How to convert DataFrame.append() to pandas.concat()?-CodePudding

In pandas 1.4.0: append() was deprecated, and the docs say to use concat() instead.

FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

Codeblock in question:

def generate_features(data, num_samples, mask):
    """
    The main function for generating features to train or evaluate on.
    Returns a pd.DataFrame()
    """
    logger.debug("Generating features, number of samples", num_samples)
    features = pd.DataFrame()

    for count in range(num_samples):
        row, col = get_pixel_within_mask(data, mask)
        input_vars = get_pixel_data(data, row, col)
        features = features.append(input_vars)
        print_progress(count, num_samples)

    return features

These are the two options I've tried, but did not work:

features = pd.concat([features],[input_vars])

and

pd.concat([features],[input_vars])

This is the line that is deprecated and throwing the error:

features = features.append(input_vars)

CodePudding user response：

You can keep the DataFrames generated in the loop in a list and concatenate them with features once you finish the loop.

In other words, replace the loop:

for count in range(num_samples):
    row, col = get_pixel_within_mask(data, mask)
    input_vars = get_pixel_data(data, row, col)
    features = features.append(input_vars)
    print_progress(count, num_samples)

with the one below:

tmp = [features]
for count in range(num_samples):
    row, col = get_pixel_within_mask(data, mask)
    input_vars = get_pixel_data(data, row, col)
    tmp.append(input_vars)
    print_progress(count, num_samples)
features = pd.concat(tmp)

You can certainly concatenate in the loop but it's more efficient to do it only once.

CodePudding user response：

This will "append" the blank df and prevent errors in the future by using the concat option

features= pd.concat([features, input_vars])

However, still, without having access to actually data and data structures this would be hard to test replicate.