I have two DataFrames:
df1 = ['Date_Time',
'Temp_1',
'Latitude',
'N_S',
'Longitude',
'E_W']
df2 = ['Date_Time',
'Year',
'Month',
'Day',
'Hour',
'Minute',
'Seconds']
As You can see both DataFrames have Date_Time as a common column. I want to Join these two DataFrames by matching Date_Time.
My current code is: df.join(df2, on='Date_Time'), but this is giving an error.
You are looking for a merge:
df1.merge(df2, on='Date_Time')
The keywords are the same as for join, but join uses only the index, see "Database-style DataFrame joining/merging".
Here's a simple example:
import pandas as pd
df1 = pd.DataFrame([[1, 2, 3]])
df2 = pd.DataFrame([[1, 7, 8],[4, 9, 9]], columns=[0, 3, 4])
In [4]: df1
Out[4]:
0 1 2
0 1 2 3
In [5]: df2
Out[5]:
0 3 4
0 1 7 8
1 4 9 9
In [6]: df1.merge(df2, on=0)
Out[6]:
0 1 2 3 4
0 1 2 3 7 8
In [7]: df1.merge(df2, on=0, how='outer')
Out[7]:
0 1 2 3 4
0 1 2 3 7 8
1 4 NaN NaN 9 9
If you try and join on a column you get an error:
In [8]: df1.join(df2, on=0)
# error!
Exception: columns overlap: array([0], dtype=int64)
df1.to_dict() and df2.to_dict() in your question? That's a strange error, and sounds like a string encoding issue...pd.to_datetime(df['Date_Time'])), it could be from another column... :S