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To create a boxplot stratified by column in Python class, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a Pandas data frame of two-dimensional, size-mutable, potentially heterogeneous tabular data.Compute the histogram of a set of data.Create a boxplot startified by column.To display the figure, use show() method.Exampleimport pandas as pd import numpy as np from matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Pandas dataframe df = pd.DataFrame({"column1": [4, 6, 7, 1, 8], "column2": [1, 5, 7, ... Read More
To plot aggregated by date pandas dataframe, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a data frame, df, of two-dimensional, size-mutable, potentially heterogeneous tabular data.Get the values of aggregated by date pandas dataframe.Plot the df (Step 3) with kind="bar".To display the figure, use show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt, dates # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create a dataframe df = pd.DataFrame(dict(data=list(pd.date_range("2021-01-01", periods=10)), value=np.linspace(1, 10, 10))) df = df.groupby('data').agg(['sum']).reset_index() ... Read More
To have the line color vary with the data index for a line graph in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Get smaller limit, dydx.Get the points and segments data points using numpy.Create a figure and a set of subplots.Create a class which, when called, linearly normalizes data into some range.Set the image array from numpy array *A*.Set the linewidth(s) for the collection.Set the colorbar for axis 1.Generate Colormap object from a list of colors i.e r, g and b.Repeat ... Read More
To return a masked array containing the same data, but with a new shape, use the ma.MaskedArray.reshape() method in Numpy. Give a new shape to the array without changing its data. The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length.The order determines whether the array data should be viewed as in C (row-major) or FORTRAN (column-major) order. Returns a masked array containing the same data, but with a new shape. The result is a view on the original array; if this is not ... Read More
To return a 1D version of self as a view, use the ma.MaskedArray.ravel() method in Numpy. The order is set using the "order" parameter, to read the elements using the index order. The ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative.The elements of a are read using this index order. ‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in Fortran-like index order, with the ... Read More
To return a 1D version of self as a view, use the ma.MaskedArray.ravel() method in Numpy. The order is set using the "order" parameter, to read the elements using the index order. The ‘F’ means to index the elements in Fortran-like index order.The elements of a are read using this index order. ‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest.The ‘C’ and ... Read More
To return a 1D version of self as a view, use the ma.MaskedArray.ravel() method in Numpy. The order is set using the "order" parameter, to read the elements using the index order. The‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest.The elements of a are read using this index order. ‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in Fortran-like index order, with ... Read More
To return a 1D version of self as a view in Python, use the ma.MaskedArray.ravel() method in Numpy.The elements of a are read using this index order. ‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest.The ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. ‘A’ means to ... Read More
To return a copy of the array collapsed into one dimension, use the ma.MaskedArray.flatten() method in Numpy. The "order" parameter is used to flatter in order. The ‘K’ order means to flatten in the order the elements occur in memory. The order ‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.A masked array is the combination of a ... Read More
To return a copy of the array collapsed into one dimension, use the ma.MaskedArray.flatten() method in Numpy. The "order" parameter is used to flatter in order. The ‘F’ order means to flatten in column-major (Fortan-style) order.The order ‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.A masked array is the combination of a standard numpy.ndarray and a mask. ... Read More