How to loop over multiple DataFrames and produce multiple list?

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I have some difficulties to create multiple lists using pandas from a list of multiple dataframes:

df1 = pd.read_csv('df1.csv')
df2 = pd.read_csv('df2.csv')   
...
dfN = pd.read_csv('df1.csv')

dfs = [df1, df2, ..., dfN] 

So far, I am able to convert each dataframe into a list by df1 = df1.values.tolist(). Since I have multiple data frames, I would like to convert each dataframe into a list with a loop.

Appreciate any suggestions!


Use list comprehensions:

dfs = [i.values.tolist() for i in dfs]

Tutorial: Advanced For Loops in Python – Dataquest, If you've already mastered the basics of iterating through Python lists, take it to the Now, let's dive into how to use for loops with different sorts of data structures. We'll use the .items() method on our dictionary to generate a key and value for If we try to iterate over a pandas DataFrame as we would a numpy array, this� How to loop over multiple DataFrames and produce multiple list? Ask Question Asked 1 year, 4 months ago. Active 1 year, 4 months ago.


same as you are storing dataframes?

lists = []
for df in dfs:
    temp_list = df.values.tolist()
    lists.append(temp_list)

This will give you a list of lists. Each list within will be values from a dataframe. Or did I understand the question incorrectly?

Edit: If you wish to name each list, then you can use a dictionary instead? Would be better than trying to create thousands of variables dynamically.

dict_of_lists = {}
for index, df in enumerate(dfs):
    listname = "list" + str(index)
    dict_of_lists[listname] = df.values.tolist()

Reading DataFrames from multiple files in a loop, That is, even if your data comes in other formats, as long as pandas has a suitable data import function, you can apply a loop or comprehension to generate a list of� Just use lapply again for additionnal data processing, including saving the dataframes to disk. data.table is a nice package if you have to deal with a ton of data. Especially rbindlist allows you to rbind all the dt (=df) contained in a list into a single one if needed (split will do the reverse).


use pd.concat to join all dataframes to one big dataframe

df_all = pd.concat(dfs,axis=1)

df_all.values.tolist()

How to make a single Pandas DataFrame from multiple `.csv` files in , csv files. Create a list of .csv filenames, and initialize an empty list to store the converted DataFrames. Iterate over the list of filenames and� 1.81 s ± 27.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) The difference it more than 2 times! We get some savings of accessing all columns by unpacking rather than accessing one by one.


Loop through multiple dataframes in python, create multiple dataframe in for loop python create pandas how to put the results of loop into a dataframe python Any help on how to loop this and make it look concise? Print the first 5 rows of the first DataFrame of the list dataframes . List is equivalent to arrays in other languages, with the extra benefit of being dynamic in size. In Python, list is a type of container in Data Structures, which is used to store multiple data at the same time. Unlike Sets, the list in Python are ordered and have a definite count. There are multiple ways to iterate over a list in Python.


Python Iterate over multiple lists simultaneously, How to iterate over rows in Pandas Dataframe � Multiple inheritance in Python � Python Lists � Striver. Check out this Author's contributed articles. For Loop over a list ; For Loop over a matrix ; For Loop Syntax and Examples For (i in vector) { Exp } Here, R will loop over all the variables in vector and do the computation written inside the exp. Let's see a few examples. Example 1: We iterate over all the elements of a vector and print the current value.


Pandas nested for loop insert multiple data on different data frames , Here, you are overwriting the year index with each loop and therefore only the last continent dataframe is remaining for years 2010-2014. We learned how to iterate over different types of data structures, and how loops can be used with pandas DataFrames and matplotlib to create multiple traces or sub-plots programmatically. Finally, we looked at some more advanced techniques that give us more control over the operation and execution of our for loops.