How to calculate percent change compared to the beginning value using pandas?

问题

I have a DataFrame and need to calculate percent change compared to the beginning of the year by companies. Is there any way to use pct_change() or other method to perform this task? Thanks!

df looks like

security    date        price
IBM         1/1/2016    100
IBM         1/2/2016    102
IBM         1/3/2016    108
AAPL        1/1/2016    1000
AAPL        1/2/2016    980
AAPL        1/3/2016    1050
AAPL        1/4/2016    1070

results I want

security    date        price   change
IBM         1/1/2016    100     NA
IBM         1/2/2016    102     2%
IBM         1/3/2016    108     8%
AAPL        1/1/2016    1000    NA
AAPL        1/2/2016    980     -2%
AAPL        1/3/2016    1050    5%
AAPL        1/4/2016    1070    7%

回答1:

Sounds like you are looking for an expanding_window version of pct_change(). This doesn't exist out of the box AFAIK, but you could roll your own:

df.groupby('security')['price'].apply(lambda x: x.div(x.iloc[0]).subtract(1).mul(100))

回答2:

This works, assuming you're already ordered by date within each possible grouping.

def pct_change(df):
    df['pct'] = 100 * (1 - df.iloc[0].price / df.price)
    return df

df.groupby('security').apply(pct_change)

回答3:

I had the same problem, but solved it his way:

(only difference was that the columns would be your company and not the row.)

for each column of my dataframe I did:

df[column] = df[column].pct_change().cumsum()

pct_change() calculates the change between now and the last value, and cumcum() adds it all together.

来源：https://stackoverflow.com/questions/35090498/how-to-calculate-percent-change-compared-to-the-beginning-value-using-pandas

标签

python

pandas

dataframe

percentage