The ultimate guide to data science interviews. Developed by FAANG engineers, it covers Python, algorithms, ML concepts, and interview questions. Get interview-ready in just a few hours.

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This course will increase your skills to crack the data science or machine learning interview. You will cover all the most common data science and ML concepts coupled with relevant interview questions.
You will start by covering Python basics as well as the most widely used algorithms and data structures. From there, you will move on to more advanced topics like feature engineering, unsupervised learning, as well as neural networks and deep learning.
This course takes a non-traditional approach to interview prep, in that it focuses on data science fundamentals instead of open-ended questions.

In all, this course will get you ready for data science interviews. By the time you finish this course, you will have reviewed all the major concepts in data science and will have a good idea of what interview questions you can expect.

Data Science Interview Handbook

Pandas is a Python library built over the NumPy package. Its key data structure is Data-Frame. Data-Frame allows a user to store and manipulate data in a tabular format.

Pandas data structures are fast and flexible. They are suitable for working with real-world data problems, and they provide a good set of functions for data scientists and data engineers. These function help the user get a  jump-start on their data, allowing them to quickly build a predictive modeling pipeline.


<h2>Creating a Pandas DataFrame
</h2>

There are multiple ways to create a Pandas DataFrame.


#Import libraries
import pandas

#Create a dictionary
data_dictionary = {
  "City":['Delhi','Bombay','Pune','Hyderabad'],
  "Population_Index": [19,21,7,9],
  "Area_Type":['Metro','Metro','Non-Metro','Metro']
}

#Create DataFrame
city_data = pandas.DataFrame(data_dictionary)
print(city_data)

In the above example, indexes are coming from 0 to 3 (the length of the data list - 1). We can provide indexes also. See the example below:


Learn about Pandas, a Python data manipulation library. Pandas is used heavily used in data science operations.


Are You Ready to Become a Data Scientist?

Python Basics

Python Libraries

More Data Science Tools

Data Structures and Algorithms - I

Data Structures and Algorithms - II

Statistics and Probability

Feature Engineering

Basics of Machine Learning

Regression

Classification

Unsupervised Learning

Advanced Topics in Machine Learning

Conclusion

Pandas

Creating a Pandas DataFrame