When it is required to find the mean deviation of the elements of a list, the ‘sum’ method and the ‘len’ method is used.ExampleBelow is a demonstration of the samemy_list = [3, 5, 7, 10, 12] print("The list is :") print(my_list) my_mean = sum(my_list) / len(my_list) my_variance = sum([((x – my_mean) ** 2) for x in my_list]) / len(my_list) my_result = my_variance ** 0.5 print("The result is :") print(result)OutputThe original list : [3, 5, 7, 10, 12] the standard deviation of list is : 3.2619012860600183ExplanationA list is defined and is displayed on the console.The ‘sum’ of the ... Read More

To find rolling mean, we will use the apply() function in Pandas. At first, let us import the required library −import pandas as pdCreate a DataFrame with 2 columns. One is an int column −dataFrame = pd.DataFrame(    {       "Car": ['Tesla', 'Mercedes', 'Tesla', 'Mustang', 'Mercedes', 'Mustang'],       "Reg_Price": [5000, 1500, 6500, 8000, 9000, 6000]    } )Group using GroupBy and find the Rolling Mean using apply() −dataFrame.groupby("Car")["Reg_Price"].apply( lambda x: x.rolling(center=False, window=2).mean()) ExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame( { ... Read More

To delete a column from a DataFrame, use del(). You can also use pop() method to delete. Just drop it using square brackets. Mention the column to be deleted in the brackets and that’s it, for example −del dataFrame[‘ColumnName’]Import the required library with an alias −import pandas as pdCreate a Pandas DataFrame −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } ) Now, delete a column “Car” from a DataFrame −del ... Read More

When it is required to sort strings based on case difference, a method is defined that takes a string as a parameter. This method uses list comprehension and ‘isupper’ and ‘islower’ methods along with list comprehension to get case difference. Their difference gives the sorted values.ExampleBelow is a demonstration of the samedef get_diff(my_string):    lower_count = len([ele for ele in my_string if ele.islower()])    upper_count = len([ele for ele in my_string if ele.isupper()])    return abs(lower_count - upper_count) my_list = ["Abc", "Python", "best", "hello", "coders"] print("The list is :") print(my_list) my_list.sort(key=get_diff) print("Sorted Strings by case ... Read More

When it is required to filter similar case strings, list comprehension can be used along with ‘isupper’ and ‘islower’ methods.ExampleBelow is a demonstration of the samemy_list = ["Python", "good", "FOr", "few", "CODERS"] print("The list is :") print(my_list) my_result = [sub for sub in my_list if sub.islower() or sub.isupper()] print("The strings with same case are :") print(my_result)OutputThe list is : ['Python', 'good', 'FOr', 'few', 'CODERS'] The strings with same case are : ['good', 'few', 'CODERS']ExplanationA list is defined and is displayed on the console.The list comprehension is used to iterate over the list and check if the strings ... Read More

When it is required to find the index value that has been repeated in a list, it is iterated over using the list comprehension and ‘enumerate’.ExampleBelow is a demonstration of the samemy_list = [4, 0, 3, 1] print("The list is :") print(my_list) my_result = [element for sub in ([index] * element for index, element in enumerate(my_list)) for element in sub] print("The result is :") print(my_result)OutputThe list is : [4, 0, 3, 1] The result is : [0, 0, 0, 0, 2, 2, 2, 3]ExplanationA list is defined and is displayed on the console.List comprehension is used to ... Read More

To select rows by integer location, use the iloc() function. Mention the index number of the row you want to select.Create a DataFrame −dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35]], index=['x', 'y', 'z'], columns=['a', 'b'])Select rows with integer location using iloc() −dataFrame.iloc[1] 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"DataFrame...", dataFrame # select rows with loc print"Select rows by passing label..." print(dataFrame.loc['z']) # select rows with integer location using iloc print"Select rows by passing integer ... Read More

When it is required to convert the suffix denomination to values, the dictionary is iterated over and the ‘replace’ method is used to convert them to values.ExampleBelow is a demonstration of the samemy_list = ["5Cr", "7M", "9B", "12L", "20Tr", "30K"] print("The list is :") print(my_list) value_dict = {"M": 1000000, "B": 1000000000, "Cr": 10000000, "L": 100000, "K": 1000, "Tr": 1000000000000} my_result = [] for element in my_list: for key in value_dict: if key in element: val = ... Read More

To select rows by passing a label, use the loc() function. Mention the index of which you want to select the row. This is the index label in our example. We have x, y and z as the index label and can be used to select rows with loc().Create a DataFrame −dataFrame = pd.DataFrame([[10, 15], [20, 25], [30, 35]], index=['x', 'y', 'z'], columns=['a', 'b'])Now, select rows with loc. We have passed the index label “z” −dataFrame.loc['z'] 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', ... Read More

To cast only a single column, use the astype() method. Let us first create a DataFrame with 2 columns. One of them is a “float64” type and another “int64” −dataFrame = pd.DataFrame( { "Reg_Price": [7000.5057, 1500, 5000, 8000, 9000.75768, 6000], "Units": [90, 120, 100, 150, 200, 130] } )Check the types −dataFrame.dtypes Let’s say we need to cast only a single column “Units” from int64 to int32. For that, use astype() −dataFrame.astype({'Units': 'int32'}).dtypesExampleFollowing is the code − import pandas as pd ... Read More