I would like to shift a column in a Pandas DataFrame, but I haven’t been able to find a method to do it from the documentation without rewriting the whole DF. Does anyone know how to do it? DataFrame:

##    x1   x2
##0  206  214
##1  226  234
##2  245  253
##3  265  272
##4  283  291

Desired output:

##    x1   x2
##0  206  nan
##1  226  214
##2  245  234
##3  265  253
##4  283  272
##5  nan  291

In [18]: a
Out[18]: 
   x1  x2
0   0   5
1   1   6
2   2   7
3   3   8
4   4   9

In [19]: a.x2 = a.x2.shift(1)

In [20]: a
Out[20]: 
   x1  x2
0   0 NaN
1   1   5
2   2   6
3   3   7
4   4   8

You need to use df.shift here.
df.shift(i) shifts the entire dataframe by i units down.

So, for i = 1:

Input:

    x1   x2  
0  206  214  
1  226  234  
2  245  253  
3  265  272    
4  283  291

Output:

    x1   x2
0  Nan  Nan   
1  206  214  
2  226  234  
3  245  253  
4  265  272 

So, run this script to get the expected output:

import pandas as pd

df = pd.DataFrame({'x1': ['206', '226', '245',' 265', '283'],
                   'x2': ['214', '234', '253', '272', '291']})

print(df)
df['x2'] = df['x2'].shift(1)
print(df)

Lets define the dataframe from your example by

>>> df = pd.DataFrame([[206, 214], [226, 234], [245, 253], [265, 272], [283, 291]], 
    columns=[1, 2])
>>> df
     1    2
0  206  214
1  226  234
2  245  253
3  265  272
4  283  291

Then you could manipulate the index of the second column by

>>> df[2].index = df[2].index+1

and finally re-combine the single columns

>>> pd.concat([df[1], df[2]], axis=1)
       1      2
0  206.0    NaN
1  226.0  214.0
2  245.0  234.0
3  265.0  253.0
4  283.0  272.0
5    NaN  291.0

Perhaps not fast but simple to read. Consider setting variables for the column names and the actual shift required.

Edit: Generally shifting is possible by df[2].shift(1) as already posted however would that cut-off the carryover.

If you don’t want to lose the columns you shift past the end of your dataframe, simply append the required number first:

    offset = 5
    DF = DF.append([np.nan for x in range(offset)])
    DF = DF.shift(periods=offset)
    DF = DF.reset_index() #Only works if sequential index

I suppose imports

import pandas as pd
import numpy as np

First append new row with NaN, NaN,... at the end of DataFrame (df).

s1 = df.iloc[0]    # copy 1st row to a new Series s1
s1[:] = np.NaN     # set all values to NaN
df2 = df.append(s1, ignore_index=True)  # add s1 to the end of df

It will create new DF df2. Maybe there is more elegant way but this works.

Now you can shift it:

df2.x2 = df2.x2.shift(1)  # shift what you want

Trying to answer a personal problem and similar to yours I found on Pandas Doc what I think would answer this question:

DataFrame.shift(periods=1, freq=None, axis=0) Shift index by desired number of periods with an optional time freq

Notes

If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data.

Hope to help future questions in this matter.

This is how I do it:

df_ext = pd.DataFrame(index=pd.date_range(df.index[-1], periods=8, closed='right'))
df2 = pd.concat([df, df_ext], axis=0, sort=True)
df2["forecast"] = df2["some column"].shift(7)

Basically I am generating an empty dataframe with the desired index and then just concatenate them together. But I would really like to see this as a standard feature in pandas so I have proposed an enhancement to pandas.