How do I calculate the first value in each group from every other value in the group to calculate change over time?

user_11320962 0
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In R I can calculate the change over time for each group in a data set like this:

df %>% 
  group_by(z) %>%
  mutate(diff = y - y[x == 0])

What is the equivalent in pandas?

I know that using pandas you can minus the first value of a column like this:

df['diff'] = df.y-df.y.iloc[0]

But how do you group by variable z?

Example data:

x   y   z
0   2   A
5   4   A
10  6   A
0   1   B
5   3   B
10  9   B

Expected output:

x   y   z   diff
0   2   A   0
5   4   A   2
10  6   A   4
0   1   B   0
5   5   B   4
10  9   B   8
dplyr pandas r python
1 Answers

You can try this.

temp = df.groupby('z').\
    apply(lambda g: g.y - g.y[0]).\
    reset_index().\
    rename(columns={'y': 'diff'}).\
    drop('z', axis=1)

df.merge(temp, how='inner', left_index=True, right_on='level_1').\
    drop('level_1', axis=1)

Return:

x   y   z   diff
0   2   A   0
5   4   A   2
10  6   A   4
0   1   B   0
5   5   B   4
10  9   B   8
user_76611580
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