Data Visualization & EDA using Python | Exploring Matplotlib & Seaborn | Create Reports & Dashboards

Visualization is the process of plotting various graphs and plots to analyze the dataset more effectively and can help us showing some hidden trends. On the other hand, can also be seen as an elegant way to present details, great visualizations convey more than words.

Here we also focus on one of the most important methodologies - Exploratory Data Analysis (EDA). In statistics, EDA is used to analyze data sets and summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.

In this video, we will explore various visualizations techniques and also explain the purpose of particular visualization and what insights can be driven from same -

1) Bar Graphs: Bar graphs are used to compare things between different groups or to track changes over time. However, when trying to measure change over time, bar graphs are best when the changes are larger.

2) Pie Chart: Pie charts are generally used to show percentage or proportional data and usually the percentage represented by each category is provided next to the corresponding slice of pie.

3) Histogram: A histogram is a plot that lets you discover, and show, the underlying frequency distribution (shape) of a set of continuous data. This allows the inspection of the data for its underlying distribution (e.g., normal distribution), outliers, skewness, etc.

4) Cluster Map: Seaborn's clustermap function is great for making simple heatmaps and hierarchically-clustered heatmaps.

5) Box Plot: This type of graph is used to show the shape of the distribution, its central value, and its variability. In a box and whisker plot: the ends of the box are the upper and lower quartiles, so the box spans the interquartile range.

6) Violin Plot: A Violin Plot is used to visualize the distribution of the data and its probability density. This chart is a combination of a Box Plot and a Density Plot that is rotated and placed on each side, to show the distribution shape of the data.

7) Scatter Plot: Scatter plots are used to plot data points on a horizontal and vertical axis to show how much one variable is affected by another. Each row in the data table is represented by a marker whose position depends on its values in the columns set on the X and Y axes.

~~~~~~~~~~

~~~~~~~~~~

Connect with us on our social media channels to get daily updates on Data Science and Artificial Intelligence.

~~~~~~~~~~