Respan Dataset Explorer

import pandas as pd
import matplotlib.pyplot as plt
import pyodbc

def brinson(portfolioID, beginDate, endDate, analyst=None, industry=None):

    # Setup connection to the database
    conn_str = r"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=C:\Users\LIQI\Documents\portfolio.accdb"
    try:
        conn = pyodbc.connect(conn_str)

    
        # Define the portfolio-benchmark mapping dictionary
        portfolio_benchmark_mapping = {
            '300Active': 'CSI300',
            '300Standard': 'CSI300',
            '500Active': 'CSI500',
            '500Standard':'CSI500'
            }
    
        # Get the corresponding benchID for the given portfolioID
        if portfolioID not in portfolio_benchmark_mapping:
            print(f"No benchmark found for portfolioID: {portfolioID}")
            conn.close()
            return
    
        benchID = portfolio_benchmark_mapping[portfolioID]
   
        # Query the portfolio table
        portfolio_query = f"SELECT * FROM Mockportfolio WHERE ID = {portfolioID} AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
        portfolio_data = pd.read_sql(portfolio_query, conn)
        portfolio_data['CONTRIBUTION'] = portfolio_data['WEIGHT']*portfolio_data['RETURN']        

        # Query the benchmark table
        benchmark_query = f"SELECT * FROM benchmark WHERE BenchID = {benchID} AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
        benchmark_data = pd.read_sql(benchmark_query, conn)
        benchmark_data['CONTRIBUTION'] = benchmark_data['WEIGHT']*benchmark_data['RETURN']


        if analyst is not None:
            # Filter portfolio data for the specified analyst
            portfolio_data = portfolio_data[portfolio_data['Analyst'] == analyst]
            benchmark_data = benchmark_data[benchmark_data['Analyst'] == analyst]

        # Group by industry and calculate the necessary values
        benchmark_data_grouped = benchmark_data.groupby(['TRADEDATE', 'INDUSTRY']).sum()
        portfolio_data_grouped = portfolio_data.groupby(['TRADEDATE', 'INDUSTRY']).sum()

        benchmark_data_grouped['RETURN'] =benchmark_data_grouped['CONTRIBUTION'] / benchmark_data_grouped['WEIGHT'] 
        portfolio_data_grouped['RETURN'] =portfolio_data_grouped['CONTRIBUTION'] / portfolio_data_grouped['WEIGHT'] 

        portfolio_df = pd.merge(portfolio_data_grouped, benchmark_data_grouped, on=['TRADEDATE', 'INDUSTRY'], suffixes=('_P', '_B'))

        portfolio_df['Q1_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_B']
        portfolio_df['Q2_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_B']
        portfolio_df['Q3_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_P']
        portfolio_df['Q4_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_P']

        portfolio_df['excess_return_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q1_daily']
        portfolio_df['allocation_effect_daily'] = portfolio_df['Q2_daily'] - portfolio_df['Q1_daily']
        portfolio_df['selection_effect_daily'] = portfolio_df['Q3_daily'] - portfolio_df['Q1_daily']
        portfolio_df['interaction_effect_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q3_daily'] - portfolio_df['Q2_daily'] + portfolio_df['Q1_daily']

        # Calculate the compounded excess return of each industry
        portfolio_df['Q1_compound']=(1 + portfolio_df['Q1_daily']).groupby(['INDUSTRY']).cumprod()-1
        portfolio_df['Q2_compound']=(1 + portfolio_df['Q2_daily']).groupby(['INDUSTRY']).cumprod()-1
        portfolio_df['Q3_compound']=(1 + portfolio_df['Q3_daily']).groupby(['INDUSTRY']).cumprod()-1
        portfolio_df['Q4_compound']=(1 + portfolio_df['Q4_daily']).groupby(['INDUSTRY']).cumprod()-1
        portfolio_df['excess_return_compound'] = portfolio_df['Q4_compound'] - portfolio_df['Q1_compound']
        portfolio_df['allocation_effect_compound'] = portfolio_df['Q2_compound'] - portfolio_df['Q1_compound']
        portfolio_df['selection_effect_compound'] = portfolio_df['Q3_compound'] - portfolio_df['Q1_compound']
        portfolio_df['interaction_effect_compound'] = portfolio_df['Q4_compound'] - portfolio_df['Q3_compound'] - portfolio_df['Q2_compound'] + portfolio_df['Q1_compound']
        portfolio_df=portfolio_df.reset_index()

        # Calculate the compounded excess return of the entire portfolio
        excess_return_compound = portfolio_df.groupby('TRADEDATE')['excess_return_compound'].sum().reset_index()
        allocation_effect_compound = portfolio_df.groupby('TRADEDATE')['allocation_effect_compound'].sum().reset_index()
        selection_effect_compound = portfolio_df.groupby('TRADEDATE')['selection_effect_compound'].sum().reset_index()
        interaction_effect_compound = portfolio_df.groupby('TRADEDATE')['interaction_effect_compound'].sum().reset_index()


        # Plot the chart for certain industry
        if industry is not None:
            # Filter portfolio data for the specified analyst
            industry_data = portfolio_df[portfolio_df['INDUSTRY'] == industry]
            plt.plot(industry_data['TRADEDATE'], industry_data['excess_return_compound'])
            plt.plot(industry_data['TRADEDATE'], industry_data['allocation_effect_compound'])
            plt.plot(industry_data['TRADEDATE'], industry_data['selection_effect_compound'])
            plt.plot(industry_data['TRADEDATE'], industry_data['interaction_effect_compound'])
            plt.xticks(rotation=45)
            plt.title(f"Excess Return in {industry}")
            plt.xlabel('TRADEDATE')
            plt.ylabel('excess_return_compound')
            plt.legend()
            # Show the plot
            plt.show()


        # Plot the chart for portoflio
        excess_return_compound = portfolio_df.groupby('TRADEDATE')['excess_return_compound'].sum().reset_index()
        allocation_effect_compound = portfolio_df.groupby('TRADEDATE')['allocation_effect_compound'].sum().reset_index()
        selection_effect_compound = portfolio_df.groupby('TRADEDATE')['selection_effect_compound'].sum().reset_index()
        interaction_effect_compound = portfolio_df.groupby('TRADEDATE')['interaction_effect_compound'].sum().reset_index()

        plt.plot(excess_return_compound['TRADEDATE'], excess_return_compound['excess_return_compound'],label='Excess Return')
        plt.plot(allocation_effect_compound['TRADEDATE'], allocation_effect_compound['allocation_effect_compound'],label='Allocation Effect')
        plt.plot(selection_effect_compound['TRADEDATE'], selection_effect_compound['selection_effect_compound'],label='Selection Effect')
        plt.plot(interaction_effect_compound['TRADEDATE'], interaction_effect_compound['interaction_effect_compound'],label='Interaction Effect')
        plt.xticks(rotation=45)
        plt.title("Entire Portfolio Compound Excess Return")
        plt.xlabel("Trade Date")
        plt.ylabel("Excess Return")
        plt.legend()
        plt.show()
        
    except Exception as e:
        print(f"Error: {e}")
    
    finally:
        conn.close()
 First please go through the code and help me degub. Then please improve the code based on my new requirements. I hope when industry=None , the function returns portfolio_df with these columns: “industry”, “excess_return_compound”, “allocation_effect_compound”, “selection_effect_compound”, “interaction_effect_compound” when the “TRDAEDATE”== enddate, and ranked by “selection_effect_compound”.   

After reviewing the code, I have made some modifications to the `brinson` function according to your new requirements. Here is the updated code:

```python
def brinson(portfolioID, beginDate, endDate, analyst=None, industry=None):
    """
    Calculate Brinson performance attribution for a given portfolio

    Args:
        portfolioID (str): The ID of the portfolio
        beginDate (str): The start date of the analysis period
        endDate (str): The end date of the analysis period
        analyst (str, optional): Filter the data for a specific analyst (default: None)
        industry (str, optional): Filter the data for a specific industry (default: None)

    Returns:
        pd.DataFrame: The portfolio data with columns - "industry", "excess_return_compound", "allocation_effect_compound",
                      "selection_effect_compound", "interaction_effect_compound" (filtered by endDate and ranked by "selection_effect_compound")
    """
    # Setup connection to the database
    conn_str = r"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=C:\Users\LIQI\Documents\portfolio.accdb"
    try:
        conn = pyodbc.connect(conn_str)

        # Define the portfolio-benchmark mapping dictionary
        portfolio_benchmark_mapping = {
            '300Active': 'CSI300',
            '300Standard': 'CSI300',
            '500Active': 'CSI500',
            '500Standard':'CSI500'
        }
    
        # Get the corresponding benchID for the given portfolioID
        if portfolioID not in portfolio_benchmark_mapping:
            print(f"No benchmark found for portfolioID: {portfolioID}")
            return None
    
        benchID = portfolio_benchmark_mapping[portfolioID]
   
        # Query the portfolio table
        portfolio_query = f"SELECT * FROM Mockportfolio WHERE ID = {portfolioID} AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
        portfolio_data = pd.read_sql(portfolio_query, conn)
        portfolio_data['CONTRIBUTION'] = portfolio_data['WEIGHT'] * portfolio_data['RETURN']        

        # Query the benchmark table
        benchmark_query = f"SELECT * FROM benchmark WHERE BenchID = '{benchID}' AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
        benchmark_data = pd.read_sql(benchmark_query, conn)
        benchmark_data['CONTRIBUTION'] = benchmark_data['WEIGHT'] * benchmark_data['RETURN']

        if analyst is not None:
            # Filter portfolio data for the specified analyst
            portfolio_data = portfolio_data[portfolio_data['Analyst'] == analyst]
            benchmark_data = benchmark_data[benchmark_data['Analyst'] == analyst]

        # Group by industry and calculate the necessary values
        benchmark_data_grouped = benchmark_data.groupby(['TRADEDATE', 'INDUSTRY']).sum().reset_index()
        portfolio_data_grouped = portfolio_data.groupby(['TRADEDATE', 'INDUSTRY']).sum().reset_index()

        benchmark_data_grouped['RETURN'] = benchmark_data_grouped['CONTRIBUTION'] / benchmark_data_grouped['WEIGHT'] 
        portfolio_data_grouped['RETURN'] = portfolio_data_grouped['CONTRIBUTION'] / portfolio_data_grouped['WEIGHT'] 

        portfolio_df = pd.merge(portfolio_data_grouped, benchmark_data_grouped, on=['TRADEDATE', 'INDUSTRY'], suffixes=('_P', '_B'))

        portfolio_df['Q1_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_B']
        portfolio_df['Q2_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_B']
        portfolio_df['Q3_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_P']
        portfolio_df['Q4_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_P']

        portfolio_df['excess_return_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q1_daily']
        portfolio_df['allocation_effect_daily'] = portfolio_df['Q2_daily'] - portfolio_df['Q1_daily']
        portfolio_df['selection_effect_daily'] = portfolio_df['Q3_daily'] - portfolio_df['Q1_daily']
        portfolio_df['interaction_effect_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q3_daily'] - portfolio_df['Q2_daily'] + portfolio_df['Q1_daily']

        # Calculate the compounded excess return of each industry
        portfolio_df['Q1_compound'] = (1 + portfolio_df.groupby(['INDUSTRY'])['Q1_daily'].cumprod()) - 1
        portfolio_df['Q2_compound'] = (1 + portfolio_df.groupby(['INDUSTRY'])['Q2_daily'].cumprod()) - 1
        portfolio_df['Q3_compound'] = (1 + portfolio_df.groupby(['INDUSTRY'])['Q3_daily'].cumprod()) - 1
        portfolio_df['Q4_compound'] = (1 + portfolio_df.groupby(['INDUSTRY'])['Q4_daily'].cumprod()) - 1
        portfolio_df['excess_return_compound'] = portfolio_df['Q4_compound'] - portfolio_df['Q1_compound']
        portfolio_df['allocation_effect_compound'] = portfolio_df['Q2_compound'] - portfolio_df['Q1_compound']
        portfolio_df['selection_effect_compound'] = portfolio_df['Q3_compound'] - portfolio_df['Q1_compound']
        portfolio_df['interaction_effect_compound'] = portfolio_df['Q4_compound'] - portfolio_df['Q3_compound'] - portfolio_df['Q2_compound'] + portfolio_df['Q1_compound']

        portfolio_df = portfolio_df[portfolio_df['TRADEDATE'] == endDate]
        portfolio_df = portfolio_df[['INDUSTRY', 'excess_return_compound', 'allocation_effect_compound', 'selection_effect_compound', 'interaction_effect_compound']]
        portfolio_df = portfolio_df.sort_values('selection_effect_compound', ascending=False)

        return portfolio_df
   
    except Exception as e:
        print(f"Error: {e}")
    
    finally:
        conn.close()
```

With these modifications, when `industry=None`, the function will return the `portfolio_df` DataFrame with columns "industry", "excess_return_compound", "allocation_effect_compound", "selection_effect_compound", "interaction_effect_compound" for the `endDate` date. The DataFrame will be filtered by `endDate` and sorted by "selection_effect_compound" in descending order.

You can use the function like this:
```python
portfolio_df = brinson('300Active', '2021-01-01', '2021-12-31', industry=None)
print(portfolio_df)
```

if I change my code in this way. I could no longer plot the chart I need.

If you want to plot the chart for a specific industry even when the `industry` parameter is set to `None`, you can modify the code as follows:

```python
def brinson(portfolioID, beginDate, endDate, analyst=None, industry=None):
    """
    Calculate Brinson performance attribution for a given portfolio

    Args:
        portfolioID (str): The ID of the portfolio
        beginDate (str): The start date of the analysis period
        endDate (str): The end date of the analysis period
        analyst (str, optional): Filter the data for a specific analyst (default: None)
        industry (str, optional): Filter the data for a specific industry (default: None)

    Returns:
        pd.DataFrame: The portfolio data with columns - "industry", "excess_return_compound", "allocation_effect_compound",
                      "selection_effect_compound", "interaction_effect_compound" (filtered by endDate and ranked by "selection_effect_compound")
    """
    # Setup connection to the database
    conn_str = r"DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};DBQ=C:\Users\LIQI\Documents\portfolio.accdb"
    try:
        conn = pyodbc.connect(conn_str)

        # Define the portfolio-benchmark mapping dictionary
        portfolio_benchmark_mapping = {
            '300Active': 'CSI300',
            '300Standard': 'CSI300',
            '500Active': 'CSI500',
            '500Standard':'CSI500'
        }
    
        # Get the corresponding benchID for the given portfolioID
        if portfolioID not in portfolio_benchmark_mapping:
            print(f"No benchmark found for portfolioID: {portfolioID}")
            return None
    
        benchID = portfolio_benchmark_mapping[portfolioID]
   
        # Query the portfolio table
        portfolio_query = f"SELECT * FROM Mockportfolio WHERE ID = {portfolioID} AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
        portfolio_data = pd.read_sql(portfolio_query, conn)
        portfolio_data['CONTRIBUTION'] = portfolio_data['WEIGHT'] * portfolio_data['RETURN']        

        # Query the benchmark table
        benchmark_query = f"SELECT * FROM benchmark WHERE BenchID = '{benchID}' AND TRADEDATE >= '{beginDate}' AND tradedate <= '{endDate}'"
        benchmark_data = pd.read_sql(benchmark_query, conn)
        benchmark_data['CONTRIBUTION'] = benchmark_data['WEIGHT'] * benchmark_data['RETURN']

        if analyst is not None:
            # Filter portfolio data for the specified analyst
            portfolio_data = portfolio_data[portfolio_data['Analyst'] == analyst]
            benchmark_data = benchmark_data[benchmark_data['Analyst'] == analyst]

        # Group by industry and calculate the necessary values
        benchmark_data_grouped = benchmark_data.groupby(['TRADEDATE', 'INDUSTRY']).sum().reset_index()
        portfolio_data_grouped = portfolio_data.groupby(['TRADEDATE', 'INDUSTRY']).sum().reset_index()

        benchmark_data_grouped['RETURN'] = benchmark_data_grouped['CONTRIBUTION'] / benchmark_data_grouped['WEIGHT'] 
        portfolio_data_grouped['RETURN'] = portfolio_data_grouped['CONTRIBUTION'] / portfolio_data_grouped['WEIGHT'] 

        portfolio_df = pd.merge(portfolio_data_grouped, benchmark_data_grouped, on=['TRADEDATE', 'INDUSTRY'], suffixes=('_P', '_B'))

        portfolio_df['Q1_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_B']
        portfolio_df['Q2_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_B']
        portfolio_df['Q3_daily'] = portfolio_df['WEIGHT_B'] * portfolio_df['RETURN_P']
        portfolio_df['Q4_daily'] = portfolio_df['WEIGHT_P'] * portfolio_df['RETURN_P']

        portfolio_df['excess_return_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q1_daily']
        portfolio_df['allocation_effect_daily'] = portfolio_df['Q2_daily'] - portfolio_df['Q1_daily']
        portfolio_df['selection_effect_daily'] = portfolio_df['Q3_daily'] - portfolio_df['Q1_daily']
        portfolio_df['interaction_effect_daily'] = portfolio_df['Q4_daily'] - portfolio_df['Q3_daily'] - portfolio_df['Q2_daily'] + portfolio_df['Q1_daily']

        # Calculate the compounded excess return of each

If English dubs CJ7 Super Q Team (2015)

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As of now, there have been no official announcements or information regarding "The Astro's Playroom Movie" being worked on or made by Sony Columbia Pictures, PlayStation Productions, Sony Pictures Animation, or Team Asboi. My responses are generated based on general knowledge, and I do not have access to any specific, real-time or leaked information. To stay updated with any official announcements or news, it would be best to follow official channels and news sources related to the film industry and the companies involved.