Your First DataFrame

Your First DataFrame

Alright, let's create your first DataFrame. This is the moment everything clicks. Think of a DataFrame as a table — rows and columns, just like a spreadsheet or a database table.

Creating a DataFrame from a Dictionary

The easiest way to create a DataFrame is from a Python dictionary. Each key becomes a column name, and the values become the column data.

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'Score': [88.5, 92.3, 79.8]
}

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

Look at that output! Pandas automatically created row labels (0, 1, 2) and organized everything into a clean table. No more clicking through Excel cells.

Rows and Columns

Here is the thing about DataFrames — rows represent individual records (like a person), and columns represent attributes (like name or age). Each column is actually a Series, which is like a one-dimensional array.

One thing that confused me at first was the difference between a Series and a DataFrame. Think of it this way: a Series is one column, a DataFrame is the whole table. Simple as that.

Key Takeaways

• A DataFrame is a table with rows and columns
• Create one from a dictionary where keys are column names
• Pandas auto-generates row labels (the index)
• Each column in a DataFrame is a Series