Question

Assume you have the Pandas DataFrame data, with the following contents:

our_columns_name column_A column_B column_C column_D column_E

our_index_name                                                   

row_name_0               9        93        71    Hello       102

row_name_1              28        64        37       my        92

row_name_2              13        91        93     name       104

row_name_3              45        29        54       is        74

row_name_4               0        36        31    Jason        36

Each column has a dtype (data type). Which of the following could be set of dtypes for this DataFrame?

Hint 1: None of the numeric values shows a decimal point. (A float shows a decimal point, while an int does not. For example, 3. is a float, while 3 is an int.)

Hint 2: A column that has strings has a dtype of object, not str.

You can create the DataFrame above with this code:

data = pd.DataFrame({'column_A': {'row_name_0': 9,

'row_name_1': 28,

'row_name_2': 13,

'row_name_3': 45,

'row_name_4': 0},

'column_B': {'row_name_0': 93,

'row_name_1': 64,

'row_name_2': 91,

'row_name_3': 29,

'row_name_4': 36},

'column_C': {'row_name_0': 71,

'row_name_1': 37,

'row_name_2': 93,

'row_name_3': 54,

'row_name_4': 31},

'column_D': {'row_name_0': 'Hello',

'row_name_1': 'my',

'row_name_2': 'name',

'row_name_3': 'is',

'row_name_4': 'Jason'},

'column_E': {'row_name_0': 102,

'row_name_1': 92,

'row_name_2': 104,

'row_name_3': 74,

'row_name_4': 36}})

• A.

  column_A     int64

  column_B   float64

  column_C   float64

  column_D    object

  column_E     int64

• B.

  column_A     int64

  column_B     int32

  column_C     int32

  column_D    int64

  column_E     int64

• C.

  column_A     int64

  column_B     int64

  column_C     int64

  column_D    object

  column_E     int64

Answer 1

#option c is correct which is

column_A     int64

column_B     int64

column_C     int64

column_D    object

column_E     int64

#source code in python:

import pandas as pd
data = pd.DataFrame({'column_A': {'row_name_0': 9,

'row_name_1': 28,

'row_name_2': 13,

'row_name_3': 45,

'row_name_4': 0},

'column_B': {'row_name_0': 93,

'row_name_1': 64,

'row_name_2': 91,

'row_name_3': 29,

'row_name_4': 36},

'column_C': {'row_name_0': 71,

'row_name_1': 37,

'row_name_2': 93,

'row_name_3': 54,

'row_name_4': 31},

'column_D': {'row_name_0': 'Hello',

'row_name_1': 'my',

'row_name_2': 'name',

'row_name_3': 'is',

'row_name_4': 'Jason'},

'column_E': {'row_name_0': 102,

'row_name_1': 92,

'row_name_2': 104,

'row_name_3': 74,

'row_name_4': 36}})
print(data.dtypes)

#output:

#if you have any doubt comment below...if you like give thumbs up.....