Pandas DataFrame compare() 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.
FeFeel 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
The compare() method compares two (2) DataFrames. This method returns the differences between them.
The syntax for this method is as follows:
DataFrame.compare(other, align_axis=1, keep_shape=False, keep_equal=False)
| Parameter | Description |
|---|---|
| other | This parameter is the object (DataFrame) to use for comparison. |
| align_axis | This parameter determines the axis to align the comparison. If zero (0) or index is selected, apply to each column. Default is 0 (column). If zero (1) or columns, apply to each row. |
| keep_shape | If set to True, all column(s) stay. If False, only the ones with differing values remain. |
| keep_equal | If set to True, keep equal values. If False, equal values display as NaN values. |
For this example, we have two (2) DataFrames. One with existing customer login credentials and one with new customer credentials. This code compares the DataFrames and returns the results (the differences).
df_custs = pd.DataFrame({('jkende', 'Vzs*@4:kNq%)'), ('sarahJ', '{M$*3zB~-a-W'), ('AmyKerr', '*7#<bSt?Y_Z<')}, columns=['username', 'password'], index=['user-a', 'user-b', 'user-c']) print(df_custs)
df_new = pd.DataFrame({('jkende', 'Vzs*@4:kNq%)'), ('sarahJ', 'xc^O3&43P'), ('AmyKerr', '*7#<bSt?Y_Z<')}, columns=['username', 'password'], index=['user-a', 'user-b', 'user-c']) print(df_new)
result = df_custs.compare(df_new) print(result)
- Line [1] creates a DataFrame from a Dictionary of Tuples and assigns it to
df_custs. - Line [2] outputs the DataFrame to the terminal.
- Line [3] creates a DataFrame from a Dictionary of Tuples and assigns it to
df_new. - Line [4] outputs the DataFrame to the terminal.
- Line [5] compares the two DataFrames. This output saves to
result. - Line [6] outputs the result to the terminal.
Output
df_custs
| | username | password | | | ---------- | -------- | -------------- | | user-a | AmyKerr | *7#<bSt?Y_Z< | | user-b | sarahJ | {M$*3zB~-a-W | | user-c | jkende | Vzs*@4:kNq%) |
df_new
| | username | password | | | ---------- | -------- | -------------- | | user-a | AmyKerr | *7#<bSt?Y_Z< | | user-b | sarahJ | xc^O3&43P | | user-c | jkende | Vzs*@4:kNq%) |
result
| | password | | | | ---------- | -------------- | --------- | | | self | other | | | user-b | {M$*3zB~-a-W | xc^O3&43P |
💡 Note: The user sarahJ resides in each DataFrame with different passwords.
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.