Data visualization plots of seaborn

Seaborn is an amazing data visualization library for statistical graphics plotting in Python.

Using Seaborn we can plot wide varieties of plots like:

1. Distribution Plots
2. Pie Chart & Bar Chart
3. Scatter Plots
4. Pair Plots
5. Heat maps

To initialize the Seaborn library, the command used is:

import seaborn as sns

Here, the data set used for visualizing plots is Drinking water data set from Kaggle.

Get your data

import pandasi
df=pandas.read_csv(‘Your Data set’)

1. Distplot

Dist plot gives us the histogram of the selected continuous variable.

import seaborn as sns
sns.distplot(df['ph'], bins = 10)

2. Joint Plot

It is the combination of the distplot of two variables. We additionally obtain a scatter plot between the variable to reflecting their linear relationship. We can customize the scatter plot into a hexagonal plot, where, more the colour intensity, the more will be the number of observations.

sns.jointplot(x = df[‘ph’], y = df[‘Hardness’], kind = ‘scatter’)

sns.jointplot(x = df['ph'], y = df['Hardness'], kind = 'hex')

3. Pair Plot

It takes all the numerical attributes of the data and plot a pairwise scatter plot for two different variables and histograms from the same variables.

sns.pairplot(df)

4. Count Plot

It counts the number of occurrences of categorical variables.

sns.countplot(df['Potability'])

5. Box Plot

It is a 5 point summary plot. It gives the information about the maximum, minimum, mean, first quartile, and third quartile of a continuous variable. Also, it equips us with knowledge of outliers.

We can plot this for a single continuous variable or can analyze different categorical variables based on a continuous variable.

sns.boxplot(y = df['ph'], x = df['Potability'])

6. Violin Plot

It is similar to the Box plot, but it gives supplementary information about the distribution too.

sns.violinplot(y = df['ph'], x = df['Potability'])

7. Strip Plot

It’s a plot between a continuous variable and a categorical variable. It plots as a scatter plot but supplementarily uses categorical encodings of the categorical variable.

sns.stripplot(y = df['ph'], x = df['Potability'])

8. Swarm Plot

It is the combination of a strip plot and a violin plot. Along with the number of data points, it also provides their respective distribution.

sns.swarmplot(y = df['ph'], x = df['Potability'])

9. Regression Plot

This is a more advanced statistical plot that provides a scatter plot along with a linear fitting of the data.

sns.lmplot(x = ‘ph’, y = ‘Hardness’, data = df, hue = ‘Potability’)

I hope this article would serve you as a tool for interrogating your data.

Thanks for Reading!!