Final Project

import pandas as pd import numpy as np import plotly.graph_objects as go import plotly.express as px dfShooting = pd.read_csv('shootings.csv') def count_clean(df, column): series = df[column].value_counts() new_df = series.to_frame(name='counts') return new_df

state_df = count_clean(dfShooting, 'state') fig = go.Figure(data=go.Choropleth( locations=state_df.index, # Spatial coordinates z = state_df['counts'], # Data to be color-coded locationmode = 'USA-states', # set of locations match entries in `locations` colorscale = 'Reds', marker_line_color='white', # line markers between states colorbar_title = "Numbers of Police Shootings", )) fig.update_layout( title_text = '2015-2020 US Police Shootings', geo_scope='usa', # limite map scope to USA ) fig.show()

dfCA = dfShooting[dfShooting.state == 'CA'] CA_df = count_clean(dfCA, 'city') city_df = pd.read_csv('CA cities.csv') df = CA_df.merge(city_df, left_on=CA_df.index, right_on='Location', how='left') df.dropna() fig = px.density_mapbox(df, lat='Latitude', lon='Longitude', z='counts', radius=20, center=dict(lat=35.5, lon=-120), zoom=5, mapbox_style="stamen-terrain") fig.show()

len(state_df) state_df.sort_values(by='counts') dfAbb = pd.read_csv('abb.csv') df = state_df.merge(dfAbb, left_on=state_df.index, right_on='abbreviation', how='left') del df['abbreviation'] df = df[['state full name', 'counts']] df.tail()[::-1]

gender = dfShooting['gender'].value_counts() age = dfShooting['age'].value_counts(bins=5) gender, age

armed = dfShooting['armed'].value_counts() mental = dfShooting['signs_of_mental_illness'].value_counts() armed, mental

filtered_df = dfShooting[['state', 'race']] sum_df = filtered_df['state'].value_counts() filtered_df['combined'] = filtered_df['state'] + ', ' + filtered_df['race'] filtered_df['counts'] = 0 filtered_df = filtered_df[['combined', 'counts']] count_df = filtered_df.groupby('combined').count() count_df['combined'] = count_df.index count_df.index = np.arange(len(count_df)) count_df[['state', 'race']] = count_df['combined'].str.split(pat=", ", expand=True) count_df = count_df.merge(sum_df, left_on='state', right_on=sum_df.index, how='left') count_df.rename(columns={'state_y': 'sum'}, inplace=True) count_df['percentage'] = count_df['counts'] / count_df['sum'] dfPercentage = pd.read_csv('US population by race.csv') final_df = count_df.merge(dfPercentage, left_on='race', right_on='self-identified race', how='left') final_df = final_df[['state', 'race', 'counts', 'percentage', 'percentage of the population']] fig = px.sunburst(final_df, path=['state', 'race'], values='percentage', color='percentage of the population', color_continuous_scale='Rdbu_r') fig.update_layout(autosize=False, width=800, height=600) fig.show()