Pandas version of rbind

Ah, this is to do with how I created the DataFrame, not with how I was combining them. The long and the short of it is, if you are creating a frame using a loop and a statement that looks like this:

Frame = Frame.append(pandas.DataFrame(data = SomeNewLineOfData))

You must ignore the index

Frame = Frame.append(pandas.DataFrame(data = SomeNewLineOfData), ignore_index=True)

Or you will have issues later when combining data.

pd.concat will serve the purpose of rbind in R.

import pandas as pddf1 = pd.DataFrame({'col1': [1,2], 'col2':[3,4]})df2 = pd.DataFrame({'col1': [5,6], 'col2':[7,8]})print(df1)print(df2)print(pd.concat([df1, df2]))

The outcome will looks like:

   col1  col20     1     31     2     4   col1  col20     5     71     6     8   col1  col20     1     31     2     40     5     71     6     8

If you read the documentation careful enough, it will also explain other operations like cbind, ..etc.

This worked for me:

import numpy as npimport pandas as pddates = np.asarray(pd.date_range('1/1/2000', periods=8))df1 = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])df2 = df1.copy()df = df1.append(df2)

Yields:

                   A         B         C         D2000-01-01 -0.327208  0.552500  0.862529  0.4931092000-01-02  1.039844 -2.141089 -0.781609  1.3076002000-01-03 -0.462831  0.066505 -1.698346  1.1231742000-01-04 -0.321971 -0.544599 -0.486099 -0.2837912000-01-05  0.693749  0.544329 -1.606851  0.5277332000-01-06 -2.461177 -0.339378 -0.236275  0.1555692000-01-07 -0.597156  0.904511  0.369865  0.8625042000-01-08 -0.958300 -0.583621 -2.068273  0.5394342000-01-01 -0.327208  0.552500  0.862529  0.4931092000-01-02  1.039844 -2.141089 -0.781609  1.3076002000-01-03 -0.462831  0.066505 -1.698346  1.1231742000-01-04 -0.321971 -0.544599 -0.486099 -0.2837912000-01-05  0.693749  0.544329 -1.606851  0.5277332000-01-06 -2.461177 -0.339378 -0.236275  0.1555692000-01-07 -0.597156  0.904511  0.369865  0.8625042000-01-08 -0.958300 -0.583621 -2.068273  0.539434

If you don't already use the latest version of pandas I highly recommend upgrading. It is now possible to operate with DataFrames which contain duplicate indices.