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28 topics
Pandas
1. Introduction
Pandas gives you labeled, heterogeneous, tabular data on top of NumPy.
✓2. Reading Data
df = pd.readcsv('data.csv')
✓3. Writing Data
df.tocsv('out.csv', index=False) # drop the index column
✓4. Exploring Data
df.head(3) # first 3 rows
✓5. Selecting Data
df = pd.DataFrame({'age':[25,30,35], 'city':['NYC','LA','SF']},
✓6. Cleaning Data
df.isna().sum() # count NaNs per column
✓7. Working with Columns
df['bonus'] = df['salary'] 0.1 # create
✓8. Sorting
df.sortvalues('age') # ascending
✓9. GroupBy
The split-apply-combine pattern: split rows into groups, apply a function, combine results.
✓10. Merge & Join
left = pd.DataFrame({'id':[1,2,3], 'name':['A','B','C']})
✓11. Pivot Tables & Reshaping
df = pd.DataFrame({
✓12. MultiIndex (hierarchical index)
idx = pd.MultiIndex.fromtuples(
✓13. String Functions (`.str` accessor)
s = pd.Series([' Alice ', 'BOB', 'charlie99'])
✓14. DateTime
df['date'] = pd.todatetime(df['date']) # parse strings
✓15. Window Functions
s = pd.Series([1, 2, 3, 4, 5])
✓16. Performance Tips
df['city'] = df['city'].astype('category') # if few unique values
✓17. Pandas Interview Questions (50+)
1. Series vs DataFrame? 1-D labeled array vs 2-D labeled table.
✓Project 1 — Cleaning a Messy Dataset
raw = pd.DataFrame({
✓Project 2 — Sales Analysis
sales = pd.DataFrame({
✓Project 3 — Employee Salary Analysis
emp = pd.DataFrame({
✓Project 4 — Titanic-style Analysis
titanic = pd.DataFrame({
✓Project 5 — Time Series Analysis
dates = pd.daterange('2024-01-01', periods=90, freq='D')
✓Project 6 — Feature Engineering & ML Preprocessing
df = pd.DataFrame({
✓Project 7 — Customer Churn Analysis
cust = pd.DataFrame({
✓Pandas Exercises
P1. Create a DataFrame from a dict. pd.DataFrame({'a':[1,2],'b':[3,4]})
✓Mixed NumPy + Pandas Exercises
M1. Convert a DataFrame column to a NumPy array. df['v'].tonumpy()
✓Quick Comparison Tables
Revision notes.
✓Final Revision Cheat Sheets
readcsv, head, info, describe, loc, iloc, groupby, agg,
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