Loop through different Pandas Dataframes

Question

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

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.

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.

For example for one DF i do:

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:

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.

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

Answer 1

You're probably looking for something like this

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?

Answer 2

Do you mean something like this?

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 apply that 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) .