# Set up Pandas in a virtual environment
# 1. Create and activate a venv
python -m venv venv
# Windows
venv\Scripts\activate
# macOS / Linux
source venv/bin/activate

# 2. Install Pandas + Jupyter
pip install pandas jupyter matplotlib

# 3. Start Jupyter
jupyter notebook

# 4. In a notebook cell:
import pandas as pd
print(pd.__version__)

# If you can read and run this, you're ready for Pandas.
# Small check: loops, dicts, functions, list comprehensions.

products = [
    {"name": "Laptop", "price": 60000, "qty": 3},
    {"name": "Phone",  "price": 25000, "qty": 5},
    {"name": "Tablet", "price": 30000, "qty": 2},
]

def total(products):
    return sum(p["price"] * p["qty"] for p in products)

print("Total:", total(products))

# If this makes sense, you're ready.
# If not, spend 1-2 weeks on Python basics first.

import pandas as pd

# Load a CSV file
df = pd.read_csv("sales.csv")

# Quick inspection
print(df.shape)          # (rows, cols)
print(df.head())         # first 5 rows
print(df.info())         # types, non-null counts
print(df.describe())     # numeric summary
print(df.columns.tolist())

# Pick a column
print(df["revenue"].head())

# Pick a few columns
print(df[["order_id", "revenue"]].head())

import pandas as pd

df = pd.read_csv("sales.csv")

# Missing values
print(df.isna().sum())

# Duplicates
df = df.drop_duplicates()

# Type fixing
df["order_date"] = pd.to_datetime(df["order_date"])
df["revenue"]    = pd.to_numeric(df["revenue"], errors="coerce")

# Fill missing values (numeric column → median)
df["revenue"] = df["revenue"].fillna(df["revenue"].median())

# Clean text columns
df["city"] = df["city"].str.strip().str.title()

# Filter to last 90 days
recent = df[df["order_date"] >= pd.Timestamp.today() - pd.Timedelta(days=90)]
print("Rows in last 90 days:", len(recent))

import pandas as pd

df = pd.read_csv("orders.csv")
df["order_date"] = pd.to_datetime(df["order_date"])

# Revenue by month and category
monthly = (
    df.assign(month=df["order_date"].dt.to_period("M"))
      .groupby(["month", "category"])
      .agg(orders=("order_id", "nunique"),
           revenue=("revenue", "sum"))
      .reset_index()
)

print(monthly.head(10))

# Pivot for a report
pivot = monthly.pivot_table(
    index="month", columns="category",
    values="revenue", aggfunc="sum"
)
print(pivot.tail())

# Sales analysis project — README outline
1. Problem:
   "Which products and cities drive revenue?"

2. Data:
   - orders.csv (order_id, date, city, product, revenue)
   - customers.csv (customer_id, city, segment)

3. Steps:
   - Clean dates, revenue, and city names
   - Merge orders with customers
   - Monthly revenue by city and product
   - Top 10 products by revenue growth

4. Results:
   - City X grew 32% QoQ
   - Product Y drove 45% of new revenue

5. Visuals:
   - Line chart: monthly revenue
   - Bar chart: top products

# Customer churn project — README outline
1. Problem:
   "Which customers are likely to churn?"

2. Data:
   - users.csv (signup_date, plan, country)
   - events.csv (user_id, event, timestamp)

3. Steps:
   - Build features: logins, purchases, support tickets
   - Define churn: no login in 30 days
   - Train a simple logistic regression
   - Segment churn risk by plan and country

4. Results:
   - Churn is 2x higher on monthly plans
   - Users with 0 logins in 14 days churn 3x more

5. Visuals:
   - Churn rate by plan
   - Feature importance chart

Daily Pandas routine (60-90 minutes):
20 min — Read one concept (e.g., groupby)
20 min — Solve 1-2 practice problems
20 min — Apply it to your own dataset
10 min — Write notes in your own words
10 min — Review yesterday's notes

Consistency beats intensity.
30 days × 1 hour = interview-ready.

Recommended practice resources:
- Pandas official 10-minute intro
- Kaggle Pandas micro-course
- "Pandas Cookbook" (book)
- StrataScratch / LeetCode Pandas problems
- Real datasets on Kaggle or data.gov.in

Build your own project alongside tutorials —
that's what gets remembered in interviews.