A Pandas DataFrame is a 2D data structure like a table with rows and columns. The values in the data frame are mutable and can be modified. Data frames are mostly used in data analysis and data manipulation. DataFrames store data in table form like databases and Excel sheets. We can easily perform arithmetic operations on them.There are many ways to create data frames. In this article, we will look into creating a pandas data frame from a generator function data frame.

Generator Function in Python

A generator function in Python is defined like a regular function, using the def keyword. However, instead of using return to send a value back to the caller, it employs the yield keyword. The presence of the yield statement within a function is what transforms it into a generator function.

Syntax:

def Func_Name():

yield statement

Here, Func_name is the name of the generator function, and the yield statement is where the function temporarily pauses, saving its state and allowing the generator to produce a value

Example:

In this example, we create a simple Python generator that yields 3 alphabets. Then we print these alphabets using Python for loop.

Output:

A
B
C

I think you might understand how a generator function works. Now, let’s learn how we can create Pandas Dataframes from the Generator function.

Creating a List from Generator() function and Making DataFrame

We are creating a DataFrame by using dataframe() function from the pandas library and passing a list that was created by a generator function.

Here, we are generating a list using the generator() function through a for loop, which creates a sequence of 5 numbers and then we are creating a DataFrame with the list using the pandas DataFrame() function.

Output:

  Numbers
0        0
1        1
2        2
3        3
4        4

Create a List from Generator expression and Making DataFrame

In this example, the generator expression [chr(ord('A') + i) for i in range(5)] is used within a for loop to create a list (alphabet_list) containing alphabets from ‘A’ to ‘E’.

Then, we use the pd.DataFrame() function to create a DataFrame (df) from the list, and the resulting DataFrame has one column named ‘Alphabets’ with values ‘A’ to ‘E’.

We created a dictionary and then converted to list using list() function.

Output:

  Alphabets
0         A
1         B
2         C
3         D
4         E

Conclusion:

The generator() function in Python is highly useful for generating values. In this article, we discussed creating a Pandas DataFrame using the generator() function and generator expression. With this tutorial, you will be to create DataFrames using both the generator function and generator expression.