Pandas 获取加载到内存中的所有数据帧的列表
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Pandas Get a List Of All Data Frames loaded into memory
提问by Kartik
I am using pandas to read several csv files into memory for processing and at some point would like to list all the data frames I have loaded into memory. Is there a simple way to do that? (I am thinking something like %ls but only for the data frames that I have available in memory)
我正在使用 Pandas 将几个 csv 文件读入内存进行处理，并且在某些时候想列出我加载到内存中的所有数据帧。有没有一种简单的方法可以做到这一点？（我正在考虑类似 %ls 的事情，但仅适用于我在内存中可用的数据帧）
回答by datawrestler
You could list all dataframes with the following:
您可以使用以下内容列出所有数据框：
import pandas as pd
# create dummy dataframes
df1 = pd.DataFrame({'Col1' : list(range(100))})
df2 = pd.DataFrame({'Col1' : list(range(100))})
# check whether all variables in scope are pandas dataframe.
# Dir() will return a list of string representations of the variables.
# Simply evaluate and test whether they are pandas dataframes
alldfs = [var for var in dir() if isinstance(eval(var), pd.core.frame.DataFrame)]
print(alldfs) # df1, df2
回答by Alex
回答by Jorge Martins
building on previous answers ... this returns a list
以以前的答案为基础……这将返回一个列表
import pandas as pd
%who_ls DataFrame
however, if you try to run a script it doesn't work
但是，如果您尝试运行脚本，则它不起作用
thus
因此
import pandas as pd
sheets=[]
for var in dir():
if isinstance(locals()[var], pd.core.frame.DataFrame) and var[0]!='_':
sheets.append(var)
since some DataFrames will have a copy for internal use only and those start with '_'
因为一些 DataFrames 将有一个仅供内部使用的副本，并且那些以“_”开头
