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
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.
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.
Source: Stackoverflow.com