Pandas pct_change(), quantile(), rank(), round(), prod(), product()

The Pandas DataFrame has several methods concerning Computations and Descriptive Stats. When applied to a DataFrame, these methods evaluate the elements and return the results.

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

• The Pandas library enables access to/from a DataFrame.
• The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required libraries.

• How to install Pandas on PyCharm
• How to install Numpy on PyCharm

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd
import numpy as np 

DataFrame pct_change()

The pct_change() method calculates and returns the percentage change between the current and prior element(s) in a DataFrame. The return value is the caller.

To fully understand this method and other methods in this tutorial from a mathematical point of view, feel free to watch this short tutorial:

The syntax for this method is as follows:

DataFrame.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)

This example calculates and returns the percentage change of four (4) fictitious stocks over three (3) months.

df = pd.DataFrame({'ASL':   [18.93, 17.03, 14.87],
                   'DBL':   [39.91, 41.46, 40.99],
                   'UXL':   [44.01, 43.67, 41.98]},
                   index=   ['2021-10-01', '2021-11-01', '2021-12-01'])

result = df.pct_change(axis='rows', periods=1)
print(result)

• Line [1] creates a DataFrame from a dictionary of lists and saves it to df.
• Line [2] uses the pc_change() method with a selected axis and period to calculate the change. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output

💡 Note: The first line contains NaN values as there is no previous row.

DataFrame quantile()

The quantile() method returns the values from a DataFrame/Series at the specified quantile and axis.

The syntax for this method is as follows:

DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')

To fully understand the interpolation parameter from a mathematical point of view, feel free to check out this tutorial:

This example uses the same stock DataFrame as noted above to determine the quantile(s).

df = pd.DataFrame({'ASL':   [18.93, 17.03, 14.87],
                   'DBL':   [39.91, 41.46, 40.99],
                   'UXL':   [44.01, 43.67, 41.98]})

result = df.quantile(0.15)
print(result)

• Line [1] creates a DataFrame from a dictionary of lists and saves it to df.
• Line [2] uses the quantile() method to calculate by setting the q (quantile) parameter to 0.15. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output

DataFrame rank()

The rank() method returns a DataFrame/Series with the values ranked in order. The return value is the same as the caller.

The syntax for this method is as follows:

DataFrame.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)

For this example, a CSV file is read in and is ranked on Population and sorted. Click here to download and move this file to the current working directory.

df = pd.read_csv("countries.csv")
df["Rank"] = df["Population"].rank()
df.sort_values("Population", inplace=True)
print(df)

• Line [1] reads in the countries.csv file and saves it to df.
• Line [2] appends a column to the end of the DataFrame (df). 
• Line [3] sorts the CSV file in ascending order.
• Line [4] outputs the result to the terminal.

Output

DataFrame round()

The round() method rounds the DataFrame output to a specified number of decimal places.

The syntax for this method is as follows:

DataFrame.round(decimals=0, *args, **kwargs)

For this example, the Bank of Canada’s mortgage rates over three (3) months display and round to three (3) decimal places.

Code Example 1

df = pd.DataFrame([(2.3455, 1.7487, 2.198)], columns=['Month 1', 'Month 2', 'Month 3']) 
result = df.round(3)
print(result)

• Line [1] creates a DataFrame complete with column names and saves it to df.
• Line [2] rounds the mortgage rates to three (3) decimal places. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output

Another way to perform the same task is with a Lambda!

Code Example 2

df = pd.DataFrame([(2.3455, 1.7487, 2.198)], 
                  columns=['Month 1', 'Month 2', 'Month 3']) 
result = df.apply(lambda x: round(x, 3))
print(result)

• Line [1] creates a DataFrame complete with column names and saves it to df.
• Line [2] rounds the mortgage rates to three (3) decimal places using a Lambda. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

💡 Note: The output is identical to that of the above.

DataFrame prod() and product()

The prod() and product() methods are identical. Both return the product of the values of a requested axis.

The syntax for these methods is as follows:

DataFrame.prod(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

DataFrame.product(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

For this example, random numbers generate, and the product on the selected axis returns.

df = pd.DataFrame({'A':   [2, 4, 6],
                   'B':   [7, 3, 5],
                   'C':   [6, 3, 1]})
                   
index_ = ['A', 'B', 'C']
df.index = index_

result = df.prod(axis=0)
print(result)

• Line [1] creates a DataFrame complete with random numbers and saves it to df.
• Line [2-3] creates and sets the DataFrame index.
• Line [3] calculates the product along axis 0. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output

Formula Example: 2*4*6=48

Further Learning Resources

This is Part 5 of the DataFrame method series.

• Part 1 focuses on the DataFrame methods abs(), all(), any(), clip(), corr(), and corrwith().
• Part 2 focuses on the DataFrame methods count(), cov(), cummax(), cummin(), cumprod(), cumsum().
• Part 3 focuses on the DataFrame methods describe(), diff(), eval(), kurtosis().
• Part 4 focuses on the DataFrame methods mad(), min(), max(), mean(), median(), and mode().
• Part 5 focuses on the DataFrame methods pct_change(), quantile(), rank(), round(), prod(), and product().
• Part 6 focuses on the DataFrame methods add_prefix(), add_suffix(), and align().
• Part 7 focuses on the DataFrame methods at_time(), between_time(), drop(), drop_duplicates() and duplicated().
• Part 8 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 9 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 10 focuses on the DataFrame methods reset_index(), sample(), set_axis(), set_index(), take(), and truncate()
• Part 11 focuses on the DataFrame methods backfill(), bfill(), fillna(), dropna(), and interpolate()
• Part 12 focuses on the DataFrame methods isna(), isnull(), notna(), notnull(), pad() and replace()
• Part 13 focuses on the DataFrame methods drop_level(), pivot(), pivot_table(), reorder_levels(), sort_values() and sort_index()
• Part 14 focuses on the DataFrame methods nlargest(), nsmallest(), swap_level(), stack(), unstack() and swap_axes()
• Part 15 focuses on the DataFrame methods melt(), explode(), squeeze(), to_xarray(), t() and transpose()
• Part 16 focuses on the DataFrame methods append(), assign(), compare(), join(), merge() and update()
• Part 17 focuses on the DataFrame methods asfreq(), asof(), shift(), slice_shift(), tshift(), first_valid_index(), and last_valid_index()
• Part 18 focuses on the DataFrame methods resample(), to_period(), to_timestamp(), tz_localize(), and tz_convert()
• Part 19 focuses on the visualization aspect of DataFrames and Series via plotting, such as plot(), and plot.area().
• Part 20 focuses on continuing the visualization aspect of DataFrames and Series via plotting such as hexbin, hist, pie, and scatter plots.
• Part 21 focuses on the serialization and conversion methods from_dict(), to_dict(), from_records(), to_records(), to_json(), and to_pickles().
• Part 22 focuses on the serialization and conversion methods to_clipboard(), to_html(), to_sql(), to_csv(), and to_excel().
• Part 23 focuses on the serialization and conversion methods to_markdown(), to_stata(), to_hdf(), to_latex(), to_xml().
• Part 24 focuses on the serialization and conversion methods to_parquet(), to_feather(), to_string(), Styler.
• Part 25 focuses on the serialization and conversion methods to_bgq() and to_coo().

Also, have a look at the Pandas DataFrame methods cheat sheet!