Programming Articles - Page 1065 of 3363

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

When it is required to check for similar elements in a matrix row, a method is defined that take a matrix as parameter. The map method is used to covert the matrix to a tuple. The matrix values are iterated over and if the frequency is greater than 1, it is displayed on the console.ExampleBelow is a demonstration of the samefrom collections import Counter def find_dupes(my_matrix):    my_matrix = map(tuple, my_matrix)    freq_dict = Counter(my_matrix)    for (row, freq) in freq_dict.items():       if freq>1:          print (row) my_matrix = [[1, 1, 0, ... Read More

When it is required to check the alternate peak elements in a list, a function is defined that iterates through the list, the adjacent elements of the array are compared and depending on this, the output is displayed on the console.ExampleBelow is a demonstration of the samedef find_peak(my_array, array_length) :    if (array_length == 1) :       return 0    if (my_array[0] >= my_array[1]) :       return 0    if (my_array[array_length - 1] >= my_array[array_length - 2]) :       return array_length - 1    for i in range(1, array_length - 1) : ... Read More

When it is required to extract paired rows, a list comprehension and the ‘all’ operator is used.ExampleBelow is a demonstration of the samemy_list = [[10, 21, 34, 21, 37], [41, 41, 52, 68, 68, 41], [12, 29], [30, 30, 51, 51]] print("The list is :") print(my_list) my_result = [row for row in my_list if all(row.count(element) % 2 == 0 for element in row)] print("The result is :") print(my_result)OutputThe list is : [[10, 21, 34, 21, 37], [41, 41, 52, 68, 68, 41], [12, 29], [30, 30, 51, 51]] The result is : [[30, 30, 51, 51]]ExplanationA list ... Read More

When it is required to get the replacement combination from the other list, the ‘combinations’ method and the ‘list’ method is used.ExampleBelow is a demonstration of the samefrom itertools import combinations my_list = [54, 98, 11] print("The list is :") print(my_list) replace_list = [8, 10] my_result = list(combinations(my_list + replace_list, len(my_list))) print("The result is :") print(my_result)OutputThe list is : [54, 98, 11] The result is : [(54, 98, 11), (54, 98, 8), (54, 98, 10), (54, 11, 8), (54, 11, 10), (54, 8, 10), (98, 11, 8), (98, 11, 10), (98, 8, 10), (11, 8, ... Read More

Use the astype() method in Pandas to convert one datatype to another. Import the required library −import pandas as pdCreate a DataFrame. Here, we have 2 columns, “Reg_Price” is a float type and “Units” int type −dataFrame = pd.DataFrame( { "Reg_Price": [7000.5057, 1500, 5000, 8000, 9000.75768, 6000], "Units": [90, 120, 100, 150, 200, 130] } ) Check the datatypes of the columns created above −dataFrame.dtypesConvert both the types to int32 −dataFrame.astype('int32').dtypes ExampleFollowing is the code −import pandas as pd # ... Read More

When it is required to sort the rows by frequency of ‘K’, a list comprehension and ‘Counter’ methods are used.ExampleBelow is a demonstration of the samefrom collections import Counter my_list = [34, 56, 78, 99, 99, 99, 99, 99, 12, 12, 32, 51, 15, 11, 0, 0] print ("The list is ") print(my_list) my_result = [item for items, c in Counter(my_list).most_common() for item in [items] * c] print("The result is ") print(my_result)OutputThe list is [34, 56, 78, 99, 99, 99, 99, 99, 12, 12, 32, 51, 15, 11, 0, 0] The result is [99, 99, 99, ... Read More