""" >>> df = pd.DataFrame({'GDP': [100, 110, 120, 130, 140, 150], ...                    'INV': [0, 10, 20, 30, 40, 50]})  >>> lagged_dataframe(df, lags=3)    GDP  INV  GDP_Int  INV_Int  GDP_Lag01  INV_Lag01  GDP_Lag02  INV_Lag02  \ 0  100    0        1        1        NaN        NaN        NaN        NaN 1  110   10        1        1      100.0        0.0        NaN        NaN 2  120   20        1        1      110.0       10.0      100.0        0.0 3  130   30        1        1      120.0       20.0      110.0       10.0 4  140   40        1        1      130.0       30.0      120.0       20.0 5  150   50        1        1      140.0       40.0      130.0       30.0 <BLANKLINE>    GDP_Lag03  INV_Lag03 0        NaN        NaN 1        NaN        NaN 2        NaN        NaN 3      100.0        0.0 4      110.0       10.0 5      120.0       20.0  """  import numpy as np import pandas as pd   def constants(df):     new_columns = ["{}_Int".format(variable) for variable in df.columns]     return pd.DataFrame(np.ones_like(df.loc[:,:]), index=df.index, columns=new_columns)  def lag(df, n):     new_columns = ["{}_Lag{:02d}".format(variable, n) for variable in df.columns]     new_df = df.shift(n)     new_df.columns = new_columns     return new_df  def lagged_dataframe(df, lags=1):     data_frames = [df]     data_frames.append(constants(df))     data_frames.extend([lag(df, i) for i in range(1, lags + 1)])     return pd.concat(data_frames, axis=1)   if __name__ == "__main__":     import doctest     doctest.testmod(verbose=True, optionflags=doctest.NORMALIZE_WHITESPACE) 

I’m creating a pandas.DataFrame out of lags from an existing pandas.DataFrame, using DataFrame.shift and pandas.concat. There are a two aspects I don’t like about this code:

1. Using np.ones_like to create constants;
2. Building up a list of lag tables and using pandas.concat to combine them.