CODEWITHSUNDEEP
pythonpython-2.7pandasdataframe
nrs90
nrs90

Reputation: 168

Splitting a type float dataframe column into two columns in Python

I have the following data frame: 

>>>    print df.head()
>>>    print df.dtypes 

                                    PRICE   
2017-01-05 11:03:58.425000+00:00  340.143
2017-01-05 11:23:37.427000+00:00  216.639
2017-01-05 11:35:10.282000+00:00  201.093
2017-01-05 12:54:10.408000+00:00  201.093
2017-01-05 15:41:38.521000+00:00  201.093

PRICE float64
dtype: object

I want to split this up into two columns, and keep the PRICE column as type float64 and convert the other column to datetime64[ns] but I'm having trouble working out how to split a column of type float64!

Desired output: 

>>>    print df.head()
>>>    print df.dtypes
                       TIMESTAMP    PRICE   
2017-01-05 11:03:58.425000+00:00  340.143
2017-01-05 11:23:37.427000+00:00  216.639
2017-01-05 11:35:10.282000+00:00  201.093
2017-01-05 12:54:10.408000+00:00  201.093
2017-01-05 15:41:38.521000+00:00  201.093

TIMESTAMP    datetime64[ns]
PRICE               float64
dtype: object

I can achieve thsi by converting the dataframe into a csv then re-reading it back into another dataframe and then renaming the columns and then converting the columns into their desired types, but it is less pythonic and takes longer to run!

Thanks

Upvotes: 2

Views: 1209

Answers (1)

jezrael
jezrael

Reputation: 863226

You need reset_index, because first column is index:

df = df.reset_index().rename(columns={'index':'TIMESTAMP'})
print (df)
                TIMESTAMP    PRICE
0 2017-01-05 11:03:58.425  340.143
1 2017-01-05 11:23:37.427  216.639
2 2017-01-05 11:35:10.282  201.093
3 2017-01-05 12:54:10.408  201.093
4 2017-01-05 15:41:38.521  201.093

Upvotes: 3

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