Descriptive Statistics - Box plot and Histogram in Google Colab Python

#BoxPlot #Histogram #Colab

Creating Box plot / whisker's plot and histogram using Google Colab #python will become simple task after watching this video. We have explained the need to use Python for chart preparation on comparison to Spreadsheet as Python helps to create charts with better visualization by writing simple codes and also it saves time which in turn will improve the productivity of Data #Analysts.

Histogram and Box plot plays a vital role in Descriptive Statistics. Both plots are used to visualized the spread range of data. Histogram shows the frequency in bar graph format whereas box plot is representation of the distribution of a variable through its quartiles.

Various types of #distribution in histogram - a) Normal Distribution, b)Right Skewed / Positively Skewed distribution, c) Left Skewed / Negatively Skewed distribution

Some the important things we need to know about box plots are,

a) Inter-Quartile Range (IQR) :Central 50% data spread, 25th Percentile to 75th Percentile of data spread. i.e., IQR = Value of 75th Percentile - Value of 25th Percentile

b) Upper Control Limit (UCL) : 75th Percentile + 1.5*IQR or Maximum

c) Lower Control Limit (LCL) : 25th Percentile - 1.5*IQR or Minimum

d) Outliers: Any value above upper control limit and any value below lower control limit

We have explained all the above statistical details related to histogram and box plot in this video.

00:00 Introduction
01:23 Creating Histogram in Google Colab Python
04:32 Right Skewed & Left Skewed Histogram
06:49 Creating Box Plot in Google Colab Python
11:56 Box plot showing relationship
14:41 Conclusion

If you have any queries please post it in comment section or at our blog - https://pakuppaivu.blogspot.com

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