Update CaseStudy.md

Author: Pankaj-Str

35 Day Pandas Basic/CaseStudy.md

@@ -74,3 +74,153 @@ This case study demonstrates common Pandas operations:
 8. Customer behavior analysis
 9. Filtering for specific conditions
 10. Exporting results to CSV files
+
+---------------------------------
+
+
+# Pandas Step-by-Step Example Guide
+*Updated: June 25, 2025*
+
+## 1. What is Pandas?
+Pandas is a powerful Python library for data manipulation and analysis. It provides data structures like Series and DataFrames, which are ideal for handling structured data, such as tabular data, time series, and more.
+
+```python
+import pandas as pd
+import numpy as np
+```
+
+## 2. Series and DataFrames
+- **Series**: A one-dimensional array-like object that can hold data of any type (integers, strings, floats, etc.). It has an index for labeling data.
+- **DataFrame**: A two-dimensional, tabular data structure with labeled rows and columns, similar to a spreadsheet or SQL table.
+
+```python
+# Creating a Series
+series = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])
+print("Series:\n", series)
+
+# Creating a DataFrame
+data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
+df = pd.DataFrame(data)
+print("\nDataFrame:\n", df)
+```
+
+## 3. Creating DataFrames
+DataFrames can be created from dictionaries, lists, or other data structures.
+
+```python
+# From a dictionary
+data = {'Product': ['Apple', 'Banana', 'Orange'], 'Price': [1.0, 0.5, 0.75]}
+df = pd.DataFrame(data, index=['P1', 'P2', 'P3'])
+print("DataFrame from dictionary:\n", df)
+
+# From a list of dictionaries
+list_data = [{'Product': 'Apple', 'Price': 1.0}, {'Product': 'Banana', 'Price': 0.5}]
+df_list = pd.DataFrame(list_data)
+print("\nDataFrame from list:\n", df_list)
+```
+
+## 4. Reading and Writing Data
+Pandas supports reading from and writing to various file formats like CSV, Excel, and JSON.
+
+```python
+# Writing DataFrame to CSV
+df.to_csv('products.csv', index=True)
+
+# Reading from CSV
+df_read = pd.read_csv('products.csv', index_col=0)
+print("Read from CSV:\n", df_read)
+```
+
+## 5. Data Types and Missing Values
+Pandas automatically infers data types, but you can inspect and handle missing values.
+
+```python
+# Checking data types
+print("Data types:\n", df.dtypes)
+
+# Introducing missing values
+df_with_na = df.copy()
+df_with_na.loc['P1', 'Price'] = np.nan
+print("\nDataFrame with missing values:\n", df_with_na)
+
+# Checking for missing values
+print("\nMissing values:\n", df_with_na.isna())
+```
+
+## 6. Indexing Methods: loc and iloc
+- `loc`: Access data by label/index.
+- `iloc`: Access data by integer position.
+
+```python
+# Using loc
+print("Using loc (select P1):\n", df.loc['P1'])
+
+# Using iloc
+print("\nUsing iloc (select first row):\n", df.iloc[0])
+```
+
+## 7. Boolean Indexing
+Filter rows based on conditions using boolean masks.
+
+```python
+# Select rows where Price > 0.6
+print("Boolean indexing (Price > 0.6):\n", df[df['Price'] > 0.6])
+```
+
+## 8. Selection Based on Conditions
+Combine conditions for more complex filtering.
+
+```python
+# Select rows where Product is 'Apple' or Price > 0.6
+condition = (df['Product'] == 'Apple') | (df['Price'] > 0.6)
+print("Complex condition:\n", df[condition])
+```
+
+## 9. Adding and Deleting Columns
+Modify DataFrames by adding or removing columns.
+
+```python
+# Adding a new column
+df['Stock'] = [100, 200, 150]
+print("After adding Stock column:\n", df)
+
+# Deleting a column
+df = df.drop('Stock', axis=1)
+print("\nAfter deleting Stock column:\n", df)
+```
+
+## 10. Handling Missing Data
+Handle missing values by filling or dropping them.
+
+```python
+# Filling missing values
+df_with_na['Price'] = df_with_na['Price'].fillna(df_with_na['Price'].mean())
+print("After filling missing values:\n", df_with_na)
+
+# Dropping rows with missing values
+df_dropped = df_with_na.dropna()
+print("\nAfter dropping missing values:\n", df_dropped)
+```
+
+## 11. Grouping and Aggregation
+Group data by a column and perform aggregations like sum, mean, etc.
+
+```python
+# Adding a Category column for grouping
+df['Category'] = ['Fruit', 'Fruit', 'Fruit']
+grouped = df.groupby('Category').agg({'Price': ['mean', 'sum']})
+print("Grouped by Category:\n", grouped)
+```
+
+## 12. Merging and Joining DataFrames
+Combine DataFrames using merge or join operations.
+
+```python
+# Creating another DataFrame
+df2 = pd.DataFrame({'Product': ['Apple', 'Banana'], 'Region': ['North', 'South']})
+
+# Merging DataFrames
+merged = pd.merge(df, df2, on='Product', how='left')
+print("Merged DataFrame:\n", merged)
+```
+