需要在行级别对 pandas dataframe 进行评估

Question

I have a scenario where my pandas data frame have a condition stored as string which I need to execute and store result as different column. Below example will help you understand better;

Existing DataFrame:

ID   Val    Cond
1     5      >10
1     15     >10

Expected DataFrame:

ID   Val    Cond    Result
1     5      >10     False
1     15     >10     True

As you see and I need to concatenate Val and Cond and do eval at row level.

Answer 1

If your conditions are formed from the basic operations (<, <=, ==, ,=, >, >=), then we can do this more efficiently using getattr . We use .str.extract to parse the condition and separate the comparison and the value. Using our dictionary we map the comparison to the Series attributes that we can then call for each unique comparison separately in a simple groupby.

import pandas as pd

print(df)
   ID  Val  Cond
0   1    5   >10
1   1   15   >10
2   1   20  ==20
3   1   25  <=25
4   1   26  <=25

# All operations we might have. 
d = {'>': 'gt', '<': 'lt', '>=': 'ge', '<=': 'le', '==': 'eq', '!=': 'ne'}

# Create a DataFrame with the LHS value, comparator, RHS value
tmp = pd.concat([df['Val'], 
                 df['Cond'].str.extract('(.*?)(\d+)').rename(columns={0: 'cond', 1: 'comp'})], 
                axis=1)
tmp[['Val', 'comp']] = tmp[['Val', 'comp']].apply(pd.to_numeric)
#   Val cond  comp
#0    5    >    10
#1   15    >    10
#2   20   ==    20
#3   25   <=    25
#4   26   <=    25
#5   10   !=    10

# Aligns on row Index
df['Result'] = pd.concat([getattr(gp['Val'], d[idx])(gp['comp']) 
                          for idx, gp in tmp.groupby('cond')])
#   ID  Val  Cond  Result
#0   1    5   >10   False
#1   1   15   >10    True
#2   1   20  ==20    True
#3   1   25  <=25    True
#4   1   26  <=25   False
#5   1   10  !=10   False

---

Simple, but inefficient and dangerous, is to eval on each row, creating a string of your condition. eval is dangerous as it can evaluate any code, so only use if you truly trust and know the data.

df['Result'] = df.apply(lambda x: eval(str(x.Val) + x.Cond), axis=1)
#    ID  Val  Cond  Result
#0   1    5   >10   False
#1   1   15   >10    True
#2   1   20  ==20    True
#3   1   25  <=25    True
#4   1   26  <=25   False
#5   1   10  !=10   False

Answer 2

You can also do something like this:

df["Result"] = [eval(x + y) for x, y in zip(df["Val"].astype(str), df["Cond"]]

Make the "Result" column by concatenating the strings df["Val"] and df["Cond"], then applying eval to that.