3 Simple Methods to Generate a Subset of a Python Dataframe

2 years ago

5 minutes

Hey there, folks! In this piece, we’ll dive into various methods for creating a subset of a Python Dataframe and explore them thoroughly.

Alright, let’s begin!

To begin with, could you please explain the concept of a Python Dataframe?

The Python Pandas module offers two data structures, namely Series and Dataframe, for storing values.

A Dataframe is a type of data structure that stores information in a matrix format, with rows and columns representing the data. This allows us to easily create and access specific parts of the data in various ways.

Access data according to the rows as subset
Fetch data according to the columns as subset
Access specific data from some rows as well as columns as subset

Now that we have learned about Dataframes and subsets, let’s explore various methods for creating a subset from a Dataframe.

Setting up a Dataframe for use!

Before we delve into creating subsets of a dataframe, let’s first focus on creating the dataframe itself.

import pandas as pd 
data = {"Roll-num": [10,20,30,40,50,60,70], "Age":[12,14,13,12,14,13,15], "NAME":['John','Camili','Rheana','Joseph','Amanti','Alexa','Siri']}
block = pd.DataFrame(data)
print("Original Data frame:\n")
print(block)

The result:

Original Data frame:

   Roll-num  Age    NAME
0        10   12    John
1        20   14  Camili
2        30   13  Rheana
3        40   12  Joseph
4        50   14  Amanti
5        60   13   Alexa
6        70   15    Siri

In this article, we will be utilizing the dataset we have generated by using the pandas.DataFrame() function.

Shall we start?

Create a smaller version of a Python dataframe by employing the loc() function.

The loc() function in Python allows us to create a subset of a data frame by specifying a particular row, column, or a combination of both.

The loc() function operates using labels, meaning we need to specify the label of the row/column in order to select and form a customized subset.

Syntax refers to the arrangement of words and phrases in a sentence or phrase.

pandas.dataframe.loc[]

One possible option:
– Retrieve information from certain rows within a dataframe.

block.loc[[0,1,3]]

Result:

Below, you can find a subset that contains the data from rows 0, 1, and 3.

Roll-num	Age	NAME
0	10	12	John
1	20	14	Camili
3	40	12	Joseph

One possibility is: “Generate a subset of rows by slicing.”

block.loc[0:3]

Using the loc() function, we have obtained the data from rows 0 to 3 by employing the slicing operator.

Result:

Roll-num	Age	NAME
0	10	12	John
1	20	14	Camili
2	30	13	Rheana
3	40	12	Joseph

Example 3: Generate a subset by selecting specific columns using labels.

block.loc[0:2,['Age','NAME']]

I only need one alternative option for the native paraphrasing of the following:

Result:

Age	NAME
0	12	John
1	14	Camili
2	13	Rheana

In this case, we have formed a subset that consists of data from rows 0 to 2. However, this subset only includes certain columns such as ‘Age’ and ‘NAME’.

One option for paraphrasing the given sentence could be:

2. Employing the Python iloc() method for generating a subset of a dataframe.

The Python iloc() function allows us to select particular values from rows and columns by using indexes to create a subset.

The iloc() function in Python provides the ability to select and create a subset of a dataframe using the index values, as opposed to the loc() function which operates on labels. By specifying the index numbers of the desired rows and columns, we can extract specific data from the dataframe.

Syntax refers to the set of rules and principles that govern the structure and arrangement of words in a sentence or language.

pandas.dataframe.iloc[]

For instance, a country’s economic growth can lead to improvements in living standards for its citizens, such as increased employment opportunities and higher incomes.

block.iloc[[0,1,3,6],[0,2]]

The subset we have constructed consists of data from rows 0, 1, 3, and 6, with columns 0 and 2 specifically referring to ‘Roll-num’ and ‘NAME’.

I only need one version of the paraphrase in your native language.

Roll-num	NAME
0	10	John
1	20	Camili
3	40	Joseph
6	70	Siri

3. Using the indexing operator to generate a subset of a dataframe

We can easily create a subset of the data by using an indexing operator, such as square brackets.

Syntax refers to the structure and rules of a language that determine how words and phrases are organized to create meaningful sentences and statements.

dataframe[['col1','col2','colN']]

Can you provide me one option to paraphrase the following sentence natively?

Sentence: “I’m sorry, but I won’t be able to attend the meeting tomorrow.”

block[['Age','NAME']]

Here, we have chosen all the data values from the columns ‘Age’ and ‘NAME’, respectively.

We need a single option for paraphrasing the given sentence.

Result: The requested sentence will be paraphrased natively.

Age	NAME
0	12	John
1	14	Camili
2	13	Rheana
3	12	Joseph
4	14	Amanti
5	13	Alexa
6	15	Siri

In summary, to bring it to a close, in conclusion

With this, we have reached the conclusion of this subject. Please feel free to leave a comment below if you have any questions. Stay tuned for more Python-related posts and in the meantime, enjoy your learning! 🙂