outliers

来源： https://stackoverflow.com/questions/22080126/specific-outliers-on-a-heat-map-matplotlib

来源： https://stackoverflow.com/questions/22080126/specific-outliers-on-a-heat-map-matplotlib

问题 I am trying to filter out some outliers from a scatter plot of GPS elevation displacements with dates I'm trying to use df.rolling to compute a median and standard deviation for each window and then remove the point if it is greater than 3 standard deviations. However, I can't figure out a way to loop through the column and compare the the median value rolling calculated. Here is the code I have so far import pandas as pd import numpy as np def median_filter(df, window): cnt = 0 median = df[

问题 I am trying to filter out some outliers from a scatter plot of GPS elevation displacements with dates I'm trying to use df.rolling to compute a median and standard deviation for each window and then remove the point if it is greater than 3 standard deviations. However, I can't figure out a way to loop through the column and compare the the median value rolling calculated. Here is the code I have so far import pandas as pd import numpy as np def median_filter(df, window): cnt = 0 median = df[

问题 I am having a pandas dataframe with several of speed values which is continuously moving values, but its a sensor data, so we often get the errors in the middle at some points the moving average seems to be not helping also, so what methods can I use to remove these outliers or peak points from the data? Example: data points = {0.5,0.5,0.7,0.6,0.5,0.7,0.5,0.4,0.6,4,0.5,0.5,4,5,6,0.4,0.7,0.8,0.9} in this data If I see the points 4, 4, 5, 6 are completely outlier values, before I have used the

问题 I have a DataFrame for a fast Fourier transformed signal. There is one column for the frequency in Hz and another column for the corresponding amplitude. I have read a post made a couple of years ago, that you can use a simple boolean function to exclude or only include outliers in the final data frame that are above or below a few standard deviations. df = pd.DataFrame({'Data':np.random.normal(size=200)}) # example dataset of normally distributed data. df[~(np.abs(df.Data-df.Data.mean())>(3

问题 I find a function to detect outliers from columns but I do not know how to remove the outliers is there a function for excluding or removing outliers from the columns Here is the function to detect the outlier but I need help in a function to remove the outliers import numpy as np import pandas as pd outliers=[] def detect_outlier(data_1): threshold=3 mean_1 = np.mean(data_1) std_1 =np.std(data_1) for y in data_1: z_score= (y - mean_1)/std_1 if np.abs(z_score) > threshold: outliers.append(y)

问题 I find a function to detect outliers from columns but I do not know how to remove the outliers is there a function for excluding or removing outliers from the columns Here is the function to detect the outlier but I need help in a function to remove the outliers import numpy as np import pandas as pd outliers=[] def detect_outlier(data_1): threshold=3 mean_1 = np.mean(data_1) std_1 =np.std(data_1) for y in data_1: z_score= (y - mean_1)/std_1 if np.abs(z_score) > threshold: outliers.append(y)

问题 I find a function to detect outliers from columns but I do not know how to remove the outliers is there a function for excluding or removing outliers from the columns Here is the function to detect the outlier but I need help in a function to remove the outliers import numpy as np import pandas as pd outliers=[] def detect_outlier(data_1): threshold=3 mean_1 = np.mean(data_1) std_1 =np.std(data_1) for y in data_1: z_score= (y - mean_1)/std_1 if np.abs(z_score) > threshold: outliers.append(y)

问题 I have climate data and I'm trying to replace outliers with NA . I'm not using boxplot(x)$out is because I have a range of values to be considered to compute the outlier. temp_range <- c(-15, 45) wind_range <- c(0, 15) humidity_range <- c(0, 100) My dataframe looks like this df with outliers (I highlighted values that should be replaced with NA according to ranges.) So temp1 and temp2 outliers must be replaced to NA according to temp_range , wind 's outliers should be replaced to NA according