Level Up Your Data Game: 15 Pandas Functions Data Scientists Need to Master for Interviews

Master the 15 most important Pandas functions every data scientist should know—complete with code, use cases, and interview-ready tips.

👋 Hey! This is Manisha Arora from PrepVector. Welcome to the Tech Growth Series, a newsletter that aims to bridge the gap between academic knowledge and practical aspects of data science. My goal is to simplify complicated data concepts, share my perspectives on the latest trends, and share my learnings from building and leading data teams.

As an aspiring or established AI/ML professional, you know that data is the lifeblood of our models. And when it comes to wrangling, cleaning, and transforming data in Python, Pandas is the undisputed champion.

It’s no surprise then that data science and machine learning interviews almost always include questions that test your proficiency with this powerful library.

To help you ace your next interview, PrepVector has curated a list of 15 essential Pandas functions, complete with code examples, use cases, and explanations.

Let’s dive in!

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🧭 Section 1: Data Ingestion & Initial Inspection

1. pd.read_csv() / pd.read_excel() / pd.read_sql()

Your entry points for loading data from external sources.

import pandas as pd

df = pd.read_csv('people.csv', encoding='utf-8', parse_dates=['date_col'])
df = pd.read_excel('workbook.xlsx', sheet_name='Sheet1')
df = pd.read_sql('SELECT * FROM my_table', con)  # `con` is a DB connection

🔹 Use Cases:

• Load data from flat files or databases

• Specify encoding or parse dates during load

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2. .head() / .tail() / .info() / .describe()

Your first-pass tools to inspect and sanity-check the dataset.

df.head()      # First few rows
df.tail()      # Last few rows
df.info()      # Summary of columns, non-null counts, types
df.describe()  # Stats for numeric columns

🔹 Use Cases:

• Identify missing values or data types

• Quick stats on distributions

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3. .loc[] / .iloc[]

For selecting specific rows and columns.

df.loc['row_label', 'col_label']  # Label-based access
df.iloc[2:5, [0, 2]]              # Index-based slicing

🔹 Use Cases:

• Extract subsets using labels or positions

• Useful in feature selection and filtering

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🧹 Section 2: Data Cleaning & Preparation

4. .fillna() / .dropna()

Handle missing values with ease.

df.fillna(0)              # Replace NaNs with 0
df.fillna(method='ffill') # Forward fill
df.dropna()               # Drop rows with NaNs

🔹 Use Cases:

• Clean up datasets for modeling

• Choose between imputing or removing missing data

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5. .drop_duplicates()

Remove redundant rows.

df.drop_duplicates()
df.drop_duplicates(subset=['col1'], keep='first')

🔹 Use Cases:

• Deduplicate records after merges

• Ensure unique values for identifiers

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Bonus: Coding Masterclass for Data Professionals

To help you move from code snippets to coding confidence, we’re launching a 6-hour Weekend Coding Masterclass—a curated, real-world coding experience

Led by Siddarth Ranganathan, Manisha Arora and Sai Kumar Bysani, this session is not a generic tutorial. It’s a guided, hands-on masterclass that mirrors the challenges you’ll face in data science and engineering roles.

This masterclass is tailored for:

• Aspiring Data Scientists and Engineers who know the basics but want to go deeper

• SQL Challenge alumni looking to sharpen Python and OOP skills

• Working professionals preparing for interviews or technical presentations

• Students who want job-ready skills and clean code habits

Schedule:

🗓️ June 21 & 22 (Weekend)
🕒 11am – 2pm EST | 8am – 11am PST
📍 100% Live on Zoom
📂 Includes recordings, cheat sheets, and project templates

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📈 Section 3: Data Aggregation & Summarization

6. .groupby() / .agg()

Aggregate data by category.

df.groupby('product')['quantity'].sum()

df.groupby('city').agg(
    total_price=('price', 'sum'),
    avg_quantity=('quantity', 'mean'),
    num_transactions=('product', 'count')
)

🔹 Use Cases:

• Summarize sales, counts, or trends

• Create aggregated KPIs by segment

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7. .merge() / .join() / pd.concat()

Combine datasets together.

pd.merge(df1, df2, on='id', how='inner')
df1.join(df2, on='id', how='left')
pd.concat([df1, df2], axis=0)

🔹 Use Cases:

• Perform relational joins (like SQL)

• Stack datasets vertically or horizontally

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🔄 Section 4: Data Transformation & Reshaping

8. .apply() / .map() / .applymap()

Apply custom logic to rows, columns, or elements.

df['score_normalized'] = df['score'].apply(lambda x: x / 100)
df['grade_desc'] = df['grade'].map({'A': 'Excellent', 'B': 'Good'})

🔹 Use Cases:

• Feature engineering

• Data cleaning or recoding

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9. .sort_values()

Sort your data.

df.sort_values(by='price', ascending=False)
df.sort_values(by=['product', 'price'], ascending=[True, False])

🔹 Use Cases:

• Identify top/bottom performers

• Prioritize rows for review

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10. .pivot_table()

Create powerful summaries in a wide format.

pd.pivot_table(
    df,
    values='quantity',
    index='city',
    columns='product',
    aggfunc='sum'
)

🔹 Use Cases:

• Summarize performance across two dimensions

• Replace SQL GROUP BY with row/column indexing

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🧠 Section 5: Feature Engineering & Time Series

11. .cut() / .qcut()

Bin continuous data.

pd.cut(scores, bins=[0, 70, 85, 100], labels=['Fail', 'Pass', 'Excellent'])
pd.qcut(scores, q=4, labels=['Q1', 'Q2', 'Q3', 'Q4'])

🔹 Use Cases:

• Create categorical variables from continuous ones

• Quantile-based segmentation

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12. .astype()

Change column data types.

df['id'] = df['id'].astype(int)
df['flag'] = df['flag'].astype('category')

🔹 Use Cases:

• Memory optimization

• Ensuring correct datatypes for ML models

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13. .resample()

Aggregate time series data.

df.resample('W').sum()     # Weekly sum
df.resample('3D').mean()   # 3-day average

🔹 Use Cases:

• Downsample/upsample data

• Temporal trend analysis

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14. .rank()

Rank rows based on column values.

df['rank'] = df['score'].rank(method='dense')

🔹 Use Cases:

• Create leaderboard-style features

• Understand relative performance

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15. .shift() / .diff() / .rolling()

Create lagged and moving features.

df['lag_1'] = df['value'].shift(1)
df['diff'] = df['value'].diff(1)
df['rolling_mean'] = df['value'].rolling(window=3).mean()

🔹 Use Cases:

• Lagged predictors in time series

• Moving averages and trend smoothing

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🎯 Final Thoughts

These 15 Pandas functions are more than just interview prep—they're daily essentials for any data professional.

• 💡 Master them with real datasets

• 💬 Understand not just the syntax, but why and when to use each

• 🛠️ Practice using them together to build robust data pipelines

You don’t just want to answer Pandas questions in interviews—you want to impress with your fluency.

Happy wrangling! 🐼