DataFrame Calculations for Financial Analysis

What will we cover?

In this lesson we will learn how to add new columns calculated from values in other columns in our DataFrame. This is similar to calculate in Excel on data from different columns.

Then we will demonstrate some useful function when working with financial data.

Finally, we will show how to remove (or drop) columns from our DataFrame.

Step 1: Load the data.

As usual we need to load the data into our DataFrame. You can get the CSV file from here.

import pandas as pd
data = pd.read_csv("AAPL.csv", index_col=0, parse_dates=True)

It is always a good habit to inspect the data with data.head() (see the video lesson or the in the Notebook link below the video for expected output).

Step 2: Create new columns in a DataFrame (Pandas)

To create a new column in our data set simply write as follows.

data['Daily chg'] = data['Close'] - data['Open']

The above statement will create a new column named Daily chg with the difference between column Close and Open.

Similarly, you can create a column with the normalized data as follows.

data['Normalized'] = data['Close'] / data['Close'].iloc[0]

This is how easy it is to work with.

Step 3: Get min and max in DataFrame columns

To find the minimum of a column.

data['Close'].min()

This will find the minimal value of the column Close.

To find the index of the minimum value use the following.

data['Close'].argmin()

You can do similar things as the following shows.

data['Normalized'].min()
data['Normalized'].argmin()
data['Close'].max()
data['Close'].argmax()

Step 4: Get the mean value of a column in a DataFrame (Pandas)

To get the mean value of a column, simply use mean().

data['Close'].mean()

Step 5: Remove / Delete columns in a DataFrame

It is always good practice to remove the columns of data we do not intend to use anymore. This can be done by using drop().

data.drop(labels=['High', 'Low', 'Adj Close', 'Volume'], axis=1, inplace=True)

Where we use the following arguments.

  • labels=[‘High’, ‘Low’, ‘Adj Close’, ‘Volume’] sets the labels of the columns we want to remove.
  • axis=1 sets the axis of the labels. Default is 0, and will look for the labels on the index. While axis 1 is the column names.
  • inplace=True says it should actually remove the columns on the DataFrame we work on. Otherwise it will return a new DataFrame without the columns.

Want to learn more?

This is part of a 2-hour full video course in 8 parts about Technical Analysis with Python.

If you are serious about learning Python for Finance check out this course.

  • Learn Python for Finance with pandas and NumPy.
  • 21 hours of video in over 180 lectures.
  • “Excellent course for anyone trying to learn to code and invest.” – Lorenzo B.

Get Python for Finance HERE.

Python for Finance

Learn Python

Learn Python A BEGINNERS GUIDE TO PYTHON

  • 70 pages to get you started on your journey to master Python.
  • How to install your setup with Anaconda.
  • Written description and introduction to all concepts.
  • Jupyter Notebooks prepared for 17 projects.

Python 101: A CRASH COURSE

  1. How to get started with this 8 hours Python 101: A CRASH COURSE.
  2. Best practices for learning Python.
  3. How to download the material to follow along and create projects.
  4. A chapter for each lesson with a descriptioncode snippets for easy reference, and links to a lesson video.

Expert Data Science Blueprint

Expert Data Science Blueprint

  • Master the Data Science Workflow for actionable data insights.
  • How to download the material to follow along and create projects.
  • A chapter to each lesson with a Description, Learning Objective, and link to the lesson video.

Machine Learning

Machine Learning – The Simple Path to Mastery

  • How to get started with Machine Learning.
  • How to download the material to follow along and make the projects.
  • One chapter for each lesson with a Description, Learning Objectives, and link to the lesson video.

Leave a Comment