It’s easy to turn a list of lists into a pandas dataframe:

import pandas as pd
df = pd.DataFrame([[1,2,3],[3,4,5]])

But how do I turn df back into a list of lists?

lol = df.what_to_do_now?
print lol
# [[1,2,3],[3,4,5]]

You could access the underlying array and call its tolist method:

>>> df = pd.DataFrame([[1,2,3],[3,4,5]])
>>> lol = df.values.tolist()
>>> lol
[[1L, 2L, 3L], [3L, 4L, 5L]]

If the data has column and index labels that you want to preserve, there are a few options.

Example data:

>>> df = pd.DataFrame([[1,2,3],[3,4,5]], \
       columns=('first', 'second', 'third'), \
       index=('alpha', 'beta')) 
>>> df
       first  second  third
alpha      1       2      3
beta       3       4      5

The tolist() method described in other answers is useful but yields only the core data – which may not be enough, depending on your needs.

>>> df.values.tolist()
[[1, 2, 3], [3, 4, 5]]

One approach is to convert the DataFrame to json using df.to_json() and then parse it again. This is cumbersome but does have some advantages, because the to_json() method has some useful options.

>>> df.to_json()
{
  "first":{"alpha":1,"beta":3},
  "second":{"alpha":2,"beta":4},"third":{"alpha":3,"beta":5}
}

>>> df.to_json(orient='split')
{
 "columns":["first","second","third"],
 "index":["alpha","beta"],
 "data":[[1,2,3],[3,4,5]]
}

Cumbersome but may be useful.

The good news is that it’s pretty straightforward to build lists for the columns and rows:

>>> columns = [df.index.name] + [i for i in df.columns]
>>> rows = [[i for i in row] for row in df.itertuples()]

This yields:

>>> print(f"columns: {columns}\nrows: {rows}") 
columns: [None, 'first', 'second', 'third']
rows: [['alpha', 1, 2, 3], ['beta', 3, 4, 5]]

If the None as the name of the index is bothersome, rename it:

df = df.rename_axis('stage')

Then:

>>> columns = [df.index.name] + [i for i in df.columns]
>>> print(f"columns: {columns}\nrows: {rows}") 

columns: ['stage', 'first', 'second', 'third']
rows: [['alpha', 1, 2, 3], ['beta', 3, 4, 5]]

I don’t know if it will fit your needs, but you can also do:

>>> lol = df.values
>>> lol
array([[1, 2, 3],
       [3, 4, 5]])

This is just a numpy array from the ndarray module, which lets you do all the usual numpy array things.

I wanted to preserve the index, so I adapted the original answer to this solution:

list_df = df.reset_index().values.tolist()

Now you can paste it somewhere else (e.g. to paste into a Stack Overflow question) and latter recreate it:

pd.Dataframe(list_df, columns=['name1', ...])
pd.set_index(['name1'], inplace=True)

Maybe something changed but this gave back a list of ndarrays which did what I needed.

list(df.values)

Note: I have seen many cases on Stack Overflow where converting a Pandas Series or DataFrame to a NumPy array or plain Python lists is entirely unecessary. If you’re new to the library, consider double-checking whether the functionality you need is already offered by those Pandas objects.

To quote a comment by @jpp:

In practice, there’s often no need to convert the NumPy array into a list of lists.

If a Pandas DataFrame/Series won’t work, you can use the built-in DataFrame.to_numpy and Series.to_numpy methods.

This is very simple:

import numpy as np

list_of_lists = np.array(df)

We can use the DataFrame.iterrows() function to iterate over each of the rows of the given Dataframe and construct a list out of the data of each row:

# Empty list 
row_list =[] 

# Iterate over each row 
for index, rows in df.iterrows(): 
    # Create list for the current row 
    my_list =[rows.Date, rows.Event, rows.Cost] 

    # append the list to the final list 
    row_list.append(my_list) 

# Print 
print(row_list) 

We can successfully extract each row of the given data frame into a list