Print full pandas index in Jupyter Notebook

Question

I have a pandas index with 380 elements and want to print the full index in Jupyter Notebook. I googled already but everything I’ve found did not help. For example this does not work:

with pd.option_context('display.max_rows', None, 'display.max_columns', None):
    print(my_index)

Neither this works:

with np.printoptions(threshold=np.inf):
    print(my_index.array)

In both cases only the first 10 and last 10 elements are shown. The elements in between are abbreviated by "…".

Asked By: principal-ideal-domain

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Source

Answer 1

IIUC, seems like you’re looking for 'display.max_seq_items'.

From the documentation :

display.max_seq_items : int or None
When pretty-printing a long sequence, no more then max_seq_items
will be printed. If items are omitted, they will be denoted by the
addition of "…" to the resulting string.

with pd.option_context('display.max_seq_items', None):
    print(my_index)

Tested it in Jupyter with :
my_index = pd.date_range(start='2023-02-01', end='2023-05-27') #len(my_index) = 116

Without the context :

DatetimeIndex(['2023-02-01', '2023-02-02', '2023-02-03', '2023-02-04', '2023-02-05', '2023-02-06', '2023-02-07', '2023-02-08', '2023-02-09',
               '2023-02-10',
               ...
               '2023-05-18', '2023-05-19', '2023-05-20', '2023-05-21', '2023-05-22', '2023-05-23', '2023-05-24', '2023-05-25', '2023-05-26',
               '2023-05-27'],
              dtype='datetime64[ns]', length=116, freq='D')

With the context :

DatetimeIndex(['2023-02-01', '2023-02-02', '2023-02-03', '2023-02-04', '2023-02-05', '2023-02-06', '2023-02-07', '2023-02-08', '2023-02-09',
               '2023-02-10', '2023-02-11', '2023-02-12', '2023-02-13', '2023-02-14', '2023-02-15', '2023-02-16', '2023-02-17', '2023-02-18',
               '2023-02-19', '2023-02-20', '2023-02-21', '2023-02-22', '2023-02-23', '2023-02-24', '2023-02-25', '2023-02-26', '2023-02-27',
               '2023-02-28', '2023-03-01', '2023-03-02', '2023-03-03', '2023-03-04', '2023-03-05', '2023-03-06', '2023-03-07', '2023-03-08',
               '2023-03-09', '2023-03-10', '2023-03-11', '2023-03-12', '2023-03-13', '2023-03-14', '2023-03-15', '2023-03-16', '2023-03-17',
               '2023-03-18', '2023-03-19', '2023-03-20', '2023-03-21', '2023-03-22', '2023-03-23', '2023-03-24', '2023-03-25', '2023-03-26',
               '2023-03-27', '2023-03-28', '2023-03-29', '2023-03-30', '2023-03-31', '2023-04-01', '2023-04-02', '2023-04-03', '2023-04-04',
               '2023-04-05', '2023-04-06', '2023-04-07', '2023-04-08', '2023-04-09', '2023-04-10', '2023-04-11', '2023-04-12', '2023-04-13',
               '2023-04-14', '2023-04-15', '2023-04-16', '2023-04-17', '2023-04-18', '2023-04-19', '2023-04-20', '2023-04-21', '2023-04-22',
               '2023-04-23', '2023-04-24', '2023-04-25', '2023-04-26', '2023-04-27', '2023-04-28', '2023-04-29', '2023-04-30', '2023-05-01',
               '2023-05-02', '2023-05-03', '2023-05-04', '2023-05-05', '2023-05-06', '2023-05-07', '2023-05-08', '2023-05-09', '2023-05-10',
               '2023-05-11', '2023-05-12', '2023-05-13', '2023-05-14', '2023-05-15', '2023-05-16', '2023-05-17', '2023-05-18', '2023-05-19',
               '2023-05-20', '2023-05-21', '2023-05-22', '2023-05-23', '2023-05-24', '2023-05-25', '2023-05-26', '2023-05-27'],
              dtype='datetime64[ns]', freq='D')

Answered By: Timeless

Answer 2

You can try

print(list(id_to_submit.index))

or

list(id_to_submit.index)

It is work for me

Answered By: Anastasia Tataurova

Print full pandas index in Jupyter Notebook

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