Reputation: 143
I want to change dtype of one data frame column (from datetime64 to object).
First of all, I create data frame:
Python 2.6.8 (unknown, Jan 26 2013, 14:35:25)
[GCC 4.7.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> values = pd.Series(i for i in range(5))
>>> dates = pd.date_range('20130101',periods=5)
>>> df = pd.DataFrame({'values': values, 'dates': dates})
>>> df
/usr/local/lib/python2.6/dist-packages/pandas/core/config.py:570: DeprecationWarning: height has been deprecated.
warnings.warn(d.msg, DeprecationWarning)
dates values
0 2013-01-01 00:00:00 0
1 2013-01-02 00:00:00 1
2 2013-01-03 00:00:00 2
3 2013-01-04 00:00:00 3
4 2013-01-05 00:00:00 4
It have two columns: one is datetime64 and other one is int64 dtype:
>>> df.dtypes
dates datetime64[ns]
values int64
dtype: object
In pandas documentation I found how to convert series to any dtypes. It looks like what I need:
>>> df['dates'].astype(object)
0 2013-01-01 00:00:00
1 2013-01-02 00:00:00
2 2013-01-03 00:00:00
3 2013-01-04 00:00:00
4 2013-01-05 00:00:00
Name: dates, dtype: object
But when I assign this series as dataframe column, I got a datetime64 dtype again.
>>> df['dates'] = df['dates'].astype(object)
>>> df.dtypes
dates datetime64[ns]
values int64
dtype: object
Please, help. How to convert data frame's column to object dtype? Thanks.
Upvotes: 11
Views: 40063
Reputation: 23
If you want to convert Date
column which is object
type to datetime64[ns] dtype;
, then following code will work:
df['Date']=pd.to_datetime(df['Date'])
Upvotes: 1
Reputation: 2771
Not proficient with lambda usage. In some simple case, df['dates'].astype(str)
would work also.
Note: it doesn't work when there are NaN in a column.
Not solution to OP, but others may find help in this question. Almost duplicate, but it's mostly talk about convert to numeric.
Upvotes: 0
Reputation: 11490
If you really want to change from datatype of datetime64[ns] to object, you could run something like this:
df['dates'] = df['dates'].apply(lambda x: str(x))
print df.types # Can verify to see that dates prints out as an object
Upvotes: 8
Reputation: 128918
Is this what you are after?
In [9]: pd.pivot_table(data=df,rows='columns',cols='rows',values='values',margins=True).T
Out[9]:
columns 2013-01-01 00:00:00 2013-01-02 00:00:00 2013-01-03 00:00:00 2013-01-04 00:00:00 2013-01-05 00:00:00 All
rows
a 0 NaN 2 3 NaN 1.666667
b NaN 1 NaN NaN 4 2.500000
All 0 1 2 3 4 2.000000
Upvotes: 0