1593497100
Heatmap Basics with Python’s Seaborn
The idea is straightforward, replace numbers with colors.
Now, this visualization style came a long way from simple color-coded tables, it became widely used with geospatial data, and its commonly applied for describing density or intensity of variables, visualize patterns, variance, and even anomalies.
Correlation Matrix — Composition of a sample of Cereals
With so many applications, this elementary method deserves some attention. In this article, we’ll go through the basics of heatmaps, and see how to create them using Matplotlib, and Seaborn.
Hands-on
We’ll use Pandas and Numpy to help us with data wrangling.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import numpy as np
The dataset for this example is a time-series of foreign exchange rates per U.S. dollar.
Instead of the usual line chart to represent the values over time, I want to try visualizing this data with a color-coded table, having the months as columns and the years as rows.
I’ll try sketching both the line chart and the heatmap, to get an understanding of this will look.
Line charts would be more effective in displaying the data; it’s easier to compare how higher a point is in the line than it is to distinguish colors.
Heatmaps will have a higher impact as they are not the conventional way of displaying this sort of data, they’ll lose some accuracy, especially in this case, since we’ll need to aggregate the values in months. But overall, they would still be able to display patterns and summarize the periods in our data.
Let’s read the dataset and rearrange the data according to the sketch.
# read file
df = pd.read_csv('data/Foreign_Exchange_Rates.csv',
usecols=[1,7], names=['DATE', 'CAD_USD'],
skiprows=1, index_col=0, parse_dates=[0])
For this example, we’ll use the columns 1 and 7, which are the ‘Time Serie’ and ‘CANADA — CANADIAN DOLLAR/US$’.
Let’s rename those columns to ‘DATE’ and ‘CAD_USD’, and since we’re passing our headers, we also need to skip the first row.
We also need to parse the first column, so the values are in a DateTime format, and we’ll define the date as our index.
Let’s make sure all our values are numbers, and remove the empty rows as well.
df['CAD_USD'] = pd.to_numeric(df.CAD_USD, errors='coerce')
df.dropna(inplace=True)
We need to aggregate those values by month. Let’s create separate columns for month and year, then we group the new columns and get the mean.
# create a copy of the dataframe, and add columns for month and year
df_m = df.copy()
df_m['month'] = [i.month for i in df_m.index]
df_m['year'] = [i.year for i in df_m.index]
# group by month and year, get the average
df_m = df_m.groupby(['month', 'year']).mean()
All that’s left to do is unstack the indexes, and we’ll have our table.
df_m = df_m.unstack(level=0)
#heatmap #seaborn #data-science #data-visualization #python
What is GEEK
Buddha Community
1626775355
Why use Python for Software Development
No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
5 Reasons to Utilize Python for Programming Web Apps
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Robust frameworks
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Progressive applications
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Summary
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
#python development services #python development company #python app development #python development #python in web development #python software development
1574339995
Learn Python Tutorial from Basic to Advance
Description
Become a Python Programmer and learn one of employer’s most requested skills of 21st century!
This is the most comprehensive, yet straight-forward, course for the Python programming language on Simpliv! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3. (Note, we also provide older Python 2 notes in case you need them)
With over 40 lectures and more than 3 hours of video this comprehensive course leaves no stone unturned! This course includes tests, and homework assignments as well as 3 major projects to create a Python project portfolio!
This course will teach you Python in a practical manner, with every lecture comes a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you!
We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we’ve got you covered!
We cover a wide variety of topics, including:
Command Line Basics
Installing Python
Running Python Code
Strings
Lists
Dictionaries
Tuples
Sets
Number Data Types
Print Formatting
Functions
Scope
Built-in Functions
Debugging and Error Handling
Modules
External Modules
Object Oriented Programming
Inheritance
Polymorphism
File I/O
Web scrapping
Database Connection
Email sending
and much more!
Project that we will complete:
Guess the number
Guess the word using speech recognition
Love Calculator
google search in python
Image download from a link
Click and save image using openCV
Ludo game dice simulator
open wikipedia on command prompt
Password generator
QR code reader and generator
You will get lifetime access to over 40 lectures.
So what are you waiting for? Learn Python in a way that will advance your career and increase your knowledge, all in a fun and practical way!
Basic knowledge
Basic programming concept in any language will help but not require to attend this tutorial
What will you learn
Learn to use Python professionally, learning both Python 2 and Python 3!
Create games with Python, like Tic Tac Toe and Blackjack!
Learn advanced Python features, like the collections module and how to work with timestamps!
Learn to use Object Oriented Programming with classes!
Understand complex topics, like decorators.
Understand how to use both the pycharm and create .py files
Get an understanding of how to create GUIs in the pycharm!
Build a complete understanding of Python from the ground up!
#Learn Python #Learn Python from Basic #Python from Basic to Advance #Python from Basic to Advance with Projects #Learn Python from Basic to Advance with Projects in a day
1656151740
Test_cov_console: Flutter Console Coverage Test
Flutter Console Coverage Test
This small dart tools is used to generate Flutter Coverage Test report to console
How to install
Add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):
dev_dependencies:
test_cov_console: ^0.2.2
How to run
run the following command to make sure all flutter library is up-to-date
flutter pub get
Running "flutter pub get" in coverage... 0.5s
run the following command to generate lcov.info on coverage directory
flutter test --coverage
00:02 +1: All tests passed!
run the tool to generate report from lcov.info
flutter pub run test_cov_console
---------------------------------------------|---------|---------|---------|-------------------|
File |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
print_cov_constants.dart | 0.00 | 0.00 | 0.00 | no unit testing|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
Optional parameter
If not given a FILE, "coverage/lcov.info" will be used.
-f, --file=<FILE> The target lcov.info file to be reported
-e, --exclude=<STRING1,STRING2,...> A list of contains string for files without unit testing
to be excluded from report
-l, --line It will print Lines & Uncovered Lines only
Branch & Functions coverage percentage will not be printed
-i, --ignore It will not print any file without unit testing
-m, --multi Report from multiple lcov.info files
-c, --csv Output to CSV file
-o, --output=<CSV-FILE> Full path of output CSV file
If not given, "coverage/test_cov_console.csv" will be used
-t, --total Print only the total coverage
Note: it will ignore all other option (if any), except -m
-p, --pass=<MINIMUM> Print only the whether total coverage is passed MINIMUM value or not
If the value >= MINIMUM, it will print PASSED, otherwise FAILED
Note: it will ignore all other option (if any), except -m
-h, --help Show this help
example run the tool with parameters
flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock
---------------------------------------------|---------|---------|---------|-------------------|
File |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
report for multiple lcov.info files (-m, --multi)
It support to run for multiple lcov.info files with the followings directory structures:
1. No root module
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
2. With root module
<root>/coverage/lcov.info
<root>/lib/src
<root>/<module_a>
<root>/<module_a>/coverage/lcov.info
<root>/<module_a>/lib/src
<root>/<module_b>
<root>/<module_b>/coverage/lcov.info
<root>/<module_b>/lib/src
...
You must run test_cov_console on <root> dir, and the report would be grouped by module, here is
the sample output for directory structure 'with root module':
flutter pub run test_cov_console --file=coverage/lcov.info --exclude=_constants,_mock --multi
---------------------------------------------|---------|---------|---------|-------------------|
File |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_a - |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
---------------------------------------------|---------|---------|---------|-------------------|
File - module_b - |% Branch | % Funcs | % Lines | Uncovered Line #s |
---------------------------------------------|---------|---------|---------|-------------------|
lib/src/ | | | | |
print_cov.dart | 100.00 | 100.00 | 88.37 |...,149,205,206,207|
lib/ | | | | |
test_cov_console.dart | 0.00 | 0.00 | 0.00 | no unit testing|
---------------------------------------------|---------|---------|---------|-------------------|
All files with unit testing | 100.00 | 100.00 | 88.37 | |
---------------------------------------------|---------|---------|---------|-------------------|
Output to CSV file (-c, --csv, -o, --output)
flutter pub run test_cov_console -c --output=coverage/test_coverage.csv
#### sample CSV output file:
File,% Branch,% Funcs,% Lines,Uncovered Line #s
lib/,,,,
test_cov_console.dart,0.00,0.00,0.00,no unit testing
lib/src/,,,,
parser.dart,100.00,100.00,97.22,"97"
parser_constants.dart,100.00,100.00,100.00,""
print_cov.dart,100.00,100.00,82.91,"29,49,51,52,171,174,177,180,183,184,185,186,187,188,279,324,325,387,388,389,390,391,392,393,394,395,398"
print_cov_constants.dart,0.00,0.00,0.00,no unit testing
All files with unit testing,100.00,100.00,86.07,""Installing
Use this package as an executable
Install it
You can install the package from the command line:
dart pub global activate test_cov_consoleUse it
The package has the following executables:
$ test_cov_console
Use this package as a library
Depend on it
Run this command:
With Dart:
$ dart pub add test_cov_consoleWith Flutter:
$ flutter pub add test_cov_consoleThis will add a line like this to your package's pubspec.yaml (and run an implicit dart pub get):
dependencies:
test_cov_console: ^0.2.2Alternatively, your editor might support dart pub get or flutter pub get. Check the docs for your editor to learn more.
Import it
Now in your Dart code, you can use:
import 'package:test_cov_console/test_cov_console.dart';example/lib/main.dart
import 'package:flutter/material.dart';
void main() {
runApp(MyApp());
}
class MyApp extends StatelessWidget {
// This widget is the root of your application.
@override
Widget build(BuildContext context) {
return MaterialApp(
title: 'Flutter Demo',
theme: ThemeData(
// This is the theme of your application.
//
// Try running your application with "flutter run". You'll see the
// application has a blue toolbar. Then, without quitting the app, try
// changing the primarySwatch below to Colors.green and then invoke
// "hot reload" (press "r" in the console where you ran "flutter run",
// or simply save your changes to "hot reload" in a Flutter IDE).
// Notice that the counter didn't reset back to zero; the application
// is not restarted.
primarySwatch: Colors.blue,
// This makes the visual density adapt to the platform that you run
// the app on. For desktop platforms, the controls will be smaller and
// closer together (more dense) than on mobile platforms.
visualDensity: VisualDensity.adaptivePlatformDensity,
),
home: MyHomePage(title: 'Flutter Demo Home Page'),
);
}
}
class MyHomePage extends StatefulWidget {
MyHomePage({Key? key, required this.title}) : super(key: key);
// This widget is the home page of your application. It is stateful, meaning
// that it has a State object (defined below) that contains fields that affect
// how it looks.
// This class is the configuration for the state. It holds the values (in this
// case the title) provided by the parent (in this case the App widget) and
// used by the build method of the State. Fields in a Widget subclass are
// always marked "final".
final String title;
@override
_MyHomePageState createState() => _MyHomePageState();
}
class _MyHomePageState extends State<MyHomePage> {
int _counter = 0;
void _incrementCounter() {
setState(() {
// This call to setState tells the Flutter framework that something has
// changed in this State, which causes it to rerun the build method below
// so that the display can reflect the updated values. If we changed
// _counter without calling setState(), then the build method would not be
// called again, and so nothing would appear to happen.
_counter++;
});
}
@override
Widget build(BuildContext context) {
// This method is rerun every time setState is called, for instance as done
// by the _incrementCounter method above.
//
// The Flutter framework has been optimized to make rerunning build methods
// fast, so that you can just rebuild anything that needs updating rather
// than having to individually change instances of widgets.
return Scaffold(
appBar: AppBar(
// Here we take the value from the MyHomePage object that was created by
// the App.build method, and use it to set our appbar title.
title: Text(widget.title),
),
body: Center(
// Center is a layout widget. It takes a single child and positions it
// in the middle of the parent.
child: Column(
// Column is also a layout widget. It takes a list of children and
// arranges them vertically. By default, it sizes itself to fit its
// children horizontally, and tries to be as tall as its parent.
//
// Invoke "debug painting" (press "p" in the console, choose the
// "Toggle Debug Paint" action from the Flutter Inspector in Android
// Studio, or the "Toggle Debug Paint" command in Visual Studio Code)
// to see the wireframe for each widget.
//
// Column has various properties to control how it sizes itself and
// how it positions its children. Here we use mainAxisAlignment to
// center the children vertically; the main axis here is the vertical
// axis because Columns are vertical (the cross axis would be
// horizontal).
mainAxisAlignment: MainAxisAlignment.center,
children: <Widget>[
Text(
'You have pushed the button this many times:',
),
Text(
'$_counter',
style: Theme.of(context).textTheme.headline4,
),
],
),
),
floatingActionButton: FloatingActionButton(
onPressed: _incrementCounter,
tooltip: 'Increment',
child: Icon(Icons.add),
), // This trailing comma makes auto-formatting nicer for build methods.
);
}
}Author: DigitalKatalis
Source Code: https://github.com/DigitalKatalis/test_cov_console
License: BSD-3-Clause license
1593497100
Heatmap Basics with Python’s Seaborn
The idea is straightforward, replace numbers with colors.
Now, this visualization style came a long way from simple color-coded tables, it became widely used with geospatial data, and its commonly applied for describing density or intensity of variables, visualize patterns, variance, and even anomalies.
Correlation Matrix — Composition of a sample of Cereals
With so many applications, this elementary method deserves some attention. In this article, we’ll go through the basics of heatmaps, and see how to create them using Matplotlib, and Seaborn.
Hands-on
We’ll use Pandas and Numpy to help us with data wrangling.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import numpy as np
The dataset for this example is a time-series of foreign exchange rates per U.S. dollar.
Instead of the usual line chart to represent the values over time, I want to try visualizing this data with a color-coded table, having the months as columns and the years as rows.
I’ll try sketching both the line chart and the heatmap, to get an understanding of this will look.
Line charts would be more effective in displaying the data; it’s easier to compare how higher a point is in the line than it is to distinguish colors.
Heatmaps will have a higher impact as they are not the conventional way of displaying this sort of data, they’ll lose some accuracy, especially in this case, since we’ll need to aggregate the values in months. But overall, they would still be able to display patterns and summarize the periods in our data.
Let’s read the dataset and rearrange the data according to the sketch.
# read file
df = pd.read_csv('data/Foreign_Exchange_Rates.csv',
usecols=[1,7], names=['DATE', 'CAD_USD'],
skiprows=1, index_col=0, parse_dates=[0])
For this example, we’ll use the columns 1 and 7, which are the ‘Time Serie’ and ‘CANADA — CANADIAN DOLLAR/US$’.
Let’s rename those columns to ‘DATE’ and ‘CAD_USD’, and since we’re passing our headers, we also need to skip the first row.
We also need to parse the first column, so the values are in a DateTime format, and we’ll define the date as our index.
Let’s make sure all our values are numbers, and remove the empty rows as well.
df['CAD_USD'] = pd.to_numeric(df.CAD_USD, errors='coerce')
df.dropna(inplace=True)
We need to aggregate those values by month. Let’s create separate columns for month and year, then we group the new columns and get the mean.
# create a copy of the dataframe, and add columns for month and year
df_m = df.copy()
df_m['month'] = [i.month for i in df_m.index]
df_m['year'] = [i.year for i in df_m.index]
# group by month and year, get the average
df_m = df_m.groupby(['month', 'year']).mean()
All that’s left to do is unstack the indexes, and we’ll have our table.
df_m = df_m.unstack(level=0)
#heatmap #seaborn #data-science #data-visualization #python
1593156510
Basic Data Types in Python | Python Web Development For Beginners
At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Table of Contents hide
III Built-in data types in Python
Mutable objects
The Size and declared value and its sequence of the object can able to be modified called mutable objects.
Mutable Data Types are list, dict, set, byte array
Immutable objects
The Size and declared value and its sequence of the object can able to be modified.
Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.
id() and type() is used to know the Identity and data type of the object
a**=25+**85j
type**(a)**
output**:<class’complex’>**
b**={1:10,2:“Pinky”****}**
id**(b)**
output**:**238989244168
Built-in data types in Python
a**=str(“Hello python world”)****#str**
b**=int(18)****#int**
c**=float(20482.5)****#float**
d**=complex(5+85j)****#complex**
e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**
f**=tuple((“python”,“easy”,“learning”))****#tuple**
g**=range(10)****#range**
h**=dict(name=“Vidu”,age=36)****#dict**
i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**
j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**
k**=bool(18)****#bool**
l**=bytes(8)****#bytes**
m**=bytearray(8)****#bytearray**
n**=memoryview(bytes(18))****#memoryview**
Numbers (int,Float,Complex)
Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.
#signed interger
age**=**18
print**(age)**
Output**:**18
Python supports 3 types of numeric data.
int (signed integers like 20, 2, 225, etc.)
float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)
complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)
A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).
String
The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.
# String Handling
‘Hello Python’
#single (') Quoted String
“Hello Python”
# Double (") Quoted String
“”“Hello Python”“”
‘’‘Hello Python’‘’
# triple (‘’') (“”") Quoted String
In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.
The operator “+” is used to concatenate strings and “*” is used to repeat the string.
“Hello”+“python”
output**:****‘Hello python’**
"python "*****2
'Output : Python python ’
#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type