Creating DataFrames

Creating DataFrames

Let me give you several ways to create DataFrames. While reading from files is common, sometimes you need to build data from scratch.

From a Dictionary

This is the most straightforward way. We covered it before, but it's worth repeating:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data)
print(df)
    

From a List of Dictionaries

When your data comes as individual records, this approach works great:

records = [
    {'Name': 'Alice', 'Age': 25},
    {'Name': 'Bob', 'Age': 30},
    {'Name': 'Charlie', 'Age': 35}
]
df = pd.DataFrame(records)
print(df)
    

Think of it like this — each dictionary is one row. Pandas stitches them together into a table automatically.

From a NumPy Array

If you're working with numerical data, this is super handy:

import numpy as np

arr = np.random.rand(4, 3)
df = pd.DataFrame(arr, columns=['A', 'B', 'C'])
print(df)
    

The array gives you the data, and you provide the column names. This is great for generating test data or working with scientific computations.

Key Takeaways

• Create from dictionaries where keys are column names
• Use a list of dictionaries for row-by-row data
• NumPy arrays work great for numerical data
• You always control the column names with the `columns` parameter