The correct answer is: B. ix
ix is a concise means of selecting data from a pandas object. It is a more flexible and powerful alternative to the traditional indexing methods, such as slicing and boolean indexing.
ix can be used to select data from a pandas object by specifying the row and column indices. For example, to select the first row and second column of a DataFrame, you would use the following syntax:
df.ix[0, 1]
ix can also be used to select data from a pandas object by specifying a range of row and column indices. For example, to select the first three rows and second and third columns of a DataFrame, you would use the following syntax:
df.ix[0:3, 1:3]
ix can also be used to select data from a pandas object by specifying a boolean mask. For example, to select all rows in a DataFrame where the value in the “A” column is greater than 10, you would use the following syntax:
df.ix[df['A'] > 10]
ix is a powerful and flexible tool for selecting data from pandas objects. It is a more concise and efficient alternative to the traditional indexing methods.
A. In is a boolean operator that is used to test whether a value is present in a list or a string. For example, the following code will return True if the value “10” is present in the list [1, 2, 3, 4, 5]:
if 10 in [1, 2, 3, 4, 5]:
print("True")
else:
print("False")
B. ix is a concise means of selecting data from a pandas object. It is a more flexible and powerful alternative to the traditional indexing methods, such as slicing and boolean indexing.
C. ipy is not a valid indexing capability in pandas.
D. None of the mentioned is not a valid answer.