pandas to_datetime() then concat() on DateTime Index
I'm trying to merge 2 DataFrames using concat, on their DateTime Index, but it's not working as I expected. I copied some of this code from the example in the documentation for this example:
import pandas as pd
df = pd.DataFrame({'year': [2015, 2016],
'month': [2, 3],
'day': [4, 5],
'value': [444,555]})
df.set_index(pd.to_datetime(df.loc[:,['year','month','day']]),inplace=True)
df.drop(['year','month','day'],axis=1,inplace=True)
df2 = pd.DataFrame(data=[222,333],
index=pd.to_datetime(['2015-02-04','2016-03-05']))
pd.concat([df,df2])
Out[1]:
value 0
2015-02-04 444.0 NaN
2016-03-05 555.0 NaN
2015-02-04 NaN 222.0
2016-03-05 NaN 333.0
Why isn't it recognizing the same dates on the index and merging accordingly? I verified that both Indexes are DateTime:
df.index
Out[2]: DatetimeIndex(['2015-02-04', '2016-03-05'], dtype='datetime64[ns]', freq=None)
df2.index
Out[3]: DatetimeIndex(['2015-02-04', '2016-03-05'], dtype='datetime64[ns]', freq=None)
Thanks.
python pandas datetime
asked May 10 '17 at 15:35
Josh D
13413
add a comment |
I'm trying to merge 2 DataFrames using concat, on their DateTime Index, but it's not working as I expected. I copied some of this code from the example in the documentation for this example:
import pandas as pd
df = pd.DataFrame({'year': [2015, 2016],
'month': [2, 3],
'day': [4, 5],
'value': [444,555]})
df.set_index(pd.to_datetime(df.loc[:,['year','month','day']]),inplace=True)
df.drop(['year','month','day'],axis=1,inplace=True)
df2 = pd.DataFrame(data=[222,333],
index=pd.to_datetime(['2015-02-04','2016-03-05']))
pd.concat([df,df2])
Out[1]:
value 0
2015-02-04 444.0 NaN
2016-03-05 555.0 NaN
2015-02-04 NaN 222.0
2016-03-05 NaN 333.0
Why isn't it recognizing the same dates on the index and merging accordingly? I verified that both Indexes are DateTime:
df.index
Out[2]: DatetimeIndex(['2015-02-04', '2016-03-05'], dtype='datetime64[ns]', freq=None)
df2.index
Out[3]: DatetimeIndex(['2015-02-04', '2016-03-05'], dtype='datetime64[ns]', freq=None)
Thanks.
python pandas datetime
asked May 10 '17 at 15:35
Josh D
13413
add a comment |
I'm trying to merge 2 DataFrames using concat, on their DateTime Index, but it's not working as I expected. I copied some of this code from the example in the documentation for this example:
import pandas as pd
df = pd.DataFrame({'year': [2015, 2016],
'month': [2, 3],
'day': [4, 5],
'value': [444,555]})
df.set_index(pd.to_datetime(df.loc[:,['year','month','day']]),inplace=True)
df.drop(['year','month','day'],axis=1,inplace=True)
df2 = pd.DataFrame(data=[222,333],
index=pd.to_datetime(['2015-02-04','2016-03-05']))
pd.concat([df,df2])
Out[1]:
value 0
2015-02-04 444.0 NaN
2016-03-05 555.0 NaN
2015-02-04 NaN 222.0
2016-03-05 NaN 333.0
Why isn't it recognizing the same dates on the index and merging accordingly? I verified that both Indexes are DateTime:
df.index
Out[2]: DatetimeIndex(['2015-02-04', '2016-03-05'], dtype='datetime64[ns]', freq=None)
df2.index
Out[3]: DatetimeIndex(['2015-02-04', '2016-03-05'], dtype='datetime64[ns]', freq=None)
Thanks.
python pandas datetime
asked May 10 '17 at 15:35
Josh D
13413
I'm trying to merge 2 DataFrames using concat, on their DateTime Index, but it's not working as I expected. I copied some of this code from the example in the documentation for this example:
import pandas as pd
df = pd.DataFrame({'year': [2015, 2016],
'month': [2, 3],
'day': [4, 5],
'value': [444,555]})
df.set_index(pd.to_datetime(df.loc[:,['year','month','day']]),inplace=True)
df.drop(['year','month','day'],axis=1,inplace=True)
df2 = pd.DataFrame(data=[222,333],
index=pd.to_datetime(['2015-02-04','2016-03-05']))
pd.concat([df,df2])
Out[1]:
value 0
2015-02-04 444.0 NaN
2016-03-05 555.0 NaN
2015-02-04 NaN 222.0
2016-03-05 NaN 333.0
Why isn't it recognizing the same dates on the index and merging accordingly? I verified that both Indexes are DateTime:
df.index
Out[2]: DatetimeIndex(['2015-02-04', '2016-03-05'], dtype='datetime64[ns]', freq=None)
df2.index
Out[3]: DatetimeIndex(['2015-02-04', '2016-03-05'], dtype='datetime64[ns]', freq=None)
Thanks.
python pandas datetime
python pandas datetime
asked May 10 '17 at 15:35
Josh D
13413
asked May 10 '17 at 15:35
Josh D
13413
asked May 10 '17 at 15:35
Josh D
13413
asked May 10 '17 at 15:35
Josh D
13413
asked May 10 '17 at 15:35
Josh D
13413
13413
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
pass axis=1 to concatenate column-wise:
In [7]:
pd.concat([df,df2], axis=1)
Out[7]:
value 0
2015-02-04 444 222
2016-03-05 555 333
Alternatively you could've joined:
In [5]:
df.join(df2)
Out[5]:
value 0
2015-02-04 444 222
2016-03-05 555 333
or merged:
In [8]:
df.merge(df2, left_index=True, right_index=True)
Out[8]:
value 0
2015-02-04 444 222
2016-03-05 555 333
answered May 10 '17 at 15:39
EdChum
170k32359310
add a comment |
You need axis=1:
pd.concat([df,df2], axis=1)
Output:
value 0
2015-02-04 444 222
2016-03-05 555 333
answered May 10 '17 at 15:40
Scott Boston
50.7k72754
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
pass axis=1 to concatenate column-wise:
In [7]:
pd.concat([df,df2], axis=1)
Out[7]:
value 0
2015-02-04 444 222
2016-03-05 555 333
Alternatively you could've joined:
In [5]:
df.join(df2)
Out[5]:
value 0
2015-02-04 444 222
2016-03-05 555 333
or merged:
In [8]:
df.merge(df2, left_index=True, right_index=True)
Out[8]:
value 0
2015-02-04 444 222
2016-03-05 555 333
answered May 10 '17 at 15:39
EdChum
170k32359310
add a comment |
pass axis=1 to concatenate column-wise:
In [7]:
pd.concat([df,df2], axis=1)
Out[7]:
value 0
2015-02-04 444 222
2016-03-05 555 333
Alternatively you could've joined:
In [5]:
df.join(df2)
Out[5]:
value 0
2015-02-04 444 222
2016-03-05 555 333
or merged:
In [8]:
df.merge(df2, left_index=True, right_index=True)
Out[8]:
value 0
2015-02-04 444 222
2016-03-05 555 333
answered May 10 '17 at 15:39
EdChum
170k32359310
add a comment |
pass axis=1 to concatenate column-wise:
In [7]:
pd.concat([df,df2], axis=1)
Out[7]:
value 0
2015-02-04 444 222
2016-03-05 555 333
Alternatively you could've joined:
In [5]:
df.join(df2)
Out[5]:
value 0
2015-02-04 444 222
2016-03-05 555 333
or merged:
In [8]:
df.merge(df2, left_index=True, right_index=True)
Out[8]:
value 0
2015-02-04 444 222
2016-03-05 555 333
answered May 10 '17 at 15:39
EdChum
170k32359310
pass axis=1 to concatenate column-wise:
In [7]:
pd.concat([df,df2], axis=1)
Out[7]:
value 0
2015-02-04 444 222
2016-03-05 555 333
Alternatively you could've joined:
In [5]:
df.join(df2)
Out[5]:
value 0
2015-02-04 444 222
2016-03-05 555 333
or merged:
In [8]:
df.merge(df2, left_index=True, right_index=True)
Out[8]:
value 0
2015-02-04 444 222
2016-03-05 555 333
answered May 10 '17 at 15:39
EdChum
170k32359310
answered May 10 '17 at 15:39
EdChum
170k32359310
answered May 10 '17 at 15:39
EdChum
170k32359310
answered May 10 '17 at 15:39
EdChum
170k32359310
170k32359310
add a comment |
add a comment |
You need axis=1:
pd.concat([df,df2], axis=1)
Output:
value 0
2015-02-04 444 222
2016-03-05 555 333
answered May 10 '17 at 15:40
Scott Boston
50.7k72754
add a comment |
You need axis=1:
pd.concat([df,df2], axis=1)
Output:
value 0
2015-02-04 444 222
2016-03-05 555 333
answered May 10 '17 at 15:40
Scott Boston
50.7k72754
add a comment |
You need axis=1:
pd.concat([df,df2], axis=1)
Output:
value 0
2015-02-04 444 222
2016-03-05 555 333
answered May 10 '17 at 15:40
Scott Boston
50.7k72754
You need axis=1:
pd.concat([df,df2], axis=1)
Output:
value 0
2015-02-04 444 222
2016-03-05 555 333
answered May 10 '17 at 15:40
Scott Boston
50.7k72754
answered May 10 '17 at 15:40
Scott Boston
50.7k72754
answered May 10 '17 at 15:40
Scott Boston
50.7k72754
answered May 10 '17 at 15:40
Scott Boston
50.7k72754
50.7k72754
add a comment |
add a comment |
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