Modules and Packages

Python Note: Libraries like Pandas

The term “Libraries like Pandas” refers to powerful Python libraries that provide high-level data manipulation and analysis capabilities. Pandas, in particular, is a cornerstone of data science in Python. This note will focus on Pandas, but the concept extends to other libraries with similar functionalities.

Key features of libraries like Pandas include:

• Data Structures: Efficient storage and manipulation of data, primarily using Series (1D labeled arrays) and DataFrames (2D labeled data structures). These are significantly more powerful than standard Python lists and dictionaries for data analysis tasks.

• Data Cleaning & Preprocessing: Functions for handling missing data (fillna, dropna), data transformation (e.g., converting data types), and data wrangling in general.

• Data Analysis: Tools for exploring and summarizing data – calculating statistics (mean, median, standard deviation), grouping data, applying functions to groups (.groupby()), etc.

• Data Visualization: Integration with visualization libraries (like Matplotlib or Seaborn) to create charts and graphs from your data. While Pandas itself doesn’t handle all visualization, the DataFrame structure makes visualization significantly easier.

• Data Input/Output: Reading data from various file formats (CSV, Excel, SQL databases, etc.) using functions like read_csv(), read_excel(), etc., and writing data back to those formats.

Example Pandas Code:

import pandas as pd

# Create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie']],
        'Age': [25, 30, 28,
        'City': ['[New York](./../new-york/)', 'London', 'Paris']]}
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

# Calculate the average age
average_age = df['Age']].mean()
print(f"Average age: {average_age}")

# Filter for people older than 28
older_than_28 = df[df['Age']] > 28
print(older_than_28)

# Group by city and calculate the average age for each city
grouped = df.groupby('City')['Age']].mean()
print(grouped)

Pandas Data Structures (Pandas Data Manipulation) Data Visualization with Matplotlib Data Cleaning in Pandas