等价于Python/pandas中的R/ddply中的transform? [英] Equivalent of transform in R/ddply in Python/pandas?
问题描述
在R的ddply函数中，您可以按组计算任何新列，并将结果附加到原始数据帧中，例如:
In R's ddply function, you can compute any new columns group-wise, and append the result to the original dataframe, such as:
ddply(mtcars, .(cyl), transform, n=length(cyl)) # n is appended to the df
在Python/熊猫中，我先进行了计算，然后合并，例如:
In Python/pandas, I have computed it first, and then merge, such as:
df1 = mtcars.groupby("cyl").apply(lambda x: Series(x["cyl"].count(), index=["n"])).reset_index()
mtcars = pd.merge(mtcars, df1, on=["cyl"])
或类似的东西.
但是，我总是觉得这很艰巨，所以一次完成所有可行吗?
However, I always feel like that's pretty daunting, so is it feasible to do it all once?
谢谢.
推荐答案
您可以通过将groupby/transform操作的结果分配给它来向DataFrame添加列:
You can add a column to a DataFrame by assigning the result of a groupby/transform operation to it:
mtcars['n'] = mtcars.groupby("cyl")['cyl'].transform('count')
import pandas as pd
import pandas.rpy.common as com
mtcars = com.load_data('mtcars')
mtcars['n'] = mtcars.groupby("cyl")['cyl'].transform('count')
print(mtcars.head())
收益
mpg cyl disp hp drat wt qsec vs am gear carb n
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 7
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 7
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 11
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 7
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 14
要添加多列，可以使用groupby/apply.确保您应用的函数返回的DataFrame与其输入具有相同的索引.例如，
To add multiple columns, you could use groupby/apply. Make sure the function you apply returns a DataFrame with the same index as its input. For example,
mtcars[['n','total_wt']] = mtcars.groupby("cyl").apply(
lambda x: pd.DataFrame({'n': len(x['cyl']), 'total_wt': x['wt'].sum()},
index=x.index))
print(mtcars.head())
收益
mpg cyl disp hp drat wt qsec vs am gear carb n total_wt
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 7 21.820
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 7 21.820
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 11 25.143
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 7 21.820
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 14 55.989
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