Tracking execution progress of np.cov with a progress bar in Python

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😎 def calcula_matriz_covarianzas_np(df_rent):
arr_rent = df_rent.values # Convierte el dataframe a un array Numpy
matriz_covarianzas = np.cov(arr_rent, rowvar=False)
return matriz_covarianzas

cov_filt1 = calcula_matriz_covarianzas_np(vls_df_filt1)

My dataframe has 70000 columns and 344 rows. Let me know how can I track the very long execution of np.cov on screen. I want to see some sort of progress bar and updated elapsed time
🤖 When dealing with long-running computations like the covariance matrix calculation on a large dataframe, you can use various methods to track progress and elapsed time. However, `numpy` itself does not provide built-in progress indicators. Instead, you’ll need to integrate with other libraries like `tqdm` for progress tracking and use Python’s time tracking facilities to monitor elapsed time. Here is a way to modify your function to include a progress bar and elapsed time tracking using `tqdm` and the `time` module:

1. **Install tqdm**: First, ensure that you have `tqdm` installed in your environment. You can install it via pip:

bash
    pip install tqdm
    


2. **Modify the Function**: Update your function to include progress reporting and elapsed time. Here’s how you can do it:

python
import numpy as np
import time
from tqdm import tqdm

def calcula_matriz_covarianzas_np(df_rent):
    arr_rent = df_rent.values  # Convierte el dataframe a un array Numpy
    
    # Initialize tqdm progress bar
    pbar = tqdm(total=arr_rent.shape[1])
    
    # Tracking time
    start_time = time.time()
    
    # Dummy loop for illustration - update with actual logic as needed
    for i in range(arr_rent.shape[1]):
        # Your per column or row operation here, for example:
        # some_operation = np.cov(arr_rent[:, i], rowvar=False)
        # In actual case, np.cov will process all at once, so this is just illustrative.
        
        # Update progress after each iteration
        pbar.update(1)
        
        # Print elapsed time
        elapsed_time = time.time() - start_time
        pbar.set_description(f"Processing. Elapsed time: {elapsed_time:.2f} seconds.")
    
    pbar.close()
    
    # Your actual operation
    matriz_covarianzas = np.cov(arr_rent, rowvar=False)
    return matriz_covarianzas

# Usage
cov_filt1 = calcula_matriz_covarianzas_np(vls_df_filt1)



This code provides a basic framework. The `tqdm` library is used to create a progress bar, and the `time` library measures the elapsed time. Since `numpy`’s `np.cov` function computes the entire covariance matrix at once, it’s not trivial to insert a progress bar that accurately reflects the calculation’s progression without significantly altering the underlying computations. Thus, the dummy loop is illustrative, showing how you might implement per-column or per-row updates in a more granularly controlled computation.

In practice, for the `np.cov` function or similar large-scale computations, consider the following:

– **Parallelize**: If possible, parallelize your computation to utilize multiple cores or machines, which might speed up the computation significantly.
– **Efficiency**: Sometimes breaking down the problem or using more efficient computation methods specific to your data’s structure can improve performance.
– **External Tools**: For very long tasks, consider using tools designed for heavy computations like Dask or Spark, which provide more sophisticated ways to handle large-scale data and can integrate with progress reporting.

Keep in mind that any sort of progress update inherently adds overhead to your computation. Ensure that this is acceptable for your use case and test to confirm that the added convenience is worth the potential slowdown in overall computation time.
😎 that is an extremely long answer. This is not acceptable
🤖 To track the progress and elapsed time of the long-running `np.cov` calculation, consider using the `tqdm` library for a progress bar and Python’s `time` module to measure time. Here’s a streamlined approach:

1. Install tqdm: `pip install tqdm`.
2. Modify your function:

“`python
import numpy as np
import time
from tqdm import tqdm

def calcula_matriz_covarianzas_np(df_rent):
arr_rent = df_rent.values
start_time = time.time() # Start time tracking

with tqdm(total=arr_rent.shape[1]) as pbar: # Initialize progress bar
matriz_covarianzas = np.cov(arr_rent, rowvar=False)
pbar.update(arr_rent.shape[1]) # Update progress bar to complete

print(f”Elapsed time: {time.time() – start_time:.2f} seconds”) # Print elapsed time
return matriz_covarianzas

cov_filt1 = calcula_matriz_covarianzas_np(vls

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