4 Pandas Plotting Function You Should Know

Pandas is a powerful package for data scientists. There are many reasons we use Pandas, e.g. Data wrangling, Data cleaning, and Data manipulation. Although, there is a method that rarely talks about regarding Pandas package and that is the Data plotting.

Data plotting, just like the name implies, is a process to plot the data into some graph or chart to visualise the data. While we have much fancier visualisation package out there, some method is just available in the pandas plotting API.

Let’s see a few selected method I choose.

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4 Pandas Plotting Function You Should Know

PANDAS: Most Used Functions in Data Science

Most useful functions for data preprocessing

When you get introduced to machine learning, the first step is to learn Python and the basic step of learning Python is to learn pandas library. We can install pandas library by pip install pandas. After installing we have to import pandas each time of the running session. The data used for example is from the UCI repository “https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records

  1. Read Data

2. Head and Tail

3. Shape, Size and Info

4. isna

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Vincent Lab

Vincent Lab


The Difference Between Regular Functions and Arrow Functions in JavaScript

Other then the syntactical differences. The main difference is the way the this keyword behaves? In an arrow function, the this keyword remains the same throughout the life-cycle of the function and is always bound to the value of this in the closest non-arrow parent function. Arrow functions can never be constructor functions so they can never be invoked with the new keyword. And they can never have duplicate named parameters like a regular function not using strict mode.

Here are a few code examples to show you some of the differences
this.name = "Bob";

const person = {
name: “Jon”,

<span style="color: #008000">// Regular function</span>
func1: <span style="color: #0000ff">function</span> () {
    console.log(<span style="color: #0000ff">this</span>);

<span style="color: #008000">// Arrow function</span>
func2: () =&gt; {
    console.log(<span style="color: #0000ff">this</span>);


person.func1(); // Call the Regular function
// Output: {name:“Jon”, func1:[Function: func1], func2:[Function: func2]}

person.func2(); // Call the Arrow function
// Output: {name:“Bob”}

The new keyword with an arrow function
const person = (name) => console.log("Your name is " + name);
const bob = new person("Bob");
// Uncaught TypeError: person is not a constructor

If you want to see a visual presentation on the differences, then you can see the video below:

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Kasey  Turcotte

Kasey Turcotte


All You Need to Know About Pandas Cut and Qcut Functions

What exactly is the difference between them?

Pandas is arguably the most popular data analysis and manipulation tool in the data science ecosystem. Thanks to the numerous functions and methods, we can play around with data freely.

The cut and qcut functions come in quite handy for many cases. The difference between them was not clear to me at first. I was able to figure it out after doing several examples.

In this article, we will do the same. The examples in this article will demonstrate how to use the cut and qcut functions and also emphasize the difference between them.

Let’s start with creating a sample data frame.

import numpy as np
import pandas as pd

df = pd.DataFrame({
  "col_a": np.random.randint(1, 50, size=50),
  "col_b": np.random.randint(20, 100, size=50),
  "col_c": np.random.random(size=50).round(2)

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Paula  Hall

Paula Hall


Three Very Useful Functions of Pandas to Summarize the Data

Pandas count, value_count, and crosstab functions in details

Pandas library is a very popular python library for data analysis. Pandas library has so many functions. This article will discuss three very useful and widely used functions for data summarizing. I am trying to explain it with examples so we can use them to their full potential.

The three functions I am talking about today are count, value_count, and crosstab.

The count function is the simplest. The value_count can do a bit more and the crosstab function does even more complicated work with simple commands.

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Jamison  Fisher

Jamison Fisher


Top 5 Pandas Functions Essential for Data Scientists [2021]

Pandas is clearly one of the most used and loved libraries when it comes to Data Science and Data Analysis with Python. What makes it special? In this tutorial, we will go over 5 such functions that make Pandas an extremely useful tool in a Data Scientist’s tool kit.

By the end of this tutorial, you’ll have the knowledge of the below functions in Pandas and how to use them for your applications:

  • value_counts
  • groupby
  • loc and iloc
  • unique and nunique
  • Cut and qcut

#data science #pandas #pandas for data science #pandas functions