循环遍历不同的 Pandas 数据帧

Loop through different Pandas Dataframes

提问by Nicolaj Jeppesen

im new to Python, and have what is probably a basis question.

我是 Python 新手，有什么可能是基础问题。

I have imported a number of Pandas Dataframes consisting of stock data for different sectors. So all columns are the same, just with different dataframe names.

我导入了许多 Pandas Dataframes，其中包含不同行业的股票数据。所以所有列都是相同的，只是数据框名称不同。

I need to do a lot of different small operations on some of the columns, and I can figure out how to do it on one Dataframe at a time, but I need to figure out how to loop over the different frames and do the same operations on each.

我需要对某些列执行许多不同的小操作，并且我可以弄清楚如何一次在一个 Dataframe 上执行此操作，但是我需要弄清楚如何遍历不同的帧并执行相同的操作在各个。

For example for one DF i do:

例如对于我做的一个 DF：

ConsumerDisc['IDX_EST_PRICE_BOOK']=1/ConsumerDisc['IDX_EST_PRICE_BOOK']
ConsumerDisc['IDX_EST_EV_EBITDA']=1/ConsumerDisc['IDX_EST_EV_EBITDA']
ConsumerDisc['INDX_GENERAL_EST_PE']=1/ConsumerDisc['INDX_GENERAL_EST_PE']
ConsumerDisc['EV_TO_T12M_SALES']=1/ConsumerDisc['EV_TO_T12M_SALES']
ConsumerDisc['CFtoEarnings']=ConsumerDisc['CASH_FLOW_PER_SH']/ConsumerDisc['TRAIL_12M_EPS']

And instead of just copying and pasting this code for the next 10 sectors, I want to to do it in a loop somehow, but I cant figure out how to access the df via variable, eg:

而不是仅仅为接下来的 10 个扇区复制和粘贴此代码，我想以某种方式在循环中执行此操作，但我无法弄清楚如何通过变量访问 df，例如：

CS=['ConsumerDisc']
CS['IDX_EST_PRICE_BOOK']=1/CS['IDX_EST_PRICE_BOOK'] 

so I could just create a list of df names and loop through it.

所以我可以创建一个 df 名称列表并循环遍历它。

Hope you can give a small example as how to do this.

希望你能举一个小例子来说明如何做到这一点。

回答by Roger Thomas

You're probably looking for something like this

你可能正在寻找这样的东西

for df in (df1, df2, df3):
    df['IDX_EST_PRICE_BOOK']=1/df['IDX_EST_PRICE_BOOK']
    df['IDX_EST_EV_EBITDA']=1/df['IDX_EST_EV_EBITDA']
    df['INDX_GENERAL_EST_PE']=1/df['INDX_GENERAL_EST_PE']
    df['EV_TO_T12M_SALES']=1/df['EV_TO_T12M_SALES']
    df['CFtoEarnings']=df['CASH_FLOW_PER_SH']/df['TRAIL_12M_EPS']

Here we're iterating over the dataframes that we've put in a tuple datasctructure, does that make sense?

在这里，我们正在迭代我们放入元组数据结构中的数据帧，这有意义吗？

回答by gold_cy

Do you mean something like this?

你的意思是这样的吗？

import pandas as pd
d = {'a' : pd.Series([1, 2, 3, 10]), 'b' : pd.Series([2, 2, 6, 8])}
z = {'d' : pd.Series([4, 2, 3, 1]), 'e' : pd.Series([21, 2, 60, 8])}
df = pd.DataFrame(d)
zf = pd.DataFrame(z)

df.head()

    a  b
0   1  2
1   2  2
2   3  6
3  10  8

df = df.apply(lambda x: 1/x)

df.head()

      a         b
0   1.0  0.500000
1   2.0  0.500000
2   3.0  0.166667
3  10.0  0.125000

You have more functions so you can create a function and then just applythat to each DataFrame. Alternatively you could also apply these lambda functions to only specific columns. So lets say you want to apply only 1/column to the every column but the last (going by your example, I am assuming it is in the end) you could do df.ix[:, :-1].apply(lambda x : 1/x).

您有更多的功能，因此您可以创建一个功能，然后将apply其添加到每个 DataFrame 中。或者，您也可以将这些 lambda 函数仅应用于特定列。因此，假设您只想将 1/column 应用于每一列，但最后一个（根据您的示例，我假设它在最后）您可以执行df.ix[:, :-1].apply(lambda x : 1/x).