Dynamic Filters and Dynamic Data Grouping

# Function to apply granularity and calculate averages
def apply_granularity(df, interval):
    # Ensure 'Start Time' is set as the index
    df = df.set_index('Start Time')
    
    aggregated_data = df.resample(interval).agg({
        'Minimum Latency': 'mean',
        'Maximum Latency': 'mean',
        'Average Latency': 'mean'
    }).dropna()
    
    return aggregated_data.round()

# Function to detect anomalies using IQR and handle NaN values
def detect_anomalies(latency_data):
    Q1 = latency_data['Maximum Latency'].quantile(0.25)
    Q3 = latency_data['Maximum Latency'].quantile(0.75)
    IQR = Q3 - Q1
    upper_bound = Q3 + 1.5 * IQR
    
    # Handle cases where upper_bound may be NaN
    if pd.isna(upper_bound):
        upper_bound = 0
    
    latency_data['Anomaly'] = latency_data['Maximum Latency'].apply(lambda x: 'Yes' if (x > upper_bound) else 'No')
    return latency_data.round(), round(upper_bound)

# Function to generate a table of latency stats
def generate_latency_table(latency_data, interval, upper_bound):
    latency_data['DateHour'] = latency_data.index.strftime('%Y-%m-%d %H:00')

    def anomaly_range(x):
        valid_anomalies = x[x > upper_bound]
        if not valid_anomalies.empty:
            return f"{int(valid_anomalies.min())}-{int(x.max())}"
        else:
            return "No anomaly"
    
    latency_data['Min Above Upper Bound'] = latency_data['Maximum Latency'].apply(lambda x: x if x > upper_bound else None)
    
    table_data = latency_data.groupby('DateHour').agg({
        'Maximum Latency': [lambda x: round(x.quantile(0.75)), lambda x: round(x.quantile(0.95))],
        'Min Above Upper Bound': 'min',
        'Anomaly': lambda x: (x == 'Yes').sum()
    })
    
    table_data.columns = ['Max. Latency 75th Percentile', 'Max. Latency 95th Percentile', 'Min Anomaly Latency', 'Anomalies Count']
    table_data = table_data.round()
    table_data['Anomalies'] = latency_data.groupby('DateHour')['Maximum Latency'].apply(anomaly_range).fillna("No anomaly")
    
    return table_data

# Granularity intervals for different time frames
daily_granularity_intervals = {"5": '5T', "10": '10T', "15": '15T', "30": '30T'}
weekly_granularity_intervals = {"240": '4H', "480": '8H', "720": '12H'}
monthly_granularity_intervals = {"720": '12H', "1440": '24H'}

The Above is the Python code that I have done to get my desired output and I can get the Excel files from this but I want to do the same thing in Power BI as a Table as well as the Line chart and Bubble chart to show the Q3, Q4 as Line chart and Count of Anomalies as Bubble chart with size variation of Bubble based on the counts of the anomalies.

Please find the data template given below. I have 14 Prob and 15 Destinations, which are distinct counts. 

The Granularity I have to focus are given as:

• 10 minutes, 20 minutes, 30 minutes for hourly data
• 1 hour, 4 hours, 8 hours, 12 hours for daily data
• 12 hours, 24 hours for Weekly and Monthly data

I hope this information can help you more to provide me help on the same.

Looking forward to hear back from you