There are several ways to create a DataFrame, ranging from manually inputting data to loading data from external files.

1. Creating DataFrame from a Dictionary

A common way to create a DataFrame is by using a dictionary, where the keys represent the column names and the values are lists (or arrays) that represent the data.

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
}

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

Output:

      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago

2. Creating DataFrame from a List of Lists

You can also create a DataFrame from a list of lists, where each inner list represents a row of data.

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
}

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

Output:

      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago

3. Creating DataFrame from CSV

You can read data directly from CSV files into a DataFrame using read_csv.

df = pd.read_csv('data.csv')  # Assuming 'data.csv' is a valid CSV file
print(df.head())