Python Libraries for Data Wrangling
Subject and UNIT: Foundation of Data Science: Unit IV: Python Libraries for Data Wrangling
Masking means to extract, modify, count or otherwise manipulate values in an array based on some criterion.
Python Libraries for Data Wrangling
Subject and UNIT: Foundation of Data Science: Unit IV: Python Libraries for Data Wrangling
Computation on NumPy arrays can be very fast, or it can be very slow. Using vectorized operations, fast computations is possible and it is implemented by using NumPy's universial functions (ufuncs).
Python Libraries for Data Wrangling
Subject and UNIT: Foundation of Data Science: Unit IV: Python Libraries for Data Wrangling
In aggregation function is one which takes multiple individual values and returns a summary. In the majority of the cases, this summary is a single value.
Python Libraries for Data Wrangling
Subject and UNIT: Foundation of Data Science: Unit IV: Python Libraries for Data Wrangling
Numpy array is a powerful N-dimensional array object which is in the form of rows and columns.
Python Libraries for Data Wrangling
Subject and UNIT: Foundation of Data Science: Unit IV: Python Libraries for Data Wrangling
NumPy, short for Numerical Python, is the core library for scientific computing in Python.
Features, Advantages and Disadvantages of Python
Subject and UNIT: Foundation of Data Science: Unit IV: Python Libraries for Data Wrangling
Python is a high-level scripting language which can be used for a wide variety of text processing, system administration and internet-related tasks.
Data Science
Subject and UNIT: Foundation of Data Science: Unit IV: Python Libraries for Data Wrangling
Data Wrangling is the process of transforming data from its original "raw" form into a more digestible format and organizing sets from various sources into a singular coherent whole for further processing.
Describing Relationships | Foundation of Data Science
Subject and UNIT: Foundation of Data Science: Unit III: Describing Relationships
Correlation refers to a relationship between two or more objects.
Data Science
Subject and UNIT: Foundation of Data Science: Unit III: Describing Relationships
Regression toward the mean refers to a tendency for scores, particularly extreme scores, to shrink toward the mean.
Data Science
Subject and UNIT: Foundation of Data Science: Unit III: Describing Relationships
Multiple linear regression is an extension of linear regression, which allows a response variable, y to be modelled as a linear function of two or more predictor variables.
Characteristics, Spurious Regression | Data Science
Subject and UNIT: Foundation of Data Science: Unit III: Describing Relationships
The primary objective of regression is to explain the variation in Y using the knowledge of X.
Properties, Formula, Example Solved Problems | Data Science
Subject and UNIT: Foundation of Data Science: Unit III: Describing Relationships
For an input x, if the output is continuous, this is called a regression problem.