Pandas quiz (15 MCQ)

-
Pandas MCQ Quiz

Pandas MCQ Quiz

1. What is Pandas mainly used for?

2. Which data structure in Pandas is 1-dimensional?

3. Pandas is built on top of which Python library?

4. Which function reads data from a CSV file?

5. What does df.head() do?

6. What is the default number of rows shown by df.head()?

7. Which method gives basic statistics of numeric columns?

8. Which command returns DataFrame shape?

9. What function removes missing values?

10. Which of the following merges two DataFrames?

11. What does df.info() provide?

12. Which of these is NOT a Pandas function?

13. Which library works closely with Pandas for plotting?

14. Which function is used to count unique values in a column?

15. How can you access the first row of a DataFrame?

Comments

Post a Comment

Popular posts from this blog

Topic1:- Introduction of Machine Learning

-
Image
What is Machine Learning (ML)? Machine Learning is a branch of Artificial Intelligence (AI) that enables computers to learn from data and improve their performance automatically without being explicitly programmed for every task.  In simple terms: Machine Learning is about teaching computers to learn patterns from data and make decisions or predictions. Example: Imagine you want to make a program that predicts whether an email is spam or not spam . You collect thousands of emails (data). You label them as spam or not spam (training data). You train an ML model on these examples. Later, the model can predict new, unseen emails as spam or not spam automatically. Types of Machine Learning Machine Learning is generally divided into three main types . Types of Machine Learning 1. Supervised Learning In supervised learning , the model learns from labeled data — data that already has the correct output Goal: To make predictions based on example input...
Read more

Topic3:- python's Important Libraries for Machine Learning

-
Image
Important Python libraries for Machine Learning and Data Science : 1. NumPy (Numerical Python) NumPy is a library used for numerical computation in Python. It provides multidimensional arrays (called ndarray ) and supports fast mathematical operations. Key Features: Efficient array operations (faster than Python lists) Mathematical functions: mean, sum, std, etc. Linear algebra, Fourier transforms, random numbers Example: import numpy as np # Create a NumPy array arr = np.array([1, 2, 3, 4, 5]) # Perform operations print("Mean:", np.mean(arr)) print("Array * 2:", arr * 2) Output: Mean: 3.0 Array * 2: [ 2 4 6 8 10] 2. Pandas     Pandas is used for data manipulation and analysis . It provides two main data structures: Series (1D) DataFrame (2D table like Excel) Key Features: Handling tabular data easily Data cleaning, merging, reshaping, filtering Reading/writing CSV, Excel, SQL files Example: import pandas as pd...
Read more

Topic 2:- Machine Learning Workflow

-
Image
Workflow  The Machine Learning (ML) workflow is a step-by-step process used to build, train, test, and evaluate an ML model. It ensures that data is properly prepared, the model is correctly trained, and its performance is accurately measured.   Main Stages of the ML Workflow ML Overview(step-by-step) 1. Data Collection Gather data from various sources such as CSV files, databases, APIs, sensors, or online datasets. Example: Collecting house price data (area, location, price). 2. Data Preprocessing Clean and prepare data before training. Handle missing values, outliers, and categorical encoding. Apply feature scaling and normalization. 3. Train/Test Split Dataset Division Split the dataset into two parts: Training Set: Used to train the model (70–80% of data) Testing Set: Used to test the model (20–30% of data)   Purpose: To check how well the model performs on unseen da...
Read more