Category: Python
What are the common built-in data types in Python?
The common built-in data types in python are: Numbers They include integers, floating-point numbers, and complex numbers. e.g., 1, 2.4, 3 + 4j List An ordered sequences of items is called a list. The elements of a list belong to different data types. Eg. [5, ‘player’, 4.7]. Tuple It is also an ordered sequence of…
Read MoreWhat are Dict and List Comprehensions in Python?
Dictionary and list comprehensions are just concise way to define dictionaries and lists. Example of list comprehension is: Var = [i for i in range (5)] The above code created a list as below- [0, 1, 2, 3, 4] Example of dictionary comprehension is – X = [i : i + 2 for I in…
Read MoreWhat are Python namespaces?
A namespace In Python refers to the name which is assigned to each object in Python. The objects are variables and functions. As each object is created, its name along with space (the address of the outer function in which the object is), gets created. The namespaces are maintained in Python like a dictionary where…
Read MoreNumPy for Data Science – Part 5
The difference between copy and view Copy View Join & split function Join array – joining means putting contents of two or more array in a single array. hstack vs vstack The major difference is that np.hstack combines NumPy arrays horizontally and np.vstack combines arrays vertically. Split – splitting breaks one array into multiple. NumPy…
Read MoreNumPy for Data Science – Part 4
Broadcasting NumPy Arrays The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array in order that they have compatible shapes. NumPy operations are usually done on pairs of arrays on an element-by-element basis. Within the simplest case, the…
Read MoreNumPy for Data Science – Part 3
Arithmetic Operations in NumPy Arrays In NumPy there are multiple functions which we can use to perform the arithmetic operation, we will be looking them one by one. The add() function can also be used to perform the same operation. The subtract() function can also be used to perform the same operation. The multiply() function…
Read MoreNumPy for Data Science – Part 2
Create NumPy Arrays with Random Numbers Data Types in NumPy Arrays Shape and Reshaping In NumPy Arrays Shape of an Array The shape of an array is the number of elements in each dimension. Get the Shape of an Array NumPy arrays have an attribute called shape that returns a tuple with each index having the number…
Read MoreNumPy for Data Science – Part 1
What is NumPy Array? An array is a grid of values and it contains information about the raw data, how to locate an element, and how to interpret an element. Numpy vs Python List Advantages of using NumPy Arrays over Python List: Let’s look at the example of NumPy Array and Python List. Importance of…
Read MoreDifference between Pandas .at and .iat Function
.at The .at and .iat index accessors are analogous to .loc and .iloc. The difference being that they will return a numpy.ndarray when pulling out a duplicate value, whereas .loc and .iloc return a Series: .iat .iat is similar to [] indexing. Because it tries to support both positional and label based indexing, I advise…
Read MoreTop 10 Pandas Functions
1 – To Read CSV and Excel files. These Functions will be used in almost every Project, They are used to read a CSV or an excel file to pandas DataFrame format. 2 – Columns Function. When we have a big dataset with many columns it will be difficult to see all columns, hence we…
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