Ramsar Sites in India

India, on its 75th year of Independence (15th August), achieved a milestone of 75 Ramsar sites. 

It recently added 11 more wetlands to the list of Ramsar sites to make total 75 Ramsar sites covering an area of 13 lakh 26 thousand 677 Hectare in the country. 

I have created an interactive map. Kindly visit the map at https://andybandra.github.io/ramsar_india/ You can see and click the map shown below.

On the map you may click an icon to get the name and state of a site, and its area in hectares.  

India is one of the Contracting Parties to Ramsar Convention, signed in Ramsar, Iran in 1971. India signed it on 1st February 1982. These are wetlands deemed to be of "international importance" under the Ramsar Convention.

What is RAMSAR Wetland Sites

The Ramsar Convention is an international treaty for the conservation and sustainable utilization of wetlands, recognizing the fundamental ecological functions of wetlands and their economic, cultural, scientific, and recreational value.

Tamil Nadu has the highest number of Ramsar Sites in India with 14 Ramsar Sites. The largest site is marked in blue icon. It is the Sundarbans National Park a national park, tiger reserve and biosphere reserve in West Bengal,

The list is available at https://en.wikipedia.org/wiki/List_of_Ramsar_sites_in_India.

75 Ramsar India Sites

KeralaDay3

Kerala Day 3

Periyar Tiger Reserve

We started from Munnar. We came down a moutain range and climed up another. I did not note down the geo locations while coming down and hence those are missing. While we did not see tigers and elephants we did see the following. Great cormorant, Woolly-necked stork, Sambar deer, Bengal monitor, White-throated kingfisher, Brahminy Kite and Gaur.
Please see the photos my facebook.

The path taken

The above interactive visualization was made using Python and Folium. I wanted to make a visualization with Folium for last 2 years!
The folium library in turn uses the leaflet library to render the web-page visualisation. Using Folium I do not have to worry about the HTML, CSS, Javascript code! Plus Python allows me to hold my data in pandas and manupulation of the data becomes convenient. Customizing markers and icons becomes easy using both the pandas and folium libraries.

Folium was not part of the standard Anaconda distribution. It had to be downloaded by using the following command.
$ conda install folium -c conda-forge
at the Anaconda promo. But before that I created a separate environment for folium (folium_project) by closing the base environment. The command was
$ conda create --name folium_project --clone base

Folium allows one to leverage the power of leaflet by just writing a few lines of code. The web-page visualization including its code is on my github account.

CSR Multi-index report in python using pandas

I wanted to make a report with multi-index columns and rows using python and pandas. 

The starting point was data in Excel file. The data was brought in pandas data frame.

I wanted an output report that looked like the report shown below.

But there was one condition. The condition was the quarters of the year has to be as per Indian financial year that starts on 1st April and end on 31st March the following year. 

What helped me was the below syntax.

Q-DEC specifies quarterly periods whose last quarter ends on the last day in December. 

Q-MAR specifies quarterly periods whose last quarter ends on the last day in March.

So, I chose Q-MAR. 

The data shown is about spend on Social and Environmental efforts of a company in India. A similar report has to be published by the company, as applicable, as per Indian laws. 

The report format can also be extended to report on financial numbers. The data itself is imaginary. 

ET top 25 - further analysis

Further to below blog, I have added a few more plots. 

A) I have added a column to dataframe to indicate if the company belongs to public (pub) sector or private (pvt) sector. The below plot (similar to the earlier blogpost) shows the plot by types of companies. 

B) I also wanted to study the distribution, so I have added a histogram. From it, we can see that most of companies are below revenue level of Rs 2,00,000 Cr (USD 28.5billion).

C) I added one more calculated column in the dataframe to show profit percentage compared to revenue in the below graph. High profits percentage companies are in private sector and are IT companies namely TCS (only company with 20% plus profit ratio) and Infosys. The other two companies are banking firms namely HDFC Bank and HDFC. The top five companies by revenue are not that profitable by the ratio of profits to revenue and most of them are public sector companies. 

The data was analyzed using python, pandas and plotted using seaborn library. 

Cleaned category list using Python3

I analyzed an excel containing a list of 300+ #unicorns using #Python and #Pandas. I made some nice charts also. 

Later I realized that the column containing the classification values of unicorns such as TravelTech, EduTeach, Ecommerce had not been written consistently. 

These similar looking classification values were written differently. 

Ecommerce was written as eCommerce, ecommerce, e-commerce and so on.  With these classification values my analysis wasn’t right. The grouping on classification values had given me incorrect analysis. These kinds of errors are common when no data validation is in place.

So started all over again. Just to describe in this post; I have taken the values and created a list. 

The existing values are given below. 

['Auto Tech', 'AutoTech', 'Digital health', 'Digital Health', 'EdTech', 'Edtech', 'Ed Tech', 'e-commerce', 'eCommerce', 'ecommerce', 'Food & Beverage', 'Food & Beverages', 'Food and Beverage', 'Health & Wellnes', 'Health & Wellness', 'IoT', 'Internet of Things', 'Sales Tech', 'SalesTech', 'On Demand', 'On-Demand', 'On-demand', 'Supply Chain & Logistics', 'Supply chain & Logistics', 'Travel Tech', 'TravelTech']

Using Python, I cleaned the list. I used #Spyder 4.0 which is beautiful. I used good old loops in the logic. I am comfortable with loops. 

The new list is given below.

['Autotech', 'Autotech',    'Digitalhealth',    'Digitalhealth',    'Edtech',  'Edtech', 'Edtech', 'Ecommerce',    'Ecommerce',    'Ecommerce',    'Food&Beverages', 'Food&Beverages', 'Food&Beverages',    'Health&Wellness', 'Health&Wellness', 'Iot', 'Internetofthings', 'Salestech', 'Salestech', 'Ondemand', 'Ondemand', 'Ondemand', 'Supplychain&Logistics', 'Supplychain&Logistics', 'Traveltech', 'Traveltech']   

The new cleaned  list is now ready for analysis. All the classification values are written consistently. 

However, there is one more iteration I have to do. IoT and ‘Internet of Things’ are shown separately.

I hope to take care of that as well shortly. 

Mutual Funds Performance

The diagram shows two sub plots. The left subplot shows the 5 large cap funds by their names ans assets (AUM) in rupees crores.

One way to judge a fund is by its AUM. The AUM has grown because investors have invested money it. The large AUM may overcome the sudden withdrawals by investors. 

But this is not the only way to evaluate performance of a fund. 

So on the right hand side I have plotted another chart indicating the performance (returns) of the fund over last 10 years for the regular scheme. All funds have given a return in excess of 10 per cent. 

10 years may be a good indicator to judge the performance. 

However both the parameters together may not be sufficient to evaluate performance of funds. There are other factors as well that are not considered in this post.

The data has been analyzed and plotted using Python3 and pandas. 

This time I imported .xls file in to pandas for analysis. 

The source of the data is https://www.amfiindia.com

Disclaimer: This post is not a suggestion or advice to invest in any particular mutual fund. Please contact your investor advisor for it. 

World Unicorns 2019

World Unicorns 2019

USA obviously is ahead with 172 unicorns. China comes second with 89 unicorns. UK has 17, with India a close behind UK with 16 unicorns.

South Korea and Germany are next with 8 each. 9 countries have between 2 to 4 unicorns. 11 countries have at least 1 unicorn. In all 26 countries have unicorns.

How I did it?

World unicorns 2019

For mapping unicorns on the world map; I started with downloading the excel file from CB Insights.

The first challenge to me was the valuation was in currency format which was read as a string in pandas. By writing a function the valuation was converted into a number.

Then built-in group by function was used to group and aggregate on count of unicorns by country.

Now I did not have country codes in my data frame. The country codes were needed later to map it using pygal library. So, I downloaded country codes file from pygal. Later I mapped it and had my original data frame have country codes in an additional column. However, the mapping did not recognize South Korea. Its official name is different. So, I had to correct it.

Then the countries were put in five buckets as per the count of unicorns in a country.

The final challenge was to convert these five data frames into dictionaries. It was repeatedly giving type error. So, I had to go back to pandas and dictionaries and read again about sub setting pandas and converting to dictionaries. Finally, with a lot of tries I could convert five data frames in to five dictionaries.

These five dictionaries were used with pygal to plot the words map.