1593984540

Data visualization is an essential, routine task in any field regardless of how small or large the data is, then be it plotting a simple sinusoidal curve or creating sophisticated, interactive dashboards for stakeholders. Python being one of the most sought after programming languages today (in the active top 3 on Stack Overflow), offers tremendous possibilities of data visualization.

Matplotlib is one such powerful plotting library for python that offers a varied spectrum of 1D, 2D, and 3D visualizations. Being a gold badge holder of matplotlib on Stack Overflow, I thought of creating a series of tutorials aimed at teaching the basic know-hows of using **python** & **matplotlib** for plotting eye-catchy figures, providing **ready-to-use, Jupyter Notebooks**.

This first post of mine will teach you the **_essential basics _**of matplotlib, taking the use case of **one-dimensional plots,** together with basic **bar** and **pie** **charts**. The whole Notebook can be downloaded/forked from my GitHub repository. Following system settings are used: Python version: 3.7.7, Matplotlib version: 3.1.3. The tutorial is expected to work on older versions, too.

To enable the inline plotting in Jupyter Notebooks, you need to use the following at the beginning of the Notebook

```
%matplotlib inline
```

If your screen supports retina display, you can enhance the resolution of the figure by using

```
%config InlineBackend.figure_format = ‘retina’
```

Matplotlib offers several cool style sheets to enhance the figure aesthetics. I will use `'fivethirtyeight'`

```
plt.style.use('fivethirtyeight')
```

The _simplest _example would be to plot a list of values. Let’s create a list of incremental values and plot it.

```
values = [5, 6, 7, 8, 9]
plt.plot(values)
```

A simple line plot

You can see that the `values`

are plotted on the *y*-axis. For plotting on the *x-y* space, you typically need two lists: one for the *x*-values and the other for the *y*-values. Please note that, by default, solid lines are used for plotting.

**Now you might be wondering how the figure was created without passing the x-values.**

By default, when we pass a single list to `plt.plot()`

, the *x*-axis assume integer values starting from zero until one less than the length of the *y*-values. In the above figure, `values`

had five elements. So the *x*-values became 0, 1, 2, 3, 4 (because indexing starts from 0 in python). In other words, the *x*-values are `range(len(values))`

, where `len(values)`

returns the length of the list i.e. 5. Therefore, the following line will also plot the same figure:

```
plt.plot(range(len(values)), values)
```

⇒ **NOTE:** If you are using some other python IDE, for instance spyder, you will need to use `plt.show()`

after the `plt.plot()`

command for the figure window to appear. You don’t need it in the Jupyter Notebook though.

#python #matplotlib #plotting #data-visualization #charts #programming

1619518440

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

…

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

1619510796

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

**Lambda function in python**: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

**Syntax: x = lambda arguments : expression**

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

1593984540

Data visualization is an essential, routine task in any field regardless of how small or large the data is, then be it plotting a simple sinusoidal curve or creating sophisticated, interactive dashboards for stakeholders. Python being one of the most sought after programming languages today (in the active top 3 on Stack Overflow), offers tremendous possibilities of data visualization.

Matplotlib is one such powerful plotting library for python that offers a varied spectrum of 1D, 2D, and 3D visualizations. Being a gold badge holder of matplotlib on Stack Overflow, I thought of creating a series of tutorials aimed at teaching the basic know-hows of using **python** & **matplotlib** for plotting eye-catchy figures, providing **ready-to-use, Jupyter Notebooks**.

This first post of mine will teach you the **_essential basics _**of matplotlib, taking the use case of **one-dimensional plots,** together with basic **bar** and **pie** **charts**. The whole Notebook can be downloaded/forked from my GitHub repository. Following system settings are used: Python version: 3.7.7, Matplotlib version: 3.1.3. The tutorial is expected to work on older versions, too.

To enable the inline plotting in Jupyter Notebooks, you need to use the following at the beginning of the Notebook

```
%matplotlib inline
```

If your screen supports retina display, you can enhance the resolution of the figure by using

```
%config InlineBackend.figure_format = ‘retina’
```

Matplotlib offers several cool style sheets to enhance the figure aesthetics. I will use `'fivethirtyeight'`

```
plt.style.use('fivethirtyeight')
```

The _simplest _example would be to plot a list of values. Let’s create a list of incremental values and plot it.

```
values = [5, 6, 7, 8, 9]
plt.plot(values)
```

A simple line plot

You can see that the `values`

are plotted on the *y*-axis. For plotting on the *x-y* space, you typically need two lists: one for the *x*-values and the other for the *y*-values. Please note that, by default, solid lines are used for plotting.

**Now you might be wondering how the figure was created without passing the x-values.**

By default, when we pass a single list to `plt.plot()`

, the *x*-axis assume integer values starting from zero until one less than the length of the *y*-values. In the above figure, `values`

had five elements. So the *x*-values became 0, 1, 2, 3, 4 (because indexing starts from 0 in python). In other words, the *x*-values are `range(len(values))`

, where `len(values)`

returns the length of the list i.e. 5. Therefore, the following line will also plot the same figure:

```
plt.plot(range(len(values)), values)
```

⇒ **NOTE:** If you are using some other python IDE, for instance spyder, you will need to use `plt.show()`

after the `plt.plot()`

command for the figure window to appear. You don’t need it in the Jupyter Notebook though.

#python #matplotlib #plotting #data-visualization #charts #programming

1599813647

Python is an interpreted, high-level, powerful general-purpose programming language. You may ask, Python’s a snake right? and Why is this programming language named after it? Well, you are in the right place to discover the answer! and I’ll also answer the why, what, and how regarding Python programming.

**People prefer Python over French** (What 😮)

According to a recent survey, in the UK, Python overtook French to be the most popular language taught in primary schools. (OMG!) 6 of 10 parents preferred their children to learn Python over French.

So hurry up🏃♂️🏃♀️(or these kids will for sure)! Get ready to learn it! Because there’s a possibility of you being hired in one of the companies mentioned below!!!

#python #python-programming #software-development #python3 #python-tips #learn-python #python-top-story #python-skills

1624291080

In Python, plotting graphs is straightforward — you can use powerful libraries like Matplotlib. But when you are running simulations, basic plots may not always be enough. You may want to show an animation that helps you understand how the state changes over time.

Luckily, it’s just as easy to create animations as it is to create plots with Matplotlib.

In this guide, you are going to learn:

- How to create a basic Matplotlib plot.
- How to create a basic animated plot using Matplotlib.
- How to create a live-updating graph of a stock price.

Matplotlib is a commonly used visualization library in Python. You can plot interactive graphs, histograms, bar charts, and so on.

#coding #python #python animations with matplotlib #animations with matplotlib #matplotlib #python animations