Get list from pandas dataframe column or row?

Question

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)] 

Answer 1

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'>