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We can create a dict for color and a value. If the same value comes up, we can use a scatter method and if the closer values have the same set of colors, that could make the plot color denser.StepsCreate a new figure, or activate an existing figure.Add an ~.axes.Axes to the figure as part of a subplot arrangement.Get the x and y values using np.random.normal() method. Draw random samples from a normal (Gaussian) distribution.Make a color list with red and blue colors.To make it denser, we can store the same color with the same value.Plot scatter point, a scatter ... Read More
In this program, we will find the standard deviation of a Pandas series. Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance.AlgorithmStep 1: Define a Pandas series Step 2: Calculate the standard deviation of the series using the std() function in the pandas library. Step 3: Print the standard deviation.Example Codeimport pandas as pd series = pd.Series([10,20,30,40,50]) print("Series: ", series) series_std = series.std() print("Standard Deviation of the series: ",series.std())OutputSeries: 0 10 1 20 2 30 3 40 4 50 dtype: int64 Standard Deviation of the series: 15.811388300841896
mean() function in the Pandas library can be used to find the mean of a series.AlgorithmStep 1: Define a Pandas series. Step 2: Use the mean() function to calculate the mean. Step 3: Print the mean.Example Codeimport pandas as pd series = pd.Series([10,20,30,40,50]) print("Pandas Series: ", series) series_mean = series.mean() print("Mean of the Pandas series:", series_mean)OutputPandas Series: 0 10 1 20 2 30 3 40 4 50 dtype: int64 Mean of the Pandas series: 30.0
To create horizontal lines for each bar in a bar plot of base R, we can use abline function and pass the same values as in the original barplot with h argument that represents horizontal with different color to make the plot a little better in terms of visualization.Example Live Demox
Using Pandas, we can create a dataframe with time and speed, and thereafter, we can use the data frame to get the desired plot.StepsConstruct a new Generator with the default BitGenerator (PCG64).Using Pandas, get a fixed frequency DatetimeIndex. From '2020-01-01' to '2021-01-01'.Draw samples from a log-normal distribution.Make a data frame with above data.Using panda dataframe create plot, with figsize = (10, 5).To show the figure, use the plt.show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt rng = np.random.default_rng(seed=1) date_day = pd.date_range(start='2020-01-01', end='2021-01-01', freq='D') traffic = rng.lognormal(sigma=2, size=date_day.size) df_day = pd.DataFrame(dict(speed=[pow(2, -i) for ... Read More
In plt.hist() method, stacked=True could help to get the heights of the bars sum to 1.StepsCreate a list of numbers.Using plt.hist(), we can draw the histogram.stacked : bool, default: FalseIf "True", multiple data are stacked on top of each other If ``False`` multiple data are arranged side by side if histtype is 'bar' or on top of each other if histtype is 'step'.density : bool, default: FalseIf "True", draw and return a probability density: each bin will display the bin's raw count divided by the total number of counts *and the bin width*.To show the figure, use the plt.show() method.Examplefrom ... Read More
In this program, we will append elements to a Pandas series. We will use the append() function for this task. Please note that we can only append a series or list/tuple of series to the existing series.AlgorithmStep1: Define a Pandas series, s1. Step 2: Define another series, s2. Step 3: Append s2 to s1. Step 4: Print the final appended series.Example Codeimport pandas as pd s1 = pd.Series([10, 20, 30, 40, 50]) s2 = pd.Series([11, 22, 33, 44, 55]) print("S1:", s1) print("S2:", s2) appended_series = s1.append(s2) print("Final Series after appending:", appended_series)OutputS1: 0 10 1 20 ... Read More
In this problem we have to sort a Pandas series. We will define an unsorted pandas series and will sort it using the sort_values() function in the Pandas library.AlgorithmStep 1: Define Pandas series. Step 2: Sort the series using sort_values() function. Step 3: Print the sorted series.Example Codeimport pandas as pd panda_series = pd.Series([18, 15, 66, 92, 55, 989]) print("Unsorted Pandas Series: ", panda_series) panda_series_sorted = panda_series.sort_values(ascending = True) print("Sorted Pandas Series: ", panda_series_sorted)OutputUnsorted Pandas Series: 0 18 1 15 2 66 3 92 4 55 5 ... Read More
First, we can create an array matrix with some np.nan value, and using imshow method, we can create a diagram for that matrix.StepsCreate a new figure, or activate an existing figure.Add an `~.axes.Axes` to the figure as part of a subplot arrangement, nrows = 1, ncols = 1, index = 1.Create a 2D array with np.nan.Display data as an image, i.e., on a 2D regular raster.Use the draw() method which draws the drawing at the given location.To show the figure, use the plt.show() method.Exampleimport numpy as np import matplotlib.pyplot as plt f = plt.figure() ax = f.add_subplot(111) a = ... Read More
This is a simple program wherein we have to reverse a numpy array. We will use numpy.flip() function for the same.AlgorithmStep 1: Import numpy. Step 2: Define a numpy array using numpy.array(). Step 3: Reverse the array using numpy.flip() function. Step 4: Print the array.Example Codeimport numpy as np arr = np.array([10,20,30,40,50]) print("Original Array: ", arr) arr_reversed = np.flip(arr) print("Reversed Array: ", arr_reversed)OutputOriginal Array: [10 20 30 40 50] Reversed Array: [50 40 30 20 10]