SKILL ORIENTED COURSE
EXPLORATORY DATA ANALYSIS USING PYTHON
DEPARTMENT OF CSE-DATA SCIENCE
Name _________________________
Name ________________________________________Branch___________________
______________________ _______Year of Study ___________________
Regd. No ________________________________Year
-------------------------------------------------------------------------------------------------------
Subject____________________________________________ __________________
Subject________________________________________________________________
VIGNAN’S NIRULA INSTITUTE OF TECHNOLOGY AND SCIENCE FOR WOMEN
GUNTUR 522005.
PEDAPALAKALURU ROAD, GUNTUR-522005.
(Affiliated to JNTU KAKINADA, Kakinada)
2024-2025
VIGNAN’S NIRULA INSTITUTE OF TECHNOLOGY AND SCIENCE FOR WOMEN
PEDAPALAKALURU ROAD, GUNTUR-522005.
GUNTUR 522005.
(Affiliated to JNTU KAKINADA, Kakinada)
CERTIFICATE
This is to certify that __________________________________________________
bearing the Regd. No ________________________is a student of _______________B. Tech
_______________Semester has completed _________________ experiments in
_____________________________________Laboratory during the academic year 2024-2025.
2024
Signature of
Head of the Department Signature of
Laboratory In Charge
Signature of
External Examiner
EXPLORATORY DATA ANALYSIS USING PYTHON
(SKILL DEVELOPMENT COURSE)
S.No. Experiment Page No.
a) Download Dataset from Kaggle using the following link :
https://www.kaggle.com/datasets/sukhmanibedi/cars4u
1. 1
b) Install python libraries required for Exploratory Data Analysis
(numpy, pandas, matplotlib, seaborn)
Perform Numpy Array basic operations and Explore Numpy Built-in
2. 3
functions.
3. Loading Dataset into pandas dataframe 6
4. Selecting rows and columns in the dataframe 7
Apply different visualization techniques using sample dataset
5. 11
a) Line Chart b) Bar Chart c) Scatter Plots d)Bubble Plot
6. Generate Scatter Plot using seaborn library for iris dataset 13
Apply following visualization Techniques for a sample dataset
7. 14
a) Area Plot b) Stacked Plot c) Pie chart d) Table Chart
Generate the following charts for a dataset.
8. 18
a) Polar Chart b)Histogram c)Lollipop chart
9. Case Study: Perform Exploratory Data Analysis with Personal Email Data 18
Perform the following operations
1. Merging Dataframes
10. 2. Reshaping with Hierarchical Indexing 22
3. Data Deduplication
4. Replacing Values
Apply different Missing Data handling techniques
1. NaN values in mathematical Operations
11. 2. Filling in missing data 24
3. Forward and Backward filling of missing values
4. Filling with index values
5. Interpolation of missing values
Apply different data transformation techniques
1. Renaming axis indexes
12. 2. Discretization and Binning 27
3. Permutation and Random Sampling
4. Dummy variables
Study the following Distribution Techniques on a sample data
1. Uniform Distribution
2. Normal Distribution
13. 3. Gamma Distribution 30
4. Exponential Distribution
5. Poisson Distribution
6. Binomial Distribution
14. Perform Data Cleaning on a sample dataset. 37
Compute measure of Central Tendency on a sample dataset
15. 39
a)Mean b)Median c)Mode
Explore Measures of Dispersion on a sample dataset
16. 40
a) Variance b) Standard Deviation c) Skewness d) Kurtosis
a) Calculating percentiles on sample dataset
17. 41
b) Calculate Inter Quartile Range(IQR) and Visualize using Box Plots
Perform the following analysis on automobile dataset.
18. 42
a) Bivariate analysis b)Multivariate analysis
19. Perform Time Series Analysis on Open Power systems dataset 44
Perform hypothesis testing using statsmodels library
20. 45
a) Z-Test b)T-Test
Develop model and Perform Model Evaluation using different metrics
21. 48
such as prediction score, R2 Score, MAE Score, MSE Score.
22. Case Study: Perform Exploratory Data Analysis with Wine Quality Dataset 49
