Best Data Sorting Tools to Buy in October 2025

Klein Tools VDV226-110 Ratcheting Modular Data Cable Crimper / Wire Stripper / Wire Cutter for RJ11/RJ12 Standard, RJ45 Pass-Thru Connectors
-
STREAMLINED INSTALLATION: MODULAR DESIGN WITH PASS-THRU PLUGS SAVES TIME.
-
ALL-IN-ONE TOOL: CRIMP, STRIP, AND CUT FOR VERSATILE CABLE HANDLING.
-
ERROR REDUCTION: ON-TOOL GUIDE ENSURES PRECISE WIRING AND OPTIMAL CONNECTIONS.



Klein Tools VDV001819 Tool Set, Cable Installation Test Set with Crimpers, Scout Pro 3 Cable Tester, Snips, Punchdown Tool, Case, 6-Piece
- ALL-IN-ONE KIT FOR VDV PROS: ESSENTIAL TOOLS, USA ASSEMBLED.
- SCOUT PRO 3 TESTER: VERSATILE, LOCATES AND TESTS MULTIPLE CABLE TYPES.
- DURABLE TOOLS: PRECISION CRIMPER AND SNIP FOR RELIABLE, EFFICIENT USE.



Klein Tools VDV226-107 Compact Ratcheting Modular Data Cable Crimper / Wire Stripper / Wire Cutter, CAT6, CAT5, CAT3, Flat-Satin Voice Cable
- FULL-CYCLE RATCHET MECHANISM ENSURES PERFECT CONNECTOR TERMINATION.
- ERGONOMIC DESIGN FOR EASY SINGLE-HAND OPERATION, BOOSTING EFFICIENCY.
- DIRECT CRIMP ACTION DELIVERS PRECISE CONTACT FOR RELIABLE CONNECTIONS.



Fluke Networks 11293000 Pro-Tool Kit IS60 with Punch Down Tool
- ERGONOMIC DUR A GRIP POUCH FOR EASY, CONVENIENT TOOL STORAGE.
- D914S PUNCH DOWN TOOL REDUCES HAND FATIGUE FOR LASTING PERFORMANCE.
- QUICK CABLE STRIPPER AND D SNIPS FOR FASTER, CLEANER WIRE WORK.



KLEIN TOOLS VDV501-851 Cable Tester Kit with Scout Pro 3 for Ethernet / Data, Coax / Video and Phone Cables, 5 Locator Remotes
-
COMPREHENSIVE TESTING: TESTS VOICE, DATA, AND VIDEO CABLES FOR CLARITY.
-
PRECISE MEASUREMENTS: MEASURES CABLE LENGTHS UP TO 2000 FEET ACCURATELY.
-
EFFICIENT FAULT DETECTION: IDENTIFIES OPEN, SHORT, MISWIRE, AND SPLIT-PAIRS.



Network Cable Untwist Tool, Dual Headed Looser Engineer Twisted Wire Separators for CAT5 CAT5e CAT6 CAT7 and Telephone (Black, 1 Piece)
-
EFFORTLESSLY UNTWIST: EASILY SEPARATES TWISTED CABLES FOR DAILY USE.
-
UNIVERSAL FIT: WORKS WITH CAT5, CAT6, CAT7 FOR VERSATILE APPLICATIONS.
-
PORTABLE DESIGN: COMPACT TOOL CONVENIENTLY STORES IN BAGS OR BOXES.


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.