Pandas with Python 2.7 Part 5 - Column Operations (Math, moving averages)

TL;DR

Learn the basic column manipulation abilities of pandas in Python, including simple math operations, moving averages, and calculating differences between columns.

Transcript

right what is going on everybody welcome to another Python and pandas tutorial video in this video we're gonna be talking about is just some quick basic column manipulation abilities with pandas so with that let's go ahead and get started make some space here and after we've created our data frame one of the most basic things that we can do is we c... Read More

Key Insights

• 🐼 Pandas offers a variety of basic column manipulation abilities, including simple math operations, moving averages, and calculating differences between columns.
• 🫥 These operations can be performed using the DataFrame syntax or by accessing columns directly using the dot notation.
• 🤣 The rolling mean function in pandas allows us to calculate moving averages, which can be useful for trend analysis.
• 🐼 The diff function in pandas helps calculate the differences between columns, providing insights into changes over time.
• 🐼 If a desired operation is not available in pandas, it is possible to map a function to the pandas data frame or series.
• 🐼 Exploring and utilizing the built-in abilities of pandas can greatly simplify data manipulation and analysis tasks.
• 😫 Pandas offers a comprehensive set of tools for working with tabular data in Python.

Questions & Answers

Q: How can we perform simple math operations on columns in pandas?

To perform simple math operations on columns in pandas, we can use the DataFrame syntax to subtract one column from another, or we can use the dot notation to access the columns directly. For example, we can calculate the difference between the high and low values by subtracting the 'high' column from the 'low' column.

Q: How can we calculate moving averages in pandas?

We can calculate moving averages in pandas using the rolling mean function. By specifying the column we want to perform the moving average on and the number of periods we want to use, we can create a new column with the moving average values. For example, we can calculate the 100-day moving average for the 'close' column.

Q: Can we perform calculations on other rows using pandas?

Yes, pandas allows us to perform calculations on other rows by using the diff function. By calling the diff method on a column, we can create a new column that shows the difference between each value and the previous value. This can be useful for analyzing the changes in a dataset over time.

Q: What can we do if a desired operation is not built-in in pandas?

If a desired operation is not built-in in pandas, we can still perform it by mapping a function to our pandas DataFrame or Series. While it may not be as efficient as using built-in functions, this approach allows us to perform customized operations on our data.

Summary & Key Takeaways

• One of the most basic things we can do with pandas is perform simple math operations on each column, such as calculating the difference between the high and low values.

• Another useful feature is the ability to calculate moving averages using the rolling mean function in pandas.

• Pandas also allows us to calculate differences between columns, showing the change between the current value and the previous value.