Python Pandas – DataFrame

Creating a DataFrame in Pandas

A DataFrame in Python’s pandas library is a two-dimensional labeled data structure that is used for data manipulation and analysis. It can handle different data types such as integers, floats, and strings. Each column has a unique label, and each row is labeled with a unique index value, which helps in accessing specific rows.

DataFrame is used in machine learning tasks which allow the users to manipulate and analyze the data sets in large size. It supports the operations such as filtering, sorting, merging, grouping and transforming data.

Features of DataFrame

• Columns can be of different types.
• Size is mutable.
• Labeled axes (rows and columns).
• Can Perform Arithmetic operations on rows and columns.

Python Pandas DataFrame Structure

You can think of a DataFrame as similar to an SQL table or a spreadsheet data representation. Let us assume that we are creating a data frame with student’s data.

Creating a pandas DataFrame

A pandas DataFrame can be created using the following constructor −

pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=None)

The parameters of the constructor are as follows −

Creating a DataFrame from Different Inputs

A pandas DataFrame can be created using various inputs like −

• Lists
• Dictionary
• Series
• Numpy ndarrays
• Another DataFrame
• External input files like CSV, JSON, HTML, Excel sheet, and more.

Create an Empty DataFrame

#import the pandas library and aliasing as pd
import pandas as pd
df = pd.DataFrame()
print(df)

Output:

Empty DataFrame
Columns: []
Index: []

Create a DataFrame from Lists

import pandas as pd
data = [1,2,3,4,5]
df = pd.DataFrame(data)
print(df)

Output:

   0
0  1
1  2
2  3
3  4
4  5