Week 6 Demo

In [1]:

notebook_path = Path(os.path.abspath("__file__")).parent
os.chdir(notebook_path)
os.getcwd()

notebook_path = Path(os.path.abspath("__file__")).parent os.chdir(notebook_path) os.getcwd()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[1], line 1
----> 1 notebook_path = Path(os.path.abspath("__file__")).parent
      2 os.chdir(notebook_path)
      3 os.getcwd()

NameError: name 'Path' is not defined

In [9]:

import pandas as pd
data = pd.read_csv('./Worcester_Police_Use_of_Force_Incidents_(July_2024).csv')

# Convert 'Date___Time' column to datetime format
data['Date___Time'] = pd.to_datetime(data['Date___Time'], format='%m/%d/%y %I:%M %p')

# Create a new column for the date
data['Date'] = data['Date___Time'].dt.date

# Group data by date and count incidents per day
incidents_per_day = data.groupby('Date').size()

import pandas as pd data = pd.read_csv('./Worcester_Police_Use_of_Force_Incidents_(July_2024).csv') # Convert 'Date___Time' column to datetime format data['Date___Time'] = pd.to_datetime(data['Date___Time'], format='%m/%d/%y %I:%M %p') # Create a new column for the date data['Date'] = data['Date___Time'].dt.date # Group data by date and count incidents per day incidents_per_day = data.groupby('Date').size()

In [3]:

import plotly.express as px
import pandas as pd

# Sample time series data
# Assuming 'incidents_per_day' is a Pandas Series with a DateTime index
# Creating some mock data for this example
date_rng = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')

# Create the line plot
fig = px.line(x=incidents_per_day.index, 
              y=incidents_per_day.values, 
              labels={'x': 'Date', 'y': 'Incidents'},
              title='Interactive Time Series with Date Range Selector',
              line_shape='spline')

# Add a range slider for selecting the time range
fig.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=1, label="1 Month", step="month", stepmode="backward"),
                dict(count=6, label="6 Months", step="month", stepmode="backward"),
                dict(count=1, label="1 Year", step="year", stepmode="backward"),
                dict(step="all", label="All Time")
            ])
        ),
        rangeslider=dict(visible=True),  # Show the range slider below the x-axis
        type="date"
    )
)

# Show the plot
fig.show()

import plotly.express as px import pandas as pd # Sample time series data # Assuming 'incidents_per_day' is a Pandas Series with a DateTime index # Creating some mock data for this example date_rng = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D') # Create the line plot fig = px.line(x=incidents_per_day.index, y=incidents_per_day.values, labels={'x': 'Date', 'y': 'Incidents'}, title='Interactive Time Series with Date Range Selector', line_shape='spline') # Add a range slider for selecting the time range fig.update_layout( xaxis=dict( rangeselector=dict( buttons=list([ dict(count=1, label="1 Month", step="month", stepmode="backward"), dict(count=6, label="6 Months", step="month", stepmode="backward"), dict(count=1, label="1 Year", step="year", stepmode="backward"), dict(step="all", label="All Time") ]) ), rangeslider=dict(visible=True), # Show the range slider below the x-axis type="date" ) ) # Show the plot fig.show()