Data

Use 'requests' library in python to send HTTP requests and receive response, also pass headers, cookies, json payload in the request

The yield keyword in Python allows functions to produce a series of values over time, making it efficient for handling large data sets or for producing a series of values for iteration.

Discover various methods to analyze the correlation between two variables in Python using functions such as numpy.corrcoef(), pandas.DataFrame.corr(), scipy.stats.pearsonr(), scipy.stats.spearmanr() and seaborn.pairplot() to understand the relationship between the variables.

Learn various methods to perform linear regression in Python using functions such as scipy.stats.linregress(), numpy.polyfit(), scikit-learn and statsmodels to model the relationship between a scalar dependent variable and one or more explanatory variables.

Learn various ways to slice a Pandas DataFrame, including using the .loc[], .iloc[], and [] accessors, boolean indexing and chaining multiple conditions to select specific rows and columns.

Learn various ways to create a Pandas DataFrame, including from a dictionary of arrays, list of dictionaries, 2D Numpy array, CSV file, SQL query, Excel file and also specifying index while creating it.

Learn various ways to store data efficiently in Python, including relational databases, NoSQL databases, data serialization, data compression, and data containers like NumPy array and Pandas DataFrame.

There are several ways to read data in Python, such as using the built-in open() function, pandas library, csv and json module, and libraries for reading data from databases.

This article explains how to do math in Python using built-in mathematical operations and functions, math module, and advanced mathematical libraries like NumPy, Scipy and Pandas for scientific computations.