Point out the correct statement.

The correct answer is: B. NaN is the standard missing data marker used in pandas.

A. If data is a list, if index is passed the values in data corresponding to the labels in the index will be pulled out. This is not always the case. For example, if the index is a list of strings and the data is a list of numbers, then the values in data will not be pulled out.

C. Series acts very similarly to a array. This is not always the case. For example, a Series can have a name, while an array cannot.

D. None of the mentioned. This is not the correct answer.

NaN stands for “Not a Number” and is used to represent missing data in pandas. It is a special value that is not equal to any other value in pandas. NaN can be used to represent missing values in any type of data, including numbers, strings, and dates.

To use NaN in pandas, you can simply assign it to a variable or use it in a calculation. For example, the following code creates a Series with two NaN values:

“`
import pandas as pd

s = pd.Series([1, 2, np.nan, 4])

print(s)

0 1
1 2
2 NaN
3 4
“`

You can also use NaN to represent missing values in a DataFrame. For example, the following code creates a DataFrame with two NaN values:

“`
import pandas as pd

df = pd.DataFrame({‘A’: [1, 2, np.nan], ‘B’: [3, 4, 5]})

print(df)

A B
0 1 3
1 2 4
2 NaN 5
“`

NaN is a very useful tool for representing missing data in pandas. It is easy to use and can be used in any type of data.