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Beginner pandas and NumPy Interview Questions and Answers

Common data analyst interview questions for freshers on NumPy and pandas, with clear answers and runnable examples: arrays vs lists, Series vs DataFrame, loc vs iloc, missing values, filtering, groupby, merge and value_counts.

Beginner5 min readUpdated
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These questions come up in nearly every data analyst screening. Answer out loud first, then open the solution. Short, precise answers with a tiny example beat memorised paragraphs.

Concepts#

1. Why use a NumPy array instead of a Python list?#

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An array stores values of one type together in memory, so NumPy runs maths on the whole array in compiled code (vectorisation): far faster than looping over a list, and much shorter to write. Arithmetic also means maths, not list operations:

Python
import numpy as np

print([1, 2, 3] * 2)
print(np.array([1, 2, 3]) * 2)
Output
[1, 2, 3, 1, 2, 3]
[2 4 6]

2. What is the difference between a Series and a DataFrame?#

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A Series is one labelled column of values; a DataFrame is a table of Series sharing one row index. df["col"] returns a Series, df[["col"]] a one-column DataFrame.

3. loc vs iloc?#

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loc selects by label (index values, column names) and includes the end of a slice; iloc selects by position and excludes the end:

Python
import pandas as pd

s = pd.Series([10, 20, 30, 40], index=["a", "b", "c", "d"])
print(s.loc["a":"c"].tolist(), s.iloc[0:2].tolist())
Output
[10, 20, 30] [10, 20]

4. How do you find and handle missing values?#

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Find them with df.isna().sum(). Then, depending on how many there are and why: drop rows (dropna, with subset or thresh), fill them (fillna with a constant, the median, a group mean, or forward fill for time series), or keep them and add a flag column. Calculations like mean() skip NaN by default.

5. What does value_counts do?#

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It counts how often each value appears in a Series, sorted from most to least common; normalize=True gives proportions.

Python
import pandas as pd

print(pd.Series(["upi", "card", "upi", "cash", "upi"]).value_counts().to_dict())
Output
{'upi': 3, 'card': 1, 'cash': 1}

Coding questions#

6. Filter rows with two conditions.#

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Use & and | with parentheses around each condition (not and/or):

Python
import pandas as pd

df = pd.DataFrame({"city": ["Mumbai", "Delhi", "Mumbai"], "amount": [250, 700, 900]})
print(df[(df["city"] == "Mumbai") & (df["amount"] > 300)])
Output
     city  amount
2  Mumbai     900

7. Total and average sales per region.#

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Python
import pandas as pd

df = pd.DataFrame({"region": ["N", "S", "N", "S", "E"], "sales": [100, 80, 140, 60, 90]})
print(df.groupby("region")["sales"].agg(["sum", "mean"]))
Output
        sum   mean
region
E        90   90.0
N       240  120.0
S       140   70.0

8. Join customers to their orders, keeping customers with no orders.#

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A left merge keeps every row of the left table:

Python
import pandas as pd

customers = pd.DataFrame({"id": [1, 2, 3], "name": ["Asha", "Ravi", "Meera"]})
orders = pd.DataFrame({"id": [1, 1, 2], "amount": [250, 120, 560]})
print(pd.merge(customers, orders, on="id", how="left"))
Output
   id   name  amount
0   1   Asha   250.0
1   1   Asha   120.0
2   2   Ravi   560.0
3   3  Meera     NaN

9. Remove duplicate rows, keeping the last.#

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Python
import pandas as pd

df = pd.DataFrame({"email": ["a@x.in", "b@x.in", "a@x.in"], "plan": ["basic", "pro", "pro"]})
print(df.drop_duplicates(subset="email", keep="last"))
Output
    email plan
1  b@x.in  pro
2  a@x.in  pro

10. Add a column based on a condition.#

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Python
import numpy as np
import pandas as pd

df = pd.DataFrame({"score": [45, 72, 88]})
df["result"] = np.where(df["score"] >= 50, "pass", "fail")
print(df)
Output
   score result
0     45   fail
1     72   pass
2     88   pass

Key takeaway#

Next: Intermediate Questions.

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