Visualization in Python

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as sns
#loading data with seaborndf_titanic = sns.load_dataset('titanic')df_iris = sns.load_dataset('iris')df_flights = sns.load_dataset('flights')
  • Univariate Plots
  • Bivariate Plots
  • Multivariate Plots
  • Numerical Variables against Categorical Variables

Univariate Plots

plt.figure(figsize=(16,8))sns.distplot(df_titanic['fare'])plt.title('Distribution of Fare in titanic')

Bivariate Plots['month'],df_flights['passengers'] )
sns.scatterplot(x='sepal_length', y='sepal_width', data=df_iris)
plt.title('scaterplot in seaborn')
plt.title('Scatter plot in matplotlib')
sns.jointplot(x=df_iris['sepal_length'], y=df_iris['sepal_width'], kind='hex')

Multivariate Plots

sns.scatterplot(x='sepal_length', y='sepal_width', hue='species', data =df_iris)plt.title('Scatterplt with hue in Seaborn')
sns.barplot(x='sex', y='fare', data= df_tiitanic, hue='class')

Numerical Variables against Categorical Variables

sns.boxplot(x='sex', y='age', data = df_titanic)
sns.boxplot(x='species', y='sepal_length', data = df_iris)
sns.violinplot(x='month', y='passengers', data=df_flights)
sns.swarmplot(y=df_iris['petal_length'], x=df_iris['species'])
sns.pairplot(df_iris, hue='species', diag_kind='hist')
g = pd.plotting.scatter_matrix(df_iris, figsize=(10,10), marker = '*', hist_kwds = {'bins': 10}, s = 60, alpha = 0.8)




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Akshar Rastogi

Akshar Rastogi

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