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Articles on Trending Technologies
Technical articles with clear explanations and examples
How are the journal entries and legal entries recorded for contingent liabilities?
There is a lawsuit between company A and company B. Company A sued company B in violating patent rights of technology. Company A claimed $ 3 billion, when the lawsuit began in 2010 but in the final verdict in 2016, company A won $ 400 million. (Company B estimated contingent liability as $800 million in their books)SolutionPrepare journal entry for year end 2010 as shown below −ParticularsDebt (million $)Credit (million $)Lawsuit loss A/c To Lawsuit liability800800Being estimated amount of lawsuit losses recordedProbability of occurrenceIf the liability is not probable and will arise soon (journal entry is not passed)Disclosure is ...
Read MoreHow to color a Seaborn boxplot based on DataFrame column name in Matplotlib?
To color a Seaborn boxplot based on dataframe column name, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a Pandas dataframe with two columns, col1 and col2.Make a boxplot with horizontal orientation.Get the boxes artists.Iterate the boxes and set the facecolor of the box.To display the figure, use show() method.Exampleimport seaborn as sns import matplotlib.pyplot as plt import pandas as pd plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame( [[2, 4], [7, 2] ], columns=['col1', 'col2']) ...
Read MoreHow to draw rounded line ends using Matplotlib?
To draw rounded line ends using matplotlib, we can use solid_capstyle='round'.StepsSet the figure size and adjust the padding between and around the subplots.Create random x and y data points using numpy.Create a figure and a set of subplots.Plot x and y data points using plot() method, with solid_capstyle in the method argument.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.random.randn(5) y = np.random.randn(5) fig, ax = plt. subplots() ln, = ax.plot(x, y, lw=10, solid_capstyle='round', color='red') plt.show()Output
Read MorePlot 95% confidence interval errorbar Python Pandas dataframes in Matplotlib
To plot 95% confidence interval errorbar Python Pandas dataframes, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Get a dataframe instance of two-dimensional, size-mutable, potentially heterogeneous tabular data.Make a dataframe with two columns, category and number.Find the mean and std of category and number.Plot y versus x as lines and/or markers with attached errorbars.To display the figure, use show() method.Exampleimport numpy as np import pandas as pd import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame() df['category'] = np.random.choice(np.arange(10), 1000, replace=True) df['number'] = ...
Read MoreHow does parameters 'c' and 'cmap' behave in a Matplotlib scatter plot?
To get a sense of how the parameters c and cmap behave in a Matplotlib scatterplot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable N to store the number of sample data.Create x and y data points using numpy.Plot x and y data points using scatter() method, color and colormap.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 50 x = np.random.randn(N) y = np.random.randn(N) plt.scatter(x, y, c=x, ...
Read MoreCreating multiple boxplots on the same graph from a dictionary, using Matplotlib
To create multiple boxplots on the same graph from a dictionary, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a dictionary, dict, with two columns.Create a figure and a set of subplots.Make a box and whisker plotSet the xtick labels using set_xticklabels() methodTo display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True data = {'col1': [3, 5, 2, 9, 1], 'col2': [2, 6, 1, 3, 4]} fig, ax = plt.subplots() ax.boxplot(data.values()) ax.set_xticklabels(data.keys()) plt.show()Output
Read MoreHow to edit the properties of whiskers, fliers, caps, etc. in a Seaborn boxplot in Matplotlib?
To edit the properties of whiskers, fliers, caps, etc. in a Seaborn boxplot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a dataframe using Pandas.Make a boxplot from the DataFrame columns.Get the boxplot's outliers, boxes, medians, and whiskers data.Print all the above data.To display the figure, use show() method.Exampleimport seaborn as sns import pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame(dict(age=[23, 45, 21, 15, 12])) _, bp = pd.DataFrame.boxplot(df, return_type='both') outliers = [flier.get_ydata() for flier ...
Read MoreHow to force Matplotlib to show the values on X-axis as integers?
To force matplotlib to show the values on X-axis as integers, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create two lists, x and y, of data points.Plot x and y using plot() method.Make a new list for only integers tick on X-axis. Use math.floor() and math.ceil() to remove the decimals and include only integers in the list.Set x and y labels.Set the title of the figure.To display the figure, use show() method.Exampleimport math from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True y ...
Read MoreHow to plot certain rows of a Pandas dataframe using Matplotlib?
To plot certain rows of a Pandas dataframe, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a Pandas data frame, df. It should be a two-dimensional, size-mutable, potentially heterogeneous tabular data.Make rows of Pandas plot. Use iloc() function to slice the df and print specific rows.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np import pandas as pd plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame(np.random.randn(10, 5), columns=list('abcde')) df.iloc[0:6].plot(y='e') print(df.iloc[0:6]) # plt.show()OutputWe have 10 rows in ...
Read MoreMoving X-axis in Matplotlib during real-time plot
To move X-axis in Matplotlib during real-time plot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Create x and y data points using numpy.Plot x and y data points using plot() method.Make an animation by repeatedly calling a function *animate* that moves the X-axis during real-time plot.To display the figure, use show() method.Exampleimport matplotlib.pylab as plt import matplotlib.animation as animation import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() x = np.linspace(0, 15, 100) ...
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