Class Notes: Data Handling Using Pandas – I

What is Pandas?

Pandas is a super useful Python library for working with data—like a supercharged Excel for coders. It helps you:

• Organize data neatly (like tables).
• Clean messy data (handling missing values, duplicates, etc.).
• Slice, filter, and analyze data quickly.

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Pandas Data Structures

Pandas has two main data containers:

1. Series: A single column of data with labels (like a list with a name tag).
2. DataFrame: A full table with rows and columns (like an Excel sheet).

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1. Series

• What? A 1D labeled array (e.g., [10, 15, 18] with index labels [0, 1, 2]).
• Features:
  • Data can be changed (mutable), but size can’t (immutable).
  • Index labels make data easy to access.

How to Create a Series:

python

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import pandas as pd  
data = [10, 15, 18, 22]  
s = pd.Series(data, index=['a', 'b', 'c', 'd'])  
print(s)  

Output:

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a    10  
b    15  
c    18  
d    22  

Cool Tricks with Series:

• Math ops: s * 2 (multiplies all values by 2).
• Filtering: s[s > 2] (shows values > 2).
• Head/Tail: s.head(3) (first 3 rows) or s.tail(2) (last 2 rows).

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2. DataFrame

• What? A 2D table (rows + columns).
• Features:
  • Columns can hold different data types (numbers, text, etc.).
  • Size and data can be changed (mutable).

How to Create a DataFrame:

python

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

Output:

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   Name  Age  
0  Alice  25  
1   Bob  30  

Working with DataFrames:

• Add a column: df['Salary'] = [5000, 6000]
• Delete a column: del df['Age'] or df.drop('Age', axis=1)
• Select data:
  • Single column: df['Name']
  • Multiple columns: df[['Name', 'Salary']]
  • Rows: df.loc[0:2] (by label) or df.iloc[0:2] (by position)

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DataFrame Operations

• Filtering: df[df['Age'] > 25] (people older than 25).
• Math: df['Salary'].sum() (total salary).
• Merge/Join: Combine two DataFrames (like SQL joins).pythonCopydf1.merge(df2, on=’ID’, how=’inner’) # Keeps matching rows only.

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Reading/Writing CSV Files

• Read CSV: data = pd.read_csv('file.csv')
• Save to CSV: df.to_csv('new_file.csv')

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Key Takeaways

• Series = 1D labeled data (single column).
• DataFrame = 2D table (rows + columns).
• Use loc/iloc to access data.
• Pandas is your best friend for data cleaning, analysis, and quick lookups!