code:

cmap = sns.diverging_palette(5, 250, as_cmap=True)
def magnify():
    rtnVal = [dict(selector="th",
                 props=[("font-size", "10px")]),
            dict(selector="td",
                 props=[('padding', "2px 2px")]),
            dict(selector="th:hover",
                 props=[("font-size", "16px")]),
            dict(selector="tr:hover td:hover",
                 props=[('max-width', '200px'),
                        ('font-size', '16px')])]
    return []


corr.style.background_gradient(cmap, axis=1)\
    .set_properties(**{'max-width': '80px', 'font-size': '14px'})\
    .set_caption("Hover to magify")\
    .set_precision(2)\
    .set_table_styles(magnify())
 
Output: