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Ecommerce Purchases Data Analysis Exercises (Pandas Practice)

  Ecommerce Purchases Data Analysis Exercises (Pandas Practice) Are you learning Python Pandas for Data Analysis ?  This hands-on exercise will help you practice real-world data analysis using an Ecommerce Purchases dataset . Follow the questions step-by-step and try solving them using Pandas . Dataset Setup First, import Pandas and load the dataset: import pandas as pd ecom = pd.read_csv('Ecommerce Purchases') You can download the dataset from any dataset websites like kaggle and UCI or comment us and we will share you the data Basic Data Exploration 1. Check the first few rows of the dataset  Use .head() to view the dataset. 2. How many rows and columns are there? Use .info() to get dataset structure. Purchase Price Analysis 3. What is the average Purchase Price? 4. What are the highest and lowest purchase prices? User Insights 5. How many people have English ( 'en' ) as their language? 6. How many people have the job title "Lawyer" ? Time-Based Analysis 7...
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Groupby in Pandas | Lecture 5

  Groupby in Pandas The groupby operation in Pandas is a powerful tool for analyzing and summarizing data. It allows you to split your data into groups based on a column or index, apply a function to each group, and combine the results. 1. Basic Syntax grouped = df.groupby("column_name") groupby() groups the data based on the values in the specified column(s). It returns a DataFrameGroupBy object, which you can aggregate, transform, or iterate over. 2. Common Operations with groupby a) Aggregation Aggregation functions like sum() , mean() , count() , etc., can be applied to grouped data. Example: import pandas as pd   # Creating a sample DataFrame data = {     "Department": ["HR", "IT", "HR", "IT", "Sales", "HR", "Sales"],     "Employee": ["Alice", "Bob", "Charlie", "David", "Eva",...
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Handling Missing Data in Python DataFrames | Lecture 4

  Handling Missing Data in Python DataFrames Missing data is a common issue in data analysis, and Pandas provides several ways to handle it. Here's an overview with examples: 1. Identifying Missing Data Missing data in a DataFrame is often represented as NaN (Not a Number). You can use these methods to detect them: isnull() : Returns True for missing values. notnull() : Returns False for missing values. Example: import pandas as pd import numpy as np   # Creating a DataFrame with missing values data = {     "Name": ["Alice", "Bob", "Charlie", None],     "Age": [25, np.nan, 30, 22],     "Score": [85, 90, np.nan, 88] } df = pd.DataFrame(data)   # Detect missing values print(df.isnull())   # Check for non-missing values print(df.notnull()) 2. Dropping Missing Data dropna() : Removes rows or columns with missing values. Example: # Drop rows with any ...
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DataFrames in Python with examples | Lecture 3

  Here’s a detailed guide to DataFrames in Python with examples: What is a DataFrame? A DataFrame is a two-dimensional, labeled data structure provided by the pandas library. It can be thought of as a table, similar to a spreadsheet, SQL table, or a dictionary of Series objects. A DataFrame is highly flexible and can handle data in various formats. Key Features Labeled axes : Rows and columns have labels (index and column names). Heterogeneous data : Can contain different types of data (integers, floats, strings, etc.). Size mutable : Rows and columns can be added or deleted. How to Create a DataFrame? 1. From a Dictionary import pandas as pd   data = {     'Name': ['Alice', 'Bob', 'Charlie'],     'Age': [25, 30, 35],     'City': ['New York', 'Los Angeles', 'Chicago'] } df = pd.DataFrame(data) print(df) Output...
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