在這篇文章中，我們將學習如何在 Python 中使用 Matplotlib 製作熱圖。在 Matplotlib 中，我們可以使用函數 imshow() 製作熱圖。imshow() 基本上將輸入數據顯示為圖像。
我們將首先使用 imshow() 開始製作帶有單線的簡單熱圖。然後通過添加軸標籤來顯示幾個簡單的自定義項，以使熱圖看起來更好。
import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns
我們將使用 Seaborn 內置數據集中可用的航班數據集。首先，我們必須將數據轉換為矩陣形式，其中月份為行，年份為列。
flights = sns.load_dataset("flights") # pivoting to make the data wide flights = flights.pivot("month", "year", "passengers") flights.head() year 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 month Jan 112 115 145 171 196 204 242 284 315 340 360 417 Feb 118 126 150 180 196 188 233 277 301 318 342 391 Mar 132 141 178 193 236 235 267 317 356 362 406 419 Apr 129 135 163 181 235 227 269 313 348 348 396 461 May 121 125 172 183 229 234 270 318 355 363 420 472
當您擁有矩陣形式的數據時，我們使用 imshow() 函數來製作簡單的熱圖。
fig, ax = plt.subplots(figsize=(10,10)) im = ax.imshow(flights)
讓我們先添加簡單的註釋，使熱圖稍微好一點。在這裡，我們使用 set_title() 函數為熱圖添加了標題。
fig, ax = plt.subplots(figsize=(10,10)) im = ax.imshow(flights) ax.set_title("Matplotlib Heatmap with imshow", size=20) fig.tight_layout() plt.savefig("heatmap_in_matplotlib_using_imshow.png", format='png',dpi=150)
如何使用 Matplotlib 製作熱圖
請注意，缺少 x 和 y 軸標籤。要添加軸標籤，讓我們從航班數據集的行名和列名中獲取月份和年份值。
months = flights.index.values months years = flights.columns.values
現在我們可以在 x 軸上添加年份，在 y 軸刻度標籤上添加月份。在 Matplotlib 中，我們可以使用 set_xticks() 和 set_yticks() 函數添加刻度標籤。
fig, ax = plt.subplots(figsize=(10,10)) im = ax.imshow(flights) # Add axis tick labels ax.set_xticks(np.arange(len(years)), labels=years) ax.set_yticks(np.arange(len(months)), labels=months) ax.set_title("Adding axis labels to Matplotlib Heatmap", size=20) fig.tight_layout() plt.savefig("axis_labels_in_heatmap_in_matplotlib.png", format='png',dpi=150)
使用 Matplotlib 將軸標籤添加到熱圖
我們可以使用 figure.colorbar() 函數添加顏色條圖例，以幫助理解數值範圍及其與顏色的關聯。有時添加的顏色條可能比熱圖略大。這裡我們使用了 shrink 參數來減小顏色條的大小
fig, ax = plt.subplots( figsize=(10,10)) im = ax.imshow(flights) cbar = ax.figure.colorbar(im, ax = ax, shrink=0.5 ) # add tick labels ax.set_xticks(np.arange(len(years)), labels=years, size=12) ax.set_yticks(np.arange(len(months)), labels=months,size=12) # Rotate the tick labels to be more legible plt.setp(ax.get_xticklabels(), rotation = 45, ha = "right", rotation_mode = "anchor") ax.set_title("Flights Data Seaborn", size=20) fig.tight_layout() plt.savefig("how_to_make_a_heatmap_with_matplotlib_Python.png", format='png',dpi=150)
如何在 Matplotlib 中使用 imshow() 製作熱圖
默認情況下，Matplotlib 的 imshow() 使用 viridis 調色板來製作熱圖。我們可以通過使用imshow() 函數的cmap參數來更改熱圖的調色板。在本例中，我們將默認調色板更改為“YlGn”。
fig, ax = plt.subplots( figsize=(10,10)) im = ax.imshow(flights, cmap="YlGn") cbar = ax.figure.colorbar(im, ax=ax, shrink=0.5 ) # add tick labels ax.set_xticks(np.arange(len(years)), labels=years, size=12) ax.set_yticks(np.arange(len(months)), labels=months, size=12) # Rotate the tick labels to be more legible plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") ax.set_title("Flights Data Seaborn", size=20) fig.tight_layout() plt.savefig("change_heatmap_color_palette_matplotlib_Python.png", format='png',dpi=150)
使用 cmap 參數更改 Matplotlib 中的熱圖調色板
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
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.
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.
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.
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.
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
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:
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
Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu" >>> LastName = "Jordan" >>> FirstName, LastName = LastName, FirstName >>> print(FirstName, LastName) ('Jordan', 'kalebu')
#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development
Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.
In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.
Heres a solution
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
But How do we do it?
If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?
The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.
There’s a variety of hashing algorithms out there such as
#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips