Remove Duplicate Rows
The dataset has duplicate rows. Remove them and keep only the first occurrence. Available Data: DataFrame df with...
pandasdata-cleaning
Beginner
3m10 pts
8100%
Python Practice Hub
pandas DataFrame Practice Questions
Select, group, merge and derive columns on a table in Python, without loops.
51 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.
51 questionsPage 2 of 4
Count Missing Values
Count the number of missing (null/NaN) values in each column. Available Data: DataFrame df with columns: name, email,...
pandasdata-cleaningBeginner2m5 pts8100%Drop Rows with Missing Values
Remove all rows that contain any missing values. Available Data: DataFrame df with columns: name, email, age, salary...
pandasdata-cleaningBeginner3m10 pts8100%Fill Missing Values
Fill missing salary values with the mean salary of the existing values. Available Data: DataFrame df with columns:...
pandasdata-cleaningIntermediate5m15 pts8100%Convert Column Types
Given a DataFrame with numeric data stored as strings, convert the price column to float type. Available Data:...
pandasdata-cleaningIntermediate3m10 pts8100%Replace Values in a Column
Replace the department name 'HR' with 'Human Resources' in the employee DataFrame. Available Data: DataFrame df with...
pandasdata-cleaningBeginner3m10 pts8100%Drop a Column
Remove the city column from the employee DataFrame. Available Data: DataFrame df with columns: name, department,...
pandasdata-cleaningBeginner2m5 pts8100%Clean Dataset: Drop Nulls and Duplicates
Clean the messy dataset by removing both null values and duplicate rows. Available Data: DataFrame df with columns:...
pandasdata-cleaningIntermediate5m15 pts8100%Drop Nulls in Specific Columns
Drop rows only where the email column is null. Keep rows with null values in other columns. Available Data: DataFrame...
pandasdata-cleaningIntermediate3m10 pts9100%Clip Outlier Values
Clip salary values to be between 60,000 and 100,000. Values below 60k should become 60k, values above 100k should...
pandasdata-cleaningIntermediate3m10 pts5100%Average Salary by Department
Calculate the average salary for each department. Available Data: DataFrame df with columns: name, department, salary,...
pandasaggregationGROUP BYIntermediate5m15 pts8100%Multiple Aggregations per Group
For each department, find the minimum, maximum, and mean salary. Available Data: DataFrame df with columns: name,...
pandasaggregationGROUP BYIntermediate5m15 pts7100%Merge Two DataFrames
Merge the orders DataFrame with the customers DataFrame to see customer names with their orders. Available Data:...
pandasaggregationmergeSwiggyIntermediate5m15 pts4100%Left Merge (Keep All Customers)
Perform a left merge to see all customers, even those without orders. Available Data: orders: order_id, customer_id,...
pandasaggregationmergeSwiggyIntermediate5m15 pts4100%Create a Pivot Table
Create a pivot table showing total quantity sold per product in each region. Available Data: DataFrame df with columns:...
pandasaggregationpivotIntermediate7m20 pts4100%
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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