Introduction to Pandas

# CHAPTER 13

Introduction to Pandas #

1. Chapter Introduction #

2. What is Pandas? #

Pandas is a software library built *on top* of NumPy. It provides high-performance, easy-to-use data structures and data analysis tools.

If NumPy is for pure math, Pandas is for data manipulation, cleaning, and preparation.

Installation & Importing:

!pip install pandas

# 'pd' is the universal industry standard alias
import pandas as pd

3. The Two Core Structures #

Pandas has two primary data structures: the Series (1D) and the DataFrame (2D).

1. The Pandas Series: A Series is essentially a single column of data. It is a 1D array, but unlike NumPy, it has an explicitly defined "Index" (labels for each row).

import pandas as pd

# Create a Series from a list
ages = pd.Series([22, 35, 58], name="Age")
print(ages)
# Output:
# 0    22
# 1    35
# 2    58
# Name: Age, dtype: int64

2. The Pandas DataFrame: A DataFrame is a 2D table (like an Excel sheet or SQL table). It is essentially a collection of Series sharing the same index.

# Create a DataFrame from a Dictionary
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "Department": ["Sales", "IT", "HR"]
}

df = pd.DataFrame(data)
print(df)
# Output:
#       Name  Age Department
# 0    Alice   25      Sales
# 1      Bob   30         IT
# 2  Charlie   35         HR

4. Data Loading #

Data Scientists rarely type out DataFrames by hand. They load them from files. Pandas can read almost any file format with a single line of code.

# The most common command in data science
# Read a CSV file into a DataFrame
df = pd.read_csv('employee_data.csv')

# Other formats:
# pd.read_excel('data.xlsx')
# pd.read_json('data.json')
# pd.read_sql('SELECT * FROM table', connection)

5. Data Inspection #

Once the data is loaded, the very first thing you do is inspect it to see what you are dealing with.

# 1. Look at the top 5 rows (CRITICAL)
print(df.head())

# 2. Look at the bottom 3 rows
print(df.tail(3))

# 3. Get the shape (Rows, Columns)
print(df.shape) 

# 4. Get a technical summary (Column names, null counts, data types)
print(df.info())

# 5. Get a statistical summary (Mean, min, max for numerical columns)
print(df.describe())

6. Mini Project: Employee Dataset Analyzer #

Let's simulate loading an employee dataset and profiling it.

import pandas as pd

# Simulating data loading
data = {
    "ID": [101, 102, 103, 104],
    "Name": ["John", "Sarah", "Mike", "Emma"],
    "Salary": [60000, 85000, 50000, 92000],
    "Years_Exp": [2, 5, 1, 7]
}
df = pd.DataFrame(data)

print("--- DATASET PREVIEW ---")
print(df.head(2))

print("\n--- QUICK STATISTICS ---")
# .describe() automatically ignores text columns like 'Name'
print(df.describe())

7. Common Mistakes #

• Printing the whole DataFrame: If you load a 1-million-row CSV and type print(df), Jupyter might freeze trying to render it. *Always* use df.head() to look at the data.

• Confusing Series and DataFrames: A Series is a single column. A DataFrame is a table. If you extract one column from a DataFrame (df['Name']), it becomes a Series.

8. MCQs #

9. Interview Questions #

• Q: What is the difference between a Pandas Series and a Pandas DataFrame?

• Q: Walk me through the exact steps (and functions) you would use immediately after loading an unknown CSV file into Pandas.

10. Summary #

11. Next Chapter Recommendation #