To fetch only capital words, we are using regex. The re module is used here and imported. Let us import all the libraries −import re import pandas as pdCreate a DataFrame −data = [['computer', 'mobile phone', 'ELECTRONICS', 'electronics'], ['KEYBOARD', 'charger', 'SMARTTV', 'camera']] dataFrame = pd.DataFrame(data)Now, extract capital words −for i in range(dataFrame.shape[1]): for ele in dataFrame[i]: if bool(re.match(r'\w*[A-Z]\w*', str(ele))): print(ele)ExampleFollowing is the code −import re import pandas as pd # create a dataframe data = [['computer', 'mobile phone', 'ELECTRONICS', 'electronics'], ... Read More

Use the isin() method to display True for infinite values. 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, 900, np.inf] } Creating DataFrame from the above dictionary of list −dataFrame = pd.DataFrame(d)Display True for infinite values −res = dataFrame.isin([np.inf, -np.inf]) ExampleFollowing is the code −import pandas as pd import numpy as np # dictionary of list d = { "Reg_Price": [7000.5057, np.inf, 5000, ... Read More

To check and display row index, use the isinf() with any(). 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, 900, np.inf] } Creating DataFrame from the above dictionary of list −dataFrame = pd.DataFrame(d)Getting row index with infinity values −indexNum = dataFrame.index[np.isinf(dataFrame).any(1)] ExampleFollowing is the code −import pandas as pd import numpy as np # dictionary of list d = { "Reg_Price": [7000.5057, np.inf, ... Read More

To count the observations, first use the groupby() and then use count() on the result. At first, import the required library −dataFrame = pd.DataFrame({'Product Name': ['Keyboard', 'Charger', 'SmartTV', 'Camera', 'Graphic Card', 'Earphone'], 'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Electronics', 'Computer', 'Mobile Phone'], 'Quantity': [10, 50, 10, 20, 25, 50]})Group the column with duplicate values −group = dataFrame.groupby("Product Category") Get the count −group.count()ExampleFollowing is the code −import pandas as pd # create a dataframe dataFrame = pd.DataFrame({'Product Name': ['Keyboard', 'Charger', 'SmartTV', 'Camera', 'Graphic Card', 'Earphone'], 'Product Category': ['Computer', 'Mobile Phone', 'Electronics', 'Electronics', 'Computer', 'Mobile Phone'], 'Quantity': [10, 50, 10, 20, ... Read More

To sort data in ascending or descending order, use sort_values() method. For ascending order, use the following is the sort_values() method −ascending=TrueImport the required library −import pandas as pd Create a DataFrame with 3 columns −dataFrame = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'BMW', 'Mustang', 'Mercedes', 'Lexus'], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 2000], "Place": ['Pune', 'Delhi', 'Mumbai', 'Hyderabad', 'Bangalore', 'Chandigarh'] } ) To sort DataFrame in ascending order according to the element frequency, we need to count the occurrences. Therefore, count() is also used with sort_values() set for asscending order sort −dataFrame.groupby(['Car'])['Reg_Price'].count().reset_index(name='Count').sort_values(['Count'], ... Read More

To get the maximum of column values, use the max() 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 maximum value of a single column “Units” using max() −print"Maximum Units from DataFrame1 = ", dataFrame1['Units'].max()In the same way, we have calculated the maximum value from the 2nd DataFrame.ExampleFollowing is the complete code −import pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame(    {   ... Read More

Use index=False to ignore index. Let us first import the required library −import pandas as pdCreate a DataFrame −dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35]], index=['x', 'y', 'z'], columns=['a', 'b'])Select rows by passing label using loc −dataFrame.loc['x'] Display DataFrame without index −dataFrame.to_string(index=False)ExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35]], index=['x', 'y', 'z'], columns=['a', 'b']) # DataFrame print"Displaying DataFrame with index...", dataFrame # select rows with loc print"Select rows by passing label..." print(dataFrame.loc['x']) # display DataFrame without index print"Displaying DataFrame without Index...", dataFrame.to_string(index=False)OutputThis will produce ... Read More

When it is required to sort the palindrome words present in a sentence, a method is defined that takes a string as a parameter and first ensures that it is a palindrome. Then it sorts all the words of a string and returns it as output.ExampleBelow is a demonstration of the samedef check_palindrome(my_string): if(my_string == my_string[::-1]): return True else: return False def print_sort_palindromes(my_sentence): my_list = [] my_result = list(my_sentence.split()) for ... Read More

When it is required to generate all possible permutations of a word in a sentence, a function is defined. This function iterates over the string and depending on the condition, the output is displayed.ExampleBelow is a demonstration of the samefrom itertools import permutations def calculate_permutations(my_string):    my_list = list(my_string.split())    permutes = permutations(my_list)    for i in permutes:       permute_list = list(i)       for j in permute_list:          print j print() my_string = "hi there" print("The string is :") print(my_string) ... Read More

When it is required to extract rows from a matrix with different data types, it is iterated over and ‘set’ is used to get the distinct types.ExampleBelow is a demonstration of the samemy_list = [[4, 2, 6], ["python", 2, {6: 2}], [3, 1, "fun"], [9, (4, 3)]] print("The list is :") print(my_list) my_result = [] for sub in my_list: type_size = len(list(set([type(ele) for ele in sub]))) if len(sub) == type_size: my_result.append(sub) print("The resultant distinct data type rows are :") print(my_result)OutputThe list is : [[4, ... Read More