# Plotting High Quality Plots in Python with Plotly and Clufflinks¶

## Introduction¶

Hello everyone, in this blog we are going to explore some of most used and simplest plots in the data analysis. If you have made your hand dirty playing with data then you might have come across at least anyone of these plots. And in Python, we have been doing these plots using Matplotlib. But above that, we have some tools like Seaborn (built on the top of Matplotlib) which gave use nice graphs. But those were not interactive plots. Plotly is all about interactivity!

This blog will be updated frequently.

• January 28 2022, started blog writing.

## Installation¶

This blog was prepared and run on the google colab and if you are trying to run codes in local computer, please install plotly first by pip install plotly. You can visit official link if you want. Then cufflinks by pip install cufflinks.

If you are running Plotly on colab then use pio.renderers.default = "colab" else choose according to your need.

## Get Dataset¶

For the purpose of visualization, we are going to look into COVID 19 Dataset publicly available on GitHub.

Since the main goal of this blog is to explore visualization not the analysis part, we will be skipping most of analysis and focus only on the plots.

## Check Missing Columns¶

First step of any data analysis is checking for missing columns.

It seems that we have lots of missing data (97%+).

## Pie Chart¶

#### Missing Values Columns¶

How about plotting the counts of missing columns in pie chart?

To make it more fast, we will be using only columns that are missing more than 100000 values.

Above plot seems little bit dirty and we could smoothen it by not providing textinfo.

## Line Chart¶

### New Cases Per day¶

The location field of our data seems to be having country name, continent name and world so we will skip those locations first. Then we will calculate the aggregated value of each day by grouping on date level

Lets first plot simple line chart with only total cases. But we could always plot more lines within it.

Above plot seems to be cool but now lets plot multiple lines at the same time on same figure.

It does not look that good because the new_deaths is not clearly visible lets draw them in sub plots so that we could see each lines distinctly.

Now its better.

We could even plot secondary y variable. Now lets plot new tests and new vaccinations side by side.

In above plot, we were able to insert two y axes.

## Scatter Plot¶

### New deaths vs New Cases¶

How about viewing the deaths vs cases in scatter plot?

It seems that most of the deaths happened while cases were little.

We could even plot secondary y. Lets visualize new tests along with them.

We could even use subplots on it.

## Bar Plot¶

How about plotting top 20 countries where most death have occured?

But first, take the aggregate data by taking maximum of total deaths column. Thanks to the author of this dataset we do not have to make our hands dirty much. Then take top 20 by using nlargest.

It seems awesome. We could play with theme also.

We could even make it horizontal.

We could even plot multiple bars at the same time. In seaborn, we could do this by using Hue but here, we only have to pass it in y. Lets plot bars of total deaths, total cases and total tests.

But total deaths is not visible clearly, lets try to use different mode of bar. We could choose one from the 'stack', 'group', 'overlay', 'relative'.

But it is still not clear. One solution is to plot in subplots.

Much better.

## Histogram Chart¶

How about viewing the distribution of totel tests done?

To see histogram of other columns in same figure we will use keys.

It does not look good as the data is not distributed properly. Lets visualize it in different plots.

## Box Plot¶

How about viewing outliers in data?