Pandas DataFrame to List of Lists

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 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) 

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 had this problem: how do I get the headers of the df to be in row 0 for writing them to row 1 in the excel (using xlsxwriter)? None of the proposed solutions worked, but they pointed me in the right direction. I just needed one line more of code

# get csv data df = pd.read_csv(filename) # combine column headers and list of lists of values lol = [df.columns.tolist()] + df.values.tolist() 

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

list(df.values) 

1. The solutions presented so far suffer from a "reinventing the wheel" approach. Quoting @AMC:

If you're new to the library, consider double-checking whether the functionality you need is already offered by those Pandas objects.

2. If you convert a dataframe to a list of lists you will lose information - namely the index and columns names.

My solution: use to_dict()

dict_of_lists = df.to_dict(orient='split') 

This will give you a dictionary with three lists: index, columns, data. If you decide you really don't need the columns and index names, you get the data with

dict_of_lists['data'] 

Not quite relate to the issue but another flavor with same expectation

converting data frame series into list of lists to plot the chart using create_distplot in Plotly

 hist_data=[] hist_data.append(map_data['Population'].to_numpy().tolist()) 

"df.values" returns a numpy array. This does not preserve the data types. An integer might be converted to a float.

df.iterrows() returns a series which also does not guarantee to preserve the data types. See: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iterrows.html

The code below converts to a list of list and preserves the data types:

rows = [list(row) for row in df.itertuples()] 

If you wish to convert a Pandas DataFrame to a table (list of lists) and include the header column this should work:

import pandas as pd def dfToTable(df:pd.DataFrame) -> list: return [list(df.columns)] + df.values.tolist() 

Usage (in REPL):

>>> df = pd.DataFrame( [["r1c1","r1c2","r1c3"],["r2c1","r2c2","r3c3"]] , columns=["c1", "c2", "c3"]) >>> df c1 c2 c3 0 r1c1 r1c2 r1c3 1 r2c1 r2c2 r3c3 >>> dfToTable(df) [['c1', 'c2', 'c3'], ['r1c1', 'r1c2', 'r1c3'], ['r2c1', 'r2c2', 'r3c3']] 

This is very simple:

import numpy as np list_of_lists = np.array(df) 

A function I wrote that allows including the index column or the header row:

def df_to_list_of_lists(df, index=False, header=False): rows = [] if header: rows.append(([df.index.name] if index else []) + [e for e in df.columns]) for row in df.itertuples(): rows.append([e for e in row] if index else [e for e in row][1:]) return rows 

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

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.