Podcast
Questions and Answers
What is the primary method used to visualize total sales by location?
What is the primary method used to visualize total sales by location?
- Line graph
- Scatter plot
- Pie chart
- Bar chart (correct)
Which data operation is used to find the number of sales by each gender and location?
Which data operation is used to find the number of sales by each gender and location?
- Group by (correct)
- Sum
- Merge
- Filter
Which method is used to extract the day from the sales date?
Which method is used to extract the day from the sales date?
- pd.to_datetime(sales['Date']).dt.day (correct)
- sales['Date'].dt.day
- pd.to_datetime(sales['Date']).day
- sales['Day'] = sales['Date'].day
What is the purpose of using the 'unstack' operation on location sales data?
What is the purpose of using the 'unstack' operation on location sales data?
Which chart would you use to display the average ratings of each location?
Which chart would you use to display the average ratings of each location?
What is the purpose of data analysis?
What is the purpose of data analysis?
Which type of analytics explains what has already occurred?
Which type of analytics explains what has already occurred?
In the data analysis process, what is the primary goal of step 4, Data Preparation?
In the data analysis process, what is the primary goal of step 4, Data Preparation?
How would you retrieve rows of sales data where the total exceeds $100?
How would you retrieve rows of sales data where the total exceeds $100?
Which command would yield the maximum sales total?
Which command would yield the maximum sales total?
What does the command 'sales.groupby('City').sum()['Total']' do?
What does the command 'sales.groupby('City').sum()['Total']' do?
Which step follows data visualization in the data analysis process?
Which step follows data visualization in the data analysis process?
Which method is used to find unique payment methods in sales data?
Which method is used to find unique payment methods in sales data?
Flashcards
Data Analysis
Data Analysis
Using statistical and logical methods to understand and interpret data; it involves inspecting, cleaning, transforming, and modeling data to find useful patterns and support decision-making.
Descriptive Analytics
Descriptive Analytics
A type of data analysis that summarizes past data to understand what happened.
Predictive Analytics
Predictive Analytics
A type of data analysis that uses past data to predict future outcomes.
Prescriptive Analytics
Prescriptive Analytics
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Data Analysis Steps
Data Analysis Steps
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Pandas
Pandas
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Data Visualization
Data Visualization
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Data Filtering
Data Filtering
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Visualizing Sales by Location
Visualizing Sales by Location
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Analyzing Customer Gender by Location
Analyzing Customer Gender by Location
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Daily Sales Trend Identification
Daily Sales Trend Identification
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Branch and Membership Analysis
Branch and Membership Analysis
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Customer Spending Analysis
Customer Spending Analysis
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Study Notes
Data Analysis Overview
- Data analysis is a systematic process applying statistical or logical techniques to describe, illustrate, condense, and evaluate data.
- The goal of data analysis is to discover useful information, inform conclusions, and support decision-making.
Types of Analytics
- Descriptive analytics: Shows what has already happened.
- Predictive analytics: Shows what could happen.
- Prescriptive analytics: Shows what should happen.
Steps in Data Analysis
- Understanding the business problem
- Analyze data requirements
- Data understanding and collection
- Data preparation
- Data visualization
- Data analysis
- Deployment
Data Loading and Manipulation (Example using Pandas)
import pandas as pd: Imports the Pandas library for data manipulationsales=pd.read_csv('sales.csv'): Reads data from a CSV file named 'sales.csv' into a Pandas DataFramesales.head(10): Displays the first 10 rows of the DataFramesales['Invoice ID']: Extracts the 'Invoice ID' columnsales['Category']: Extracts the 'Category' columnsales['Category'].unique(): Identifies and displays unique categoriessales.tail(): Displays the last 10 rows of the DataFrame- Filtering Data (e.g., selecting rows where 'Gender' is 'Male'):
sales[sales['Gender']=='Male']
- Filtering and Displaying specific number of rows (e.g., first 10 rows where 'Gender' = 'Male'):
sales[sales['Gender']=='Male'].head(10)
- Filtering based on a condition (e.g., Total > 100):
sales[sales['Total']>100]
- Obtaining Summaries
sales.sum()['Quantity']:Calculates the sum of the 'Quantity' column.sales.max(): Calculates the maximum value in each column.sales.max()['Total']: Calculates the maximum value in the 'Total' column.sales.min()['Total']: Calculates the minimum value in the 'Total' column.sales.mean()['Total']: Calculates the mean of the 'Total' column
- Grouping and Aggregation (e.g., summarizing sales by city):
sales.groupby('City').sum()['Total']- Sums total sales for each city.
- Plotting (using Matplotlib):
import matplotlib.pyplot as plt: Imports necessary library for plotting.plt.bar(location, sales.groupby('Location').sum()['Total']): Example code to create a bar chart representing total sales per location.plt.plot(): Generating line graphs.plt.pie(): Generating pie charts
Additional Examples (Specific Analysis Tasks)
- Finding the highest and lowest sales locations
- Finding the most and least popular product lines (using categories)
- Identifying the days of the month with the highest sales
Additional Data Analysis Functions Examples (Grouping, Aggregating and Plotting)
sales.groupby('Month').sum()['Total']: Sum the 'Total' column by month.sales.groupby(['Category','Gender']).count()['Rating']: Counts 'Rating' based on category and gender.sales.groupby(['Category','Gender']).count()['Invoice ID']: Counts 'Invoice ID' based on category and gender.sales.groupby('Date').sum()['Total']: Calculating sums by date.- Unstacking data
sales.unstack(level=0)
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