Which of the following is true?

• If data is an ndarray, index must be the same length as data.

• Series is a one-dimensional labeled array capable of holding any data type.

• Both of the above

• None of the above

Pandas series is a combination of________

• Records arranged in row and column

• Collection of 1 dimensional data and associated index

• Collection of 2 dimensional data

• Collection of 3 dimensional data

Which of the following are ways to access series elements?

• Using for loop

• Indexing

• Slicing

• All of these

Which of the following is correct statement to create a series of multiple table of 3?

• s = pd.Series(range(3,31,3))

• s = pd.Series(range(3,3*11))

• s = pd.Series(range(3,3,3))

• All of these

To specify datatype int16 for a Series object, you can write :

• pd.Series(data = array, dtype = int16)

• pd.Series(data = array, dtype = numpy.int16)

• pd.Series(data = array.dtype = pandas.int16)

• all of the above

Pandas supports which of the following types of indexes?

• Positional and Labelled Indexing

• Numbered and Valued Indexing

• Row and Column Indexing

• Loop Indexing

Which of the following is correct statement to create a series of multiple repeated values 44,55 for three times?

• s = pd.Series([44,55],3)

• s = pd.Series(np.tile[44,55],3)

• s = pd.Series(44,55,3)

• s = pd.Series(range(44 and 55, 3))

Which of the following correct statement for creating empty series? (Assume that pandas library is already imported as pd)

• ser = pd.Series(NaN)

• ser = pd.Series(None)

• ser = pd.Series()

• ser = pd.Series

Which of the following is correct statement for creating a series to assign None to all series elements of 5 elements?

• s = pd.Series()

• s = pd.Series(None,index=[1,2,3,4,5])

• s = pd.Series(none,5)

• s = pd.Series(none*5)

Which one of the following is correct statement to create series 35,38,41,44 using a python sequence?

• s = pd.Series(range(35,44,3))

• s = pd.Series(range(35,45,3))

• s = pd.Series(range(35 to 45,3))

• s = pd.Series(range(35-45,3))