# Loading in the pandas module
import pandas as pd
# Reading in the data
deaths = pd.read_csv('datasets/deaths.csv')
# Print out the shape of the dataset
print(deaths.shape)
# Printing out the first 5 rows
print(deaths.head())
(489, 3)
Death X coordinate Y coordinate
0 1 51.513418 -0.137930
1 1 51.513418 -0.137930
2 1 51.513418 -0.137930
3 1 51.513361 -0.137883
4 1 51.513361 -0.137883
# Summarizing the content of deaths
deaths.info()
# Define the new names of your columns
newcols = {
'Death': 'death_count',
'X coordinate': 'x_latitude',
'Y coordinate': 'y_longitude'
}
# Rename your columns
deaths.rename(columns=newcols, inplace=True)
# Describe the dataset
deaths.describe()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 489 entries, 0 to 488
Data columns (total 3 columns):
Death 489 non-null int64
X coordinate 489 non-null float64
Y coordinate 489 non-null float64
dtypes: float64(2), int64(1)
memory usage: 11.5 KB
# Create `locations` by subsetting only Latitude and Longitude from the dataset
locations = deaths[['x_latitude', 'y_longitude']]
# Create `deaths_list` by transforming the DataFrame to list of lists
deaths_list = locations.values.tolist()
# Check the length of the list
len(deaths_list)
# Plot the data on map (map location is provided) using folium and for loop for plotting all the points
import folium
map = folium.Map(location=[51.5132119,-0.13666], tiles='Stamen Toner', zoom_start=17)
for point in range(0, len(deaths_list)):
folium.CircleMarker(deaths_list[point], radius=8, color='red', fill=True, fill_color='red', opacity = 0.4).add_to(map)
map
# Import the data
pumps = pd.read_csv('datasets/pumps.csv')
# Subset the DataFrame and select just ['X coordinate', 'Y coordinate'] columns
locations_pumps = pumps[['X coordinate', 'Y coordinate']]
# Transform the DataFrame to list of lists in form of ['X coordinate', 'Y coordinate'] pairs
pumps_list = locations_pumps.values.tolist()
# Create a for loop and plot the data using folium (use previous map + add another layer)
map1 = map
for point in range(0, len(pumps_list)):
folium.Marker(pumps_list[point], popup=pumps['Pump Name'][point]).add_to(map1)
map1
# Import the data the right way
dates = pd.read_csv('datasets/dates.csv', parse_dates=['date'])
# Set the Date when handle was removed (8th of September 1854)
handle_removed = pd.to_datetime('1854/9/8')
# Create new column `day_name` in `dates` DataFrame with names of the day
dates['day_name'] = dates['date'].dt.day_name()
# Create new column `handle` in `dates` DataFrame based on a Date the handle was removed
dates['handle'] = dates.date > handle_removed
# Check the dataset and datatypes
dates.info()
# Create a comparison of how many cholera deaths and attacks there were before and after the handle was removed
dates.groupby(['handle']).sum()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 43 entries, 0 to 42
Data columns (total 6 columns):
order 43 non-null int64
date 43 non-null datetime64[ns]
attacks 43 non-null int64
deaths 43 non-null int64
day_name 43 non-null object
handle 43 non-null bool
dtypes: bool(1), datetime64[ns](1), int64(3), object(1)
memory usage: 1.8+ KB
import bokeh
from bokeh.plotting import output_notebook, figure, show
output_notebook(bokeh.resources.INLINE)
# Set up figure
p = figure(plot_width=900, plot_height=450, x_axis_type='datetime', tools='lasso_select, box_zoom, save, reset, wheel_zoom',
toolbar_location='above', x_axis_label='Date', y_axis_label='Number of Deaths/Attacks',
title='Number of Cholera Deaths/Attacks before and after 8th of September 1854 (removing the pump handle)')
# Plot on figure
p.line(dates['date'], dates['deaths'], color='red', alpha=1, line_width=3, legend='Cholera Deaths')
p.circle(dates['date'], dates['deaths'], color='black', nonselection_fill_alpha=0.2, nonselection_fill_color='grey')
p.line(dates['date'], dates['attacks'], color='black', alpha=1, line_width=2, legend='Cholera Attacks')
show(p)
# Based on John Snow's map and the data John Snow collected, what would you say?
john_snow_knows_nothing = False