11 Pandas Built-in Functions You Should Know

I’ve been using pandas for a few years and each time I feel I am typing too much, I google the operation and I usually find a shorter way of doing it — a new pandas trick!

I learned about these functions recently and I deem them essential because of ease of use.

By reading this article, you’ll learn:

1. How to retrieve a column value from a Dataframe
2. How to change a column value in a Dataframe
3. The proper way of adding a new column to a DataFrame
4. How to retrieve a Series or a DataFrame
5. How to create a DataFrame from a pandas Series
6. Filter with between function
7. Fix the order of the rows with reindex function
8. Advanced usages of Describe function
9. Text search with regex
10. Bigger than memory datasets with pandas
11. How to save the disk space

#pandas #data-science #programming #python

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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

…

#pandas: most used functions in data science #pandas #data science #function #used python data #most used functions in data science

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:

#arrow functions #javascript #regular functions #arrow functions vs normal functions #difference between functions and arrow functions

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)
})
df.head()

#artificial-intelligence #pandas #programming #data-science #python #all you need to know about pandas cut and qcut functions

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.

#data-science #programming #artificial-intelligence #three very useful functions of pandas to summarize the data #pandas #functions

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