Creating a Pandas DataFrame

Pandas DataFrame comes is a powerful tool that allows us to store and manipulate data in a structured way, similar to an Excel spreadsheet or a SQL table. A DataFrame is similar to a table with rows and columns. It helps in handling large amounts of data, performing calculations, filtering information with ease.

Creating an Empty DataFrame

An empty DataFrame in pandas is a table with no data but can have defined column names and indexes. It is useful for setting up a structure before adding data dynamically. An empty DataFrame can be created just by calling a dataframe constructor. 

import pandas as pd

df = pd.DataFrame()

print(df)

Empty DataFrame
Columns: []
Index: []

Creating a DataFrame from a List

A simple way to create a DataFrame is by using a single list. Pandas automatically assigns index values to the rows when you pass a list.

• Each item in the list becomes a row.
• The DataFrame consists of a single unnamed column.

import pandas as pd

lst = ['Geeks', 'For', 'Geeks', 'is', 
            'portal', 'for', 'Geeks']

df = pd.DataFrame(lst)
print(df)

        0
0   Geeks
1     For
2   Geeks
3      is
4  portal
5     for
6   Geeks

Creating DataFrame from dict of Numpy Array

We can create a Pandas DataFrame using a dictionary of NumPy arrays. Each key in the dictionary represents a column name and the corresponding NumPy array provides the values for that column.

import numpy as np
import pandas as pd

data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
df = pd.DataFrame(data, columns=['A', 'B', 'C'])
print(df)

   A  B  C
0  1  2  3
1  4  5  6
2  7  8  9

Creating a DataFrame from a List of Dictionaries  

We can also create dataframe using List of Dictionaries. It represents data where each dictionary corresponds to a row. This method is useful for handling structured data from APIs or JSON files. It is commonly used in web scraping and API data processing since JSON responses often contain lists of dictionaries.

import pandas as pd

dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
        'degree': ["MBA", "BCA", "M.Tech", "MBA"],
        'score':[90, 40, 80, 98]}

df = pd.DataFrame(dict)

print(df)

     name  degree  score
0  aparna     MBA     90
1  pankaj     BCA     40
2  sudhir  M.Tech     80
3   Geeku     MBA     98

To understand more methods of creating dataframe in detail refer to:

• Different ways to create Pandas Dataframe
• Create pandas dataframe from lists using zip
• Create a Pandas DataFrame from List of Dicts