Best Data Sorting Tools to Buy in November 2025

1

Klein Tools VDV001819 Tool Set, Cable Installation Test Set with Crimpers, Scout Pro 3 Cable Tester, Snips, Punchdown Tool, Case, 6-Piece

• COMPLETE KIT: ALL ESSENTIAL TOOLS FOR VDV PROS, MADE IN USA!
• VERSATILE TESTER: LOCATES & TESTS COAX, DATA, AND PHONE CABLES EASILY!
• DURABLE SNIP & STRIPPER: PRECISION TOOLS FOR EFFICIENT CABLE WORK!

BUY & SAVE

$224.99

2

Klein Tools VDV226-110 Ratcheting Modular Data Cable Crimper / Wire Stripper / Wire Cutter for RJ11/RJ12 Standard, RJ45 Pass-Thru Connectors

• STREAMLINED INSTALLATION: EFFICIENT MODULAR TOOL FOR QUICK SETUP.

• 3-IN-1 FUNCTIONALITY: COMBINES STRIPPER, CRIMPER, AND CUTTER.

• CONNECT WITH CONFIDENCE: FULL-CYCLE MECHANISM ENSURES SECURE TERMINATIONS.

BUY & SAVE

$45.50 $49.97

Save 9%

3

KNIPEX Tools - Electrician's Shears (9505155SBA)

• PRECISION PERFORMANCE TRUSTED BY TRADESMAN GLOBALLY.
• ERGONOMIC DESIGN ENSURES COMFORT DURING EVERY TASK.
• PROVEN DURABILITY FOR REAL-WORLD APPLICATIONS AND CONDITIONS.

BUY & SAVE

$25.43

4

Solsop Pass Through RJ45 Crimp Tool Kit Ethernet Crimper CAT5 Cat5e Cat6 Crimping Tool Kit

• SPEED UP PREP WORK: PASS THROUGH TECHNOLOGY CUTS WIRING TIME IN HALF!

• COMPACT & EFFICIENT: CRIMPS RJ45 CONNECTORS NEATLY ON PAIRED CABLES.

• ERROR-PROOF DESIGN: BUILT-IN WIRING DIAGRAM PREVENTS COSTLY MISTAKES!

BUY & SAVE

$35.35

5

Klein Tools VDV427-300 Impact Punchdown Tool with 66/110 Blade, Reliable CAT Cable Connections, Adjustable Force, Includes Pick and Spudger

• EFFORTLESS TERMINATION: CUTS AND TERMINATES WIRES IN ONE SWIFT STEP.

• WIDE COMPATIBILITY: WORKS WITH 66/110 PANELS FOR VERSATILE NETWORKING.

• CUSTOMIZABLE IMPACT: ADJUSTABLE SETTINGS FOR PERFECT WIRE SEATING.

BUY & SAVE

$37.96 $39.97

Save 5%

6

Network Cable Untwist Tool, Dual Headed Looser Engineer Twisted Wire Separators for CAT5 CAT5e CAT6 CAT7 and Telephone (Black, 1 Piece)

• EFFORTLESSLY UNTWIST CABLES FOR EFFICIENT NETWORK SETUP AND REPAIRS.

• COMPACT DESIGN FITS EASILY IN BAGS; PERFECT FOR ON-THE-GO USE!

• COMPATIBLE WITH VARIOUS CABLE TYPES-ENHANCE YOUR TOOLKIT TODAY!

BUY & SAVE

$11.29

+

ONE MORE?

To sort a Pandas DataFrame, you can use the sort_values() method. It allows you to sort the DataFrame by one or more columns.

Here is an example of how to sort a Pandas DataFrame:

# Import pandas library import pandas as pd

Create a sample DataFrame

data = {'Name': ['John', 'Adam', 'Kate', 'Emma'], 'Age': [25, 30, 20, 35], 'Salary': [50000, 70000, 40000, 60000]}

df = pd.DataFrame(data)

Sort the DataFrame by a single column

sorted_df = df.sort_values(by='Age')

Print the sorted DataFrame

print(sorted_df)

This code will sort the DataFrame based on the 'Age' column. The resulting DataFrame will be:

Name Age Salary 2 Kate 20 40000 0 John 25 50000 1 Adam 30 70000 3 Emma 35 60000

You can also sort the DataFrame by multiple columns. To do that, provide a list of column names to the by parameter:

sorted_df = df.sort_values(by=['Age', 'Salary'])

The DataFrame will then be sorted by the 'Age' column first, and in case of ties, it will use the 'Salary' column to break the tie.

Note that the sort_values() method by default sorts the data in ascending order. If you want to sort in descending order, you can set the ascending parameter to False:

sorted_df = df.sort_values(by='Age', ascending=False)

This will sort the DataFrame in descending order based on the 'Age' column.

Remember to assign the sorted DataFrame to a new variable or overwrite the original DataFrame if you want to keep the sorted data.

How to sort a Pandas DataFrame by a specific range of values?

To sort a Pandas DataFrame by a specific range of values, you can use the iloc indexing method along with the sorting function sort_values().

Here's an example of how to sort a DataFrame by a specific range:

import pandas as pd

Create DataFrame

data = {'Name': ['John', 'Alice', 'Bob', 'Charlie', 'Jane'], 'Age': [25, 30, 18, 35, 28], 'Salary': [50000, 60000, 40000, 70000, 55000]}

df = pd.DataFrame(data)

Sort DataFrame by a specific range of values

sorted_df = df.sort_values(by='Age').iloc[1:4]

print(sorted_df)

Output:

 Name  Age  Salary

4 Jane 28 55000 0 John 25 50000 1 Alice 30 60000

In the above example, we sort the DataFrame df by the 'Age' column using the sort_values() function. Then, we use iloc[1:4] to select only the rows from index 1 to 3 (excluding index 4). Finally, we store the sorted DataFrame in sorted_df and print it.

How to sort a Pandas DataFrame by absolute values?

To sort a Pandas DataFrame by absolute values, you can use the sort_values() function along with the key parameter to specify the sorting criteria. Here is an example:

import pandas as pd

Create a sample DataFrame

data = {'A': [-1, 4, -3, 0, 2], 'B': [-5, 1, 7, -2, 6]} df = pd.DataFrame(data)

Sort the DataFrame by absolute values of column 'A'

df_sorted = df.sort_values(by='A', key=lambda x: abs(x))

print(df_sorted)

Output:

A B 1 4 1 0 -1 -5 2 -3 7 4 2 6 3 0 -2

In the above example, we use the sort_values() function and specify the by parameter as 'A' to sort based on column 'A'. We use the key parameter and define a lambda function to compute the absolute of each value in column 'A'. This way, the sorting is done based on the absolute values of the column.

How to sort a Pandas DataFrame without modifying the original DataFrame?

To sort a Pandas DataFrame without modifying the original DataFrame, you can use the sort_values() method with the inplace=False parameter. This will create a new sorted DataFrame without affecting the original DataFrame.

Here's an example:

import pandas as pd

Create a sample DataFrame

data = {'Name': ['John', 'Emma', 'David', 'Sophia'], 'Age': [34, 28, 42, 25], 'Country': ['USA', 'Canada', 'Canada', 'USA']} df = pd.DataFrame(data)

Sort the DataFrame by 'Age' column in ascending order without modifying the original DataFrame

sorted_df = df.sort_values('Age', inplace=False)

Print the sorted DataFrame

print(sorted_df)

Output:

Name  Age Country

3 Sophia 25 USA 1 Emma 28 Canada 0 John 34 USA 2 David 42 Canada

In this example, the sort_values() method is used to sort the DataFrame by the 'Age' column in ascending order. The inplace=False parameter ensures that the original DataFrame (df) remains unmodified, and the sorted DataFrame is stored in the sorted_df variable.

How to sort a Pandas DataFrame based on a partial string match in a column?

To sort a Pandas DataFrame based on a partial string match in a column, you can use the str.contains() method combined with the sort_values() method. Here's an example:

import pandas as pd

Create a sample DataFrame

data = { 'City': ['New York', 'Chicago', 'Los Angeles', 'San Francisco'], 'Country': ['USA', 'USA', 'USA', 'USA'] } df = pd.DataFrame(data)

Sort the DataFrame by a partial string match in the 'City' column

partial_match = 'an' # Partial string to match sorted_df = df[df['City'].str.contains(partial_match)].sort_values('City')

print(sorted_df)

Output:

        City Country

2 Los Angeles USA 3 San Francisco USA

In this example, the DataFrame is sorted based on a partial string match in the 'City' column. The str.contains() method is used to check if a partial match exists, and then the sort_values() method is used to sort the DataFrame based on the matched values in the 'City' column.