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Median separates the higher half from the lower half of the data. Use the fillna() method and set the median to fill missing columns with median. At first, let us import the required libraries with their respective aliases −import pandas as pd import numpy as npCreate a DataFrame with 2 columns. We have set the NaN values using the Numpy np.NaN −dataFrame = pd.DataFrame( { "Car": ['Lexus', 'BMW', 'Audi', 'Bentley', 'Mustang', 'Tesla'], "Units": [100, 150, np.NaN, 80, np.NaN, np.NaN] } )Find median of the column values with NaN i.e, for Units columns here. ... Read More
The Dataframe.loc is used to access a group of rows and columns by label or a boolean array. We will append a list to a DataFrame using loc. Let us first create a DataFrame. The data is in the form of lists of team rankings for our example −# data in the form of list of team rankings Team = [['India', 1, 100], ['Australia', 2, 85], ['England', 3, 75], ['New Zealand', 4 , 65], ['South Africa', 5, 50], ['Bangladesh', 6, 40]] # Creating a DataFrame and adding columns dataFrame = pd.DataFrame(Team, columns=['Country', 'Rank', 'Points'])Following is the row to be ... Read More
Mode is the value that appears the most in a set of values. Use the fillna() method and set the mode to fill missing columns with mode. At first, let us import the required libraries with their respective aliases −import pandas as pd import numpy as npCreate a DataFrame with 2 columns. We have set the NaN values using the Numpy np.NaN −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Lexus', 'Mustang', 'Bentley', 'Mustang'], "Units": [100, 150, np.NaN, 80, np.NaN, np.NaN] } )Find mode of the column values with NaN i.e, for Units columns ... Read More
We can search DataFrame for a specific value. Use iloc to fetch the required value and display the entire row. At first, import the required library −import pandas as pdCreate a DataFrame with 4 columns −dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000], "Units_Sold": [ 100, 120, 150, 110, 200, 250] })Let’s search Car with Registeration Price 500 −for i in range(len(dataFrame.Car)): if 5000 == dataFrame.Reg_Price[i]: indx = iNow, display the found value −dataFrame.iloc[indx] ExampleFollowing is ... Read More
The sort_index() is used to sort index in ascending and descending order. If you won’t mention any parameter, then index sorts in ascending order.At first, import the required library −import pandas as pdCreate a new DataFrame. It has unsorted indexes −dataFrame = pd.DataFrame([100, 150, 200, 250, 250, 500],index=[4, 8, 2, 9, 15, 11],columns=['Col1'])Sort the indexes −dataFrame.sort_index() ExampleFollowing is the code −import pandas as pd dataFrame = pd.DataFrame([100, 150, 200, 250, 250, 500],index=[4, 8, 2, 9, 15, 11],columns=['Col1']) print"DataFrame...",dataFrame print"Sort index...",dataFrame.sort_index()OutputThis will produce the following output −DataFrame... Col1 4 100 8 150 2 200 9 250 15 250 11 500 Sort index... Col1 2 200 4 100 8 150 9 250 11 500 15 250
To add a prefix to all the column names, use the add_prefix() method. At first, import the required Pandas library −import pandas as pdCreate a DataFrame with 4 columns −dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000], "Units_Sold": [ 100, 120, 150, 110, 200, 250] })Add a prefix to _column to every column using add_prefix() −dataFrame.add_prefix('column_') ExampleFollowing is the code −import pandas as pd # creating dataframe dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": ... Read More
To draw the largest polygon from a set of points in matplotlib, we can take the following steps −Import "Polygon" from matplotlib.patches.Set the figure size and adjust the padding between and around the subplots.Create a list of data points for the largest polygon.Get the polygon instance.Create a figure and a set of subplots.Add a polygon instance patch.Set the x and y scale limit.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Polygon plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True y = np.array([[1, 1], [0.5, 1.5], [2, 1], [1, 2], [2, ... Read More
To change the face color of a plot using 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.Create a figure and a set of subplots.Plot the x and y data points using plot() method with color=yellow and linewidth=7.Set the facecolor of the axes, using set_facecolor().To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import numpy as np # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Create x and y data points x ... Read More
To plot a masked surface plot using Python, Numpy and 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.Add an 'ax' to the figure as part of a subplot arrangement.Return the coordinate matrices from coordinate vectors, pi and theta.Create x, y and z with masked data points.Create a surface plot with x, y, and z data points.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = ... Read More
To draw lattices and graphs with networkx, we can take the following steps −Import networkx and pyplot.Set the figure size and adjust the padding between and around the subplots.Use nx.grid_2d_graph(3, 3) to get a two-dimensional grid graph. The grid graph has each node connected to its four nearest neighbors.Draw the graph G with Matplotlib.To display the figure, use show() method.Example# Import networkx and pyplot import networkx as nx from matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Draw the graph G = nx.grid_2d_graph(3, 3) nx.draw(G, node_size=100) plt.show()OutputIt ... Read More