

In [1]:

    
import pandas as pd
import sys
%matplotlib inline



In [2]:

    
print 'Python version ' + sys.version
print 'Pandas version ' + pd.__version__









    



Python version 2.7.5 |Anaconda 2.1.0 (64-bit)| (default, Jul  1 2013, 12:37:52) [MSC v.1500 64 bit (AMD64)]
Pandas version 0.15.2

Merge

I have two dataframes that have dates as their index. The problem is that one of the dataframes has a timestamp and this is preventing me from adding the dataframes together. How can I match up the time stamps?



In [3]:

    
df1 = pd.DataFrame({'col1':[pd.Timestamp('20130102000030'),
                         pd.Timestamp('2013-01-03 00:00:30'),
                         pd.Timestamp('1/4/2013 000030')],
                 'col2':[1,10,18]
                 })
df1









    Out[3]:






  
    
      
      col1
      col2
    
  
  
    
      0
      2013-01-02 00:00:30
        1
    
    
      1
      2013-01-03 00:00:30
       10
    
    
      2
      2013-01-04 00:00:30
       18



In [4]:

    
df1 = df1.set_index('col1')
df1









    Out[4]:






  
    
      
      col2
    
    
      col1
      
    
  
  
    
      2013-01-02 00:00:30
        1
    
    
      2013-01-03 00:00:30
       10
    
    
      2013-01-04 00:00:30
       18



In [5]:

    
d = {'col2':[22,10,113]}

i = [pd.Timestamp('20130102'),
     pd.Timestamp('2013-01-03'),
     pd.Timestamp('1/4/2013')]
                 

df2 = pd.DataFrame(data=d, index = i)
df2.index.name = 'col1'
df2



In [6]:

    
# If we try to add the data frames together, we do not get the results we want.
df2+df1









    Out[6]:






  
    
      
      col2
    
    
      col1
      
    
  
  
    
      2013-01-02 00:00:00
      NaN
    
    
      2013-01-02 00:00:30
      NaN
    
    
      2013-01-03 00:00:00
      NaN
    
    
      2013-01-03 00:00:30
      NaN
    
    
      2013-01-04 00:00:00
      NaN
    
    
      2013-01-04 00:00:30
      NaN



In [7]:

    
# Make the index of df2 the same as the index of df1
# Fill the missing values with previous known value
#
#2013-01-02 00:00:00 => 22
#2013-01-02 00:00:30 => 22 
#2013-01-03 00:00:00 => 10
#2013-01-03 00:00:00 => 10
#2013-01-04 00:00:00 => 113
#2013-01-04 00:00:00 => 113
df2.reindex(df1.index, method='pad')









    Out[7]:






  
    
      
      col2
    
    
      col1
      
    
  
  
    
      2013-01-02 00:00:30
        22
    
    
      2013-01-03 00:00:30
        10
    
    
      2013-01-04 00:00:30
       113



In [8]:

    
# Now we can add them
df2 = df2.reindex(df1.index, method='pad')
df1+df2









    Out[8]:






  
    
      
      col2
    
    
      col1
      
    
  
  
    
      2013-01-02 00:00:30
        23
    
    
      2013-01-03 00:00:30
        20
    
    
      2013-01-04 00:00:30
       131

How do I add two dataframes together by row?



In [9]:

    
df1 = pd.DataFrame([1,2,3])
df1



In [10]:

    
df2 = pd.DataFrame([4,5,6])
df2



In [11]:

    
pd.concat([df1,df2])

How do I join two data frames by index?



In [12]:

    
d = {'col1':[22,10,113]}

i = [pd.Timestamp('1/1/2013'),
     pd.Timestamp('1/2/2013'),
     pd.Timestamp('1/3/2013')]
                 

df1 = pd.DataFrame(data=d, index = i)
df1



In [13]:

    
d = {'col2':[5,5]}

i = [pd.Timestamp('1/1/2013'),
     pd.Timestamp('1/3/2013')]
                 

df2 = pd.DataFrame(data=d, index = i)
df2



In [14]:

    
df1.merge(df2, left_index=True, right_index=True, how='left')

Author: David Rojas

	col1	col2
0	2013-01-02 00:00:30	1
1	2013-01-03 00:00:30	10
2	2013-01-04 00:00:30	18

	col2
col1
2013-01-02 00:00:30	1
2013-01-03 00:00:30	10
2013-01-04 00:00:30	18

	col2
col1
2013-01-02 00:00:00	NaN
2013-01-02 00:00:30	NaN
2013-01-03 00:00:00	NaN
2013-01-03 00:00:30	NaN
2013-01-04 00:00:00	NaN
2013-01-04 00:00:30	NaN