Date-based indexing and slicing in Data Analysis Python - Mini Project: Build & Apply

Date-based indexing and slicing

📖 Scenario: You work in a small company that tracks daily sales data. You want to analyze sales for specific dates and date ranges to understand trends.

🎯 Goal: Learn how to create a time series data structure and use date-based indexing and slicing to select data for specific dates and ranges.

📋 What You'll Learn

Create a pandas Series with dates as the index

Use a date string to select data for a single day

Use date strings to slice data for a range of days

Print the selected data

💡 Why This Matters

🌍 Real World

Date-based indexing is useful in finance, sales, weather data, and any field where time series data is analyzed.

💼 Career

Data analysts and data scientists often need to filter and analyze data by dates to find trends and patterns.

1

Create a pandas Series with dates as index

Import pandas as pd. Create a pandas Series called sales with these values: [200, 220, 250, 210, 230] and use these dates as the index: ['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05']. Convert the dates to datetime format using pd.to_datetime().

Data Analysis Python

# Your code here

Hint

Use pd.to_datetime() to convert the list of date strings to datetime objects before using them as the index.

2

Create a date string to select a single day

Create a variable called single_date and set it to the string '2024-06-03'.

Data Analysis Python

import pandas as pd

dates = pd.to_datetime(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05'])
sales = pd.Series([200, 220, 250, 210, 230], index=dates)
# Your code here

Hint

Just assign the string '2024-06-03' to the variable single_date.

3

Select sales data for the single date and a date range

Use single_date to select the sales value for that day from sales and store it in sales_single. Then create two variables start_date and end_date with values '2024-06-02' and '2024-06-04'. Use these to slice sales for the date range and store it in sales_range.

Data Analysis Python

import pandas as pd

dates = pd.to_datetime(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05'])
sales = pd.Series([200, 220, 250, 210, 230], index=dates)
single_date = '2024-06-03'
# Your code here

Hint

Use sales[single_date] to get one day and sales[start_date:end_date] to get a range.

4

Print the selected sales data

Print sales_single and then print sales_range to show the sales for the single date and the date range.

Data Analysis Python

import pandas as pd

dates = pd.to_datetime(['2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05'])
sales = pd.Series([200, 220, 250, 210, 230], index=dates)
single_date = '2024-06-03'
sales_single = sales[single_date]
start_date = '2024-06-02'
end_date = '2024-06-04'
sales_range = sales[start_date:end_date]
# Your code here

Hint

Use two print() statements: one for sales_single and one for sales_range.