import pandas as pd import numpy as np from matplotlib import pyplot as plt import seaborn as sn import plotly.express as px sn.set_style(style= 'whitegrid')

url = 'https://raw.githubusercontent.com/Qalinle1996/DS-portofolio/main/History_of_Mass_Shootings_in_the_USA.csv' data = pd.read_csv(url) data.head()

data.columns

data.info()

# convert the date to datetime type data['Date'] = pd.to_datetime(data['Date'])

data.info()

# Add a year column to the dataframe data['year'] = data.Date.dt.year data

data['City'].nunique()

data['State'].nunique()

data['State'].unique()

data['Date'].min()

data['Date'].max()

data.describe()

# histogram of death column distribution death = data[(data['Dead'] >0)] px.histogram(death, x = 'Dead', template = 'plotly_dark', title = 'The distribution of the death cases from every mass-shooting from 1924 to 2022', nbins = 100)

# histogram of death column px.histogram(data, x = 'Injured', template = 'plotly_dark', title = 'The distribution of the Injured cases from every mass-shooting from 1924 to 2022', nbins = 100)

# # histogram of death column px.histogram(data, x = 'Total', template = 'plotly_dark', title = 'The distribution of the Total cases from every mass-shooting from 1924 to 2022', nbins = 100)

# create a new dataframe dead_data = data # set year column as an index dead_data = dead_data.set_index('year') plt.figure(figsize = (20,15)) plt.plot(dead_data['Dead'], color = 'r' , lw = 5 , alpha = 1, linestyle=':' , label = 'Dead per year') plt.title('Mass shooting fatal cases per year from 1924. US', fontsize = 20) plt.xlabel('Dead People', fontsize = 20) plt.ylabel('Frequency', fontsize = 20) plt.xticks(fontsize = 18) plt.yticks(fontsize = 18) plt.show()

# create a new dataframe injured_data = data # set year column as an index injured_data = injured_data.set_index('year') plt.figure(figsize = (20,15)) plt.plot(injured_data['Injured'], color = 'b' , lw = 2 , alpha = 1, linestyle='-' ) plt.title('Mass shooting injured cases per year from 1924. US', fontsize = 20) plt.xlabel('Number if Injured People through the year', fontsize = 20) plt.ylabel('Frequency', fontsize = 30) plt.xticks(fontsize = 18) plt.yticks(fontsize = 18) plt.show()

# create a new dataframe total_data = data # set year column as an index total_data = total_data.set_index('year') plt.figure(figsize = (20,15)) plt.plot(total_data['Total'], color = 'g' , lw = 3 , alpha = 0.7, linestyle='-' ) plt.title('Mass shooting Total cases - Dead and Injured - per year from 1924. US', fontsize = 20) plt.xlabel('Number of Injured + dead through the year', fontsize = 20) plt.ylabel('Frequency', fontsize = 20) plt.xticks(fontsize = 18) plt.yticks(fontsize = 18) plt.show()

# pd.options.display.max_rows = 65 # Create an empty list of all years in the data set Years = [2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997, 1996, 1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979, 1978, 1977, 1976, 1975, 1974, 1973, 1972, 1971, 1970, 1968, 1966, 1965, 1959, 1958, 1949, 1946, 1945, 1935, 1933, 1929, 1927, 1924] # Reverse the years values years = Years[::-1] grouped_by_year = data.groupby(['year'])['Dead'].sum().to_frame() grouped_by_year.reset_index() grouped_by_year['Years']= years grouped_by_year= grouped_by_year.sort_values(by = 'Dead' , ascending = False) grouped_by_year px.bar(data_frame = grouped_by_year , x = 'Years' , y = 'Dead' , color = 'Dead', template = 'plotly_dark', title = 'Bar char, comparison of mass shooting fatal cases from 1924 to 2022 in the USA' )

# group the data by the death column to select grouped_by_states_dead_cases = data.groupby('State')['Dead'].sum().to_frame().sort_values('Dead', ascending = False) grouped_by_states_dead_cases = grouped_by_states_dead_cases.reset_index() grouped_by_states_dead_cases = grouped_by_states_dead_cases.head(10) px.bar(grouped_by_states_dead_cases, x = 'State', y = 'Dead', title = 'Bar char, comparison of mass shooting fatal cases, 1924 to 2022. top 10 states in the USA.', template = 'plotly_dark' )

grouped_by_state_total_cases = data.groupby('State')['Total'].sum().to_frame().sort_values('Total', ascending = False) grouped_by_state_total_cases = grouped_by_state_total_cases.reset_index() grouped_by_state_total_cases = grouped_by_state_total_cases.head(20) px.bar(grouped_by_state_total_cases, x = 'State', y = 'Total', color = 'Total', template = 'plotly_dark', title = 'States with the highest mass shooting victims both fatal and non-fatal. top 20 ')

temp_df = data[(data['Dead'] >=4)] mass_shooting_per_state = temp_df.groupby('State')['Date'].count().to_frame().reset_index() mass_shooting_per_state = mass_shooting_per_state.rename(columns = {'Date':'num_of_mass-shooting'}) mass_shooting_per_state = mass_shooting_per_state.sort_values(by = 'num_of_mass-shooting', ascending = False) mass_shooting_per_state = mass_shooting_per_state.head(20) px.bar(mass_shooting_per_state, x = 'State' , y = 'num_of_mass-shooting', template = 'plotly_dark', color = 'num_of_mass-shooting', title = 'Number of mass shootings in the United States between 1924 and July 2022. Top 20 States')

temp = data[(data['Dead'] >=4)] mass_shooting_per_year = temp.groupby('year')['Date'].count().to_frame().reset_index() mass_shooting_per_year = mass_shooting_per_year.rename(columns = {'Date':'num_of_mass-shooting_per_year'}) mass_shooting_per_year = mass_shooting_per_year.sort_values(by = 'num_of_mass-shooting_per_year', ascending = False) mass_shooting_per_year = mass_shooting_per_year.head(20) px.bar(mass_shooting_per_year, x = 'year', y = 'num_of_mass-shooting_per_year', color = 'num_of_mass-shooting_per_year', template = 'plotly_dark', title = 'Bar char, number of mass shooting per year from 1924 to 2022 in the USA. top 20')

# create a dataframe where atleasts one person deid and three people injured dead_injured_at_least_four = data[(data['Dead'] > 0) & (data['Injured'] >= 3)] # plot a treemap to show the concentration of total mass shooting casualties and group them by the states they happened. # from 1924 to 2022 fig = px.treemap(dead_injured_at_least_four, path=['State'], values='Total', color='Dead') fig.show()

!pip install wordcloud

from wordcloud import WordCloud text = data['Description'].values wordcloud = WordCloud().generate(str(text)) plt.figure(figsize =(40,30)) plt.imshow(wordcloud) plt.axis("off") plt.show()