docs: add comprehensive introduction to Pandas library for Python

Author: Sandesh

docs/python/pandas-introduction.md

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+---
+id: pandas-introduction
+title: Introduction to Pandas
+sidebar_label: Pandas
+description: Learn the basics of the Pandas Python library, including Series, DataFrame, data input/output, and basic data analysis, to kickstart your ML/DS workflow.
+sidebar_position: 20
+tags:
+  [
+    Python,
+    Pandas,
+    Data Analysis,
+    DataFrame,
+    Series,
+    Python Library,
+    Machine Learning,
+    Data Science,
+    Python Basics
+  ]
+slug: /python/pandas-introduction
+---
+
+
+# Introduction to Pandas
+
+Pandas is one of the most essential libraries in the Python data ecosystem.  
+It provides rich, high-level data structures and tools designed for fast and flexible data manipulation, analysis, and visualization.  
+
+If you're working in **data science**, **machine learning**, or **analytics**, Pandas is your foundation for cleaning, transforming, and understanding data.  
+It sits beautifully on top of **NumPy**, integrating seamlessly with other libraries like **Matplotlib**, **Seaborn**, and **Scikit-learn**.
+
+---
+
+## 1. Why Pandas?
+
+Working with raw data in Python used to mean juggling lists, dictionaries, and loops.  
+Pandas simplifies all that by introducing *two powerful data structures* — the **Series** and the **DataFrame** — that behave much like spreadsheet tables or SQL tables.
+
+Some reasons Pandas is so popular:
+
+- Handles large datasets efficiently.
+- Provides built-in methods for aggregation, cleaning, and reshaping.
+- Easily reads and writes data from multiple sources like CSV, Excel, JSON, and SQL.
+- Integrates tightly with visualization and machine learning libraries.
+
+---
+
+## 2. Installation
+
+If Pandas isn’t already installed, you can add it via pip:
+
+```bash
+pip install pandas
+```
+
+You can also install it with Anaconda (which includes Pandas by default):
+
+```bash
+conda install pandas
+```
+
+---
+
+## 3. Core Data Structures
+
+### Series
+
+A **Series** is a one-dimensional labeled array. You can think of it as a single column in a spreadsheet.
+
+```python
+import pandas as pd
+
+# Create a simple Series
+s = pd.Series([100, 200, 300, 400])
+print(s)
+```
+
+**Output:**
+```
+0    100
+1    200
+2    300
+3    400
+dtype: int64
+```
+
+Each element has an **index** (on the left) and a **value** (on the right).  
+You can assign your own custom index too:
+
+```python
+s = pd.Series([10, 20, 30], index=['A', 'B', 'C'])
+print(s['B'])  # Accessing by label → 20
+```
+
+---
+
+### DataFrame
+
+A **DataFrame** is a two-dimensional labeled data structure — essentially a table with rows and columns.
+
+```python
+data = {
+    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
+    'Age': [25, 30, 35, 40],
+    'City': ['Delhi', 'Mumbai', 'Chennai', 'Kolkata']
+}
+
+df = pd.DataFrame(data)
+print(df)
+```
+
+**Output:**
+```
+      Name  Age     City
+0    Alice   25    Delhi
+1      Bob   30   Mumbai
+2  Charlie   35  Chennai
+3    David   40  Kolkata
+```
+
+Each column in a DataFrame is actually a Series.  
+You can access them individually:
+
+```python
+df['Name']       # Access a column
+df[['Name', 'Age']]  # Access multiple columns
+df.loc[2]        # Access a row by label
+df.iloc[0]       # Access a row by position
+```
+
+---
+
+## 4. Reading and Writing Data (Input/Output)
+
+One of Pandas’ greatest strengths is its ability to easily load data from many file formats.  
+Here are some commonly used functions:
+
+| Format | Read | Write |
+|:--------|:------|:------|
+| CSV | `pd.read_csv()` | `DataFrame.to_csv()` |
+| Excel | `pd.read_excel()` | `DataFrame.to_excel()` |
+| JSON | `pd.read_json()` | `DataFrame.to_json()` |
+| SQL | `pd.read_sql()` | `DataFrame.to_sql()` |
+
+### Example: CSV Files
+
+```python
+# Reading from a CSV file
+df = pd.read_csv('employees.csv')
+
+# Writing to a CSV file
+df.to_csv('employees_cleaned.csv', index=False)
+```
+
+By default, Pandas assumes that the first row of your CSV file contains column names.  
+You can customize this behavior with parameters like `header=None` or `names=[...]`.
+
+---
+
+## 5. Basic Data Exploration
+
+Once your data is loaded into a DataFrame, Pandas provides a variety of methods for quick exploration.
+
+```python
+df.head()        # Displays the first 5 rows
+df.tail()        # Displays the last 5 rows
+df.shape         # Returns (rows, columns)
+df.columns       # Lists all column names
+df.dtypes        # Shows data types for each column
+df.info()        # Summary: column names, types, nulls, memory usage
+df.describe()    # Statistical summary of numeric columns
+```
+
+**Example:**
+```python
+print(df.describe())
+```
+
+**Output:**
+```
+             Age
+count   4.000000
+mean   32.500000
+std     6.454972
+min    25.000000
+25%    28.750000
+50%    32.500000
+75%    36.250000
+max    40.000000
+```
+
+---
+
+## 6. Data Selection and Filtering
+
+Pandas allows flexible data filtering using both labels and conditions.
+
+```python
+# Select a single column
+df['Age']
+
+# Select multiple columns
+df[['Name', 'City']]
+
+# Conditional filtering
+df[df['Age'] > 30]
+
+# Combining multiple conditions
+df[(df['Age'] > 25) & (df['City'] == 'Delhi')]
+```
+
+You can also use `.loc[]` for label-based selection or `.iloc[]` for position-based selection:
+
+```python
+df.loc[1:3, ['Name', 'City']]
+df.iloc[0:2, 0:2]
+```
+
+---
+
+## 7. Data Cleaning Basics
+
+Real-world data is messy. Pandas makes cleaning painless.
+
+### Handling Missing Values
+
+```python
+df.isnull()         # Check for missing values
+df.dropna()         # Drop rows with missing values
+df.fillna(0)        # Fill missing values with a placeholder
+```
+
+### Renaming Columns
+
+```python
+df.rename(columns={'Name': 'Employee_Name'}, inplace=True)
+```
+
+### Changing Data Types
+
+```python
+df['Age'] = df['Age'].astype(float)
+```
+
+---
+
+## 8. Sorting and Grouping
+
+Sorting your data:
+```python
+df.sort_values(by='Age', ascending=False)
+```
+
+Grouping (e.g., aggregating data by a category):
+```python
+grouped = df.groupby('City')['Age'].mean()
+print(grouped)
+```
+
+**Output:**
+```
+City
+Chennai    35.0
+Delhi      25.0
+Kolkata    40.0
+Mumbai     30.0
+Name: Age, dtype: float64
+```
+
+---
+
+## 9. Basic Data Analysis
+
+Let’s see some quick examples of what you can do once your data is cleaned:
+
+```python
+# Mean age
+df['Age'].mean()
+
+# Count how many from each city
+df['City'].value_counts()
+
+# Filter and sort together
+df[df['Age'] > 30].sort_values(by='Age', ascending=False)
+```
+
+---
+
+## 10. Visualizing Data with Pandas
+
+Pandas integrates with **Matplotlib**, allowing quick visualization directly from your DataFrame.
+
+```python
+import matplotlib.pyplot as plt
+
+df['Age'].plot(kind='bar', title='Age Distribution')
+plt.xlabel('Index')
+plt.ylabel('Age')
+plt.show()
+```
+
+For more advanced visualizations, you can use libraries like Seaborn or Plotly with your Pandas data.
+
+---
+
+## 11. Summary
+
+Pandas provides a clean, efficient interface for everything from data cleaning to basic analysis.  
+It’s one of the first libraries every data professional should master because it forms the backbone of nearly every ML and data science workflow in Python.
+
+**Next Steps:**
+- Explore advanced Pandas operations (merging, reshaping, pivoting)
+- Learn how Pandas integrates with NumPy and visualization libraries
+- Try using Pandas in a small data project — like analyzing a CSV dataset from Kaggle
+
+---
+
+**References:**
+- [Official Pandas Documentation](https://pandas.pydata.org/)
+- [10 Minutes to Pandas (Official Guide)](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html)
+- [Pandas API Reference](https://pandas.pydata.org/pandas-docs/stable/reference/index.html)