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Pandas pct_change() to compute percent change across columns/rows

This question originally appeared on Quora. I see what you did there. Before I go into why, let’s talk about why you shouldn’t be doing it. Mainly because pandas % change() is an awful way to do it. Here’s the synopsis from the documentation: “”” The % operator is a unary operator that returns the remainder of one number divided by another. In other words, it returns the number left over when its first argument is divided by its second argument. The % operator has a single integer argument and a single integer result. Note that the non-integer arguments must be integers. “””

Pandas pct_change() to compute percent change across columns/rows Pandas pct_change() is a Python function that can be used to get the percent change across columns/rows of a DataFrame. The function does that using a Pandas formula. In this blog post, we will explain how to use pct_change() and then explain how to use it to calculate percent change across columns/rows of a DataFrame.

Code a Python function that takes in a list of data, computes the percent change across each column or row and returns the results. The function should have a single parameter, which is a list of data.

In this lesson, we will see how to calculate the percentage change for the values in each column. By default, the pandas function pct_change() calculates for each value in the column the percentage change from the previous item in the column. Another way to think about it is this. Calculates the percentage change from the immediately preceding row. The pandas function pct_change() is extremely useful for comparing the percentage change in time series data. Pandas pct_change() word-image-6560 First we load the Pandas library and create some dummy time series data. Import pandas as pd Let’s create a data frame with the profits of the top technology companies over the last four years. We have income by company in the form of lists. year=[2017, 2018, 2019, 2020] facebook=[159340000, 221120000, 184850000, 291460000] google=[12662000000, 307360000, 343430000, 402690000] microsoft=[254890000, 165710000, 39240000, 442810000] From these four lists we can create a Pandas data frame. df = pd.DataFrame({facebook:facebook, google:google, microsoft:microsoft}, index=year) In our toy time series data, we have three columns (companies) and a year as an index. df facebook google microsoft 2017 159340000 12662000000 254890000 2018 221120000 307360000 16571000000 2019 184850000 343430000 39240000000 2020 291460000 40269000000 4428100000000

Pandas function pct_change() to calculate the percentage change

We can use the pct_change() function and calculate the percentage change in sales for each company from the previous year. df.pct_change() You can see that the first line is NaN because there is nothing before it. And for the other lines, we have the percentage change in profit from one year to the next. facebook google microsoft 2017 NaN NaN 2018 0.387724 1.427421 -0.349876 2019 -0.164029 0.117354 1.367992 2020 0.576738 0.172553 0.128466 By default, pct_change() calculates with rows. df.pct_change(axis=rows) We can also calculate the rate of change relative to the columns with the axis=columns argument. Since the percentage change of column makes no sense in this example, we transpose the dataframe before using pct_change(). df.T.pct_change(axis=columns) 2017 2018 2019 2020 facebook NaN 0.387724 -0.164029 0.576738 google NaN 1.427421 0.117354 0.172553 microsoft NaN -0.349876 1.367992 0.128466 We can also specify the rows we want to use to calculate the percentage change using the period argument. A z. B. to calculate the percentage change from the two previous years or series, the argument period=2 is used. Here, we essentially compare 2019 sales to 2017 sales and 2020 sales to 2020 sales. That’s why we have NaN in the first two lines. This argument is extremely useful when comparing quarterly changes in profit/sales. f.pct_change(periods=2) facebook google microsoft 2017 NaN NaN 2018 NaN NaN 2019 0.160098 1.712289 0.539488 2020 0.318108 0.310157 1.672198I have been playing a lot of Pokemon these days. I started the series as a boy with the Red / Blue version in 1996, but the most recent games came out in the 3DS and the franchise feels like it is a lot more than just a game. This gave me the opportunity to learn how the games work, how the world works and how things work.. Read more about pandas pct_change freq example and let us know what you think.

Frequently Asked Questions

How do you calculate percentage change in pandas?

Pandas pct_change() is a simple python function that lets you compute percentage change across two columns or rows. For example, given two numeric columns named a and b, we can calculate the percentage change in a by calling: print(pct_change(a, b)) All of the tutorials I’ve seen for pandas’ pct_change() function simply show the following: print(pct_change(A, B)) which is obviously not very useful as an actual function.

How do I find the percentage difference between two columns in pandas?

* If you want to find differences/percentages between two columns in pandas, then you have to use pandas-pct_change() (the pct_change() function is also known as the “% change” function). In the previous section, we learned how to use the pct_change() function in pandas to compute the percent change across columns. However, there is a cool little trick you can do to get a relative percent change based on only 2 columns, namely:

How do you calculate percent change in Python?

If you want to calculate the percent change of a column or row of a data frame, you can do this using Pandas PctChange() function. Let’s say we are interested to know how our weight has changed across the year. We have a data frame with weight (in pounds), date (in date), and age (in years): weight_2012 = pd.DataFrame(pd.Series([1, 25, 35, 45, 55], columns=[‘weight’], index=[‘year’]) weight_2012 1 25 35 45 55 Python is a great language for scientific computations. It has a good set of scientific data types to handle big data, and a powerful library to do numerical computations. However, Python doesn’t have a built-in function to perform a percent change calculation.

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