Best Pandas Data Manipulation Tools to Buy in January 2026

1

GoodsFilter Jewelry Display Stand Ring Holder,Cute Panda Room Decor,Necklace Organizer Display Bracelet Earrings and Ring Tray Jewelry Holder,Panda Gifts for Christmas Valentine's Day Birthday

• ADORABLE PANDA DESIGN: PERFECT BLEND OF CUTENESS AND PRACTICALITY!

• DURABLE CONSTRUCTION: HIGH-QUALITY RESIN ENSURES LONG-LASTING USE.

• VERSATILE GIFT IDEA: IDEAL FOR ANYONE-MOMS, FRIENDS, AND MORE!

BUY & SAVE

$12.69

2

Calm Collective Peaceful Panda Breathing Trainer Light for Calming Stress, Anxiety Relief Items for ADHD, Mindfulness Meditation Tools for Depression, Great Self Care and Mental Health Gifts

• PROMOTES STRESS RELIEF: PROVEN BREATHING EXERCISES FOR RELAXATION.

• EASY-TO-USE DESIGN: COLOR PROMPTS FOR EFFORTLESS MEDITATION PRACTICE.

• VERSATILE & PORTABLE: PERFECT FOR HOME, WORK, AND ON-THE-GO SERENITY.

BUY & SAVE

$22.95

3

2Pcs Rose Gold Metal Ruler Hollow Brass Rulers 6 Inch Panda Metal Bookmarks Straight Edge Rulers Office Products for Students Bullet Journal Ruler Art Drafting Tools and Drafting Kits

• DUAL-PURPOSE: RULERS THAT ALSO SERVE AS STYLISH BOOKMARKS!
• ACCURATE 6-INCH MEASUREMENT FOR SEAMLESS DRAWING AND PLANNING.
• DURABLE DESIGN WITH ELEGANT PATTERNS, PERFECT FOR ANY PROJECT.

BUY & SAVE

$5.99

4

Presence The Meditating Panda, Guided Visual Meditation Tool for Practicing Mindfulness, 3 in 1 Breathing Light with Night Light and Noise Machine, 4-7-8 Breathing for Relaxation and Stress Relief

• 3-IN-1 RELAXATION TOOL: ENHANCE CALM WITH BREATHING LIGHT, SOUNDS, AND NIGHT LIGHT.

• MINDFUL BREATHING MADE EASY: CALMING 4-7-8 METHOD FOR ALL AGES, GUIDED VISUALLY OR AUDIBLY.

• VERSATILE FOR ANY SETTING: USE AT HOME, SCHOOL, OR WORK FOR FOCUS AND RELAXATION ANYTIME.

BUY & SAVE

$19.99

5

ARFUKA Cute Panda Bottle Opener Keychain - Portable Beer & Soda Opener Keyring, Durable Beverage Opener Tool for Men Women (Gift Idea)

• COOL & FUNCTIONAL: OPEN BOTTLES WHILE KEEPING KEYS ORGANIZED.
• DURABLE STAINLESS STEEL FOR LONG-LASTING USE AND RELIABILITY.
• PERFECT GIFT FOR ANY OCCASION: CHRISTMAS, BIRTHDAYS, AND MORE!

BUY & SAVE

$4.59

6

TINDTOP 3 Sets Punch Needle Kits, Panda Punch Embroidery Kits for Adults Beginner, Tool with Punch Needle Fabric, Hoops, Yarns and Sewing Needles

• COMPLETE KIT: ALL YOU NEED FOR EFFORTLESS EMBROIDERY BEGINNERS!
• EASY-TO-USE WITH PRE-PRINTED PATTERNS AND STEP-BY-STEP INSTRUCTIONS.
• PERFECT DIY GIFT FOR HOLIDAYS AND SPECIAL OCCASIONS-SURPRISE LOVED ONES!

BUY & SAVE

$14.79

7

Panda Brothers Montessori Screwdriver Board Set - Wooden Montessori Toys for 4 Year Old Kids and Toddlers, Sensory Bin, Fine Motor Skills, STEM Toys

• TEACH PRACTICAL SKILLS & BOOST MOTOR SKILLS WITH FUN, HANDS-ON PLAY!

• ECO-FRIENDLY WOODEN DESIGN ENSURES SAFE, LONG-LASTING ENJOYMENT FOR KIDS.

• PERFECT GIFT FOR TODDLERS, TURNING LEARNING INTO CREATIVE FUN TIME!

BUY & SAVE

$17.95 $19.95

Save 10%

+

ONE MORE?

To group by data in a column with pandas, you can use the groupby() function along with the column you want to group by. This function allows you to split the data into groups based on a particular column, and then perform operations on these groups. You can then apply various aggregation functions to calculate statistics for each group, such as mean, count, sum, etc. Grouping data in a column with pandas is a powerful tool for analyzing and summarizing your data based on specific categories or criteria.

How to sort grouped data in pandas?

You can sort grouped data in pandas using the sort_values method on the groupby object. Here's an example:

import pandas as pd

Create a sample DataFrame

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

Group the data by the 'category' column

grouped = df.groupby('category')

Sort the grouped data by the 'value' column

sorted_grouped = grouped.apply(lambda x: x.sort_values(by='value'))

Display the sorted grouped data

print(sorted_grouped)

In this example, we first group the data by the 'category' column. Then, we use the apply method to sort each group by the 'value' column. Finally, we display the sorted grouped data using the print function.

How to perform group by operations in pandas?

To perform group by operations in Pandas, you can use the groupby() method. Here is a step-by-step guide on how to do this:

1. Import the Pandas library:

import pandas as pd

1. Create a DataFrame:

data = {'Name': ['Alice', 'Bob', 'Charlie', 'Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35, 28, 32, 37], 'Salary': [50000, 60000, 70000, 55000, 65000, 75000]} df = pd.DataFrame(data)

1. Perform a group by operation on the DataFrame:

grouped = df.groupby('Name')

1. Perform an aggregation operation on the grouped data:

grouped_mean = grouped.mean()

1. You can also perform multiple group by operations and aggregations:

double_grouped = df.groupby(['Name', 'Age']) double_grouped_mean = double_grouped.mean()

1. You can also apply custom aggregation functions using the agg() method:

custom_aggregation = grouped.agg({'Salary': 'mean', 'Age': 'max'})

That's it! You have successfully performed group by operations in Pandas.

How to filter data after grouping in pandas?

After grouping the data in pandas using the groupby function, you can filter the data using the filter function.

Here is an example of how to filter data after grouping in pandas:

import pandas as pd

Create a sample DataFrame

data = {'Category': ['A', 'A', 'B', 'B', 'A', 'B'], 'Value': [10, 20, 30, 40, 50, 60]}

df = pd.DataFrame(data)

Group the data by the 'Category' column

grouped = df.groupby('Category')

Filter the data to only include groups where the sum of 'Value' is greater than 50

filtered_data = grouped.filter(lambda x: x['Value'].sum() > 50)

print(filtered_data)

In this example, we first group the data by the 'Category' column. Then we use the filter function along with a lambda function to filter the groups based on a condition. In this case, we are filtering groups where the sum of the 'Value' column is greater than 50.

You can adjust the filter condition as needed to filter the grouped data based on different criteria.

How to group data in a column with pandas?

To group data in a column with pandas, you can use the groupby() function. Here is a step-by-step guide on how to do this:

1. Import the pandas library:

import pandas as pd

1. Create a DataFrame with your data:

data = {'Category': ['A', 'B', 'A', 'B', 'A', 'A'], 'Value': [10, 20, 15, 25, 30, 35]} df = pd.DataFrame(data)

1. Group the data by the 'Category' column:

grouped = df.groupby('Category')

1. Perform an aggregation operation on the grouped data, such as finding the sum of the values in each group:

result = grouped.sum() print(result)

This will group the data in the 'Category' column and calculate the sum of the 'Value' column for each group. You can also perform other aggregation operations, such as finding the mean, median, minimum, or maximum value for each group.

Additionally, you can also group by multiple columns by passing a list of column names to the groupby() function:

grouped = df.groupby(['Category', 'City'])

This will group the data by both the 'Category' and 'City' columns.