## Violin Plot for Data Analysis

**Violin Plot** is a method to visualize the distribution of numerical data of different variables. It is similar to Box Plot but with a rotated plot on each side, giving more information about the density estimate on the y-axis.

The density is mirrored and flipped over and the resulting shape is filled in, creating an image resembling a violin. The advantage of a violin plot is that it can show nuances in the distribution that aren’t perceptible in a boxplot. On the other hand, the boxplot more clearly shows the outliers in the data.

Violin Plots hold more information than the box plots, they are less popular. Because of their unpopularity, their meaning can be harder to grasp for many readers not familiar with the violin plot representation.

To get the link to Iris Data, click – here.

**Attribute Information about data set:**

Attribute Information: -> sepal length in cm -> sepal width in cm -> petal length in cm -> petal width in cm -> class: Iris Setosa Iris Versicolour Iris Virginica Number of Instances: 150 Summary Statistics: Min Max Mean SD Class Correlation sepal length: 4.3 7.9 5.84 0.83 0.7826 sepal width: 2.0 4.4 3.05 0.43 -0.4194 petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) petal width: 0.1 2.5 1.20 0.76 0.9565 (high!) Class Distribution: 33.3% for each of 3 classes.

**Loading Libraries**

`import` `numpy as np` `import` `pandas as pd` `import` `seaborn as sns` `from` `matplotlib ` `import` `pyplot` `import` `seaborn` |

**Loading Data**

`data ` `=` `pd.read_csv(` `"Iris.csv"` `)` ` ` `print` `(data.head(` `10` `))` |

**Output:**

**Description**

`data.describe()` |

**Output:**

**Info**

`data.info()` |

**Output:**

**Describing ‘SepalLengthCm’ parameter of Iris dataset.**

`data[` `"SepalLengthCm"` `].describe()` |

**Output:**

count 150.000000 mean 5.843333 std 0.828066 min 4.300000 25% 5.100000 50% 5.800000 75% 6.400000 max 7.900000 Name: SepalLengthCm, dtype: float64

**Code #1:** Violin Plot for ‘SepalLengthCm’ Parameter.

`fig, ax ` `=` `pyplot.subplots(figsize ` `=` `(` `9` `, ` `7` `))` `sns.violinplot( ax ` `=` `ax, y ` `=` `data[` `"SepalLengthCm"` `] )` |

**Output:**

As u can see we have a higher density between 5 and 6. That is very significant because as in the SepalLengthCm description, a mean value is at 5.43.

**Code #2:** Violin Plot for ‘SepalLengthWidth’ Parameter.

`fig, ax ` `=` `pyplot.subplots(figsize ` `=` `(` `9` `, ` `7` `))` `sns.violinplot(ax ` `=` `ax, y ` `=` `data[` `"SepalWidthCm"` `] )` |

**Output:**

Here also, Higher density is at the mean = 3.05

**Code #3:** Violin Plot comparing ‘SepalLengthCm’ and ‘SepalWidthCm’.

`fig, ax ` `=` `pyplot.subplots(figsize ` `=` `(` `9` `, ` `7` `))` `sns.violinplot(ax ` `=` `ax, data ` `=` `data.iloc[:, ` `1` `:` `3` `])` |

**Output:**

**Code #4:** Violin Plot comparing ‘SepalLengthCm’ species wise.

`fig, ax ` `=` `pyplot.subplots(figsize ` `=` `(` `9` `, ` `7` `))` `sns.violinplot(ax ` `=` `ax, x ` `=` `data[` `"Species"` `], ` ` ` `y ` `=` `data[` `"SepalLengthCm"` `] )` |

**Output:**

Last Updated on October 23, 2021 by admin