median

问题 I have a complicated problem I am trying to solve. Please bear with me and feel free to ask any questions. I am quite new to SQL and having difficulty with this... I need to count the median of a group of values. Now the values are not given in a table. The values are derived from a table based on hourly occurrences grouped by date. Here's the sample table from where data is pooled. CREATE TABLE Table22( Request_Number BIGINT NOT NULL ,Request_Received_Date DATETIME NOT NULL ); INSERT INTO

问题 Below is the schema for the athena table I wish to calculate median for 'parameter_value' group by standard_lab_parameter_name & units. For this I followed link : https://docs.aws.amazon.com/redshift/latest/dg/r_MEDIAN.html But on running the query select median(parameter_value) from table_name group by standard_lab_parameter_name, units It throws error SYNTAX_ERROR: line 1:8: Function median not registered Any help? Or if some alternative query would be great 回答1: Athena is based on Presto 0

问题 Below is the schema for the athena table I wish to calculate median for 'parameter_value' group by standard_lab_parameter_name & units. For this I followed link : https://docs.aws.amazon.com/redshift/latest/dg/r_MEDIAN.html But on running the query select median(parameter_value) from table_name group by standard_lab_parameter_name, units It throws error SYNTAX_ERROR: line 1:8: Function median not registered Any help? Or if some alternative query would be great 回答1: Athena is based on Presto 0

问题 I have a python pandas dataframe with several columns and one column has 0 values. I want to replace the 0 values with the median or mean of this column. data is my dataframe artist_hotness is the column mean_artist_hotness = data['artist_hotness'].dropna().mean() if len(data.artist_hotness[ data.artist_hotness.isnull() ]) > 0: data.artist_hotness.loc[ (data.artist_hotness.isnull()), 'artist_hotness'] = mean_artist_hotness I tried this, but it is not working. 回答1: I think you can use mask and

问题 I have a python pandas dataframe with several columns and one column has 0 values. I want to replace the 0 values with the median or mean of this column. data is my dataframe artist_hotness is the column mean_artist_hotness = data['artist_hotness'].dropna().mean() if len(data.artist_hotness[ data.artist_hotness.isnull() ]) > 0: data.artist_hotness.loc[ (data.artist_hotness.isnull()), 'artist_hotness'] = mean_artist_hotness I tried this, but it is not working. 回答1: I think you can use mask and

问题 Closed. This question is not reproducible or was caused by typos. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 12 months ago . Improve this question I have the following data frame (here just a tiny part from a big one) ID= c(1,1,1,2,2,2,2,3,3) week = c(1,1,2,1,1,2,2,1,2) X = c(3.3,4.23,5.6,12,3.1,4.3,5.9,6.1,5.3) Y = c(1.3,2.4,6.8,5.5,4.3,3,6.6,2.6,5.7) TS_DF = data.frame(ID,week,X,Y) I would like to

问题 My dataframe look like this Plate Sample LogRatio P1 S1 0.42 P1 S2 0.23 P2 S3 0.41 P3 S4 0.36 P3 S5 0.18 I have calculated the median of each plate (but it's probably not the best idea to start like this) grouped = df.groupby("Plate") medianesPlate = grouped["LogRatio"].median() And I want to add a column on my dataframe CorrectedLogRatio = LogRatio-median(plate) I suppose with : df["CorrectedLogRatio"] = LogRatio-median(plate) To have something like this : Plate Sample LogRatio

问题 When applying a rank filter with ImageMagick such as erode or delate or median, it takes the minimum ( erode ) or maximum ( dilate ) or middle ( median ) value of all pixels within a certain radius or custom shape around each source pixel. Is it also possible to take a custom rank of the surrounding pixel values? For example when using a 5x5 square, with the erode or dilate or median filters, respectively the lowest, highest, of middle value of the 25 pixels surrounding each pixel is taken.

问题 I have a simple numpy array. array([[10, 0, 10, 0], [ 1, 1, 0, 0] [ 9, 9, 9, 0] [ 0, 10, 1, 0]]) I would like to take the median of each column, individually, of this array. However, there are a few 0 values in various places which I would like to ignore in the calculation of the medians. To further complicate, I would like to keep the columns with only 0 entries as having the median of 0 . In this manner, those columns would serve as a bit of a place holder, keeping the dimensions of the

问题 I have a simple numpy array. array([[10, 0, 10, 0], [ 1, 1, 0, 0] [ 9, 9, 9, 0] [ 0, 10, 1, 0]]) I would like to take the median of each column, individually, of this array. However, there are a few 0 values in various places which I would like to ignore in the calculation of the medians. To further complicate, I would like to keep the columns with only 0 entries as having the median of 0 . In this manner, those columns would serve as a bit of a place holder, keeping the dimensions of the