Python Libraries Pandas example

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Pandas: Powerful Data Analysis in Python

Pandas is a Python library specifically designed for working with structured data – think of data organized in tables, like spreadsheets or SQL databases. It makes data manipulation and analysis much easier and more intuitive.

Key Features & Why it's Useful:

  • DataFrame: This is the core data structure in Pandas. A DataFrame is like a table with rows and columns, where each column can hold different types of data (numbers, strings, dates, etc.).
  • Series: A Series is a one-dimensional labeled array, essentially a single column of a DataFrame.
  • Data Cleaning & Preparation: Pandas provides tools to handle missing data, filter data, transform data types, and generally clean up messy datasets.
  • Data Manipulation: You can easily select, filter, group, merge, and reshape data in DataFrames.
  • Data Analysis: Pandas offers functions for calculating statistics (mean, median, standard deviation, etc.), finding correlations, and performing other data analysis tasks.
  • Input/Output: Pandas can read data from and write data to various file formats (CSV, Excel, SQL databases, JSON, etc.).
  • Integration with other libraries: Pandas works seamlessly with NumPy, Matplotlib, and other data science libraries.



import pandas as pd

# Create a DataFrame from a dictionary
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 28],
        'City': ['New York', 'London', 'Paris']}

df = pd.DataFrame(data)

print(df)
# Output:
#       Name  Age      City
# 0    Alice   25  New York
# 1      Bob   30    London
# 2  Charlie   28     Paris

# Select a column
print(df['Name'])
# Output:
# 0      Alice
# 1        Bob
# 2    Charlie
# Name: Name, dtype: object

# Calculate the average age
average_age = df['Age'].mean()
print(average_age) # Output: 27.666666666666668
Simple Example:

 



Data Loading, Cleaning, and Basic Analysis with Pandas



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