CODEWITHSUNDEEP
pythonlistpandasloopsdataframe
Nabih Bawazir
Nabih Bawazir

Reputation: 7265

How do I build multiple list automatically

I have hundereds of dataframe, let say the name is df1,..., df250, I need to build list by a column of those dataframe. Usually I did manually, but today data is to much, and to prone to mistakes

Here's what I did

list1 = df1['customer_id'].tolist()
list2 = df2['customer_id'].tolist()
..
list250 = df250['customer_id'].tolist()

This is so manual, can we make this in easier way?

Upvotes: 2

Views: 63

Answers (3)

Sharku
Sharku

Reputation: 1092

Using exec function enables you to execute python code stored in a string:

for i in range(1,251):
   s = "list"+str(i)+" = df"+str(i)+"['customer_id'].tolist()"
   exec(s)

Upvotes: 1

Sergei
Sergei

Reputation: 470

I'd use next code. In this case there's no need to manually create list of DataFrames.

cust_lists = {'list{}'.format(i): globals()['df{}'.format(i)]['customer_id'].tolist()
                   for i in range(1, 251)}

Now you can access you lists from cust_lists dict by the name, like this:

`cust_lists['list1']`

or 

`list1`

Upvotes: 1

jpp
jpp

Reputation: 164773

The easier way is to take a step back and make sure you put your dataframes in a collection such as list or dict. You can then perform operations easily in a scalable way.

For example:

dfs = {1: df1, 2: df2, 3: df3, ... , 250: df250}

lists = {k: v['customer_id'].tolist() for k, v in dfs.items()}

You can then access the results as lists[1], lists[2], etc.

There are other benefits. For example, you are no longer polluting the namespace, you save the effort of explicitly defining variable names, you can easily store and transport related collections of objects.

Upvotes: 3

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