You are working with a dataset stored in a CSV file and need to perform some analysis on it. The dataset is stored in a file named "final-data-mod3.csv" and can be read into a pandas DataFrame using the provided code snippet.

Write a function `analyze_data()` that takes no parameters and returns the following information about the dataset:

1. The total number of rows in the DataFrame.
2. The number of unique feature vectors in the DataFrame.
3. The number of missing values (NaNs) in each column of the DataFrame.
4. The average value for each numerical column in the DataFrame.

Your function should return a dictionary with keys "total_rows", "unique_vectors", "missing_values", and "average_values". The corresponding values should be the results of the above calculations.

For example, if the DataFrame has 1000 rows, 500 unique feature vectors, 10 missing values in column "A", and an average of 3.5 in column "B", the returned dictionary would be:
```
{
  "total_rows": 1000,
  "unique_vectors": 500,
  "missing_values": {
    "A": 10
  },
  "average_values": {
    "B": 3.5
  }
}
```

Assume that the DataFrame stored in the CSV file has already been loaded into the 'df' variable.
user
Sure, here is a Python function that performs the required analysis and returns a dictionary in the desired format. Ensure that you have properly loaded the CSV file into the 'df' variable as mentioned in your instruction.

```python
import pandas as pd

def analyze_data():
    df = pd.read_csv('final-data-mod3.csv')

    # Calculate total number of rows
    total_rows = len(df)

    # Calculate number of unique feature vectors
    unique_vectors = df.dropna(axis=0).shape[0]

    # Calculate number of missing values in each column
    missing_values = {}
    for col in df.columns:
        missing_values[col] = len(df[df[col].isna()])

    # Calculate average value for numerical columns
    average_values = {}
    for col in df.columns:
        if df[col].isnull().sum() == len(df):
            average_values[col] = None
        else:
            average_values[col] = df[col].mean()

    return {
        'total_rows': total_rows,
        'unique_vectors': unique_vectors,
        'missing_values': missing_values,
        'average_values': average_values
    }
```

This function first imports the `pandas` library, reads the `final-data-mod3.csv` file into the 'df' variable, and calculates the required statistics. The `dropna(axis=0)` method is used to remove the rows with missing values before calculating the number of unique feature vectors. The `isnull()` method is used to check for missing values in columns. The `isnull().sum()` method is used to count the number of missing values in each column.

Note that if a column contains only NaN values, the `mean()` method will raise an error. In such cases, the function in the dictionary returns `None` for the `average_values`.
assistant
You are working with a dataset stored in a CSV file and need to perform some analysis on it. The dataset is stored in a file named "final-data-mod3.csv" and can be read into a pandas DataFrame using the provided code snippet.

Write a function `analyze_data()` that takes no parameters and returns the following information about the dataset:

1. The total number of rows in the DataFrame.
2. The number of unique feature vectors in the DataFrame.
3. The number of missing values (NaNs) in each column of the DataFrame.
4. The average value for each numerical column in the DataFrame.

Your function should return a dictionary with keys "total_rows", "unique_vectors", "missing_values", and "average_values". The corresponding values should be the results of the above calculations.

For example, if the DataFrame has 1000 rows, 500 unique feature vectors, 10 missing values in column "A", and an average of 3.5 in column "B", the returned dictionary would be:
```
{
  "total_rows": 1000,
  "unique_vectors": 500,
  "missing_values": {
    "A": 10
  },
  "average_values": {
    "B": 3.5
  }
}
```

Assume that the DataFrame stored in the CSV file has already been loaded into the 'df' variable.
user
Sure, here's a function that should do what you're asking for. This function uses the pandas library to perform the required operations on the DataFrame.

```python
import pandas as pd
import numpy as np

# Assuming that the DataFrame is already loaded into the 'df' variable
# df = pd.read_csv('final-data-mod3.csv')

def analyze_data():
    # Total number of rows in the DataFrame
    total_rows = df.shape[0]

    # Number of unique feature vectors in the DataFrame
    # Assuming that the feature vectors are the rows themselves
    unique_vectors = df.dropna().drop_duplicates().shape[0]

    # Number of missing values (NaNs) in each column of the DataFrame
    missing_values = df.isna().sum().to_dict()

    # Average value for each numerical column in the DataFrame
    # We use the select_dtypes function to select only numerical columns
    average_values = df.select_dtypes(include=np.number).mean().to_dict()

    # Create the dictionary with the results
    result_dict = {
        "total_rows": total_rows,
        "unique_vectors": unique_vectors,
        "missing_values": missing_values,
        "average_values": average_values
    }

    return result_dict
```

This function calculates the total number of rows, the number of unique feature vectors (assuming that the feature vectors are the rows themselves), the number of missing values in each column, and the average value for each numerical column. The results are then stored in a dictionary and returned.
assistant