dataframe

问题 The title wasn't too clear but here's an example. Suppose I have: person apple orange type Alice 11 23 apple Bob 14 20 orange and I want to get this column person new_col Alice 11 Bob 20 so we get the column 'apple' for row 'Alice' and 'orange' for row 'Bob'. I'm thinking iterrows, but that would be slow. Are there faster ways to do this? 回答1: Use DataFrame.lookup: df['new_col'] = df.lookup(df.index, df['type']) print (df) person apple orange type new_col 0 Alice 11 23 apple 11 1 Bob 14 20

问题 I am trying to reorder a data frame based on the median value associated with a column ID. I have a dataframe with a column of IDs and 2 columns of values. ID <- c("a","a","a","b","b","b","c","c","c","c") alpha <- c(3,4,5,9,11,13,1,1,1,0) beta <- c(2,3,4,3,4,5,4,5,6,7) df <- data.frame(ID,alpha,beta) ID alpha beta 1 a 3 2 2 a 4 3 3 a 5 4 4 b 9 3 5 b 11 4 6 b 13 5 7 c 1 4 8 c 1 5 9 c 1 6 10 c 0 7 I want to reorder this dataframe so that the column ID is in an order based on the decending means

问题 The title wasn't too clear but here's an example. Suppose I have: person apple orange type Alice 11 23 apple Bob 14 20 orange and I want to get this column person new_col Alice 11 Bob 20 so we get the column 'apple' for row 'Alice' and 'orange' for row 'Bob'. I'm thinking iterrows, but that would be slow. Are there faster ways to do this? 回答1: Use DataFrame.lookup: df['new_col'] = df.lookup(df.index, df['type']) print (df) person apple orange type new_col 0 Alice 11 23 apple 11 1 Bob 14 20

问题 I am trying to reorder a data frame based on the median value associated with a column ID. I have a dataframe with a column of IDs and 2 columns of values. ID <- c("a","a","a","b","b","b","c","c","c","c") alpha <- c(3,4,5,9,11,13,1,1,1,0) beta <- c(2,3,4,3,4,5,4,5,6,7) df <- data.frame(ID,alpha,beta) ID alpha beta 1 a 3 2 2 a 4 3 3 a 5 4 4 b 9 3 5 b 11 4 6 b 13 5 7 c 1 4 8 c 1 5 9 c 1 6 10 c 0 7 I want to reorder this dataframe so that the column ID is in an order based on the decending means

问题 trying tell r to read through the rows of my dataframe and add the column with the highest value in the row to a new column in the dataframe called "MOST_COMMON_CANCER" I tried the following code but got an error. BASE_DF2 <- BASE_DF2%>%mutate(MOST_COMMON_CANCER=colnames(BASE_DF2[8:26])[max.col(BASE_DF2[8:26],ties.method="first")],.keep="all",.after=c_INCS_RATE) Error: Problem with `mutate()` input `MOST_COMMON_CANCER`. x Input `MOST_COMMON_CANCER` can't be recycled to size 1. i Input `MOST

问题 Suppose I want to compare the content of two dataframes, but not the column names (or index names). Is it possible to achieve this without renaming the columns? For example: df = pd.DataFrame({'A': [1,2], 'B':[3,4]}) df_equal = pd.DataFrame({'a': [1,2], 'b':[3,4]}) df_diff = pd.DataFrame({'A': [1,2], 'B':[3,5]}) In this case, df is df_equal but different to df_diff , because the values in df_equal has the same content, but the ones in df_diff . Notice that the column names in df_equal are

问题 I have a dataframe in the following structure: cNames | cValues | number [a,b,c] | [1,2,3] | 10 [a,b,d] | [55,66,77]| 20 I would like to transpose - create columns from the names in cNames . But I can't manage to achieve this with transpose because I want a column for each value in the list. The needed output: a | b | c | d | number 1 | 2 | 3 | NaN | 10 55 | 66 | NaN | 77 | 20 How can I achieve this result? Thanks! The code to create the DF: d = {'cNames': [['a','b','c'], ['a','b','d']],

问题 I have a data frame like below. I would like to find unique rows (uniqueness). But in this data I have 'NA'. I like if all value in one row with NA value is the same with other rows (like rows: 1,2,5) I want to ignore it, but if not same (like rows : 2,4) I like to keep it as unique row. For example, in rows 1 ,2 and 6 all values except NA are the same so because NA can be value '1 and 3' I like to remove this row and just keep row 2. Also, in row 6 values 2 and 3 (exclude NA) are the same as

问题 For a research project, I need to process every individual's information from the website into an excel file. I have copied and pasted everything I need from the website onto a single column in an excel file, and I loaded that file using PANDAS. However, I need to present each individual's information horizontally instead of vertically like it is now. For example, this is what I have right now. I only have one column of unorganized data. df= pd.read_csv("ior work.csv", encoding = "ISO-8859-1"

问题 For a research project, I need to process every individual's information from the website into an excel file. I have copied and pasted everything I need from the website onto a single column in an excel file, and I loaded that file using PANDAS. However, I need to present each individual's information horizontally instead of vertically like it is now. For example, this is what I have right now. I only have one column of unorganized data. df= pd.read_csv("ior work.csv", encoding = "ISO-8859-1"