通过将不同数据框的2列中的值视为范围来为数据框分配值

Question

The following code explains the scenario, I have a dataframe(df_ticker) with 3 columns

import pandas as pd 
df_ticker = pd.DataFrame({'Min_val': [22382.729,36919.205,46735.164,62247.61], 'Max_val': [36901.758,46716.06,62045.06,182727.05],
           'Ticker':['$','$$','$$$','$$$$']})
df_ticker`

df_ticker My second dataframe contains 2 columns

df_values = pd.DataFrame({'Id':[1,2,3,4,5,6],'sal_val': [3098,45639.987,65487.4,56784.8,8,736455]})
df_values  `

df_values

For every value in df_values ['sal_val'], I want to check in which range it falls in df_ticker [Max_val] and df_ticker [min_val] and assign df_ticker [ticker] accordingly.
Sample output would be something like this, sample_output
In the sample output, sal_val=3098 is greater than or equal to Min_val=22382.729 and less than or equal to max_val=36901.75, it was assigned ticker=$

I tried the following,

  df_values['ticker']=df_ticker.\
loc[((df_values['sal_val']>=df_ticker['Min_val'])| (df_values['sal_val']<=df_ticker['Max_val']))]['Ticker']
df_values  

It failed with error "ValueError: Can only compare identically-labeled Series objects"

Any solutions for this issue?

Answer 1

One way is to define a custom mapping function and use pd.Series.apply .

def mapper(x, t):
    if x < t['Min_val'].min():
        index = 0
    elif x >= t['Max_val'].max():
        index = -1
    else:
        index = next((idx for idx, (i, j) in enumerate(zip(t['Min_val'], t['Max_val']))\
                      if i <= x < j), None)

    return t['Ticker'].iloc[index] if index is not None else None

df_values['Ticker'] = df_values['sal_val'].apply(mapper, t=df_ticker)

Result

   Id     sal_val Ticker
0   1    3098.000      $
1   2   45639.987     $$
2   3   65487.400   $$$$
3   4   56784.800    $$$
4   5       8.000      $
5   6  736455.000   $$$$

Explanation

• pd.Series.apply accepts a custom mapping function as an input.
• The mapping function takes each entry in sal_val and compares it to values in df_ticker via an if / else structure.
• The first 2 if statements deal with minimum and maximum boundaries.
• The final else statement uses a generator, which cycles through each row in df_ticker and finds the index of values where the input is within the range of Min_val and Max_val .
• Finally, we use the index and feed it into df_ticker['Ticker'] via .iloc integer accessor.