我将Rmarkdown与reticulate包一起使用来将python和R编织在一起。但是,将Pandas DataFrame转换为R Dataframe的过程似乎并不一致。
以下是可重现的示例:
---
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{python}
import pandas as pd
df = pd.DataFrame({'a':4, 'b':5, 'c':9}, index=[0])
print(df)
```
```{r}
library(reticulate)
df2 <- reticulate::py$df
print(df2)
print(reticulate::py$df)
```
预期结果:
我希望对数据框进行粗略渲染(3次),如下所示:
## a b c
## 0 4 5 9
## a b c
## 0 4 5 9
## a b c
## 0 4 5 9
实际结果:
import pandas as pd
df = pd.DataFrame({'a':4, 'b':5, 'c':9}, index=[0])
print(df)
## a b c
## 0 4 5 9
library(reticulate)
df2 <- reticulate::py$df
print(df2)
## a b
## 1 <environment: 0x000000001dddb808> <environment: 0x000000001decdc58>
## c
## 1 <environment: 0x000000001e000918>
print(reticulate::py$df)
## a b
## 1 <environment: 0x000000001e807f78> <environment: 0x000000001e8fd480>
## c
## 1 <environment: 0x000000001e9ee608>
```
请注意,数据框可以从python正确打印。进入R后,R数据框对象似乎已损坏。
这是我的会话信息:
## R version 3.5.2 (2018-12-20)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17134)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] reticulate_1.10.0.9004
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.0 lattice_0.20-38 digest_0.6.16 rprojroot_1.3-2
## [5] grid_3.5.2 jsonlite_1.6 backports_1.1.2 magrittr_1.5
## [9] evaluate_0.11 stringi_1.1.7 Matrix_1.2-15 rmarkdown_1.10
## [13] tools_3.5.2 stringr_1.3.1 yaml_2.2.0 compiler_3.5.2
## [17] htmltools_0.3.6 knitr_1.20
答案 0 :(得分:0)
我能够做到,但功能顺序对我来说有点不同。我将网状封装与其他R封装一起加载。
我用Python完成了大部分工作,然后将其转换为R以使用DT包通过Excel和.CSV导出按钮进行数据视图。
output:
html_document:
toc: false
toc_depth: 1
---
```{r, loadPython, echo=F}
library(reticulate)
library(tidyverse)
library(DT)
```
```{python, echo=T}
# working with pandas df objects continues from other work
predictions = ts.make_predictions(model,
series + ' SARIMAX',
start=len(train),
end= len(train) + len(oos_exog)-1,
exog_data=oos_exog)
# make the OOS intervals
intervals = ts.get_oos_conf_interval(model=model,
steps_ahead=short_horizon,
exog_data = oos_exog)
# this is raw output
print(intervals)
```
```{r, echo=T}
# convert the pandas df object to R DF
r_df <- reticulate::py$intervals
# make a function to make fancy tables in R Markdown using DT package
makeTable <- function(df, end_col){
datatable(df, extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = list("excel", "csv")
)) %>%
formatRound(columns = c(1:end_col), digits = 0)
}
r_df
# output the table
makeTable(r_df, end_col=4)
```