During an early live coding session at a data science bootcamp, the mentor casually said, “Let’s just initialise the pandas dataframe.” That one word—just—made it sound simple, but for anyone new to pandas, creating a dataframe from scratch can feel as tricky as solving a Rubik’s cube blindfolded.
But here’s the truth: once you understand the structure and logic, working with a pandas dataframe becomes second nature. Whether you're a beginner in Python or pursuing a data science course, mastering the basics of dataframes is your gateway to the data world.
What Is a Pandas DataFrame?
A pandas DataFrame is essentially a two-dimensional labelled data structure with columns of potentially different data types. Think of it as an Excel spreadsheet or an SQL table in memory – only more powerful and flexible.
Developers created Pandas (styled as pandas) as a software library in Python to support data manipulation and analysis.
| Feature | Description |
| Structure | Two-dimensional, with rows and columns |
| Data Types | Can store int, float, string, datetime, etc. |
| Indexing | Row and column labels for fast lookups |
| Operations | Slicing, filtering, merging, cleaning, etc. |
India's tech space is booming, and with that comes a rising demand for tech professionals. If you're enrolled in a data science course or just starting, you can’t avoid pandas dataframe operations. From fintech firms in Mumbai to e-commerce giants in Bengaluru, every data team uses it.
Step-by-Step: How to Create a DataFrame in Pandas
Pandas gained its advantage by being one of the first Python DataFrame libraries, which helped it build the largest community and a mature ecosystem. However, some of its early design choices now appear outdated when compared to modern standards of usability and scalability.
Although it remains the most widely used library with a broad and active ecosystem, pandas continue to adapt and evolve as they keep pace with newer, more advanced libraries.
Let’s walk through the most common ways to initialise a DataFrame in pandas.
1. From a Dictionary
import pandas as pd
data = {'Name': ['Anita', 'Rohit', 'Zoya'], 'Age': [28, 34, 22]}
df = pd.DataFrame(data)
print(df)
This is the easiest way to go from raw data to a structured table.
2. From a List of Lists
data = [['Anita', 28], ['Rohit', 34], ['Zoya', 22]]
df = pd.DataFrame(data, columns=['Name', 'Age'])
print(df)
Perfect when working with nested list outputs from APIs or raw JSON.
3. From a CSV File
df = pd.read_csv('students.csv')
Often used in real-world projects where you sort datasets externally.
4. Using NumPy Arrays
import numpy as np
arr = np.array([[10, 20], [30, 40]])
df = pd.DataFrame(arr, columns=['Maths', 'Science'])
Great when combining DataFrames in pandas with machine learning workflows.
Common Sources to Create a DataFrame
| Data Source | Best Used For |
| Dictionary | Clean, labelled data with named fields |
| List of Lists | Nested structures or simple tabular data |
| CSV or Excel | Data stored in external files |
| NumPy Arrays | Numerical data and machine learning inputs |
Common Pitfalls and How to Avoid Them
- Column Mismatch: While trying to combine two DataFrames pandas, make sure column names match exactly.
- Missing Data: Watch out for NaNs and use .fillna() or .dropna() accordingly.
- Index Issues: Set or reset indexes deliberately. Default indexes can create confusion later.
How to Combine Two DataFrames in Pandas
Combining datasets is common when working with multiple sources, and pandas make this surprisingly easy.
1. Using concat()
pd.concat([df1, df2])
Use it when the two DataFrames have the same columns.
2. Using merge()
df1.merge(df2, on='ID')
Perfect for joining on a common key, like SQL JOINs.
3. Using join()
df1.join(df2)
Ideal when you want to join on indexes.
Why Imarticus Learning Recommends Pandas for Data Science
If you're learning Python for data science through a structured data science course, you're bound to spend a good chunk of time on pandas. At Imarticus Learning, the curriculum focuses heavily on practical skills like how to combine two DataFrames pandas, clean and wrangle data, and set up a DataFrame in pandas from scratch.
Their trainers emphasise not just theory but real industry cases. Whether you're analysing user data for a fintech app or building dashboards for an FMCG brand, Panda's toolkit becomes your go-to essential.
Working with Panda's DataFrame structures is no longer a nice-to-have skill. It’s a non-negotiable part of being job-ready in data science. If you want to succeed in India’s fast-growing analytics job market, get hands-on with pandas, understand how to combine two DataFrames pandas, and truly own the process of working with a DataFrame in pandas.
If you’re looking to gain these skills the right way, a certified data science course from Imarticus Learning is the right place to start. With the right training and consistent practice, you won’t just write code—you’ll write solutions.
Postgraduate Programme in Data Science and Analytics – Your Gateway to Growth
Imarticus Learning presents the Postgraduate Programme in Data Science and Analytics, a career-focused course built with 100% job assurance to help fresh graduates and early-stage professionals from a tech background thrive in today’s data-driven world.
The programme delivers specific skills that represent what top corporate entities look for in contemporary data analysts. Students benefit from the Postgraduate Programme in Data Science and Analytics because it delivers a foundational understanding of Python, SQL, and data analytics combined with Power BI and Tableau training.
Students receive job-specific training through coursework that combines practical applications that directly create workplace success. Imarticus Learning ensures its students access 10 interviews through partnerships with more than 500 top recruitment firms as part of its employment assurance programme.
Learners benefit from live, interactive sessions led by expert faculty who employ immersive teaching methods to simulate actual industry roles in data science. Join the Postgraduate Programme in Data Science and Analytics at Imarticus Learning today and move one step closer to your dream job.
FAQ
- What is a pandas DataFrame, and why is it used?
A Pandas DataFrame is a two-dimensional table-like data structure in Python used to store, filter, and manipulate datasets—essential in data analysis.
- How do you combine two DataFrames in pandas?
To combine two DataFrames pandas style, use methods like concat(), merge(), or join() depending on whether you're stacking, aligning by index, or key.
- Is it necessary to reset the index when combining two DataFrames?
Yes. Always check indexes while merging. Not resetting may result in misaligned data. Use .reset_index() if needed before you combine two DataFrames in pandas.
- How do pandas DataFrames help in a data science course?
A data science course will teach you to use pandas DataFrame for data wrangling, preprocessing, and visualisation—foundational for machine learning tasks.
- Can I read data from a CSV file into a pandas dataframe?
Absolutely. Use pd.read_csv('filename.csv') to load CSVs directly into a DataFrame in pandas, one of the most common file input methods in real-world projects.
- How does Imarticus Learning teach pandas for data science?
Imarticus Learning includes practical modules that focus on real datasets, guiding learners to create and combine two DataFrames pandas style through projects.
