Learn Python & Pandas in 20 Minutes (project files included!)

Are you ready to learn Python? 🐍

Python is a powerful addition to any data professional's stack of tools. Its data analysis library, Pandas, is a workhorse used daily by analysts, data scientists, and data engineers alike.

And the best part? ANYONE can start learning Python and Pandas in just a matter of minutes – for free!

In this demo, we’ll play the role of an Analytics Consultant for a local restaurant. 🍝
We've been given flat files storing information on the restaurant's menu items and orders for the past three months, and our task is to identify opportunities to streamline the business.

Using Python & Pandas, we'll explore the data and generate visualizations that help pinpoint order seasonality, menu item performance, and more.

Along the way, we’ll walk through the steps of an exploratory analysis:

✔️ Read & profile data from flat files
✔️ Join data across multiple tables
✔️ Apply filtering, sorting and aggregation methods
✔️ Visualize data with charts and graphs

…all in less than 20 minutes!

If you’re excited to learn more and build job-ready, expert-level skills, check out our Python courses and learning paths at Maven Analytics!

🔗 Helpful links:

👉 Full Data Analysis with Python & Pandas Course:

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⏱️ Timestamps:

00:00 - Intro
00:17 - Project Brief
01:21 - Review of Tables
03:13 - STEP 1: Read & Profile the Data
06:46 - STEP 2: Join Data Across Multiple Tables
11:15 - STEP 3: Apply Filtering, Sorting & Aggregation Methods
12:39 - STEP 4: Visualize Data with Charts & Graphs
19:23 - Project Recap
19:34 - Resources and Next Steps
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[envsaf]

Very nice quick run to data exploration

[DanielOnyedikachiOkwandu]

Hello please the link to download the datasets you were analysing isn't in the downloadable link

[gurvindersingharora]

Hello Sir,

When are you going to add more case Studies of Advanced DAX in your Udemy Advanced DAX Course ?

I want to create custom solutions the way the students from SQL do !

Please upgrade your Course

Thanks 😊

[ByTyoma]

These files are incorrect. These are not the files you use in the video

[debrecenisandorcontrolling]

I really enjoyed the video, especially the heatmap. However, since the video was uploaded, the dataset has changed. Instead of a timestamp column, the dataset now contains separate date and time columns, which require a different transformation compared to the video.

1. Combine and transform into a datetime column.
2. Insert the new column at the beginning.

3. Finally, delete the date and time columns.

my solution to transform:
orders_df =

# Combine date and time into a timestamp column with explicit formatting to prevent UserWarning

order_timestamp = + " " + orders_df["order_time"], format="%m/%d/%y %I:%M:%S %p")

# insert timestamp to the beginning
orders_df.insert(0, "order_timestamp", order_timestamp)

# delete date and time cols
orders_df = orders_df.drop(["order_date", "order_time"], axis= 1)

[ajinkyashinde]

Why do we need to use python for this? We can creat this analysis using excel, power bi