Programming Articles - Page 1052 of 3363

To get the columns shared by two DataFrames, use the intersect1d() method. This method is provided by numpy, so you need to import Numpy also with Pandas. Let us first import the required libraries −import pandas as pd import numpy as npCreate two DataFrames −# creating dataframe1 dataFrame1 = pd.DataFrame({"Car": ['Bentley', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000], "Units_Sold": [ 100, 110, 150, 80, 200, 90] }) # creating dataframe2 dataFrame2 = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Units_Sold": [ 100, 110, 150, 80, 200, 90] ... Read More

To merge two Pandas DataFrame with common column, use the merge() function and set the ON parameter as the column name.At first, let us import the pandas library with an alias −import pandas as pdLet us create the 1st DataFrame −dataFrame1 = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Next, create the 2nd DataFrame −dataFrame2 = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Mercedes', 'Jaguar'], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000] } )Now, merge ... Read More

To merge two Pandas DataFrame, use the merge() function. Just set both the DataFrames as a parameter of the merge() function.At first, let us import the required library with alias “pd” −import pandas as pdCreate the 1st DataFrame −# Create DataFrame1 dataFrame1 = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90]    } )Next, create the 2nd DataFrame −# Create DataFrame2 dataFrame2 = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Mercedes', 'Jaguar'], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000] } ... Read More

The iloc method is an integer-location based indexing for selection by position. We are using iloc to append a list to a DataFrame.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 appended −myList = ["Sri Lanka", 7, ... Read More

To add anew column with constant value, use the square bracket i.e. the index operator and set that value.At first, import the required library −import pandas as pdCreating a DataFrame with 4 columns −dataFrame = pd.DataFrame({"Car": ['Bentley', 'Lexus', 'BBMW', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000], "Units_Sold": [ 100, 110, 150, 80, 200, 90] })Adding a new column with a constant value. The new column names is set in the square bracket −dataFrame['Mileage'] = 15 ExampleFollowing is the complete code −import pandas as pd # creating dataframe dataFrame = ... Read More

To check, use the isinf() method. To find the count of infinite values, use sum(). At first, let us import the required libraries with their respective aliases −import pandas as pd import numpy as npCreate a dictionary of list. We have set the infinity values using the Numpy np.inf −d = { "Reg_Price": [7000.5057, np.inf, 5000, np.inf, 9000.75768, 6000] } Creating dataframe from the above dictionary of listdataFrame = pd.DataFrame(d)Checking for infinite values using isinf() and displaying the countcount = np.isinf(dataFrame).values.sum() ExampleFollowing is the code −import pandas as pd import numpy as np # dictionary of list d = ... Read More

We can create a Pivot Table with multiple columns. To create a Pivot Table, use the pandas.pivot_table to create a spreadsheet-style pivot table as a DataFrame.At first, import the required library −import pandas as pdCreate a DataFrame with Team records −dataFrame = pd.DataFrame({'Team ID': {0: 5, 1: 9, 2: 6, 3: 11, 4: 2, 5: 7 }, 'Team Name': {0: 'India', 1: 'Australia', 2: 'Bangladesh', 3: 'South Africa', 4: 'Sri Lanka', 5: 'England'}, 'Team Points': {0: 95, 1: 93, 2: 42, 3: 60, 4: 80, 5: 55}, 'Team Rank': {0: 'One', 1: 'Two', 2: 'Six', 3: 'Four', 4: 'Three', 5: ... Read More

To get the minimum of column values, use the min() function. At first, import the required Pandas library −import pandas as pdNow, create a DataFrame with two columns −dataFrame1 = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Finding the minimum value of a single column “Units” using min() −print"Minimum Units from DataFrame1 = ", dataFrame1['Units'].min() In the same way, we have calculated the minimum value from the 2nd DataFrame.ExampleFollowing is the complete code −import pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame(    { ... Read More

To exponentially scale the Y-axis with matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Inintialize a variable dt for steps.Create x and y data points using numpy.Plot the x and y data points using numpy.Set the exponential scale for the Y-axis, using plt.yscale('symlog').To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True dt = 0.01 x = np.arange(-50.0, 50.0, dt) y = np.arange(0, 100.0, dt) plt.plot(x, y) plt.yscale('symlog') plt.show()OutputIt will produce the following ... Read More

When it is required to convert a list of list to a list of set, the ‘map’, ‘set’, and ‘list’ methods are used.ExampleBelow is a demonstration of the samemy_list = [[2, 2, 2, 2], [1, 2, 1], [1, 2, 3], [1, 1], [0]] print("The list of lists is: ") print(my_list) my_result = list(map(set, my_list)) print("The resultant list is: ") print(my_result)OutputThe list of lists is: [[2, 2, 2, 2], [1, 2, 1], [1, 2, 3], [1, 1], [0]] The resultant list is: [set([2]), set([1, 2]), set([1, 2, 3]), set([1]), set([0])]ExplanationA list of list is defined and is displayed ... Read More