Category: Data Analysis

It’s with no doubt that SQL and Python are the powerful and mostly used languages for working with data. These tools are nowadays used in highbred fashion. Window functions are data manipulation constructs that enables us to operate on a

Pandas gives us powerful and easy to use functions for statistical analysis. We can perform different operations and deliver powerful insights with pandas. Statistics is at the centre of any analytical and data science solution. With it we can understand

“Math is the language of the universe” so they say. When it comes to data the only language that data can be able to speak to and communicate facts and truth is Maths. Mathematics is at the heart of data

Dates play an important role in our life and so in our analyses. Most data analyses are based on a specific timeline. Working with dates in any analytical use-case is a fundamental task that each data practitioner has encountered with.

Data comes in different structures either numeric, text, images, videos or audio. Ability to comfortably work with different data format is key for any data scientist and analytics practitioner. Pandas provides us with powerful functions to clean and analyse text

More often we need to change the way our data appears to perform deeper analyses. To achieve this Pandas gives us various simple techniques that allows us to reshape and transform our data accordingly. We can change the columns to

Pandas provides us with a powerful apply() function to iterate through each value in a data frame and compute certain operations. The apply function is an alternative to creating a loop structure to iterate through a data frame. Similar to

Missingn data is a data quality issue and a common problem that every data practitioner has to deal with. Missing data affects the accuracy of analysis and the performance of the model. Most machine learning algorithms are sensitive to missing

More often, data that is required for analytical purpose comes from different isolated data sources. We need to integrate these desperate data into a unified dataset useful for analytical and modelling purpose. The task for combining and integrating data is

One of the most powerful capability of pandas is to slice based on different criteria. Pandas provides us with different approaches to slice data in both series and dataframe data structures to return the required subset. These approaches include selection