Python Matplotlib Library with Examples

What Is Python Matplotlib?

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+.

Pyplot is a Matplotlib module which provides a MATLAB-like interface. Matplotlib is designed to be as usable as MATLAB, with the ability to use Python and the advantage of being free and open-source. matplotlib.pyplot is a plotting library used for 2D graphics in the python programming language. It can be used in python scripts, shell, web application servers, and other graphical user interface toolkits.

There are several toolkits that are available that extend python Matplotlib functionality.

  • Basemap: It is a map plotting toolkit with various map projections, coastlines, and political boundaries.
  • Cartopy: It is a mapping library featuring object-oriented map projection definitions, and arbitrary point, line, polygon and image transformation capabilities.
  • Excel tools: Matplotlib provides utilities for exchanging data with Microsoft Excel.
    Mplot3d: It is used for 3-D plots.
  • Natgrid: It is an interface to the “natgrid” library for irregular gridding of the spaced data.
  • GTK tools: mpl_toolkits.gtktools provides some utilities for working with GTK. This toolkit ships with matplotlib, but requires pygtk.
  • Qt interface
  • Mplot3d: The mplot3d toolkit adds simple 3D plotting capabilities to matplotlib by supplying an axes object that can create a 2D projection of a 3D scene.
  • matplotlib2tikz: export to Pgfplots for smooth integration into LaTeX documents.

Types of Plots
There are various plots which can be created using python Matplotlib. Some of them are listed below:

  • Bar Graph
  • Histogram
  • Scatter Plot
  • Line Plot
  • 3D plot
  • Area Plot
  • Pie Plot
  • Image Plot

We will demonstrate some of them in detail.

But before that, let me show you elementary codes in python matplotlib in order to generate a simple graph.

So, with three lines of code, you can generate a basic graph using python matplotlib.

Let us see how can we add title, labels to our graph created by python matplotlib library to bring in more meaning to it. Consider the below example:

You can even try many styling techniques to create a better graph by changing the width or color of a particular line or what if you want to have some grid lines, there you need styling!

The style package adds support for easy-to-switch plotting “styles” with the same parameters as a matplotlibrc file.

There are a number of pre-defined styles provided by matplotlib. For example, there’s a pre-defined style called “ggplot”, which emulates the aesthetics of ggplot (a popular plotting package for R). To use this style, just add:

To list all available styles, use:

So, let me show you how to add style to a graph using python matplotlib. First, you need to import the style package from python matplotlib library and then use styling functions as shown in below code:

Now, we will understand the different kinds of plots. Let’s start with the bar graph!

Matplotlib: Bar Graph
A bar graph uses bars to compare data among different categories. It is well suited when you want to measure the changes over a period of time. It can be plotted vertically or horizontally. Also, the vital thing to keep in mind is that longer the bar, the greater is the value. Now, let us practically implement it using python matplotlib.

When I run this code, it generates a figure like below:


In the above plot, I have displayed a comparison between the distance covered by two cars BMW and Audi over a period of 5 days. Next, let us move on to another kind of plot using python matplotlib – Histogram

Matplotlib – Histogram
Let me first tell you the difference between a bar graph and a histogram. Histograms are used to show a graphical representation of the distribution of numerical data whereas a bar chart is used to compare different entities.

It is an estimate of the probability distribution of a continuous variable (quantitative variable) and was first introduced by Karl Pearson. It is a kind of bar graph.

To construct a histogram, the first step is to “bin” the range of values — that is, divide the entire range of values into a series of intervals — and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent and are often (but are not required to be) of equal size.

Basically, histograms are used to represent data given in the form of some groups or we can say when you have arrays or a very long list. X-axis is about bin ranges where Y-axis talks about frequency. So, if you want to represent the age-wise population in form of the graph then histogram suits well as it tells you how many exist in certain group range or bin if you talk in the context of histograms.

In the below code, I have created the bins in the interval of 10 which means the first bin contains elements from 0 to 9, then 10 to 19 and so on.

When I run this code, it generates a figure like below:

As you can see in the above plot, Y-axis tells about the age groups that appear with respect to the bins. Our biggest age group is between 40 and 50.

Matplotlib: Scatter Plot
A scatter plot is a type of plot that shows the data as a collection of points. The position of a point depends on its two-dimensional value, where each value is a position on either the horizontal or vertical dimension. Usually, we need scatter plots in order to compare variables, for example, how much one variable is affected by another variable to build a relation out of it.
Consider the below example:

As you can see in the above graph, I have plotted two scatter plots based on the inputs specified in the above code. The data is displayed as a collection of points having ‘high-income low salary’ and ‘low-income high salary.’

Scatter plot with groups
Data can be classified into several groups. The code below demonstrates:

The purpose of using “plt.figure()” is to create a figure object. It’s a Top-level container for all plot elements.

The whole figure is regarded as the figure object. It is necessary to explicitly use “plt.figure()”when we want to tweak the size of the figure and when we want to add multiple Axes objects in a single figure.

fig.add_subplot() is used to control the default spacing of the subplots.
For example, “111” means “1×1 grid, first subplot” and “234” means “2×3 grid, 4th subplot”.

You can easily understand by the following picture:

Next, let us understand the area plot or you can also say Stack plot using python matplotlib.

Matplotlib: Area Plot
Area plots are pretty much similar to the line plot. They are also known as stack plots. These plots can be used to display the evolution of the value of several groups on the same graphic. The values of each group are displayed on top of each other. It allows checking on the same figure the evolution of both the total of a numeric variable and the importance of each group.

A line chart forms the basis of an area plot, where the region between the axis and the line is represented by colors.

The above-represented graph shows how an area plot can be plotted for the present scenario. Each shaded area in the graph shows a particular bike with the frequency variations denoting the distance covered by the bike on different days. Next, let us move to our last yet most frequently used plot – Pie chart.

Matplotlib: Pie Chart
In a pie plot, statistical data can be represented in a circular graph where the circle is divided into portions i.e. slices of pie that denote a particular data, that is, each portion is proportional to different values in the data. This sort of plot can be mainly used in mass media and business.

In the above-represented pie plot, the bikes scenario is illustrated, and I have divided the circle into 4 sectors, each slice represents a particular bike and the percentage of distance traveled by it. Now, if you have noticed these slices adds up to 24 hrs, but the calculation of pie slices is done automatically for you. In this way, the pie charts are really useful as you don’t have to be the one who calculates the percentage of the slice of the pie.

Matplotlib: 3D Plot
Plotting of data along x, y, and z axes to enhance the display of data represents the 3-dimensional plotting. 3D plotting is an advanced plotting technique that gives us a better view of the data representation along the three axes of the graph.

Line Plot 3D

In the above-represented 3D graph, a line graph is illustrated in a 3-dimensional manner. We make use of a special library to plot 3D graphs which are given in the following syntax.
Syntax for plotting 3D graphs:

The import Axes3D is mainly used to create an axis by making use of the projection=3d keyword. This enables a 3-dimensional view of any data that can be written along with the above-mentioned code.

Surface Plot 3D

By default, it will be colored in shades of a solid color, but it also supports color mapping by supplying the cmap argument.

The rstride and cstride kwargs set the stride used to sample the input data to generate the graph. If 1k by 1k arrays are passed in, the default values for the strides will result in a 100×100 grid being plotted. Defaults to 10. Raises a ValueError if both stride and count kwargs (see next section) are provided.

Matplotlib: Image Plot

Matplotlib: Working With Multiple Plots
I have discussed multiple types of plots in python matplotlib such as bar plot, scatter plot, pie plot, area plot, etc. Now, let me show you how to handle multiple plots.

Data Analysis with Pandas & Python

What is Data Analysis?
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.
In this article, I have used Pandas to know more about doing data analysis.
Mainly pandas have two data structures, series, data frames, and Panel.

Installation
The easiest way to install pandas is to use pip:

or, Download it from here.

  • pandas Series

pandas series can be used for the one-dimensional labeled array.

Labels can be accessed using index attribute
print(a.index)

You can use array indexing or labels to access data in the series.
You can use array indexing or labels to access data in the series
print(a[1])
print(a[‘test4’])

You can also apply mathematical operations on pandas series.
b = a * 2
c = a ** 1.5
print(b)
print(c)

You can even create a series of heterogeneous data.
s = pd.Series([‘test1’, 1.2, 3, ‘test2’], index=[‘test3’, ‘test4’, 2, ‘4.3’])

print(s)

  • pandas DataFrame

pandas DataFrame is a two-dimensional array with heterogeneous data.i.e., data is aligned in a tabular fashion in rows and columns.
Structure
Let us assume that we are creating a data frame with the student’s data.

Name Age Gender Rating
Steve 32 Male 3.45
Lia 28 Female 4.6
Vin 45 Male 3.9
Katie 38 Female 2

You can think of it as an SQL table or a spreadsheet data representation.
The table represents the data of a sales team of an organization with their overall performance rating. The data is represented in rows and columns. Each column represents an attribute and each row represents a person.
The data types of the four columns are as follows −

Column Type
Name String
Age Integer
Gender String
Rating Float

Key Points
• Heterogeneous data
• Size Mutable
• Data Mutable

A pandas DataFrame can be created using the following constructor −
pandas.DataFrame( data, index, columns, dtype, copy)

•  data
data takes various forms like ndarray, series, map, lists, dict, constants and also another DataFrame.
•  index
For the row labels, the Index to be used for the resulting frame is Optional Default np.arrange(n) if no index is passed.
•  columns
For column labels, the optional default syntax is – np.arrange(n). This is only true if no index is passed.
•  dtype
The data type of each column.
•  copy
This command (or whatever it is) is used for copying of data if the default is False.

There are many methods to create DataFrames.
• Lists
• dict
• Series
• Numpy ndarrays
• Another DataFrame

Creating DataFrame from the dictionary of Series
The following method can be used to create DataFrames from a dictionary of pandas series.

print(df)

print(df.index)

print(df.columns)

Creating DataFrame from list of dictionaries
l = [{‘orange’: 32, ‘apple’: 42}, {‘banana’: 25, ‘carrot’: 44, ‘apple’: 34}]
df = pd.DataFrame(l, index=[‘test1’, ‘test2’])

print(df)

You might have noticed that we got a DataFrame with NaN values in it. This is because we didn’t the data for that particular row and column.

Creating DataFrame from Text/CSV files
Pandas tool comes in handy when you want to load data from a CSV or a text file. It has built-in functions to do this for use.

df = pd.read_csv(‘happiness.csv’)

Yes, we created a DataFrame from a CSV file. This dataset contains the outcome of the European quality of life survey. This dataset is available here. Now we have stored the DataFrame in df, we want to see what’s inside. First, we will see the size of the DataFrame.

print(df.shape)

It has 105 Rows and 4 Columns. Instead of printing out all the data, we will see the first 10 rows.
df.head(10)

There are many more methods to create a DataFrames. But now we will see the basic operation on DataFrames.

Operations on DataFrame
We’ll recall the DataFrame we made earlier.

print(df)

Now we want to create a new row column from current columns. Let’s see how it is done.
df[‘column3’] = (2 * df[‘column1’] + 3 * df[‘column2’])/5

We have created a new column column3 from column1 and  column2. We’ll create one more using boolean.
df[‘flag’] = df[‘column1’] > 99.5

We can also remove columns.
column3 = df.pop(‘column3’)

print(column3)

print(df)

Descriptive Statistics using pandas
It’s very easy to view descriptive statistics of a dataset using pandas. We are gonna use, Biomass data collected from this source. Let’s load the data first.

url = ‘https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/DAAG/biomass.csv’
df = pd.read_csv(url)
df.head()

We are not interested in the unnamed column. So, let’s delete that first. Then we’ll see the statistics with one line of code.

It’s simple as that. We can see all the statistics. Count, mean, standard deviation and other statistics. Now we are gonna find some other metrics which are not available in the describe() summary.

Mean :
print(df.mean())

Min and Max
print(df.min())

print(df.max())

Pairwise Correlation
df.corr()

Data Cleaning
We need to clean our data. Our data might contain missing values, NaN values, outliers, etc. We may need to remove or replace that data. Otherwise, our data might make any sense.
We can find null values using the following method.

print(df.isnull().any())

We have to remove these null values. This can be done by the method shown below.

newdf = df.dropna()

print(newdf.shape)

print(newdf.shape)

Pandas .Panel()
A panel is a 3D container of data. The term Panel data is derived from econometrics and is partially responsible for the name pandas − pan(el)-da(ta)-s.
The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data. They are −
• items − axis 0, each item corresponds to a DataFrame contained inside.
• major_axis − axis 1, it is the index (rows) of each of the DataFrames.
• minor_axis − axis 2, it is the columns of each of the DataFrames.

A Panel can be created using the following constructor −
The parameters of the constructor are as follows −
• data – Data takes various forms like ndarray, series, map, lists, dict, constants and also another DataFrame
• items – axis=0
• major_axis – axis=1
• minor_axis – axis=2
• dtype – the Data type of each column
• copy – Copy data. Default, false

A Panel can be created using multiple ways like −
• From ndarrays
• From dict of DataFrames
• From 3D ndarray

print(p)

Note − Observe the dimensions of the empty panel and the above panel, all the objects are different.

From dict of DataFrame Objects

print(p)

Selecting the Data from Panel
Select the data from the panel using −
• Items
• Major_axis
• Minor_axis

Using Items

print p[‘Item1’]

We have two items, and we retrieved item1. The result is a DataFrame with 4 rows and 3 columns, which are the Major_axis and Minor_axis dimensions.

Using major_axis
Data can be accessed using the method panel.major_axis(index).

Using minor_axis
Data can be accessed using the method panel.minor_axis(index).

print(p.minor_xs(1))

 

How to protect files from overwriting with noclobber in bash

This tip is for people who have ever hosed important files by using > when they meant to use >>. Add the following line to .bashrc:

set -o noclobber

The noclobber option prevents you from overwriting existing files with the > operator.

If the redirection operator is ‘>’, and the noclobber option to the set builtin has been enabled, the redirection will fail if the file whose name results from the expansion of word exists and is a regular file. If the redirection operator is ‘>|’, or the redirection operator is ‘>’ and the noclobber option is not enabled, the redirection is attempted even if the file named by word exists.

Example:

 

Run:

noclobber

 

Manhole service in Twisted Application.

What is Manhole?

Manhole is an in-process service, that will accept UNIX domain socket connections and present the stack traces for all threads and an interactive prompt.

Using it we can access and modify objects or definition in the running application, like change or add the method in any class, change the definition of any method of class or module.

This allows us to make modifications in running an application without restarting the application, it makes work easy like debugging the application, you are able to check the values of the object while the program is running.

How to configure it?

Once you run above snippet, the service will start on TCP port 2222.

You need to use SSH command to get login into the service.

See below how it looks like.

Here In the first login, we change the value in DATA dictionary in running application, as we can see we get the new value in the second login.

Simple port scanner in python

a port scanner is an application designed to probe a server or host for open ports. Such an application may be used by administrators to verify the security policies of their networks and by attackers to identify network services running on a host and exploit vulnerabilities.

port-scanner.py

Example

port-scannin

Howto use ssh as VPN tunnel

SSH is typically used to log into a remote machine and execute commands, but it also supports tunneling, forwarding TCP ports and X11 connections.

What is SSH Tunneling?

A tunneling protocol may, for example, allow a foreign protocol to run over a network that does not support that particular protocol, such as running IPv6 over IPv4.

SSH tunneling is a method of transporting arbitrary networking data over an encrypted SSH connection. It can be used to add encryption to legacy applications. … It also provides a way to secure the data traffic of any given application using port forwarding, basically tunneling any TCP/IP port over SSH.

sshuttle

sshuttle is not exactly a VPN, and not exactly port forwarding. It’s kind of both, and kind of neither.

It’s like a VPN, since it can forward every port on an entire network, not just ports you specify. Conveniently, it lets you use the “real” IP addresses of each host rather than faking port numbers on localhost.

On the other hand, the way it works is more like ssh port forwarding than a VPN. Normally, a VPN forwards your data one packet at a time, and doesn’t care about individual connections; ie. it’s “stateless” with respect to the traffic. sshuttle is the opposite of stateless; it tracks every single connection.

Installation

 sudo pip install sshuttle

Example

$ sshuttle --dns -v -r <remote-host> 0/0

ssh-tunnel

* This will forward all connections including DNS requests…

Usage

Working with Spinner in Android (Single Selector)

Spinner

Spinners provide a quick way to select one value from a set then we called it a single selector. A spinner shows its currently selected value from set. In the default state, it shows 0 index value from a set. Touching the spinner displays a dropdown menu or dialog with all other available values in the set, So we can be called it a dropdown.

Dropdown

 

 

 

 

 

 

Let’s check, how we can use it in an application.

Spinner integration has 3 key classes:-

1.  Spinner

2. Spinner Adapter

3. Spinner OnItemSelectedListener

So we will discuss above these classes also, with integration. You can add a spinner to your layout XML file. You can use the below sample code –

Spinner Adapter uses for bind between data set and spinner widget and it manages view for the spinner row item. We can use data as an array of string. See sample code –

Above string array, we will use for the display. So now we need to get reference Spinner which we defined in the XML file. We can use the below sample code –

So now we need to set values in ArrayAdapter. and We set this ArrayAdapter in Spinner. Use below code –

When the user selects an item from the drop-down, the Spinner object receives an on-item-selected event. for manage event, we can use OnItemSelectedListener in the Spinner. Sample code –

onItemSelected gives us a selected position of the spinner.

That’s it. Happy Coding 🙂

Reference:-

https://developer.android.com/guide/topics/ui/controls/spinner#java

Migrate an Existing Project to AndroidX

AndroidX is an open-source project by Google that provides a major improvement to the original Android Support Library. AndroidX is replaced the Support Library. Like support library, Google keeps AndroidX is independently from the Android OS and provides backward-compatibility across Android releases. AndroidX package structure is to encourage smaller and more focused libraries.

AndroidX replaces the original support library APIs with packages in the androidx namespace. Only the package and import names changed. Class, method, and field names did not change in migration.

Example:-

android.support.v7.widget.RecyclerView is changed to androidx.recyclerview.widget.RecyclerView

android.support.v7 is replced by androidx.

Migrating existing project:-

Before start migration, we need to make sure to follow the below things:-

1.  Android studio version should be higher than 3.2, You can check your android studio version from About Android Studio section. also use the latest Gradle version. Check project level Build.Gradle file to change the version.

2. Target SDK version and Compile SDK version should be 28 or greater. Check your app level Build.Gradle file to change target and compile SDK version.

3. Take a backup of your project. 

4. Add below properties to gradle.properties file. You can find this file on Project Level.

A.) android.useAndroidX: set to true, the Android plugin uses the AndroidX library instead of a Support Library. The value is false by default.

B.) android.enableJetifier: set to true, the Android plugin automatically migrates existing third-party libraries to use AndroidX. The value is false by default.

Let’s start migration:-

1.  Click Refactor from the menu in Android studio.

2. Then Click on Migrate to AndroidX from Refactor dropdown.

3. After that, It will ask you to take a backup of the whole project. If you have already taken the backup then ignore this step.

4. After the backup process clicks on Migrate, It will show list all support library where we are using in this project. Just click on Do Refactor and wait for some time.

5. After some time, you can see the project all support library replaced by the AndroidX library. Most of the support library will merge automatically and few we need to replaced manually. If you found any error,  Fix it manually. and test your app carefully. The application could crash due to incorrect auto-correction during migration.

That’s it. Enjoy Coding using AndroidX. 🙂

Reference:-

https://developer.android.com/jetpack/androidx/migrate

TabLayout Example using ViewPager and Fragments in Android

If you are using the latest android application then you have noticed that now days android is following a design pattern. This is material design and it came with Android Lollipop (5.0). Though we can still use this design pattern for the older versions (>4.0) by using the support libraries. One of the component of material design is TabLayout. So in this TablLayout Example we will see how we can implement it in our android application.

What is TabLayout ?

Android TabLayout provides horizontal layout to display tabs. We can display more screens in a single screen using tabs. User can swipe the tabs quickly as you can see in the image below.

Creating a new project and add necessary libraries

Open Android Studio and create a new project. I have created DemoTabLayout.

After create a new project, First of all we have to need include design libraries in the dependencies section of our build.gradle file so include this libraries in your build.gradle file by below line.

Remove action bar from style

Now since we will be using Android Toolbar and TabLayout classes to show tabs, lets remove the action bar from layout by using styles,

Go to style.xml file and change the parent of theme which you are using in your app.Change parent with “Theme.AppCompat.Light.NoActionBar”. So now your style file will look like below code

Creating fragments

We are creating an application which will have three tabs, So let’s define three fragment and their layouts.

First Fragment

It’s layout

 

Second Fragment

it’s layout

Third Fragment

its layout

Now we will define a view pager adapter to create tab swipe functionality.

Now we will create an Activity named MainActivity which will hold the tabs . This Activity will have two part first is code file and second is layout file. layout file code given below

We can see above that here we add a Toolbar, second is Tablayout and third thing is Viewpager. Toolbar is for showing application name and menu icon. TabLayout is for showing Tabs and viewpager is for holding fragments. Now we write code for attach this layout with fragments in MainActivity code file. Code is given below:

That’s all, Now you can run your project.

Happy coding…

DatePicker Example in Android

In android, DatePicker is a control which will allow users to select the date by day, month and year in our application user interface.

The images of datePicker are given blow

Create DatePicker with xml

We can create DatePicker using <Datepicker> element in xml file, which is given below

There are two types of view of DatePicker

1. Complete Calendar view

2. Spinner View

Complete Calendar view :- We can create complete calendar view with below code

This is output

Spinner view :- We can create complete calendar view with below code

This is output

Now we will create an example of datePicker in which we will set date picker on edit text and we will create date picker using java code also. In this example we will create a Textview and a button also. On button click we will show date in TextView.

Create a new android application using android studio and give name DatePickerDemo.

Now Open activity_main.xml file from \res\layout path and write code which is given below

activity_main.xml

Now open MainActivity.java file and write code which is given below

MainActivity.java

In java code you can see that we set an Onclicklistener on EditText, when we click on EditText then will see calendar dialog, From which we can select date.

Output of example

That’s all . This is the final output of example.

Happy Coding…

Shell script wrapper function for sending messages through Pushover

Pushover makes it easy to get real-time notifications on your Android, iPhone, iPad, and Desktop (Android Wear and Apple Watch, too!)

You can use this shell function anywhere in your script.

Example:

Note: you need to update API tokens and title above

Fetch Contacts From Native Phonebook

Import Contacts In iOS

Contacts are We are going to use built in Contacts.framework to import all contacts in our app. To display a list of contacts, you must have access to the user’s address book. Apple does a great job of protecting the user’s privacy, so you can’t read any of their contacts’ data without asking the user for permission. Similar restrictions apply to access the user’s camera, location, photos, and more.

Whenever we need access to privacy-sensitive information, you are required to specify this in your app’s Info.plist file. This file keeps track of many of your app’s properties, such as its display name, supported interface orientations, and, in the case of accessing a user’s contacts, Info.plist also contains information about why you need access to the user’s contacts.

Let’s go step by step:-

  • Add usage description in Info.plist file for contacts.

Open Plist file and click on plus button to add new row for contact usage description.

Add Privacy – Contacts Usage Description in key

Select Type as String

Write the usage purpose of contacts in your app.

  • Import Contacts Framework in your class.

 

  •  Request for Contact permission

 

Above two function will check Contact authorisation status. If not determined it will show alert for contact permission. Keep in mind you can ask Contact permission only once. Once user denied you can just open Setting screen for enable Contact permission.

  • Fetch Contact Using CNContactStore:-

 

We create a CNContactStore instance and this object is used to communicate directly with the Contacts system on iOS. In this method, we wrap our code in a do-catch statement because two of the methods we use are throwing methods.We can retrieve different values using different Keys like first name, last name, contact image, mobile number, address, emails etc. We then create an array that contains a number of constant keys. These keys directly relate to the information your app has access too.

There is different container Group in Native phonebook. We can retrieve Contacts from different Container according our need. Here we are retrieving contacts of all Groups using store.containers(matching: nil) and iterate it one by one to fetch contacts.

store.unifiedContacts will return array of CNContact which you can store in Your app data or in your app’s database and display contacts In your own tableview Format.

Important:-

In iOS 13, apple have added a new entitlement that is needed if you wish to access the notes for contacts. The entitlement is com.apple.developer.contacts.notes. You can request permission to use this entitlement for an app being put in the App Store.

The reason it was added is primarily for privacy reasons — the notes field can contain any information you might have on the contact; and a lot of times this information is significantly more sensitive than just the contact information.

 

Happy Coding.