How To Find An Outlier In A Box Plot

What is Box plot and the condition of outliers?

Box plot is a data visualization plotting function. It shows the min, max, median, start quartile, and third quartile. All of the things volition exist explained briefly. All of the holding of box plot tin be accessed by dataframe.column_name.describe() function.

Aspects of a box plot

Here is a well distributed data-set.

              data                            =              [              0              ,                            i              ,                            ii              ,                            3              ,                            4              ,                            5              ,                            6              ]            
              df                            =              pd.DataFrame(information, columns                            =              [              'Num'              ])            
              df            

Output:

pytohn-box-plot

Now plotting the information frame using box plot,

plt.figure(figsize = ( 10 , 7 ))

df.boxplot()

Caption of the different parts of the box plot

The maximum and the minimum is the max and min value of the data-set. fifty percentile is the median of the data-set. The first quartile is the median of the data between the min to 50% and the third quartile is the median of the data between l% to max. The outliers will be the values that are out of the (1.5*interquartile range) from the 25 or 75 percentile.

Methods of finding the values

Utilize the median to divide the ordered information gear up into 2 halves.
1) If there is an odd number of data points in the original ordered data prepare, practice not include the median (the primal value in the ordered list) in either half.
ii) If there is an even number of data points in the original ordered data set, carve up this data set exactly in half.
The lower quartile value is the median of the lower half of the data. The upper quartile value is the median of the upper one-half of the data.
An extreme value is considered to exist an outlier if it is at least 1.5 interquartile ranges below the first quartile, or at least 1.5 interquartile ranges to a higher place the third quartile.

Different Cases of Box Plot

Allow us see different cases of box plots with unlike examples and permit'south try to sympathise each 1 of them.

```
Now for the data = [0, one, ii, three, 6, half-dozen, 6]
```
Here the median of the data is iii, min is 0 and max is 6. The commencement quartile is 1.5 but after fifty% to max values, all of the data is half-dozen. So the 3rd quartile and the max values are the same.
```
For the data = [0, one, 2, 3, 4, v, 9]              
```
Hither the median is three. For the tertiary quartile, the values are 4, 5 and 9. And then the 3rd quartile is 5 and the max value is nine.
```
For the information = [0, ane, two, iii, four, 5, ten]
```
Unlike the previous one, the max value is v because the third quartile is 4.five and the interquartile range is (four.5-1.v)=>iii. And so, 1.5*three is 4.5 and tertiary quartile(4.v)+four.v=>9. Then ten is larger than the limit 9, thus information technology becomes an outlier.

Description

The box plot seem useful to detect outliers only it has several other uses besides. Box plots take up less space and are therefore especially useful for comparing distributions between several groups or sets of data. It is a direct representation of the Probability Density Function which indicates the distribution of data.