Posts (page 15)
-
6 min readIf you want to plot outside of a matplotlib plot, you can achieve this by using the plt.axes() function to create an additional set of axes within the plot. By specifying the position and size of these axes, you can plot data outside of the original plot area. This allows you to overlay multiple plots or create annotations outside of the main plot.To plot outside of the main plot area, you can also use the plt.figure() function to create a new figure with a separate plot window.
-
3 min readTo resave an image without borders in matplotlib, you can use the imwrite() function from the matplotlib library. This function allows you to save the image without any padding or borders that may have been included in the original image. Simply pass the desired file path and image as arguments to the imwrite() function, and the image will be saved without any borders. This can be particularly useful when you want to manipulate or display the image without any unwanted whitespace around it.
-
7 min readTo compare stocks using tools and platforms, you can start by selecting a reliable financial website or app that provides stock comparison features. Look for tools that allow you to input multiple stock symbols and analyze their performance side by side.Once you have chosen a platform, enter the stock symbols you want to compare and review the data provided. You can compare various metrics such as stock prices, market capitalization, earnings per share, and dividend yield.
-
5 min readTo remove a specific tick on an axis in matplotlib, you can use the set_ticks method and pass in a list of the desired ticks you want to keep on the axis. This will effectively remove the specific tick you want to get rid of. Alternatively, you can use the set_xticks or set_yticks methods to set the ticks for the x-axis or y-axis respectively, excluding the specific tick you wish to remove.
-
6 min readWhen comparing stock prices and market trends, it is important to consider both quantitative and qualitative factors. Quantitatively, you can compare the current stock prices of different companies within the same industry to see which ones are performing better or worse. You can also analyze historical stock prices to see if there are any patterns or trends that can help you make more informed decisions.
-
4 min readTo plot two lists of tuples with Matplotlib, you can first unpack the tuples into two separate lists of x and y coordinates. Then, you can use Matplotlib's plt.plot() function to plot the points on a graph. Make sure to import matplotlib.pyplot as plt at the beginning of your code. You can also customize the appearance of the plot by adding labels, titles, and formatting options. Finally, call plt.show() to display the plot.
-
6 min readWhen comparing the volatility of different stocks, there are several factors to consider. One common metric used to measure volatility is the standard deviation of a stock's returns over a certain period of time. A higher standard deviation indicates higher volatility.Another measure of volatility is beta, which compares the stock's price movements to the overall market.
-
3 min readIn matplotlib, you can set the color range by using the vmin and vmax parameters in the imshow function. These parameters allow you to specify the minimum and maximum values for the color map. By setting the vmin and vmax values, you can control the color range and ensure that the colors in your plot accurately reflect the data values. This can be particularly useful when working with datasets that have a wide range of values and you want to emphasize certain parts of the data.
-
8 min readWhen comparing stock dividends and yields, it is important to understand the differences between the two metrics.Dividends represent the portion of a company's profits that are distributed to shareholders. Dividends are typically paid on a regular basis, such as quarterly or annually. Dividends are expressed in terms of a dollar amount per share, and can be compared across different stocks to see which companies offer higher dividend payments.
-
7 min readTo create a custom gradient with matplotlib, you can use the LinearSegmentedColormap class from the matplotlib.colors module. This class allows you to define a colormap with multiple colors and their corresponding positions along the gradient.First, you need to define a list of colors that you want to include in your custom gradient. Each color should be specified as a tuple of RGBA values.
-
4 min readTo annotate a vertical line in matplotlib, you can use the plt.axvline() function. This function takes the x-coordinate where you want the vertical line to be placed as one of its arguments. You can also provide additional arguments such as the line style, color, and label for the annotation. After adding the vertical line to your plot, you can use the plt.annotate() function to add text or other annotations at a specific position relative to the line.
-
8 min readComparing stocks using financial ratios involves evaluating key metrics such as price-to-earnings (P/E) ratio, price-to-book (P/B) ratio, return on equity (ROE), earnings per share (EPS), and many more. These ratios provide insights into a company's financial health, profitability, and valuation.To compare stocks effectively, investors should analyze multiple ratios to gain a comprehensive understanding of the company's performance relative to its peers and industry benchmarks.