Shift column in pandas dataframe up by one?
PythonPandasDataframePython Problem Overview
I've got a pandas dataframe. I want to 'lag' one of my columns. Meaning, for example, shifting the entire column 'gdp' up by one, and then removing all the excess data at the bottom of the remaining rows so that all columns are of equal length again.
df =
y gdp cap
0 1 2 5
1 2 3 9
2 8 7 2
3 3 4 7
4 6 7 7
df_lag =
y gdp cap
0 1 3 5
1 2 7 9
2 8 4 2
3 3 7 7
Anyway to do this?
Python Solutions
Solution 1 - Python
In [44]: df['gdp'] = df['gdp'].shift(-1)
In [45]: df
Out[45]:
y gdp cap
0 1 3 5
1 2 7 9
2 8 4 2
3 3 7 7
4 6 NaN 7
In [46]: df[:-1]
Out[46]:
y gdp cap
0 1 3 5
1 2 7 9
2 8 4 2
3 3 7 7
Solution 2 - Python
shift column gdp up:
df.gdp = df.gdp.shift(-1)
and then remove the last row
Solution 3 - Python
To easily shift by 5 values for example and also get rid of the NaN rows, without having to keep track of the number of values you shifted by:
d['gdp'] = df['gdp'].shift(-5)
df = df.dropna()
Solution 4 - Python
Time is going. And current Pandas documentation recommend this way:
df.loc[:, 'gdp'] = df.gdp.shift(-1)
Solution 5 - Python
df.gdp = df.gdp.shift(-1) ## shift up
df.gdp.drop(df.gdp.shape[0] - 1,inplace = True) ## removing the last row
Solution 6 - Python
First shift the column:
df['gdp'] = df['gdp'].shift(-1)
Second remove the last row which contains an NaN Cell:
df = df[:-1]
Third reset the index:
df = df.reset_index(drop=True)