You Are Probably Not Making The Most of Pandas “read_csv” Function

It is more than just reading the file

Pandas is arguably the most popular data analysis and manipulation library. What I think makes Pandas widely-used is having a large number of powerful and versatile functions.

Pandas functions usually do a fine job with the default settings. However, they offer much more if you use the parameters efficiently. In this article, we will elaborate on the read_csv function to make the most of it.

The read_csv is one of the most commonly used Pandas functions. It creates a dataframe by reading data from a csv file. However, it is almost always executed with the default settings.

If you ever read through the documentation, you would notice the read_csv function has many parameters. These parameters add functionality and flexibility to the function.

For instance, if the csv file contains a column of dates, it will be stored in the dataframe with object data type. However, in order to use Pandas datetime functions under dt accessor, we need to have the dates with datetime data type. We can always convert the data type after reading the data. A more practical way is to handle this task while reading the data.

The parse_dates parameter accomplishes this task. Let’s do an example. I have a sample csv file with 3 columns.

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You Are Probably Not Making The Most of Pandas “read_csv” Function

You Are Probably Not Making The Most of Pandas “read_csv” Function

It is more than just reading the file

Pandas is arguably the most popular data analysis and manipulation library. What I think makes Pandas widely-used is having a large number of powerful and versatile functions.

Pandas functions usually do a fine job with the default settings. However, they offer much more if you use the parameters efficiently. In this article, we will elaborate on the read_csv function to make the most of it.

The read_csv is one of the most commonly used Pandas functions. It creates a dataframe by reading data from a csv file. However, it is almost always executed with the default settings.

If you ever read through the documentation, you would notice the read_csv function has many parameters. These parameters add functionality and flexibility to the function.

For instance, if the csv file contains a column of dates, it will be stored in the dataframe with object data type. However, in order to use Pandas datetime functions under dt accessor, we need to have the dates with datetime data type. We can always convert the data type after reading the data. A more practical way is to handle this task while reading the data.

The parse_dates parameter accomplishes this task. Let’s do an example. I have a sample csv file with 3 columns.

#artificial-intelligence #programming #python #machine-learning #data-science #you are probably not making the most of pandas “read_csv” function

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

Paula  Hall

Paula Hall

1623551520

3 Pandas Functions That Will Make Your Life Easier

A practical guide with worked through examples

Pandas is a prevalent data analysis and manipulation in the data science ecosystem. It provides lots of versatile functions and methods to perform efficient data analysis.

In this article, we will cover 3 Pandas functions that will expedite and simplify the data analysis process.

1. Convert_dtypes

2. Pipe

3. Plot

Conclusion

#programming #python #machine-learning #data-science #pandas functions that will make your life easier #pandas functions

Vincent Lab

Vincent Lab

1605017502

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

Paula  Hall

Paula Hall

1623578460

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