Matplotlib was initially designed with only two-dimensional plotting in mind. Around the time of the 1.0 release, some three-dimensional plotting utilities were built on top of Matplotlib's two-dimensional display, and the result is a %matplotlib inline import numpy as np import matplotlib.pyplot as plt Another way to change the color of the plot is to make use of the color parameter. You can pass the name of the color or the hexadecimal value of the color to the color parameter. Take a look at the following example:matplotlib.pyplot.subplot(nrows, ncols, index, **kwargs)In arguments, we have three integers to specify, the number of plots in a row and in a column, then at which index the plot should be. You can consider it as a grid and we are drawing on its cells.""" Figure 22 """ # Plotting a scatter plot of 'AdjOpen' and 'AdjClose' of AAPL stock data.plot(x='AdjOpen', y='AdjClose', kind='scatter') plt.show() Interestingly, we only need to specify column names of a dataframe data for x and y coordinates along with the argument kind which gets resulted in the output as shown below:# Save the figure plt.savefig("bubbleplot.png", transparent=True, dpi=120) 12. How to draw Histograms, Boxplots and Time SeriesThe methods to draw different types of plots are present in pyplot (plt) as well as Axes. The below example shows basic examples of few of the commonly used plot types.

In the first method, we directly make a call to plot method on the dataframe data sliced with Volume column. Whereas in the second method, we use the hist method provided by matplotlib.pyplot module to plot the histogram. Both methods plot the same result as shown above. In the next section of the Python matplotlib tutorial, we are going to learn something interesting, how to customise your own plots.*import matplotlib*.pyplot as plt import numpy as np x = np.linspace(-10, 9, 20) y = x ** 3 plt.subplot(2,2,1) plt.plot(x, y, 'b*-') plt.subplot(2,2,2) plt.plot(x, y, 'y--') plt.subplot(2,2,3) plt.plot(x, y, 'b*-') plt.subplot(2,2,4) plt.plot(x, y, 'y--') The first attribute to the subplot function is the rows that the subplots will have and the second parameter species the number of columns for the subplot. A value of 2,2 species that there will be four graphs. The third argument is the position at which the graph will be displayed. The positions start from top-left. Plot with position 1 will be displayed at first row and first column. Similarly, plot with position 2 will be displayed in first row and second column.

我用matplotlib.pyplot画图，然后给图加标题的时候，怎么在标题里插入变量？ import matplotlib.pyplot as plt # Import Data df = pd.read_csv("https://github.com/selva86/datasets/raw/master/economics.csv") x = df['date']; y1 = df['psavert']; y2 = df['unemploy'] # Plot Line1 (Left Y Axis) fig, ax1 = plt.subplots(1,1,figsize=(16,7), dpi= 80) ax1.plot(x, y1, color='tab:red') # Plot Line2 (Right Y Axis) ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis ax2.plot(x, y2, color='tab:blue') # Just Decorations!! ------------------- # ax1 (left y axis) ax1.set_xlabel('Year', fontsize=20) ax1.set_ylabel('Personal Savings Rate', color='tab:red', fontsize=20) ax1.tick_params(axis='y', rotation=0, labelcolor='tab:red' ) # ax2 (right Y axis) ax2.set_ylabel("# Unemployed (1000's)", color='tab:blue', fontsize=20) ax2.tick_params(axis='y', labelcolor='tab:blue') ax2.set_title("Personal Savings Rate vs Unemployed: Plotting in Secondary Y Axis", fontsize=20) ax2.set_xticks(np.arange(0, len(x), 60)) ax2.set_xticklabels(x[::60], rotation=90, fontdict={'fontsize':10}) plt.show() This is just to give a hint of what’s possible with seaborn. Maybe I will write a separate post on it. However, the official seaborn page has good examples for you to start with. import matplotlib.pyplot as plt plt.plot([1,2,3,4]) plt.ylabel('some numbers') #为y轴加注释 plt.show(). import numpy as np import matplotlib.pyplot as plt #. evenly sampled time at 200ms intervals t = np.arange(0., 5., 0.2) # red dashes, blue squares and green triangles plt.plot(t, t, 'r--', t, t..

The ax1 and ax2 objects, like plt, has equivalent set_title, set_xlabel and set_ylabel functions. Infact, the plt.title() actually calls the current axes set_title() to do the job.""" Method 1 """ plt.scatter(x='AdjOpen', y='AdjClose', data=data) plt.show() """ Method 2 """ plt.scatter(x=data['AdjOpen'], y=data['AdjClose']) plt.show() The method one uses the argument data which specifies the data source, whereas the second method directly uses dataframe slicing and hence, there is no need to specify the data argument.

The syntax you’ve seen so far is the Object-oriented syntax, which I personally prefer and is more intuitive and pythonic to work with. import matplotlib.pyplot as plt. plt.title('Nuage de points avec Matplotlib') plt.xlabel('x') plt.ylabel('y') plt.savefig('ScatterPlot_07.png') plt.show(). Points with different size

- The range or limit of the x and y axis can be set by using the xlim() and ylim() functions of pyplot respectively.
- g that the path of Python is set in environment variables, you just need to use the pip command to install matplotlib package to get started.
- The goal of this tutorial is to make you understand ‘how plotting with matplotlib works’ and make you comfortable to build full-featured plots with matplotlib.
- # import external libraries import matplotlib import matplotlib.pyplot as plt import numpy as np # Sample Data for plotting t = np.arange(0.0, 2.0, 0.01) s = 1 + np.sin(2 * np.pi * t) # Note that using plt.subplots below is equivalent to using # fig = plt.figure() and then ax = fig.add_subplot(111) fig, ax..
- Here, the fmt parameters g refers to the green color, o refers to circle markers and - refers to a continuous line to be plotted. This formatting technique allows us to format a line plot in virtually any way we like. It is possible to change marker style by tweaking marker parameter in the fmt string as shown below:
- PyCharm's auto-
**import**(Command-Space-Space) feature is great, but unfortunately doesn't work for the most common**imports**of all! Probably the two lines I type most often in Python are**import**numpy as np and from matplotlib**import**pyplot as**plt**. The first time I use numpy in each file, I still have t - from matplotlib import pyplot as plt import numpy as np. xa = np.linspace(0, 12, 100) ya = np.sin(xa)*np.exp(-xa/4). plt.plot(xa, ya) plt.show(). Setting the line colour and style using a string

You can go through the following blog to understand how to install and import Python packages. Thus, you have understood how to import matplotlib. Before we start plotting graphs, let us first understand the key terms in the next section of the Python matplotlib tutorial. Since the exponent in $y=x^{3}$ is $3$, the power is bound to have negative values for negative values of $x$. Therefore, for visibility of negative values in the $y$-axis, we need to move the $x$-axis to the centre of the graph. ax.spines['bottom'] is thus positioned to centre.

- #importing the libraries import numpy as np import matplotlib.pyplot as plt#assign x, y values x = np.arange(1,101) y = np.random.random(100)#plot plt.scatter(x,y, marker = 'o', color = 'g') plt.title('Scatter plot') plt.xlabel('X') plt.ylabel('Y') plt.show
- However, sometimes you might want to construct the legend on your own. In that case, you need to pass the plot items you want to draw the legend for and the legend text as parameters to plt.legend() in the following format:
- To verify that Matplotlib is installed, try to invoke Matplotlib's version at the Python REPL. Use the commands below that include calling the .__version__ an attribute common to most Python packages
- """ Example 12 - Technique 2""" # Plot lists and show them plt.plot(y, 'go-', y2, 'b*--') # Plot axes labels and show the plot plt.xlabel('Index Values') plt.ylabel('Elements in Lists') plt.show() Essentially, the plot method makes it very easy to plot sequential data structure such as list, NumPy arrays, pandas series, etc. Similar to plotting lists, we can plot NumPy arrays directly via the plot method. Let us plot NumPy one dimensional array. As we are executing codes directly in IPython console, calling the plt.show() is not required and hence, we will not be calling the same in subsequent examples. However, remember, it is absolutely necessary to call it while writing Python code to show a plot.
- """ Example 6 """ # Defining coordinates to be plotted on X and Y axes respectively x = [1.3, 2.9, 3.1, 4.7, 5.6, 6.5, 7.4, 8.8, 9.2, 10] y = [95, 42, 69, 11, 49, 32, 74, 62, 25, 32] # Plot lists 'x' and 'y' plt.plot(x, y) # Plot axes labels and show the plot plt.xlabel('X-axis Label') plt.ylabel('Y-axis Label') plt.show() The above code plots values in the list x along the X-axis and values in the list y along the Y-axis. The output is shown below:

Suppose you want to draw a specific type of plot, say a scatterplot, the first thing you want to check out are the methods under plt (type plt and hit tab or type dir(plt) in python prompt).**Next, we plot the close_prices using the plot method and specify its color to be purple using the color argument**. Similar to the plot method, Python matplotlib provides bar method to draw bar plots which takes two arguments: the first argument to be plotted on the X-axis and second argument to be plotted along the y-axis. For our example, values on X-axis happens to be a date (specified by volume.index), and value for each bar on the Y-axis is provided using the recently created volume series. After that, we plot grids on both plots. Finally, we display both plots. As can be seen above, Python matplotlib rendered a decent chart. However, it misses some key components such as title, legends, etc.Now we can plot any column of a data dataframe by calling plot method on it. In the example given below, we plot the recent 100 data points from the Volume column of the dataframe:

Below is the Matplotlib code to plot the function $y=x^{2}$. It is a simple straight-forward code; the bulk of it in the middle is for setting the axes. As the exponent of $x$ is $2$, there will only be positive values of $y$, so we can position ax.spines['bottom'] at the bottom. plt.style.available # ['seaborn-dark', 'seaborn-darkgrid', 'seaborn-ticks', 'fivethirtyeight', # 'seaborn-whitegrid', 'classic', '_classic_test', 'fast', 'seaborn-talk', # 'seaborn-dark-palette', 'seaborn-bright', 'seaborn-pastel', 'grayscale', # 'seaborn-notebook', 'ggplot', 'seaborn-colorblind', 'seaborn-muted', # 'seaborn', 'Solarize_Light2', 'seaborn-paper', 'bmh', 'tableau-colorblind10', # 'seaborn-white', 'dark_background', 'seaborn-poster', 'seaborn-deep'] import matplotlib as mpl mpl.rcParams.update({'font.size': 18, 'font.family': 'STIXGeneral', 'mathtext.fontset': 'stix'}) def plot_sine_cosine_wave(style='ggplot'): plt.style.use(style) plt.figure(figsize=(7,4), dpi=80) X = np.linspace(0,2*np.pi,1000) plt.plot(X,np.sin(X)); plt.plot(X,np.cos(X)) plt.xticks(ticks=np.arange(0, 440/57.2985, 90/57.2985), labels = [r'$0$',r'$\frac{\pi}{2}$',r'$\pi$',r'$\frac{3\pi}{2}$',r'$2\pi$']) # 1 radian = 57.2985 degrees plt.gca().set(ylim=(-1.25, 1.25), xlim=(-.5, 7)) plt.title(style, fontsize=18) plt.show() plot_sine_cosine_wave('seaborn-notebook') plot_sine_cosine_wave('ggplot') plot_sine_cosine_wave('bmh') Visualize matplotlib marker styles. import matplotlib.pyplot as plt import numpy as np from matplotlib.lines import Line2D #. Define the plot plt.ylim(-1.0, 1.0) plt.title(Example for matplotlib markers, fontweight=bold, fontsize=20) plt.xlabel(rx-axis label, fontsize=20) plt.ylabel(r.. The below code will create the stacked histogram using Python's Matplotlib library. To plot, we have to pass the parameter stacked = True in the plt.hist () which informs Matplotlib library to perform the stacking task

- The %matplotlib inline is a jupyter notebook specific command that let’s you see the plots in the notbook itself.
- import matplotlib.pyplot as plt import numpy as np x = np.linspace(-10, 9, 20) y = x ** 3 figure = plt.figure() axes = figure.add_axes([0.2, 0.2, 0.8, 0.8]) axes.plot(x, y, 'b') axes.set_xlabel('X Axis') axes.set_ylabel('Y Axis') axes.set_title('Cube function') You can see that the output is similar to the one we got in the last section but this time we used the object-oriented approach.
- # Arrays which defines color and size of each data point color = np.random.rand(10) size = np.random.randint(50, 100, 10) Now that we have data points ready, we can plot a scatter plot out of them as below:
- # GridSpec Approach 2 import matplotlib.gridspec as gridspec fig = plt.figure() gs = gridspec.GridSpec(2, 2, height_ratios=[2,1], width_ratios=[1,2]) for g in gs: ax = fig.add_subplot(g) fig.tight_layout()
- import matplotlib.pyplot as plt import numpy as np x = np.linspace(-10, 9, 20) y = x ** 3 z = x ** 2 figure = plt.figure() axes = figure.add_axes([0,0,1,1]) axes.plot(x, z, color = "purple" ,label="Square Function") axes.plot(x, y, color = "#FF0000", label="Cube Function") axes.legend(loc=4) Output:
- """ Example 12 - Technique 1""" # Define two lists y = [95, 42, 69, 11, 49, 32, 74, 62, 25, 32] y2 = [35, 52, 96, 77, 36, 66, 50, 12, 35, 63] # Plot lists and show them plt.plot(y, 'go-') plt.plot(y2, 'b*--') # Plot axes labels and show the plot plt.xlabel('Index Values') plt.ylabel('Elements in Lists') plt.show()
- plt.figure(figsize=(10,7)) # 10 is width, 7 is height plt.plot([1,2,3,4,5], [1,2,3,4,10], 'go', label='GreenDots') # green dots plt.plot([1,2,3,4,5], [2,3,4,5,11], 'b*', label='Bluestars') # blue stars plt.title('A Simple Scatterplot') plt.xlabel('X') plt.ylabel('Y') plt.xlim(0, 6) plt.ylim(0, 12) plt.legend(loc='best') plt.show()

We can change the font size of a plot with the help of a function called rc(). The rc() function is used to customize the rc settings. To use rc() to change font size, use the syntax below: Explore and run machine learning code with Kaggle Notebooks | Using data from no data sources..

You can actually create more than one plots on one canvas using Matplotlib. To do so, you have to use the subplot function which specifies the location and the plot number. Take a look at the following example: import matplotlib.pyplot as plt import numpy as np # 100 linearly spaced numbers x = np.linspace(-5,5,100) # the function, which is y = x^2 here y = x**2 # setting the axes at the centre fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.spines['left'].set_position('center') ax.spines['bottom'].set_position('zero') ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') # plot the function plt.plot(x,y, 'r') # show the plot plt.show() Cubic Equation Next, we will plot the simplest cubic function $y=x^{3}$. *matplotlib*.pyplot.xlim([starting_point, ending_point]) *matplotlib*.pyplot.ylim([starting_point, ending_point])Consider the example below to set x axis limit for the plot:Moving forward, the same notation is followed by pandas DataFrame object and visualizing data within a dataframe becomes more intuitive and less quirky. Before we attempt to plot data directly from a dataframe, let us create a new dataframe and populate it. We fetch the stock data of AAPL ticker that we will be using for illustration purposes throughout the remaining part of the Python matplotlib tutorial.

*""" Example 2 """ fig = plt*.figure() # Creating subplot/axes ax = fig.add_subplot(111) # Setting axes/plot title ax.set_title('An Axes Title') # Setting X-axis and Y-axis limits ax.set_xlim([0.5, 4.5]) ax.set_ylim([-3, 7]) # Setting X-axis and Y-axis labels ax.set_ylabel('Y-Axis Label') ax.set_xlabel('X-Axis Label') # Showing the plot plt.show() Using all these customization techniques, we have been able to evolve the dull-looking price series chart to a nice and attractive graphic which is not only easy to understand but presentable also. However, we have restricted ourselves to plotting only a single chart. Let us brace ourselves and learn to apply these newly acquired customization techniques to multiple plots.ax1.set(title='Scatterplot Greendots', xlabel='X', ylabel='Y', xlim=(0,6), ylim=(0,12)) ax2.set(title='Scatterplot Bluestars', xlabel='X', ylabel='Y', xlim=(0,6), ylim=(0,12)) 5. Object Oriented Syntax vs Matlab like SyntaxA known ‘problem’ with learning matplotlib is, it has two coding interfaces:

- The axes object that was returned here would be a 2D-NumPy array, and each item in the array is one of the subplots. Therefore, when we want to work with one of these axes, we can index it and use that item's methods. Let us add the title to each subplot using the axes methods.
- We first import matplotlib.pyplot as plt. This allows us to reference the plot as plt. We creates an axes for the plot with the line, axes= plt.axes(). we then set the x-axis range using the axes.set_xlim() function
- First, we extract the AdjVolume column from the data dataframe into a volume which happens to be pandas series object. Then, we create a figure with sub-plots having two rows and a single column. This is achieved using nrows and ncols arguments respectively. The sharex argument specifies that both sub-plots will share the same x-axis. Likewise, we also specify the figure size using the figsize argument. These two subplots are unpacked into two axes: ax1 and ax2 respectively. Once, we have the axes, desired charts can be plotted on them.
- import matplotlib.pyplot as plt xpoints = [0.2, 0.4, 0.6] for p in xpoints: plt.axvline(p, label='pyplot vertical line') plt.legend() plt.show()The output will be:
- import matplotlib.pyplot as plt import numpy as np x = np.linspace(-10, 9, 20) y = x ** 3 z = x ** 2 fig, axes = plt.subplots(nrows=2, ncols=3) In the output you will see 6 plots in 2 rows and 3 columns as shown below:

Similarly, Python matplotlib provides yticks method that can be used to customize the values on the Y-axis. Apart from the rotation argument, there are a bunch of other parameters that can be provided xticks and yticks to customize them further. We change the font size, color and orientation of ticks along the axes using the appropriate arguments within these methods in the following example:So far in this Python matplotlib tutorial, we have discussed a lot about laying things out, but we haven't really discussed anything about plotting data yet. Python Matplotlib has various plotting functions. Many more than we will discuss and cover here. However, a full list or gallery1 can be a bit overwhelming at first. Hence, we will condense it down and attempt to start with simpler plotting and then move towards more complex plotting. The plot method of pyplot is one of the most widely used methods in Python Matplotlib to plot the data. The syntax to call the plot method is shown below:We can use the streamplot() function to plot the streamlines of a vector. We can also map the colors and width of the different parameters such as speed time etc.

Pyplot is a Matplotlib module which provides a MATLAB-like interface.[10] Matplotlib is designed to be as usable as MATLAB, with the ability to use SageMath - uses Matplotlib to draw plots. SciPy (modules plt and gplt). wxPython (module wx.lib.plot.py). Plotly - for interactive, online Matplotlib and.. Stack plot is an extension of bar chart or line chart which breaks down data from different categories and stack them together so that comparison between the values from different categories can easily be made.

import numpy as np import matplotlib.pyplot as plt #创建figure（）对象； fig=plt.figure(1) #figure对象的add_axes()可以在其中创建一个axes对象， #add_axes()参数为[left,bottom,width,height]的列表，取值范围在0与1之间； ax= The matplotlib.pyplot.subplots method provides a way to plot multiple plots on a single figure. Given the number of rows and columns, it returns a tuple (fig, ax), giving a single figure fig with an import matplotlib.pyplot as plt import numpy as np. fig, ax = plt.subplots(2, 2) x = np.linspace(0, 8, 1000)

from matplotlib import pyplot as plt plt.subplot(1, 2, 1) x1 = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] y1 = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50] plt.plot(x1, y1, color = "c") plt.subplot(1, 2, 2) x2 = [40, 50, 60, 70, 80, 90, 100] y2 = [40, 50, 60, 70, 80, 90, 100] plt.plot(x2, y2, color = "m") plt.show()The first thing is to define the location of the plot. In the first subplot, 1, 2, 1 states that we have 1 row, 2 columns and the current plot is going to be plotted at index 1. Similarly, 1, 2, 2 tells that we have 1 row, 2 columns but this time the plot at index 2.# Texts, Arrows and Annotations Example plt.figure(figsize=(14,7), dpi=80) X = np.linspace(0, 8*np.pi, 1000) # Text Relative to DATA plt.text(0.50, 0.02, "Text relative to the DATA centered at : (0.50, 0.02)", transform=plt.gca().transData, fontsize=14, ha='center', color='blue') # Text Relative to AXES plt.text(0.50, 0.02, "Text relative to the AXES centered at : (0.50, 0.02)", transform=plt.gca().transAxes, fontsize=14, ha='center', color='blue') # Text Relative to FIGURE plt.text(0.50, 0.02, "Text relative to the FIGURE centered at : (0.50, 0.02)", transform=plt.gcf().transFigure, fontsize=14, ha='center', color='blue') plt.gca().set(ylim=(-2.0, 2.0), xlim=(0, 2)) plt.title('Placing Texts Relative to Data, Axes and Figure', fontsize=18) plt.show() **Alright, notice instead of the intended scatter plot, plt**.plot drew a line plot. That’s because of the default behaviour.

plt.style.available Let us set the style to one of the pre-defined style known as 'fivethirtyeight' and plot the chart.A pie type is a circular chart where different categories are marked as part of the circle. The larger the share of the category, larger will be the portion that it will occupy on the chart.Let’s begin by making a simple but full-featured scatterplot and take it from there. Let’s see what plt.plot() creates if you an arbitrary sequence of numbers.The first thing we will do is change the default plot size. By default, the size of the Matplotlib plots is 6 x 4 inches. The default size of the plots can be checked using this command:

import matplotlib.pyplot as plt. Are the above statements equivalent? Which is more readable/better form? import matplotlib.pyplot as plt gives an unfamiliar reader a hint that pyplot is a module, rather than a function which could be incorrectly assumed from the first form fig, ax = plt.subplots() Both versions of code produce the same output. However, the latter version is cleaner.

That means, the plt keeps track of what the current axes is. So whatever you draw with plt.{anything} will reflect only on the current subplot.*In the above code, we created a dictionary dictionary with four key-value pairs*. Values in key a and b contain fifty random values to be plotted on a scatter plot. Key c contains fifty random integers and key d contains fifty positive floats which represent color and size respectively for each scatter data point. Then, a call to plt.scatter is made along with all keys and the dictionary as the value to data. The argument c within the call refers to color to be used and the argument s represents the size of a data point. These arguments c and s are optional. A simple scatter plot with the same color and size gets plotted when we omit these optional arguments as shown in the following example:

import numpy as np import matplotlib.pyplot as plt. 図全体がFigureでグラフがAxesになる。matplotlib.pyplotクラスのメソッドはコマンドのように扱える Few commonly used short hand format examples are: * 'r*--' : ‘red stars with dashed lines’ * 'ks.' : ‘black squares with dotted line’ (‘k’ stands for black) * 'bD-.' : ‘blue diamonds with dash-dot line’.mpl.rcParams.update({'font.size': 18, 'font.family': 'STIXGeneral', 'mathtext.fontset': 'stix'}) After modifying a plot, you can rollback the rcParams to default setting using:With a dataframe, plot method is a convenience to plot all of the columns with labels. In other words, if we plot multiple columns, it would plot labels of each column as well.

""" Example 8 """ # Plot line with green color plt.plot(y, 'g') # Plot axes labels and show the plot plt.xlabel('Index Values') plt.ylabel('Elements in List Y') plt.show() In the above example, we call the plot method directly on pandas Series object ts. Alternatively, we could have called plt.plot(ts). Calling ts.plot() is equivalent to calling plt.plot(ts) and both calls would result in almost the same output as shown above. Additionally, the plot() method on pandas object supports almost every attribute that plt.plot() supports for formatting. For example, calling the plot method on pandas objects with a color attribute would result in a plot with color mentioned by its value. This is shown below:You can do that by creating two separate subplots, aka, axes using plt.subplots(1, 2). This creates and returns two objects: * the figure * the axes (subplots) inside the figureTo create a pie chart in Matplot lib, the pie class is used. The first parameter to the class constructor is the list of numbers for each category. Comma-separated list of categories is passed as the argument to the labels attribute. List of colors for each category is passed to the colors attribute. If set to true, shadow attribute creates shadows around different categories on the pie chart. Finally, the explode attribute breaks the pie chart into individual parts.

In plt.subplot(1,2,1), the first two values, that is (1,2) specifies the number of rows (1) and columns (2) and the third parameter (1) specifies the position of current subplot. The subsequent plt functions, will always draw on this current subplot.Matplotlib is a library in Python that creates 2D graphs to visualize data. Visualization always helps in better analysis of data and enhance the decision-making abilities of the user. In this matplotlib tutorial, we will plot some graphs and change some properties like fonts, labels, ranges, etc, import matplotlib.pyplot as plt import numpy as np # 100 linearly spaced numbers x = np.linspace(-5,5,100) # the function, which is y = x^3 here y = x**3 # setting the axes at the centre fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.spines['left'].set_position('center') ax.spines['bottom'].set_position('center') ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') # plot the function plt.plot(x,y, 'g') # show the plot plt.show() Trigonometric Functions Here we plot the trigonometric function $y=\text{sin}(x)$ for the values of $x$ between $-\pi$ and $\pi$. The linspace() method has its interval set from $-\pi$ to $\pi$. Below is the Matplotlib code to plot the function $y=x^{2}$. It is a simple straight-forward code; the bulk of it in the middle is for setting the axes. As the exponent of $x$ is $2$, there will only be positive values of $y$, so we can position ax.spines['bottom'] at the bottom. import matplotlib.pyplot as plt

- In this tutorial, we will learn how to draw vector graphics in PyQt5. In Python, the PyQt5 module is the easiest module you can use for drawing purposes as you will see. Before getting started, let’s create a window using PyQt5 where we will be drawing our graphics. Create a PyQt5 window To create […]
- Adding legends to a plot is very straightforward using Matplotlib library. All you have to do is to pass the value for the label parameter of the plot function. Then after calling the plot function, you just need to call the legend function. Take a look at the following example:
- """ Example 7 """ # Defining 'y' coordinates y = [95, 42, 69, 11, 49, 32, 74, 62, 25, 32] # Plot list 'y' plt.plot(y) # Plot axes labels and show the plot plt.xlabel('Index Values') plt.ylabel('Elements in List Y') plt.show() Here, we define a list called y that contains values to be plotted on Y-axis. The output is shown below:
- Note that you import the pyplot module of the matplotlib library under the alias plt. Secondly, pyplot is a module in the matplotlib package. That's why you often see matplotlib.pyplot in code. The module provides an interface that allows you to implicitly and automatically create figures and axes to..
- A figure can have more than one Axes on it. In Python matplotlib, the easiest way is to use plt.subplots() call to create a figure and add the axes to it automatically. Axes will be on a regular grid system. For example,
- import matplotlib.pyplot as plt. (I was using matplotlib to graph the training history of the model. import matplotlib.pyplot as plt # crash!!! This is a known issue due to some library conflicts in the installation, which should hopefully be fixed in a future release
- In this example, 2,2,1 means 2 rows, 2 columns, and the plot will be at index 1. Similarly, 2,2,2 means 2 rows, 2 columns, and the plot will be at index 2 of the grid.

- The plot method generates a line plot by default when called on pandas data structures. However, it can also produce a variety of other charts as we will see later in this Python matplotlib tutorial. Having said that, let us head forward to plot scatter plots.
- import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates. days, impressions = np.loadtxt(page-impressions.csv, unpack=True, converters={ 0: mdates.strpdate2num('%Y-%m-%d')})
- plt.text and plt.annotate adds the texts and annotations respectively. If you have to plot multiple texts you need to call plt.text() as many times typically in a for-loop.
- import matplotlib.pyplot as plt plt.plot([4,8,13,17,20],[54, 67, 98, 78, 45]) plt.show(). Notice that we represented the x and y points as lists. In this case, the result will be as follows: The line in the figure above is the default line that gets drawn for us, in terms of shape and color

fig, ax = plt.subplots(figsize = (9,4)) ax = sns.distplot(data['Sales'], kde=True, rug=True, bins = 10). Scatter plot. import seaborn as sns import pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-plotting - справочник по графикам в pandas с использованием Matplotlib https.. fig = plt.figure() <Figure size 432x288 with 0 Axes> Upon running the above example, nothing happens really. It only creates a figure of size 432 x 288 with 0 Axes. Also, Python Matplotlib will not show anything until told to do so. Python will wait for a call to show method to display the plot. This is because we might want to add some extra features to the plot before displaying it, such as title and label customization. Hence, we need to call plt.show() method to show the figure as shown below:

# GridSpec Approach 1 import matplotlib.gridspec as gridspec fig = plt.figure() grid = plt.GridSpec(2, 3) # 2 rows 3 cols plt.subplot(grid[0, :2]) # top left plt.subplot(grid[0, 2]) # top right plt.subplot(grid[1, :1]) # bottom left plt.subplot(grid[1, 1:]) # bottom right fig.tight_layout() plt.axvline(x=0, ymin=0, ymax=1, **kwargs)In this syntax: x is the coordinate for x axis. This point is from where the line would be generated vertically. ymin is the bottom of the plot, ymax is the top of the plot. **kwargs are the properties of the line such as color, label, line style, etc.The output we got shows that the data distribution indeed resembles a normal distribution. Apart from bins argument, other arguments that can be provided to hist are color and histtype. There are lot many arguments that can be provided, but we will keep our discussion limited to these few arguments only. The color of a histogram can be changed using the color argument. The histtype argument takes some of the pre-defined values such as bar, barstacked, step and stepfilled. The below example illustrates the usage of these arguments.By changing the style, we get a fair idea about how styles play an important role to change the look of charts cosmetically while plotting them.

# Scatterplot with varying size and color of points import pandas as pd midwest = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv") # Plot fig = plt.figure(figsize=(14, 7), dpi= 80, facecolor='w', edgecolor='k') plt.scatter('area', 'poptotal', data=midwest, s='dot_size', c='popdensity', cmap='Reds', edgecolors='black', linewidths=.5) plt.title("Bubble Plot of PopTotal vs Area\n(color: 'popdensity' & size: 'dot_size' - both are numeric columns in midwest)", fontsize=16) plt.xlabel('Area', fontsize=18) plt.ylabel('Poptotal', fontsize=18) plt.colorbar() plt.show() import matplotlib.pyplot as plt import matplotlib.patches as patches import mpl_toolkits.mplot3d.art3d as art3d fig = plt . figure() ax 11/26/2018 Matplotlib — Programming for Data Analysis 7/22 plt . show() ( Source code , png , hires.png , pdf ) You can also stack graphs you..

Matplotlib ist eine Programmbibliothek für die Programmiersprache Python, die es erlaubt mathematische Darstellungen aller Art anzufertigen. Matplotlib kann mit Python 2.x und 3.x verwendet werden und funktioniert auf allen gängigen Betriebssystemen label one of the line properties. legend() is the MATLAB function which enables label on the plot. Finally, show() will open the plot or graph screen.""" Figure 28 """ # Method 1 # Creating a histogram using a dataframe method data['Volume'].plot(kind='hist') plt.show() # Method 2 plt.hist(data['Volume']) plt.ylabel('Frequency') plt.show() import matplotlib.pyplot as plt ypoints = [0.2, 0.4, 0.6, 0.68] colors = ['b', 'k', 'y', 'm'] for p, c in zip(ypoints, colors): plt.axhline(p, label='line: {}'.format(p), c=c) plt.legend() plt.show()The code is the same, we have an array of four points of y axis and different colors this time. Both arrays are merged together with zip() function, iterated through the final array and axhline() plots the lines as shown in the output below:

* In matplotlib*.pyplot various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting Of course, each figure can contain as many axes and subplots as your heart desires: import matplotlib.pyplot as plt plt.figure(1) # the first figure.. The lower left corner of the axes has (x,y) = (0,0) and the top right corner will correspond to (1,1).Both plt.subplot2grid and plt.GridSpec lets you draw complex layouts. Below is a nice plt.subplot2grid example.

For example, the format 'go-' has 3 characters standing for: ‘green colored dots with solid line’. By omitting the line part (‘-‘) in the end, you will be left with only green dots (‘go’), which makes it draw a scatterplot.from matplotlib import pyplot as plt x1 = [40, 50, 60, 70, 80, 90, 100] y1 = [40, 50, 60, 70, 80, 90, 100] plt.plot(x1, y1) plt.xlabel('Like Geeks X Axis') plt.ylabel('Like Geeks Y Axis') plt.clf() plt.show()In this code, we created a plot and defined labels as well. After that we have used the clf() function to clear the plot as follows:

- mpl.rcParams.update(mpl.rcParamsDefault) # reset to defaults Matplotlib comes with pre-built styles which you can look by typing:
- """ Figure 26 """ # Creating an array array = np.random.normal(0, 1, 10000) # Creating a histogram and plotting it plt.hist(array, color='purple', histtype='step') plt.xlabel('Bins') plt.ylabel('Frequency') plt.show()
- %matplotlib inline %config InlineBackend.figure_format='retina' import numpy as np from matplotlib import pyplot as plt import matplotlib. When preparing plots for a paper, I collected some customizations to the matplotlib defaults to improve their appearance

So, what you can do instead is to use a higher level package like seaborn, and use one of its prebuilt functions to draw the plot. import numpy as np import matplotlib.pyplot as plt. Keywords: python, matplotlib, pylab, example, codex (see Search examples) **Following the fmt string notation, we changed the color of a line to green using the character g which refers to the line color**. Likewise, markers are added using the same notation as shown below:

- import numpy as np import matplotlib.pyplot as plt. x = np.linspace(0.0,100,50) y = np.random.uniform(low=0,high=10,size=50) #. passing 2,2 as parameters indicates that you will # get 4 subplots (2 rows and 2 columns) fig, axes = plt.subplots(2,2) #
- from matplotlib import pyplot as plt x1 = [40, 50, 60, 70, 80, 90, 100] y1 = [40, 50, 60, 70, 80, 90, 100] plt.plot(x1, y1) plt.xlabel('Like Geeks X Axis') plt.ylabel('Like Geeks Y Axis') plt.show()In the above example, we have regular x and y arrays for x and y coordinates respectively. Then plt.xlabel() generates a text for x axis and plt.ylabel() generates a text for y axis.
- Matplotlib是基于python的开源科学测绘包，基于python软件基金会许可证发布。 大量的文档和例子、集成了Python和Numpy科学计算包、以及自动化能力，是作为Linux环境中进行科学画图的可靠选择的几个原因
- This is how vertical subplots are drawn. To plot horizontal graphs, change the subplot rows and columns values as:
- In addition to changing the default size of the graph, you can also change the figure size for specific graphs. To do so, you need to pass a value for the figsize parameter of the subplots function. The value for the figsize parameter should be passed in the form of a tuple where the first value corresponds to the width while the second value corresponds to the hight of the graph. Look at the following example to see how to change the size of a specific plot:

**Congratulations if you reached this far**. Because we literally started from scratch and covered the essential topics to making matplotlib plots.# Data points for scatter plot x = [1.3, 2.9, 3.1, 4.7, 5.6, 6.5, 7.4, 8.8, 9.2, 10] y = [95, 42, 69, 11, 49, 32, 74, 62, 25, 32] In addition to these lists, we would be defining two more NumPy arrays color and size which determines the color and size respectively of each data point while plotting the scatter plot.You can get a reference to the current (subplot) axes with plt.gca() and the current figure with plt.gcf(). Likewise, plt.cla() and plt.clf() will clear the current axes and figure respectively.

Visualizing data trends is one of the most important tasks in data science and machine learning. The choice of data mining and machine learning algorithms depends heavily on the patterns identified in the dataset during data visualization phase. In this article, we will see how we can perform different types of data visualizations in Python. We will use Python's Matplotlib library which is the de facto standard for data visualization in Python.You can generate multiple plots in the same figure with the help of the subplot() function of Python pyplot.import matplotlib.pyplot as plt xpoints = [0.2, 0.4, 0.6] colors = ['g', 'c', 'm'] for p, c in zip(xpoints, colors): plt.axvline(p, label='line: {}'.format(p), c=c) plt.legend() plt.show()In this example, we have an array of lines and an array of Python color symbols. Using the zip() function, both arrays are merged together: the first element of xpoints[] with the first element of the color[] array. This way, first line = green, second line = cyan, etc.However, since the original purpose of matplotlib was to recreate the plotting facilities of matlab in python, the matlab-like-syntax is retained and still works.

- """ Figure 25 """ # Creating an array array = np.random.normal(0, 1, 10000) # Creating a histogram plt.hist(array) plt.xlabel('Bins') plt.ylabel('Frequency') plt.show()
- HMS Core is the platform for the basic open capabilities in Huawei Mobile Services, and HMS Core 4.0, which was released on January 15, 2020, r...
- plt.savefig(fname, **kwargs)Where fname is the name of the file. The destination or path can also be specified along with the name of the file. The kwargs parameter is optional. It’s used to change the orientation, format, facecolor, quality, dpi, etc.
- Alright, compare the above code with the object oriented (OO) version. The OO version might look a but confusing because it has a mix of both ax1 and plt commands.
- import matplotlib.pyplot as plt

To reiterate, the Python matplotlib tutorial is an excerpt from the Python Basics Handbook, which was created for both; beginners who are starting out in Python as well as accomplished traders who can use it as a handy reference while coding their strategy.In Python matplotlib, this is the simplest code possible to plot a histogram with minimal arguments. We create a range of values and simply provide it to the hist method and let it perform the rest of the things (creating bins, segregating each value to the corresponding bin, plotting, etc.). The hist method also takes bins as an optional argument. If this argument is specified, bins will be created as per the specified value, otherwise, it will create bins on its own. To illustrate this, we explicitly specify the number of bins in the above code and generate the plot. The modified code and output is shown below:** import matplotlib**.pyplot as plt. Plotting Matplotlib Pie Chart. In the matplotlib plt.pie chart blog, we learn how to plot one and multiple pie charts with a real-time example using the plt.pie() method Matplotlib allows to make scatter plots with python using the plot function. This page aims to provide a few elements of customization. For every example, we need a few libraries and to create a datase from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np

import matplotlib.pyplot as plt %matplotlib inline matplotlib.pyplot is usually imported as plt. It is the core object that contains the methods to create all sorts of charts and features in a plot.Learn the bleeding edge of AI in the most practical way: By getting hands-on with Python, TensorFlow, Keras, and OpenCV. Go a little deeper...

Thus, in this Python matplotlib tutorial, we started with the basics of figure and plots, gradually learning various types of charts and their nitty-gritty along the way, and finally, we learned customization and took a sneak-peek into plotting multiple plots within the same chart.So, how to recreate the above multi-subplots figure (or any other figure for that matter) using matlab-like syntax? **import** **matplotlib.pyplot** **as** **plt** %matplotlib inline #. Plot **plt**.plot([1,2,3,4,10]) #> [<matplotlib.lines.Line2D at 0x10edbab70>]. I just gave a list of numbers to **plt**.plot() and it drew a line chart automatically. It assumed the values of the X-axis to start from zero going up to as many items..

If you want to get more practice, try taking up couple of plots listed in the top 50 plots starting with correlation plots and try recreating it.First, we will install matplotlib, then we will start plotting some basics graphs. Before that, let’s see some of the graphs that matplotlib can draw.This second axes will have the Y-axis on the right activated and shares the same x-axis as the original ax. Then, whatever you draw using this second axes will be referenced to the secondary y-axis. The remaining job is to just color the axis and tick labels to match the color of the lines.Axes is where the plotting occurs. The axes are effectively the area that we plot data on. Each Axes has an X-Axis and a Y-Axis.

""" Figure 36 """ # Importing DateTime from DateTime library from datetime import datetime # Plotting text on date 2014-1-7 and price 120 plt.text(datetime(2014,1,7), 120, 'Mean Price', size=15, color='r') The text method takes three compulsory arguments: x, y and t which specifies the coordinates on X and Y-axis and text respectively. Also, we use a datetime sub-module from a datetime library to specify a date on the X-axis as the plot we are generating has dates on the X-axis. The chart with text indicating the mean price is shown below:""" Figure 15 """ # Importing necessary libraries import pandas as pd import numpy as np # Creating pandas Series with 50 samples drawn from normal distribution ts = pd.Series(np.random.normal(size=50), index=pd.date_range(start='1/1/2019', periods=50)) # Plotting pandas Series ts.plot() import matplotlib.pyplot as plt. pltに対してグラフの情報を登録して描画する。 座標軸のラベル等の設定方法は、どのグラフでも共通

To better understand the working of scatter plots, let us resort to our old friends: lists x and y that we defined earlier while learning line plots and build scatter plots on them. To refresh our memory, we re-define the same lists below:Now that we have got a good understanding of plotting various types of charts and their basic formatting techniques, we can delve deeper and look at some more formatting techniques. We already learned that Python matplotlib does not add any styling components on its own. It will plot a simple plain chart by default. We as a user need to specify whatever customization we need. We start with a simple line plot and will keep on making it better. The following example shows plotting of close prices of the AAPL ticker that is available with us in the dataframe data.plt.plot([1,2,3,4,5], [1,2,3,4,10], 'go', label='GreenDots') plt.plot([1,2,3,4,5], [2,3,4,5,11], 'b*', label='Bluestars') plt.title('A Simple Scatterplot') plt.xlabel('X') plt.ylabel('Y') plt.legend(loc='best') # legend text comes from the plot's label parameter. plt.show() import matplotlib.pyplot as plt. The alias plt is commonly used for matplotlib's pyplot library and will look familiar to other programmers. In our first example, we will also import numpy with the line import numpy as np

Matplotlib forms basis for visualization in Python import matplotlib.pyplot as plt #. We will use the Seaborn library import seaborn as sns sns.set() #. Using the kind parameter, you can change the type of the plot to, for example, a bar chart. matplotlib is generally quite flexible for customizing plots import matplotlib.pyplot as plt import numpy as np # 100 linearly spaced numbers x = np.linspace(-np.pi,np.pi,100) # the function, which is y = sin(x) here y = np.sin(x) # setting the axes at the centre fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.spines['left'].set_position('center') ax.spines['bottom'].set_position('center') ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') # plot the function plt.plot(x,y, 'b') # show the plot plt.show() Let us plot it together with two more functions, $y=2\text{sin}(x)$ and $y=3\text{sin}(x)$. This time, we label the functions.

plt.subplot(2, 1, 1) plt.subplot(2, 1, 2)This means we have 2 rows and 1 column. The output will be like this:plt.savefig('AAPL_chart.png') Executing the above code will save the chart we plotted above with the name AAPL_chart.png. I cannot import matplotlib.pyplot. This is what I did on Windows to make pyplot work. pip uninstall matplotlib python -m pip install --upgrade pip pip install matplotlib # Plot inside a plot plt.style.use('seaborn-whitegrid') fig, ax = plt.subplots(figsize=(10,6)) x = np.linspace(-0.50, 1., 1000) # Outer Plot ax.plot(x, x**2) ax.plot(x, np.sin(x)) ax.set(xlim=(-0.5, 1.0), ylim=(-0.5,1.2)) fig.tight_layout() # Inner Plot inner_ax = fig.add_axes([0.2, 0.55, 0.35, 0.35]) # x, y, width, height inner_ax.plot(x, x**2) inner_ax.plot(x, np.sin(x)) inner_ax.set(title='Zoom In', xlim=(-.2, .2), ylim=(-.01, .02), yticks = [-0.01, 0, 0.01, 0.02], xticks=[-0.1,0,.1]) ax.set_title("Plot inside a Plot", fontsize=20) plt.show() mpl.rcParams.update(mpl.rcParamsDefault) # reset to defaults Interestingly, almost all methods of axes objects in Python Matplotlib exist as a method in the pyplot module. For example, we can call plt.xlable('X-Axis Label') to set the label of X-axis (plt being an alias for pyplot), which in turn calls ax.set_xlabel('X-Axis Label') on whichever axes is current.If you want to see more data analysis oriented examples of a particular plot type, say histogram or time series, the top 50 master plots for data analysis will give you concrete examples of presentation ready plots. This is a very useful tool to have, not only to construct nice looking plots but to draw ideas to what type of plot you want to make for your data.