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.

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.

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.

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

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

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

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

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

add a comment |

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