Recipe Objective

Have you ever feel a need to visualize the data in various form. Visualizing the data give us a better idea how our dataset is distributed.

So this is the recipe on how we use seaborn to visualise a Pandas dataframe.

Step 1 - Import the library

We have imported various modules like pandas, random, matplotlib and seaborn which will be need for the dataset.

Step 2 - Setting up the Data

We have created a empty dataset and then by using random function we have created set of random data and stored in X and Y. We have used print function to print the dataset. df = pd.DataFrame() df['x'] = random.sample(range(1, 50), 25) df['y'] = random.sample(range(1, 100), 25) print(); print(df.head()) print(); print(df.tail())

Step 3 - Ploting different Plots

So we will be ploting different plots by using seaborn.

• First we are ploting Scatterplot by passing the required parameters
sns.lmplot('x', 'y', data=df, fit_reg=False)
• Now we are ploting a regression line which fits the data
sns.lmplot('x', 'y', data=df, fit_reg=True)
• Now we are ploting a density plot for the data
sns.kdeplot(df.y); plt.show() sns.kdeplot(df.y, df.x); plt.show() sns.distplot(df.x); plt.show()
• Now we are ploting a histogram for the data
plt.hist(df.x, alpha=.3) sns.rugplot(df.x) plt.show()
• Now we are ploting a Boxplot for the data
sns.boxplot([df.y, df.x]) plt.show()
• Now we are ploting a Violin Plot for the data
sns.violinplot([df.y, df.x]) plt.show()
• Now we are ploting a Heatmap for the data
sns.heatmap([df.y, df.x], annot=False, fmt="d") plt.show()
• Finally we are ploting a clustermap for the data
sns.clustermap(df) plt.show()

    x   y
0  15  22
1  36  61
2  39  71
3   3  46
4  38  85

     x   y
20   6  49
21  19  20
22   9  73
23  33  79
24  40  59