Company productivity dashboard 📊

employee_engagement = df_activities.groupby('employee_id').size()\ .reset_index(name='activity_count')\ .merge(df_employees[['employee_id', 'name', 'team']], on='employee_id')\ .sort_values('activity_count', ascending=False)

team_composition = df_employees.groupby(['team', 'role']).size().unstack()

def get_team_insights(team_name: str, df_employees: pd.DataFrame = df_employees, df_activities: pd.DataFrame = df_activities) -> dict: """Generate comprehensive insights for a specific team. :param team_name: str, name of the team for which to generate insights. :param df_employees: pd.DataFrame, DataFrame containing employee data. :param df_activities: pd.DataFrame, DataFrame containing activity data. :return: dict, dictionary containing team insights. """ team_members = df_employees[df_employees['team'] == team_name] team_activities = df_activities.merge( df_employees[df_employees['team'] == team_name][['employee_id', 'name', 'role']], on='employee_id' ) team_size = len(team_members) insights = { 'basic_stats': { 'team_size': team_size, 'total_activities': len(team_activities), 'avg_activities_per_person': round(len(team_activities) / team_size, 2) if team_size > 0 else 0, 'avg_productivity': round(team_activities['productivity_score'].mean(), 2) if team_size > 0 else 0 }, 'team_composition': team_members.groupby('role').size().to_dict(), 'activity_breakdown': team_activities['activity_type'].value_counts().to_dict(), 'top_performers': team_activities.groupby('name')['productivity_score'].mean().nlargest(3).to_dict(), 'most_active_members': team_activities.groupby('name').size().nlargest(3).to_dict(), 'recent_activities': team_activities.sort_values('date', ascending=False).head(5)[ ['date', 'name', 'activity_type', 'productivity_score'] ].to_dict('records') } return insights

if member_stats: employee_id = df_employees[df_employees['name'] == person]['employee_id'].iloc[0] df_employees['join_date'] = pd.to_datetime(df_employees['join_date']) if df_activities['date'].dtype == 'O': # Check if the type is object, which means it might not be a datetime df_activities['date'] = pd.to_datetime(df_activities['date']) insights = get_employee_insights(employee_id, df_employees, df_activities) employee_viz = visualize_employee_insights(employee_id, df_employees, df_activities) employee_viz.show()

def compare_with_team(employee_id, df_employees, df_activities): """ Compare employee metrics with their team averages. """ employee = df_employees[df_employees['employee_id'] == employee_id].iloc[0] team = employee['team'] employee_activities = df_activities[df_activities['employee_id'] == employee_id] employee_metrics = { 'productivity': employee_activities['productivity_score'].mean(), 'duration': employee_activities['duration_hours'].mean(), 'activity_count': len(employee_activities) } team_members = df_employees[df_employees['team'] == team]['employee_id'].tolist() team_members.remove(employee_id) team_activities = df_activities[df_activities['employee_id'].isin(team_members)] team_metrics = { 'productivity': team_activities['productivity_score'].mean(), 'duration': team_activities['duration_hours'].mean(), 'activity_count': len(team_activities) / len(team_members) } fig = go.Figure() metrics = ['productivity', 'duration', 'activity_count'] employee_values = [employee_metrics[m] for m in metrics] team_values = [team_metrics[m] for m in metrics] fig.add_trace(go.Bar( name='Employee', x=metrics, y=employee_values, marker_color='rgb(26, 118, 255)' )) fig.add_trace(go.Bar( name='Team Average', x=metrics, y=team_values, marker_color='rgb(158, 158, 158)' )) fig.update_layout( title=f"{employee['name']} vs {team} team average", barmode='group', height=400 ) return fig