Every Python skill a 12-18 LPA data job expects

Skills 1-4: load, look, clean, filter

1. 01

   Read any file

   df = pd.read_csv('orders.csv')
   df = pd.read_excel('sales.xlsx')

   CSV, Excel, even a folder of files.

2. 02

   Inspect before touching

   df.info()
   df.isna().sum()

   Types, nulls, ranges. First 2 minutes, always.

3. 03

   Clean

   df = df.drop_duplicates()
   df['city'] = df['city'].str.strip()

   Duplicates, blanks, extra spaces, wrong types.

4. 04

   Filter

   df[df['amount'] > 500]
   df.query("city == 'Pune'")

   Same as WHERE in SQL.

Skills 5-10: group, join, pivot, ship

1. 05

   groupby + agg

   df.groupby('city').agg(
       total=('amount', 'sum'),
       orders=('order_id', 'count'))

   Your GROUP BY, with named outputs.

2. 06

   merge

   df.merge(cust, on='cust_id', how='left')

   LEFT JOIN two tables on a key.

3. 07

   pivot_table

   df.pivot_table(index='city', columns='month',
       values='amount', aggfunc='sum', fill_value=0)

   Excel pivot, in one line.

4. 08

   Dates

   df['dt'] = pd.to_datetime(df['dt'])
   df['month'] = df['dt'].dt.month

   Convert, then pull month or year.

5. 09

   MoM growth + running total

   m['growth'] = m['sales'].pct_change()
   df['cum'] = df['amount'].cumsum()

   The window-function questions, in pandas.

6. 10

   Hand it over

   out.to_excel('report.xlsx')

   Managers want Excel, not a notebook.

SQL you know, pandas you need

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