Prompt Title: Excel Scientific Notation.

To determine whether a value in a Pandas DataFrame is in scientific notation, even if it contains string numbers, you can use a regular expression to check if it matches the pattern of scientific notation. Here's an example code snippet:

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import pandas as pd
import numpy as np

# create a sample dataframe
df = pd.DataFrame({
    'A': [1.23e6, '456', 7.89e-4, '1.2e+03', 'foo'],
    'B': [1.23e6, 456, 7.89e-4, 1.2e+03, 'bar']
})

# define a regular expression to match scientific notation
sci_notation_pattern = r'^[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?$'

# define a lambda function to check for scientific notation
def is_scientific_notation(x):
    if isinstance(x, (int, float, np.number)):
        # if the value is a number, convert to a string first
        return bool(re.match(sci_notation_pattern, str(x)))
    elif isinstance(x, str):
        # if the value is a string, check if it matches the pattern directly
        return bool(re.match(sci_notation_pattern, x))
    else:
        # for other types, assume it's not in scientific notation
        return False

# apply the function to each value in the dataframe
sci_notation_mask = df.applymap(is_scientific_notation)

# print the resulting mask
print(sci_notation_mask)

This will produce a boolean mask indicating which values in the DataFrame are in scientific notation. The regular expression used in this example matches the standard format for scientific notation, allowing for an optional sign (+/-) before the exponent and optional decimal points. The lambda function checks whether the value is a number or a string and applies the regular expression accordingly. In the example above, the resulting mask would be:

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       A      B
0   True  True
1  False  False
2   True  True
3   True  True
4  False  False

This indicates that the values in columns "A" with indices 0, 2, and 3 are in scientific notation, while all other values are not.