The Top 10 Python topics for data scientists that you need to know to master in Data Science

Python is a powerful and versatile programming language that is widely used in the field of data science. It offers a wide range of tools and libraries for data manipulation, analysis, and visualization. In this article, we will discuss the top 10 most important Python topics for data scientists. These topics will lay the groundwork for understanding and working with data in Python, allowing data scientists to analyse and interpret data more efficiently and effectively.

1. Functions: Functions are a fundamental concept in Python that data scientists frequently use. They enable you to organize and reuse code, making it more efficient, readable, and maintainable. Functions are defined in Python by using the def keyword, followed by the function name and a set of parentheses that may contain parameters.
2. Lambda Functions: Lambda functions, also known as anonymous functions, are a way to create small, one-time-use functions in Python. They are defined using the 'lambda' keyword, followed by one or more arguments, a colon, and a single expression. Lambda functions are useful for data scientists because they enable the creation of simple functions without the need for naming them or including a full function definition.
3. Panda Data frame: Pandas is a Python data manipulation library that is both powerful and flexible. A data scientist can use DataFrames to perform a variety of data manipulation tasks, such as filtering, aggregation, and group-by operations, as well as handle missing values and perform data cleaning tasks.
4. Pandas Series: It is a useful tool for data scientists in many ways, it can be used to clean and pre-process data by handling missing values and converting data types. Data scientists can also investigate data distribution and distribution by producing summary statistics and visualizations.
5. Lists: Lists are a fundamental data structure that every data scientist will use regularly. In Python, the list datatype is a versatile and powerful tool for storing and manipulating data. Lists have the advantage of storing multiple items of different types, making them ideal for storing and processing data with varying characteristics.
6. Numpy Array: It is a powerful library for numerical computing in Python. Numpy arrays are especially useful for data scientists because they allow you to perform mathematical operations on entire arrays instead of looping through the array and applying the operation to each element individually.
7. Dictionaries:  Dictionaries are a fundamental data structure in Python that data scientists commonly used to store and retrieve data. Dictionaries are useful for data scientists because they allow you to store data in an understandable and accessible format.
8. Sets: Sets are another fundamental data structure in Python that data scientists frequently use. Sets are useful for data scientists because they allow them to easily remove duplicates from a dataset and perform common set operations like union, intersection, and difference.
9. Apply: In Python, the apply () method is commonly used with the panda library, which is a powerful library for data manipulation and analysis. The apply () method is useful for data scientists because it allows them to perform complex operations on large datasets without having to write explicit loops.
10. Map: The map () function in Python is a built-in function, it is similar to the apply () method in pandas, but it is intended for use with simple iterables rather than DataFrames or Series. For data scientists, the map () function is useful for applying a simple operation to each element of a dataset.

These are some of the most important python topics for data scientists that you must know to excel in the field of data science.