rasika1206/data-science-

data-science

ASSIGNMENT-LAB INTRODUCTION

ACTIVITY 1:

1. Write a Python program to select the 'name' and 'score' columns from the following DataFrame:

   Sample DataFrame:

          exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],

           'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],

          'attempts': [1, 3, 4, 3, 5, 3, 6, 1, 7, 1] } 

PROGRAM:

import pandas as pd
import numpy as np
exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],
            'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],
            'attempts': [1, 3, 4, 3, 5, 3, 6, 1, 7, 1]}

df = pd.DataFrame(exam_data)
selected_columns = df[['name', 'score']]

print(selected_columns)

OUTPUT:

2. For the above dataframe, Write a program to select the data who's attempt is greater than 3.

PROGRAM:

import pandas as pd
import numpy as np
exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],
             'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],
             'attempts': [1, 3, 4, 3, 5, 3, 6, 1, 7, 1]}

df = pd.DataFrame(exam_data)
selected_data = df[df['attempts'] > 3]

print(selected_data)

OUTPUT:

3. Write python code for indexing rows and columns based on the following conditions:

Assume we have the following dataframe:

data = {'name': ['Alice', 'Bob', 'Charlie', 'Dave'],

    'age': [25, 35, 40, 28],

    'gender': ['F', 'M', 'M', 'M'],

    'salary': [50000, 70000, 60000, 80000]}

df = pd.DataFrame(data)

a. Select rows where age is greater than 30:

b. Select rows where name contains 'e':

c. Select rows where gender is 'M' and salary is greater than 65000:

d. Select columns 'name' and 'age'

PROGRAM:

import pandas as pd

# Create a DataFrame
data = {'name': ['Alice', 'Bob', 'Charlie', 'Dave'],
        'age': [25, 35, 40, 28],
        'gender': ['F', 'M', 'M', 'M'],
        'salary': [50000, 70000, 60000, 80000]}

df = pd.DataFrame(data)

# a. Select rows where age is greater than 30
print("Rows where age is greater than 30:")
print(df[df['age'] > 30])

# b. Select rows where name contains 'e'
print("\nRows where name contains 'e':")
print(df[df['name'].str.contains('e')])

# c. Select rows where gender is 'M' and salary is greater than 65000
print("\nRows where gender is 'M' and salary is greater than 65000:")
print(df[(df['gender'] == 'M') & (df['salary'] > 65000)])

# d. Select columns 'name' and 'age'
print("\nColumns 'name' and 'age':")
print(df[['name', 'age']])

OUTPUT: