The main motive of any supervised machine learning algorithm is to reduce the model error. What is a model error? It is the difference between the true and the predicted values.

It consists of 3 parts — Bias Error, Variance Error, and Irreducible Error.

NumPy is a python library used for working with arrays. It stands for Numerical Python. Numpy provides many functions to work on linear algebra, arrays, and matrices making it a widely used tool in data analysis.

The main advantage is that it is incredibly fast as it has bindings to C-libraries.

In this blog post, I will be discussing 51 important NumPy functions you need to know, before starting to work on any arrays.

If you are a beginner and want to learn more about the NumPy library, check out my blog post series on `Complete Guide to NumPy for Beginners`

. …

For a basic understanding of what is NumPy and what are the functions available in NumPy, check out my blog post series on Complete Guide to NumPy for Beginners.

Link: Complete Guide to Numpy for Beginners

In this blog post, I will be discussing some important operations that you can perform in NumPy arrays, which will be useful for you.

In case, you have printed the array, you might have seen that the array gets truncated while displaying. That is only some of the values at the start and some of the values at the end are getting displayed.

`import numpy as np`

x = np.random.randn(10,10)…

*Ever wondered why there is an (N-1) in the equation of calculating standard deviation or variance for a sample? That N-1 is referred to as the **Bessel’s Correction.*

**Terms you need to know before moving on to the next part:**

**Population:** Population contains all the members that are present in the data

**Sample:** Sample contains some members of the data

**Standard Deviation: **Standard deviation is a measure of how spread out a dataset is.

Let’s look at the formula of standard deviation for both population and sample.

The formula for Population Standard Deviation is:

In this blog post, I will be discussing how to plot boxplot in python. If you don’t know what is boxplot and how to interpret it, then check out my blog post on Understanding Box Plots for Beginners — A Complete Guide.

I will be using the heart disease dataset from Kaggle for the examples. You can download the dataset from the link given below:

First, import all the required libraries, that you will need to explore the dataset — `numpy`

,`pandas`

,`matplotlib`

,`seaborn`

.

`import numpy as np`

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

Then import the dataset using `pandas`

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I was watching a video of astonishing facts about space, and I came across this —` A teaspoon full of a neutron star would weigh a billion tons`

, that's astonishing. Let's just do a little check of this with our closest neutron star.

RX J1856.5−3754 is the closest neutron star to our planet. It’s around 400 light-years away from earth, which is around 3.784e+15 km. The neutron star is present in the constellation of Corona Australis.

Based on the observations from Hubble Space Telescope and Chandra X-Ray Observatory, the size of the star was estimated.

A box plot is one of the standard plots used in Exploratory Data Analysis to analyze the distribution of the data. It provides a graphical summary to identify the distribution and skewness of the dataset. This is how a box plot looks like:

This is my 3rd and final blog post on NumPy in which I will be discussing operations, joining, splitting, and filtering of arrays and also about different math functions available in NumPy.

If you haven’t checked out my first 2 blog posts on Numpy discussing initializing a NumPy array, indexing, and basic functions available. Then check out the link below:

http://www.letsdiscussstuff.in/complete-guide-to-numpy-for-beginners-part-1/

http://www.letsdiscussstuff.in/complete-guide-to-numpy-for-beginners-part-2/

In the previous blog post, I have discussed various ways of initializing NumPy arrays. In today’s blog post, I will be discussing how indexing and slicing take place in a NumPy array and also about some basic functions available in NumPy.

You can check out my previous blog post on this link: Complete Guide to Numpy for Beginners — Part 1

In the next and the last blog post on NumPy, I will be discussing operators and different math functions like `np.sin()`

and `np.exp()`

which are available in NumPy.

- Indexing in Numpy Arrays
- Indexing in 2-D Numpy Arrays
- Slicing with varying step…

NumPy is a python library used for working with arrays. It stands for Numerical Python. Numpy provides many functions to work on linear algebra, arrays, and matrices making it a widely used tool in data analysis.

The main advantage is that it is incredibly fast as it has bindings to C-libraries.

This is the first of a 3-part series of blog posts on Numpy Library. The topics included respectively are — Initialization of a Numpy Library, Numpy Operations, and Indexing, Numpy Functions.

Originally Published on My Website — Let’s Discuss Stuff

- Installing Numpy Library
- Numpy Arrays
- Creating an evenly spaced NumPy array using…

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