Create a Pivot Table
Create a pivot table showing total quantity sold per product in each region. Available Data: DataFrame df with columns:...
pandasaggregationpivot
Intermediate
7m20 pts
1100%
Python Practice Hub
pandas DataFrame Practice Questions
Select, group, merge and derive columns on a table in Python, without loops.
54 questionsFreeRuns in your browser
- loc / iloc
- Select rows and columns by label or position.
- groupby + agg
- Group rows and summarise each group.
- merge
- Join two DataFrames, like a SQL join.
What you will practice
- Select and filter with loc, iloc and boolean masks instead of chained indexing
- Aggregate with groupby().agg() and reshape the result with reset_index and pivot_table
- Join frames using merge with the right how= and on= arguments, and check the row count afterwards
- Replace row-by-row loops and apply() with vectorised column arithmetic and np.where
- Handle missing data with isna, fillna and dropna before the numbers reach a report
Read the full overview
A pandas DataFrame is a labelled, two-dimensional table in Python, and most analysis with it comes down to four moves: select the rows and columns you need, group and aggregate them, merge in another table, and derive new columns without looping. Doing that vectorised, with whole-column operations instead of a Python for loop, is what keeps the code both short and fast. These questions run real pandas in the browser against seeded DataFrames, so the output you check is the actual frame.
54 questionsPage 3 of 4
Calculate Revenue per Product
Calculate the total revenue (quantity * price) for each product. Available Data: DataFrame df with columns: date,...
pandasaggregationtransformationSwiggy+1Intermediate5m15 pts1100%Customer Order Summary
Create a summary showing each customer's total orders, total amount, and average amount. Available Data: DataFrame...
pandasaggregationGROUP BYSwiggyAdvanced7m20 pts1100%Cross-Tabulation
Create a cross-tabulation showing the count of employees per department and city. Available Data: DataFrame df with...
pandasaggregationIntermediate5m15 pts1100%Add Group Aggregation as Column
Add a column dept_avg_salary showing the average salary of each employee's department. Available Data: DataFrame df...
pandasaggregationGROUP BYAdvanced7m20 pts1100%Convert Names to Uppercase
Convert all employee names to uppercase. Available Data: DataFrame df with columns: name, department, salary, age, city...
pandasstring-operationsBeginner2m5 pts1100%Filter by String Pattern
Find all employees whose names start with the letter 'A' or 'E'. Available Data: DataFrame df with columns: name,...
pandasstring-operationsfilteringIntermediate5m15 pts1100%Extract First Name from Full Name
Given a DataFrame with full names, extract just the first name. Available Data: DataFrame df with a full_name column...
pandasstring-operationsIntermediate5m15 pts1100%Parse Date Strings
Convert the date column from strings to datetime objects and extract the month. Available Data: DataFrame df with...
pandasdatetimeUberIntermediate5m15 pts1100%Monthly Sales Summary
Calculate total quantity sold per month. Available Data: DataFrame df with columns: date, product, quantity, price,...
pandasdatetimeaggregationUber+1Intermediate7m20 pts1100%Top Earner per Department
Find the highest-paid employee in each department. Available Data: DataFrame df with columns: name, department, salary,...
pandasanalysisGROUP BYAmazonAdvanced8m25 pts2100%Salary Percentile Ranking
Add a salary_percentile column showing each employee's percentile rank within the company. Available Data: DataFrame df...
pandasanalysisstatisticsGoogleAdvanced5m20 pts1100%Student Grade Analysis
Calculate each student's average score across all subjects and assign a letter grade. Available Data: DataFrame df with...
pandasanalysistransformationAdvanced10m25 pts1100%Complete Orders Analysis
Merge orders with customers and find the total spending per customer for completed orders only. Sort by total spending...
pandasanalysismergeGROUP BY+2Advanced10m30 pts0—Build a Sales Report
Create a comprehensive sales report DataFrame showing per-product metrics. Available Data: DataFrame df with columns:...
pandasanalysisaggregationSwiggyAdvanced10m30 pts0—
FAQ
pandas DataFrame practice: common questions
Short answers to what people get stuck on most. Tap a question to expand it.
How does pandas treat NaN in groupby sums and means?
Aggregations skip NaN by default, so a group still returns a total from its non-missing values.
- sum(), mean() and count() all ignore NaN.
- An all-NaN group sums to 0 unless you pass min_count=1 (shown here).
- Check df.isna().sum() first so you know how much data is being skipped.
SQL
df.groupby("city")["sales"].sum() # all-NaN group -> 0.0
df.groupby("city")["sales"].sum(min_count=1) # all-NaN group -> NaNWhat is the difference between loc and iloc?
loc selects by label, iloc selects by integer position.
- loc uses index values and column names, and slices include the end label.
- iloc uses positions, and slices exclude the end like normal Python.
- loc also takes boolean masks, the usual way to filter rows (shown here).
- Avoid chained indexing like df[mask]["col"] = x: it may assign to a copy (SettingWithCopyWarning).
SQL
df.loc[df["sales"] > 100, "city"] # label + boolean mask
df.iloc[0:2, 0] # first two rows, first columnHow do I join two DataFrames?
Use merge, which works like a SQL join. Choose how: inner, left, right or outer.
- Check the row count after merging: a many-to-many match multiplies rows.
- validate="many_to_one" makes pandas raise an error if the right key is not unique (shown here).
- Use pd.concat instead to stack DataFrames when there is no key to match on.
Python
orders.merge(
customers,
on="customer_id",
how="left",
validate="many_to_one",
)Related practice topics
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