添加到多索引数据帧时如何保持词法排序状态？

Question

Say I construct a dataframe with pandas, having multi-indexed columns:

mi      = pd.MultiIndex.from_product([['trial_1', 'trial_2', 'trial_3'], ['motor_neuron','afferent_neuron','interneuron'], ['time','voltage','calcium']])
ind     = np.arange(1,11)
df      = pd.DataFrame(np.random.randn(10,27),index=ind, columns=mi)

Link to image of output dataframe

Say I want only the voltage data from trial 1. I know that the following code fails, because the indices are not sorted lexically:

idx    = pd.IndexSlice
df.loc[:,idx['trial_1',:,'voltage']]

As explained in another post , the solution is to sort the dataframe's indices, which works as expected:

dfSorted    = df.sortlevel(axis=1)
dfSorted.loc[:,idx['trial_1',:,'voltage']]

I understand why this is necessary. However, say I want to add a new column:

dfSorted.loc[:,('trial_1','interneuron','scaledTime')] = 100 * dfSorted.loc[:,('trial_1','interneuron','time')]

Now dfSorted is not sorted anymore, since the new column was tacked onto the end, rather than snuggled into order. Again, I have to call sortlevel before selecting multiple columns.

I feel this makes for repetitive, bug-prone code, especially when adding lots of columns to the much bigger dataframe in my own project. Is there a (preferably clean-looking) way of inserting new columns in lexical order without having to call sortlevel over and over again?

Answer 1

One approach would be to use filter which does a text filter on the column names:

In [117]: df['trial_1'].filter(like='voltage')
Out[117]:
   motor_neuron afferent_neuron interneuron
        voltage         voltage     voltage
1     -0.548699        0.986121   -1.339783
2     -1.320589       -0.509410   -0.529686