Suppose we have a dataframe and we calculate as percent change between rows
y_axis = [1,2,3,4,5,6,7,8,9]
x_axis = [100,105,115,95,90,88,110,100,0]
DF = pd.DataFrame({'Y':y_axis, 'X':x_axis})
DF = DF[['Y','X']]
DF['PCT'] = DF['X'].pct_change()
Y X PCT
0 1 100 NaN
1 2 105 0.050000
2 3 115 0.095238
3 4 95 -0.173913
4 5 90 -0.052632
5 6 88 -0.022222
6 7 110 0.250000
7 8 100 -0.090909
8 9 0 -1.000000
That way it starts from the first row. I want calculate pct_change()
starting from the last row.
One way to do it
DF['Reverse'] = list(reversed(x_axis))
DF['PCT_rev'] = DF['Reverse'].pct_change()
pct_rev = DF.PCT_rev.tolist()
DF['_PCT_'] = list(reversed(pct_rev))
DF2 = DF[['Y','X','PCT','_PCT_']]
Y X PCT _PCT_
0 1 100 NaN -0.047619
1 2 105 0.050000 -0.086957
2 3 115 0.095238 0.210526
3 4 95 -0.173913 0.055556
4 5 90 -0.052632 0.022727
5 6 88 -0.022222 -0.200000
6 7 110 0.250000 0.100000
7 8 100 -0.090909 inf
8 9 0 -1.000000 NaN
But that is a very ugly and inefficient solution. I was wondering if there are more elegant solutions?
DF.assign(_PCT_=DF.X.pct_change(-1))
Y X PCT _PCT_
0 1 100 NaN -0.047619
1 2 105 0.050000 -0.086957
2 3 115 0.095238 0.210526
3 4 95 -0.173913 0.055556
4 5 90 -0.052632 0.022727
5 6 88 -0.022222 -0.200000
6 7 110 0.250000 0.100000
7 8 100 -0.090909 inf
8 9 0 -1.000000 NaN
Series.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)
periods : int, default 1 Periods to shift for forming percent change
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.pct_change.html
I deleted my other answer because @su79eu7k 's is way better.
You can cut your time in half by using the underlying arrays. But you also have to suppress a warning.
a = DF.X.values
DF.assign(_PCT_=np.append((a[:-1] - a[1:]) / a[1:], np.nan))
Y X PCT _PCT_
0 1 100 NaN -0.047619
1 2 105 0.050000 -0.086957
2 3 115 0.095238 0.210526
3 4 95 -0.173913 0.055556
4 5 90 -0.052632 0.022727
5 6 88 -0.022222 -0.200000
6 7 110 0.250000 0.100000
7 8 100 -0.090909 inf
8 9 0 -1.000000 NaN
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