python-3.x

问题 This question already has answers here : “Function ________ at 0x01D57aF0” return in python (2 answers) Closed 4 years ago . I wrote a new function and when I execute it, I get an error: <function read_grades at 0x000001F69E0FC8C8> Ok so here is my code: def add(x, y): z = x / y * 100 return z def calc_grade(perc): if perc < 50: return "1" if perc < 60: return "2" if perc < 75: return "3" if perc < 90: return "4" if perc >= 90: return "5" def calc_command(): num1 = input("Input your points: "

问题 This question already has answers here : “Function ________ at 0x01D57aF0” return in python (2 answers) Closed 4 years ago . I wrote a new function and when I execute it, I get an error: <function read_grades at 0x000001F69E0FC8C8> Ok so here is my code: def add(x, y): z = x / y * 100 return z def calc_grade(perc): if perc < 50: return "1" if perc < 60: return "2" if perc < 75: return "3" if perc < 90: return "4" if perc >= 90: return "5" def calc_command(): num1 = input("Input your points: "

问题 Purpose I have turned a double for loop into a single for loop using vectorization . I would like to now get rid of the last loop . I want to slice an Nx3 array of coordinates and calculate distances between the sliced portion and the remaining portion without using a for loop . Two cases (1) the slice is always 3x3 . (2) the slice is variable i.e., Mx3 where M is always significantly smaller than N Vectorizing the interaction of 1 row of the slice interacting with the remainder is

问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 6 years ago . Improve this question I am new to Python and trying to create a user interface with options to insert, delete and update data. THe data will be manipulated in a text file. I wanted to accept option from user and call respective function to do the activity. One alternative that I found

问题 Dynamically create string from pandas column I have three data frame like below one is df and another one is anomalies:- d = {'10028': [0], '1058': [25], '20120': [29], '20121': [22],'20122': [0], '20123': [0], '5043': [0], '5046': [0]} df1 = pd.DataFrame(data=d) Basically anomalies in a mirror copy of df just in anomalies the value will be 0 or 1 which indicates anomalies where value is 1 and non-anomaly where value is 0 d = {'10028': [0], '1058': [1], '20120': [1], '20121': [0],'20122': [0]

问题 My objective is to count the frequency of number in num_lst to the range in num_range. And display the output to a dictionary where key is the range, value is the frequencies of numbers within the range from num_lst. I've seen many posts and most are using numpy or pandas to solve it. However I want to find traditional ways to solve this without using np and pd. Can anyone give me the right direction. num_range = [(0.0, 20.0), (20.0, 40.0), (40.0, 60.0), (60.0, 80.0), (80.0, 100.0)] num_lst =

问题 My objective is to count the frequency of number in num_lst to the range in num_range. And display the output to a dictionary where key is the range, value is the frequencies of numbers within the range from num_lst. I've seen many posts and most are using numpy or pandas to solve it. However I want to find traditional ways to solve this without using np and pd. Can anyone give me the right direction. num_range = [(0.0, 20.0), (20.0, 40.0), (40.0, 60.0), (60.0, 80.0), (80.0, 100.0)] num_lst =

问题 Now i use optbinning module to binning all logstic regression modeling varible. however optbinning module need to use only one variable ,such as variable = "REGION_POPULATION_RELATIVE" x = df[variable].values y = df.TARGET.values from optbinning import OptimalBinning optb = OptimalBinning(name=variable, dtype="numerical", solver="ls", max_n_prebins=100, min_prebin_size=0.001, time_limit=50) optb.fit(x, y) how can i use loop to get binning result for all variable ? i try to codeing variable

问题 Now i use optbinning module to binning all logstic regression modeling varible. however optbinning module need to use only one variable ,such as variable = "REGION_POPULATION_RELATIVE" x = df[variable].values y = df.TARGET.values from optbinning import OptimalBinning optb = OptimalBinning(name=variable, dtype="numerical", solver="ls", max_n_prebins=100, min_prebin_size=0.001, time_limit=50) optb.fit(x, y) how can i use loop to get binning result for all variable ? i try to codeing variable

问题 df=pd.read_excel(filename) What attributes should I add to exclude header and footer? 回答1: Head over here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html you will find pandas.read_excel(io, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=False, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, verbose=False, parse_dates=False, date_parser