使用pandas和numpy的表的平均索引

Question

I have been playing with pandas for a few hours now, I was wondering whether there is another faster way to add an extra column to your table which consists of the average of that row? I am creating a new list which contains the mean and then I am incorporating it in the data frame.

This is my code:

import numpy as np
import pandas as pd
userdata={"A":[2,5],"B":[4,6]}
tab=pd.DataFrame((userdata), columns=["A","B"])
lst=[np.mean([tab.loc[i,"A"],tab.loc[i,"B"]]) for i in range(len(tab.index))]
tab["Average of A and B"]=pd.DataFrame(lst)
tab

Answer 1

try df.mean(1) with assign . df.mean(1) tells pandas to calculate the mean along axis=1 (rows). axis=0 is the default.

df.assign(Mean=df.mean(1))

This produces a copy of df with added column.

To alter the existing dataframe

df['Mean'] = df.mean(1)

---

demo

tab.assign(Mean=tab.mean(1))

   A  B  Mean
0  2  4   3.0
1  5  6   5.5

Answer 2

A NumPy solution would be to work with the underlying array data for performance -

tab['average'] = tab.values.mean(1)

To choose specific columns, like 'A' and 'B' -

tab['average'] = tab[['A','B']].values.mean(1)

Runtime test -

In [41]: tab = pd.DataFrame(np.random.randint(0,9,(10000,10)))

# @piRSquared's soln
In [42]: %timeit tab.assign(Mean=tab.mean(1))
1000 loops, best of 3: 615 µs per loop

In [43]: tab = pd.DataFrame(np.random.randint(0,9,(10000,10)))

In [44]: %timeit tab['average'] = tab.values.mean(1)
1000 loops, best of 3: 297 µs per loop


In [37]: tab = pd.DataFrame(np.random.randint(0,9,(10000,100)))

# @piRSquared's soln
In [38]: %timeit tab.assign(Mean=tab.mean(1))
100 loops, best of 3: 4.71 ms per loop

In [39]: tab = pd.DataFrame(np.random.randint(0,9,(10000,100)))

In [40]: %timeit tab['average'] = tab.values.mean(1)
100 loops, best of 3: 3.6 ms per loop