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Get list from pandas dataframe column or row?

I have a dataframe df imported from an Excel document like this: 

cluster load_date   budget  actual  fixed_price
A   1/1/2014    1000    4000    Y
A   2/1/2014    12000   10000   Y
A   3/1/2014    36000   2000    Y
B   4/1/2014    15000   10000   N
B   4/1/2014    12000   11500   N
B   4/1/2014    90000   11000   N
C   7/1/2014    22000   18000   N
C   8/1/2014    30000   28960   N
C   9/1/2014    53000   51200   N

I want to be able to return the contents of column 1 df['cluster'] as a list, so I can run a for-loop over it, and create an Excel worksheet for every cluster.

Is it also possible to return the contents of a whole column or row to a list? e.g. 

list = [], list[column1] or list[df.ix(row1)]
Pandas dataframe columns are a pandas series when you pull them out, which you can then call .tolist() on to turn them into a python list
From v0.24 onwards, .values will NO LONGER BE the preferred method for accessing underlying numpy arrays. See this answer.
Important note: Converting a Pandas Series to list or NumPy array is often unnecessary, and it almost certainly is in OP's case.
Also, there is no need to read the overly long answers for such a trivial question. df.to_numpy().tolist() should be fine for most use cases.
Simply typecast using list(x)

s
samlima

Pandas DataFrame columns are Pandas Series when you pull them out, which you can then call x.tolist() on to turn them into a Python list. Alternatively you cast it with list(x). 

import pandas as pd

data_dict = {'one': pd.Series([1, 2, 3], index=['a', 'b', 'c']),
             'two': pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}

df = pd.DataFrame(data_dict)

print(f"DataFrame:\n{df}\n")
print(f"column types:\n{df.dtypes}")

col_one_list = df['one'].tolist()

col_one_arr = df['one'].to_numpy()

print(f"\ncol_one_list:\n{col_one_list}\ntype:{type(col_one_list)}")
print(f"\ncol_one_arr:\n{col_one_arr}\ntype:{type(col_one_arr)}")

Output: 

DataFrame:
   one  two
a  1.0    1
b  2.0    2
c  3.0    3
d  NaN    4

column types:
one    float64
two      int64
dtype: object

col_one_list:
[1.0, 2.0, 3.0, nan]
type:<class 'list'>

col_one_arr:
[ 1.  2.  3. nan]
type:<class 'numpy.ndarray'>
I can't get my head around the style of the docs, because it's almost always straight syntax, where as I need syntax and example. E.g. Syntax would be to create a set: use the set keyword, and a list: Accompanying example: alist = df.cluster.tolist(). Until pandas is written in this way I will struggle. it's getting there, there are some examples now, but not for every method.
Thanks @Ben, great Answer! Can you tell me about the Dataframe method, Ive never seen that before... seems like you are converting a dinctionary to a df? df = DataFrame(d)?
One of the default ways to make a dataframe is to pass it a list of dictionaries with matching keys.
@yoshiserry most of the common functions now have example usage in their documentation, below the syntax and argument listing. You can also see 15 minutes to pandas for more beginner level examples.
@Ben I hadn't seen you are still active on SO, I wanted to mention that I submitted a decently sized edit to this answer, so let me know what you think :)
A
AMC

This returns a numpy array: 

arr = df["cluster"].to_numpy()

This returns a numpy array of unique values: 

unique_arr = df["cluster"].unique()

You can also use numpy to get the unique values, although there are differences between the two methods: 

arr = df["cluster"].to_numpy()
unique_arr = np.unique(arr)
What are the differences between the two methods?
@ProQ pandas unique method does not sort unlike np.unique() therefore it is faster. For more info look at pandas.pydata.org/pandas-docs/stable/reference/api/… and numpy.org/devdocs/reference/generated/numpy.unique.html. The docs are pretty solid for both the methods.
But the question is about a python list?
H
Hrvoje

Example conversion:

Numpy Array -> Panda Data Frame -> List from one Panda Column

Numpy Array 

data = np.array([[10,20,30], [20,30,60], [30,60,90]])

Convert numpy array into Panda data frame 

dataPd = pd.DataFrame(data = data)
    
print(dataPd)
0   1   2
0  10  20  30
1  20  30  60
2  30  60  90

Convert one Panda column to list

pdToList = list(dataPd['2'])

Why show the array creation code twice, as if it were an important part of the solution? Why even create that array at all, in fact? Isn't df = pd.DataFrame(data=[[10, 20, 30], [20, 30, 60], [30, 60, 90]]) more straightforward? Also, note the variable name and whitespace which follow Python style conventions. Iterate over list as a proof What does that prove, exactly? That it's a list?
Agreed - numpy array stuff is irrelevant to the question
m
marc_s

As this question attained a lot of attention and there are several ways to fulfill your task, let me present several options.

Those are all one-liners by the way ;)

Starting with: 

df
  cluster load_date budget actual fixed_price
0       A  1/1/2014   1000   4000           Y
1       A  2/1/2014  12000  10000           Y
2       A  3/1/2014  36000   2000           Y
3       B  4/1/2014  15000  10000           N
4       B  4/1/2014  12000  11500           N
5       B  4/1/2014  90000  11000           N
6       C  7/1/2014  22000  18000           N
7       C  8/1/2014  30000  28960           N
8       C  9/1/2014  53000  51200           N

Overview of potential operations: 

ser_aggCol (collapse each column to a list)
cluster          [A, A, A, B, B, B, C, C, C]
load_date      [1/1/2014, 2/1/2014, 3/1/2...
budget         [1000, 12000, 36000, 15000...
actual         [4000, 10000, 2000, 10000,...
fixed_price      [Y, Y, Y, N, N, N, N, N, N]
dtype: object


ser_aggRows (collapse each row to a list)
0     [A, 1/1/2014, 1000, 4000, Y]
1    [A, 2/1/2014, 12000, 10000...
2    [A, 3/1/2014, 36000, 2000, Y]
3    [B, 4/1/2014, 15000, 10000...
4    [B, 4/1/2014, 12000, 11500...
5    [B, 4/1/2014, 90000, 11000...
6    [C, 7/1/2014, 22000, 18000...
7    [C, 8/1/2014, 30000, 28960...
8    [C, 9/1/2014, 53000, 51200...
dtype: object


df_gr (here you get lists for each cluster)
                             load_date                 budget                 actual fixed_price
cluster                                                                                         
A        [1/1/2014, 2/1/2014, 3/1/2...   [1000, 12000, 36000]    [4000, 10000, 2000]   [Y, Y, Y]
B        [4/1/2014, 4/1/2014, 4/1/2...  [15000, 12000, 90000]  [10000, 11500, 11000]   [N, N, N]
C        [7/1/2014, 8/1/2014, 9/1/2...  [22000, 30000, 53000]  [18000, 28960, 51200]   [N, N, N]


a list of separate dataframes for each cluster

df for cluster A
  cluster load_date budget actual fixed_price
0       A  1/1/2014   1000   4000           Y
1       A  2/1/2014  12000  10000           Y
2       A  3/1/2014  36000   2000           Y

df for cluster B
  cluster load_date budget actual fixed_price
3       B  4/1/2014  15000  10000           N
4       B  4/1/2014  12000  11500           N
5       B  4/1/2014  90000  11000           N

df for cluster C
  cluster load_date budget actual fixed_price
6       C  7/1/2014  22000  18000           N
7       C  8/1/2014  30000  28960           N
8       C  9/1/2014  53000  51200           N

just the values of column load_date
0    1/1/2014
1    2/1/2014
2    3/1/2014
3    4/1/2014
4    4/1/2014
5    4/1/2014
6    7/1/2014
7    8/1/2014
8    9/1/2014
Name: load_date, dtype: object


just the values of column number 2
0     1000
1    12000
2    36000
3    15000
4    12000
5    90000
6    22000
7    30000
8    53000
Name: budget, dtype: object


just the values of row number 7
cluster               C
load_date      8/1/2014
budget            30000
actual            28960
fixed_price           N
Name: 7, dtype: object


============================== JUST FOR COMPLETENESS ==============================


you can convert a series to a list
['C', '8/1/2014', '30000', '28960', 'N']
<class 'list'>


you can convert a dataframe to a nested list
[['A', '1/1/2014', '1000', '4000', 'Y'], ['A', '2/1/2014', '12000', '10000', 'Y'], ['A', '3/1/2014', '36000', '2000', 'Y'], ['B', '4/1/2014', '15000', '10000', 'N'], ['B', '4/1/2014', '12000', '11500', 'N'], ['B', '4/1/2014', '90000', '11000', 'N'], ['C', '7/1/2014', '22000', '18000', 'N'], ['C', '8/1/2014', '30000', '28960', 'N'], ['C', '9/1/2014', '53000', '51200', 'N']]
<class 'list'>

the content of a dataframe can be accessed as a numpy.ndarray
[['A' '1/1/2014' '1000' '4000' 'Y']
 ['A' '2/1/2014' '12000' '10000' 'Y']
 ['A' '3/1/2014' '36000' '2000' 'Y']
 ['B' '4/1/2014' '15000' '10000' 'N']
 ['B' '4/1/2014' '12000' '11500' 'N']
 ['B' '4/1/2014' '90000' '11000' 'N']
 ['C' '7/1/2014' '22000' '18000' 'N']
 ['C' '8/1/2014' '30000' '28960' 'N']
 ['C' '9/1/2014' '53000' '51200' 'N']]
<class 'numpy.ndarray'>

code: 

# prefix ser refers to pd.Series object
# prefix df refers to pd.DataFrame object
# prefix lst refers to list object

import pandas as pd
import numpy as np

df=pd.DataFrame([
        ['A',   '1/1/2014',    '1000',    '4000',    'Y'],
        ['A',   '2/1/2014',    '12000',   '10000',   'Y'],
        ['A',   '3/1/2014',    '36000',   '2000',    'Y'],
        ['B',   '4/1/2014',    '15000',   '10000',   'N'],
        ['B',   '4/1/2014',    '12000',   '11500',   'N'],
        ['B',   '4/1/2014',    '90000',   '11000',   'N'],
        ['C',   '7/1/2014',    '22000',   '18000',   'N'],
        ['C',   '8/1/2014',    '30000',   '28960',   'N'],
        ['C',   '9/1/2014',    '53000',   '51200',   'N']
        ], columns=['cluster', 'load_date',   'budget',  'actual',  'fixed_price'])
print('df',df, sep='\n', end='\n\n')

ser_aggCol=df.aggregate(lambda x: [x.tolist()], axis=0).map(lambda x:x[0])
print('ser_aggCol (collapse each column to a list)',ser_aggCol, sep='\n', end='\n\n\n')

ser_aggRows=pd.Series(df.values.tolist()) 
print('ser_aggRows (collapse each row to a list)',ser_aggRows, sep='\n', end='\n\n\n')

df_gr=df.groupby('cluster').agg(lambda x: list(x))
print('df_gr (here you get lists for each cluster)',df_gr, sep='\n', end='\n\n\n')

lst_dfFiltGr=[ df.loc[df['cluster']==val,:] for val in df['cluster'].unique() ]
print('a list of separate dataframes for each cluster', sep='\n', end='\n\n')
for dfTmp in lst_dfFiltGr:
    print('df for cluster '+str(dfTmp.loc[dfTmp.index[0],'cluster']),dfTmp, sep='\n', end='\n\n')

ser_singleColLD=df.loc[:,'load_date']
print('just the values of column load_date',ser_singleColLD, sep='\n', end='\n\n\n')

ser_singleCol2=df.iloc[:,2]
print('just the values of column number 2',ser_singleCol2, sep='\n', end='\n\n\n')

ser_singleRow7=df.iloc[7,:]
print('just the values of row number 7',ser_singleRow7, sep='\n', end='\n\n\n')

print('='*30+' JUST FOR COMPLETENESS '+'='*30, end='\n\n\n')

lst_fromSer=ser_singleRow7.tolist()
print('you can convert a series to a list',lst_fromSer, type(lst_fromSer), sep='\n', end='\n\n\n')

lst_fromDf=df.values.tolist()
print('you can convert a dataframe to a nested list',lst_fromDf, type(lst_fromDf), sep='\n', end='\n\n')

arr_fromDf=df.values
print('the content of a dataframe can be accessed as a numpy.ndarray',arr_fromDf, type(arr_fromDf), sep='\n', end='\n\n')

as pointed out by cs95 other methods should be preferred over pandas .values attribute from pandas version 0.24 on see here. I use it here, because most people will (by 2019) still have an older version, which does not support the new recommendations. You can check your version with print(pd.__version__)

R
Ramin Melikov

If your column will only have one value something like pd.series.tolist() will produce an error. To guarantee that it will work for all cases, use the code below: 

(
    df
        .filter(['column_name'])
        .values
        .reshape(1, -1)
        .ravel()
        .tolist()
)
Gosh all this needed for the one value case? Catch that case with an except clause?
What's wrong with: list(df['column_name']) - it will work with one item.
@MrR If you need to chain your methods, then wrapping it into a list by list() will be breaking the consistency. Also, code is clearer this way as we're seeing step-by-step what is happening and we can at any point put a # in front of each or multiple lines to modify code and see how each line changes the df.
N
Natasha

Assuming the name of the dataframe after reading the excel sheet is df, take an empty list (e.g. dataList), iterate through the dataframe row by row and append to your empty list like- 

dataList = [] #empty list
for index, row in df.iterrows(): 
    mylist = [row.cluster, row.load_date, row.budget, row.actual, row.fixed_price]
    dataList.append(mylist)

Or, 

dataList = [] #empty list
for row in df.itertuples(): 
    mylist = [row.cluster, row.load_date, row.budget, row.actual, row.fixed_price]
    dataList.append(mylist)

No, if you print the dataList, you will get each rows as a list in the dataList.

Variable and function names should follow the lower_case_with_underscores style. What advantage does this solution have over the existing ones, exactly? Also, I really discourage the use of attribute-style access on Series and DataFrames.
C
Coddy

If you do df.T.values.tolist() it generates list of lists of column values.

k
kamran kausar 
 amount = list()
    for col in df.columns:
        val = list(df[col])
        for v in val:
            amount.append(v)

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