When it is required to find sequences of an upper case letter followed by lower case using regular expression, a method named ‘match_string’ is defined that uses the ‘search’ method to match a regular expression. Outside the method, the string is defined, and the method is called on it by passing the string.ExampleBelow is a demonstration of the sameimport re def match_string(my_string):    pattern = '[A-Z]+[a-z]+$'    if re.search(pattern, my_string):       return('The string meets the required condition ')    else:       return('The string doesnot meet the required condition ') print("The string is ... Read More

When it is required to remove all characters except for letters and numbers, regular expressions are used. A regular expression is defined, and the string is subjected to follow this expression.ExampleBelow is a demonstration of the sameimport re my_string = "python123:, .@! abc" print ("The string is : ") print(my_string) result = re.sub('[\W_]+', '', my_string) print ("The expected string is :") print(result)OutputThe string is : python123:, .@! abc The expected string is : python123abcExplanationThe required packages are imported.A string is defined and is displayed on the console.A regular expression is defined, and the string is subjected ... Read More

When it is required to find the least frequent character in a string, ‘Counter’ is used to get the count of letters. The ‘min’ method is used to get the minimum of values in the string, i.e every letter’s count is stored along with the letter. The minimum is obtained.ExampleBelow is a demonstration of the samefrom collections import Counter my_str = "highland how" print ("The string is : ") print(my_str) my_result = Counter(my_str) my_result = min(my_result, key = my_result.get) print ("The minimum of all characters in the string is : ") print(my_result)OutputThe string is : highland ... Read More

To fill NaN with Linear Interpolation, use the interpolate() method on the Pandas series. At first, import the required libraries −import pandas as pd import numpy as npCreate a Pandas series with some NaN values. We have set the NaN using the numpy np.nan −d = pd.Series([10, 20, np.nan, 40, 50, np.nan, 70, np.nan, 90, 100]) Find linear interpolation −d.interpolate()ExampleFollowing is the code −import pandas as pd import numpy as np # pandas series d = pd.Series([10, 20, np.nan, 40, 50, np.nan, 70, np.nan, 90, 100]) print"Series...", d # interpolate print"Linear Interpolation...", d.interpolate()OutputThis will produce the following ... Read More

To group dataframe rows into list, use the apply() function. At first, let us import the require library −import pandas as pdCreate DataFrame with 2 columns −dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Grouping DataFrame into list with apply(list) −dataFrame = dataFrame.groupby('Car')['Units'].apply(list) ExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": ... Read More

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

When it is required to group strings by K length using a suffix, a simple iteration and the ‘try’ and ‘except’ blocks are used.ExampleBelow is a demonstration of the samemy_list = ['peek', "leak", 'creek', "weak", "good", 'week', "wood", "sneek"] print("The list is :") print(my_list) K = 3 print("The value of K is ") print(K) my_result = {} for element in my_list: suff = element[-K : ] try: my_result[suff].append(element) except: my_result[suff] = [element] print("The ... Read More

When it is required to replace the elements of the list by greatest neighbours, a simple iteration along with the ‘if’ and ‘else’ condition is used.ExampleBelow is a demonstration of the samemy_list = [41, 25, 24, 45, 86, 37, 18, 99] print("The list is :") print(my_list) for index in range(1, len(my_list) - 1): my_list[index] = my_list[index - 1] if my_list[index - 1] > my_list[index + 1] else my_list[index + 1] print("The resultant list is :") print(my_list)OutputThe list is : [41, 25, 24, 45, 86, 37, 18, 99] The resultant list is : [41, ... Read More

When it is required to filter dictionaries with ordered values, the ‘sorted’ method along with the list comprehension is used.ExampleBelow is a demonstration of the samemy_list = [{'python': 2, 'is': 8, 'fun': 10}, {'python': 1, 'for': 10, 'coding': 9}, {'cool': 3, 'python': 4}] print("The list is :") print(my_list) my_result = [index for index in my_list if sorted( list(index.values())) == list(index.values())] print("The resultant dictionary is :") print(my_result)OutputThe list is : [{'python': 2, 'fun': 10, 'is': 8}, {'python': 1, 'coding': 9, 'for': 10}, {'python': 4, 'cool': 3}] The resultant dictionary ... Read More

To filter DataFrame between two dates, use the dataframe.loc. At first, import the required library −import pandas as pdCreate a Dictionary of lists with date records −d = {'Car': ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'], 'Date_of_Purchase': ['2021-07-10', '2021-08-12', '2021-06-17', '2021-03-16', '2021-02-19', '2021-08-22']    }Creating dataframe from the above dictionary of listsdataFrame = pd.DataFrame(d) Fetch car purchased between two dates i.e. 1st Date: 2021-05-10 and 2nd Date: 2021-08-25 −resDF = dataFrame.loc[(dataFrame["Date_of_Purchase"] >= "2021-05-10") & (dataFrame["Date_of_Purchase"] = "2021-05-10") & (dataFrame["Date_of_Purchase"] Read More