Generate an example dataframe

import random
import string
import numpy as np
df = pd.DataFrame(
    columns=[random.choice(string.ascii_uppercase) for i in range(5)],
    data=np.random.rand(10,5))
df
          V         O         C         X         E
0  0.060255  0.341051  0.288854  0.740567  0.236282
1  0.933778  0.393021  0.547383  0.469255  0.053089
2  0.994518  0.156547  0.917894  0.070152  0.201373
3  0.077694  0.685540  0.865004  0.830740  0.605135
4  0.760294  0.838441  0.905885  0.146982  0.157439
5  0.116676  0.340967  0.400340  0.293894  0.220995
6  0.632182  0.663218  0.479900  0.931314  0.003180
7  0.726736  0.276703  0.057806  0.624106  0.719631
8  0.677492  0.200079  0.374410  0.962232  0.915361
9  0.061653  0.984166  0.959516  0.261374  0.361677

Now I want to filter a dataframe using the values in the first column, but since I make heavy use of chaining (e.g. df.T.replace(0, np.nan).pipe(np.log2).mean(axis=1).fillna(0).pipe(func)) I need a much more compact notation for the operation. Normally you'd do something like

df[df.iloc[:, 0] < 0.5]
          V         O         C         X         E
0  0.060255  0.341051  0.288854  0.740567  0.236282
3  0.077694  0.685540  0.865004  0.830740  0.605135
5  0.116676  0.340967  0.400340  0.293894  0.220995
9  0.061653  0.984166  0.959516  0.261374  0.361677

but the awkwardly redundant syntax is horrible for chaining. I want to replace it with a .query(), and normally you'd use the column name like df.query('V < 0.5'), but here I want to be able to query the table by column index number instead of by name. So in the example, I've deliberately randomized the column names. I also can not use the table name in the query like df.query('@df[0] < 0.5') since in a long chain, the intermediate result has no name.

I'm hoping there is some syntax such as df.query('_[0] < 0.05') where I can refer to the source table as some symbol _.

CodePudding user response：

You can use lambda functions in loc, which passes in the dataframe. You can then use iloc for your positional indexing. So you could do:

df.loc[lambda x: x.iloc[:, 0] > 0.5]

This should work in a method chain.

CodePudding user response：

For a single column with index:

df.query(f"{df.columns[0]}<0.5")

          V         O         C         X         E
0  0.060255  0.341051  0.288854  0.740567  0.236282
3  0.077694  0.685540  0.865004  0.830740  0.605135
5  0.116676  0.340967  0.400340  0.293894  0.220995
9  0.061653  0.984166  0.959516  0.261374  0.361677

For multiple columns with index:

idx = [0,1]
col = df.columns[np.r_[idx]]
val = 0.5
query = ' and '.join([f"{i} < {val}" for i in col])
# V < 0.5 and O < 0.5
print(df.query(query))

          V         O         C         X         E
0  0.060255  0.341051  0.288854  0.740567  0.236282
5  0.116676  0.340967  0.400340  0.293894  0.220995

CodePudding user response：

You can using f-string notation in df.query:

df.query(f'{df.columns[0]} < .5')

Output:

          J         M         O         R         N
3  0.114554  0.131948  0.650307  0.672486  0.688872
4  0.272368  0.745900  0.544068  0.504299  0.434122
6  0.418988  0.023691  0.450398  0.488476  0.787383
7  0.040440  0.220282  0.263902  0.660016  0.955950

Update using "walrus" operator in python 3.8

Let's try this:

((dfout := df.T.replace(0, np.nan).pipe(np.log2).mean(axis=1).fillna(0).to_frame(name='values'))
             .query(f'{dfout.columns[0]} > -2'))

output:

     values
N -1.356779
O -1.202353
M -1.591623
T -1.557801