pandas DataFrames are the backbone of data analysis in Python. They provide a powerful, flexible way to work with structured data. In this guide, you’ll learn multiple ways to create DataFrames.

Prerequisites

First, make sure you have pandas installed:

pip install pandas

Then import it in your Python script:

import pandas as pd

Method 1: From a Dictionary

The most common way to create a DataFrame is from a Python dictionary:

import pandas as pd

# Create a DataFrame from a dictionary
df = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'city': ['New York', 'London', 'Tokyo']
})

print(df)

Output:

      name  age      city
0    Alice   25  New York
1      Bob   30    London
2  Charlie   35     Tokyo

Method 2: From a List of Dictionaries

You can also create a DataFrame from a list of dictionaries where each dictionary represents a row:

data = [
    {'name': 'Alice', 'age': 25, 'city': 'New York'},
    {'name': 'Bob', 'age': 30, 'city': 'London'},
    {'name': 'Charlie', 'age': 35, 'city': 'Tokyo'}
]

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

Method 3: From a CSV File

Reading data from a CSV file is extremely common:

# Read CSV file
df = pd.read_csv('data.csv')

# Read with specific options
df = pd.read_csv('data.csv', 
                 sep=',',           # delimiter
                 header=0,          # row number for column names
                 index_col=None)    # column to use as index

Method 4: From NumPy Arrays

If you’re working with numerical data, you can create a DataFrame from NumPy arrays:

import numpy as np
import pandas as pd

# Create from NumPy array
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
df = pd.DataFrame(data, columns=['A', 'B', 'C'])

print(df)

Inspecting Your DataFrame

After creating a DataFrame, use these methods to explore it:

# First few rows
df.head()

# Last few rows
df.tail()

# Shape (rows, columns)
df.shape

# Column info
df.info()

# Summary statistics
df.describe()

# Column names
df.columns

Summary

Creating DataFrames in Python with pandas is versatile:

Use dictionaries for quick DataFrame creation
Use pd.read_csv() for loading CSV files
Use NumPy arrays for numerical data
Always inspect your DataFrame with .head(), .info(), and .describe()

Now you’re ready to start analyzing data with pandas!

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