Pandas DataFrame clip() Method – Be on the Right Side of Change (original) (raw)
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.
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
The clip() method assigns values outside the boundary to boundary values. Thresholds can be singular values or array-like, and in the latter case, the clipping is performed element-wise in the specified axis.
The syntax for this method is as follows:
DataFrame.clip(lower=None, upper=None, axis=None, inplace=False, *args, **kwargs)
| Parameter | Description |
|---|---|
| lower | This parameter is the minimum threshold value. By default, the value is None. |
| upper | This parameter is the maximum threshold value. By default, the value is None. |
| axis | If zero (0) or index is selected, apply to each column. Default 0.If one (1) apply to each row. |
| inplace | This parameter aligns the object with lower and upper along the specified axis. |
| *args | – |
| **kwargs | Additional keywords have no effect. |
For this example, Rivers Clothing is having a sale on Pants in sizes Medium and Large. Unfortunately, these prices are greater than the sale price of $25.00 and need to be modified.
df_prices = pd.DataFrame({'Tops': [10.22, 12.45, 17.45], 'Tanks': [9.99, 10.99, 11.99], 'Pants': [24.95, 26.95, 32.95], 'Sweats': [18.99, 19.99, 21.99]})
index_ = ['Small', 'Medium', 'Large'] df_prices.index = index_
result = df_inv.clip(10, 25, axis='rows') print(result)
- Line [1] creates a DataFrame from a Dictionary of Lists and saves it to
df_inv. - Line [2-3] creates and sets the index for the DataFrame (Small/Medium/Large).
- Line [4] checks each element for the lower and upper limits and updates accordingly. The output saves to the
resultvariable. - Line [5] outputs the result to the terminal.
Output
| | Tops | Tanks | Pants | Sweats | | | ------ | ----- | ----- | --------- | ----- | | Small | 10.22 | 10.00 | 24.95 | 18.99 | | Medium | 12.45 | 10.99 | 25.00 | 19.99 | | Large | 17.45 | 11.99 | 25.00 | 21.99 |
More Pandas DataFrame Methods
Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:
Also, check out the full cheat sheet overview of all Pandas DataFrame methods.