Introduction to Python Pandas

Pandas is a powerful Python library used for data manipulation and analysis. It provides data structures and functions that make it easy to work with structured data.

Key Concepts

1. Data Structures

• Series: A one-dimensional labeled array that can hold any data type.
• DataFrame: A two-dimensional labeled data structure, similar to a table in a database or a spreadsheet.

Example:

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

Example:

import pandas as pd
s = pd.Series([1, 2, 3, 4])
print(s)

2. Data Manipulation

• Indexing and Selecting Data: You can access rows and columns using labels or indices.
• Filtering Data: You can filter data based on conditions.

Example:

print(df[df['Age'] > 30])  # Selects rows where Age is greater than 30

Example:

print(df['Name'])  # Selects the 'Name' column
print(df.loc[0])   # Selects the first row

3. Data Cleaning

• Handling Missing Values: Pandas provides functions to detect and fill or drop missing values.

Example:

df.fillna(0)  # Replaces NaN with 0

4. Data Aggregation and Grouping

• GroupBy: You can group data by one or more columns and perform aggregate functions.

Example:

grouped = df.groupby('Age').count()
print(grouped)

5. Data Visualization

• While Pandas itself is not a visualization library, it can integrate with libraries like Matplotlib for plotting data.

Example:

import matplotlib.pyplot as plt
df['Age'].plot(kind='bar')
plt.show()

Conclusion

Pandas is an essential tool for anyone working with data in Python. Its intuitive data structures and vast functionality make data manipulation straightforward and efficient. Whether you are analyzing data, cleaning datasets, or preparing data for visualization, Pandas has the tools you need.