Best Data Analysis Tools to Buy in February 2026

1

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

• ONE-STEP WIRE TERMINATION: CUTS AND TERMINATES CAT CABLES EFFORTLESSLY.
• COMPATIBLE WITH MULTIPLE PANELS: WORKS WITH 66/110 FOR VERSATILE USE.
• CUSTOMIZABLE IMPACT FORCE: ADJUSTABLE SETTINGS FOR OPTIMAL PERFORMANCE.

BUY & SAVE

2

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

• STREAMLINE INSTALLATIONS WITH PASS-THRU RJ45 CONNECTORS FOR EFFICIENCY.
• ALL-IN-ONE TOOL: STRIP, CRIMP, AND CUT FOR VERSATILE DATA CABLING.
• ENSURE SECURE CONNECTIONS WITH FULL-CYCLE RATCHET AND ERROR MINIMIZATION.

BUY & SAVE

3

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

• EFFORTLESSLY UNTWIST CABLES: PERFECT FOR CAT5 TO CAT7 NETWORKS.

• COMPACT DESIGN: PORTABLE TOOL EASILY FITS IN BAGS OR WORKBOXES.

• PREVENTS DAMAGE: SAFELY SEPARATES TWISTED WIRES WITHOUT HARM.

BUY & SAVE

Save 20%

4

KNIPEX Tools - Electrician's Shears (9505155SBA)

• STAINLESS STEEL BLADES WITH FINE SERRATION FOR CLEAN, PRECISE CUTS.
• INTEGRATED CABLE CUTTER FOR EASY SLICING OF SOFT WIRES.
• CONVENIENT BELT HOLSTER FOR QUICK ACCESS ON THE GO.

BUY & SAVE

5

Mini Wire Stripper, 6 Pcs Network Wire Stripper Punch Down Cutter for Network Wire Cable, RJ45/Cat5/CAT-6 Data Cable, Telephone Cable and Computer UTP Cable

• COMPACT 6-PIECE SET IN VIBRANT COLORS, PERFECT FOR ON-THE-GO USE.
• VERSATILE TOOL FOR STRIPPING VARIOUS CABLE TYPES, HOME OR OFFICE.
• SAFE DESIGN WITH A FINGER LOOP FOR SECURE HANDLING AND EASY TRANSPORT.

BUY & SAVE

6

KNIPEX Ergostrip Universal Stripping Tool for Right-Handers, 135 mm, Round Cable, Coax Cables, Data Cables, Wire Stripper, 16 95 01 SB

• FAST, PRECISE STRIPPING FOR ALL COMMON INSTALLATION CABLES.
• ERGONOMIC PISTOL GRIP FOR EFFORTLESS CUTTING AND STRIPPING.
• SLIM TOOL ENDS FOR ACCESS IN TIGHT SPACES-PERFECT FOR ANY JOB!

BUY & SAVE

7

Klein Tools VDV500-820 Wire Tracer Tone Generator and Probe Kit Continuity Tester for Ethernet, Telephone, Speaker, Coax, Video, and Data Cables, RJ45, RJ11, RJ12

• PROFESSIONAL GRADE ACCURACY: ENSURES EFFICIENT TRACING OF NON-ENERGIZED WIRING.
• VERSATILE SIGNAL TRANSMISSION: 5 TONE CADENCES, SIGNALS UP TO 1,000 FEET.
• SECURE CONNECTIONS: RUGGED CLIPS AND EASY PLUG-INS ENSURE STABLE USAGE.

BUY & SAVE

Save 10%

8

Klein Tools 32933 Klein Tools 32933 Impact Driver, SAE 7-in-1 Impact Rated Socket Set, 3 Flip Sockets with 6 Hex Driver Sizes and 1/4-Inch Bit Holder, 5-Inch Shaft

• VERSATILE 7-IN-1 DESIGN: EASILY TACKLE VARIOUS TASKS WITH FLIP SOCKETS.

• IMPACT-RATED DURABILITY: BUILT FOR HEAVY-DUTY APPLICATIONS WITH IMPACT TOOLS.

• COLOR-CODED CONVENIENCE: QUICK SIZE IDENTIFICATION FOR EFFICIENT SOCKET CHANGES.

BUY & SAVE

+

ONE MORE?

To split data hourly in pandas, first you need to convert the date column to a datetime object if it is not already in that format. Then, you can use the resample function with the frequency set to 'H' (hourly) to group the data by hour. This will create a new DataFrame with data aggregated by hour. You can then perform any further analysis or transformations on this hourly data as needed.

How to resample data hourly in pandas?

You can resample data hourly in pandas by using the resample() method along with the H frequency parameter. Here's an example:

import pandas as pd

Create a sample DataFrame

data = {'datetime': pd.date_range('2022-01-01 00:00:00', periods=100, freq='30T'), 'value': range(100)} df = pd.DataFrame(data)

Set the 'datetime' column as the index

df.set_index('datetime', inplace=True)

Resample the data hourly and calculate the mean

hourly_data = df.resample('H').mean()

print(hourly_data)

In this example, we first create a sample DataFrame with a datetime column and a value column. We then set the datetime column as the index of the DataFrame. Finally, we use the resample() method to resample the data to an hourly frequency ('H') and calculate the mean value for each hour.

You can also use other aggregation functions such as sum, count, etc. by passing them as an argument to the resample() method.

What is the most effective method for categorizing data into hourly increments in pandas?

The most effective method for categorizing data into hourly increments in pandas is to use the pd.to_datetime() function to convert the timestamp column into a datetime object, and then use the dt.hour property to extract the hour from the datetime object. You can then create a new column with the hourly increments.

import pandas as pd

Create a sample DataFrame

data = {'timestamp': ['2022-01-01 08:30:00', '2022-01-01 09:45:00', '2022-01-01 11:10:00']} df = pd.DataFrame(data)

Convert timestamp column to datetime object

df['timestamp'] = pd.to_datetime(df['timestamp'])

Extract the hour from the timestamp column

df['hour'] = df['timestamp'].dt.hour

Print the DataFrame with hourly increments

print(df)

This will output:

        timestamp  hour

0 2022-01-01 08:30:00 8 1 2022-01-01 09:45:00 9 2 2022-01-01 11:10:00 11

You can then use the groupby() function to group the data by hour and perform any further analysis or aggregation as needed.

How to handle missing values in hourly data with pandas?

There are several ways to handle missing values in hourly data with pandas:

1. Drop rows with missing values: You can simply drop rows that contain missing values using the dropna() method.

df.dropna(inplace=True)

1. Fill missing values with a specific value: You can fill missing values with a specific value (such as 0) using the fillna() method.

df.fillna(0, inplace=True)

1. Fill missing values with the previous or next value: You can fill missing values with the previous or next value in the column using the ffill() or bfill() methods.

df.fillna(method='ffill', inplace=True) # fill missing values with the previous value df.fillna(method='bfill', inplace=True) # fill missing values with the next value

1. Interpolate missing values: You can interpolate missing values based on the values before and after the missing values using the interpolate() method.

df.interpolate(inplace=True)

Choose the method that best fits your data and analysis requirements.

How to categorize data into hourly increments in pandas?

To categorize data into hourly increments in pandas, you can use the pd.Grouper function in combination with the groupby method. Here is an example code snippet to accomplish this:

import pandas as pd

Create a sample DataFrame

df = pd.DataFrame({ 'date': pd.date_range(start='2022-01-01', end='2022-01-03', freq='30T'), 'value': range(48) })

Convert the 'date' column to datetime type

df['date'] = pd.to_datetime(df['date'])

Categorize the data into hourly increments

hourly_data = df.groupby(pd.Grouper(key='date', freq='1H')).sum()

print(hourly_data)

In this example, we first create a sample DataFrame with a 'date' column and a 'value' column. We then convert the 'date' column to datetime type using pd.to_datetime. Lastly, we group the data by hourly increments using groupby(pd.Grouper(key='date', freq='1H')) and aggregate the values by summing them.