To divide the data frame row values by row standard deviation in R, we can follow the below steps −First of all, create a data frame.Then, use apply function to divide the data frame row values by row standard deviation.Creating the data frameLet's create a data frame as shown below − Live Demo> x y df dfOn executing, the above script generates the below output(this output will vary on your system due to randomization) −      x     y 1   1.48  0.86 2  -0.14 -0.58 3  -0.25  1.22 4   0.18  0.25 5   0.50  0.68 6  -1.34 ... Read More

To subtract column values from column means in R data frame, we can follow the below steps −First of all, create a data frame.Then, find the column means using colMeans function.After that, subtract column values from column means.Creating the data frameLet's create a data frame as shown below − Live Demo> x1 x2 x3 df dfOn executing, the above script generates the below output(this output will vary on your system due to randomization) −   x1  x2  x3 1  54  73  57 2  79  52  92 3  87  51  47 4  13  12   1 5  70  90  19 6  15  99 ... Read More

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 More

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()OutputRead More

To show date and time on the X-axis in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a list of dates and y values.Get the current axis.Set the major date formatter and locator.Plot x and y values using plot() method.To display the figure, use show() method.Examplefrom datetime import datetime as dt from matplotlib import pyplot as plt, dates as mdates plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True dates = ["01/02/2020", "01/03/2020", "01/04/2020"] x_values = [dt.strptime(d, "%m/%d/%Y").date() for d in dates] y_values = [1, 2, 3] ax ... Read More

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 More

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 More

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()OutputRead More

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 More

To plot Pandas data frames in Pie charts using Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a dataframe of two-dimensional, size-mutable, potentially heterogeneous tabular data.Plot the dataframe with activities index using pie() methodTo display the figure, use show() method.Exampleimport 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({'activities': ['sleep', 'exercise', 'work', 'study'],                                     'hours': [8, 1, 9, 6]}) df.set_index('activities').plot.pie(y='hours', legend=False,                                     autopct='%1.1f%%') plt.show()Output