Seaborn.ecdfplot() method

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The Seaborn.edcfplot() method is used to plot empirical cumulative distribution functions.

The percentage or count of observations in a dataset that fall below each distinct value is represented by an empirical cumulative distribution function. It has the benefit that each observation is immediately displayed, as opposed to a histogram or density plot, therefore no binning or smoothing settings need to be changed. It also makes it easier to compare different distributions directly.

A drawback is that there may be a lack of clarity, instinctively in the link between the plot's appearance and the fundamental characteristics of the distribution, such as its central tendency, variance, and existence of any bimodality.

Syntax

Following is the syntax of the seaborn.ecdfplot() method −

seaborn.ecdfplot(data=None, *, x=None, y=None, hue=None, weights=None, stat='proportion', complementary=False, palette=None, hue_order=None, hue_norm=None, log_scale=None, legend=True, ax=None, **kwargs

Parameters

Some of the parameters of the above-mentioned method are as follows.

let us load the seaborn library and the dataset before moving on to the developing the plots.

Loading the seaborn library

To load or import the seaborn library the following line of code can be used.

Import seaborn as sns

Loading the dataset

in this article, we will make use of the Tips dataset inbuilt in the seaborn library. the following command is used to load the dataset.

tips=sns.load_dataset("tips")

The below mentioned command is used to view the first 5 rows in the dataset. This enables us to understand what variables can be used to plot a graph.

tips.head()

the below is the output for the above piece of code.

index,total_bill,tip,sex,smoker,day,time,size
0,16.99,1.01,Female,No,Sun,Dinner,2
1,10.34,1.66,Male,No,Sun,Dinner,3
2,21.01,3.5,Male,No,Sun,Dinner,3
3,23.68,3.31,Male,No,Sun,Dinner,2
4,24.59,3.61,Female,No,Sun,Dinner,4

Now that we have loaded the data, we will move on to plotting the data.

Example 1

In this example, we will see how a empirical cumulative distribution function plot is plotted for a single variable in a dataset.

import seaborn as sns
import matplotlib.pyplot as plt
tips=sns.load_dataset("tips")
tips.head()
sns.ecdfplot(data=tips, x="tip")
plt.show()

Output

the output of the above code is seen below.

Example 2

In this example, we will see the usage of the stat parameter. It can take values proportion and count. It is used to specify the distribution statistic to compute.

Passing “proportion” to the stat parameter. The code to do so is mentioned below.

import seaborn as sns
import matplotlib.pyplot as plt
tips=sns.load_dataset("tips")
tips.head()
sns.ecdfplot(data=tips, x="tip",hue="time",stat="proportion")
plt.show()

Output

The output of the following line of code is as follows

Passing “count” to the stat parameter. The code to so is mentioned below.

import seaborn as sns
import matplotlib.pyplot as plt
tips=sns.load_dataset("tips")
tips.head()
sns.ecdfplot(data=tips, x="tip",hue="time",stat="count")
plt.show()

The output of the following line of code is as follows

Example 3

The complementary parameter of the seaborn.ecdfplot() method is used to draw the complementary of the cumulative distribution function and it is computed using 1-CDF. If it is True, then the complementary is printed. The same can be seen below.

import seaborn as sns
import matplotlib.pyplot as plt
tips=sns.load_dataset("tips")
tips.head()
sns.ecdfplot(data=tips, x="tip",hue="time",stat="proportion",complementary=True)
plt.show()

Output

the following output will be plotted for the above given code snippet.

Example 4

In this example, we will see how to not send any parameters but still be able to plot data on the screen.

The dataset is handled as wide-form and a histogram is created for each numeric column if neither x nor y are assigned.

The below line of code can also be used in a scenario where there are multiple columns with the same name or beginning with the same name in a dataset.

import seaborn as sns
import matplotlib.pyplot as plt
tips=sns.load_dataset("tips")
tips.head()
sns.ecdfplot(data=tips.filter(like="total_", axis="columns"))
plt.show()

Output

the output for the code snippet is as follows.