So I have a dataset that contains history of a specific tag from a start to end date. I’ll be comparing rows based on the a date column, if they’re similar by month, day and year, I’ll add those to a temporary list by the value of the next column and then once I have ..
I need to optimize this piece of code, the for loop takes the longest time and I think this is not the best way to write the loop def insert_scenarios(self, repeated_data_array): # establish count to control loop to insert scenarios count = 0 print("Inserting Scenarios") # iterate through array to insert scenario keys, write "history" ..
I need to load zoo.csv file in the url: https://www.kaggle.com/uciml/zoo-animal-classification and then print the information of that data set in python. Please help me do this. I have tried quite a lot of ways. But all of them are throwing errors Source: Python..
I want to create an images dataset as .mat file for celebrity face detection and recognition in python. We are unable to a .mat file of images dataset for training and classification purpose of an image. My question is how to create an image dataset and how to save and load that dataset for testing ..
I’m trying to define the file processing methods before parsing the file. Consequently, I’m getting ‘NoneType’ object has no attribute ‘fillna error. import zipfile import pathlib import statistics import pandas as pd import numpy as np class DataProcessing: def __init__(self, df=None, file=None, duplicates=None, uninformative=None, mhealth_dataset=None): self.df = df self.file = file self.duplicates = duplicates self.uninformative ..
I want to extract and process all the files in a zipped file? import re import zipfile import pathlib import pandas as pd # Download mHealth dataset def parse(zip_file): # Extract all the files in output directory with zipfile.ZipFile(zip_file, "r") as zfile: for file in zfile.extractall(): if file.is_file(): old_name = file.stem extension = file.suffix directory ..
I am trying to implement a neural network model using numpy with a dataset loaded using sklearn’s make_moonsand all functions are set. While creating my dataset I have used a random_state so that input doesn’t change. After training my model, I am trying to plot decision boundaries of my predictions and I see that input ..
I have some very large tables I am trying to filter for multiple specific values. For the sake of simplicity, let’s say a table resembles the following: Column1 Column2 Column3 1 4 7 2 5 8 3 6 9 I would want to create a new table which only includes the rows where Column2 equals ..
In the function down below, I am trying to normalize prices (converting them to percentage differences from the starting price) in dataframes contained in a list of dataframes. Each dataframe has two columns: date and price. def normalize_windows(window_data: List[DataFrame]): starting_price = window_data[‘price’].values for window in window_data: for index, row in window.iterrows(): window.at[index, ‘price’] = (row[‘price’] ..
ds = xr.open_dataset(‘./input_file.nc’) ds2 = ds.where(ds.total_precip > 0, drop=True) print(ds2) This code replaces some zero values with Nan, but i still see many 0 values not getting dropped. If i change the condition to ds.total_precip == 0 I get a smaller data set with all total_precip values = 0. Am I missing something? Is there ..