Write a Python program with suitable comments using pandas that performs the following tasks after
reading the file.
identify missing values,
remove duplicate records,
replace inconsistent values,
fill missing salary values using an appropriate method, and
rename the column headings.
(d) Explain different techniques used for handling missing data. Discuss situations where each technique is
appropriate. (3 Marks)
8
(e) A dataset contains the ages of 500 customers ranging from 18 to 80 years. Explain different ways to
classify these customers into age groups using discretisation (binning). (4 Marks)
(f) Write Python code to detect outliers using the interquartile range method. Also, explain the strategies for
handling outliers in data with the help of suitable examples. (4 Marks)
Q2:
(a) Two data files contain customer details and purchase details having the same format. These files may
contain overlapped data. Write and explain the Python program to merge the two files. Make and state
assumptions, if any (3 Marks)
(b) Explain hierarchical indexing. Illustrate how hierarchical indexing can be used for reshaping datasets
with suitable examples. (3 Marks)
(c) A supermarket dataset contains the following fields: (3 Marks)
Region
Product Category
Sales
Profit
Write a Python program to perform the following tasks:
group data by Region,
calculate total sales,
calculate average profit.
(d) Why is the need to create groups during data analysis? Explain with the help of an example. Explain the
working of the groupby() function in pandas. Discuss grouping using dictionaries, functions and Series
in Python with suitable examples.
(e) Make a synthetic dataset containing monthly sales of three different products for one year. Write a
Python program using Matplotlib and/or Seaborn to generate a line plot and a scatter plot. Make suitable
assumptions.
(f) Write a Python code to demonstrate the purpose of figures, axes, labels, legends, annotations and subplot
arrangements with suitable examples.
Data wrangling is the process of cleaning, transforming, and organizing raw data into a suitable format for analysis and visualization. Python's pandas library provides efficient functions for handling missing values, duplicates, inconsistent entries, and data transformation. ```python # Import pandas import pandas as pd # Read the CSV file df = pd.read_csv("employee_data.csv") # Display missing values in each column print("Missing Values:") print(df.isnull().sum()) # Remove duplicate records df = df.drop_duplicates() # Replace inconsistent values in Gender column df["Gender"] = df["Gender"].replace({ "M": "Male", "F": "Female", "male": "Male", "female": "Female" }) # Fill missing salary values with median salary df["Salary"] = df["Salary"].fillna(df["Salary"].median()) # Rename column headings df = df.rename(columns={ "EmpID": "Employee_ID", "EmpName": "Employee_Name", "Dept": "Department", "Salary": "Monthly_Salary" }) print(df.head()) ``` Missing data handling techniques are essential because incomplete data may reduce the accuracy of analysis. Deletion methods include listwise deletion, where rows containing missing values are removed. This method is appropriate when only a small percentage of records is missing and sufficient data remain. Pairwise deletion removes only missing values needed for a particular analysis and is ______ ____ ________ ________ ________ ____ _______ _________ __________.
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