Data Science

Pandas DataFrames: Essential Guide to Data Analysis

📅 December 05, 2025 ⏱️ 1 min read 👁️ 3 views 🏷️ Data Science

Pandas is the essential library for data manipulation and analysis in Python.

Creating DataFrames


import pandas as pd

# From dictionary
df = pd.DataFrame({
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'city': ['NYC', 'LA', 'Chicago']
})

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

Basic Operations


# View data
print(df.head())
print(df.info())
print(df.describe())

# Select columns
ages = df['age']
subset = df[['name', 'age']]

# Filter rows
adults = df[df['age'] >= 18]
filtered = df[(df['age'] > 25) & (df['city'] == 'NYC')]

Data Aggregation


# Group by
grouped = df.groupby('city')['age'].mean()

# Multiple aggregations
agg_df = df.groupby('city').agg({
    'age': ['mean', 'min', 'max'],
    'name': 'count'
})

Data Cleaning


# Handle missing values
df.dropna()  # Remove rows with NA
df.fillna(0)  # Fill NA with value

# Remove duplicates
df.drop_duplicates()

# Rename columns
df.rename(columns={'old_name': 'new_name'})

Pandas makes data analysis efficient and intuitive!

🏷️ Tags:
pandas data science python data analysis dataframe

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