CODEWITHSUNDEEP
pythonpandasdataframedatetimeclipboard
Alex
Alex

Reputation: 19104

Pandas read DataFrame with datetime columns from clipboard

I see a lot of DataFrames posted to StackOverflow that look like:

          a                  dt         b
0 -0.713356 2015-10-01 00:00:00 -0.159170
1 -1.636397 2015-10-01 00:30:00 -1.038110
2 -1.390117 2015-10-01 01:00:00 -1.124016

I still haven't figured out a good way to copy these into my interpreter using .read_clipboard (argument list in .read_table docs).

I thought the key was the parse_dates parameter:

parse_dates : boolean or list of ints or names or list of lists or dict, default False
* boolean. If True -> try parsing the index.
* list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column.
* list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column.
* dict, e.g. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’

pd.read_clipboard(parse_dates={'dt': [1, 2]}) raises the exception NotImplementedError: file structure not yet supported.

When I try skipping the first row pd.read_clipboard(parse_dates=[[1, 2]], names=['a', 'dt1', 'dt2', 'b'], skiprows=1, header=None) I get the same exception.

How do others do this?

Upvotes: 10

Views: 654

Answers (2)

Alex
Alex

Reputation: 19104

In case it helps someone, here is what I do now:

df = pd.read_clipboard(skiprows=1, names=['a', 'dt1', 'dt2', 'b'])
df['dt'] = pd.to_datetime(df['dt1'] + ' ' + df['dt2'])
df = df[['a', 'dt', 'b']]

Upvotes: 1

cs95
cs95

Reputation: 402263

This is what I do. First, ensure that your columns have two spaces in between them:

          a                  dt         b
0 -0.713356  2015-10-01 00:00:00  -0.159170
1 -1.636397  2015-10-01 00:30:00  -1.038110
2 -1.390117  2015-10-01 01:00:00  -1.124016

Notice, the datetime column has one space between the date and time. This is important. Next, I use something like this to load it in:

df = pd.read_clipboard(sep='\s{2,}', parse_dates=[1], engine='python')
df

             a                  dt         b
0  0 -0.713356 2015-10-01 00:00:00 -0.159170
1  1 -1.636397 2015-10-01 00:30:00 -1.038110
2  2 -1.390117 2015-10-01 01:00:00 -1.124016


df.dtypes

a             object
dt    datetime64[ns]
b            float64
dtype: object

Yes, this isn't a fully automated process, but as long as you're dealing with small dataframes to copy in, it isn't that bad. Although I'm open to seeing better alternatives.

Upvotes: 10

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