Pandas Interview Questions PDF Downloads

Key takeaways

• Know the DataFrame and Series, and the difference between loc (labels) and iloc (positions).
• groupby with agg is the core summarising tool; merge is the core joining tool.
• Handle missing data deliberately with isna, fillna and dropna.
• Use vectorised operations instead of loops for speed.
• Check shapes and row counts after every merge or filter.

What is pandas interview questions?

What data do the examples use?

The examples use a small made-up table of orders. Run this first to follow along.

import pandas as pd

df = pd.DataFrame({
    "order_id": [1, 2, 3, 4, 5, 6],
    "city": ["Delhi", "Noida", "Delhi", "Pune", "Noida", "Delhi"],
    "category": ["A", "B", "A", "C", "B", "C"],
    "amount": [500, 250, 300, None, 900, 120],
    "date": ["2026-01-05", "2026-01-09", "2026-02-02", "2026-02-11", "2026-02-20", "2026-03-01"],
})
print(df.shape)

What is the difference between a Series and a DataFrame?

A Series is a one-dimensional labelled array, such as one column. A DataFrame is a two-dimensional table of Series that share an index.

How do I inspect a DataFrame?

df.head(), df.info() (types and non-null counts), df.describe() (summary statistics), df.shape, df.dtypes and df.columns. Always inspect before cleaning.

What is the difference between loc and iloc?

loc selects by label and includes both ends of a slice. iloc selects by integer position and excludes the end.

print(df.loc[0:2, "city"].tolist())
print(df.iloc[0:2, 1].tolist())

How do I filter rows?

Use boolean conditions with &, | and ~, with each condition in parentheses.

print(df[(df["city"] == "Delhi") & (df["amount"] > 200)]["order_id"].tolist())

df.query("city == 'Delhi' and amount > 200") gives the same result in a readable form.

How do I handle missing values?

print(df["amount"].isna().sum())
filled = df["amount"].fillna(df["amount"].median())
print(filled.tolist())
clean = df.dropna(subset=["amount"])
print(len(clean))

Choose between dropping and filling by asking why values are missing, and record your choice.

How does groupby work?

groupby splits data into groups, applies a function and combines the results.

summary = df.groupby("city")["amount"].agg(["count", "sum", "mean"])
print(summary)

Use .reset_index() to turn the group keys back into columns, and agg with several functions or named outputs for more control.

What is the difference between merge, join and concat?

merge combines tables on key columns like a SQL join, with how set to inner, left, right or outer. join merges on the index. concat stacks tables vertically or side by side.

cities = pd.DataFrame({"city": ["Delhi", "Noida"], "state": ["Delhi", "UP"]})
m = df.merge(cities, on="city", how="left")
print(m[["order_id", "state"]].fillna("NA").values.tolist())

After a merge, compare the row count with what you expect to catch duplicated keys.

How do I work with dates?

df["date"] = pd.to_datetime(df["date"])
df["month"] = df["date"].dt.month
print(df.groupby("month")["order_id"].count().tolist())

Use dt.year, dt.day_name() and resample for time series.

How do I create a pivot table?

pt = df.pivot_table(index="city", columns="category", values="amount", aggfunc="sum", fill_value=0)
print(pt)

What are apply, map and vectorised operations?

Vectorised operations such as df["amount"] * 2 run in optimised code and are fast. map transforms a Series with a dictionary or function. apply runs a function per row or column and is slower; use it only when no vectorised form exists.

How do I find and remove duplicates?

df.duplicated() flags repeats and df.drop_duplicates(subset=["order_id"]) removes them. Decide which row to keep before dropping.

How do I speed up pandas?

Use vectorised operations, choose efficient types such as category for repeated text, read only needed columns, process large files in chunks and avoid loops over rows. Measure before optimising.

What is the SettingWithCopy warning?

It warns that you may be changing a copy of a slice instead of the original. Avoid chained indexing; use .loc to assign, or make an explicit copy with .copy().

What practical tasks are common?

Typical tasks are: load a CSV and report missing values per column, find the top three categories by total amount, compute month-on-month change, merge two tables and find rows without a match, and reshape wide data to long with melt. Practise each on a small table so you can write the code quickly, explain every step and check the result with a second method such as a manual total.

How do I explain my pandas projects?

Say which dataset you used, how you cleaned it, which grouping or merging steps produced the key result, what chart you made and how you checked your numbers. Mention one bug you found, such as duplicated rows after a merge, and how you fixed it. A concrete example shows real hands-on use.

Download Pandas Interview Questions PDF

Sources and further reading

• pandas documentation
• pandas user guide