Analyzing various crimes committed against women in India in the span of 2001–2014
“No woman has to be part of physical abuse. Women have to feel like they are not alone.”
― Salma Hayek
In 2020, India celebrated it’s 73rd Independence day on 15th August. Despite India being a democratic and free country, majority of women in India are still struggling and fighting daily for their basic rights and freedom. On daily basis, we keep seeing news related to violence against women through social media or newspaper. Majority portion of the girls from urban area reports getting sexually harassed in public or at workplace.
Our main aim of the analysis is to be aware of the current situation and how the rate of violence against women in India in the span of year 2001–2014 has only increased with time.
Getting the Dataset-
For this analysis, we are selecting the below dataset, which has well categorized data of various types of crimes happening with women in India, along with the state, district and the year , when it was committed.
Also for better insight into the analysis, do refer to my Jovian Notebook here .
Analyzing our dataset -
The first step for Data Analysis is Data Cleaning, which is to remove unnecessary or noisy data from our dataset. After the process of data cleaning, our final dataset is shown below.
It has data of various crimes like Rape, Domestic Violence, Deaths caused by dowry, assault as well as insult to a women’s modesty and also cases of importation of girls.
At first, we analyse the total number of women who has been a victim of violence in India. We find that in the span of 2001–2014, that there is more than 5 million females have been a victim of assault or harassment, in India, in the span of this 14 years.
We then processed the data and took help of Matplotlib Library function to present each category of crimes using simple and basic bar charts for our visual analysis of the the given data per category.
Here the black bar graph represents the analysis of rape cases in India, consecutively, pink does for Kidnapping and abduction cases, blue for death caused by dowry, cyan for assaulting a women for her modesty , yellow for domestic violence and red for cases of importation of girls, in India.
From observing each of the bar graphs, we can conclude that crime under each category has increased and 2014 was the year where highest number of cases has been reported, which can also be proved by the below code -
count_df = crimes_df.groupby('Year')[['STATE/UT']].count()
count_dfWhich gives us the result -
We also take help of seaborn library function to use heat-map for better analysis of how more cases from each state started coming up and how the reporting ONLY increased over the years.
This result of analysis can also be taken on a better note, the reason being, that there might have been more cases of domestic violence or assault which were never reported previously, but with passing of time, women got courage to came forward and report the wrong things that was happening with them.
More detailed analysis-
With such increase in violence over the years, there is some more detailed analysis to be done “State-wise”, like which are the places that has reported the maximum number of cases under each crime category. We will try to find answer to that through the below code-
max_rape_cases = crimes_df.sort_values('Rape', ascending = False).head(10)
max_rape_casesThe above code helps us in finding the states where 10 highest reported rape cases in India, in the span of year 2001–2014.
We see that, Madhya Pradesh reported the maximum number of rape cases in the year 2014.
Similarly, after trying to analyse the top 10 highest cases reported under each category of crimes, we find out, Uttar Pradesh in 2014, reported the highest number of cases of “Death due to Dowry”, maximum cases of Domestic Violence cases came from West Bengal in the year 2014 and maximum of Importation of girls has been reported in the state of Bihar in the year 2011.
We also try to analyse the TOTAL number of cases from each state under each category over the years 2001–2014, using the code below -
counts_df = crimes_df.groupby('STATE/UT')[['Rape', 'Kidnapping_Abduction', 'Dowry_Deaths','Domestic_violence', 'Importation_of_Girls']].sum()
counts_dfAfter this we try to find out the top 5 states where maximum number of cases has been reported in TOTAL, under each category of crime, one by one. At first starting with the cases of Rape -
counts_df.sort_values(by = 'Rape', ascending = False).head(5)Which gives us the output -
This gives us the conclusion that the maximum number of rape cases over the year 2001–2014 happened in Madhya Pradesh followed by UP, Maharashtra, West Bengal and Rajasthan.
Same way we try to analyze the other categories, as well and by the analysis we find that Uttar Pradesh has reported the highest number of cases under “Kidnapping and Abduction” , where Rajasthan, Assam, West Bengal and Bihar follows the list.
For cases of Domestic Violence, West Bengal has reported the highest number of cases , where Andhra Pradesh, Rajasthan, Uttar Pradesh and Maharashtra follows the list and Bihar has reported the highest number of Importation of girl’s cases , where Jharkhand, West Bengal, Madhya Pradesh and Karnataka follows the list.
Summary
This analysis gave us a very thought-provoking insight into the state of women and girls in our society and how unsafe they are. Being a developed and independent country, it’s all of our duty to make sure that every girl or women are safe and has their basic rights, because that is how our nation will progress further!
