How to read a TSV file into pandas dataframe with datetime as the rownames?

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>>> data
                  Open
Date
2020-06-15  182.809924
2020-06-16  191.040258
2020-06-17  193.159706
2020-06-18  192.139603
2020-06-19  196.685578
...                ...         ...         ...         ...       ...        ...           ...
2021-06-08  255.160004
2021-06-09  253.809998
2021-06-10  254.289993
2021-06-11  257.989990
2021-06-14  257.899994

[252 rows x 1 columns]
>>> data['Open']
Date
2020-06-15    182.809924
2020-06-16    191.040258
2020-06-17    193.159706
2020-06-18    192.139603
2020-06-19    196.685578
                 ...
2021-06-08    255.160004
2021-06-09    253.809998
2021-06-10    254.289993
2021-06-11    257.989990
2021-06-14    257.899994
Name: Open, Length: 252, dtype: float64
>>> data.index
DatetimeIndex(['2020-06-15', '2020-06-16', '2020-06-17', '2020-06-18',
               '2020-06-19', '2020-06-22', '2020-06-23', '2020-06-24',
               '2020-06-25', '2020-06-26',
               ...
               '2021-06-01', '2021-06-02', '2021-06-03', '2021-06-04',
               '2021-06-07', '2021-06-08', '2021-06-09', '2021-06-10',
               '2021-06-11', '2021-06-14'],
              dtype='datetime64[ns]', name='Date', length=252, freq=None)
>>> type(data)
<class 'pandas.core.frame.DataFrame'>

I have the above data already in python.

If I have the input data in TSV format, how to read it so that it is the same as above? (<TAB> is a TAB character.)

Date<TAB>Open
2020-06-15<TAB>182.809924
...
2021-06-14<TAB>257.899994
pandas dataframe csv python
1 Answers

You can use read_csv() with \t as the delimiter:

data = pd.read_csv('data.tsv', sep='\t', parse_dates=['Date'], index_col='Date')

#                   Open
# Date                  
# 2020-06-15  182.809924
# ...                ...
# 2021-06-14  257.899994

data.index

# DatetimeIndex(['2020-06-15', '2021-06-14'], dtype='datetime64[ns]', name='Date', freq=None)
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