take the last value of row that is not nan and create a array consisting of these values pandas dataframe in python

You can use the pandas.DataFrame.apply method along with numpy to achieve this. Here's an example:

main.py
import pandas as pd
import numpy as np

# create sample dataframe
df = pd.DataFrame({'A': [1, 2, np.nan, 4],
                   'B': [5, np.nan, 7, 8],
                   'C': [9, 10, 11, np.nan]})

# define a function to get the last non-nan value in a row
def last_non_nan(row):
    last_value = np.nan
    for value in row.values[::-1]:
        if not np.isnan(value):
            last_value = value
            break
    return last_value

# apply the function to each row
last_vals = df.apply(last_non_nan, axis=1)

# convert to array
last_vals_array = last_vals.to_numpy()

print(last_vals_array) # [9, 8, 11, 4]
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In this example, we start by creating a sample dataframe with some missing values. We then define a function last_non_nan to get the last non-nan value in a row. This function simply loops over the values in the row (in reverse order), and returns the first non-nan value it encounters.

We then apply this function to each row of the dataframe using df.apply(last_non_nan, axis=1). This returns a pandas Series with the last non-nan value of each row.

Finally, we convert the Series to a numpy array using the to_numpy method, and print the result.