When it is required to randomly insert elements K times, the ‘random’ package and methods from the random package along with a simple iteration is used.ExampleBelow is a demonstration of the same −import random my_list = [34, 12, 21, 56, 8, 9, 0, 3, 41, 11, 90] print("The list is : " ) print(my_list) print("The list after sorting is : " ) my_list.sort() print(my_list) to_add_list = ["Python", "Object", "oriented", "language", 'cool'] K = 3 print("The value of K is ") print(K) for element in range(K): index = random.randint(0, len(my_list)) ... Read More

When it is required to find all the combinations in a list with a specific condition, a simple iteration, the ‘isinstance’ method, the ‘append’ method and indexing are used.ExampleBelow is a demonstration of the same −print("Method definition begins") def merge_the_vals(my_list_1, my_list_2, K): index_1 = 0 index_2 = 0 while(index_1 < len(my_list_1)): for i in range(K): yield my_list_1[index_1] index_1 += 1 ... Read More

To query the columns of a Pandas DataFrame, use the query(). We are querying to filter records. At first, let us create a Pandas DataFramedataFrame = pd.DataFrame({"Product": ["SmartTV", "PenDrive", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})Using query() to query columns with conditions −print(dataFrame.query('Opening_Stock >=500 & Closing_Stock < 1000 & Product.str.startswith("P").values'))ExampleFollowing is the complete code −import pandas as pd dataFrame = pd.DataFrame({"Product": ["SmartTV", "PenDrive", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) print"DataFrame...", dataFrame # using query() to query columns print"Querying columns to filter records..." print(dataFrame.query('Opening_Stock >=500 & Closing_Stock ... Read More

To select multiple rows from a DataFrame, set the range using the : operator. At first, import the require pandas library with alias −import pandas as pdNow, create a new Pandas DataFrame −dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35], [40, 45]], index=['w', 'x', 'y', 'z'], columns=['a', 'b'])Select multiple rows using the : operator −dataFrame[0:2]ExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35], [40, 45]], index=['w', 'x', 'y', 'z'], columns=['a', 'b']) # DataFrame print"DataFrame...", dataFrame # select rows with loc print"Select rows by passing label..." print(dataFrame.loc['z']) ... Read More

To select a column from a DataFrame, just fetch it using square brackets. Mention the column to select in the brackets and that’s it, for exampledataFrame[‘ColumnName’]At first, import the required library −import pandas as pdNow, create a DataFrame. We have two columns in it −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )To select only a single column, mention the column name using the square bracket as shown below. Here, our ... Read More

To merge Pandas DataFrame, use the merge() function. The outer join is implemented on both the DataFrames by setting under the “how” parameter of the merge() function i.e. −how = “outer”At first, let us import the pandas library with an alias −import pandas as pdLet us create DataFrame1 −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Let us now create DataFrame2 −dataFrame2 = pd.DataFrame( { ... Read More

To merge two Pandas DataFrame with common column, use the merge() function and set the ON parameter as the column name. To set NaN for unmatched values, use the “how” parameter and set it left or right. That would mean, merging left or right.At first, let us import the pandas library with an alias −import pandas as pdLet us create DataFrame1 −dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Let us ... Read More

To drop multiple levels from a multi-level column index, use the columns.droplevel() repeatedly. We have used the Multiindex.from_tuples() is used to create indexes column-wise.At first, create indexes column-wise −items = pd.MultiIndex.from_tuples([("Col 1", "Col 1", "Col 1"), ("Col 2", "Col 2", "Col 2"), ("Col 3", "Col 3", "Col 3")])Next, create a multiindex array and form a multiindex dataframe −arr = [np.array(['car', 'car', 'car', 'bike', 'bike', 'bike', 'truck', 'truck', 'truck']), np.array(['valueA', 'valueB', 'valueC', 'valueA', 'valueB', 'valueC', 'valueA', 'valueB', 'valueC'])] # forming multiindex dataframe dataFrame = pd.DataFrame(np.random.randn(9, 3), index=arr, columns=items)Label the index −dataFrame.index.names = ['level 0', 'level 1'] Drop a level ... Read More

To drop a level from a multi-level column index, use the columns.droplevel(). We have used the Multiindex.from_tuples() is used to create indexes column-wise.At first, create indexes column-wise −items = pd.MultiIndex.from_tuples([("Col 1", "Col 1", "Col 1"), ("Col 2", "Col 2", "Col 2"), ("Col 3", "Col 3", "Col 3")])Next, create a multiindex array and form a multiindex dataframearr = [np.array(['car', 'car', 'car', 'bike', 'bike', 'bike', 'truck', 'truck', 'truck']),    np.array(['valueA', 'valueB', 'valueC', 'valueA', 'valueB', 'valueC', 'valueA', 'valueB', 'valueC'])] # forming multiindex dataframe dataFrame = pd.DataFrame(np.random.randn(9, 3), index=arr, columns=items)Label the index −dataFrame.index.names = ['level 0', 'level 1']Drop a level at index ... Read More

To group Pandas dataframe, we use groupby(). To sort grouped dataframe in ascending order, use sort_values(). The size() method is used to get the dataframe size.For ascending order sort, use the following in sort_values() −ascending=TrueAt first, create a pandas dataframe −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'], "Reg_Price": [1000, 1400, 1000, 900, 1700, 900] } )Next, group according to Reg_Price column and sort in ascending order −dataFrame.groupby('Reg_Price').size().sort_values(ascending=True)ExampleFollowing is the code −import pandas as pd # dataframe ... Read More