seaborn histogram code example

Example 1: plot histogram in seaborn

sns.distplot(gapminder['lifeExp'], kde=False, color='red', bins=100)
plt.title('Life Expectancy', fontsize=18)
plt.xlabel('Life Exp (years)', fontsize=16)
plt.ylabel('Frequency', fontsize=16)

Example 2: distribution seaborn

x = np.random.normal(size=100)
sns.distplot(x);

Example 3: how to show mean values on histogram in seaborn

import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline

sns.distplot(xgb_errors, kde=True, rug=True);
plt.axvline(np.median(xgb_errors),color='b', linestyle='--')

Example 4: scatter density plot seaborn

>>> iris = sns.load_dataset("iris")
>>> g = sns.jointplot("sepal_width", "petal_length", data=iris,
...                   kind="kde", space=0, color="g")

Example 5: mean =[0,0] covariance = [[1,0],[0,100]] ds = np.random.multivariate_normal(mean,covariance,500) dframe = pd.DataFrame(ds, columns=['col1', 'col2']) fig = sns.kdeplot(dframe).get_figure() fig.savefig('kde1.png')

mean =[0,0]
covariance = [[1,0],[0,100]]

ds = np.random.multivariate_normal(mean,covariance,500)

dframe = pd.DataFrame(ds, columns=['col1', 'col2'])

fig = sns.kdeplot(dframe).get_figure()
fig.savefig('kde1.png')

Example 6: kde = false

kdebool, optional
Whether to plot a gaussian kernel density estimate.