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DataFrames in Pandas

In this tutorial, we'll delve into DataFrames in Pandas, a powerful two-dimensional labeled data structure widely used for data manipulation and analysis. We'll cover the fundamentals of DataFrames, how to create them, access their attributes, and perform basic operations.

Introduction to DataFrames​

A DataFrame in Pandas is a two-dimensional labeled data structure with columns of potentially different types. You can think of it as a spreadsheet, SQL table, or a dictionary of Series objects. While Series represent one-dimensional data, DataFrames offer a higher dimensionality, allowing for more complex data representation and manipulation.

Key Features of DataFrames:​

  • Two-dimensional structure
  • Columns of potentially different types
  • Labels for both rows (index) and columns

Axes in DataFrames:​

  • The zero axis refers to the index (rows).
  • The one axis refers to columns.

Creating DataFrames​

Method 1: Using a Dictionary of Lists​

import pandas as pd

# Define data as a dictionary with lists inside
data = {'A': [value1, value2, value3],
        'B': [value1, value2, value3],
        'C': [value1, value2, value3]}

# Create DataFrame using pd.DataFrame
df = pd.DataFrame(data)

# Display DataFrame
print(df)

Method 2: Using a List of Lists with Specified Index and Columns​

# Define data as a list of lists
data = [[value1, value2, value3],
        [value1, value2, value3],
        [value1, value2, value3]]

# Specify index and columns
index = ['index1', 'index2', 'index3']
columns = ['A', 'B', 'C']

# Create DataFrame
df = pd.DataFrame(data, index=index, columns=columns)

# Display DataFrame
print(df)

Accessing Data in DataFrames​

Accessing Columns​

You can access columns using dot notation or indexing:

# Access column 'A' using dot notation
print(df.A)

# Access column 'A' using indexing
print(df['A'])

DataFrame Attributes​

Shape:​

# Shape of DataFrame
print(df.shape)  # Output: (rows, columns)

Size:​

# Size of DataFrame
print(df.size)  # Output: total number of elements

Index:​

# Index of DataFrame
print(df.index)

Columns:​

# Columns of DataFrame
print(df.columns)

Axis:​

# Axis information
print(df.axis)

Data Types:​

# Data types of columns
print(df.dtypes)

Values:​

# Values of DataFrame as an array
print(df.values)