Seaborn: Making Sense of Data through Advanced Visualization

Making Sense of Data through Advanced Visualization

• While working in statistical data analysis project, we need Numpy, Pandas, Matplotlib and Seaborn libraries. So we need to import them in our Python code ‒

In [11]: import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

• Now we need to load some data set present in the csv file. There are various csv files present on the internet which can be downloaded and can be practiced for data analysis. I have downloaded one such csv file named flight.csv. We will load this data set using the Pandas read_csv() function.

In [12]: flights = pd.read_csv("d:/flights.csv")

flights.head()

•  The output will be ‒

•  We can also using info() function to print the summary of data frame, It returns information regarding data type, column data types, non‒null values and memory usage. For example ‒

In [13]: flights.info()

<class pandas.core.frame.DataFrame">

 RangeIndex: 144 entries, 0 to 143

Data columns (total 3 columns):

dtypes: int64(2), object(1)

memory usage: 3.5+ KB

•  Now let us discuss various plotting techniques in seaborn.

1) Bar plot

•  The barplot is used to aggregate the categorical data according to some methods and by default it's the mean. This function computes the mean for each categorical variable and represents it with bars and its confidence interval with error bars.

•  To use this plot we choose a categorical column for the x‒axis and a numerical column for the y‒axis. For example ‒ From the file healthexp.csv we can find out the health expenses spent by each country in US Dollars.

In [1]: import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

health = pd.read_csv("d:/healthexp.csv")

res = sns.barplot(x='Country',y='Spending USD', data = health)

plt.show()

Output

Code explanation: In above code,

•  We have imported numpy, pandas, matplotlib and seaborn libraries.

•  Then we have loaded healthexp.csv file using read_csv function.

•  There are two columns in this file healthexp.csv Country and Spending_USD We will make use of them for displaying bar plot. On x‒axis the Country name will be displayed and on y‒axis the Spending_USD will be displayed. Thus we can have a plot for the amount spent by each country for healthcare. For that purpose we have written the code as

 res = sns.barplot(x='Country',y='Spending_USD', data = health)

•  In the above output, the thin vertical line represents the error bar. We can remove this error bar if we pass the parameter errorbar = None. For example

res = sns.barplot(x='Country',y='Spending_USD',data = health,errorbar=None)

• Then the output will be ‒

•  Finally using plt.show() function of Matplotlib the plot will be displayed.

•  Python Seaborn allows the users to assign colors to the bars. The bar chart below will convert all the bars to green color.

res = medi sns.barplot(x='Country',y='Spending_USD", data = health,color='green')

 

2) Countplot

The countplot() function in the Python Seaborn library returns the count of total values for each category using bars. Following plot represents number of males and females survived in the Titanic boat sink incidence. This data is available in titanic.csv

In [1]: import numpy as np

import pandas as pd

import matplotlib.pyplot as plt copalbrog?

import seaborn as sns

# read dataset

titanic = pd.read_csv("d:/titanic.csv")

print(titanic.head())

# create plot

sns.countplot(x = 'Pclass',hue='Sex', data = titanic)

plt.title('Survivors')

plt.show()

Output

Code explanation: In above code

•  Initially we have imported all the required libraries such as numpy, pandas, matplotlib and seaborn.

•  We have read the csv file named titanic.csv. This file is available on the internet, it can be downloaded for the purpose of learning data analysis. I have stored it at the D drive. Hence the command for reading this csv file is ‒

titanic = pd.read_csv("d:/titanic.csv")

•  Then we are displaying only first 5 rows of the titanic.csv file using the command

print(titanic.head())

•  Then using countplot of seaborn the Pclass data is displayed. Pclass refers to passenger class (1st, 2nd, 3rd), and is a proxy for socio‒economic class. The hue is used to visualize the data of different categories in one plot. Hence the column Sex will be passed as a value to hue so that count of males and females in each class who are survived will be displayed. The data is in the object titanic. Hence we write the code as ‒

sns.countplot(x = 'Pclass', hue='sex',data = titanic)

•  The title to this plot is Survivors. For this the code is

plt.title('Survivors')

•  Finally the plot is displayed as output using the command plt.show()

 

3) Distribution plot

• This plot is used basically for univariant set of observations and visualizes it through a histogram i.e. only one observation and hence we choose one particular column of the dataset. The displot() function is used to create distribution of any continuous data.

In [5]: import numpy as np

import pandas as pd

import matplotlib.pyplot as plt.

import seaborn as sns

#read dataset

health = pd.read_csv("d:/healthexp.csv")

#display sample

print(health.head())

#create plot

res =sns.displot(x='Spending USD', kde = True, bins = 20, data = health)

plt.show()

Output

Code explanation: In above code,

 • Initially we have imported all the required libraries such as numpy, pandas, matplotlib and seaborn.

•  We have read the csv file named healthexp.csv. This file is available on the internet, it can be downloaded for the purpose of learning data analysis. I have stored it at the D drive. Hence the command for reading this csv file is ‒

health = pd.read_csv("d:/healthexp.csv")

• Then we are displaying only first 5 rows of the titanic.csv file using the command

 print(health.head())

• Then using displot of seaborn the Spending _USD data is displayed. The Spending_USD refers to amount spent by each country on health. Kernel Density Estimate (KDE) plot is a powerful tool for estimating the probability density function of continuous or non‒ parametric data. So the kde = True. bins is used to set the number of bins you want in your plot and it actually depends on your dataset. Hence we write the code as –

res =sns.displot(x='Spending_USD', kde = True, bins = 20, data = health)

•  Finally the plot is displayed as output using the command plt.show()

 

4) Heatmap

•  Heatmap allows us to represent the data in matrix‒like form. In this Heatmap, more common values or higher activities brighter colors basically reddish colors are used and to represent less common or activity values, darker colors are preferred.

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

data1 = np.random.randint(low=1,high=100,size=(10,10))

#display sample

print(data1)

#create plot

res =sns.heatmap(data = data1)

plt.show()

Output

Code explanation:

•  At the beginning of the code, all the necessary Python library files are imported.

• Then using the random.randint function the data set is obtained. The random.randint( ) is a NumPy library function that returns an array of random integers that are discrete uniform distribution of the specified dtype in the half‒open interval [low, high).

•  The sample is then displayed using print method.

• Then using heatmap function and plt.show() function the map is displayed for this sample data set.

 

5) Scatterplot

•  Scatter plots are the graphs that present the relationship between two variables in a data‒set. It represents data points on a two‒dimensional plane or on a Cartesian system. The independent variable or attribute is plotted on the X‒axis, while the dependent variable is plotted on the Y‒axis. Scatter plots instantly report a large volume of data.

•  Following example demonstrates the scatter plot in which how much US Dollars are spent by each country is displayed.

Demo example

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

health = pd.read_csv("d:/healthexp.csv")

# Display sample

print(health.head())

# Create plot

res=sns.scatterplot(x='Country',y='Spending_USD',hue='Life_Expectancy',data=health) plt.show()

Output

Code explanation: In above code,

•  We have imported required library files such as matplotlib, pandas, numpy and seaborn.

•  Then using read_csv function we have read the healthexp.csv file.

•  First five records are displayed on the console.

• Then using scatterplot function the graph is plotted.

•  Finally using plt.show() function the graph is displayed as output.

 

6) Linear regression plot

•  For linear regression plot, the implot method is used. It draws the scatterplot onto the FacetGrid.

• Following example demonstrates the year‒wise expenditure in Dollars by each country. The linear regression line is represented in the scatter plot.

Demo example

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

health = pd.read_csv("d:/healthexp.csv")

#display sample

print(health.head())

#create plot

res =sns.lmplot(x='Year',y='Spending_USD',hue='Country', data=health)

plt.show()

Output

Code explanation: In above code,

•  We have imported required library files such as matplotlib, pandas, numpy and seaborn.

•  Then using read_csv function we have read the healthexp.csv file.

•  First five records are displayed on the console.

•  Then using implot function the graph is plotted.

•  Finally using plt.show() function the graph is displayed as output.

 

7) Boxplot

•  It is used to display the summary of the set of data values having properties like minimum, first quartile, median, third quartile and maximum.

•  In the box plot, a box is created from the first quartile to the third quartile, a vertical line is also there which goes through the box at the median. Here x‒axis denotes the data to be plotted while the y‒axis shows the frequency distribution.

In [7]: import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

health = pd.read_csv("d:/healthexp.csv")

#display sample

print(health.head())

#create plot

res sns.boxplot (x='Country',y='Life Expectancy', data=health)

plt.show()

Output

 

8) Pairplot

• The pairplot represents the pairwise relationships in a dataset. Pair plot is used to understand the best set of features to explain a relationship between two variables or to form the most separated clusters.

Example code

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

flights = pd.read_csv("d:/flights.csv")

print(flights.head())

res = sns.pairplot(flights)

plt.show()

Output

•  As you can see, the above output clearly compares between the year and the number of passengers in different ways.