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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
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 More
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 More
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) ... Read More
To update a bar plot dynamically in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure.Make a list of data points and colors.Plot the bars with data and colors, using bar() method.Using FuncAnimation() class, make an animation by repeatedly calling a function, animation, that sets the height of the bar and facecolor of the bars.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import animation as animation, pyplot as plt, cm plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = ... Read More
To draw more type of lines in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot x and y data points using plot() method, with an array of dashes.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(-10, 10, 100) y = np.sin(x) plt.plot(x, y, dashes=[1, 1, 2, 1, 3], linewidth=7, color='red') plt.show()Output