apply

A.整数索引： 用整数索引在pandas中 与python的list与truple有一点不同，如果在Series的默认row Index为数字的话，用整数索引会出错，那么我们就应该将index更改掉，改成其它。 1. import numpy as np import pandas as pd obj=pd.Series(np.arange(3.)) print(obj) print(obj[-1]) 看得出来如果这样会报错！ 因为在我们创建Series的时候index 为0，1，2 .而我们obj[-1] 表达的意思和它起冲突了，所以程序想知道我们想要什么就困难。我们需要的倒数第一个，而它以为我们是要index为-1的这个数据，所以就会报错。那么我们创建时将index更改掉，那么就可以了！ obj1=pd.Series(np.arange(3.),index=['a','b','c']) print(obj1[-1]) 2. 其实也可以这样索引，从输出中看出每个的不同，这里我用的是创建的第一个对象obj，不是obj1 print(obj[:1]) print(obj.loc[:1]) print(obj.iloc[:1]) B.算数与数据对齐： padnas一个特点是，两个相同的数据结构相加，那么他们的索引值对应相加，如果这两个，其中一个有不同的index，那么相加后

I'm using R, and I have two data.frames, A and B . They both have 6 rows, but A has 25000 columns (genes), and B has 30 columns. I'd like to apply a function with two arguments f(x,y) where x is every column of A and y is every column of B . So far it looks like this: i = 1 for (x in A){ j = 1 for (y in B){ out[i,j] <- f(x,y) j = j + 1 } i = i + 1 } I have two issues with this: from my Python programming I associate keeping track of counters like this as crufty, and from my R programming I am nervous of for loops. However, I can't quite see how to apply apply (or even if I should apply apply )

问题描述：css和js文件导入不到html中，出现错误：10Refused to apply style from ‘’ because its MIME type (‘text/html’) is not a supported stylesheet MIME type, and strict MIME checking is enabled. 在引入css和js库之前添加代码 解决问题 参考资料 文章来源: https://blog.csdn.net/wongnoubo/article/details/89716000

I am trying to apply some transformations to all the elements in a dataframe. When using the regular apply functions, I get a matrix back and not a dataframe. Is there a way to get a dataframe directly without adding as.data.frame to each line? df = data.frame(a = LETTERS[1:5], b = LETTERS[6:10]) apply(df, 1, tolower) #Matrix apply(df, 2, tolower) #Matrix sapply(df, tolower) #Matrix as.data.frame(sapply(df, tolower)) # Can I avoid "as.data.frame"? We can use lapply and assign it back to 'df' df[] <- lapply(df, tolower) The [] preserves the same structure as the original dataset. Using apply

How do I achieve row-wise iteration using purrr::map? Here's how I'd do it with a standard row-wise apply. df <- data.frame(a = 1:10, b = 11:20, c = 21:30) lst_result <- apply(df, 1, function(x){ var1 <- (x[['a']] + x[['b']]) var2 <- x[['c']]/2 return(data.frame(var1 = var1, var2 = var2)) }) However, this is not too elegant, and I would rather do it with purrr. May (or may not) be faster, too. You can use pmap for row-wise iteration. The columns are used as the arguments of whatever function you are using. In your example you would have a three-argument function. For example, here is pmap