The indexing operator is the square brackets for creating a subset dataframe. Let us first create a Pandas DataFrame. We have 3 columns in the DataFramedataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Creating a subset with a single columndataFrame[['Product']]Creating a subset with multiple columnsdataFrame[['Opening_Stock', 'Closing_Stock']]ExampleFollowing is the complete codeimport pandas as pd dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) print"DataFrame...", dataFrame print"Displaying a subset using indexing operator:", dataFrame[['Product']] print"Displaying a subset with multiple columns:", dataFrame[['Opening_Stock', 'Closing_Stock']]OutputThis will ... Read More

The numpy where() method can be used to filter Pandas DataFrame. Mention the conditions in the where() method. At first, let us import the required libraries with their respective aliasimport pandas as pd import numpy as npWe will now create a Pandas DataFrame with Product records dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Use numpy where() to filter DataFrame with 2 ConditionsresValues1 = np.where((dataFrame['Opening_Stock']>=700) & (dataFrame['Closing_Stock']< 1000)) print"Filtered DataFrame Value = ", dataFrame.loc[resValues1] Let us use numpy where() again to filter DataFrame with 3 conditionsresValues2 = np.where((dataFrame['Opening_Stock']>=500) & (dataFrame['Closing_Stock']< 1000) ... Read More

To sum all the rows of a DataFrame, use the sum() function and set the axis value as 1. The value axis 1 will add the row values.At first, let us create a DataFrame. We have Opening and Closing Stock columns in itdataFrame = pd.DataFrame({"Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Finding sum of row values. Axis is set 1 to add row valuesdataFrame = dataFrame.sum(axis = 1) ExampleFollowing is the complete code import pandas as pd dataFrame = pd.DataFrame({"Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) print"DataFrame...", dataFrame # finding sum of ... Read More

To delete a row from a DataFrame, use the drop() method and set the index label as the parameter.At first, let us create a DataFrame. We have index label as w, x, y, and z:dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35], [40, 45]], index=['w', 'x', 'y', 'z'], columns=['a', 'b'])Now, let us use the index label and delete a row. Here, we will delete a row with index label 'w'.dataFrame = dataFrame.drop('w') ExampleFollowing is the codeimport pandas as pd # Create DataFrame dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35], [40, 45]], index=['w', 'x', 'y', 'z'], columns=['a', 'b']) ... Read More

To append rows to a DataFrame, use the append() method. Here, we will create two DataFrames and append one after the another.At first, import the pandas library with an alias −import pandas as pdNow, create the 1st DataFramedataFrame1 = pd.DataFrame(    { "Car": ['BMW', 'Lexus', 'Audi', 'Jaguar'] } )Create the 2nd DataFramedataFrame2 = pd.DataFrame( { "Car": ['Mercedes', 'Tesla', 'Bentley', 'Mustang'] } )Next, append rows to the enddataFrame1 = dataFrame1.append(dataFrame2)ExampleFollowing is the codeimport pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Jaguar'] } ) print"DataFrame1 ...", dataFrame1 # Find ... Read More

To create a subset by choosing specific values from columns based on indexes, use the iloc() method. Let us first import the pandas libraryimport pandas as pdCreate a Pandas DataFrame with Product records. We have 3 columns in itdataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Creating a subset with 2 columns and 1st 2 rows using iloc(print"Displaying a subset using iloc() = ", dataFrame.iloc[0:2, 0:2] ExampleFollowing is the complete codeimport pandas as pd dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) ... Read More

To create a subset of DataFrame by column name, use the square brackets. Use the DataFrame with square brackets (indexing operator) and the specific column name like this −dataFrame[‘column_name’]At first, import the required library with alias −import pandas as pdCreate a Pandas DataFrame with Product records −dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Let us fetch a subset i.e. we are fetching only Product column recordsdataFrame['Product']ExampleFollowing is the codeimport pandas as pd dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) ... Read More

When it is required to assign an alphabet to every element of an integer list, the ‘ascii_lowercase’ method, and the list comprehension are used.ExampleBelow is a demonstration of the same −import string my_list = [11, 51, 32, 45, 21, 66, 12, 58, 90, 0] print("The list is : " ) print(my_list) print("The list after sorting is : " ) my_list.sort() print(my_list) temp_val = {} my_counter = 0 for element in my_list: if element in temp_val: continue temp_val[element] = string.ascii_lowercase[my_counter] my_counter ... Read More

When it is required to get the dictionaries with unique value lists, the ‘set’ operator and the list methods are used, along with a simple iteration.ExampleBelow is a demonstration of the same −my_dictionary = [{'Python' : 11, 'is' : 22}, {'fun' : 11, 'to' : 33}, {'learn' : 22}, {'object':9}, {'oriented':11}] print("The dictionary is : " ) print(my_dictionary) my_result = list(set(value for element in my_dictionary for value in element.values())) print("The resultant list is : ") print(my_result) print("The resultant list after sorting is : ") my_result.sort() print(my_result)OutputThe dictionary is : [{'Python': 11, 'is': 22}, {'fun': 11, 'to': ... Read More

When it is required to get the mean of the matrix elements, the ‘mean’ method from the ‘Numpy’ package is used after it has been imported into the environment.ExampleBelow is a demonstration of the same −import numpy as np my_matrix = np.matrix('[24, 41; 35, 25]') print("The matrix is : " ) print(my_matrix) my_result = my_matrix.mean() print("The result is : ") print(my_result)OutputThe matrix is : [[24 41] [35 25]] The result is : 31.25ExplanationThe required packages are imported into the environment.A matrix is created using the Numpy package.It is displayed on the console.The mean of the matrix is ... Read More