DataFrame.md

DataFrame()

• DataFrame is a 2D data structure that organizes data into rows and columns.
• It is like a spreadsheet, but with more features.
• DataFrames can be created from a variety of data sources, such as CSV files, Excel files, and SQL databases.

DataFrames can be used to perform a variety of tasks, such as:

1. Cleaning and manipulating data
2. Analyzing data trends
3. Visualizing data using charts and graphs

Benefits of using DataFrames:

1. Flexibility: DataFrames can be used to store a variety of data types, including numeric, categorical, and string data.
2. Scalability: DataFrames can scale to handle large datasets.
3. Performance: DataFrames are optimized for performance, so you can quickly and easily perform complex data operations.
4. Ease of use: DataFrames are easy to use, even for beginners.

DataFrames can be created in multiple ways:

A. Using List

import pandas as pd

# Create a list of lists:
list_of_lists = [['Kirankumar', 27, 'Mumbai'], ['Sumit', 26, 'Patna'], ['Suraj', 27, 'Bangalore']]

# Convert the list of lists to a DataFrame:
df = pd.DataFrame(list_of_lists, columns=['Name', 'Age', 'City'])

# Print the DataFrame:
print(df)

B. Using zip

import pandas as pd

# Create lists of fields:
name = ['Kirankumar', 'Suraj', 'Sumit']
age = [27, 27, 26]
city = ['Mumbai', 'Bangalore', 'Patna']

# Combine the multiple lists into single list object:
df = pd.DataFrame(data=zip(name, age, city), columns=['Name', 'Age', 'City'])

# Print the DataFrame:
print(df)

C. Using List of Dictionaries

import pandas as pd

# Create a list of dictionaries:
list_of_dictionaries = [{'Name': 'Kirankumar', 'Age': 27, 'Occupation': 'Data Scientist'}, {'Name': 'Suraj', 'Age': 27, 'Occupation': 'DevOps'}, {'Name': 'Sumit', 'Age': 26, 'Occupation': 'Python Developer'}]

# Convert the list of dictionaries to a DataFrame
df = pd.DataFrame(list_of_dictionaries)

# Print the DataFrame
print(df)